public repository of the uncongeniality_analysis. Initial publication

.gitignore

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+# PyCharm IDE
+.idea/
+*.iml
+
+# Byte-compiled / optimized / DLL files
+__pycache__/
+
+# Environments
+.env
+.venv
+env/
+venv/
+ENV/
+
+# Logfiles
+.log

LICENSE

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+uncongeniality_analysis source code is provided under the GPLv3 license.
+
+*************************************************************************************
+
+
+GNU GENERAL PUBLIC LICENSE
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+Version 3, 29 June 2007
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README.md

@@ -0,0 +1,120 @@
+# uncongeniality_analysis
+
+## Project Contributors
+
+This analysis project has been executed by Gerrit Anders and Jürgen Buder from IWM Tuebingen.
+For project-related queries, please contact Gerrit Anders at g.anders@iwm-tuebingen.de.
+
+## Project Overview
+
+This repository host a general data analysis framework employed to investigate reply behaviour and polarization in the 
+comment section of "Spiegel Online" (SPON). The research, focuses on understanding uncongeniality within a large 
+online sample and examining polarization in online discussions.
+
+The dataset for analysis can be found on the [Open Science Framework](https://osf.io/t6eph).
+
+## Setup
+To set up the analysis, follow the steps below:
+
+### Clone the Repository
+
+```bash
+git clone --branch public https://gitea.iwm-tuebingen.de/ganders/project_SPON1_code.git
+```
+
+### Install non-python prerequisites
+
+In order to run the analysis R needs to be installed. The analysis was conducted using R version 4.1.1.
+Please install R from the [official website](https://cran.r-project.org/).
+In addition, development tools is recommended. This can be done via apt-get on Linux systems:
+
+```bash
+sudo apt-get update
+sudo apt-get install build-essential
+```
+
+Furthermore, to enable the generation of pdf reports, pandoc and texlive needs to be installed.
+pandoc installation can be done via apt-get on Linux systems:
+
+```bash
+sudo apt-get install pandoc
+sudo apt-get install texlive-latex-recommended
+```
+
+The code runs without these functionalities if in the config file the pdf flag is set to false.
+Please note that the markdown versions of result reports use relative paths to images,
+thus they will only display those while being in the `results_reports` folder (in contrast to pdf reports)
+
+### Install requirements
+
+The code was tested under python 3.10.2.
+It is recommended to run the code in a virtual environment. To create a virtual environment, run the following commands:
+
+```bash
+python3 -m venv venv
+source venv/bin/activate
+```
+
+To install the required python packages, run:
+
+```bash
+pip3 install -r requirements.txt
+```
+
+## Running analysis
+
+To run analysis with these frameworks one needs to adapt the configuration file `config.yaml` to the desired settings.
+Adapt the `data_path` to the directory in which the dataset that is available on the 
+[Open Science Framework](https://osf.io/t6eph) is stored.
+
+To replicate the analysis provided in "Polarizing reply patterns in comment sections of a large German news outlet"
+all other settings can be unchanged. 
+
+The analysis can be run by executing the following command:
+
+```bash
+python3 main.py
+```
+
+### Configuring analysis
+
+This framework allows to run a wide range of analyses on a dataset or subsets by defining analysis jobs as yaml files.
+Such files consist of four parts:
+- `preprocessing`: defines all subsets of the dataset that will be targeted in the analysis
+- `descriptive`: defines all descriptive analyses that will be conducted
+- `analysis`: defines all other analysis jobs that will be conducted (e.g. regression, correlation, etc.)
+- `visualizations`: defines all visualizations that will be created (e.g. histograms, scatterplots, etc.)
+
+Examples for all supported analysis and their arguments can be found in the `analysis_config_templates` folder.
+The general structure of an analysis job consists of a tag that names the analysis followed by a list of arguments.
+The `name` argument is mandatory and is used for identification and naming of the output files.
+Please note that `dataset` argument refers to the names of the datasets in preprocessing. Some other analysis 
+(e.g. forest plots) require addition information referring to specific models also defined in the analysis job.
+
+### Output
+
+An analysis job creates three types of outputs:
+- A markdown report in the `results_reports` folder which for each analysis give the settings of the analysis and the result
+- A pdf report in the `results_reports` folder which is the conversion of the pdf (that can be shared)
+- For each analysis a file in the `results` folder that contains the results of the analysis and is named the same as the result
+
+### Contributing: Extending the framework
+
+If you want to extend the framework with new analysis, you can do so by following these steps:
+- Fork the repository
+- add your analysis function class to the `analysis_functions` folder
+- write a wrapper function for your analysis that takes a list of job arguments and calls the analysis.
+- add the wrapper function to the `analysis.py` file and extend it to create a list of analysis jobs for the 
+newly created analysis type
+- create a parameter dataclass in the `data_classes` folder that is inherited from `GeneralParameters`.
+- Add your dataclass to the `constructor.py` in order for it to be readable from the job yaml file.
+- Extend `utils/helper_logging.py` to log the settings for your analysis in order for them to be documented in the report.
+- Either use the extended analysis framework or create a merge request for it to be included in the main repository.
+
+## License
+
+See the LICENSE file for the GNU General Public License v3.0 related details.
+
+## Contact
+
+For queries, feedback, or issue reporting, please e-mail Gerrit Anders at g.anders@iwm-tuebingen.de.

analysis_config_templates/template_analysis_bayesian_regression.yaml

@@ -0,0 +1,10 @@
+---
+analysis:
+  - !bayesian_regression
+    name: "Example_bayes_regression"
+    dataset: "data"
+    independent_variables:
+      - 'bayes-corrected (q=0.25) valence'
+      - 'totalvotes'
+    dependent_variable: 'number O(n+1)-replies'
+...

analysis_config_templates/template_analysis_comparison_variance_in_and_between_group.yaml

@@ -0,0 +1,8 @@
+---
+analysis:
+  - !comparison_variance_in_and_between_group
+    name: "Example_comparison_variance_in_and_between_group"
+    dataset: "data"
+    variable: 'bayes-corrected (q=0.25) variance'
+    group: 'user_id'
+...

analysis_config_templates/template_analysis_effect_of_up_and_downvotes.yaml

@@ -0,0 +1,22 @@
+---
+analysis:
+  - !linear_regression
+    name: "Example_linear_regression"
+    dataset: "data"
+    independent_variables:
+      - 'bayes-corrected (q=0.25) valence'
+      - 'totalvotes'
+    dependent_variable: 'number O(n+1)-replies'
+    standardize: false
+
+  - !increase_per_up_and_downvote_from_totalvotes_and_valence
+    name: "Example_increase_per_up_and_downvote"
+    dataset: "data"
+    weight_as_distribution_quantile: true
+    weight_m: 0.25
+    model_name: "Example_linear_regression"
+    step:
+      - 0
+      - 1
+    startpoint: "average"
+...

analysis_config_templates/template_analysis_get_function_inverse_bayes_transformed_regression.yaml

@@ -0,0 +1,17 @@
+---
+analysis:
+  - !linear_regression
+    name: "Example_linear_regression"
+    dataset: "data"
+    independent_variables:
+      - 'bayes-corrected (q=0.25) valence'
+      - 'totalvotes'
+    dependent_variable: 'number O(n+1)-replies'
+    standardize: false
+    report_effect_size: false
+
+  - !function_inverse_bayes_transformed_regression
+    name: "function_Example"
+    dataset: "data"
+    model_name: "Example_linear_regression"
+...

analysis_config_templates/template_analysis_grouped_linear_regression.yaml

@@ -0,0 +1,17 @@
+---
+analysis:
+  - !linear_regression_grouped
+    name: "Example_grouped_linear_regression"
+    dataset: "data"
+    independent_variables:
+      - 'bayes-corrected (q=0.25) valence'
+      - 'totalvotes'
+    dependent_variable: 'number O(n+1)-replies'
+    aggregation_functions:
+      - 'mean'
+      - 'sum'
+      - 'sum'
+    group_by: 'user_id'
+    standardize: false
+    print_detailed_coefficients: true
+...

analysis_config_templates/template_analysis_linear_regression.yaml

@@ -0,0 +1,11 @@
+---
+analysis:
+  - !linear_regression
+    name: "Example_linear_regression"
+    dataset: "data"
+    independent_variables:
+      - 'bayes-corrected (q=0.25) valence'
+      - 'totalvotes'
+    dependent_variable: 'number O(n+1)-replies'
+    standardize: false
+...

analysis_config_templates/template_analysis_paired_ttest.yaml

@@ -0,0 +1,8 @@
+---
+analysis:
+  - !paired_ttest
+    name: "Example_paired_ttest"
+    dataset: "data"
+    variable_1: 'bayes-corrected (q=0.25) extremity'
+    variable_2: 'mean bayes-corrected (q=0.25) extremity of replies'
+...

analysis_config_templates/template_analysis_pearson_correlation.yaml

@@ -0,0 +1,8 @@
+---
+analysis:
+  - !pearson_correlation
+    name: "Example_pearson_correlation"
+    dataset: "data"
+    variable_1: 'bayes-corrected (q=0.25) extremity'
+    variable_2: 'mean bayes-corrected (q=0.25) extremity of replies'
+...

analysis_config_templates/template_analysis_ttest.yaml

@@ -0,0 +1,8 @@
+---
+analysis:
+  - !ttest
+    name: "Example_ttest"
+    dataset: "data"
+    variable_1: 'bayes-corrected (q=0.25) extremity'
+    variable_2: 'mean bayes-corrected (q=0.25) extremity of replies'
+...

analysis_config_templates/template_descriptive_create_descriptive_aggregated.yaml

@@ -0,0 +1,11 @@
+---
+descriptive:
+  - !descriptive_aggregated
+    name: "Example_overview"
+    dataset: "data"
+    variables:
+      - 'Count'
+      - 'totalvotes'
+    aggregation_function: "sum"
+    group_by: "user_id"
+...

analysis_config_templates/template_descriptive_create_descriptive_overview.yaml

@@ -0,0 +1,40 @@
+---
+descriptive:
+  - !descriptive_overview
+    name: "Example_overview"
+    dataset: "data"
+    group_by: "order"
+    metrics:
+      - operation: "count"
+        column: null
+      - operation: "sum"
+        column: "number O(n+1)-replies"
+      - operation: "count_nonzero"
+        column: "number O(n+1)-replies"
+      - operation: "count_nonzero"
+        column: "totalvotes"
+      - operation: "sum"
+        column: "totalvotes"
+      - operation: "sum"
+        column: "upvotes"
+      - operation: "sum"
+        column: "downvotes"
+      - operation: "count_nonzero"
+        column: "totalvotes"
+      - operation: "mean"
+        column: "valence"
+      - operation: "std_dev"
+        column: "valence"
+      - operation: "mean"
+        column: "bayes-corrected (q=0.25) valence"
+      - operation: "std_dev"
+        column: "bayes-corrected (q=0.25) valence"
+      - operation: "mean"
+        column: "extremity"
+      - operation: "std_dev"
+        column: "extremity"
+      - operation: "mean"
+        column: "bayes-corrected (q=0.25) extremity"
+      - operation: "std_dev"
+        column: "bayes-corrected (q=0.25) extremity"
+...

analysis_config_templates/template_descriptive_percentage_of_dataset_under_condtion.yaml

@@ -0,0 +1,9 @@
+---
+descriptive:
+  - !percentage_of_dataset_under_condition
+    name: "Example_percentage_of_dataset_under_condition"
+    dataset: "data"
+    variable: "totalvotes"
+    comparison: "smaller"
+    condition: 10
+...

analysis_config_templates/template_plot_barchart.yaml

@@ -0,0 +1,13 @@
+---
+visualization:
+  - !barchart
+    name: 'Example_barchart'
+    dataset: "data"
+    variable_x_axis: 'bayes-corrected (q=0.25) extremity'
+    variable_y_axis: None
+    x_axis_label: 'bayes-corrected (q=0.25) extremity'
+    y_axis_label: 'Count'
+    chart_orientation: 'h'
+    sort_order: 'ascending'
+    title: 'Barchart'
+...

analysis_config_templates/template_plot_boxplot.yaml

@@ -0,0 +1,11 @@
+---
+visualization:
+  - !boxplot
+    name: 'Example_boxplot'
+    dataset: "data"
+    variable_1: 'bayes-corrected (q=0.25) extremity'
+    variable_2: 'mean bayes-corrected (q=0.25) extremity of replies'
+    x_axis_label: ''
+    y_axis_label: 'Extremity value'
+    title: 'Box plot comparing bayes-corrected extremity with the mean extremity of replies'
+...

analysis_config_templates/template_plot_contourplot.yaml

@@ -0,0 +1,27 @@
+---
+analysis:
+  - !linear_regression
+    name: "Example_linear_regression"
+    dataset: "data"
+    independent_variables:
+      - 'bayes-corrected (q=0.25) valence'
+      - 'totalvotes'
+    dependent_variable: 'number O(n+1)-replies'
+    standardize: false
+    report_effect_size: false
+
+  - !function_inverse_bayes_transformed_regression
+    name: "function_Example"
+    dataset: "data"
+    model_name: "Example_linear_regression"
+
+visualization:
+  - !contourplot
+    name: "Example_surfaceplot"
+    dataset: "data"
+    function_name: "function_Example"
+    x_axis_maximum: 20
+    y_axis_maximum: 20
+    x_axis_label: "downvotes"
+    y_axis_label: "upvotes"
+...

analysis_config_templates/template_plot_count_distribution.yaml

@@ -0,0 +1,15 @@
+---
+visualization:
+  - !count_distribution
+    name: 'Example_count_distribution'
+    dataset: "data"
+    variable: 'user_id'
+    x_axis_label: 'Number of comments'
+    y_axis_label: 'Number of users'
+    x_axis_limits:
+      - 0
+      - 10
+    x_axis_logarithmic_scaling: false
+    y_axis_logarithmic_scaling: false
+    title: 'Distribution of Comments over Users'
+...

analysis_config_templates/template_plot_densityplot.yaml

@@ -0,0 +1,10 @@
+---
+visualization:
+  - !densityplot
+    name: 'Example_densityplot'
+    dataset: "data"
+    variable_x_axis: 'bayes-corrected (q=0.25) extremity'
+    variable_y_axis: 'mean bayes-corrected (q=0.25) extremity of replies'
+    data_breakpoints:
+      - 0
+...

analysis_config_templates/template_plot_forestplot.yaml

@@ -0,0 +1,37 @@
+---
+analysis:
+  - !linear_regression
+    name: "Example_linear_regression_subset_1"
+    dataset: "data_subset_1"
+    independent_variables:
+      - 'bayes-corrected (q=0.25) valence'
+      - 'totalvotes'
+    dependent_variable: 'number O(n+1)-replies'
+    standardize: true
+
+  - !linear_regression
+    name: "Example_linear_regression_subset_2"
+    dataset: "data_subset_2"
+    independent_variables:
+      - 'bayes-corrected (q=0.25) valence'
+      - 'totalvotes'
+    dependent_variable: 'number O(n+1)-replies'
+    standardize: true
+
+visualization:
+  - !forestplot:
+    name: "Example_forestplot"
+    regression_model_names:
+        - "Example_linear_regression_subset_1"
+        - "Example_linear_regression_subset_2"
+    regression_model_labels:
+        - "Subset 1"
+        - "Subset 2"
+    coefficient_names:
+        - "bayes-corrected (q=0.25) valence"
+        - "totalvotes"
+    x_axis_minimum: 0
+    dotsize: 5
+    x_axis_label: "Standardized coefficient (95% Confidence Interval)"
+...
+

analysis_config_templates/template_plot_forestplot_paired_ttest.yaml

@@ -0,0 +1,27 @@
+---
+analysis:
+  - !paired_ttest
+    name: "Example_paired_ttest_subset_1"
+    dataset: "data_subset_1"
+    variable_1: 'bayes-corrected (q=0.25) extremity'
+    variable_2: 'mean bayes-corrected (q=0.25) extremity of replies'
+
+  - !paired_ttest
+    name: "Example_paired_ttest_subset_2"
+    dataset: "data_subset_2"
+    variable_1: 'bayes-corrected (q=0.25) extremity'
+    variable_2: 'mean bayes-corrected (q=0.25) extremity of replies'
+
+visualization:
+  - !forestplot:
+    name: "Example_forestplot_paired_ttest"
+    paired_ttest_names:
+      - "Example_paired_ttest_subset_1"
+      - "Example_paired_ttest_subset_2"
+    paired_ttest_labels:
+      - "Subset 1"
+      - "Subset 2"
+    x_axis_minimum: 0
+    dotsize: 5
+    x_axis_label: "Mean difference bayes-corrected (q=0.25) extremity (95% Confidence Interval)"
+...

analysis_config_templates/template_plot_grouped_histogram.yaml

@@ -0,0 +1,12 @@
+---
+visualization:
+  - !grouped_histogram
+    name: "Example_grouped_histogram"
+    dataset: "data"
+    group_by: 'user_id'
+    aggregation_variable: 'bayes-corrected (q=0.25) valence'
+    aggregation_function: 'mean'
+    x_axis_label: 'Valence'
+    y_axis_label: 'Number of users'
+    title: 'Histogram of Mean Valence per User'
+...

analysis_config_templates/template_plot_heatmap.yaml

@@ -0,0 +1,17 @@
+---
+visualization:
+  - !heatmap
+    name: "Example_heatmap"
+    dataset: "data"
+    axis_variables:
+      - 'bayes-corrected (q=0.25) valence'
+      - 'totalvotes'
+    heat_variable: 'number O(n+1)-replies'
+    axis_maxima:
+      - 1
+      - 40
+    axis_minima:
+      - 0
+      - 0
+    logarithmic_heat_scaling: 'false'
+...

analysis_config_templates/template_plot_hexbinplot.yaml

@@ -0,0 +1,12 @@
+---
+visualization:
+  - !hexbinplot
+    name: "Example_hexbinplot"
+    dataset: "data"
+    variable_x_axis: 'bayes-corrected (q=0.25) extremity'
+    variable_y_axis: 'mean bayes-corrected (q=0.25) extremity of replies'
+    x_axis_maximum: 1
+    y_axis_maximum: 1
+    trendline: false
+    logarithmic_hex_scaling: false
+...

analysis_config_templates/template_plot_histogram.yaml

@@ -0,0 +1,12 @@
+---
+visualization:
+  - !histogram
+    name: 'Descriptive_histogram_comments_over_totalvotes'
+    dataset: "data"
+    variable: 'totalvotes'
+    x_axis_label: 'Number of total votes'
+    y_axis_label: 'Number of comments'
+    x_axis_logarithmic_scaling: false
+    y_axis_logarithmic_scaling: true
+    title: ''
+...

analysis_config_templates/template_plot_percentage_stacked_barchart.yaml

@@ -0,0 +1,14 @@
+---
+visualization:
+  - !percentage_stacked_barchart
+    name: 'Example_percentage_stacked_barchart'
+    dataset: "data"
+    variable_x_axis: 'section'
+    variables_to_compare:
+      - 'upvotes'
+      - 'downvotes'
+    x_axis_label: 'Section'
+    chart_orientation: 'h'
+    sort_order: 'ascending'
+    title: 'Stacked Barchart of Upvotes and Downvotes by Section'
+...

analysis_config_templates/template_plot_ridgelineplot.yaml

@@ -0,0 +1,10 @@
+---
+visualization:
+  - !ridgelineplot
+    name: "Example_ridgelineplot"
+    dataset: "data"
+    variable_x_axis: 'bayes-corrected (q=0.25) extremity'
+    variable_y_axis: 'mean bayes-corrected (q=0.25) extremity of replies'
+    data_breakpoints:
+      - 0.5
+...

analysis_config_templates/template_plot_simple_scatterplot.yaml

@@ -0,0 +1,8 @@
+---
+visualization:
+  - !simple_scatterplot
+    name: "Example_simple_scatterplot"
+    dataset: "data"
+    variable_x_axis: 'bayes-corrected (q=0.25) extremity'
+    variable_y_axis: 'mean bayes-corrected (q=0.25) extremity of replies'
+...

analysis_config_templates/template_plot_stacked_barchart.yaml

@@ -0,0 +1,14 @@
+---
+visualization:
+  - !stacked_barchart
+    name: 'Example_stacked_barchart'
+    dataset: "data"
+    variable_x_axis: 'section'
+    variable_y_axis: None
+    x_axis_label: 'section'
+    y_axis_label: 'Count'
+    hue: 'order'
+    chart_orientation: 'h'
+    sort_order: 'ascending'
+    title: 'Stacked Barchart of Comments by Section and Order'
+...

analysis_config_templates/template_plot_surfaceplot.yaml

@@ -0,0 +1,31 @@
+---
+analysis:
+  - !linear_regression
+    name: "Example_linear_regression"
+    dataset: "data"
+    independent_variables:
+      - 'bayes-corrected (q=0.25) valence'
+      - 'totalvotes'
+    dependent_variable: 'number O(n+1)-replies'
+    standardize: false
+    report_effect_size: false
+
+  - !function_inverse_bayes_transformed_regression
+    name: "function_Example"
+    dataset: "data"
+    model_name: "Example_linear_regression"
+
+visualization:
+  - !surfaceplot
+    name: "Example_surfaceplot"
+    dataset: "data"
+    function_name: "function_Example"
+    x_axis_maximum: 20
+    y_axis_maximum: 20
+    x_axis_label: "downvotes"
+    y_axis_label: "upvotes"
+    z_axis_label: "replies"
+    elevation_angle: 45
+    azimuth_angle: 205
+    title: 'Effect of up- and downvotes according to example linear regression'
+...

analysis_config_templates/template_plot_violinplot.yaml

@@ -0,0 +1,8 @@
+---
+visualization:
+  - !violinplot
+    name: "Example_violinplot"
+    dataset: "data"
+    variable_x_axis: 'bayes-corrected (q=0.25) extremity'
+    variable_y_axis: 'mean bayes-corrected (q=0.25) extremity of replies'
+...

analysis_jobs/analysis_job_manuscript.yaml

@@ -0,0 +1,667 @@
+---
+preprocessing:
+  data_order0:
+  - method: data_order
+    param: 0
+  data_order1:
+  - method: data_order
+    param: 1
+  data_politics:
+  - method: data_order
+    param: 0
+  - method: data_section
+    param: 'Politics'
+  data_foreign_affairs:
+  - method: data_order
+    param: 0
+  - method: data_section
+    param: 'Foreign affairs'
+  data_science:
+  - method: data_order
+    param: 0
+  - method: data_section
+    param: 'Science'
+  data_economy:
+  - method: data_order
+    param: 0
+  - method: data_section
+    param: 'Economy'
+  data_miscellaneous:
+  - method: data_order
+    param: 0
+  - method: data_section
+    param: 'Miscellaneous'
+  data_culture:
+  - method: data_order
+    param: 0
+  - method: data_section
+    param: 'Culture'
+  data_sports:
+  - method: data_order
+    param: 0
+  - method: data_section
+    param: 'Sports'
+  data_mobility:
+  - method: data_order
+    param: 0
+  - method: data_section
+    param: 'Mobility'
+  data_internet:
+  - method: data_order
+    param: 0
+  - method: data_section
+    param: 'Internet'
+  data_health:
+  - method: data_order
+    param: 0
+  - method: data_section
+    param: 'Health'
+  data_order0_with_minimum_one_vote:
+  - method: data_order
+    param: 0
+  - method: exclude_data_with_value
+    param: {'column': 'totalvotes', 'value': 0}
+
+descriptive:
+  - !descriptive_overview
+    name: "Extended_Data_Table_1_Descriptive_Data_for_different_comment_levels"
+    dataset: "data"
+    group_by: "order"
+    metrics:
+      - operation: "count"
+        column: null
+      - operation: "count_nonzero"
+        column: "totalvotes"
+      - operation: "sum"
+        column: "totalvotes"
+      - operation: "mean"
+        column: "totalvotes"
+      - operation: "std_dev"
+        column: "totalvotes"
+      - operation: "sum"
+        column: "upvotes"
+      - operation: "sum"
+        column: "downvotes"
+      - operation: "mean"
+        column: "bayes-corrected (q=0.25) valence"
+      - operation: "std_dev"
+        column: "bayes-corrected (q=0.25) valence"
+      - operation: "mean"
+        column: "bayes-corrected (q=0.25) extremity"
+      - operation: "std_dev"
+        column: "bayes-corrected (q=0.25) extremity"
+
+  - !descriptive_overview
+    name: "Extended_Data_Table_2_Descriptive_Data_for_different_news_categories"
+    dataset: "data"
+    group_by: "section"
+    metrics:
+      - operation: "count"
+        column: null
+      - operation: "sum"
+        column: "number O(n+1)-replies"
+      - operation: "count_nonzero"
+        column: "number O(n+1)-replies"
+      - operation: "count_nonzero"
+        column: "totalvotes"
+      - operation: "sum"
+        column: "totalvotes"
+      - operation: "sum"
+        column: "upvotes"
+      - operation: "sum"
+        column: "downvotes"
+      - operation: "count_nonzero"
+        column: "totalvotes"
+      - operation: "mean"
+        column: "valence"
+      - operation: "std_dev"
+        column: "valence"
+      - operation: "mean"
+        column: "bayes-corrected (q=0.25) valence"
+      - operation: "std_dev"
+        column: "bayes-corrected (q=0.25) valence"
+      - operation: "mean"
+        column: "extremity"
+      - operation: "std_dev"
+        column: "extremity"
+      - operation: "mean"
+        column: "bayes-corrected (q=0.25) extremity"
+      - operation: "std_dev"
+        column: "bayes-corrected (q=0.25) extremity"
+
+analysis:
+  - !linear_regression
+    name: "Evidence_uncongeniality_simplest_model_linear_regression_only_valence_non_standardized"
+    dataset: "data_order0"
+    independent_variables:
+      - 'valence'
+    dependent_variable: 'number O(n+1)-replies'
+    standardize: false
+    report_effect_size: true
+
+  - !linear_regression
+    name: "Evidence_uncongeniality_preregistered_model"
+    dataset: "data_order0"
+    independent_variables:
+      - 'bayes-corrected (q=0.25) valence'
+      - 'totalvotes'
+    dependent_variable: 'number O(n+1)-replies'
+    standardize: true
+    report_effect_size: true
+
+  - !linear_regression
+    name: "Evidence_uncongeniality_stability_against_variation_in_weight_q5"
+    dataset: "data_order0"
+    independent_variables:
+      - 'bayes-corrected (q=0.5) valence'
+      - 'totalvotes'
+    dependent_variable: 'number O(n+1)-replies'
+    standardize: true
+
+  - !linear_regression
+    name: "Evidence_uncongeniality_stability_against_variation_in_weight_q75"
+    dataset: "data_order0"
+    independent_variables:
+      - 'bayes-corrected (q=0.75) valence'
+      - 'totalvotes'
+    dependent_variable: 'number O(n+1)-replies'
+    standardize: true
+
+  - !linear_regression
+    name: "Evidence_uncongeniality_stability_against_variation_in_weight__no_bayes_correction"
+    dataset: "data_order0"
+    independent_variables:
+      - 'valence'
+      - 'totalvotes'
+    dependent_variable: 'number O(n+1)-replies'
+    standardize: true
+
+  - !linear_regression_grouped
+    name: "Evidence_uncongeniality_robustness_analysis_on_person_level"
+    dataset: "data_order0"
+    independent_variables:
+      - 'bayes-corrected (q=0.25) valence'
+      - 'totalvotes'
+    dependent_variable: 'number O(n+1)-replies'
+    aggregation_functions:
+      - 'mean'
+      - 'sum'
+      - 'sum'
+    group_by: 'user_id'
+    standardize: true
+    print_detailed_coefficients: true
+
+  - !linear_regression_grouped
+    name: "Evidence_uncongeniality_robustness_analysis_on_section_level"
+    dataset: "data_order0"
+    independent_variables:
+      - 'bayes-corrected (q=0.25) valence'
+      - 'totalvotes'
+    dependent_variable: 'number O(n+1)-replies'
+    aggregation_functions:
+      - 'mean'
+      - 'sum'
+      - 'sum'
+    group_by: 'section'
+    standardize: true
+    print_detailed_coefficients: true
+
+  - !linear_regression
+    name: "Evidence_uncongenialty_section_politics"
+    dataset: "data_politics"
+    independent_variables:
+      - 'bayes-corrected (q=0.25) valence'
+      - 'totalvotes'
+    dependent_variable: 'number O(n+1)-replies'
+    standardize: true
+
+  - !linear_regression
+    name: "Evidence_uncongenialty_section_affairs"
+    dataset: "data_foreign_affairs"
+    independent_variables:
+      - 'bayes-corrected (q=0.25) valence'
+      - 'totalvotes'
+    dependent_variable: 'number O(n+1)-replies'
+    standardize: true
+
+  - !linear_regression
+    name: "Evidence_uncongenialty_section_science"
+    dataset: "data_science"
+    independent_variables:
+      - 'bayes-corrected (q=0.25) valence'
+      - 'totalvotes'
+    dependent_variable: 'number O(n+1)-replies'
+    standardize: true
+
+  - !linear_regression
+    name: "Evidence_uncongenialty_section_economy"
+    dataset: "data_economy"
+    independent_variables:
+      - 'bayes-corrected (q=0.25) valence'
+      - 'totalvotes'
+    dependent_variable: 'number O(n+1)-replies'
+    standardize: true
+
+  - !linear_regression
+    name: "Evidence_uncongenialty_section_miscellaneous"
+    dataset: "data_miscellaneous"
+    independent_variables:
+      - 'bayes-corrected (q=0.25) valence'
+      - 'totalvotes'
+    dependent_variable: 'number O(n+1)-replies'
+    standardize: true
+
+  - !linear_regression
+    name: "Evidence_uncongenialty_section_culture"
+    dataset: "data_culture"
+    independent_variables:
+      - 'bayes-corrected (q=0.25) valence'
+      - 'totalvotes'
+    dependent_variable: 'number O(n+1)-replies'
+    standardize: true
+
+  - !linear_regression
+    name: "Evidence_uncongenialty_section_sports"
+    dataset: "data_sports"
+    independent_variables:
+      - 'bayes-corrected (q=0.25) valence'
+      - 'totalvotes'
+    dependent_variable: 'number O(n+1)-replies'
+    standardize: true
+
+  - !linear_regression
+    name: "Evidence_uncongenialty_section_mobility"
+    dataset: "data_mobility"
+    independent_variables:
+      - 'bayes-corrected (q=0.25) valence'
+      - 'totalvotes'
+    dependent_variable: 'number O(n+1)-replies'
+    standardize: true
+
+  - !linear_regression
+    name: "Evidence_uncongenialty_section_internet"
+    dataset: "data_internet"
+    independent_variables:
+      - 'bayes-corrected (q=0.25) valence'
+      - 'totalvotes'
+    dependent_variable: 'number O(n+1)-replies'
+    standardize: true
+
+  - !linear_regression
+    name: "Evidence_uncongenialty_section_health"
+    dataset: "data_health"
+    independent_variables:
+      - 'bayes-corrected (q=0.25) valence'
+      - 'totalvotes'
+    dependent_variable: 'number O(n+1)-replies'
+    standardize: true
+
+  - !linear_regression
+    name: "Evidence_uncongeniality_robustness_order1"
+    dataset: "data_order1"
+    independent_variables:
+      - 'bayes-corrected (q=0.25) valence'
+      - 'totalvotes'
+    dependent_variable: 'number O(n+1)-replies'
+    standardize: true
+
+  - !linear_regression
+    name: "Evidence_uncogeniality_model_with_seperate_upvotes_downvotes"
+    dataset: "data_order0"
+    independent_variables:
+      - 'upvotes'
+      - 'downvotes'
+    dependent_variable: 'number O(n+1)-replies'
+    standardize: true
+
+  - !linear_regression
+    name: "Evidence_antagonism_preregistered_model"
+    dataset: "data_order0"
+    independent_variables:
+      - 'mean bayes-corrected (q=0.25) valence of replies'
+    dependent_variable: 'bayes-corrected (q=0.25) valence'
+    standardize: true
+
+  - !linear_regression
+    name: "Evidence_antagonism_stability_against_variation_in_weight_q5"
+    dataset: "data_order0"
+    independent_variables:
+      - 'mean bayes-corrected (q=0.5) valence of replies'
+    dependent_variable: 'bayes-corrected (q=0.5) valence'
+    standardize: true
+
+  - !linear_regression
+    name: "Evidence_antagonism_stability_against_variation_in_weight_q75"
+    dataset: "data_order0"
+    independent_variables:
+      - 'mean bayes-corrected (q=0.75) valence of replies'
+    dependent_variable: 'bayes-corrected (q=0.75) valence'
+    standardize: true
+
+  - !linear_regression
+    name: "Evidence_antagonism_stability_against_variation_in_weight_no_bayes_correction"
+    dataset: "data_order0"
+    independent_variables:
+      - 'mean valence of replies'
+    dependent_variable: 'valence'
+    standardize: true
+
+  - !linear_regression
+    name: "Evidence_antagonism_section_politics"
+    dataset: "data_politics"
+    independent_variables:
+      - 'mean bayes-corrected (q=0.25) valence of replies'
+    dependent_variable: 'bayes-corrected (q=0.25) valence'
+    standardize: true
+
+  - !linear_regression
+    name: "Evidence_antagonism_section_affairs"
+    dataset: "data_foreign_affairs"
+    independent_variables:
+      - 'mean bayes-corrected (q=0.25) valence of replies'
+    dependent_variable: 'bayes-corrected (q=0.25) valence'
+    standardize: true
+
+  - !linear_regression
+    name: "Evidence_antagonism_section_science"
+    dataset: "data_science"
+    independent_variables:
+      - 'mean bayes-corrected (q=0.25) valence of replies'
+    dependent_variable: 'bayes-corrected (q=0.25) valence'
+    standardize: true
+
+  - !linear_regression
+    name: "Evidence_antagonism_section_economy"
+    dataset: "data_economy"
+    independent_variables:
+      - 'mean bayes-corrected (q=0.25) valence of replies'
+    dependent_variable: 'bayes-corrected (q=0.25) valence'
+    standardize: true
+
+  - !linear_regression
+    name: "Evidence_antagonism_section_miscellaneous"
+    dataset: "data_miscellaneous"
+    independent_variables:
+      - 'mean bayes-corrected (q=0.25) valence of replies'
+    dependent_variable: 'bayes-corrected (q=0.25) valence'
+    standardize: true
+
+  - !linear_regression
+    name: "Evidence_antagonism_section_culture"
+    dataset: "data_culture"
+    independent_variables:
+      - 'mean bayes-corrected (q=0.25) valence of replies'
+    dependent_variable: 'bayes-corrected (q=0.25) valence'
+    standardize: true
+
+  - !linear_regression
+    name: "Evidence_antagonism_section_sports"
+    dataset: "data_sports"
+    independent_variables:
+      - 'mean bayes-corrected (q=0.25) valence of replies'
+    dependent_variable: 'bayes-corrected (q=0.25) valence'
+    standardize: true
+
+  - !linear_regression
+    name: "Evidence_antagonism_section_mobility"
+    dataset: "data_mobility"
+    independent_variables:
+      - 'mean bayes-corrected (q=0.25) valence of replies'
+    dependent_variable: 'bayes-corrected (q=0.25) valence'
+    standardize: true
+
+  - !linear_regression
+    name: "Evidence_antagonism_section_internet"
+    dataset: "data_internet"
+    independent_variables:
+      - 'mean bayes-corrected (q=0.25) valence of replies'
+    dependent_variable: 'bayes-corrected (q=0.25) valence'
+    standardize: true
+
+  - !linear_regression
+    name: "Evidence_antagonism_section_health"
+    dataset: "data_health"
+    independent_variables:
+      - 'mean bayes-corrected (q=0.25) valence of replies'
+    dependent_variable: 'bayes-corrected (q=0.25) valence'
+    standardize: true
+
+  - !linear_regression
+    name: "Evidence_antagonism_robustness_order1"
+    dataset: "data_order1"
+    independent_variables:
+      - 'mean bayes-corrected (q=0.25) valence of replies'
+    dependent_variable: 'bayes-corrected (q=0.25) valence'
+    standardize: true
+
+  - !paired_ttest
+    name: "Evidence_polarization_paired_ttest_extremity"
+    dataset: "data_order0"
+    variable_1: 'bayes-corrected (q=0.25) extremity'
+    variable_2: 'mean bayes-corrected (q=0.25) extremity of replies'
+
+  - !paired_ttest
+    name: "Evidence_polarization_stability_against_variation_in_weight_paired_ttest_q5"
+    dataset: "data_order0"
+    variable_1: 'bayes-corrected (q=0.5) extremity'
+    variable_2: 'mean bayes-corrected (q=0.5) extremity of replies'
+
+  - !paired_ttest
+    name: "Evidence_polarization_stability_against_variation_in_weight_paired_ttest_q75"
+    dataset: "data_order0"
+    variable_1: 'bayes-corrected (q=0.75) extremity'
+    variable_2: 'mean bayes-corrected (q=0.75) extremity of replies'
+
+  - !paired_ttest
+    name: "Evidence_polarization_stability_against_variation_in_weight_paired_ttest_bayes"
+    dataset: "data_order0"
+    variable_1: 'extremity'
+    variable_2: 'mean extremity of replies'
+
+  - !paired_ttest
+    name: "Evidence_polarization_robustness_paired_ttest_order1"
+    dataset: "data_order1"
+    variable_1: 'bayes-corrected (q=0.25) extremity'
+    variable_2: 'mean bayes-corrected (q=0.25) extremity of replies'
+
+  - !paired_ttest
+    name: "Evidence_polarization_paired_ttest_extremity_politics"
+    dataset: "data_politics"
+    variable_1: 'bayes-corrected (q=0.25) extremity'
+    variable_2: 'mean bayes-corrected (q=0.25) extremity of replies'
+
+  - !paired_ttest
+    name: "Evidence_polarization_paired_ttest_extremity_foreign_affairs"
+    dataset: "data_foreign_affairs"
+    variable_1: 'bayes-corrected (q=0.25) extremity'
+    variable_2: 'mean bayes-corrected (q=0.25) extremity of replies'
+
+  - !paired_ttest
+    name: "Evidence_polarization_paired_ttest_extremity_science"
+    dataset: "data_science"
+    variable_1: 'bayes-corrected (q=0.25) extremity'
+    variable_2: 'mean bayes-corrected (q=0.25) extremity of replies'
+
+  - !paired_ttest
+    name: "Evidence_polarization_paired_ttest_extremity_economy"
+    dataset: "data_economy"
+    variable_1: 'bayes-corrected (q=0.25) extremity'
+    variable_2: 'mean bayes-corrected (q=0.25) extremity of replies'
+
+  - !paired_ttest
+    name: "Evidence_polarization_paired_ttest_extremity_miscellaneous"
+    dataset: "data_miscellaneous"
+    variable_1: 'bayes-corrected (q=0.25) extremity'
+    variable_2: 'mean bayes-corrected (q=0.25) extremity of replies'
+
+  - !paired_ttest
+    name: "Evidence_polarization_paired_ttest_extremity_culture"
+    dataset: "data_culture"
+    variable_1: 'bayes-corrected (q=0.25) extremity'
+    variable_2: 'mean bayes-corrected (q=0.25) extremity of replies'
+
+  - !paired_ttest
+    name: "Evidence_polarization_paired_ttest_extremity_sports"
+    dataset: "data_sports"
+    variable_1: 'bayes-corrected (q=0.25) extremity'
+    variable_2: 'mean bayes-corrected (q=0.25) extremity of replies'
+
+  - !paired_ttest
+    name: "Evidence_polarization_paired_ttest_extremity_mobility"
+    dataset: "data_mobility"
+    variable_1: 'bayes-corrected (q=0.25) extremity'
+    variable_2: 'mean bayes-corrected (q=0.25) extremity of replies'
+
+  - !paired_ttest
+    name: "Evidence_polarization_paired_ttest_extremity_internet"
+    dataset: "data_internet"
+    variable_1: 'bayes-corrected (q=0.25) extremity'
+    variable_2: 'mean bayes-corrected (q=0.25) extremity of replies'
+
+  - !paired_ttest
+    name: "Evidence_polarization_paired_ttest_extremity_health"
+    dataset: "data_health"
+    variable_1: 'bayes-corrected (q=0.25) extremity'
+    variable_2: 'mean bayes-corrected (q=0.25) extremity of replies'
+
+visualization:
+  - !hexbinplot
+    name: "Fig_2a"
+    dataset: "data_order0"
+    variable_x_axis: 'bayes-corrected (q=0.25) valence'
+    variable_y_axis: 'number O(n+1)-replies'
+    y_axis_maximum: 40
+    trendline: True
+    logarithmic_hex_scaling: True
+
+  - !forestplot
+    name: "Fig_2b"
+    regression_model_names:
+      - "Evidence_uncongenialty_section_politics"
+      - "Evidence_uncongenialty_section_foreign_affairs"
+      - "Evidence_uncongenialty_section_science"
+      - "Evidence_uncongenialty_section_economy"
+      - "Evidence_uncongenialty_section_miscellaneous"
+      - "Evidence_uncongenialty_section_culture"
+      - "Evidence_uncongenialty_section_sports"
+      - "Evidence_uncongenialty_section_mobility"
+      - "Evidence_uncongenialty_section_internet"
+      - "Evidence_uncongenialty_section_health"
+    regression_model_labels:
+      - "Politics"
+      - "Foreign Affairs"
+      - "Science"
+      - "Economy"
+      - "Miscellaneous"
+      - "Culture"
+      - "Sports"
+      - "Mobility"
+      - "Internet"
+      - "Health"
+    coefficient_names:
+        - "bayes-corrected (q=0.25) valence"
+        - "totalvotes"
+    x_axis_minimum: -0.6
+    dotsize: 2
+    x_axis_label: "Standardized coefficient (95% Confidence Interval)"
+
+  - !heatmap
+    name: "Fig_2c"
+    dataset: "data_order0_with_minimum_one_vote"
+    axis_variables:
+      - 'upvotes'
+      - 'downvotes'
+    heat_variable: 'number O(n+1)-replies'
+    axis_maxima:
+      - 20
+      - 20
+    axis_minima:
+      - 0
+      - 0
+    logarithmic_heat_scaling: 'false'
+
+  - !densityplot
+    name: 'Fig_3a'
+    dataset: "data_order0"
+    variable_x_axis: 'mean bayes-corrected (q=0.25) valence of replies'
+    variable_y_axis: 'bayes-corrected (q=0.25) valence'
+    data_breakpoints:
+      - 0
+
+  - !forestplot
+    name: "Fig_3b"
+    regression_model_names:
+      - "Evidence_antagonism_section_politics"
+      - "Evidence_antagonism_section_foreign_affairs"
+      - "Evidence_antagonism_section_science"
+      - "Evidence_antagonism_section_economy"
+      - "Evidence_antagonism_section_miscellaneous"
+      - "Evidence_antagonism_section_culture"
+      - "Evidence_antagonism_section_sports"
+      - "Evidence_antagonism_section_mobility"
+      - "Evidence_antagonism_section_internet"
+      - "Evidence_antagonism_section_health"
+    regression_model_labels:
+      - "Politics"
+      - "Foreign Affairs"
+      - "Science"
+      - "Economy"
+      - "Miscellaneous"
+      - "Culture"
+      - "Sports"
+      - "Mobility"
+      - "Internet"
+      - "Health"
+    coefficient_names:
+        - 'mean bayes-corrected (q=0.25) valence of replies'
+    x_axis_minimum: -0.1
+    dotsize: 2
+    x_axis_label: "Standardized coefficient (95% Confidence Interval)"
+
+  - !violinplot
+    name: "Fig_4a"
+    dataset: "data_order0"
+    variable_x_axis: 'bayes-corrected (q=0.25) extremity'
+    variable_y_axis: 'mean bayes-corrected (q=0.25) extremity of replies'
+    x_axis_label: ''
+    y_axis_label: 'Extremity value'
+    title: ''
+
+  - !forestplot_paired_ttest
+    name: "Fig_4b"
+    paired_ttest_names:
+        - "Evidence_polarization_paired_ttest_extremity_politics"
+        - "Evidence_polarization_paired_ttest_extremity_affairs"
+        - "Evidence_polarization_paired_ttest_extremity_science"
+        - "Evidence_polarization_paired_ttest_extremity_economy"
+        - "Evidence_polarization_paired_ttest_extremity_miscellaneous"
+        - "Evidence_polarization_paired_ttest_extremity_culture"
+        - "Evidence_polarization_paired_ttest_extremity_sports"
+        - "Evidence_polarization_paired_ttest_extremity_mobility"
+        - "Evidence_polarization_paired_ttest_extremity_internet"
+        - "Evidence_polarization_paired_ttest_extremity_health"
+    paired_ttest_labels:
+      - "Politics"
+      - "Foreign Affairs"
+      - "Science"
+      - "Economy"
+      - "Miscellaneous"
+      - "Culture"
+      - "Sports"
+      - "Mobility"
+      - "Internet"
+      - "Health"
+    x_axis_minimum: -0.06
+    dotsize: 2
+    x_axis_label: "Mean difference bayes-corrected (q=0.25) extremity (95% Confidence Interval)"
+
+  - !histogram
+    name: 'Extended_Fig_1'
+    dataset: "data"
+    variable: 'totalvotes'
+    x_axis_label: 'Number of total votes'
+    y_axis_label: 'Number of comments'
+    x_axis_logarithmic_scaling: false
+    y_axis_logarithmic_scaling: true
+    title: ''
+...

config.yaml

@@ -0,0 +1,6 @@
+---
+data_path: 'put-path-to-the-data-directory-here'
+dataset_name: '2024-02-28_preprocessed_data.parquet'
+analysis_job_file: 'analysis_jobs/analysis_job_manuscript.yaml'
+create_pdf_report: true
+...

main.py

@@ -0,0 +1,60 @@
+import pandas as pd
+import yaml
+import logging
+import datetime
+from pathlib import Path
+
+from src.analysis import run_analyses
+from src.preprocessor import Preprocessing
+
+from src.data_loading_and_saving.constructor import custom_constructor
+from src.data_loading_and_saving.create_results_report import (
+    create_markdown_report,
+    create_pdf_report,
+)
+
+
+def main():
+    yaml.SafeLoader.add_multi_constructor("!", custom_constructor)
+
+    with open("config.yaml", "r") as file:
+        config = yaml.safe_load(file)
+
+    path_to_data: str = config["data_path"]
+    name_data: str = config["dataset_name"]
+    name_analysis_job_file: str = config["analysis_job_file"]
+    bool_create_pdf_report: bool = config["create_pdf_report"]
+
+    log_filename: str = "log.log"
+    todays_date: str = datetime.date.today().strftime("%B %d, %Y")
+    output_name: str = f"{todays_date}_analysis_report"
+    logging.basicConfig(
+        filename=log_filename,
+        filemode="w",
+        format="%(message)s",
+        level=logging.INFO,
+    )
+
+    preprocessor: Preprocessing = Preprocessing(path_to_data, name_data)
+
+    with open(name_analysis_job_file, "r") as file:
+        analysis_config = yaml.safe_load(file)
+
+    datasets: dict[str, pd.DataFrame] = preprocessor.preprocess_datasets(analysis_config["preprocessing"])
+
+    run_analyses(analysis_config, datasets)
+
+    create_markdown_report(
+        log_filename=Path(log_filename),
+        output_name=Path(output_name),
+        output_dir=Path("results_reports"),
+    )
+
+    if bool_create_pdf_report:
+        create_pdf_report(
+            markdown_filename=Path(output_name), output_dir=Path("results_reports")
+        )
+
+
+if __name__ == "__main__":
+    main()

requirements.txt

@@ -0,0 +1,13 @@
+statsmodels==0.14.2
+numpy==2.0.0
+scipy==1.13.1
+scikit-learn==1.5.0
+matplotlib==3.9.0
+seaborn==0.13.2
+pandas==2.2.2
+rpy2==3.5.16
+pyarrow==16.1.0
+pingouin==0.5.4
+attrs==23.2.0
+pyyaml==6.0.1
+pypandoc==1.13

results/Evidence_antagonism_preregistered_model.txt

@@ -0,0 +1,25 @@
+                                   OLS Regression Results                                   
+============================================================================================
+Dep. Variable:     bayes-corrected (q=0.25) valence   R-squared:                       0.021
+Model:                                          OLS   Adj. R-squared:                  0.021
+Method:                               Least Squares   F-statistic:                 5.020e+04
+Date:                              Mon, 22 Jul 2024   Prob (F-statistic):               0.00
+Time:                                      09:31:51   Log-Likelihood:                -221.34
+No. Observations:                           2392896   AIC:                             446.7
+Df Residuals:                               2392894   BIC:                             472.1
+Df Model:                                         1                                         
+Covariance Type:                          nonrobust                                         
+====================================================================================================================
+                                                       coef    std err          t      P>|t|      [0.025      0.975]
+--------------------------------------------------------------------------------------------------------------------
+const                                                0.1246      0.000    796.722      0.000       0.124       0.125
+mean bayes-corrected (q=0.25) valence of replies    -0.0351      0.000   -224.063      0.000      -0.035      -0.035
+==============================================================================
+Omnibus:                   426104.077   Durbin-Watson:                   1.729
+Prob(Omnibus):                  0.000   Jarque-Bera (JB):           131391.270
+Skew:                          -0.336   Prob(JB):                         0.00
+Kurtosis:                       2.070   Cond. No.                         1.00
+==============================================================================
+
+Notes:
+[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.

results/Evidence_antagonism_robustness_order1.txt

@@ -0,0 +1,25 @@
+                                   OLS Regression Results                                   
+============================================================================================
+Dep. Variable:     bayes-corrected (q=0.25) valence   R-squared:                       0.057
+Model:                                          OLS   Adj. R-squared:                  0.057
+Method:                               Least Squares   F-statistic:                 9.915e+04
+Date:                              Mon, 22 Jul 2024   Prob (F-statistic):               0.00
+Time:                                      09:31:54   Log-Likelihood:             2.1429e+05
+No. Observations:                           1630262   AIC:                        -4.286e+05
+Df Residuals:                               1630260   BIC:                        -4.286e+05
+Df Model:                                         1                                         
+Covariance Type:                          nonrobust                                         
+====================================================================================================================
+                                                       coef    std err          t      P>|t|      [0.025      0.975]
+--------------------------------------------------------------------------------------------------------------------
+const                                                0.1419      0.000    854.072      0.000       0.142       0.142
+mean bayes-corrected (q=0.25) valence of replies    -0.0523      0.000   -314.877      0.000      -0.053      -0.052
+==============================================================================
+Omnibus:                   101738.374   Durbin-Watson:                   1.753
+Prob(Omnibus):                  0.000   Jarque-Bera (JB):            62821.338
+Skew:                          -0.351   Prob(JB):                         0.00
+Kurtosis:                       2.343   Cond. No.                         1.00
+==============================================================================
+
+Notes:
+[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.

results/Evidence_antagonism_section_affairs.txt

@@ -0,0 +1,25 @@
+                                   OLS Regression Results                                   
+============================================================================================
+Dep. Variable:     bayes-corrected (q=0.25) valence   R-squared:                       0.019
+Model:                                          OLS   Adj. R-squared:                  0.019
+Method:                               Least Squares   F-statistic:                     8343.
+Date:                              Mon, 22 Jul 2024   Prob (F-statistic):               0.00
+Time:                                      09:31:53   Log-Likelihood:                -43060.
+No. Observations:                            440260   AIC:                         8.612e+04
+Df Residuals:                                440258   BIC:                         8.615e+04
+Df Model:                                         1                                         
+Covariance Type:                          nonrobust                                         
+====================================================================================================================
+                                                       coef    std err          t      P>|t|      [0.025      0.975]
+--------------------------------------------------------------------------------------------------------------------
+const                                                0.1353      0.000    336.404      0.000       0.134       0.136
+mean bayes-corrected (q=0.25) valence of replies    -0.0367      0.000    -91.341      0.000      -0.038      -0.036
+==============================================================================
+Omnibus:                   129058.321   Durbin-Watson:                   1.735
+Prob(Omnibus):                  0.000   Jarque-Bera (JB):            32315.635
+Skew:                          -0.421   Prob(JB):                         0.00
+Kurtosis:                       1.974   Cond. No.                         1.00
+==============================================================================
+
+Notes:
+[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.

results/Evidence_antagonism_section_culture.txt

@@ -0,0 +1,25 @@
+                                   OLS Regression Results                                   
+============================================================================================
+Dep. Variable:     bayes-corrected (q=0.25) valence   R-squared:                       0.032
+Model:                                          OLS   Adj. R-squared:                  0.032
+Method:                               Least Squares   F-statistic:                     3435.
+Date:                              Mon, 22 Jul 2024   Prob (F-statistic):               0.00
+Time:                                      09:31:54   Log-Likelihood:                -1315.0
+No. Observations:                            102305   AIC:                             2634.
+Df Residuals:                                102303   BIC:                             2653.
+Df Model:                                         1                                         
+Covariance Type:                          nonrobust                                         
+====================================================================================================================
+                                                       coef    std err          t      P>|t|      [0.025      0.975]
+--------------------------------------------------------------------------------------------------------------------
+const                                                0.1253      0.001    163.518      0.000       0.124       0.127
+mean bayes-corrected (q=0.25) valence of replies    -0.0449      0.001    -58.610      0.000      -0.046      -0.043
+==============================================================================
+Omnibus:                    19234.419   Durbin-Watson:                   1.748
+Prob(Omnibus):                  0.000   Jarque-Bera (JB):             5689.759
+Skew:                          -0.334   Prob(JB):                         0.00
+Kurtosis:                       2.057   Cond. No.                         1.00
+==============================================================================
+
+Notes:
+[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.

results/Evidence_antagonism_section_economy.txt

@@ -0,0 +1,25 @@
+                                   OLS Regression Results                                   
+============================================================================================
+Dep. Variable:     bayes-corrected (q=0.25) valence   R-squared:                       0.017
+Model:                                          OLS   Adj. R-squared:                  0.017
+Method:                               Least Squares   F-statistic:                     5484.
+Date:                              Mon, 22 Jul 2024   Prob (F-statistic):               0.00
+Time:                                      09:31:53   Log-Likelihood:                 24023.
+No. Observations:                            316428   AIC:                        -4.804e+04
+Df Residuals:                                316426   BIC:                        -4.802e+04
+Df Model:                                         1                                         
+Covariance Type:                          nonrobust                                         
+====================================================================================================================
+                                                       coef    std err          t      P>|t|      [0.025      0.975]
+--------------------------------------------------------------------------------------------------------------------
+const                                                0.1474      0.000    369.619      0.000       0.147       0.148
+mean bayes-corrected (q=0.25) valence of replies    -0.0295      0.000    -74.054      0.000      -0.030      -0.029
+==============================================================================
+Omnibus:                    28321.195   Durbin-Watson:                   1.760
+Prob(Omnibus):                  0.000   Jarque-Bera (JB):            17536.904
+Skew:                          -0.450   Prob(JB):                         0.00
+Kurtosis:                       2.278   Cond. No.                         1.00
+==============================================================================
+
+Notes:
+[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.

results/Evidence_antagonism_section_health.txt

@@ -0,0 +1,25 @@
+                                   OLS Regression Results                                   
+============================================================================================
+Dep. Variable:     bayes-corrected (q=0.25) valence   R-squared:                       0.043
+Model:                                          OLS   Adj. R-squared:                  0.043
+Method:                               Least Squares   F-statistic:                     1211.
+Date:                              Mon, 22 Jul 2024   Prob (F-statistic):          1.61e-259
+Time:                                      09:31:54   Log-Likelihood:                -439.22
+No. Observations:                             27005   AIC:                             882.4
+Df Residuals:                                 27003   BIC:                             898.9
+Df Model:                                         1                                         
+Covariance Type:                          nonrobust                                         
+====================================================================================================================
+                                                       coef    std err          t      P>|t|      [0.025      0.975]
+--------------------------------------------------------------------------------------------------------------------
+const                                                0.1074      0.001     71.776      0.000       0.104       0.110
+mean bayes-corrected (q=0.25) valence of replies    -0.0521      0.001    -34.794      0.000      -0.055      -0.049
+==============================================================================
+Omnibus:                     6746.889   Durbin-Watson:                   1.761
+Prob(Omnibus):                  0.000   Jarque-Bera (JB):             1324.435
+Skew:                          -0.197   Prob(JB):                    2.53e-288
+Kurtosis:                       1.989   Cond. No.                         1.00
+==============================================================================
+
+Notes:
+[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.

results/Evidence_antagonism_section_internet.txt

@@ -0,0 +1,25 @@
+                                   OLS Regression Results                                   
+============================================================================================
+Dep. Variable:     bayes-corrected (q=0.25) valence   R-squared:                       0.028
+Model:                                          OLS   Adj. R-squared:                  0.028
+Method:                               Least Squares   F-statistic:                     1805.
+Date:                              Mon, 22 Jul 2024   Prob (F-statistic):               0.00
+Time:                                      09:31:54   Log-Likelihood:                -3477.5
+No. Observations:                             63079   AIC:                             6959.
+Df Residuals:                                 63077   BIC:                             6977.
+Df Model:                                         1                                         
+Covariance Type:                          nonrobust                                         
+====================================================================================================================
+                                                       coef    std err          t      P>|t|      [0.025      0.975]
+--------------------------------------------------------------------------------------------------------------------
+const                                                0.1191      0.001    117.001      0.000       0.117       0.121
+mean bayes-corrected (q=0.25) valence of replies    -0.0433      0.001    -42.490      0.000      -0.045      -0.041
+==============================================================================
+Omnibus:                    21454.701   Durbin-Watson:                   1.721
+Prob(Omnibus):                  0.000   Jarque-Bera (JB):             4028.801
+Skew:                          -0.319   Prob(JB):                         0.00
+Kurtosis:                       1.939   Cond. No.                         1.00
+==============================================================================
+
+Notes:
+[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.

results/Evidence_antagonism_section_miscellaneous.txt

@@ -0,0 +1,25 @@
+                                   OLS Regression Results                                   
+============================================================================================
+Dep. Variable:     bayes-corrected (q=0.25) valence   R-squared:                       0.028
+Model:                                          OLS   Adj. R-squared:                  0.028
+Method:                               Least Squares   F-statistic:                     6790.
+Date:                              Mon, 22 Jul 2024   Prob (F-statistic):               0.00
+Time:                                      09:31:54   Log-Likelihood:                -13916.
+No. Observations:                            235551   AIC:                         2.784e+04
+Df Residuals:                                235549   BIC:                         2.786e+04
+Df Model:                                         1                                         
+Covariance Type:                          nonrobust                                         
+====================================================================================================================
+                                                       coef    std err          t      P>|t|      [0.025      0.975]
+--------------------------------------------------------------------------------------------------------------------
+const                                                0.1362      0.001    257.499      0.000       0.135       0.137
+mean bayes-corrected (q=0.25) valence of replies    -0.0436      0.001    -82.403      0.000      -0.045      -0.043
+==============================================================================
+Omnibus:                    52959.753   Durbin-Watson:                   1.732
+Prob(Omnibus):                  0.000   Jarque-Bera (JB):            15867.344
+Skew:                          -0.409   Prob(JB):                         0.00
+Kurtosis:                       2.027   Cond. No.                         1.00
+==============================================================================
+
+Notes:
+[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.

results/Evidence_antagonism_section_mobility.txt

@@ -0,0 +1,25 @@
+                                   OLS Regression Results                                   
+============================================================================================
+Dep. Variable:     bayes-corrected (q=0.25) valence   R-squared:                       0.024
+Model:                                          OLS   Adj. R-squared:                  0.024
+Method:                               Least Squares   F-statistic:                     1726.
+Date:                              Mon, 22 Jul 2024   Prob (F-statistic):               0.00
+Time:                                      09:31:54   Log-Likelihood:                 10825.
+No. Observations:                             69253   AIC:                        -2.165e+04
+Df Residuals:                                 69251   BIC:                        -2.163e+04
+Df Model:                                         1                                         
+Covariance Type:                          nonrobust                                         
+====================================================================================================================
+                                                       coef    std err          t      P>|t|      [0.025      0.975]
+--------------------------------------------------------------------------------------------------------------------
+const                                                0.1109      0.001    141.050      0.000       0.109       0.112
+mean bayes-corrected (q=0.25) valence of replies    -0.0327      0.001    -41.551      0.000      -0.034      -0.031
+==============================================================================
+Omnibus:                     6922.840   Durbin-Watson:                   1.814
+Prob(Omnibus):                  0.000   Jarque-Bera (JB):             2381.203
+Skew:                          -0.195   Prob(JB):                         0.00
+Kurtosis:                       2.179   Cond. No.                         1.00
+==============================================================================
+
+Notes:
+[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.

results/Evidence_antagonism_section_politics.txt

@@ -0,0 +1,25 @@
+                                   OLS Regression Results                                   
+============================================================================================
+Dep. Variable:     bayes-corrected (q=0.25) valence   R-squared:                       0.018
+Model:                                          OLS   Adj. R-squared:                  0.018
+Method:                               Least Squares   F-statistic:                 1.166e+04
+Date:                              Mon, 22 Jul 2024   Prob (F-statistic):               0.00
+Time:                                      09:31:53   Log-Likelihood:                 34045.
+No. Observations:                            621929   AIC:                        -6.809e+04
+Df Residuals:                                621927   BIC:                        -6.806e+04
+Df Model:                                         1                                         
+Covariance Type:                          nonrobust                                         
+====================================================================================================================
+                                                       coef    std err          t      P>|t|      [0.025      0.975]
+--------------------------------------------------------------------------------------------------------------------
+const                                                0.1305      0.000    449.326      0.000       0.130       0.131
+mean bayes-corrected (q=0.25) valence of replies    -0.0314      0.000   -107.983      0.000      -0.032      -0.031
+==============================================================================
+Omnibus:                    78154.602   Durbin-Watson:                   1.733
+Prob(Omnibus):                  0.000   Jarque-Bera (JB):            31765.731
+Skew:                          -0.357   Prob(JB):                         0.00
+Kurtosis:                       2.155   Cond. No.                         1.00
+==============================================================================
+
+Notes:
+[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.

results/Evidence_antagonism_section_science.txt

@@ -0,0 +1,25 @@
+                                   OLS Regression Results                                   
+============================================================================================
+Dep. Variable:     bayes-corrected (q=0.25) valence   R-squared:                       0.028
+Model:                                          OLS   Adj. R-squared:                  0.028
+Method:                               Least Squares   F-statistic:                 1.007e+04
+Date:                              Mon, 22 Jul 2024   Prob (F-statistic):               0.00
+Time:                                      09:31:53   Log-Likelihood:                 27583.
+No. Observations:                            345534   AIC:                        -5.516e+04
+Df Residuals:                                345532   BIC:                        -5.514e+04
+Df Model:                                         1                                         
+Covariance Type:                          nonrobust                                         
+====================================================================================================================
+                                                       coef    std err          t      P>|t|      [0.025      0.975]
+--------------------------------------------------------------------------------------------------------------------
+const                                                0.0723      0.000    190.132      0.000       0.072       0.073
+mean bayes-corrected (q=0.25) valence of replies    -0.0381      0.000   -100.372      0.000      -0.039      -0.037
+==============================================================================
+Omnibus:                    59103.072   Durbin-Watson:                   1.791
+Prob(Omnibus):                  0.000   Jarque-Bera (JB):            12955.369
+Skew:                          -0.052   Prob(JB):                         0.00
+Kurtosis:                       2.057   Cond. No.                         1.00
+==============================================================================
+
+Notes:
+[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.

results/Evidence_antagonism_section_sports.txt

@@ -0,0 +1,25 @@
+                                   OLS Regression Results                                   
+============================================================================================
+Dep. Variable:     bayes-corrected (q=0.25) valence   R-squared:                       0.032
+Model:                                          OLS   Adj. R-squared:                  0.032
+Method:                               Least Squares   F-statistic:                     3344.
+Date:                              Mon, 22 Jul 2024   Prob (F-statistic):               0.00
+Time:                                      09:31:54   Log-Likelihood:                -6723.8
+No. Observations:                            100071   AIC:                         1.345e+04
+Df Residuals:                                100069   BIC:                         1.347e+04
+Df Model:                                         1                                         
+Covariance Type:                          nonrobust                                         
+====================================================================================================================
+                                                       coef    std err          t      P>|t|      [0.025      0.975]
+--------------------------------------------------------------------------------------------------------------------
+const                                                0.1246      0.001    152.318      0.000       0.123       0.126
+mean bayes-corrected (q=0.25) valence of replies    -0.0473      0.001    -57.827      0.000      -0.049      -0.046
+==============================================================================
+Omnibus:                    28267.899   Durbin-Watson:                   1.740
+Prob(Omnibus):                  0.000   Jarque-Bera (JB):             6368.870
+Skew:                          -0.345   Prob(JB):                         0.00
+Kurtosis:                       1.975   Cond. No.                         1.00
+==============================================================================
+
+Notes:
+[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.

results/Evidence_antagonism_stability_against_variation_in_weight_no_bayes_correction.txt

@@ -0,0 +1,25 @@
+                            OLS Regression Results                            
+==============================================================================
+Dep. Variable:                valence   R-squared:                       0.010
+Model:                            OLS   Adj. R-squared:                  0.010
+Method:                 Least Squares   F-statistic:                 2.337e+04
+Date:                Mon, 22 Jul 2024   Prob (F-statistic):               0.00
+Time:                        09:31:53   Log-Likelihood:            -4.8218e+05
+No. Observations:             2392896   AIC:                         9.644e+05
+Df Residuals:                 2392894   BIC:                         9.644e+05
+Df Model:                           1                                         
+Covariance Type:            nonrobust                                         
+===========================================================================================
+                              coef    std err          t      P>|t|      [0.025      0.975]
+-------------------------------------------------------------------------------------------
+const                       0.1158      0.000    604.951      0.000       0.115       0.116
+mean valence of replies    -0.0293      0.000   -152.877      0.000      -0.030      -0.029
+==============================================================================
+Omnibus:                   785394.853   Durbin-Watson:                   1.750
+Prob(Omnibus):                  0.000   Jarque-Bera (JB):           152455.997
+Skew:                          -0.323   Prob(JB):                         0.00
+Kurtosis:                       1.946   Cond. No.                         1.00
+==============================================================================
+
+Notes:
+[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.

results/Evidence_antagonism_stability_against_variation_in_weight_q5.txt

@@ -0,0 +1,25 @@
+                                   OLS Regression Results                                  
+===========================================================================================
+Dep. Variable:     bayes-corrected (q=0.5) valence   R-squared:                       0.027
+Model:                                         OLS   Adj. R-squared:                  0.027
+Method:                              Least Squares   F-statistic:                 6.556e+04
+Date:                             Mon, 22 Jul 2024   Prob (F-statistic):               0.00
+Time:                                     09:31:52   Log-Likelihood:             3.9215e+05
+No. Observations:                          2392896   AIC:                        -7.843e+05
+Df Residuals:                              2392894   BIC:                        -7.843e+05
+Df Model:                                        1                                         
+Covariance Type:                         nonrobust                                         
+===================================================================================================================
+                                                      coef    std err          t      P>|t|      [0.025      0.975]
+-------------------------------------------------------------------------------------------------------------------
+const                                               0.1323      0.000    996.732      0.000       0.132       0.133
+mean bayes-corrected (q=0.5) valence of replies    -0.0340      0.000   -256.042      0.000      -0.034      -0.034
+==============================================================================
+Omnibus:                   168653.316   Durbin-Watson:                   1.726
+Prob(Omnibus):                  0.000   Jarque-Bera (JB):           107460.980
+Skew:                          -0.396   Prob(JB):                         0.00
+Kurtosis:                       2.328   Cond. No.                         1.00
+==============================================================================
+
+Notes:
+[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.

results/Evidence_antagonism_stability_against_variation_in_weight_q75.txt

@@ -0,0 +1,25 @@
+                                   OLS Regression Results                                   
+============================================================================================
+Dep. Variable:     bayes-corrected (q=0.75) valence   R-squared:                       0.032
+Model:                                          OLS   Adj. R-squared:                  0.032
+Method:                               Least Squares   F-statistic:                 8.012e+04
+Date:                              Mon, 22 Jul 2024   Prob (F-statistic):               0.00
+Time:                                      09:31:52   Log-Likelihood:             8.8112e+05
+No. Observations:                           2392896   AIC:                        -1.762e+06
+Df Residuals:                               2392894   BIC:                        -1.762e+06
+Df Model:                                         1                                         
+Covariance Type:                          nonrobust                                         
+====================================================================================================================
+                                                       coef    std err          t      P>|t|      [0.025      0.975]
+--------------------------------------------------------------------------------------------------------------------
+const                                                0.1411      0.000   1303.270      0.000       0.141       0.141
+mean bayes-corrected (q=0.75) valence of replies    -0.0306      0.000   -283.054      0.000      -0.031      -0.030
+==============================================================================
+Omnibus:                    95205.666   Durbin-Watson:                   1.729
+Prob(Omnibus):                  0.000   Jarque-Bera (JB):           102788.717
+Skew:                          -0.491   Prob(JB):                         0.00
+Kurtosis:                       2.742   Cond. No.                         1.00
+==============================================================================
+
+Notes:
+[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.

results/Evidence_polarization_paired_ttest_extremity.txt

@@ -0,0 +1,8 @@
+Mean of bayes-corrected (q=0.25) extremity: 0.28634078314814315
+Mean of mean bayes-corrected (q=0.25) extremity of replies: 0.31853427098636283
+Standard Deviation of bayes-corrected (q=0.25) extremity: 0.12461005214018245
+Standard Deviation of mean bayes-corrected (q=0.25) extremity of replies: 0.09803757310470287
+Degrees of Freedom: 2392895
+Cohen's d: -0.28714996199978216
+T-statistic: -396.76675511778956
+P-value: 0.0

results/Evidence_polarization_paired_ttest_extremity_culture.txt

@@ -0,0 +1,8 @@
+Mean of bayes-corrected (q=0.25) extremity: 0.2873043034163312
+Mean of mean bayes-corrected (q=0.25) extremity of replies: 0.3180681433274033
+Standard Deviation of bayes-corrected (q=0.25) extremity: 0.12427097360816901
+Standard Deviation of mean bayes-corrected (q=0.25) extremity of replies: 0.10041204122831116
+Degrees of Freedom: 102304
+Cohen's d: -0.27231114070182555
+T-statistic: -77.26207861609845
+P-value: 0.0

results/Evidence_polarization_paired_ttest_extremity_economy.txt

@@ -0,0 +1,8 @@
+Mean of bayes-corrected (q=0.25) extremity: 0.28753090601867964
+Mean of mean bayes-corrected (q=0.25) extremity of replies: 0.3206172046668397
+Standard Deviation of bayes-corrected (q=0.25) extremity: 0.12269603857552688
+Standard Deviation of mean bayes-corrected (q=0.25) extremity of replies: 0.09544219919767762
+Degrees of Freedom: 316427
+Cohen's d: -0.3010114266220678
+T-statistic: -144.89599610520233
+P-value: 0.0

results/Evidence_polarization_paired_ttest_extremity_foreign_affairs.txt

@@ -0,0 +1,8 @@
+Mean of bayes-corrected (q=0.25) extremity: 0.30983360408913946
+Mean of mean bayes-corrected (q=0.25) extremity of replies: 0.330913534598374
+Standard Deviation of bayes-corrected (q=0.25) extremity: 0.1266220167440838
+Standard Deviation of mean bayes-corrected (q=0.25) extremity of replies: 0.0994658270407316
+Degrees of Freedom: 440259
+Cohen's d: -0.18514479506979328
+T-statistic: -116.67457613500132
+P-value: 0.0

results/Evidence_polarization_paired_ttest_extremity_health.txt

@@ -0,0 +1,8 @@
+Mean of bayes-corrected (q=0.25) extremity: 0.286001211119296
+Mean of mean bayes-corrected (q=0.25) extremity of replies: 0.32344058185785135
+Standard Deviation of bayes-corrected (q=0.25) extremity: 0.12360412242902419
+Standard Deviation of mean bayes-corrected (q=0.25) extremity of replies: 0.09582069057098505
+Degrees of Freedom: 27004
+Cohen's d: -0.3385470290175242
+T-statistic: -48.09524752175683
+P-value: 0.0

results/Evidence_polarization_paired_ttest_extremity_internet.txt

@@ -0,0 +1,8 @@
+Mean of bayes-corrected (q=0.25) extremity: 0.30568494651578504
+Mean of mean bayes-corrected (q=0.25) extremity of replies: 0.33706126033387757
+Standard Deviation of bayes-corrected (q=0.25) extremity: 0.12135285517757544
+Standard Deviation of mean bayes-corrected (q=0.25) extremity of replies: 0.09268724557998224
+Degrees of Freedom: 63078
+Cohen's d: -0.2905871965145026
+T-statistic: -63.21801300923011
+P-value: 0.0

results/Evidence_polarization_paired_ttest_extremity_miscellaneous.txt

@@ -0,0 +1,8 @@
+Mean of bayes-corrected (q=0.25) extremity: 0.3045872088628839
+Mean of mean bayes-corrected (q=0.25) extremity of replies: 0.33005502824126426
+Standard Deviation of bayes-corrected (q=0.25) extremity: 0.12405998014131653
+Standard Deviation of mean bayes-corrected (q=0.25) extremity of replies: 0.09742991339150692
+Degrees of Freedom: 235550
+Cohen's d: -0.22832386975048508
+T-statistic: -97.05206575930157
+P-value: 0.0

results/Evidence_polarization_paired_ttest_extremity_mobility.txt

@@ -0,0 +1,8 @@
+Mean of bayes-corrected (q=0.25) extremity: 0.25434099233474056
+Mean of mean bayes-corrected (q=0.25) extremity of replies: 0.3002874727751491
+Standard Deviation of bayes-corrected (q=0.25) extremity: 0.1194543498720806
+Standard Deviation of mean bayes-corrected (q=0.25) extremity of replies: 0.09661488578718154
+Degrees of Freedom: 69252
+Cohen's d: -0.4229377864257918
+T-statistic: -93.24696971910268
+P-value: 0.0

results/Evidence_polarization_paired_ttest_extremity_politics.txt

@@ -0,0 +1,8 @@
+Mean of bayes-corrected (q=0.25) extremity: 0.2747813213977206
+Mean of mean bayes-corrected (q=0.25) extremity of replies: 0.31051648819461664
+Standard Deviation of bayes-corrected (q=0.25) extremity: 0.1232411698734475
+Standard Deviation of mean bayes-corrected (q=0.25) extremity of replies: 0.09815038738028235
+Degrees of Freedom: 621928
+Cohen's d: -0.3207697725588003
+T-statistic: -224.4339595489235
+P-value: 0.0

results/Evidence_polarization_paired_ttest_extremity_science.txt

@@ -0,0 +1,8 @@
+Mean of bayes-corrected (q=0.25) extremity: 0.25732194943047365
+Mean of mean bayes-corrected (q=0.25) extremity of replies: 0.3019777399435376
+Standard Deviation of bayes-corrected (q=0.25) extremity: 0.1187657730515952
+Standard Deviation of mean bayes-corrected (q=0.25) extremity of replies: 0.09498121080140695
+Degrees of Freedom: 345533
+Cohen's d: -0.4152747999524859
+T-statistic: -212.56678640514008
+P-value: 0.0

results/Evidence_polarization_paired_ttest_extremity_sports.txt

@@ -0,0 +1,8 @@
+Mean of bayes-corrected (q=0.25) extremity: 0.30601102207250513
+Mean of mean bayes-corrected (q=0.25) extremity of replies: 0.328439915246921
+Standard Deviation of bayes-corrected (q=0.25) extremity: 0.12292708240128108
+Standard Deviation of mean bayes-corrected (q=0.25) extremity of replies: 0.098047183761713
+Degrees of Freedom: 100070
+Cohen's d: -0.20172544463043993
+T-statistic: -55.9671976011527
+P-value: 0.0

results/Evidence_polarization_robustness_paired_ttest_order1.txt

@@ -0,0 +1,8 @@
+Mean of bayes-corrected (q=0.25) extremity: 0.29265411081901965
+Mean of mean bayes-corrected (q=0.25) extremity of replies: 0.316766141686027
+Standard Deviation of bayes-corrected (q=0.25) extremity: 0.11701339959130957
+Standard Deviation of mean bayes-corrected (q=0.25) extremity of replies: 0.09812627267575441
+Degrees of Freedom: 1630261
+Cohen's d: -0.2232935227954181
+T-statistic: -248.9875068375778
+P-value: 0.0

results/Evidence_polarization_stability_against_variation_in_weight_paired_ttest_bayes.txt

@@ -0,0 +1,8 @@
+Mean of extremity: 0.2786279465660722
+Mean of mean extremity of replies: 0.33064022086792666
+Standard Deviation of extremity: 0.15566001726472525
+Standard Deviation of mean extremity of replies: 0.15685179947476463
+Degrees of Freedom: 2392895
+Cohen's d: -0.332863548235494
+T-statistic: -441.7826610833192
+P-value: 0.0

results/Evidence_polarization_stability_against_variation_in_weight_paired_ttest_q5.txt

@@ -0,0 +1,8 @@
+Mean of bayes-corrected (q=0.5) extremity: 0.2934997056888845
+Mean of mean bayes-corrected (q=0.5) extremity of replies: 0.31880240669265064
+Standard Deviation of bayes-corrected (q=0.5) extremity: 0.10366027656607042
+Standard Deviation of mean bayes-corrected (q=0.5) extremity of replies: 0.07259709613375841
+Degrees of Freedom: 2392895
+Cohen's d: -0.28275329909468133
+T-statistic: -394.7125869249032
+P-value: 0.0

results/Evidence_polarization_stability_against_variation_in_weight_paired_ttest_q75.txt

@@ -0,0 +1,8 @@
+Mean of bayes-corrected (q=0.75) extremity: 0.3010823980840001
+Mean of mean bayes-corrected (q=0.75) extremity of replies: 0.32039106933723704
+Standard Deviation of bayes-corrected (q=0.75) extremity: 0.08248076963764756
+Standard Deviation of mean bayes-corrected (q=0.75) extremity of replies: 0.05223289636934443
+Degrees of Freedom: 2392895
+Cohen's d: -0.2796984844303324
+T-statistic: -391.6388789093796
+P-value: 0.0

results/Evidence_uncogeniality_model_with_seperate_upvotes_downvotes.txt

@@ -0,0 +1,26 @@
+                              OLS Regression Results                             
+=================================================================================
+Dep. Variable:     number O(n+1)-replies   R-squared:                       0.194
+Model:                               OLS   Adj. R-squared:                  0.194
+Method:                    Least Squares   F-statistic:                 7.311e+05
+Date:                   Mon, 22 Jul 2024   Prob (F-statistic):               0.00
+Time:                           09:31:51   Log-Likelihood:            -1.0415e+07
+No. Observations:                6069971   AIC:                         2.083e+07
+Df Residuals:                    6069968   BIC:                         2.083e+07
+Df Model:                              2                                         
+Covariance Type:               nonrobust                                         
+==============================================================================
+                 coef    std err          t      P>|t|      [0.025      0.975]
+------------------------------------------------------------------------------
+const          1.1129      0.001   2037.629      0.000       1.112       1.114
+upvotes        0.0893      0.001    162.278      0.000       0.088       0.090
+downvotes      0.6433      0.001   1168.654      0.000       0.642       0.644
+==============================================================================
+Omnibus:                  3179849.625   Durbin-Watson:                   1.812
+Prob(Omnibus):                  0.000   Jarque-Bera (JB):        138815450.026
+Skew:                           1.836   Prob(JB):                         0.00
+Kurtosis:                      26.138   Cond. No.                         1.13
+==============================================================================
+
+Notes:
+[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.

results/Evidence_uncongeniality_preregistered_model.txt

@@ -0,0 +1,26 @@
+                              OLS Regression Results                             
+=================================================================================
+Dep. Variable:     number O(n+1)-replies   R-squared:                       0.220
+Model:                               OLS   Adj. R-squared:                  0.220
+Method:                    Least Squares   F-statistic:                 6.744e+05
+Date:                   Mon, 22 Jul 2024   Prob (F-statistic):               0.00
+Time:                           09:31:43   Log-Likelihood:            -8.1863e+06
+No. Observations:                4786218   AIC:                         1.637e+07
+Df Residuals:                    4786215   BIC:                         1.637e+07
+Df Model:                              2                                         
+Covariance Type:               nonrobust                                         
+====================================================================================================
+                                       coef    std err          t      P>|t|      [0.025      0.975]
+----------------------------------------------------------------------------------------------------
+const                                1.1760      0.001   1922.382      0.000       1.175       1.177
+bayes-corrected (q=0.25) valence    -0.4349      0.001   -707.468      0.000      -0.436      -0.434
+totalvotes                           0.5207      0.001    847.067      0.000       0.520       0.522
+==============================================================================
+Omnibus:                  2282674.662   Durbin-Watson:                   1.758
+Prob(Omnibus):                  0.000   Jarque-Bera (JB):         64040137.713
+Skew:                           1.723   Prob(JB):                         0.00
+Kurtosis:                      20.586   Cond. No.                         1.10
+==============================================================================
+
+Notes:
+[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.

results/Evidence_uncongeniality_robustness_analysis_on_person_level.txt

@@ -0,0 +1 @@
+totalvotes:  208.8619281171 (CI: [ 208.5635392284,  209.1603170058])

results/Evidence_uncongeniality_robustness_analysis_on_section_level.txt

@@ -0,0 +1 @@
+totalvotes:  444292.7728500224 (CI: [ 403421.6792516428,  485163.8664484020])

results/Evidence_uncongeniality_robustness_order1.txt

@@ -0,0 +1,26 @@
+                              OLS Regression Results                             
+=================================================================================
+Dep. Variable:     number O(n+1)-replies   R-squared:                       0.136
+Model:                               OLS   Adj. R-squared:                  0.136
+Method:                    Least Squares   F-statistic:                 3.982e+05
+Date:                   Mon, 22 Jul 2024   Prob (F-statistic):               0.00
+Time:                           09:31:50   Log-Likelihood:            -6.2998e+06
+No. Observations:                5050120   AIC:                         1.260e+07
+Df Residuals:                    5050117   BIC:                         1.260e+07
+Df Model:                              2                                         
+Covariance Type:               nonrobust                                         
+====================================================================================================
+                                       coef    std err          t      P>|t|      [0.025      0.975]
+----------------------------------------------------------------------------------------------------
+const                                0.6133      0.000   1636.095      0.000       0.613       0.614
+bayes-corrected (q=0.25) valence    -0.2055      0.000   -548.027      0.000      -0.206      -0.205
+totalvotes                           0.2575      0.000    686.512      0.000       0.257       0.258
+==============================================================================
+Omnibus:                  2832727.339   Durbin-Watson:                   1.864
+Prob(Omnibus):                  0.000   Jarque-Bera (JB):         85433368.019
+Skew:                           2.153   Prob(JB):                         0.00
+Kurtosis:                      22.684   Cond. No.                         1.03
+==============================================================================
+
+Notes:
+[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.

results/Evidence_uncongeniality_simplest_model_linear_regression_only_valence_non_standardized.txt

@@ -0,0 +1,25 @@
+                              OLS Regression Results                             
+=================================================================================
+Dep. Variable:     number O(n+1)-replies   R-squared:                       0.077
+Model:                               OLS   Adj. R-squared:                  0.077
+Method:                    Least Squares   F-statistic:                 4.005e+05
+Date:                   Mon, 22 Jul 2024   Prob (F-statistic):               0.00
+Time:                           09:31:42   Log-Likelihood:            -8.5881e+06
+No. Observations:                4786218   AIC:                         1.718e+07
+Df Residuals:                    4786216   BIC:                         1.718e+07
+Df Model:                              1                                         
+Covariance Type:               nonrobust                                         
+==============================================================================
+                 coef    std err          t      P>|t|      [0.025      0.975]
+------------------------------------------------------------------------------
+const          1.4225      0.001   1845.132      0.000       1.421       1.424
+valence       -1.3913      0.002   -632.878      0.000      -1.396      -1.387
+==============================================================================
+Omnibus:                  2883084.941   Durbin-Watson:                   1.828
+Prob(Omnibus):                  0.000   Jarque-Bera (JB):         98618092.392
+Skew:                           2.349   Prob(JB):                         0.00
+Kurtosis:                      24.736   Cond. No.                         3.42
+==============================================================================
+
+Notes:
+[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.

results/Evidence_uncongeniality_stability_against_variation_in_weight__no_bayes_correction.txt

@@ -0,0 +1,26 @@
+                              OLS Regression Results                             
+=================================================================================
+Dep. Variable:     number O(n+1)-replies   R-squared:                       0.199
+Model:                               OLS   Adj. R-squared:                  0.199
+Method:                    Least Squares   F-statistic:                 5.941e+05
+Date:                   Mon, 22 Jul 2024   Prob (F-statistic):               0.00
+Time:                           09:31:47   Log-Likelihood:            -8.2498e+06
+No. Observations:                4786218   AIC:                         1.650e+07
+Df Residuals:                    4786215   BIC:                         1.650e+07
+Df Model:                              2                                         
+Covariance Type:               nonrobust                                         
+==============================================================================
+                 coef    std err          t      P>|t|      [0.025      0.975]
+------------------------------------------------------------------------------
+const          1.1760      0.001   1897.046      0.000       1.175       1.177
+valence       -0.3745      0.001   -601.728      0.000      -0.376      -0.373
+totalvotes     0.5306      0.001    852.573      0.000       0.529       0.532
+==============================================================================
+Omnibus:                  2293481.647   Durbin-Watson:                   1.752
+Prob(Omnibus):                  0.000   Jarque-Bera (JB):         63398255.054
+Skew:                           1.739   Prob(JB):                         0.00
+Kurtosis:                      20.487   Cond. No.                         1.09
+==============================================================================
+
+Notes:
+[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.

results/Evidence_uncongeniality_stability_against_variation_in_weight_q5.txt

@@ -0,0 +1,26 @@
+                              OLS Regression Results                             
+=================================================================================
+Dep. Variable:     number O(n+1)-replies   R-squared:                       0.229
+Model:                               OLS   Adj. R-squared:                  0.229
+Method:                    Least Squares   F-statistic:                 7.096e+05
+Date:                   Mon, 22 Jul 2024   Prob (F-statistic):               0.00
+Time:                           09:31:44   Log-Likelihood:            -8.1590e+06
+No. Observations:                4786218   AIC:                         1.632e+07
+Df Residuals:                    4786215   BIC:                         1.632e+07
+Df Model:                              2                                         
+Covariance Type:               nonrobust                                         
+===================================================================================================
+                                      coef    std err          t      P>|t|      [0.025      0.975]
+---------------------------------------------------------------------------------------------------
+const                               1.1760      0.001   1933.368      0.000       1.175       1.177
+bayes-corrected (q=0.5) valence    -0.4582      0.001   -749.070      0.000      -0.459      -0.457
+totalvotes                          0.5147      0.001    841.341      0.000       0.513       0.516
+==============================================================================
+Omnibus:                  2271398.527   Durbin-Watson:                   1.760
+Prob(Omnibus):                  0.000   Jarque-Bera (JB):         63503192.358
+Skew:                           1.712   Prob(JB):                         0.00
+Kurtosis:                      20.513   Cond. No.                         1.11
+==============================================================================
+
+Notes:
+[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.

results/Evidence_uncongeniality_stability_against_variation_in_weight_q75.txt

@@ -0,0 +1,26 @@
+                              OLS Regression Results                             
+=================================================================================
+Dep. Variable:     number O(n+1)-replies   R-squared:                       0.236
+Model:                               OLS   Adj. R-squared:                  0.236
+Method:                    Least Squares   F-statistic:                 7.380e+05
+Date:                   Mon, 22 Jul 2024   Prob (F-statistic):               0.00
+Time:                           09:31:45   Log-Likelihood:            -8.1372e+06
+No. Observations:                4786218   AIC:                         1.627e+07
+Df Residuals:                    4786215   BIC:                         1.627e+07
+Df Model:                              2                                         
+Covariance Type:               nonrobust                                         
+====================================================================================================
+                                       coef    std err          t      P>|t|      [0.025      0.975]
+----------------------------------------------------------------------------------------------------
+const                                1.1760      0.001   1942.187      0.000       1.175       1.177
+bayes-corrected (q=0.75) valence    -0.4762      0.001   -781.029      0.000      -0.477      -0.475
+totalvotes                           0.5081      0.001    833.387      0.000       0.507       0.509
+==============================================================================
+Omnibus:                  2256599.632   Durbin-Watson:                   1.761
+Prob(Omnibus):                  0.000   Jarque-Bera (JB):         62251699.550
+Skew:                           1.700   Prob(JB):                         0.00
+Kurtosis:                      20.338   Cond. No.                         1.12
+==============================================================================
+
+Notes:
+[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.

results/Evidence_uncongenialty_section_affairs.txt

@@ -0,0 +1,26 @@
+                              OLS Regression Results                             
+=================================================================================
+Dep. Variable:     number O(n+1)-replies   R-squared:                       0.237
+Model:                               OLS   Adj. R-squared:                  0.237
+Method:                    Least Squares   F-statistic:                 1.380e+05
+Date:                   Mon, 22 Jul 2024   Prob (F-statistic):               0.00
+Time:                           09:31:48   Log-Likelihood:            -1.5539e+06
+No. Observations:                 890221   AIC:                         3.108e+06
+Df Residuals:                     890218   BIC:                         3.108e+06
+Df Model:                              2                                         
+Covariance Type:               nonrobust                                         
+====================================================================================================
+                                       coef    std err          t      P>|t|      [0.025      0.975]
+----------------------------------------------------------------------------------------------------
+const                                1.1789      0.001    802.397      0.000       1.176       1.182
+bayes-corrected (q=0.25) valence    -0.4979      0.001   -337.303      0.000      -0.501      -0.495
+totalvotes                           0.5435      0.001    368.179      0.000       0.541       0.546
+==============================================================================
+Omnibus:                   415616.007   Durbin-Watson:                   1.775
+Prob(Omnibus):                  0.000   Jarque-Bera (JB):          8567668.092
+Skew:                           1.765   Prob(JB):                         0.00
+Kurtosis:                      17.782   Cond. No.                         1.10
+==============================================================================
+
+Notes:
+[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.

results/Evidence_uncongenialty_section_culture.txt

@@ -0,0 +1,26 @@
+                              OLS Regression Results                             
+=================================================================================
+Dep. Variable:     number O(n+1)-replies   R-squared:                       0.243
+Model:                               OLS   Adj. R-squared:                  0.243
+Method:                    Least Squares   F-statistic:                 3.781e+04
+Date:                   Mon, 22 Jul 2024   Prob (F-statistic):               0.00
+Time:                           09:31:48   Log-Likelihood:            -3.6290e+05
+No. Observations:                 235911   AIC:                         7.258e+05
+Df Residuals:                     235908   BIC:                         7.258e+05
+Df Model:                              2                                         
+Covariance Type:               nonrobust                                         
+====================================================================================================
+                                       coef    std err          t      P>|t|      [0.025      0.975]
+----------------------------------------------------------------------------------------------------
+const                                0.9173      0.002    395.396      0.000       0.913       0.922
+bayes-corrected (q=0.25) valence    -0.3334      0.002   -142.771      0.000      -0.338      -0.329
+totalvotes                           0.5075      0.002    217.346      0.000       0.503       0.512
+==============================================================================
+Omnibus:                    99947.806   Durbin-Watson:                   1.805
+Prob(Omnibus):                  0.000   Jarque-Bera (JB):           886847.368
+Skew:                           1.813   Prob(JB):                         0.00
+Kurtosis:                      11.779   Cond. No.                         1.12
+==============================================================================
+
+Notes:
+[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.

results/Evidence_uncongenialty_section_economy.txt

@@ -0,0 +1,26 @@
+                              OLS Regression Results                             
+=================================================================================
+Dep. Variable:     number O(n+1)-replies   R-squared:                       0.196
+Model:                               OLS   Adj. R-squared:                  0.196
+Method:                    Least Squares   F-statistic:                 7.576e+04
+Date:                   Mon, 22 Jul 2024   Prob (F-statistic):               0.00
+Time:                           09:31:48   Log-Likelihood:            -1.0058e+06
+No. Observations:                 620776   AIC:                         2.012e+06
+Df Residuals:                     620773   BIC:                         2.012e+06
+Df Model:                              2                                         
+Covariance Type:               nonrobust                                         
+====================================================================================================
+                                       coef    std err          t      P>|t|      [0.025      0.975]
+----------------------------------------------------------------------------------------------------
+const                                1.1396      0.002    734.230      0.000       1.137       1.143
+bayes-corrected (q=0.25) valence    -0.3478      0.002   -223.518      0.000      -0.351      -0.345
+totalvotes                           0.4695      0.002    301.664      0.000       0.466       0.473
+==============================================================================
+Omnibus:                   202475.900   Durbin-Watson:                   1.799
+Prob(Omnibus):                  0.000   Jarque-Bera (JB):          1088427.374
+Skew:                           1.479   Prob(JB):                         0.00
+Kurtosis:                       8.773   Cond. No.                         1.08
+==============================================================================
+
+Notes:
+[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.

results/Evidence_uncongenialty_section_health.txt

@@ -0,0 +1,26 @@
+                              OLS Regression Results                             
+=================================================================================
+Dep. Variable:     number O(n+1)-replies   R-squared:                       0.257
+Model:                               OLS   Adj. R-squared:                  0.257
+Method:                    Least Squares   F-statistic:                     8576.
+Date:                   Mon, 22 Jul 2024   Prob (F-statistic):               0.00
+Time:                           09:31:48   Log-Likelihood:                -86794.
+No. Observations:                  49462   AIC:                         1.736e+05
+Df Residuals:                      49459   BIC:                         1.736e+05
+Df Model:                              2                                         
+Covariance Type:               nonrobust                                         
+====================================================================================================
+                                       coef    std err          t      P>|t|      [0.025      0.975]
+----------------------------------------------------------------------------------------------------
+const                                1.3371      0.006    212.544      0.000       1.325       1.349
+bayes-corrected (q=0.25) valence    -0.4685      0.006    -73.917      0.000      -0.481      -0.456
+totalvotes                           0.6228      0.006     98.259      0.000       0.610       0.635
+==============================================================================
+Omnibus:                    17663.533   Durbin-Watson:                   1.771
+Prob(Omnibus):                  0.000   Jarque-Bera (JB):           106942.347
+Skew:                           1.595   Prob(JB):                         0.00
+Kurtosis:                       9.459   Cond. No.                         1.13
+==============================================================================
+
+Notes:
+[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.

results/Evidence_uncongenialty_section_internet.txt

@@ -0,0 +1,26 @@
+                              OLS Regression Results                             
+=================================================================================
+Dep. Variable:     number O(n+1)-replies   R-squared:                       0.256
+Model:                               OLS   Adj. R-squared:                  0.256
+Method:                    Least Squares   F-statistic:                 2.267e+04
+Date:                   Mon, 22 Jul 2024   Prob (F-statistic):               0.00
+Time:                           09:31:48   Log-Likelihood:            -2.1421e+05
+No. Observations:                 131977   AIC:                         4.284e+05
+Df Residuals:                     131974   BIC:                         4.284e+05
+Df Model:                              2                                         
+Covariance Type:               nonrobust                                         
+====================================================================================================
+                                       coef    std err          t      P>|t|      [0.025      0.975]
+----------------------------------------------------------------------------------------------------
+const                                1.0804      0.003    320.014      0.000       1.074       1.087
+bayes-corrected (q=0.25) valence    -0.4040      0.003   -118.355      0.000      -0.411      -0.397
+totalvotes                           0.5375      0.003    157.450      0.000       0.531       0.544
+==============================================================================
+Omnibus:                    54168.298   Durbin-Watson:                   1.825
+Prob(Omnibus):                  0.000   Jarque-Bera (JB):           590918.640
+Skew:                           1.674   Prob(JB):                         0.00
+Kurtosis:                      12.811   Cond. No.                         1.16
+==============================================================================
+
+Notes:
+[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.

results/Evidence_uncongenialty_section_miscellaneous.txt

@@ -0,0 +1,26 @@
+                              OLS Regression Results                             
+=================================================================================
+Dep. Variable:     number O(n+1)-replies   R-squared:                       0.246
+Model:                               OLS   Adj. R-squared:                  0.246
+Method:                    Least Squares   F-statistic:                 7.921e+04
+Date:                   Mon, 22 Jul 2024   Prob (F-statistic):               0.00
+Time:                           09:31:48   Log-Likelihood:            -8.1045e+05
+No. Observations:                 485006   AIC:                         1.621e+06
+Df Residuals:                     485003   BIC:                         1.621e+06
+Df Model:                              2                                         
+Covariance Type:               nonrobust                                         
+====================================================================================================
+                                       coef    std err          t      P>|t|      [0.025      0.975]
+----------------------------------------------------------------------------------------------------
+const                                1.1141      0.002    602.981      0.000       1.110       1.118
+bayes-corrected (q=0.25) valence    -0.4406      0.002   -237.533      0.000      -0.444      -0.437
+totalvotes                           0.5508      0.002    296.904      0.000       0.547       0.554
+==============================================================================
+Omnibus:                   308614.044   Durbin-Watson:                   1.795
+Prob(Omnibus):                  0.000   Jarque-Bera (JB):         33388300.741
+Skew:                           2.187   Prob(JB):                         0.00
+Kurtosis:                      43.411   Cond. No.                         1.09
+==============================================================================
+
+Notes:
+[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.

results/Evidence_uncongenialty_section_mobility.txt

@@ -0,0 +1,26 @@
+                              OLS Regression Results                             
+=================================================================================
+Dep. Variable:     number O(n+1)-replies   R-squared:                       0.198
+Model:                               OLS   Adj. R-squared:                  0.198
+Method:                    Least Squares   F-statistic:                 1.449e+04
+Date:                   Mon, 22 Jul 2024   Prob (F-statistic):               0.00
+Time:                           09:31:48   Log-Likelihood:            -1.9705e+05
+No. Observations:                 117051   AIC:                         3.941e+05
+Df Residuals:                     117048   BIC:                         3.941e+05
+Df Model:                              2                                         
+Covariance Type:               nonrobust                                         
+====================================================================================================
+                                       coef    std err          t      P>|t|      [0.025      0.975]
+----------------------------------------------------------------------------------------------------
+const                                1.3476      0.004    353.887      0.000       1.340       1.355
+bayes-corrected (q=0.25) valence    -0.3144      0.004    -80.973      0.000      -0.322      -0.307
+totalvotes                           0.5090      0.004    131.111      0.000       0.501       0.517
+==============================================================================
+Omnibus:                    32287.766   Durbin-Watson:                   1.796
+Prob(Omnibus):                  0.000   Jarque-Bera (JB):           111823.546
+Skew:                           1.377   Prob(JB):                         0.00
+Kurtosis:                       6.917   Cond. No.                         1.22
+==============================================================================
+
+Notes:
+[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.

results/Evidence_uncongenialty_section_politics.txt

@@ -0,0 +1,26 @@
+                              OLS Regression Results                             
+=================================================================================
+Dep. Variable:     number O(n+1)-replies   R-squared:                       0.209
+Model:                               OLS   Adj. R-squared:                  0.209
+Method:                    Least Squares   F-statistic:                 1.708e+05
+Date:                   Mon, 22 Jul 2024   Prob (F-statistic):               0.00
+Time:                           09:31:48   Log-Likelihood:            -2.1743e+06
+No. Observations:                1295105   AIC:                         4.349e+06
+Df Residuals:                    1295102   BIC:                         4.349e+06
+Df Model:                              2                                         
+Covariance Type:               nonrobust                                         
+====================================================================================================
+                                       coef    std err          t      P>|t|      [0.025      0.975]
+----------------------------------------------------------------------------------------------------
+const                                1.1182      0.001    981.264      0.000       1.116       1.120
+bayes-corrected (q=0.25) valence    -0.3909      0.001   -341.822      0.000      -0.393      -0.389
+totalvotes                           0.5079      0.001    444.124      0.000       0.506       0.510
+==============================================================================
+Omnibus:                   680589.819   Durbin-Watson:                   1.782
+Prob(Omnibus):                  0.000   Jarque-Bera (JB):         49094495.451
+Skew:                           1.699   Prob(JB):                         0.00
+Kurtosis:                      32.971   Cond. No.                         1.09
+==============================================================================
+
+Notes:
+[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.

results/Evidence_uncongenialty_section_science.txt

@@ -0,0 +1,26 @@
+                              OLS Regression Results                             
+=================================================================================
+Dep. Variable:     number O(n+1)-replies   R-squared:                       0.253
+Model:                               OLS   Adj. R-squared:                  0.253
+Method:                    Least Squares   F-statistic:                 9.746e+04
+Date:                   Mon, 22 Jul 2024   Prob (F-statistic):               0.00
+Time:                           09:31:48   Log-Likelihood:            -1.0810e+06
+No. Observations:                 575190   AIC:                         2.162e+06
+Df Residuals:                     575187   BIC:                         2.162e+06
+Df Model:                              2                                         
+Covariance Type:               nonrobust                                         
+====================================================================================================
+                                       coef    std err          t      P>|t|      [0.025      0.975]
+----------------------------------------------------------------------------------------------------
+const                                1.6458      0.002    787.663      0.000       1.642       1.650
+bayes-corrected (q=0.25) valence    -0.3951      0.002   -184.289      0.000      -0.399      -0.391
+totalvotes                           0.7495      0.002    349.574      0.000       0.745       0.754
+==============================================================================
+Omnibus:                   194870.309   Durbin-Watson:                   1.765
+Prob(Omnibus):                  0.000   Jarque-Bera (JB):          1100608.449
+Skew:                           1.527   Prob(JB):                         0.00
+Kurtosis:                       9.050   Cond. No.                         1.26
+==============================================================================
+
+Notes:
+[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.

results/Evidence_uncongenialty_section_sports.txt

@@ -0,0 +1,26 @@
+                              OLS Regression Results                             
+=================================================================================
+Dep. Variable:     number O(n+1)-replies   R-squared:                       0.256
+Model:                               OLS   Adj. R-squared:                  0.256
+Method:                    Least Squares   F-statistic:                 3.965e+04
+Date:                   Mon, 22 Jul 2024   Prob (F-statistic):               0.00
+Time:                           09:31:48   Log-Likelihood:            -3.4768e+05
+No. Observations:                 230524   AIC:                         6.954e+05
+Df Residuals:                     230521   BIC:                         6.954e+05
+Df Model:                              2                                         
+Covariance Type:               nonrobust                                         
+====================================================================================================
+                                       coef    std err          t      P>|t|      [0.025      0.975]
+----------------------------------------------------------------------------------------------------
+const                                0.8891      0.002    390.420      0.000       0.885       0.894
+bayes-corrected (q=0.25) valence    -0.3918      0.002   -171.548      0.000      -0.396      -0.387
+totalvotes                           0.4784      0.002    209.473      0.000       0.474       0.483
+==============================================================================
+Omnibus:                   109314.794   Durbin-Watson:                   1.837
+Prob(Omnibus):                  0.000   Jarque-Bera (JB):          1540320.347
+Skew:                           1.926   Prob(JB):                         0.00
+Kurtosis:                      15.063   Cond. No.                         1.08
+==============================================================================
+
+Notes:
+[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.

results/Extended_Data_Table_1_Descriptive_Data_for_different_comment_levels.csv

@@ -0,0 +1,6 @@
+,total_count,totalvotes_nonzero,totalvotes_sum,totalvotes_mean,totalvotes_std_dev,upvotes_sum,downvotes_sum,bayes-corrected (q=0.25) valence_mean,bayes-corrected (q=0.25) valence_std_dev,bayes-corrected (q=0.25) extremity_mean,bayes-corrected (q=0.25) extremity_std_dev
+Total,20161317,14706588,154821490,7.679135742967585,11.556761173578584,102022297,52799193,0.17704715667464238,0.2021943857837279,0.3158469490984941,0.11041613015478122
+0,6069971,4786218,77964965,12.84437190886085,15.885943210670801,50729878,27235087,0.17395713322300024,0.2242844895596835,0.3081309024315796,0.11996477068190904
+1,6755090,5050120,46320518,6.857128180379536,9.434159387282262,31970589,14349929,0.19288237536756858,0.19202605012773946,0.3198901628191536,0.10879194365502998
+2,3786555,2608297,18126241,4.787000584964433,7.119372189759389,11414766,6711475,0.16402235924006153,0.19272989539907023,0.31758620689084427,0.10383240571773994
+3,3549701,2261953,12409766,3.496003184493567,5.5015115601260955,7907064,4502702,0.16325036748997546,0.1823236133759817,0.3211412527772316,0.09881505843280065

results/Extended_Data_Table_2_Descriptive_Data_for_different_news_categories.csv

@@ -0,0 +1,28 @@
+,total_count,number O(n+1)-replies_sum,number O(n+1)-replies_nonzero,totalvotes_nonzero,totalvotes_sum,upvotes_sum,downvotes_sum,valence_mean,valence_std_dev,bayes-corrected (q=0.25) valence_mean,bayes-corrected (q=0.25) valence_std_dev,extremity_mean,extremity_std_dev,bayes-corrected (q=0.25) extremity_mean,bayes-corrected (q=0.25) extremity_std_dev
+Total,20161317,14091458,7495456,14706588,154821490,102022297,52799193,0.1809732449640069,0.3221017025345512,0.17704715667464238,0.2021943857837279,0.3263665242005636,0.17316382137734357,0.3158469490984941,0.11041613015478122
+Backstage,2638,1309,916,2091,19339,14013,5326,0.23396256608765395,0.306585082923225,0.21223527077342733,0.18627809433377085,0.34578357774960666,0.1706915190446471,0.3284342540070973,0.10947346803113846
+Career,125360,84283,47627,94139,991285,688567,302718,0.23144672748128672,0.31736287806178626,0.207642429386839,0.20352379529383485,0.3562556537779823,0.165432505887087,0.33650872389557185,0.10619423486404499
+Community,2546,1519,921,1691,10943,7543,3400,0.22997949625046876,0.31967854887735475,0.200451242569652,0.17131680735740445,0.35250476788410146,0.17545113771119852,0.3275935830615364,0.09841736341890532
+Culture,783764,492965,283683,594634,7485201,4965916,2519285,0.18924208094858397,0.31430902283965584,0.18237701691103533,0.2075772292325427,0.3257271885165046,0.16883248354886274,0.3165073708092221,0.11183033084670414
+Economy,2532709,1753030,981305,1832061,15418671,10477493,4941178,0.19695589659717094,0.3200608942654652,0.1874348176320727,0.18779895991104745,0.33178431101377187,0.17649287038142622,0.3182130588344701,0.10772151446952792
+Family,49628,31670,18194,38744,504399,350538,153861,0.2207067677046886,0.3014587355657185,0.2041527930238584,0.2020069369216948,0.33366309594582666,0.1680957128606617,0.32246786364599994,0.11428810172513852
+Fitness,3010,2211,1182,2183,22484,14215,8269,0.15967373329129744,0.30431877346003455,0.1619996073865862,0.1864234078141263,0.29418798473390506,0.17756989413975396,0.29151785340526726,0.1149792183614986
+Foreign affairs,3677268,2544425,1330773,2734274,33483913,22653979,10829934,0.2002575026917707,0.325382737606063,0.1904666416894296,0.21644036248740706,0.34356756409069306,0.16714752082493223,0.32992358948836636,0.1107027200883534
+Health,232501,170195,87992,169188,1861800,1220143,641657,0.18424450969159725,0.32460476601354893,0.17814240568123157,0.20780662621629975,0.3319043469304209,0.17074351373987692,0.3200033262093227,0.10944699632311113
+History,72480,47028,26802,56445,679183,472071,207112,0.22095860634303593,0.31534867450645176,0.2039420491794974,0.2117808348179994,0.3471692447301984,0.1665525064085087,0.3331839376145845,0.11121773599665685
+International,1778,661,443,1021,5800,3874,1926,0.19351189270788866,0.3781743471452822,0.18492958397224973,0.194320028301713,0.3900060650559993,0.16806412167150314,0.3495507394881325,0.08766877038980984
+Internet,498610,333659,186807,367308,3674903,2466723,1208180,0.2091647658832731,0.3286639549837689,0.19303117978437134,0.2079459094850678,0.3515812393300087,0.16781033736005121,0.3332794356286173,0.10537720414529439
+Miscellaneous,1962726,1352139,729325,1449191,17475106,11899133,5575973,0.20487949983503603,0.32223613216900404,0.1930421103534628,0.21138823123717118,0.3434851196383367,0.16682222414119258,0.3292431100186108,0.10905631859394622
+Mobility,554408,415352,219481,421827,3502371,2196167,1306204,0.16054863670067138,0.31191065777214666,0.1618961069183528,0.18002270627566236,0.30120556091206946,0.1798309742036998,0.2958943291943663,0.10998330012774073
+Politics,5116347,3451139,1901059,3675657,39155173,25667532,13487641,0.17444454738877926,0.3163335278430911,0.17315213087529358,0.19604468474970993,0.31674249710795505,0.17370081307098184,0.3084535160005122,0.11058794557807118
+Psychology,77714,49836,28755,59103,731898,505589,226309,0.20632700233260906,0.31154092970651803,0.1944627043194951,0.20658764113640296,0.3333799252712208,0.1687757327928834,0.3224848844257549,0.1127179346512217
+Relationships,8131,4828,2914,6585,117075,86625,30450,0.24777040590992844,0.29358367752069375,0.22795252407820465,0.21253014623413818,0.34813759810576583,0.1623966700762943,0.33698553182471686,0.11775078466218207
+Science,3525557,2774136,1307843,2480281,21660444,12904848,8755596,0.13124914209737715,0.3254561015718625,0.14358107029299916,0.1929315922045766,0.30217351871367176,0.17843527137661813,0.29726966358820944,0.10920227830101752
+Services,15,6,4,13,70,49,21,0.11337188452573069,0.33244948852144557,0.16113358674678163,0.17243290163606495,0.2928590640129101,0.17757621269031232,0.3027156286778409,0.09718850862453934
+Sports,742645,458996,266832,573957,6603661,4457164,2146497,0.19390481300491805,0.324823993978004,0.1866839749335055,0.21488052454498108,0.3392841168420786,0.1673196240006354,0.3276705379187469,0.1093840089279375
+Start,59059,38288,22794,45209,446297,312161,134136,0.23012121622760803,0.3130036249047111,0.20696856220124274,0.20060384009293042,0.35070753025975687,0.16712187857191027,0.33303728389868575,0.10782854662594989
+Style,30611,17243,10890,24054,237133,168395,68738,0.24331081020636638,0.3088338815884642,0.2153432930645136,0.20011272670414704,0.35698752853489446,0.1647288201407465,0.3384781050249686,0.10535514942392003
+Tests,14585,8163,5413,11604,99542,73441,26101,0.27363177574221587,0.2996883510847475,0.23290915494157782,0.1893771434271009,0.37215267353257064,0.1618158234175073,0.3471946880707655,0.10229356775332507
+Total,2638,2185,922,1915,17354,9336,8018,0.0677696797423411,0.3072866586840947,0.10190539473940045,0.18182325609165528,0.2587321299251362,0.17900538958637705,0.266230689221851,0.11332285476209328
+Travel,84136,55950,32431,63101,614135,404586,209549,0.19389251358743367,0.31251346847520517,0.18297252866496708,0.19464504647573025,0.32412318682777963,0.1737874145135004,0.3130547750304372,0.1114047246370484
+Your SPIEGEL,453,242,148,312,3310,2196,1114,0.18208463027330246,0.2940537652828505,0.17712455278668807,0.1727453899902895,0.29483491204446555,0.1803382964418267,0.29333866069487885,0.10993924168770208

src/__init__.py

@@ -0,0 +1,2 @@
+"""Functions to handle data and perform analysis on Spiegel Online Data"""
+

src/analysis.py

@@ -0,0 +1,174 @@
+from statsmodels.regression.linear_model import RegressionResults
+
+from src.analysis_functions.comparison_variance_in_and_between_group import (
+    ComparisonVariance,
+)
+from src.analysis_functions.descriptive import DescriptiveAnalysis
+from src.analysis_functions.regression import Regression
+from src.analysis_functions.specific_analysis.increase_per_up_and_downvote import (
+    InfluenceOfUpAndDownvotesOnReplies,
+)
+from src.analysis_functions.ttest import TTest
+from src.analysis_functions.pearson_correlation import PearsonCorrelation
+
+from src.analysis_functions.visualization import DataVisualizer
+from src.analysis_wrappers.comparison_variance_in_and_between_group_wrapper import (
+    run_comparison_variance_in_and_between_group,
+)
+from src.analysis_wrappers.descriptive_wrapper import run_descriptive_analysis
+
+from src.analysis_wrappers.regression_wrapper import run_regression
+from src.analysis_wrappers.pearson_correlation_wrapper import run_pearson_correlation
+from src.analysis_wrappers.specific_analysis_wrappers.get_function_inverse_bayes_transformed_regression import (
+    run_get_function_inverse_bayes_transformed_regression,
+)
+from src.analysis_wrappers.specific_analysis_wrappers.increase_per_up_and_downvote_wrapper import (
+    run_report_influence_of_up_and_downvotes_on_replies,
+)
+from src.analysis_wrappers.ttest_wrapper import run_ttest
+from src.analysis_wrappers.visualization_wrapper import run_visualization
+from src.data_classes.parameters_analysis_comparison_variance_in_and_between_group import (
+    ComparisonVarianceInAndBetweenGroupParameters,
+)
+from src.data_classes.parameters_analysis_get_function_inverse_bayes_transformed_regression import (
+    GetFunctionInverseBayesTransformedRegressionParameters,
+)
+from src.data_classes.parameters_analysis_influence_of_up_and_downvotes import (
+    InfluenceOfVotesParameters,
+)
+
+from src.data_classes.parameters_analysis_regression import (
+    BayesianRegressionParameters,
+    LinearRegressionParameters,
+    GroupedLinearRegressionParameters,
+)
+from src.data_classes.parameters_analysis_pearson_correlation import (
+    PearsonCorrelationParameters,
+)
+from src.data_classes.parameters_analysis_ttest import (
+    TTestParameters,
+    PairedTTestParameters,
+)
+from src.utils.helper_functions import FunctionData
+
+
+def run_analyses(analyses: dict, preprocessed_datasets: dict) -> None:
+    """
+    Orchestrates the execution of various statistical analyses and visualizations based on a configuration file.
+
+    This function initializes analysis and visualization classes, assigns preprocessed data to analyses,
+    categorizes analyses by type, and sequentially executes them. It supports descriptive statistics,
+    regression analyses, t-tests, Pearson correlation analyses, comparison of variance, influence of up and downvotes,
+    and data visualization. The function ensures that the necessary data and results are passed between analyses
+    and visualizations as required.
+
+    Parameters
+    ----------
+    analyses : dict
+        A dictionary containing configurations for different categories of analyses
+        (descriptive, analysis, visualization) and their parameters.
+    preprocessed_datasets : dict
+        A dictionary mapping dataset names to their preprocessed forms. This data is used across various analyses.
+
+    Raises
+    ------
+    ValueError
+        If an unknown analysis type is encountered in the configuration.
+    """
+    regression: Regression = Regression()
+    comparison_variance: ComparisonVariance = ComparisonVariance()
+    pearson_correlation: PearsonCorrelation = PearsonCorrelation()
+    ttest: TTest = TTest()
+    influence_up_and_downvotes: InfluenceOfUpAndDownvotesOnReplies = InfluenceOfUpAndDownvotesOnReplies()
+
+    descriptive: DescriptiveAnalysis = DescriptiveAnalysis()
+    visualizer: DataVisualizer = DataVisualizer()
+
+    for category in ["descriptive", "analysis", "visualization"]:
+        if category in analyses:
+            for analysis in analyses[category]:
+                analysis.data = preprocessed_datasets[analysis.dataset]
+
+    regression_analyses: list = []
+    pearson_correlation_analyses: list = []
+    ttest_analyses: list = []
+    comparison_variance_in_and_between_group_analyses: list = []
+    influence_up_and_downvotes_analyses: list = []
+    get_function_inverse_bayes_transformed_regression_analyses: list = []
+
+    if "descriptive" in analyses:
+        run_descriptive_analysis(descriptive, analyses["descriptive"])
+
+    for analysis in analyses.get("analysis", []):
+        if isinstance(
+            analysis,
+            (
+                LinearRegressionParameters,
+                GroupedLinearRegressionParameters,
+                BayesianRegressionParameters,
+            ),
+        ):
+            regression_analyses.append(analysis)
+        elif isinstance(analysis, ComparisonVarianceInAndBetweenGroupParameters):
+            comparison_variance_in_and_between_group_analyses.append(analysis)
+        elif isinstance(analysis, PearsonCorrelationParameters):
+            pearson_correlation_analyses.append(analysis)
+        elif isinstance(analysis, (TTestParameters, PairedTTestParameters)):
+            ttest_analyses.append(analysis)
+        elif isinstance(analysis, InfluenceOfVotesParameters):
+            influence_up_and_downvotes_analyses.append(analysis)
+        elif isinstance(
+            analysis, GetFunctionInverseBayesTransformedRegressionParameters
+        ):
+            get_function_inverse_bayes_transformed_regression_analyses.append(analysis)
+        else:
+            raise ValueError(f"Unknown analysis type for analysis: {analysis}")
+
+    regression_results: dict[str, RegressionResults] = {}
+    if regression_analyses:
+        regression_results: dict[str, RegressionResults] = run_regression(
+            regression, regression_analyses
+        )
+
+    if comparison_variance_in_and_between_group_analyses:
+        run_comparison_variance_in_and_between_group(
+            comparison_variance, comparison_variance_in_and_between_group_analyses
+        )
+
+    if pearson_correlation_analyses:
+        run_pearson_correlation(pearson_correlation, pearson_correlation_analyses)
+
+    ttest_results: dict[str, tuple[float, float, float]] = {}
+    if ttest_analyses:
+        ttest_results: dict[str, tuple[float, float, float]] = run_ttest(
+            ttest, ttest_analyses
+        )
+
+    if influence_up_and_downvotes_analyses:
+        if regression_results == {}:
+            raise ValueError(
+                "Regression results are required for the influence of up and downvotes analysis"
+            )
+        run_report_influence_of_up_and_downvotes_on_replies(
+            influence_up_and_downvotes,
+            influence_up_and_downvotes_analyses,
+            regression_results,
+        )
+
+    functions: dict[str, FunctionData] = {}
+    if get_function_inverse_bayes_transformed_regression_analyses:
+        functions: dict[
+            str, FunctionData
+        ] = run_get_function_inverse_bayes_transformed_regression(
+            get_function_inverse_bayes_transformed_regression_analyses,
+            regression_results,
+        )
+
+    if "visualization" in analyses:
+        run_visualization(
+            visualizer,
+            analyses["visualization"],
+            regression_results,
+            functions,
+            ttest_results,
+        )

src/analysis_functions/comparison_variance_in_and_between_group.py

@@ -0,0 +1,91 @@
+import os
+from pathlib import Path
+
+import pandas as pd
+
+from src.data_loading_and_saving.print_and_save_results import print_and_save_result
+
+
+class ComparisonVariance:
+    """
+    A class used to calculate the Pearson's correlation coefficient between two variables.
+
+    Attributes:
+    ----------
+        print_result: bool
+            A flag to determine if the result should be printed.
+        save_result: bool
+            A flag to determine if the result should be saved.
+        filepath: str
+            The directory where the result should be saved.
+
+    Methods
+    -------
+        calculate_correlation(data: pd.DataFrame, first_group_name: str, second_group_name: str, name_save_file: Path):
+            This method calculates Pearson's correlation coefficient for the given columns
+    """
+
+    def __init__(
+        self,
+        print_result: bool = True,
+        save_result: bool = True,
+        filepath: str = "results/",
+    ):
+        """
+        Constructs all the necessary attributes for the PearsonCorrelation object.
+
+        Parameters
+        ----------
+            print_result: bool
+                A flag to determine if the result should be printed.
+            save_result: bool
+                A flag to determine if the result should be saved.
+            filepath: str
+                The directory where the result should be saved.
+        """
+        self.print_result: bool = print_result
+        self.save_result: bool = save_result
+        self.filepath: str = filepath
+        if not os.path.isdir(filepath):
+            os.makedirs(filepath)
+
+    def compare_ingroup_intergroup_variance(
+        self, data: pd.DataFrame, variable: str, group: str, name_save_file: Path
+    ) -> None:
+        """
+        Compares the variance within a group to the variance between groups for a specified variable.
+
+        This method calculates the total variance of the variable, the average variance of the variable
+        within each group, and the variance of the variable between groups. It then prints and/or saves
+        these results based on the object's attributes.
+
+        Parameters
+        ----------
+        data : pd.DataFrame
+            The dataset containing the variable and group columns.
+        variable : str
+            The name of the column representing the variable for which variance is calculated.
+        group : str
+            The name of the column representing the groups.
+        name_save_file : Path
+            The name of the file to which the result will be saved (if save_result is True).
+        """
+        total_var: float = data[variable].var()
+
+        between_group_var: float = data.groupby(group)[variable].var().mean()
+
+        within_group_var: float = total_var - between_group_var
+
+        results: str = f"""
+        Total variance: {total_var}
+        Between-group variance: {between_group_var}
+        Within-group variance: {within_group_var}
+        """
+
+        print_and_save_result(
+            self.print_result,
+            self.save_result,
+            self.filepath,
+            results,
+            name_save_file,
+        )

src/analysis_functions/descriptive.py

@@ -0,0 +1,351 @@
+import os
+from typing import Union, Optional
+from pathlib import Path
+
+import numpy as np
+import pandas as pd
+
+from src.data_classes.parameters_descriptive_overview import Metric
+from src.data_loading_and_saving.print_and_save_results import print_and_save_result
+
+
+class DescriptiveAnalysis:
+    """
+    A class used to perform descriptive data analysis.
+
+    Attributes
+    ----------
+    print_result: bool
+        A flag used to indicate if the function should print the result to the standard output.
+    save_result: bool
+        A flag used to indicate if the function should save the result to a file.
+    filepath: str
+        The directory path where the result files will be saved if save_result is True.
+    ...
+
+    Methods
+    -------
+    create_descriptives_for_metrics(data, metrics, name_save_file, group_by=None):
+        Performs multiple descriptive analyses on the provided dataset and either prints or saves the result.
+    create_descriptive_aggregated_for_metrics(data, variables, aggregation_function, group_by, name_save_file):
+        Performs descriptive analysis on the provided dataset with aggregation and grouping, and either prints
+        or saves the result.
+    give_percentage_of_dataset_under_condition(data, variable, comparison, condition, name_save_file):
+        Calculates and prints/saves the percentage of the dataset that meets a specified condition.
+    _compute_metrics(group_name, data, metrics, group_column=None):
+        Helper method to compute specified metrics on the data.
+    _count_values(data, column):
+        Counts the non-null values in a specified column or in the dataframe if no column is specified.
+    _count_nonzero(data, column):
+        Counts the non-zero/True values in a specified column.
+    _count_unique(data, column):
+        Counts the unique values in a specified column.
+    _sum_values(data, column):
+        Sums the values in a specified column.
+    _mean_values(data, column):
+        Calculates the mean of the values in a specified column.
+    _std_dev(data, column):
+        Calculates the standard deviation of the values in a specified column.
+    """
+
+    def __init__(
+        self,
+        print_result: bool = True,
+        save_result: bool = True,
+        filepath: str = "results/",
+    ):
+        """
+        Constructs all the necessary attributes for the DescriptiveAnalysis object.
+
+        Parameters
+        ----------
+        print_result: bool
+            A flag to determine if the result should be printed.
+        save_result: bool
+            A flag to determine if the result should be saved.
+        filepath: str
+            The directory where the result should be saved.
+        """
+        self.print_result: bool = print_result
+        self.save_result: bool = save_result
+        self.filepath: str = filepath
+        if not os.path.isdir(filepath):
+            os.makedirs(filepath)
+
+    def create_descriptives_for_metrics(
+        self,
+        data: pd.DataFrame,
+        metrics: list[Metric],
+        name_save_file: Path,
+        group_by: Optional[str] = None,
+    ) -> None:
+        """
+        This method performs multiple descriptive analysis on the provided dataset and either prints or saves the result
+
+        Parameters
+        ----------
+        data: pandas.DataFrame
+            The input dataframe which contains the data.
+        metrics: MetricList
+            The list of metrics to be calculated.
+        name_save_file: str
+            The name of the file to which the result will be saved (if self.save_result is True).
+        group_by: str
+            The column to group by. This gives the option to perform the analysis on each unique group member.
+
+        Returns
+        -------
+        None
+        """
+        dataframes: list = []
+        result_dataframes: pd.DataFrame = self._compute_metrics("Total", data, metrics)
+        dataframes.append(result_dataframes)
+
+        if group_by:
+            grouped_dataframe = data.groupby(group_by)
+            for group_name, group in grouped_dataframe:
+                result_dataframes: pd.DataFrame = self._compute_metrics(
+                    group_name, group, metrics
+                )
+                dataframes.append(result_dataframes)
+
+        result_dataframes: pd.DataFrame = pd.concat(dataframes)
+        result_dataframes.set_index(result_dataframes.columns[0], inplace=True)
+
+        print_and_save_result(
+            self.print_result,
+            self.save_result,
+            self.filepath,
+            result_dataframes,
+            name_save_file,
+        )
+
+    def create_descriptive_aggregated_for_metrics(
+        self,
+        data: pd.DataFrame,
+        variables: list[str],
+        aggregation_function: str,
+        group_by: str,
+        name_save_file: Path,
+    ) -> None:
+        """
+        This method performs multiple descriptive analysis on the provided dataset with grouping.
+
+        Parameters
+        ----------
+        data : pd.DataFrame
+            The dataset to perform descriptive analysis on.
+        variables : list[str]
+            The list of columns we want information on.
+        aggregation_function : str
+            The method to aggregate the data. Either 'sum' or 'mean'.
+        group_by : str
+            The column to group by.
+        name_save_file : str
+            The name for saving the result.
+
+        Returns
+        -------
+        None
+        """
+        if aggregation_function == "sum":
+            if "Count" in variables:
+                variables.remove("Count")
+                grouped_data: pd.DataFrame = (
+                    data.groupby(group_by)[variables]
+                    .sum()
+                    .aggregate(["mean", "std", "max", "min"])
+                )
+
+                count_data: pd.DataFrame = (
+                    data.groupby(group_by)
+                    .size()
+                    .reset_index(name="Count")["Count"]
+                    .aggregate(["mean", "std", "max", "min"])
+                )
+                grouped_data: pd.DataFrame = pd.concat(
+                    [count_data, grouped_data], axis=1
+                )
+            else:
+                grouped_data: pd.DataFrame = (
+                    data.groupby(group_by)[variables]
+                    .sum()
+                    .aggregate(["mean", "std", "max", "min"])
+                )
+
+        elif aggregation_function == "mean":
+            if "Count" in variables:
+                variables.remove("Count")
+                grouped_data: pd.DataFrame = (
+                    data.groupby(group_by)[variables]
+                    .mean()
+                    .aggregate(["mean", "std", "max", "min"])
+                )
+
+                count_data: pd.DataFrame = (
+                    data.groupby(group_by)
+                    .size()
+                    .reset_index(name="Count")["Count"]
+                    .aggregate(["mean", "std", "max", "min"])
+                )
+                grouped_data: pd.DataFrame = pd.concat(
+                    [count_data, grouped_data], axis=1
+                )
+            else:
+                grouped_data: pd.DataFrame = (
+                    data.groupby(group_by)[variables]
+                    .mean()
+                    .aggregate(["mean", "std", "max", "min"])
+                )
+
+        else:
+            raise ValueError(f"Invalid aggregation: {aggregation_function}")
+
+        print_and_save_result(
+            self.print_result,
+            self.save_result,
+            self.filepath,
+            grouped_data,
+            name_save_file,
+        )
+
+    def give_percentage_of_dataset_under_condition(
+        self,
+        data: pd.DataFrame,
+        variable: str,
+        comparison: str,
+        condition: Union[int, float],
+        name_save_file: Path,
+    ) -> None:
+        """
+        Calculates the percentage of the dataset that meets a specified condition and either prints or saves the result.
+
+        This method evaluates a condition on a specified column of the dataset and calculates the percentage of rows
+        that meet this condition. The result can be printed to the console or saved to a file, depending on the
+        object's attributes.
+
+        Parameters
+        ----------
+        data : pd.DataFrame
+            The dataset to evaluate the condition on.
+        variable : str
+            The column name on which the condition will be applied.
+        comparison : str
+            The type of comparison to perform. Valid options are "smaller", "larger", or "not".
+        condition : Union[int, float]
+            The value to compare against the data in the specified column.
+        name_save_file : Path
+            The path (including filename) where the result should be saved if saving is enabled.
+
+        Returns
+        -------
+        None
+        """
+        total_data: int = len(data)
+        conditional_data: int = 0
+        if comparison == "smaller":
+            conditional_data: int = len(data[data[variable] <= condition])
+        elif comparison == "larger":
+            conditional_data: int = len(data[data[variable] >= condition])
+        elif comparison == "not":
+            conditional_data: int = len(data[data[variable] != condition])
+        else:
+            ValueError("Invalid comparison type")
+
+        percentage: float = conditional_data / total_data * 100
+
+        percentage_result: str = (
+            f"The percentage of the dataset under that condition is {percentage} %"
+        )
+
+        print_and_save_result(
+            self.print_result,
+            self.save_result,
+            self.filepath,
+            percentage_result,
+            name_save_file,
+        )
+
+    def _compute_metrics(
+        self, group_name, data: pd.DataFrame, metrics: list[Metric], group_column=None
+    ) -> pd.DataFrame:
+        """
+        Computes specified metrics on the given dataset and returns the results in a DataFrame.
+
+        This method iterates over a list of Metric objects, each defining an operation (e.g., count, sum, mean) and
+        a column on which the operation is to be performed. It constructs a result dictionary where each key is a
+        metric name with the operation and column, and the value is the result of the operation. This dictionary is
+        then converted to a DataFrame and returned.
+
+        Parameters
+        ----------
+        group_name : str
+            The name of the group for which metrics are being computed. This is used as a prefix in the result
+            dictionary keys if not None.
+        data : pd.DataFrame
+            The dataset on which the metrics are to be computed.
+        metrics : list[Metric]
+            A list of Metric objects, each specifying an operation and a column.
+        group_column : str, optional
+            The name of the column by which the data was grouped, if any. This is used in the result dictionary keys.
+
+        Returns
+        -------
+        pd.DataFrame
+            A DataFrame containing the computed metrics, with each row representing the results for a group (if
+            group_name is not None) or the entire dataset.
+        """
+        result_dict: dict = {group_column: group_name} if group_name is not None else {}
+        for metric in metrics:
+            operation: str = metric.operation
+            column: str = metric.column
+            if operation == "count":
+                result_dict[
+                    f'{column if column else "total"}_count'
+                ] = self._count_values(data, column)
+            elif operation == "count_nonzero":
+                result_dict[f"{column}_nonzero"] = self._count_nonzero(data, column)
+            elif operation == "count_unique":
+                result_dict[f"{column}_unique"] = self._count_unique(data, column)
+            elif operation == "sum":
+                result_dict[f"{column}_sum"] = self._sum_values(data, column)
+            elif operation == "mean":
+                result_dict[f"{column}_mean"] = self._mean_values(data, column)
+            elif operation == "std_dev":
+                result_dict[f"{column}_std_dev"] = self._std_dev(data, column)
+
+        return pd.DataFrame(result_dict, index=[0])
+
+    @staticmethod
+    def _count_values(data: pd.DataFrame, column: str) -> int:
+        return len(data[column].dropna()) if column else len(data)
+
+    @staticmethod
+    def _count_nonzero(data: pd.DataFrame, column: str) -> int:
+        data_type = data[column].dtype
+        if data_type == bool:
+            return len(data[data[column] == True])
+        return len(data[data[column] != 0])
+
+    @staticmethod
+    def _count_unique(data: pd.DataFrame, column: str) -> int:
+        first_element = data[column].dropna().iloc[0]
+        if isinstance(first_element, (list, np.ndarray)):
+            flattend_entries = pd.Series(
+                [item for sublist in data[column] for item in sublist]
+            )
+            return flattend_entries.nunique()
+        else:
+            return data[column].nunique()
+
+    @staticmethod
+    def _sum_values(data: pd.DataFrame, column: str) -> Union[int, float]:
+        return data[column].sum()
+
+    @staticmethod
+    def _mean_values(data: pd.DataFrame, column: str) -> Union[int, float]:
+        return data[column].mean()
+
+    @staticmethod
+    def _std_dev(data: pd.DataFrame, column: str) -> Union[int, float]:
+        return data[column].std()

src/analysis_functions/pearson_correlation.py

@@ -0,0 +1,94 @@
+import os
+from pathlib import Path
+
+import pandas as pd
+from scipy.stats import pearsonr
+
+from src.data_loading_and_saving.print_and_save_results import print_and_save_result
+
+
+class PearsonCorrelation:
+    """
+    A class used to calculate the Pearson's correlation coefficient between two variables.
+
+    Attributes:
+    ----------
+        print_result: bool
+            A flag to determine if the result should be printed.
+        save_result: bool
+            A flag to determine if the result should be saved.
+        filepath: str
+            The directory where the result should be saved.
+
+    Methods
+    -------
+        calculate_correlation(data: pd.DataFrame, first_group_name: str, second_group_name: str, name_save_file: Path):
+            This method calculates Pearson's correlation coefficient for the given columns
+    """
+
+    def __init__(
+        self,
+        print_result: bool = True,
+        save_result: bool = True,
+        filepath: str = "results/",
+    ):
+        """
+        Constructs all the necessary attributes for the PearsonCorrelation object.
+
+        Parameters
+        ----------
+        print_result: bool
+            A flag to determine if the result should be printed.
+        save_result: bool
+            A flag to determine if the result should be saved.
+        filepath: str
+            The directory where the result should be saved.
+        """
+        self.print_result: bool = print_result
+        self.save_result: bool = save_result
+        self.filepath: str = filepath
+        if not os.path.isdir(filepath):
+            os.makedirs(filepath)
+
+    def calculate_correlation(
+        self,
+        data: pd.DataFrame,
+        first_group_name: str,
+        second_group_name: str,
+        name_save_file: Path,
+    ) -> None:
+        """
+        This method calculates Pearson's correlation coefficient for the given columns
+        and either prints or saves the result based on the object properties.
+
+        Parameters
+        ----------
+            data: pd.DataFrame
+                The data frame containing the data.
+            first_group_name: str
+                The name of the first group.
+            second_group_name: str
+                The name of the second group.
+            name_save_file: Path
+                The name of the file where the result should be saved.
+        """
+        data: pd.DataFrame = data[[first_group_name, second_group_name]].dropna()
+        group_1: pd.DataFrame = data[first_group_name]
+        group_2: pd.DataFrame = data[second_group_name]
+
+        pearson_correlation: float
+        p_value: float
+        pearson_correlation, p_value = pearsonr(group_1, group_2)
+
+        pearson_correlation_summary: str = (
+            f"Pearson correlation between {first_group_name} and {second_group_name}: {pearson_correlation}\n"
+            f"P-value: {p_value}"
+        )
+
+        print_and_save_result(
+            self.print_result,
+            self.save_result,
+            self.filepath,
+            pearson_correlation_summary,
+            name_save_file,
+        )

src/analysis_functions/regression.py

@@ -0,0 +1,502 @@
+import math
+import os
+from typing import Union
+from pathlib import Path
+
+import numpy as np
+import pandas as pd
+from rpy2 import robjects as ro
+from rpy2.robjects import pandas2ri, Formula
+from sklearn.preprocessing import StandardScaler
+from statsmodels import api as sm
+from statsmodels.iolib.summary import Summary
+from statsmodels.regression.linear_model import RegressionResults
+
+from src.data_loading_and_saving.print_and_save_results import print_and_save_result
+from src.utils.handle_r_dependencies import BAS
+
+
+class Regression:
+    """
+    A class used to perform Linear and Bayesian regression analyses.
+
+    ...
+
+    Attributes
+    ----------
+    print_result: bool
+        A flag used to indicate if the function should print the result to the standard output.
+    save_result: bool
+        A flag used to indicate if the function should save the result to a file.
+    filepath: str
+        The directory path where the result files will be saved if save_result is True.
+
+    Methods
+    -------
+    linear_regression(data, x_vector, y, standardize, name_save_file) -> RegressionResults:
+        Performs an OLS (Ordinary Least Squares) linear regression on the provided dataset.
+    linear_regression_grouped(data, x_vector, y, dictionary_aggregation_methods_for_data_columns,
+        column_to_group_by, standardize, name_save_file) -> RegressionResults:
+        Performs Linear Regression on the data grouped by the given column as per the aggregation dictionary.
+    predict_percentage_increase_between_liner_model_points(model, data_point_1, data_point_2) -> float:
+        Calculates the percentage increase in fitted values between two different data points using a given model.
+    report_effect_size_of_model(model, name_save_file) -> None:
+        Computes and reports the effect size of the model
+        (r-squared, Cohen's f, and equivalent Cohen's d for the regression model).
+    bayesian_regression(data, x_vector, y, name_save_file) -> None:
+        Performs a Bayesian linear regression on the provided dataset.
+    _create_dataframe_of_bayesian_regression(summary, x_vector) -> pd.DataFrame:
+        Transforms the provided Bayesian regression summary into a DataFrame.
+    _clean_names(name) -> str:
+        Cleans the input string by replacing certain characters with underscores or removing them.
+    _clean_column_names(dataframe) -> pd.DataFrame:
+        Cleans the DataFrame column names using the "_clean_names" method.
+    _get_clean_x_vector(x_vector) -> list[str]:
+        Cleans the elements of the input list using the "_clean_names" method.
+    """
+
+    def __init__(
+        self,
+        print_result: bool = True,
+        save_result: bool = True,
+        filepath: str = "results/",
+    ):
+        """
+        Initializes the Regression object with the provided parameters.
+
+        Parameters
+        ----------
+        print_result: bool, optional
+            A flag to indicate if the function should print the result to the standard output. Default is True.
+        save_result: bool, optional
+            A flag to indicate if the function should save the result to a file. Default is True.
+        filepath: str, optional
+            The directory path where the result files will be saved if save_result is True. Default is "results/".
+        """
+        self.print_result: bool = print_result
+        self.save_result: bool = save_result
+        self.filepath: str = filepath
+        if not os.path.isdir(filepath):
+            os.makedirs(filepath)
+
+    def linear_regression(
+        self,
+        data: pd.DataFrame,
+        x_vector: list[str],
+        y: str,
+        standardize: bool = False,
+        report_effect_size: bool = False,
+        name_save_file: Path = "",
+    ) -> RegressionResults:
+        """
+        This method performs Ordinary Least Squares (OLS) Linear Regression using the given parameters
+        and either prints or saves the result based on the Regression object properties.
+
+        Parameters
+        ----------
+        data: pandas.DataFrame
+            The input dataframe which contains the data.
+        x_vector: list
+            The list of column names to be used as independent variables in the regression model.
+        y: str
+            The column name to be used as dependent variable in the regression model.
+        standardize: bool
+            If the independent variables should be standardized before fitting for better comparison.
+        report_effect_size: bool
+            If the effect size of the model should be reported (r-squared, Cohen's f, and equivalent Cohen's d).
+        name_save_file: str
+            The name of the file to which the result will be saved (if self.save_result is True).
+
+        Returns
+        -------
+        model: RegressionResults
+            The fitted regression model.
+        """
+        data: pd.DataFrame = data.dropna(subset=x_vector)
+        data: pd.DataFrame = data.dropna(subset=y)
+
+        x_vector_data: pd.DataFrame = data[x_vector]
+        y_data: pd.Series = data[y]
+
+        if standardize:
+            scaler: StandardScaler = StandardScaler()
+            x_vector_data_standardized: np.ndarray = scaler.fit_transform(x_vector_data)
+            x_vector_data: pd.DataFrame = pd.DataFrame(
+                x_vector_data_standardized, columns=x_vector
+            )
+            x_vector_data.set_index(y_data.index, inplace=True)
+
+        x_vector_data: pd.DataFrame = sm.add_constant(x_vector_data)
+
+        model: RegressionResults = sm.OLS(y_data, x_vector_data).fit()
+
+        linear_regression_summary: Summary = model.summary()
+
+        print_and_save_result(
+            self.print_result,
+            self.save_result,
+            self.filepath,
+            linear_regression_summary,
+            name_save_file,
+        )
+
+        if report_effect_size:
+            name_save_file_effect_size: Path = name_save_file.with_stem(
+                name_save_file.stem + "_effect_size"
+            )
+            self._report_effect_size_of_model(model, name_save_file_effect_size)
+
+        return model
+
+    def linear_regression_grouped(
+        self,
+        data: pd.DataFrame,
+        x_vector: list[str],
+        y: str,
+        dictionary_aggregation_methods_for_data_columns: dict[str, str],
+        column_to_group_by: str,
+        standardize: bool = False,
+        report_effect_size: bool = False,
+        print_detailed_coefficients: bool = False,
+        name_save_file: Path = "",
+    ) -> RegressionResults:
+        """
+        This method performs OLS Linear Regression on the data grouped by the group_by
+        as per the aggregation dictionary and then the performs regression analysis.
+
+        Parameters
+        ----------
+        data: pandas.DataFrame
+            The input dataframe which contains the data.
+        x_vector: list
+            The list of column names to be used as independent variables in the regression model.
+        y: str
+            The column name to be used as dependent variable in the regression model.
+        dictionary_aggregation_methods_for_data_columns: dict
+            The dictionary specifying how to aggregate each column before regression.
+            The keys of the dictionary should include all columns in X and y.
+        column_to_group_by: str
+            The column name to group the data by before aggregating.
+        name_save_file: str
+            The name of the file to which the result will be saved (if self.save_result is True).
+        standardize: bool
+            If the regression should be performed standardized instead to return beta factors.
+        report_effect_size: bool
+            If the effect size of the model should be reported (r-squared, Cohen's f, and equivalent Cohen's d).
+        print_detailed_coefficients: bool
+            If the coefficients should be printed separately with 10 point float accuracy and 95% CI.
+
+        Returns
+        -------
+        model: RegressionResults
+            The fitted regression model.
+        """
+
+        all_cols = set(x_vector + [y])
+        if not all_cols.issubset(
+            set(dictionary_aggregation_methods_for_data_columns.keys())
+        ):
+            raise ValueError(
+                "dictionary_aggregation_methods_for_data_columns should contain all columns from X and y."
+            )
+
+        if column_to_group_by not in data.columns:
+            raise ValueError(
+                f"'{column_to_group_by}' column to group by not found in data."
+            )
+
+        grouped_data = (
+            data.groupby(column_to_group_by)
+            .agg(dictionary_aggregation_methods_for_data_columns)
+            .reset_index()
+        )
+
+        model = self.linear_regression(
+            data=grouped_data,
+            x_vector=x_vector,
+            y=y,
+            standardize=standardize,
+            report_effect_size=report_effect_size,
+            name_save_file=name_save_file,
+        )
+
+        if print_detailed_coefficients:
+            coefficients = model.params
+            confidence_intervals = model.conf_int()
+            for name, coefficient in coefficients.items():
+                confidence_interval = confidence_intervals.loc[name]
+                detailed_coefficient_information: str = f"{name}: {coefficient: .10f} (CI: [{confidence_interval[0]: .10f}, {confidence_interval[1]: .10f}])"
+                print_and_save_result(
+                    self.print_result,
+                    self.save_result,
+                    self.filepath,
+                    detailed_coefficient_information,
+                    name_save_file,
+                )
+
+        return model
+
+    @staticmethod
+    def predict_percentage_increase_between_liner_model_points(
+        model: RegressionResults,
+        data_point_1: Union[list, np.array],
+        data_point_2: Union[list, np.array],
+    ) -> float:
+        """
+        Calculate the percentage increase in fitted values between two different data points using a given model.
+
+        Parameters
+        ----------
+        model: statsmodels.regression.linear_model.OLS
+            The fitted regression model.
+        data_point_1: list or numpy.array
+            The first data point (a vector of X values).
+        data_point_2: list or numpy.array
+            The second data point (a vector of X values).
+
+        Returns
+        -------
+        percentage_increase: float
+            The percentage increase in fitted value from data_point1 to data_point2.
+        """
+        if not isinstance(data_point_1, pd.Series):
+            data_point_1 = pd.Series(data_point_1, index=model.model.exog_names[1:])
+        if not isinstance(data_point_2, pd.Series):
+            data_point_2 = pd.Series(data_point_2, index=model.model.exog_names[1:])
+
+        if len(data_point_1) != len(data_point_2):
+            raise ValueError("Data points must have the same dimension.")
+        if len(data_point_1) != len(model.params) - 1:
+            raise ValueError("Dimensions of data points and model do not match.")
+
+        x_vector_data: pd.DataFrame = pd.DataFrame(
+            [data_point_1, data_point_2], columns=model.model.data.orig_exog.columns[1:]
+        )
+        x_vector_data: pd.DataFrame = sm.add_constant(x_vector_data)
+
+        predictions_for_both_datapoints: np.ndarray = model.predict(x_vector_data)
+
+        percentage_increase: float = (
+            (predictions_for_both_datapoints[1] - predictions_for_both_datapoints[0])
+            / predictions_for_both_datapoints[0]
+        ) * 100
+
+        return percentage_increase
+
+    def _report_effect_size_of_model(
+        self, model: RegressionResults, name_save_file: Path
+    ) -> None:
+        """
+        Computes and reports the effect size of the model (r-squared, Cohen's f and equivalent Cohen's d
+        for the regression model) and either print or save it based on the Regression object properties.
+
+        Parameters
+        ----------
+        model: RegressionResults
+            The regression model results obtained from statsmodels regression.
+        name_save_file: Path
+            The name of the file to which the result will be saved if self.save_result is True.
+        """
+        r_squared: int = model.rsquared
+        f_squared: float = r_squared / (1 - r_squared)
+        cohens_f: float = math.sqrt(f_squared)
+        equivalent_cohens_d: float = 2 * cohens_f
+
+        effect_size_model: str = f"""
+        R-Square R^2: {r_squared}
+        Cohen's f: {cohens_f}
+        Equivalent Cohen's d: {equivalent_cohens_d}
+        """
+
+        print_and_save_result(
+            self.print_result,
+            self.save_result,
+            self.filepath,
+            effect_size_model,
+            name_save_file,
+        )
+
+    def bayesian_regression(
+        self, data: pd.DataFrame, x_vector: list[str], y: str, name_save_file: Path
+    ) -> None:
+        """
+        This method performs Bayesian Regression using the given parameters and either
+        prints or saves the result based on the Regression object properties.
+
+        the results can be interpreted in the following way:
+        - `P(B != 0 | Y)`: This column gives the probability that the coefficient,
+            associated with the factor (the B-value), is not zero given the data Y.
+            This is a way of measuring the relevance and significance of the predictive factor.
+            The higher the score, the more likely it is that the factor has an impact on your outcome variable.
+        - `Model 1, Model 2, Model 3, etc`: These columns represent different models that have been built.
+            The values in these columns are the specific coefficient values for each factor within each model.
+        - `BF`: BF is short for Bayes Factor which indicates the strength of evidence for a model as
+            compared to an alternative model. Higher BF values indicate stronger evidence for a model.
+        - `PostProbs`: Posterior probabilities for each model. These add up to 1 across all models and
+            give the probability of each model being the "best" model given the data.
+        - `R2`:  Shows the proportion of the variance for a dependent variable that's explained by
+            independent variables in the model. Close to 1 indicates the model explains a large amount of the variance.
+        - `dim`: The dimensionality of the model, which is basically the number of parameters in each model.
+        - `logmarg`: It is the logarithm of the marginal likelihood for each model.
+            This is a factor that is used in determining the posterior probabilities of each model.
+
+        Parameters
+        ----------
+        data: pandas.DataFrame
+            The input dataframe which contains the data.
+        x_vector: list
+            The list of column names to be used as independent variables in the regression model.
+        y: str
+            The column name to be used as dependent variable in the regression model.
+        name_save_file: str
+            The name of the file to which the result will be saved (if self.save_result is True).
+
+        Returns
+        -------
+        None
+        """
+        data: pd.DataFrame = data.dropna(subset=x_vector)
+
+        pandas2ri.activate()
+
+        data: pd.DataFrame = self._clean_column_names(data)
+        x_vector: list[str] = self._get_clean_x_vector(x_vector)
+        y: str = self._clean_names(y)
+
+        formula: Formula = Formula(y + " ~ " + " + ".join(x_vector))
+
+        data_r = pandas2ri.py2rpy(data)
+
+        bayesian_model = BAS.bas_lm(
+            formula=formula,
+            data=data_r,
+            method="MCMC",
+            prior="ZS-null",
+            modelprior=BAS.uniform(),
+        )
+
+        bayesian_regression_summary: np.ndarray = ro.r["summary"](bayesian_model)
+
+        try:
+            dataframe_bayesian_regression_summary = (
+                self._create_dataframe_of_bayesian_regression(
+                    bayesian_regression_summary, x_vector
+                )
+            )
+        except ValueError:
+            raise ValueError(
+                "The number of bayesian models does not match the expected values."
+            )
+
+        print_and_save_result(
+            self.print_result,
+            self.save_result,
+            self.filepath,
+            dataframe_bayesian_regression_summary,
+            name_save_file,
+        )
+
+    @staticmethod
+    def _create_dataframe_of_bayesian_regression(
+        summary: np.ndarray, x_vector: list[str]
+    ) -> pd.DataFrame:
+        """
+        Create a DataFrame from the summary of Bayesian regression results.
+
+        Parameters
+        ----------
+        summary: np.ndarray
+            The Bayesian regression summary obtained from performing a Bayesian linear regression.
+        x_vector: list[str]
+            The list of column names used as independent variables in the Bayesian regression model.
+
+        Returns
+        -------
+        pd.DataFrame
+            A DataFrame that presents the Bayesian regression's summary, including the probability
+            of the regression coefficients not being zero, model number, intercept, independent variables,
+            Bayes Factor (BF), posterior probabilities (PostProbs), R squared (R2), dimension (dim),
+            and log marginal likelihood (logmarg).
+        """
+        base_columns: list[str] = ["P(B != 0 | Y)"]
+        model_columns: list[str] = [
+            "model {}".format(i + 1) for i in range(summary.shape[1] - 1)
+        ]
+        columns: list[str] = base_columns + model_columns
+
+        index: list[str] = [
+            "Intercept",
+            *x_vector,
+            "BF",
+            "PostProbs",
+            "R2",
+            "dim",
+            "logmarg",
+        ]
+
+        dataframe_summary: pd.DataFrame = pd.DataFrame(
+            summary, columns=columns, index=index
+        )
+
+        return dataframe_summary
+
+    @staticmethod
+    def _clean_names(name: str) -> str:
+        """
+        This method performs cleaning of individual name (could be column name or value in X).
+        Cleaning includes replacing certain characters with underscores or removing them.
+
+        Parameters
+        ----------
+        name: str
+            Original name (un-cleaned).
+
+        Returns
+        -------
+        str
+            Cleaned up name.
+        """
+        return (
+            name.replace(" ", "_")
+            .replace("(", "")
+            .replace(")", "")
+            .replace("-", "_")
+            .replace("+", "_")
+            .replace("=", "")
+            .replace(".", "")
+        )
+
+    def _clean_column_names(self, dataframe: pd.DataFrame) -> pd.DataFrame:
+        """
+        This method performs cleaning of DataFrame column names using the method clean_names.
+        It cleans a deep copy to not alter the original dataframe.
+
+        Parameters
+        ----------
+        dataframe: pd.DataFrame
+            DataFrame with original (un-cleaned) column names.
+
+        Returns
+        -------
+        pd.DataFrame
+            DataFrame with cleaned column names.
+        """
+        dataframe: pd.DataFrame = dataframe.copy()
+        dataframe.columns = [self._clean_names(col) for col in dataframe.columns]
+        return dataframe
+
+    def _get_clean_x_vector(self, x_vector: list[str]) -> list[str]:
+        """
+        This method uses clean_names to clean the individual elements/column names in x_vector.
+
+        Parameters
+        ----------
+        x_vector: list[str]
+            List with original (un-cleaned) elements.
+
+        Returns
+        -------
+        list[str]
+            List where all elements have been cleaned.
+        """
+        return [self._clean_names(x) for x in x_vector]

src/analysis_functions/specific_analysis/get_function_inverse_bayes_transformed_regression.py

@@ -0,0 +1,57 @@
+from collections import namedtuple
+from typing import NamedTuple
+
+import pandas as pd
+from statsmodels.regression.linear_model import RegressionResults
+
+from src.utils.helper_functions import FunctionData
+
+
+def get_function_inverse_bayes_transformed_regression(data: pd.DataFrame, model: RegressionResults) -> FunctionData:
+    """
+    Calculate and return a function representing the inverse Bayes transformed regression.
+
+    This function constructs a parameter tuple for the regression function, and then returns
+    a FunctionData object containing the regression
+    function and its parameters.
+
+    Parameters
+    ---------
+    data: pd.DataFrame
+        The dataset containing the 'bayes-corrected (q=0.25) valence' column.
+    model: RegressionResults
+    The regression model results from which the gradient and intercept parameters are extracted.
+
+    Returns
+    -------
+    FunctionData: An object containing the regression function and its parameters.
+    """
+    average_valence: float = data["bayes-corrected (q=0.25) valence"].mean()
+
+    Param = namedtuple(
+        "Param",
+        [
+            "average_valence",
+            "gradient_valence",
+            "gradient_totalvotes",
+            "intercept",
+        ],
+    )
+
+    def function(x: float, y: float, parameters: Param) -> float:
+        return (
+            parameters.gradient_valence
+            * (-1 * (x / (x + y) + parameters.average_valence / (x + y)) - 0.5)
+            + parameters.gradient_totalvotes * (x + y)
+            + parameters.intercept
+        )
+
+    params: NamedTuple = Param(
+        average_valence=average_valence,
+        gradient_valence=model.params.iloc[1],
+        gradient_totalvotes=model.params.iloc[2],
+        intercept=model.params.iloc[0],
+    )
+
+    function_data: FunctionData = FunctionData(function, params)
+    return function_data

src/analysis_functions/specific_analysis/increase_per_up_and_downvote.py

@@ -0,0 +1,168 @@
+import os
+from pathlib import Path
+from typing import Union, Optional
+
+import pandas as pd
+from statsmodels.regression.linear_model import RegressionResults
+
+from src.analysis_functions.regression import Regression
+from src.data_loading_and_saving.print_and_save_results import print_and_save_result
+from src.utils.helper_functions import (
+    calculate_inverse_bayes_correction,
+    transform_to_bayes_corrected_valence,
+)
+
+
+class InfluenceOfUpAndDownvotesOnReplies:
+    """
+    A class used to perform a dataset specific analysis on the influence of upvotes and downvotes
+    on the number of replies based on a linear regression model for totalvotes and valence
+
+    Attributes
+    ----------
+    print_result: bool
+        A flag used to indicate if the function should print the result to the standard output.
+    save_result: bool
+        A flag used to indicate if the function should save the result to a file.
+    filepath: str
+        The directory path where the result files will be saved if save_result is True.
+    ...
+
+    Methods
+    -------
+    report_increase_per_up_and_downvote_from_totalvotes_and_valence
+        (data: pd.DataFrame, weight_as_distribution_quantile: bool, weight_m: float, model: RegressionResults,
+        step: list, startpoint: Union[str, list], name_save_file: Optional[Path]) -> None:
+        Gives the % increase in reply likelihood for a given step in upvotes and downvotes
+        according to a linear regression model
+    """
+
+    def __init__(
+        self,
+        print_result: bool = True,
+        save_result: bool = True,
+        filepath: str = "results/",
+    ):
+        """
+        Constructs all the necessary attributes for the InfluenceOfUpAndDownvotesOnReplies object.
+
+        Parameters
+        ----------
+        print_result: bool
+            A flag to determine if the result should be printed.
+        save_result: bool
+            A flag to determine if the result should be saved.
+        filepath: str
+            The directory where the result should be saved.
+        """
+        self.print_result: bool = print_result
+        self.save_result: bool = save_result
+        self.filepath: str = filepath
+        if not os.path.isdir(filepath):
+            os.makedirs(filepath)
+
+    def report_increase_per_up_and_downvote_from_totalvotes_and_valence(
+        self,
+        data: pd.DataFrame,
+        weight_as_distribution_quantile: bool,
+        weight_m: float,
+        model: RegressionResults,
+        step: list = None,
+        startpoint: Union[str, list] = "average",
+        name_save_file: Optional[Path] = None,
+    ) -> None:
+        """
+        Reports the percentage increase in reply likelihood for specified upvote and downvote steps
+        from a given startpoint, using a linear regression model.
+
+        Parameters
+        ----------
+        data : pd.DataFrame
+            The dataset containing 'totalvotes', 'valence', and other relevant metrics.
+        weight_as_distribution_quantile : bool
+            Determines if weight_m should be treated as a quantile value for weighting totalvotes.
+        weight_m : float
+            The weight factor or quantile value for calculating weighted measures.
+        model : RegressionResults
+            The fitted linear regression model used for prediction.
+        step : list, optional
+            A list containing the step increase for upvotes and downvotes respectively.
+        startpoint : Union[str, list], optional
+            The starting point for calculation. Can be 'average' or a list of [totalvotes, bayes_corrected_valence].
+        name_save_file : Optional[Path], optional
+            The name of the file to save the result to.
+
+        Returns
+        -------
+        None
+        """
+        startpoint_totalvotes: int = 0
+        startpoint_bayes_corrected_valence: int = 0
+        average_valence: float = data["valence"].mean()
+
+        if isinstance(startpoint, str) and startpoint.lower() == "average":
+            startpoint_totalvotes: float = data["totalvotes"].mean()
+            startpoint_bayes_corrected_valence: float = data[
+                "bayes-corrected (q=0.25) valence"
+            ].mean()
+
+        if isinstance(startpoint, list):
+            startpoint_totalvotes: Union[int, float] = startpoint[0]
+            startpoint_bayes_corrected_valence: Union[int, float] = startpoint[1]
+
+        else:
+            ValueError("startpoint must be a valid point or 'average'")
+
+        if weight_as_distribution_quantile:
+            weight_m: float = data["totalvotes"].quantile(q=weight_m)
+
+        non_bayes_corrected_valence: float = (
+            calculate_inverse_bayes_correction(
+                bayes_corrected_value=startpoint_bayes_corrected_valence,
+                volume=startpoint_totalvotes,
+                weight_factor_m=weight_m,
+                average_measure=average_valence,
+            )
+        )
+
+        downvote_equivalent_average_bayes_corrected_valence: float = (
+                - (non_bayes_corrected_valence - 0.5) * startpoint_totalvotes
+        )
+
+        average_bayes_corrected_negativty_plus: float = transform_to_bayes_corrected_valence(
+            upvotes=startpoint_totalvotes
+            - downvote_equivalent_average_bayes_corrected_valence
+            + step[0],
+            downvotes=downvote_equivalent_average_bayes_corrected_valence + step[1],
+            average_valence=average_valence,
+            weight_factor_m=weight_m,
+        )
+
+        increase_per_step: float = (
+            Regression().predict_percentage_increase_between_liner_model_points(
+                model=model,
+                data_point_1=[
+                    startpoint_bayes_corrected_valence,
+                    startpoint_totalvotes,
+                ],
+                data_point_2=[
+                    average_bayes_corrected_negativty_plus,
+                    startpoint_totalvotes + step[0] + step[1],
+                ],
+            )
+        )
+
+        result = f"""
+        your startpoint was: bayes-correced-valence {startpoint_bayes_corrected_valence}, totalvotes {startpoint_totalvotes}
+        for a step with {step[0]} upvotes and {step[1]} downvotes increase
+        you obtain an endpoint of: bayes-correced-valence {average_bayes_corrected_negativty_plus}, totalvotes {startpoint_totalvotes + step[0] + step[1]}
+        the increase in replies is {increase_per_step} %
+        """
+
+        print_and_save_result(
+            self.print_result,
+            self.save_result,
+            self.filepath,
+            result,
+            name_save_file,
+        )

src/analysis_functions/ttest.py

@@ -0,0 +1,195 @@
+import os
+import pandas as pd
+import numpy as np
+from scipy import stats as stats
+from scipy.stats import ttest_ind, ttest_rel
+from pathlib import Path
+
+from src.data_loading_and_saving.print_and_save_results import print_and_save_result
+
+
+class TTest:
+    """
+    A class used to perform a T-test analysis.
+
+    ...
+
+    Attributes
+    ----------
+    print_result: bool
+        A flag used to indicate if the function should print the result to the standard output.
+    save_result: bool
+        A flag used to indicate if the function should save the result to a file.
+    filepath: str
+        The directory path where the result files will be saved if save_result is True.
+
+    Methods
+    -------
+    perform_ttest(data, first_group_name, second_group_name, name_save_file) -> None:
+        Performs an independent samples T-test for the two specified groups in the provided dataset.
+    perform_paired_ttest(data, first_group_name, second_group_name, name_save_file) -> tuple[float, float, float]:
+        Performs a paired samples T-test on the two specified groups in the provided dataset.
+        Returns the mean difference with the 95% confidence interval.
+    """
+
+    def __init__(
+        self,
+        print_result: bool = True,
+        save_result: bool = True,
+        filepath: str = "results/",
+    ):
+        """
+        Initializes the TTest object with the provided parameters.
+
+        Parameters
+        ----------
+        print_result: bool, optional
+            A flag to indicate if the function should print the result to the standard output. Default is True.
+        save_result: bool, optional
+            A flag to indicate if the function should save the result to a file. Default is True.
+        filepath: str, optional
+            The directory path where the result files will be saved if save_result is True. Default is "results/".
+        """
+        self.print_result: bool = print_result
+        self.save_result: bool = save_result
+        self.filepath: str = filepath
+        if not os.path.isdir(filepath):
+            os.makedirs(filepath)
+
+    def perform_ttest(
+        self,
+        data: pd.DataFrame,
+        first_group_name: str,
+        second_group_name: str,
+        name_save_file: Path,
+    ) -> None:
+        """
+        This method performs a T-test for the means of two independent samples of scores using
+        the given columns and either prints or saves the result based on the TTest object properties.
+
+        Parameters
+        ----------
+        data: pandas.DataFrame
+            The input dataframe which contains the data.
+        first_group_name: str
+            The name of the first column to be used in the t-test.
+        second_group_name: str
+            The name of the second column to be used in the t-test.
+        name_save_file: str
+            The name of the file to which the result will be saved (if self.save_result is True).
+
+        Returns
+        -------
+        None
+        """
+        group_1: pd.DataFrame = data[first_group_name].dropna()
+        group_2: pd.DataFrame = data[second_group_name].dropna()
+
+        degrees_of_freedom: int = len(group_1) + len(group_2) - 2
+
+        t_statistic: float
+        p_value: float
+        t_statistic, p_value = ttest_ind(group_1, group_2)
+
+        mean_group_1: float = group_1.mean()
+        mean_group_2: float = group_2.mean()
+
+        standard_deviation_group_1: float = group_1.std()
+        standard_deviation_group_2: float = group_2.std()
+
+        ttest_summary: str = (
+            f"Mean of {first_group_name}: {mean_group_1}\n"
+            f"Mean of {second_group_name}: {mean_group_2}\n"
+            f"Standard Deviation of {first_group_name}: {standard_deviation_group_1}\n"
+            f"Standard Deviation of {second_group_name}: {standard_deviation_group_2}\n"
+            f"Degrees of Freedom: {degrees_of_freedom}\n"
+            f"T-statistic: {t_statistic}\n"
+            f"P-value: {p_value}"
+        )
+
+        print_and_save_result(
+            self.print_result,
+            self.save_result,
+            self.filepath,
+            ttest_summary,
+            name_save_file,
+        )
+
+    def perform_paired_ttest(
+        self,
+        data: pd.DataFrame,
+        first_group_name: str,
+        second_group_name: str,
+        name_save_file: Path,
+    ) -> tuple[float, float, float]:
+        """
+        Performs a paired sample t-test and calculates the effect size (Cohen's d) using the given columns
+
+        Parameters
+        ----------
+        data : pd.DataFrame
+            The DataFrame containing the data of two related groups to be compared
+        first_group_name : str
+            The name of the first group (column)
+        second_group_name : str
+            the name of the second group (column)
+        name_save_file: Path
+            The path of the file to save the result in.
+
+        Returns
+        -------
+        mean_difference : float
+            The mean difference between the two samples
+        confidence_interval[0] : float
+            The lower bound of the 95% confidence interval
+        confidence_interval[1] : float
+            The upper bound of the 95% confidence interval
+        """
+
+        data: pd.DataFrame = data[[first_group_name, second_group_name]].dropna()
+        group_1: pd.DataFrame = data[first_group_name]
+        group_2: pd.DataFrame = data[second_group_name]
+        degrees_of_freedom: int = len(group_1) - 1
+
+        t_statistic: float
+        p_value: float
+        t_statistic, p_value = ttest_rel(group_1, group_2)
+
+        mean_group_1: float = group_1.mean()
+        mean_group_2: float = group_2.mean()
+        standard_deviation_group_1: float = group_1.std()
+        standard_deviation_group_2: float = group_2.std()
+        pooled_standard_deviation: float = np.sqrt(
+            (standard_deviation_group_1**2 + standard_deviation_group_2**2) / 2
+        )
+        cohens_d: float = (mean_group_1 - mean_group_2) / pooled_standard_deviation
+
+        mean_difference: float = mean_group_1 - mean_group_2
+        standard_error_difference: float = np.std(group_1 - group_2, ddof=1) / np.sqrt(
+            len(group_1)
+        )
+
+        confidence_interval: np.ndarray[float] = stats.t.interval(
+            0.95, len(group_1) - 1, loc=mean_difference, scale=standard_error_difference
+        )
+
+        paired_ttest_summary: str = (
+            f"Mean of {first_group_name}: {mean_group_1}\n"
+            f"Mean of {second_group_name}: {mean_group_2}\n"
+            f"Standard Deviation of {first_group_name}: {standard_deviation_group_1}\n"
+            f"Standard Deviation of {second_group_name}: {standard_deviation_group_2}\n"
+            f"Degrees of Freedom: {degrees_of_freedom}\n"
+            f"Cohen's d: {cohens_d}\n"
+            f"T-statistic: {t_statistic}\n"
+            f"P-value: {p_value}"
+        )
+
+        print_and_save_result(
+            self.print_result,
+            self.save_result,
+            self.filepath,
+            paired_ttest_summary,
+            name_save_file,
+        )
+
+        return mean_difference, confidence_interval[0], confidence_interval[1]

src/analysis_wrappers/comparison_variance_in_and_between_group_wrapper.py

@@ -0,0 +1,42 @@
+from src.analysis_functions.comparison_variance_in_and_between_group import (
+    ComparisonVariance,
+)
+from src.data_classes.parameters_analysis_comparison_variance_in_and_between_group import (
+    ComparisonVarianceInAndBetweenGroupParameters,
+)
+from src.utils.helper_logging import log_comparison_variance_details
+
+
+def run_comparison_variance_in_and_between_group(
+    comparison_variance: ComparisonVariance,
+    analyses_list: list[ComparisonVarianceInAndBetweenGroupParameters],
+):
+    """
+    Run a comparison between the mean variance in a variable grouped by a condition and the variance
+    between group members based on a list of parameter objects.
+
+    This function iterates over a list of ComparisonVarianceInAndBetweenGroupParameters objects,
+    and then reports the total variance, variance between group members and mean variance of group members for
+     each pair of variables specified in the ComparisonVarianceInAndBetweenGroupParameters object.
+
+    Parameters
+    ----------
+    comparison_variance: ComparisonVariance
+        An instance of the ComparisonVariance class.
+    analyses_list: list[ComparisonVarianceInAndBetweenGroupParameters]
+        A list of ComparisonVarianceInAndBetweenGroupParameters objects.
+        Each object contains the parameters for a single variation comparison analysis.
+
+    Returns
+    -------
+    None
+    """
+    for analysis_config in analyses_list:
+        log_comparison_variance_details(analysis_config)
+
+        comparison_variance.compare_ingroup_intergroup_variance(
+            data=analysis_config.data,
+            variable=analysis_config.variable,
+            group=analysis_config.group,
+            name_save_file=analysis_config.name_save_file,
+        )

src/analysis_wrappers/descriptive_wrapper.py

@@ -0,0 +1,73 @@
+from src.analysis_functions.descriptive import DescriptiveAnalysis
+from src.data_classes.parameters_descriptive_aggregated import (
+    DescriptiveAggregatedParameters,
+)
+from src.data_classes.parameters_descriptive_overview import (
+    DescriptiveOverviewParameters,
+)
+from src.data_classes.parameters_descriptive_percentage_of_dataset_under_condition import (
+    DescriptivePercentageOfDatasetUnderConditionParameters,
+)
+
+from src.data_classes.parameters_general import GeneralParameters
+
+from src.utils.helper_logging import log_descriptive_analysis_details
+
+
+def run_descriptive_analysis(
+    descriptive: DescriptiveAnalysis, analyses_list: list[GeneralParameters]
+):
+    """
+    Run descriptive analyses based on a list of parameter objects.
+
+    This function iterates over a list of GeneralParameters objects,
+    logs the details of each analysis, and then performs the appropriate
+    descriptive analysis based on the type of the Parameters object.
+
+    Parameters
+    ----------
+    descriptive: DescriptiveAnalysis
+        An instance of the DescriptiveAnalysis class.
+    analyses_list: list[PlotParameters]
+        A list of PlotParameters objects.
+        Each object contains the parameters for a single visualization.
+
+    Raises
+    ------
+    ValueError: If the type of the GeneralParameters object is not recognized or not a descriptive analysis.
+
+    Returns
+    -------
+    None
+    """
+    for analysis_config in analyses_list:
+        log_descriptive_analysis_details(analysis_config)
+        if isinstance(analysis_config, DescriptiveAggregatedParameters):
+            descriptive.create_descriptive_aggregated_for_metrics(
+                data=analysis_config.data,
+                variables=analysis_config.variables,
+                aggregation_function=analysis_config.aggregation_function,
+                group_by=analysis_config.group_by,
+                name_save_file=analysis_config.name_save_file,
+            )
+        elif isinstance(analysis_config, DescriptiveOverviewParameters):
+            descriptive.create_descriptives_for_metrics(
+                data=analysis_config.data,
+                metrics=analysis_config.metrics,
+                group_by=analysis_config.group_by,
+                name_save_file=analysis_config.name_save_file,
+            )
+        elif isinstance(
+            analysis_config, DescriptivePercentageOfDatasetUnderConditionParameters
+        ):
+            descriptive.give_percentage_of_dataset_under_condition(
+                data=analysis_config.data,
+                variable=analysis_config.variable,
+                comparison=analysis_config.comparison,
+                condition=analysis_config.condition,
+                name_save_file=analysis_config.name_save_file,
+            )
+        else:
+            raise ValueError(
+                f"Invalid type of descriptive analysis requested {type(analysis_config)}"
+            )

src/analysis_wrappers/pearson_correlation_wrapper.py

@@ -0,0 +1,39 @@
+from src.analysis_functions.pearson_correlation import PearsonCorrelation
+from src.data_classes.parameters_analysis_pearson_correlation import (
+    PearsonCorrelationParameters,
+)
+from src.utils.helper_logging import log_pearson_correlation_details
+
+
+def run_pearson_correlation(
+    pearson_correlation: PearsonCorrelation,
+    analyses_list: list[PearsonCorrelationParameters],
+):
+    """
+    Run Pearson correlation analyses based on a list of parameter objects.
+
+    This function iterates over a list of PearsonCorrelationParameters objects,
+    and then calculates the Pearson correlation for each pair of variables specified
+    in the PearsonCorrelationParameters object.
+
+    Parameters
+    ----------
+    pearson_correlation: PearsonCorrelation
+        An instance of the PearsonCorrelation class.
+    analyses_list: list[PearsonCorrelationParameters
+        A list of PearsonCorrelationParameters objects.
+        Each object contains the parameters for a single Pearson correlation analysis.
+
+    Returns
+    -------
+    None
+    """
+    for analysis_config in analyses_list:
+        log_pearson_correlation_details(analysis_config)
+
+        pearson_correlation.calculate_correlation(
+            data=analysis_config.data,
+            first_group_name=analysis_config.variable_1,
+            second_group_name=analysis_config.variable_2,
+            name_save_file=analysis_config.name_save_file,
+        )

src/analysis_wrappers/regression_wrapper.py

@@ -0,0 +1,85 @@
+from statsmodels.regression.linear_model import RegressionResults
+
+from src.analysis_functions.regression import Regression
+
+from src.data_classes.parameters_analysis_regression import (
+    RegressionParameters,
+    BayesianRegressionParameters,
+    LinearRegressionParameters,
+    GroupedLinearRegressionParameters,
+)
+from src.utils.helper_logging import log_regression_details
+
+
+def run_regression(
+    regression: Regression, analyses_list: list[RegressionParameters]
+) -> dict[str, RegressionResults]:
+    """
+    Run regression analyses based on a list of parameter objects.
+
+    This function iterates over a list of RegressionParameters objects,
+    and then performs the appropriate regression analysis based on the
+    type of the RegressionParameters object.
+
+    Parameters
+    ----------
+    regression: Regression
+        An instance of the Regression class.
+    analyses_list: list[RegressionParameters]
+        A list of RegressionParameters objects.
+        Each object contains the parameters for a single regression analysis.
+
+    Raises
+    ------
+    ValueError: If the type of the RegressionParameters object is not recognized.
+
+    Returns
+    -------
+    dict[str, RegressionResults]
+        A dictionary of regression results, where the key is the name of the analysis
+        and the value is the regression results.
+    """
+
+    regression_results: dict = {}
+
+    for analysis_config in analyses_list:
+        result = None
+
+        if isinstance(analysis_config, LinearRegressionParameters):
+            log_regression_details(analysis_config, "Linear")
+
+            result = regression.linear_regression(
+                data=analysis_config.data,
+                x_vector=analysis_config.independent_variables,
+                y=analysis_config.dependent_variable,
+                standardize=analysis_config.standardize,
+                name_save_file=analysis_config.name_save_file,
+            )
+        elif isinstance(analysis_config, BayesianRegressionParameters):
+            log_regression_details(analysis_config, "Bayesian")
+
+            regression.bayesian_regression(
+                data=analysis_config.data,
+                x_vector=analysis_config.independent_variables,
+                y=analysis_config.dependent_variable,
+                name_save_file=analysis_config.name_save_file,
+            )
+        elif isinstance(analysis_config, GroupedLinearRegressionParameters):
+            log_regression_details(analysis_config, "Grouped")
+
+            result = regression.linear_regression_grouped(
+                data=analysis_config.data,
+                x_vector=analysis_config.independent_variables,
+                y=analysis_config.dependent_variable,
+                dictionary_aggregation_methods_for_data_columns=analysis_config.dictionary_aggregation_methods,
+                column_to_group_by=analysis_config.group_by,
+                standardize=analysis_config.standardize,
+                print_detailed_coefficients=analysis_config.print_detailed_coefficients,
+                name_save_file=analysis_config.name_save_file,
+            )
+        else:
+            raise ValueError(f"Unknown parameter type: {type(analysis_config)}")
+
+        regression_results[analysis_config.name]: RegressionResults = result
+
+    return regression_results

src/analysis_wrappers/specific_analysis_wrappers/get_function_inverse_bayes_transformed_regression.py

@@ -0,0 +1,52 @@
+import pandas as pd
+from statsmodels.regression.linear_model import RegressionResults
+
+from src.analysis_functions.specific_analysis.get_function_inverse_bayes_transformed_regression import (
+    get_function_inverse_bayes_transformed_regression,
+)
+from src.data_classes.parameters_analysis_get_function_inverse_bayes_transformed_regression import (
+    GetFunctionInverseBayesTransformedRegressionParameters,
+)
+from src.utils.helper_functions import FunctionData
+from src.utils.helper_logging import (
+    log_get_function_inverse_bayes_transformed_regression,
+)
+
+
+def run_get_function_inverse_bayes_transformed_regression(
+    analyses_list: list[GetFunctionInverseBayesTransformedRegressionParameters],
+    regression_models: dict[str, RegressionResults],
+) -> dict[str, FunctionData]:
+    """
+    Executes the inverse Bayes transformed regression analysis for a list of analysis configurations.
+
+    This function iterates over a list of analysis configurations, logs the configuration details,
+    performs the inverse Bayes transformed regression analysis using the specified regression model,
+    and collects the results in a dictionary.
+
+    Parameters
+    ----------
+    analyses_list : list[GetFunctionInverseBayesTransformedRegressionParameters]
+        A list of parameters for each analysis to be run. Each item in the list is an instance
+        of GetFunctionInverseBayesTransformedRegressionParameters, which includes the data and model name.
+    regression_models : dict[str, RegressionResults]
+        A dictionary mapping model names to their corresponding fitted regression models.
+
+    Returns
+    -------
+    dict[str, FunctionData]
+        A dictionary mapping the name of each analysis to its resulting FunctionData object.
+    """
+    functions: dict = {}
+
+    for analysis_config in analyses_list:
+        log_get_function_inverse_bayes_transformed_regression(analysis_config)
+
+        function: FunctionData = get_function_inverse_bayes_transformed_regression(
+            data=analysis_config.data,
+            model=regression_models[analysis_config.model_name],
+        )
+
+        functions[analysis_config.name] = function
+
+    return functions

src/analysis_wrappers/specific_analysis_wrappers/increase_per_up_and_downvote_wrapper.py

@@ -0,0 +1,51 @@
+from statsmodels.regression.linear_model import RegressionResults
+
+from src.analysis_functions.specific_analysis.increase_per_up_and_downvote import (
+    InfluenceOfUpAndDownvotesOnReplies,
+)
+from src.data_classes.parameters_analysis_influence_of_up_and_downvotes import (
+    InfluenceOfVotesParameters,
+)
+from src.utils.helper_logging import log_influence_of_up_and_downvotes_on_replies
+
+
+def run_report_influence_of_up_and_downvotes_on_replies(
+    influence_of_up_and_downvotes: InfluenceOfUpAndDownvotesOnReplies,
+    analyses_list: list[InfluenceOfVotesParameters],
+    regression_models: dict[str, RegressionResults],
+) -> None:
+    """
+    Run specific analyses to find the effect of up-and downvotes on replies based on a list of parameter objects.
+
+    This function iterates over a list of InfluenceOfVotesParameters objects,
+    and then calculates the influence of up-and downvotes on replies based on
+    the parameters in each object and a given regression model.
+
+
+    Parameters
+    ----------
+    influence_of_up_and_downvotes: InfluenceOfUpAndDownvotesOnReplies
+        An instance of the InfluenceOfUpAndDownvotesOnReplies class.
+    analyses_list: list[PearsonCorrelationParameters
+        A list of PearsonCorrelationParameters objects.
+        Each object contains the parameters for a single Pearson correlation analysis.
+    regression_models: dict[str, RegressionResults]
+        A dictionary of regression models, where the key is the model name and the value is the regression model.
+        Created via the regression_wrappers.run_regression function.
+
+    Returns
+    -------
+    None
+    """
+    for analysis_config in analyses_list:
+        log_influence_of_up_and_downvotes_on_replies(analysis_config)
+
+        influence_of_up_and_downvotes.report_increase_per_up_and_downvote_from_totalvotes_and_valence(
+            data=analysis_config.data,
+            weight_as_distribution_quantile=analysis_config.weight_as_distribution_quantile,
+            weight_m=analysis_config.weight_m,
+            model=regression_models[analysis_config.model_name],
+            step=analysis_config.step,
+            startpoint=analysis_config.startpoint,
+            name_save_file=analysis_config.name_save_file,
+        )

src/analysis_wrappers/ttest_wrapper.py

@@ -0,0 +1,57 @@
+from src.analysis_functions.ttest import TTest
+from src.data_classes.parameters_analysis_ttest import (
+    TTestParameters,
+    PairedTTestParameters,
+)
+from src.utils.helper_logging import log_ttest_details
+
+
+def run_ttest(
+    ttest: TTest, analyses_list: list[TTestParameters]
+) -> dict[str, tuple[float, float, float]]:
+    """
+    Run t-test analyses based on a list of parameter objects.
+
+    This function iterates over a list of TTestParameters objects,
+    logs the details of each analysis, and then performs the appropriate
+    t-test analysis based on the type of the TTestParameters object.
+
+    Parameters
+    ----------
+    ttest: TTest
+        An instance of the TTest class.
+    analyses_list: list[TTestParameters]
+        A list of TTestParameters objects.
+        Each object contains the parameters for a single t-test analysis.
+
+    Returns
+    -------
+    dict[str, tuple[float, float, float]]
+    """
+    ttest_results: dict = {}
+
+    for analysis_config in analyses_list:
+        result = None
+
+        if isinstance(analysis_config, PairedTTestParameters):
+            log_ttest_details(analysis_config, "Paired")
+
+            result = ttest.perform_paired_ttest(
+                data=analysis_config.data,
+                first_group_name=analysis_config.variable_1,
+                second_group_name=analysis_config.variable_2,
+                name_save_file=analysis_config.name_save_file,
+            )
+
+        else:
+            log_ttest_details(analysis_config, "")
+            ttest.perform_ttest(
+                data=analysis_config.data,
+                first_group_name=analysis_config.variable_1,
+                second_group_name=analysis_config.variable_2,
+                name_save_file=analysis_config.name_save_file,
+            )
+
+        ttest_results[analysis_config.name] = result
+
+    return ttest_results

src/analysis_wrappers/visualization_wrapper.py

@@ -0,0 +1,360 @@
+from statsmodels.regression.linear_model import RegressionResults
+
+from src.analysis_functions.visualization import DataVisualizer
+from src.data_classes.parameters_plot_barchart import BarChartPlotParameters
+from src.data_classes.parameters_plot_boxplot import BoxPlotParameters
+from src.data_classes.parameters_plot_contourplot import ContourPlotParameters
+from src.data_classes.parameters_plot_count_distribution import (
+    CountDistributionPlotParameters,
+)
+from src.data_classes.parameters_plot_densityplot import DensityPlotParameters
+from src.data_classes.parameters_plot_forestplot import ForestPlotParameters
+from src.data_classes.parameters_plot_forestplot_paired_ttest import (
+    ForestPlotPairedTTestParameters,
+)
+from src.data_classes.parameters_plot_grouped_histogram import (
+    GroupedHistogramParameters,
+)
+from src.data_classes.parameters_plot_heatmap import HeatmapParameters
+from src.data_classes.parameters_plot_hexbinplot import HexbinPlotParameters
+from src.data_classes.parameters_plot_histogram import HistogramPlotParameters
+from src.data_classes.parameters_plot_percentage_stacked_barchart import (
+    PercentageStackedBarChartPlotParameters,
+)
+from src.data_classes.parameters_plot_ridgelineplot import RidgelineParameters
+from src.data_classes.parameters_plot_simple_scatterplot import (
+    SimpleScatterPlotParameters,
+)
+from src.data_classes.parameters_plot_stacked_barchart import (
+    StackedBarChartPlotParameters,
+)
+from src.data_classes.parameters_plot_surfaceplot import SurfacePlotParameters
+from src.data_classes.parameters_plot_violinplot import ViolinPlotParameters
+from src.data_classes.parameters_visualization import PlotParameters
+from src.utils.helper_functions import FunctionData
+from src.utils.helper_logging import log_visualization_details
+
+
+def run_visualization(
+    visualizer: DataVisualizer,
+    analyses_list: list[PlotParameters],
+    regression_results: dict[str, RegressionResults],
+    functions: dict[str, FunctionData],
+    ttest_results: dict[str, tuple[float, float, float]],
+) -> None:
+    """
+    Run visualization analyses based on a list of parameter objects.
+
+    This function iterates over a list of PlotParameters objects,
+    logs the details of each analysis, and then performs the appropriate
+    visualization based on the type of the PlotParameters object.
+
+    Parameters
+    ----------
+    visualizer: DataVisualizer
+        An instance of the DataVisualizer class.
+    analyses_list: list[PlotParameters]
+        A list of PlotParameters objects.
+        Each object contains the parameters for a single visualization.
+    regression_results: dict[str, RegressionResults]
+        A dictionary mapping regression model names to their results.
+    functions: dict[str, FunctionData]
+        A dictionary mapping function names to their data.
+    ttest_results: dict[str, tuple[float, float, float]]
+        A dictionary mapping paired t-test names to their results.
+
+    Raises
+    ------
+    ValueError: If the type of the PlotParameters object is not recognized.
+
+    Returns
+    -------
+    None
+    """
+    for analysis_config in analyses_list:
+        log_visualization_details(analysis_config)
+        if isinstance(analysis_config, BarChartPlotParameters):
+            visualizer.create_bar_chart(
+                data=analysis_config.data,
+                variable_x_axis=analysis_config.variable_x_axis,
+                variable_y_axis=analysis_config.variable_y_axis,
+                x_axis_label=analysis_config.x_axis_label,
+                y_axis_label=analysis_config.y_axis_label,
+                chart_orientation=analysis_config.chart_orientation,
+                sort_order=analysis_config.sort_order,
+                title=analysis_config.title,
+                name_save_file=analysis_config.name_save_file,
+                custom_order=analysis_config.custom_order,
+            )
+        elif isinstance(analysis_config, BoxPlotParameters):
+            visualizer.create_box_plot(
+                data=analysis_config.data,
+                variable_1=analysis_config.variable_1,
+                variable_2=analysis_config.variable_2,
+                x_axis_label=analysis_config.x_axis_label,
+                y_axis_label=analysis_config.y_axis_label,
+                title=analysis_config.title,
+                name_save_file=analysis_config.name_save_file,
+            )
+        elif isinstance(analysis_config, ContourPlotParameters):
+            visualizer.create_contour_plot(
+                function=functions[analysis_config.function_name],
+                x_axis_maximum=analysis_config.x_axis_maximum,
+                y_axis_maximum=analysis_config.y_axis_maximum,
+                x_axis_label=analysis_config.x_axis_label,
+                y_axis_label=analysis_config.y_axis_label,
+                title=analysis_config.title,
+                name_save_file=analysis_config.name_save_file,
+            )
+        elif isinstance(analysis_config, HistogramPlotParameters):
+            visualizer.create_histogram(
+                data=analysis_config.data,
+                variable=analysis_config.variable,
+                x_axis_limits=analysis_config.x_axis_limits,
+                x_axis_logarithmic_scaling=analysis_config.x_axis_logarithmic_scaling,
+                y_axis_logarithmic_scaling=analysis_config.y_axis_logarithmic_scaling,
+                x_axis_label=analysis_config.x_axis_label,
+                y_axis_label=analysis_config.y_axis_label,
+                title=analysis_config.title,
+                name_save_file=analysis_config.name_save_file,
+            )
+        elif isinstance(analysis_config, CountDistributionPlotParameters):
+            visualizer.create_count_distribution(
+                data=analysis_config.data,
+                variable=analysis_config.variable,
+                x_axis_limits=analysis_config.x_axis_limits,
+                x_axis_logarithmic_scaling=analysis_config.x_axis_logarithmic_scaling,
+                y_axis_logarithmic_scaling=analysis_config.y_axis_logarithmic_scaling,
+                x_axis_label=analysis_config.x_axis_label,
+                y_axis_label=analysis_config.y_axis_label,
+                title=analysis_config.title,
+                name_save_file=analysis_config.name_save_file,
+            )
+        elif isinstance(analysis_config, DensityPlotParameters):
+            visualizer.create_density_plot(
+                data=analysis_config.data,
+                variable_x_axis=analysis_config.variable_x_axis,
+                variable_y_axis=analysis_config.variable_y_axis,
+                data_breakpoints=analysis_config.data_breakpoints,
+                title=analysis_config.title,
+                name_save_file=analysis_config.name_save_file,
+            )
+        elif isinstance(analysis_config, ForestPlotParameters):
+            visualizer.create_forest_plot(
+                regression_models=_get_labeled_models_for_forest_plot(
+                    analysis_config, regression_results
+                ),
+                coefficient_names=analysis_config.coefficient_names,
+                sort_by_size=analysis_config.sort_by_size,
+                x_axis_minimum=analysis_config.x_axis_minimum,
+                x_axis_maximum=analysis_config.x_axis_maximum,
+                dotsize=analysis_config.dotsize,
+                colors=analysis_config.colors,
+                name_save_file=analysis_config.name_save_file,
+            )
+        elif isinstance(analysis_config, ForestPlotPairedTTestParameters):
+            visualizer.create_forest_plot_paired_ttest(
+                paired_ttests=_get_labeled_ttests_for_forest_plot(
+                    analysis_config, ttest_results
+                ),
+                x_axis_minimum=analysis_config.x_axis_minimum,
+                x_axis_maximum=analysis_config.x_axis_maximum,
+                dotsize=analysis_config.dotsize,
+                name_save_file=analysis_config.name_save_file,
+            )
+        elif isinstance(analysis_config, GroupedHistogramParameters):
+            visualizer.create_grouped_histogram(
+                data=analysis_config.data,
+                group_by=analysis_config.group_by,
+                aggregation_column=analysis_config.aggregation_variable,
+                aggregation_function=analysis_config.aggregation_function,
+                x_axis_label=analysis_config.x_axis_label,
+                y_axis_label=analysis_config.y_axis_label,
+                title=analysis_config.title,
+                name_save_file=analysis_config.name_save_file,
+            )
+        elif isinstance(analysis_config, HeatmapParameters):
+            visualizer.create_heatmap(
+                data=analysis_config.data,
+                axis_variables=analysis_config.axis_variables,
+                heat_variable=analysis_config.heat_variable,
+                max_values_axes=analysis_config.axis_maxima,
+                min_values_axes=analysis_config.axis_minima,
+                log_option=analysis_config.logarithmic_heat_scaling,
+                name_save_file=analysis_config.name_save_file,
+            )
+        elif isinstance(analysis_config, HexbinPlotParameters):
+            visualizer.create_hexbin_plot(
+                data=analysis_config.data,
+                variable_x_axis=analysis_config.variable_x_axis,
+                variable_y_axis=analysis_config.variable_y_axis,
+                x_axis_maximum=analysis_config.x_axis_maximum,
+                y_axis_maximum=analysis_config.y_axis_maximum,
+                trendline=analysis_config.trendline,
+                log_scale=analysis_config.logarithmic_hex_scaling,
+                title=analysis_config.title,
+                name_save_file=analysis_config.name_save_file,
+            )
+        elif isinstance(analysis_config, PercentageStackedBarChartPlotParameters):
+            visualizer.create_percentage_stacked_bar_chart(
+                data=analysis_config.data,
+                variable_x_axis=analysis_config.variable_x_axis,
+                variables_to_compare=analysis_config.variables_to_compare,
+                chart_orientation=analysis_config.chart_orientation,
+                sort_order=analysis_config.sort_order,
+                title=analysis_config.title,
+                name_save_file=analysis_config.name_save_file,
+            )
+        elif isinstance(analysis_config, RidgelineParameters):
+            visualizer.create_ridgeline_plot(
+                data=analysis_config.data,
+                x_axis_variable=analysis_config.variable_x_axis,
+                y_axis_variable=analysis_config.variable_y_axis,
+                data_breakpoints=analysis_config.data_breakpoints,
+                title=analysis_config.title,
+                name_save_file=analysis_config.name_save_file,
+            )
+        elif isinstance(analysis_config, StackedBarChartPlotParameters):
+            visualizer.create_stacked_bar_chart(
+                data=analysis_config.data,
+                variable_x_axis=analysis_config.variable_x_axis,
+                variable_y_axis=analysis_config.variable_y_axis,
+                x_axis_label=analysis_config.x_axis_label,
+                y_axis_label=analysis_config.y_axis_label,
+                hue=analysis_config.hue,
+                chart_orientation=analysis_config.chart_orientation,
+                sort_order=analysis_config.sort_order,
+                title=analysis_config.title,
+                name_save_file=analysis_config.name_save_file,
+                custom_order=analysis_config.custom_order,
+            )
+        elif isinstance(analysis_config, SimpleScatterPlotParameters):
+            visualizer.create_scatter_plot_simple(
+                data=analysis_config.data,
+                variable_x_axis=analysis_config.variable_x_axis,
+                variable_y_axis=analysis_config.variable_y_axis,
+                title=analysis_config.title,
+                name_save_file=analysis_config.name_save_file,
+            )
+        elif isinstance(analysis_config, SurfacePlotParameters):
+            visualizer.generate_surface_plot(
+                function=functions[analysis_config.function_name],
+                x_axis_maximum=analysis_config.x_axis_maximum,
+                y_axis_maximum=analysis_config.y_axis_maximum,
+                x_axis_label=analysis_config.x_axis_label,
+                y_axis_label=analysis_config.y_axis_label,
+                z_axis_label=analysis_config.z_axis_label,
+                elevation_angle=analysis_config.elevation_angle,
+                azimuth_angle=analysis_config.azimuth_angle,
+                title=analysis_config.title,
+                name_save_file=analysis_config.name_save_file,
+            )
+        elif isinstance(analysis_config, ViolinPlotParameters):
+            visualizer.create_violin_plot(
+                data=analysis_config.data,
+                variable_x_axis=analysis_config.variable_x_axis,
+                variable_y_axis=analysis_config.variable_y_axis,
+                x_axis_label=analysis_config.x_axis_label,
+                y_axis_label=analysis_config.y_axis_label,
+                title=analysis_config.title,
+                name_save_file=analysis_config.name_save_file,
+            )
+        else:
+            raise ValueError(
+                f"Invalid type of visualization requested {type(analysis_config)}"
+            )
+
+
+def _get_labeled_models_for_forest_plot(
+    analysis_config: ForestPlotParameters,
+    regression_results: dict[str, RegressionResults],
+) -> dict:
+    """
+    Maps regression model names to their labels for forest plot visualization.
+
+    This function takes an analysis configuration for a forest plot and a dictionary
+    of regression results. It maps the specified regression model names to their
+    corresponding labels as defined in the analysis configuration. This mapping is
+    used for labeling the models in the forest plot visualization.
+
+    Parameters
+    ----------
+    analysis_config : ForestPlotParameters
+        The configuration parameters for the forest plot, including the names and
+        labels of the regression models to be used.
+    regression_results : dict[str, RegressionResults]
+        A dictionary mapping model names to their corresponding fitted regression models.
+
+    Returns
+    -------
+    dict
+        A dictionary mapping the labels (as specified in the analysis configuration)
+        to the corresponding regression models.
+    """
+    mapping_regression_model_names_to_labels: dict = {
+        name: label
+        for name, label in zip(
+            analysis_config.regression_model_names,
+            analysis_config.regression_model_labels,
+        )
+        if name in regression_results.keys()
+    }
+
+    selected_models: dict = {
+        name: regression_results[name]
+        for name in mapping_regression_model_names_to_labels.keys()
+    }
+
+    labeled_models: dict = {
+        mapping_regression_model_names_to_labels[name]: model
+        for name, model in selected_models.items()
+    }
+
+    return labeled_models
+
+
+def _get_labeled_ttests_for_forest_plot(
+    analysis_config: ForestPlotPairedTTestParameters,
+    ttest_results: dict[str, tuple[float, float, float]],
+) -> dict:
+    """
+    Maps paired t-test names to their labels for forest plot visualization.
+
+    This function takes an analysis configuration for a forest plot that includes
+    paired t-tests and a dictionary of t-test results. It maps the specified t-test
+    names to their corresponding labels as defined in the analysis configuration.
+    This mapping is used for labeling the t-tests in the forest plot visualization.
+
+    Parameters
+    ----------
+    analysis_config : ForestPlotPairedTTestParameters
+        The configuration parameters for the forest plot, including the names and
+        labels of the paired t-tests to be used.
+    ttest_results : dict[str, tuple[float, float, float]]
+        A dictionary mapping t-test names to their results (t-statistic, p-value, and
+        degrees of freedom).
+
+    Returns
+    -------
+    dict
+        A dictionary mapping the labels (as specified in the analysis configuration)
+        to the corresponding t-test results.
+    """
+    mapping_ttest_names_to_labels: dict = {
+        name: label
+        for name, label in zip(
+            analysis_config.paired_ttest_names, analysis_config.paired_ttest_labels
+        )
+        if name in ttest_results.keys()
+    }
+
+    selected_ttests: dict = {
+        name: ttest_results[name] for name in mapping_ttest_names_to_labels.keys()
+    }
+
+    labeled_ttests: dict = {
+        mapping_ttest_names_to_labels[name]: ttest
+        for name, ttest in selected_ttests.items()
+    }
+
+    return labeled_ttests

src/data_classes/parameters_analysis_comparison_variance_in_and_between_group.py

@@ -0,0 +1,10 @@
+from attrs import field, define
+from attrs.validators import instance_of
+
+from src.data_classes.parameters_general import GeneralParameters
+
+
+@define
+class ComparisonVarianceInAndBetweenGroupParameters(GeneralParameters):
+    variable: str = field(validator=instance_of(str))
+    group: str = field(validator=instance_of(str))

src/data_classes/parameters_analysis_get_function_inverse_bayes_transformed_regression.py

@@ -0,0 +1,9 @@
+from attrs import field, define
+from attrs.validators import instance_of
+
+from src.data_classes.parameters_general import GeneralParameters
+
+
+@define
+class GetFunctionInverseBayesTransformedRegressionParameters(GeneralParameters):
+    model_name: str = field(validator=instance_of(str))

src/data_classes/parameters_analysis_influence_of_up_and_downvotes.py

@@ -0,0 +1,21 @@
+from typing import Iterable, Union
+
+from attrs import field, define
+from attrs.validators import instance_of, deep_iterable
+
+from src.data_classes.parameters_general import GeneralParameters
+
+
+@define
+class InfluenceOfVotesParameters(GeneralParameters):
+    weight_as_distribution_quantile: bool = field(validator=instance_of(bool))
+    weight_m: Union[int, float] = field(validator=instance_of((int, float)))
+    model_name: str = field(validator=instance_of(str))
+    step: list[int] = field(
+        validator=deep_iterable(
+            member_validator=instance_of(int), iterable_validator=instance_of(Iterable)
+        ),
+    )
+    startpoint: Union[str, list[Union[int, float]]] = field(
+        validator=instance_of((str, list))
+    )

src/data_classes/parameters_analysis_pearson_correlation.py

@@ -0,0 +1,10 @@
+from attrs import field, define
+from attrs.validators import instance_of
+
+from src.data_classes.parameters_general import GeneralParameters
+
+
+@define
+class PearsonCorrelationParameters(GeneralParameters):
+    variable_1: str = field(validator=instance_of(str))
+    variable_2: str = field(validator=instance_of(str))

src/data_classes/parameters_analysis_regression.py

@@ -0,0 +1,61 @@
+from typing import Iterable
+from pathlib import Path
+
+from attrs import field, define
+from attrs.validators import instance_of, deep_iterable, optional
+from attr import attrib
+
+from src.data_classes.parameters_general import GeneralParameters
+from src.utils.helper_conversion import create_dictionary_of_aggregation_methods
+
+
+@define
+class RegressionParameters(GeneralParameters):
+    dependent_variable: str = field(validator=instance_of(str))
+    independent_variables: list[str] = field(
+        validator=deep_iterable(
+            member_validator=instance_of(str), iterable_validator=instance_of(Iterable)
+        ),
+    )
+
+
+@define
+class BayesianRegressionParameters(RegressionParameters):
+    pass
+
+
+@define
+class LinearRegressionParameters(RegressionParameters):
+    standardize: bool = field(default=False, validator=instance_of(bool))
+    report_effect_size: bool = field(
+        default=False, validator=optional(instance_of(bool))
+    )
+
+
+@define
+class GroupedLinearRegressionParameters(RegressionParameters):
+    aggregation_functions: list[str] = field(
+        default=[],
+        validator=deep_iterable(
+            member_validator=instance_of(str), iterable_validator=instance_of(list)
+        ),
+    )
+    group_by: str = field(default="sum", validator=instance_of(str))
+    standardize: bool = field(default=False, validator=instance_of(bool))
+    report_effect_size: bool = field(
+        default=False, validator=optional(instance_of(bool))
+    )
+    print_detailed_coefficients: bool = field(
+        default=False, validator=optional(instance_of(bool))
+    )
+    dictionary_aggregation_methods = attrib(init=False)
+
+    def __attrs_post_init__(self):
+        self.name_save_file = Path(f"{self.name}")
+        self.dictionary_aggregation_methods: dict = (
+            create_dictionary_of_aggregation_methods(
+                self.independent_variables,
+                self.dependent_variable,
+                self.aggregation_functions,
+            )
+        )

src/data_classes/parameters_analysis_ttest.py

@@ -0,0 +1,14 @@
+from attrs import field, define
+from attrs.validators import instance_of
+
+from src.data_classes.parameters_general import GeneralParameters
+
+
+@define
+class TTestParameters(GeneralParameters):
+    variable_1: str = field(validator=instance_of(str))
+    variable_2: str = field(validator=instance_of(str))
+
+
+class PairedTTestParameters(TTestParameters):
+    pass

src/data_classes/parameters_descriptive_aggregated.py

@@ -0,0 +1,15 @@
+from attr import attrib
+from attrs import field, define
+from attrs.validators import instance_of, deep_iterable
+from src.data_classes.parameters_general import GeneralParameters
+
+
+@define
+class DescriptiveAggregatedParameters(GeneralParameters):
+    variables: list[str] = field(
+        validator=deep_iterable(
+            member_validator=instance_of(str), iterable_validator=instance_of(list)
+        )
+    )
+    aggregation_function: str = field(validator=instance_of(str))
+    group_by: str = attrib(validator=instance_of(str))

src/data_classes/parameters_descriptive_overview.py

@@ -0,0 +1,26 @@
+from pathlib import Path
+from typing import Union, Any
+
+from attr import attrib
+from attrs import field, define
+from attrs.validators import instance_of, optional, deep_iterable
+from src.data_classes.parameters_general import GeneralParameters
+
+
+@define
+class Metric:
+    operation: str = field(validator=instance_of(str))
+    column: str = field(validator=optional(instance_of(str)))
+
+
+@define
+class DescriptiveOverviewParameters(GeneralParameters):
+    metrics: list[Union[Any, Metric]] = field(validator=instance_of(list))
+    group_by: str = attrib(validator=instance_of(str))
+
+    def __attrs_post_init__(self):
+        self.metrics = [
+            Metric(**metric) if isinstance(metric, dict) else metric
+            for metric in self.metrics
+        ]
+        self.name_save_file: Path = Path(f"{self.name}")

src/data_classes/parameters_descriptive_percentage_of_dataset_under_condition.py

@@ -0,0 +1,12 @@
+from typing import Union
+
+from attrs import field, define
+from attrs.validators import instance_of
+from src.data_classes.parameters_general import GeneralParameters
+
+
+@define
+class DescriptivePercentageOfDatasetUnderConditionParameters(GeneralParameters):
+    variable: str = field(validator=instance_of(str))
+    comparison: str = field(validator=instance_of(str))
+    condition: Union[int, float] = field(validator=instance_of((int, float)))

src/data_classes/parameters_general.py

@@ -0,0 +1,17 @@
+import pandas as pd
+from attr import attrib
+
+from attrs import field, define
+from attrs.validators import instance_of
+from pathlib import Path
+
+
+@define
+class GeneralParameters:
+    name: str = field(validator=instance_of(str))
+    dataset: str = field(validator=instance_of(str))
+    data: pd.DataFrame = attrib(init=False)
+    name_save_file: Path = attrib(init=False)
+
+    def __attrs_post_init__(self):
+        self.name_save_file: Path = Path(f"{self.name}")

src/data_classes/parameters_plot_barchart.py

@@ -0,0 +1,21 @@
+from typing import Optional
+
+from attrs import field, define
+from attrs.validators import instance_of, optional, deep_iterable
+from src.data_classes.parameters_visualization import PlotParameters
+
+
+@define
+class BarChartPlotParameters(PlotParameters):
+    variable_x_axis: str = field(validator=instance_of(str))
+    variable_y_axis: Optional[str] = field(validator=optional(instance_of(str)))
+    chart_orientation: str = field(validator=instance_of(str))
+    sort_order: str = field(validator=instance_of(str))
+    custom_order: Optional[list[str]] = field(
+        default=None,
+        validator=optional(
+            deep_iterable(
+                member_validator=instance_of(str), iterable_validator=instance_of(list)
+            )
+        ),
+    )

src/data_classes/parameters_plot_boxplot.py

@@ -0,0 +1,9 @@
+from attrs import field, define
+from attrs.validators import instance_of, deep_iterable
+from src.data_classes.parameters_visualization import PlotParameters
+
+
+@define
+class BoxPlotParameters(PlotParameters):
+    variable_1: str = field(validator=instance_of(str))
+    variable_2: str = field(validator=instance_of(str))

src/data_classes/parameters_plot_contourplot.py

@@ -0,0 +1,13 @@
+from typing import Union
+
+from attrs import field, define
+from attrs.validators import instance_of
+from src.data_classes.parameters_visualization import PlotParameters
+
+
+@define
+class ContourPlotParameters(PlotParameters):
+    function_name: str = field(validator=instance_of(str))
+    x_axis_maximum: Union[int, float] = field(validator=instance_of((int, float)))
+    y_axis_maximum: Union[int, float] = field(validator=instance_of((int, float)))
+    dataset: str = "data"

src/data_classes/parameters_plot_count_distribution.py

@@ -0,0 +1,22 @@
+from typing import Union, Optional
+
+from attrs import field, define
+from attrs.validators import instance_of, deep_iterable, optional
+from src.data_classes.parameters_visualization import PlotParameters
+
+
+@define
+class CountDistributionPlotParameters(PlotParameters):
+    variable: str = field(validator=instance_of(str))
+    x_axis_limits: Optional[list[Union[int, float]]] = field(
+        default=None,
+        validator=optional(
+            deep_iterable(
+                member_validator=instance_of((int, float)),
+                iterable_validator=instance_of(list),
+            )
+        ),
+        kw_only=True,
+    )
+    x_axis_logarithmic_scaling: bool = field(validator=instance_of(bool))
+    y_axis_logarithmic_scaling: bool = field(validator=instance_of(bool))

src/data_classes/parameters_plot_densityplot.py

@@ -0,0 +1,17 @@
+from typing import Union
+
+from attrs import field, define
+from attrs.validators import instance_of, deep_iterable
+from src.data_classes.parameters_visualization import PlotParameters
+
+
+@define
+class DensityPlotParameters(PlotParameters):
+    variable_x_axis: str = field(validator=instance_of(str))
+    variable_y_axis: str = field(validator=instance_of(str))
+    data_breakpoints: list[Union[int, float]] = field(
+        validator=deep_iterable(
+            member_validator=instance_of((int, float)),
+            iterable_validator=instance_of(list),
+        )
+    )

src/data_classes/parameters_plot_forestplot.py

@@ -0,0 +1,41 @@
+from typing import Union, Optional
+
+from attrs import field, define
+from attrs.validators import instance_of, deep_iterable, optional
+from src.data_classes.parameters_visualization import PlotParameters
+
+
+@define
+class ForestPlotParameters(PlotParameters):
+    regression_model_names: list[str] = field(
+        validator=deep_iterable(
+            member_validator=instance_of(str), iterable_validator=instance_of(list)
+        )
+    )
+    regression_model_labels: list[str] = field(
+        validator=[
+            deep_iterable(
+                member_validator=instance_of(str),
+                iterable_validator=instance_of(list),
+            ),
+        ],
+    )
+    coefficient_names: list[str] = field(
+        validator=deep_iterable(
+            member_validator=instance_of(str), iterable_validator=instance_of(list)
+        )
+    )
+    sort_by_size: Optional[bool] = field(
+        default=False, validator=optional(instance_of(bool))
+    )
+    x_axis_minimum: Optional[Union[int, float]] = field(
+        default=None, validator=optional(instance_of((int, float)))
+    )
+    x_axis_maximum: Optional[Union[int, float]] = field(
+        default=None, validator=optional(instance_of((int, float)))
+    )
+    dotsize: Optional[int] = field(default=5, validator=optional(instance_of(int)))
+    colors: Optional[list[str]] = field(
+        default=["orange", "royalblue", "forestgreen", "firebrick"], validator=optional(instance_of(list))
+    )
+    dataset: str = "data"

src/data_classes/parameters_plot_forestplot_paired_ttest.py

@@ -0,0 +1,30 @@
+from typing import Union, Optional
+
+from attrs import field, define
+from attrs.validators import instance_of, deep_iterable, optional
+from src.data_classes.parameters_visualization import PlotParameters
+
+
+@define
+class ForestPlotPairedTTestParameters(PlotParameters):
+    paired_ttest_names: list[str] = field(
+        validator=deep_iterable(
+            member_validator=instance_of(str), iterable_validator=instance_of(list)
+        )
+    )
+    paired_ttest_labels: list[str] = field(
+        validator=[
+            deep_iterable(
+                member_validator=instance_of(str),
+                iterable_validator=instance_of(list),
+            ),
+        ],
+    )
+    x_axis_minimum: Optional[Union[int, float]] = field(
+        default=None, validator=optional(instance_of((int, float)))
+    )
+    x_axis_maximum: Optional[Union[int, float]] = field(
+        default=None, validator=optional(instance_of((int, float)))
+    )
+    dotsize: Optional[int] = field(default=5, validator=optional(instance_of(int)))
+    dataset: str = "data"

src/data_classes/parameters_plot_grouped_histogram.py

@@ -0,0 +1,10 @@
+from attrs import field, define
+from attrs.validators import instance_of
+from src.data_classes.parameters_visualization import PlotParameters
+
+
+@define
+class GroupedHistogramParameters(PlotParameters):
+    group_by: str = field(validator=instance_of(str))
+    aggregation_variable: str = field(validator=instance_of(str))
+    aggregation_function: str = field(validator=instance_of(str))

src/data_classes/parameters_plot_heatmap.py

@@ -0,0 +1,28 @@
+from typing import Iterable, Union
+
+from attrs import field, define
+from attrs.validators import instance_of, deep_iterable
+from src.data_classes.parameters_visualization import PlotParameters
+
+
+@define
+class HeatmapParameters(PlotParameters):
+    axis_variables: list[str] = field(
+        validator=deep_iterable(
+            member_validator=instance_of(str), iterable_validator=instance_of(Iterable)
+        ),
+    )
+    heat_variable: str = field(validator=instance_of(str))
+    axis_maxima: list[Union[int, float]] = field(
+        validator=deep_iterable(
+            member_validator=instance_of((int, float)),
+            iterable_validator=instance_of(Iterable),
+        ),
+    )
+    axis_minima: list[Union[int, float]] = field(
+        validator=deep_iterable(
+            member_validator=instance_of((int, float)),
+            iterable_validator=instance_of(Iterable),
+        ),
+    )
+    logarithmic_heat_scaling: str = field(validator=instance_of(str))

src/data_classes/parameters_plot_hexbinplot.py

@@ -0,0 +1,19 @@
+from typing import Union, Optional
+
+from attrs import field, define
+from attrs.validators import instance_of, optional
+from src.data_classes.parameters_visualization import PlotParameters
+
+
+@define
+class HexbinPlotParameters(PlotParameters):
+    variable_x_axis: str = field(validator=instance_of(str))
+    variable_y_axis: str = field(validator=instance_of(str))
+    x_axis_maximum: Optional[Union[int, float]] = field(
+        default=None, validator=optional(instance_of((int, float))), kw_only=True
+    )
+    y_axis_maximum: Optional[Union[int, float]] = field(
+        default=None, validator=optional(instance_of((int, float))), kw_only=True
+    )
+    trendline: bool = field(validator=instance_of(bool))
+    logarithmic_hex_scaling: bool = field(validator=instance_of(bool))

src/data_classes/parameters_plot_histogram.py

@@ -0,0 +1,22 @@
+from typing import Union, Optional
+
+from attrs import field, define
+from attrs.validators import instance_of, deep_iterable, optional
+from src.data_classes.parameters_visualization import PlotParameters
+
+
+@define
+class HistogramPlotParameters(PlotParameters):
+    variable: str = field(validator=instance_of(str))
+    x_axis_limits: Optional[list[Union[int, float]]] = field(
+        default=None,
+        validator=optional(
+            deep_iterable(
+                member_validator=instance_of((int, float)),
+                iterable_validator=instance_of(list),
+            )
+        ),
+        kw_only=True,
+    )
+    x_axis_logarithmic_scaling: bool = field(validator=instance_of(bool))
+    y_axis_logarithmic_scaling: bool = field(validator=instance_of(bool))

src/data_classes/parameters_plot_percentage_stacked_barchart.py

@@ -0,0 +1,17 @@
+from typing import Optional
+
+from attrs import field, define
+from attrs.validators import instance_of, optional, deep_iterable
+from src.data_classes.parameters_visualization import PlotParameters
+
+
+@define
+class PercentageStackedBarChartPlotParameters(PlotParameters):
+    variable_x_axis: str = field(validator=instance_of(str))
+    variables_to_compare: list[str] = field(
+        validator=deep_iterable(
+            member_validator=instance_of(str), iterable_validator=instance_of(list)
+        )
+    )
+    chart_orientation: str = field(validator=instance_of(str))
+    sort_order: str = field(validator=instance_of(str))

src/data_classes/parameters_plot_ridgelineplot.py

@@ -0,0 +1,17 @@
+from typing import Iterable, Union
+
+from attrs import field, define
+from attrs.validators import instance_of, deep_iterable
+from src.data_classes.parameters_visualization import PlotParameters
+
+
+@define
+class RidgelineParameters(PlotParameters):
+    variable_x_axis: str = field(validator=instance_of(str))
+    variable_y_axis: str = field(validator=instance_of(str))
+    data_breakpoints: list[Union[int, float]] = field(
+        validator=deep_iterable(
+            member_validator=instance_of((int, float)),
+            iterable_validator=instance_of(list),
+        )
+    )

src/data_classes/parameters_plot_simple_scatterplot.py

@@ -0,0 +1,9 @@
+from attrs import field, define
+from attrs.validators import instance_of, deep_iterable
+from src.data_classes.parameters_visualization import PlotParameters
+
+
+@define
+class SimpleScatterPlotParameters(PlotParameters):
+    variable_x_axis: str = field(validator=instance_of(str))
+    variable_y_axis: str = field(validator=instance_of(str))

src/data_classes/parameters_plot_stacked_barchart.py

@@ -0,0 +1,22 @@
+from typing import Optional
+
+from attrs import field, define
+from attrs.validators import instance_of, optional, deep_iterable
+from src.data_classes.parameters_visualization import PlotParameters
+
+
+@define
+class StackedBarChartPlotParameters(PlotParameters):
+    variable_x_axis: str = field(validator=instance_of(str))
+    variable_y_axis: Optional[str] = field(validator=optional(instance_of(str)))
+    hue: str = field(validator=instance_of(str))
+    chart_orientation: str = field(validator=instance_of(str))
+    sort_order: str = field(validator=instance_of(str))
+    custom_order: Optional[list[str]] = field(
+        default=None,
+        validator=optional(
+            deep_iterable(
+                member_validator=instance_of(str), iterable_validator=instance_of(list)
+            )
+        ),
+    )

src/data_classes/parameters_plot_surfaceplot.py

@@ -0,0 +1,16 @@
+from typing import Union
+
+from attrs import field, define
+from attrs.validators import instance_of
+from src.data_classes.parameters_visualization import PlotParameters
+
+
+@define
+class SurfacePlotParameters(PlotParameters):
+    function_name: str = field(validator=instance_of(str))
+    x_axis_maximum: Union[int, float] = field(validator=instance_of((int, float)))
+    y_axis_maximum: Union[int, float] = field(validator=instance_of((int, float)))
+    z_axis_label: str = field(validator=instance_of(str))
+    elevation_angle: Union[int, float] = field(validator=instance_of((int, float)))
+    azimuth_angle: Union[int, float] = field(validator=instance_of((int, float)))
+    dataset: str = "data"

src/data_classes/parameters_plot_violinplot.py

@@ -0,0 +1,9 @@
+from attrs import field, define
+from attrs.validators import instance_of, deep_iterable
+from src.data_classes.parameters_visualization import PlotParameters
+
+
+@define
+class ViolinPlotParameters(PlotParameters):
+    variable_x_axis: str = field(validator=instance_of(str))
+    variable_y_axis: str = field(validator=instance_of(str))

src/data_classes/parameters_visualization.py

@@ -0,0 +1,19 @@
+from typing import Optional
+
+from attr import define, field
+from attr.validators import instance_of, optional
+
+from src.data_classes.parameters_general import GeneralParameters
+
+
+@define
+class PlotParameters(GeneralParameters):
+    x_axis_label: Optional[str] = field(
+        default=None, validator=optional(instance_of(str)), kw_only=True
+    )
+    y_axis_label: Optional[str] = field(
+        default=None, validator=optional(instance_of(str)), kw_only=True
+    )
+    title: Optional[str] = field(
+        default=None, validator=optional(instance_of(str)), kw_only=True
+    )

src/data_loading_and_saving/constructor.py

@@ -0,0 +1,143 @@
+import yaml
+
+from src.data_classes.parameters_analysis_comparison_variance_in_and_between_group import (
+    ComparisonVarianceInAndBetweenGroupParameters,
+)
+from src.data_classes.parameters_analysis_get_function_inverse_bayes_transformed_regression import (
+    GetFunctionInverseBayesTransformedRegressionParameters,
+)
+from src.data_classes.parameters_analysis_influence_of_up_and_downvotes import (
+    InfluenceOfVotesParameters,
+)
+from src.data_classes.parameters_analysis_regression import (
+    BayesianRegressionParameters,
+    LinearRegressionParameters,
+    GroupedLinearRegressionParameters,
+)
+from src.data_classes.parameters_analysis_ttest import (
+    TTestParameters,
+    PairedTTestParameters,
+)
+from src.data_classes.parameters_analysis_pearson_correlation import (
+    PearsonCorrelationParameters,
+)
+from src.data_classes.parameters_descriptive_aggregated import (
+    DescriptiveAggregatedParameters,
+)
+from src.data_classes.parameters_descriptive_overview import (
+    DescriptiveOverviewParameters,
+)
+from src.data_classes.parameters_descriptive_percentage_of_dataset_under_condition import (
+    DescriptivePercentageOfDatasetUnderConditionParameters,
+)
+from src.data_classes.parameters_plot_barchart import BarChartPlotParameters
+
+from src.data_classes.parameters_plot_boxplot import BoxPlotParameters
+from src.data_classes.parameters_plot_contourplot import ContourPlotParameters
+from src.data_classes.parameters_plot_count_distribution import (
+    CountDistributionPlotParameters,
+)
+from src.data_classes.parameters_plot_densityplot import DensityPlotParameters
+from src.data_classes.parameters_plot_forestplot import ForestPlotParameters
+from src.data_classes.parameters_plot_forestplot_paired_ttest import ForestPlotPairedTTestParameters
+from src.data_classes.parameters_plot_grouped_histogram import (
+    GroupedHistogramParameters,
+)
+from src.data_classes.parameters_plot_heatmap import HeatmapParameters
+from src.data_classes.parameters_plot_hexbinplot import HexbinPlotParameters
+from src.data_classes.parameters_plot_histogram import HistogramPlotParameters
+from src.data_classes.parameters_plot_percentage_stacked_barchart import (
+    PercentageStackedBarChartPlotParameters,
+)
+from src.data_classes.parameters_plot_ridgelineplot import RidgelineParameters
+from src.data_classes.parameters_plot_simple_scatterplot import (
+    SimpleScatterPlotParameters,
+)
+from src.data_classes.parameters_plot_stacked_barchart import (
+    StackedBarChartPlotParameters,
+)
+from src.data_classes.parameters_plot_surfaceplot import SurfacePlotParameters
+from src.data_classes.parameters_plot_violinplot import ViolinPlotParameters
+
+
+def custom_constructor(loader: yaml.Loader, tag_suffix: str, node: yaml.Node):
+    """
+    This function handles custom YAML tags and creates an instance of the appropriate class based on the tag suffix.
+
+    Parameters:
+    loader (yaml.Loader): The YAML loader instance.
+    tag_suffix (str): The suffix of the YAML tag. This determines the class to be instantiated.
+    node (yaml.Node): The YAML node to be transformed into a Python dictionary.
+
+    Returns:
+    _class: An instance of either LinearRegressionParameters or BayesianRegressionParameters class,
+        depending on the tag suffix.
+
+    Raises:
+    ValueError: If the tag suffix is not a supported type of analysis or visualization.
+    """
+    if tag_suffix == "descriptive_aggregated":
+        _class = DescriptiveAggregatedParameters
+    elif tag_suffix == "descriptive_overview":
+        _class = DescriptiveOverviewParameters
+    elif tag_suffix == "percentage_of_dataset_under_condition":
+        _class = DescriptivePercentageOfDatasetUnderConditionParameters
+    elif tag_suffix == "comparison_variance_in_and_between_group":
+        _class = ComparisonVarianceInAndBetweenGroupParameters
+    elif tag_suffix == "linear_regression":
+        _class = LinearRegressionParameters
+    elif tag_suffix == "bayesian_regression":
+        _class = BayesianRegressionParameters
+    elif tag_suffix == "linear_regression_grouped":
+        _class = GroupedLinearRegressionParameters
+    elif tag_suffix == "increase_per_up_and_downvote_from_totalvotes_and_valence":
+        _class = InfluenceOfVotesParameters
+    elif tag_suffix == "function_inverse_bayes_transformed_regression":
+        _class = GetFunctionInverseBayesTransformedRegressionParameters
+    elif tag_suffix == "ttest":
+        _class = TTestParameters
+    elif tag_suffix == "paired_ttest":
+        _class = PairedTTestParameters
+    elif tag_suffix == "pearson_correlation":
+        _class = PearsonCorrelationParameters
+    elif tag_suffix == "barchart":
+        _class = BarChartPlotParameters
+    elif tag_suffix == "boxplot":
+        _class = BoxPlotParameters
+    elif tag_suffix == "contourplot":
+        _class = ContourPlotParameters
+    elif tag_suffix == "histogram":
+        _class = HistogramPlotParameters
+    elif tag_suffix == "count_distribution":
+        _class = CountDistributionPlotParameters
+    elif tag_suffix == "densityplot":
+        _class = DensityPlotParameters
+    elif tag_suffix == "forestplot":
+        _class = ForestPlotParameters
+    elif tag_suffix == "forestplot_paired_ttest":
+        _class = ForestPlotPairedTTestParameters
+    elif tag_suffix == "grouped_histogram":
+        _class = GroupedHistogramParameters
+    elif tag_suffix == "heatmap":
+        _class = HeatmapParameters
+    elif tag_suffix == "hexbinplot":
+        _class = HexbinPlotParameters
+    elif tag_suffix == "percentage_stacked_barchart":
+        _class = PercentageStackedBarChartPlotParameters
+    elif tag_suffix == "ridgelineplot":
+        _class = RidgelineParameters
+    elif tag_suffix == "simple_scatterplot":
+        _class = SimpleScatterPlotParameters
+    elif tag_suffix == "stacked_barchart":
+        _class = StackedBarChartPlotParameters
+    elif tag_suffix == "surfaceplot":
+        _class = SurfacePlotParameters
+    elif tag_suffix == "violinplot":
+        _class = ViolinPlotParameters
+    else:
+        raise ValueError(
+            f"Unexpected tag suffix: {tag_suffix}. Please check for typos or if the analysis is not supported"
+        )
+
+    instance_data = loader.construct_mapping(node, deep=True)
+    return _class(**instance_data)

src/data_loading_and_saving/create_results_report.py

@@ -0,0 +1,202 @@
+import datetime
+import os
+import re
+from pathlib import Path
+
+import pypandoc
+
+
+def create_markdown_report(log_filename: Path, output_name: Path, output_dir: Path):
+    """
+    Generates a markdown report from a specified log file.
+
+    This function reads a log file, processes the content by adding markdown image syntax for any detected plot paths,
+    and then writes the processed content into a markdown file named after the `output_name` parameter in the specified
+    `output_dir`. The report includes a header with the current date and the processed log content.
+
+    Parameters
+    ----------
+    log_filename : Path
+        The path to the log file to be processed.
+    output_name : Path
+        The base name for the output markdown file (without extension).
+    output_dir : Path
+        The directory where the markdown file will be saved.
+    """
+    with open(log_filename, "r") as log_file:
+        log_content: str = log_file.read()
+        log_lines: list[str] = log_content.split("\n")
+
+    processed_log_content: str = ""
+    for line in log_lines:
+        if "Plot saved at" in line:
+            image_path: str = re.search("Plot saved at (.*)", line).group(1)
+            line += f"\n\n![](../{image_path})\n"
+        processed_log_content += line + "\n"
+
+    todays_date: str = datetime.date.today().strftime("%B %d, %Y")
+    markdown_content: str = f"""# Analysis Results for {todays_date}
+
+{processed_log_content}
+
+    """
+
+    with open(f"{output_dir}/{output_name}.md", "w") as md_file:
+        md_file.write(markdown_content)
+
+
+def create_pdf_report(markdown_filename: Path, output_dir: Path, font_size: str = "8pt"):
+    """
+    Generates a PDF report from a markdown file.
+    This function converts a markdown file into a PDF file directly using pypandoc,
+    preserving all formatting including code blocks and embedded images.
+    The PDF file is saved in the specified `output_dir` with the same base name as the `markdown_filename`.
+
+    Parameters
+    ----------
+    markdown_filename : Path
+        The path to the markdown file to be converted into PDF.
+    output_dir : Path
+        The directory where the PDF file will be saved.
+    font_size : str, optional
+        The font size for the PDF (default is '8pt').
+    """
+    os.chdir(output_dir)
+
+    markdown_path: Path = Path(f"{markdown_filename}.md")
+    new_md_file = split_tables_in_markdown(markdown_path)
+    new_md_file = split_regression_long_vars(new_md_file)
+    output_pdf_path: Path = Path(f"{markdown_filename}.pdf")
+
+    fontsize_number = font_size[:-2]
+
+    header_includes = f"""
+    \\usepackage[utf8]{{inputenc}}
+    \\usepackage{{lmodern}}
+    \\usepackage{{adjustbox}}
+    \\usepackage{{anyfontsize}}
+    \\usepackage{{geometry}}
+    \\usepackage{{listings}} 
+    \\lstset{{basicstyle=\\fontsize{{{fontsize_number}}}{{{int(fontsize_number) * 0.9}}}\selectfont}}
+    \\fontsize{{{fontsize_number}}}{{{int(fontsize_number) * 1.2}}}\\selectfont
+    """
+
+    table_settings = f"""
+    \\usepackage{{etoolbox}}
+    \\BeforeBeginEnvironment{{tabular}}{{\\begin{{adjustbox}}{{max width=\\textwidth}}}}
+    \\AfterEndEnvironment{{tabular}}{{\\end{{adjustbox}}}}
+    """
+
+    extra_args = [
+        '--pdf-engine=xelatex',
+        '--variable', f'geometry:top=1in, bottom=1in, left=1in, right=1in',
+        '--variable', f'header-includes:{header_includes}',
+        '--variable', f'header-includes:{table_settings}',
+        '-s'
+    ]
+
+    pypandoc.convert_file(new_md_file, 'pdf', outputfile=output_pdf_path, extra_args=extra_args)
+
+    os.remove(Path(f"{markdown_filename}_split_tables.md"))
+    os.remove(Path(f"{markdown_filename}_split_tables_split_regression.md"))
+
+
+def split_tables_in_markdown(file_path: str, max_width=100) -> Path:
+    """
+    Process a markdown file and split tables that are wider than `max_width` into smaller tables.
+    """
+    with open(file_path, 'r') as f:
+        lines = f.readlines()
+
+    new_lines: list = []
+    table_buffer: list = []
+    for line in lines:
+        if line.startswith('|'):
+            table_buffer.append(line.rstrip('\n'))
+        else:
+            if table_buffer:
+                new_lines.extend(split_table_into_smaller_ones(table_buffer, max_width))
+                table_buffer: list = []
+
+            new_lines.append(line.rstrip('\n'))
+
+    if table_buffer:
+        new_lines.extend(split_table_into_smaller_ones(table_buffer, max_width))
+
+    new_file_path = str(file_path).replace('.md', '_split_tables.md')
+    with open(new_file_path, 'w') as f:
+        f.write('\n'.join(new_lines))
+
+    return Path(new_file_path)
+
+
+def split_table_into_smaller_ones(table_lines, max_width: int) -> list[str]:
+    """
+    Given the lines of a table and a maximum width, split the table into multiple smaller tables
+    """
+    new_lines: list = []
+    headers: str = table_lines[0].split('|')[1:-1]
+    rows: list = [row.split('|')[1:-1] for row in table_lines[1:]]
+
+    current_width: int = 0
+    start_col: int = 0
+    for end_col, header in enumerate(headers, start=1):
+        current_width += len(header) + 3
+        if current_width > max_width or end_col == len(headers):
+            new_headers: str = headers[start_col:end_col]
+            new_lines.append('|' + '|'.join(new_headers) + '|')
+            for row in rows:
+                new_cells: str = row[start_col:end_col]
+                new_lines.append('|' + '|'.join(new_cells) + '|')
+            new_lines.append('\n')
+            current_width: int = 0
+            start_col: str = end_col
+
+    return new_lines
+
+
+def split_regression_long_vars(file_path: Path, max_width: int=105) -> Path:
+    """ Process a regression result file and split variables that have too long lines
+    (length > `max_width`) into smaller strings. """
+    with open(file_path, 'r') as f:
+        lines = f.readlines()
+
+    new_lines: list = []
+    is_ols_block, is_const_met = False, False
+
+    for line in lines:
+        if 'OLS Regression Results' in line:
+            is_ols_block = True
+        if line.strip() == '```':
+            is_ols_block = False
+        if is_ols_block:
+            if 'const' in line:
+                is_const_met = True
+            if line.strip().startswith('==='):
+                is_const_met = False
+            if is_const_met:
+                if set(line.strip()) not in [{'-', '+', '='}, {' '}, {''}]:
+                    split_line = line.split(' ')
+                    temp_line = ''
+                    new_line = []
+                    for word in split_line:
+                        if (len(temp_line + word) <= max_width or '[' in word or ']' in word) and not word.startswith('----'):
+                            temp_line += ' ' + word
+                        else:
+                            new_line.append(temp_line.strip())
+                            temp_line = word
+                    if temp_line:
+                        new_line.append(temp_line.strip())
+                    new_lines.extend(new_line)
+                else:
+                    new_lines.append(line.strip()[:max_width])
+            else:
+                new_lines.append(line.rstrip('\n'))
+        else:
+            new_lines.append(line.rstrip('\n'))
+
+    new_file_path = Path(str(file_path).replace('.md', '_split_regression.md'))
+    with open(new_file_path, 'w') as f:
+        f.write('\n'.join(new_lines))
+
+    return new_file_path

src/data_loading_and_saving/print_and_save_results.py

@@ -0,0 +1,63 @@
+from typing import Union
+from pathlib import Path
+import logging
+
+import pandas as pd
+from statsmodels.iolib.summary import Summary
+from statsmodels.regression.linear_model import RegressionResults
+
+
+def print_and_save_result(
+    print_result: bool,
+    save_result: bool,
+    filepath: str,
+    result: Union[str, Summary, RegressionResults, pd.DataFrame],
+    name_save_file: Path,
+) -> None:
+    """
+    Prints and/or saves the analysis result based on the specified conditions.
+
+    This function is designed to handle the output of statistical analysis results, allowing for both printing to the
+    console and saving to a file. The type of the result (e.g., string, DataFrame) determines the format of the saved file.
+    Logging is used to record the result in a consistent format, facilitating debugging and record-keeping.
+
+    Parameters
+    ----------
+    print_result : bool
+        A flag indicating whether to print the result to the console.
+    save_result : bool
+        A flag indicating whether to save the result to a file.
+    filepath : str
+        The base path where the result file will be saved. It is used in conjunction with `name_save_file`
+        to construct the full file path.
+    result : Union[str, Summary, RegressionResults, pd.DataFrame]
+        The result of the analysis. Can be a string, a Summary object, a RegressionResults object, or a pandas DataFrame
+    name_save_file : Path
+        The name of the file (without extension) to which the result will be saved.
+        The extension is determined by the type of `result`.
+
+    Returns
+    -------
+    None
+    """
+    pd.set_option('display.max_columns', None)
+    pd.set_option('display.width', 1000)
+
+    if isinstance(result, pd.DataFrame):
+        result_str = result.to_markdown()
+        logging.info(result_str)
+    else:
+        result_str = str(result)
+        logging.info("```")
+        logging.info(result_str)
+        logging.info("```")
+
+    if print_result:
+        print(result_str)
+    if save_result:
+        if type(result) == pd.DataFrame:
+            result.to_csv(f"{filepath}{name_save_file}.csv", index=True)
+
+        else:
+            with open(f"{filepath}{name_save_file}.txt", "w") as f:
+                f.write(result_str)

src/data_loading_and_saving/save_plot.py

@@ -0,0 +1,36 @@
+import logging
+from typing import Optional
+
+from matplotlib import pyplot as plt
+
+
+def save_plot(save_plots: bool, filepath: str, plot: plt, name: Optional[str], file_format: str = "png"):
+    """
+    This method saves the generated plot to a file.
+
+    Parameters
+    ----------
+    save_plots: bool
+        A boolean flag to determine whether to save the plot or not.
+    filepath: str
+        The path to the directory where the plot will be saved.
+    plot: plt
+        The matplotlib.pyplot instance.
+    name: str
+        The name of the file to which the plot will be saved (if self.save_plots is True).
+    file_format: str
+        The file format in which plot will be saved. By default, it is 'png'.
+
+    Returns
+    -------
+    None
+    """
+
+    if save_plots:
+        output_path = f"{filepath}{name}.{file_format}"
+        plot.savefig(output_path, bbox_inches="tight", dpi=300)
+        plot.show()
+        plot.close()
+        logging.info(f"Plot saved at {output_path}")
+    else:
+        plot.show()

src/preprocessor.py

@@ -0,0 +1,121 @@
+from typing import Any
+
+import pandas as pd
+
+
+class Preprocessing:
+    """
+    A class for preprocessing datasets loaded from a parquet file.
+
+    This class provides methods to preprocess datasets according to a given configuration. It supports operations such as
+    filtering data by order, section, popular sections, users with more comments, and excluding data based on specific values.
+
+    Attributes
+    ----------
+    data : pd.DataFrame
+        The dataset loaded from the specified parquet file.
+
+    Methods
+    -------
+    preprocess_datasets(preprocessing_config):
+        Applies a series of preprocessing steps to the dataset based on the provided configuration and returns the modified datasets.
+    _apply_chain(data, chain):
+        Applies a chain of preprocessing steps to the given data.
+    full_data():
+        Returns the full dataset without any preprocessing.
+    data_order(data, order):
+        Filters the dataset to include only the data with the specified order.
+    data_section(data, section):
+        Filters the dataset to include only the data from the specified section.
+    popular_sections(data, threshold):
+        Filters the dataset to include only the data from sections with more than a specified number of entries.
+    users_with_more_comments(data, num_comments):
+        Filters the dataset to include only the data from users with more than a specified number of comments.
+    exclude_data_with_value(data, param):
+        Excludes data from the dataset based on a specified column value.
+    """
+
+    def __init__(self, path_to_file: str, name_data: str):
+        """
+        Initializes the Preprocessing class with data loaded from a specified parquet file.
+
+        Parameters
+        ----------
+        path_to_file : str
+            The path to the directory containing the parquet file.
+        name_data : str
+            The name of the parquet file from which to load the data.
+        """
+        self.data = pd.read_parquet(path_to_file + name_data)
+
+    def preprocess_datasets(self, preprocessing_config) -> dict[str, pd.DataFrame]:
+        """
+        Applies a series of preprocessing steps to the dataset based on the provided configuration.
+
+        Parameters
+        ----------
+        preprocessing_config : dict
+            A dictionary where keys are dataset names and values are lists of preprocessing steps (as dictionaries) to be applied.
+
+        Returns
+        -------
+        dict[str, pd.DataFrame]
+            A dictionary of preprocessed datasets.
+        """
+        datasets = {}
+        for dataset_name, config in preprocessing_config.items():
+            datasets[dataset_name]: pd.DataFrame = self._apply_chain(self.data.copy(), config)
+        datasets["data"] = self.full_data()
+        return datasets
+
+    def _apply_chain(self, data: pd.DataFrame, chain: list) -> pd.DataFrame:
+        """
+        Applies a chain of preprocessing steps to the given data.
+
+        Parameters
+        ----------
+        data : pd.DataFrame
+            The dataset to preprocess.
+        chain : list
+            A list of dictionaries, each representing a preprocessing step with a method name and optional parameters.
+
+        Returns
+        -------
+        pd.DataFrame
+            The preprocessed dataset.
+        """
+        for step in chain:
+            method_name = step["method"]
+            param = step.get("param")
+            method = getattr(self, method_name)
+            data = method(data, param)
+        return data
+
+    def full_data(self):
+        return self.data
+
+    @staticmethod
+    def data_order(data: pd.DataFrame, order: int) -> pd.DataFrame:
+        return data.loc[data["order"] == order]
+
+    @staticmethod
+    def data_section(data: pd.DataFrame, section: str) -> pd.DataFrame:
+        return data.loc[data["section"] == section]
+
+    @staticmethod
+    def popular_sections(data: pd.DataFrame, threshold: int = 1000) -> pd.DataFrame:
+        counts = data["section"].value_counts()
+        popular_sections = counts[counts > threshold].index.tolist()
+        return data[data["section"].isin(popular_sections)]
+
+    @staticmethod
+    def users_with_more_comments(data: pd.DataFrame, num_comments: int = 10) -> pd.DataFrame:
+        counts_user = data["user_id"].value_counts()
+        users_with_10_comments = counts_user[counts_user > num_comments].index.tolist()
+        return data[data["user_id"].isin(users_with_10_comments)]
+
+    @staticmethod
+    def exclude_data_with_value(data: pd.DataFrame, param: dict) -> pd.DataFrame:
+        column: str = param['column']
+        value: Any = param['value']
+        return data[data[column] != value]

src/utils/handle_r_dependencies.py

@@ -0,0 +1,40 @@
+from rpy2.robjects import packages as rpackages
+
+
+def is_r_package_installed(package_name: str) -> bool:
+    """
+    Checks if a given R package is installed.
+
+    Parameters
+    ----------
+    package_name : str
+        The name of the R package to check.
+
+    Returns
+    -------
+    bool
+        True if the package is installed, False otherwise.
+    """
+    return rpackages.isinstalled(package_name)
+
+
+def install_r_package(package_name: str):
+    """
+    Installs a given R package using CRAN mirror.
+
+    This function selects the first CRAN mirror and installs the specified R package.
+
+    Parameters
+    ----------
+    package_name : str
+        The name of the R package to install.
+    """
+    utils = rpackages.importr("utils")
+    utils.chooseCRANmirror(ind=1)
+    utils.install_packages(package_name)
+
+
+if not is_r_package_installed("BAS"):
+    install_r_package("BAS")
+
+BAS = rpackages.importr("BAS")

src/utils/helper_conversion.py

@@ -0,0 +1,87 @@
+import inspect
+from typing import Any
+
+
+def create_dictionary_of_aggregation_methods(
+    dependent_variables: list[str],
+    independent_variable: str,
+    aggregation_functions: list[str],
+) -> dict[str, str]:
+    """
+    Creates a dictionary mapping each variable (dependent and independent) to its specified aggregation function.
+
+    This function is designed to facilitate the aggregation of data by dynamically creating a mapping of variables
+    to their corresponding aggregation functions. This is particularly useful in data analysis and preprocessing
+    where different variables may require different methods of aggregation.
+
+    Parameters
+    ----------
+    dependent_variables : list[str]
+        A list of strings representing the names of the dependent variables.
+    independent_variable : str
+        A string representing the name of the independent variable.
+    aggregation_functions : list[str]
+        A list of strings representing the aggregation functions to be applied to each variable. The order of functions
+        in this list should correspond to the order of variables in `dependent_variables`
+        followed by the `independent_variable`.
+
+    Returns
+    -------
+    dict[str, str]
+        A dictionary where keys are variable names (both dependent and independent) and values are the corresponding
+        aggregation functions as strings.
+
+    Raises
+    ------
+    ValueError
+        If the total number of variables (dependent + independent)
+        does not match the number of provided aggregation functions.
+
+    Example
+    -------
+    >>> create_dictionary_of_aggregation_methods(['var1', 'var2'], 'var3', ['sum', 'mean', 'count'])
+    {'var1': 'sum', 'var2': 'mean', 'var3': 'count'}
+    """
+    variables: list[str] = dependent_variables + [independent_variable]
+
+    if len(variables) != len(aggregation_functions):
+        raise ValueError(
+            "The number of variables must match the number of aggregation functions."
+        )
+
+    return {
+        variable: aggregation_function
+        for variable, aggregation_function in zip(variables, aggregation_functions)
+    }
+
+
+def get_data_name(data: Any) -> str or None:
+    """
+    Retrieves the variable name of the input data as it appears in the caller's scope.
+
+    This function uses introspection to look back into the caller's local variables and find the name of the variable
+    that references the data passed to this function. This can be useful for debugging or when dynamically generating
+    output based on variable names.
+
+    Parameters
+    ----------
+    data : any
+        The data object whose variable name is to be found.
+
+    Returns
+    -------
+    str or None
+        The name of the variable as a string if found; otherwise, None.
+
+    Example
+    -------
+    >>> my_data = [1, 2, 3]
+    >>> get_data_name(my_data)
+    'my_data'
+    """
+    callers_local_vars = inspect.currentframe().f_back.f_locals.items()
+    data_name = [
+        variable_name for variable_name, variable_value in callers_local_vars if variable_value is data
+    ]
+    if data_name:
+        return data_name[0]

src/utils/helper_functions.py

@@ -0,0 +1,102 @@
+"""Minor helper functions"""
+
+
+class FunctionData:
+    def __init__(self, func, params):
+        self.func = func
+        self.params = params
+
+    def __call__(self, x, y):
+        return self.func(x, y, self.params)
+
+
+def transform_to_bayes_corrected_valence(
+    upvotes: int, downvotes: int, average_valence: float, weight_factor_m: float
+) -> float:
+    """
+    Transforms the upvotes and downvotes to its bayesian corrected value.
+
+    Parameters
+    ----------
+    upvotes: int
+        The number of upvotes.
+    downvotes: int
+        The number of downvotes.
+    weight_factor_m: float
+        The weight factor m.
+    average_valence: float
+        The average valence.
+
+    Returns
+    -------
+    bayes_corrected_value: float
+        The bayes_corrected_value between 0 and 1.
+    """
+    valence = -(downvotes / (downvotes + upvotes)) + 0.5
+    totalvotes = upvotes + downvotes
+
+    bayes_corrected_value = caluculate_bayes_correction(
+        valence, totalvotes, weight_factor_m, average_valence
+    )
+    return bayes_corrected_value
+
+
+def caluculate_bayes_correction(
+    measure: float, volume: float, weight: float, average_measure: float
+) -> float:
+    """
+    Calculates the bayes_corrected_value between 0 and 1 weighing in the volume.
+
+    Parameters
+    ----------
+    measure: float
+        The measure.
+    volume: float
+        The volume.
+    weight: float
+        The weight factor m. Weighting how strongly the volume is considered
+    average_measure: float
+        The average_measure.
+
+    Returns
+    -------
+    bayes_corrected_measure: float
+        The bayes_corrected_value between 0 and 1
+    """
+    bayes_corrected_measure = (
+        volume / (volume + weight) * measure
+        + weight / (volume + weight) * average_measure
+    )
+
+    return bayes_corrected_measure
+
+
+def calculate_inverse_bayes_correction(
+    bayes_corrected_value: float,
+    volume: float,
+    weight_factor_m: float,
+    average_measure: float,
+) -> float:
+    """
+    Calculates the inverse bayes_corrected_value between 0 and 1.
+
+    Parameters
+    ----------
+    bayes_corrected_value: float
+        The bayes_corrected_value between 0 and 1.
+    volume: float
+        The average_totalvotes.
+    weight_factor_m: float
+        The weight_factor.
+    average_measure: float
+        The average_measure.
+
+    Returns
+    -------
+    downvotes: float
+        The downvotes corresponding to the bayes_corrected_value for a given number of total votes.
+    """
+    inverse_bayes_corrected_value = (
+        ((volume + weight_factor_m)/volume) * (bayes_corrected_value - weight_factor_m/(volume + weight_factor_m) * average_measure)
+    )
+    return inverse_bayes_corrected_value

src/utils/helper_logging.py

@@ -0,0 +1,260 @@
+import logging
+
+from src.data_classes.parameters_analysis_regression import (
+    LinearRegressionParameters,
+    BayesianRegressionParameters,
+    GroupedLinearRegressionParameters,
+)
+from src.data_classes.parameters_descriptive_aggregated import (
+    DescriptiveAggregatedParameters,
+)
+from src.data_classes.parameters_descriptive_overview import (
+    DescriptiveOverviewParameters,
+)
+from src.data_classes.parameters_descriptive_percentage_of_dataset_under_condition import (
+    DescriptivePercentageOfDatasetUnderConditionParameters,
+)
+from src.data_classes.parameters_plot_barchart import BarChartPlotParameters
+from src.data_classes.parameters_plot_boxplot import BoxPlotParameters
+from src.data_classes.parameters_plot_contourplot import ContourPlotParameters
+from src.data_classes.parameters_plot_count_distribution import (
+    CountDistributionPlotParameters,
+)
+from src.data_classes.parameters_plot_densityplot import DensityPlotParameters
+from src.data_classes.parameters_plot_forestplot import ForestPlotParameters
+from src.data_classes.parameters_plot_forestplot_paired_ttest import (
+    ForestPlotPairedTTestParameters,
+)
+from src.data_classes.parameters_plot_grouped_histogram import (
+    GroupedHistogramParameters,
+)
+from src.data_classes.parameters_plot_heatmap import HeatmapParameters
+from src.data_classes.parameters_plot_hexbinplot import HexbinPlotParameters
+from src.data_classes.parameters_plot_histogram import HistogramPlotParameters
+from src.data_classes.parameters_plot_percentage_stacked_barchart import (
+    PercentageStackedBarChartPlotParameters,
+)
+from src.data_classes.parameters_plot_ridgelineplot import RidgelineParameters
+from src.data_classes.parameters_plot_simple_scatterplot import (
+    SimpleScatterPlotParameters,
+)
+from src.data_classes.parameters_plot_stacked_barchart import (
+    StackedBarChartPlotParameters,
+)
+from src.data_classes.parameters_plot_surfaceplot import SurfacePlotParameters
+from src.data_classes.parameters_plot_violinplot import ViolinPlotParameters
+
+
+def log_descriptive_analysis_details(config):
+    logging.info(f"Descriptive Analysis: {config.name_save_file}")
+    logging.info("```")
+    logging.info(f"Data: {config.dataset}")
+    if isinstance(config, DescriptiveAggregatedParameters):
+        logging.info(f"Variables: {config.variables}")
+        logging.info(f"Aggregation Function: {config.aggregation_function}")
+        logging.info(f"Group By: {config.group_by}")
+    elif isinstance(config, DescriptiveOverviewParameters):
+        logging.info(f"Metrics: {config.metrics}")
+        logging.info(f"Group By: {config.group_by}")
+    elif isinstance(config, DescriptivePercentageOfDatasetUnderConditionParameters):
+        logging.info(f"Variable: {config.variable}")
+        logging.info(f"Comparison: {config.comparison}")
+        logging.info(f"Condition: {config.condition}")
+    else:
+        raise ValueError(
+            f"Invalid type of descriptive analysis requested {type(config)}"
+        )
+    logging.info("```")
+
+
+def log_regression_details(config, regression_type: str):
+    logging.info(f"{regression_type} Regression Analysis: {config.name_save_file}")
+    logging.info("```")
+    logging.info(f"Independent Variables: {config.independent_variables}")
+    logging.info(f"Dependent Variable: {config.dependent_variable}")
+    logging.info(f"Data: {config.dataset}")
+    if isinstance(config, LinearRegressionParameters):
+        logging.info(f"Standardize: {config.standardize}")
+        logging.info(f"Report effect size: {config.report_effect_size}")
+    elif isinstance(config, BayesianRegressionParameters):
+        pass
+    elif isinstance(config, GroupedLinearRegressionParameters):
+        logging.info(f"Independent Variables: {config.independent_variables}")
+        logging.info(f"Grouped by: {config.group_by}")
+        logging.info(f"Aggregation methods: {config.dictionary_aggregation_methods}")
+        logging.info(f"Standardize: {config.standardize}")
+        logging.info(f"Report effect size: {config.report_effect_size}")
+        logging.info(
+            f"Print detailed coefficients: {config.print_detailed_coefficients}"
+        )
+    else:
+        raise ValueError(f"Unknown parameter type: {type(config)}")
+
+    logging.info("```")
+
+
+def log_get_function_inverse_bayes_transformed_regression(config):
+    logging.info(
+        f"Get Function Inverse Bayes Transformed Regression: {config.name_save_file}"
+    )
+    logging.info("```")
+    logging.info(f"Data: {config.data}")
+    logging.info(f"Model Name: {config.model_name}")
+    logging.info("```")
+
+
+def log_influence_of_up_and_downvotes_on_replies(config):
+    logging.info(f"Influence of Up and Downvotes on Replies: {config.name_save_file}")
+    logging.info("```")
+    logging.info(
+        f"Weight as Distribution Quantile: {config.weight_as_distribution_quantile}"
+    )
+    logging.info(f"Weight m: {config.weight_m}")
+    logging.info(f"Model Name: {config.model_name}")
+    logging.info(f"Step: {config.step}")
+    logging.info(f"Startpoint: {config.startpoint}")
+    logging.info("```")
+
+
+def log_comparison_variance_details(config):
+    logging.info(f"Comparison Variance Analysis: {config.name_save_file}")
+    logging.info("```")
+    logging.info(f"Variable: {config.variable}")
+    logging.info(f"Group: {config.group}")
+    logging.info(f"Data: {config.data}")
+    logging.info("```")
+
+
+def log_pearson_correlation_details(config):
+    logging.info(f"Pearson Correlation Analysis: {config.name_save_file}")
+    logging.info("```")
+    logging.info(f"Variable 1: {config.variable_1}")
+    logging.info(f"Variable 2: {config.variable_2}")
+    logging.info(f"Data: {config.dataset}")
+    logging.info("```")
+
+
+def log_ttest_details(config, ttest_type: str):
+    logging.info(f"{ttest_type} TTest Analysis: {config.name_save_file}")
+    logging.info("```")
+    logging.info(f"Variable 1: {config.variable_1}")
+    logging.info(f"Variable 2: {config.variable_2}")
+    logging.info(f"Data: {config.dataset}")
+    logging.info("```")
+
+
+def log_visualization_details(config):
+    logging.info(f"Visualization: {config.name_save_file}")
+    logging.info("```")
+    logging.info(f"Data: {config.dataset}")
+    logging.info(f"Title: {config.title}")
+    if isinstance(config, BarChartPlotParameters):
+        logging.info("Creating Bar Chart")
+        logging.info(f"Variable X: {config.variable_x_axis}")
+        logging.info(f"Variable Y: {config.variable_y_axis}")
+        logging.info(f"Chart Orientation: {config.chart_orientation}")
+        logging.info(f"Sort Order: {config.sort_order}")
+    elif isinstance(config, BoxPlotParameters):
+        logging.info("Creating Box Plot")
+        logging.info(f"Variable 1: {config.variable_1}")
+        logging.info(f"Variable 2: {config.variable_2}")
+        logging.info(f"X-Axis Label: {config.x_axis_label}")
+        logging.info(f"Y-Axis Label: {config.y_axis_label}")
+    elif isinstance(config, ContourPlotParameters):
+        logging.info("Creating Contour Plot")
+        logging.info(f"Function Name: {config.function_name}")
+        logging.info(f"X-Axis Maximum: {config.x_axis_maximum}")
+        logging.info(f"Y-Axis Maximum: {config.y_axis_maximum}")
+    elif isinstance(config, HistogramPlotParameters):
+        logging.info("Creating Histogram Plot")
+        logging.info(f"Variable: {config.variable}")
+        logging.info(f"X-Axis Limits: {config.x_axis_limits}")
+        logging.info(f"X-Axis Logarithmic Scaling: {config.x_axis_logarithmic_scaling}")
+        logging.info(f"Y-Axis Logarithmic Scaling: {config.y_axis_logarithmic_scaling}")
+    elif isinstance(config, CountDistributionPlotParameters):
+        logging.info("Creating Count Distribution Plot")
+        logging.info(f"Variable: {config.variable}")
+        logging.info(f"X-Axis Limits: {config.x_axis_limits}")
+        logging.info(f"X-Axis Logarithmic Scaling: {config.x_axis_logarithmic_scaling}")
+        logging.info(f"Y-Axis Logarithmic Scaling: {config.y_axis_logarithmic_scaling}")
+    elif isinstance(config, DensityPlotParameters):
+        logging.info("Creating Density Plot")
+        logging.info(f"Variable X: {config.variable_x_axis}")
+        logging.info(f"Variable Y: {config.variable_y_axis}")
+        logging.info(f"Data Breakpoints: {config.data_breakpoints}")
+    elif isinstance(config, ForestPlotParameters):
+        logging.info("Creating Forest Plot")
+        logging.info(f"Regression Model Names: {config.regression_model_names}")
+        logging.info(f"Coefficient Names: {config.coefficient_names}")
+        logging.info(f"X-Axis Minimum: {config.x_axis_minimum}")
+        logging.info(f"X-Axis Maximum: {config.x_axis_maximum}")
+        logging.info(f"Dotsize: {config.dotsize}")
+    elif isinstance(config, ForestPlotPairedTTestParameters):
+        logging.info("Creating Forest Plot Paired TTest")
+        logging.info(f"Paired TTest Names: {config.paired_ttest_names}")
+        logging.info(f"X-Axis Minimum: {config.x_axis_minimum}")
+        logging.info(f"X-Axis Maximum: {config.x_axis_maximum}")
+        logging.info(f"Dotsize: {config.dotsize}")
+    elif isinstance(config, GroupedHistogramParameters):
+        logging.info("Creating Grouped Histogram")
+        logging.info(f"Group By: {config.group_by}")
+        logging.info(f"Aggregation Variable: {config.aggregation_variable}")
+        logging.info(f"Aggregation Function: {config.aggregation_function}")
+        logging.info(f"X-Axis Label: {config.x_axis_label}")
+        logging.info(f"Y-Axis Label: {config.y_axis_label}")
+    elif isinstance(config, HeatmapParameters):
+        logging.info("Creating Heatmap")
+        logging.info(f"Axis Variables: {config.axis_variables}")
+        logging.info(f"Heat Variable: {config.heat_variable}")
+        logging.info(f"Max Axis Values: {config.axis_maxima}")
+        logging.info(f"Min Axis Values: {config.axis_minima}")
+        logging.info(f"Log Scaling: {config.logarithmic_heat_scaling}")
+    elif isinstance(config, HexbinPlotParameters):
+        logging.info("Creating Hexbin Plot")
+        logging.info(f"Variable X: {config.variable_x_axis}")
+        logging.info(f"Variable Y: {config.variable_y_axis}")
+        logging.info(f"X Axis Maximum: {config.x_axis_maximum}")
+        logging.info(f"Y Axis Maximum: {config.y_axis_maximum}")
+        logging.info(f"Trendline: {config.trendline}")
+        logging.info(f"Log Scaling: {config.logarithmic_hex_scaling}")
+    elif isinstance(config, PercentageStackedBarChartPlotParameters):
+        logging.info("Creating Percentage Stacked Bar Chart")
+        logging.info(f"Variable X: {config.variable_x_axis}")
+        logging.info(f"Variables to compare: {config.variables_to_compare}")
+        logging.info(f"Chart Orientation: {config.chart_orientation}")
+        logging.info(f"Sort Order: {config.sort_order}")
+    elif isinstance(config, RidgelineParameters):
+        logging.info("Creating Ridgeline Plot")
+        logging.info(f"Variable X: {config.variable_x_axis}")
+        logging.info(f"Variable Y: {config.variable_y_axis}")
+        logging.info(f"Data Breakpoints: {config.data_breakpoints}")
+    elif isinstance(config, SimpleScatterPlotParameters):
+        logging.info("Creating Simple Scatter Plot")
+        logging.info(f"Variable X: {config.variable_x_axis}")
+        logging.info(f"Variable Y: {config.variable_y_axis}")
+    elif isinstance(config, StackedBarChartPlotParameters):
+        logging.info("Creating Bar Chart")
+        logging.info(f"Variable X: {config.variable_x_axis}")
+        logging.info(f"Variable Y: {config.variable_y_axis}")
+        logging.info(f"Hue: {config.hue}")
+        logging.info(f"Chart Orientation: {config.chart_orientation}")
+        logging.info(f"Sort Order: {config.sort_order}")
+    elif isinstance(config, SurfacePlotParameters):
+        logging.info("Creating Surface Plot")
+        logging.info(f"Function Name: {config.function_name}")
+        logging.info(f"X-Axis Maximum: {config.x_axis_maximum}")
+        logging.info(f"Y-Axis Maximum: {config.y_axis_maximum}")
+        logging.info(f"X-Axis Label: {config.x_axis_label}")
+        logging.info(f"Y-Axis Label: {config.y_axis_label}")
+        logging.info(f"Z-Axis Label: {config.z_axis_label}")
+        logging.info(f"Elevation Angle: {config.elevation_angle}")
+        logging.info(f"Azimuth Angle: {config.azimuth_angle}")
+    elif isinstance(config, ViolinPlotParameters):
+        logging.info("Creating Violin Plot")
+        logging.info(f"Variable X: {config.variable_x_axis}")
+        logging.info(f"Variable Y: {config.variable_y_axis}")
+        logging.info(f"X-Axis Label: {config.x_axis_label}")
+        logging.info(f"Y-Axis Label: {config.y_axis_label}")
+    else:
+        raise ValueError(f"Invalid type of visualization requested {type(config)}")
+    logging.info("```")