118 lines
3.9 KiB
R
118 lines
3.9 KiB
R
#' ---
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#' title: "HMC data cleaning"
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#' author: "Nora Wickelmaier"
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#' date: 2025-10-14
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#' ---
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# clear workspace
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rm(list = ls())
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# Read anonymized data
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dat1 <- read.csv("HMC_wave1_anonymized.csv", na.strings = c("-99", "NA"))
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dat2 <- read.csv("HMC_wave2_anonymized.csv", na.strings = c("-99", "NA"))
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dat3 <- read.csv("HMC_wave3_anonymized.csv", na.strings = c("-99", "NA"))
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dat4 <- read.csv("HMC_wave4_anonymized.csv", na.strings = c("-99", "NA"))
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dat5 <- read.csv("HMC_wave5_anonymized.csv", na.strings = c("-99", "NA"))
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dat6 <- read.csv("HMC_wave6_anonymized.csv", na.strings = c("-99", "NA"))
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# Check if subjects are double within waves
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which(table(dat1$subj_id) > 1)
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which(table(dat2$subj_id) > 1)
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which(table(dat3$subj_id) > 1)
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which(table(dat4$subj_id) > 1)
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which(table(dat5$subj_id) > 1)
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which(table(dat6$subj_id) > 1)
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dat1[dat1$subj_id == names(which(table(dat1$subj_id) > 1)),
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c("StartDate", "subj_id", "Duration_in_seconds")]
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dat3[dat3$subj_id == names(which(table(dat3$subj_id) > 1)),
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c("StartDate", "subj_id", "Duration_in_seconds")]
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# Only keep first entry
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dat1 <- subset(dat1, !duplicated(dat1$subj_id))
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dat3 <- subset(dat3, !duplicated(dat3$subj_id))
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# Remove empty variables wave 1
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which(apply(dat1, 2, function(x) is.na(x) |> sum()) == nrow(dat1)) |> names()
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dat1 <- subset(dat1, select = !grepl("^Q1.*", names(dat1)))
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# Check for other variables that are empty, but are supposed to be filled
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empty_var_w1 <- which(apply(dat1, 2, function(x) is.na(x) |> sum()) == nrow(dat1)) |>
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names() |> print()
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empty_var_w2 <- which(apply(dat2, 2, function(x) is.na(x) |> sum()) == nrow(dat2)) |>
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names() |> print()
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empty_var_w3 <- which(apply(dat3, 2, function(x) is.na(x) |> sum()) == nrow(dat3)) |>
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names() |> print()
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empty_var_w4 <- which(apply(dat4, 2, function(x) is.na(x) |> sum()) == nrow(dat4)) |>
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names() |> print()
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empty_var_w5 <- which(apply(dat5, 2, function(x) is.na(x) |> sum()) == nrow(dat5)) |>
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names() |> print()
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empty_var_w6 <- which(apply(dat6, 2, function(x) is.na(x) |> sum()) == nrow(dat6)) |>
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names() |> print()
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# Problems in wave 1
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# Theory: *After* data collection survey was altered and a new variable was
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# created. Data presentation was correct. (Angelica and Nora checked it via view
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# response in Qualtrics.)
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#
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# delg_tsk_typs_3 needs to be deleted
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# delg_tsk_typs_4 --> delg_tsk_typs_3
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# delg_tsk_typs_5 --> delg_tsk_typs_4
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# delg_tsk_typs_6 --> delg_tsk_typs_5
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# delg_tsk_typs_7 --> delg_tsk_typs_6
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# delg_tsk_typs_8 --> delg_tsk_typs_7
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# Rename variables as above
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dat1$delg_tsk_typs_3 <- dat1$delg_tsk_typs_4
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dat1$delg_tsk_typs_4 <- dat1$delg_tsk_typs_5
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dat1$delg_tsk_typs_5 <- dat1$delg_tsk_typs_6
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dat1$delg_tsk_typs_6 <- dat1$delg_tsk_typs_7
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dat1$delg_tsk_typs_7 <- dat1$delg_tsk_typs_8
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dat1$delg_tsk_typs_8 <- NULL
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# fav_Icecream is a bot detection item --> should it be removed?
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# Read codebook and rename variables for all waves
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cb <- openxlsx::read.xlsx("../HMC_codebook.xlsx")
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rename_data <- function(data, wave) {
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data <- data[, cb$qualtricsname[cb[[wave]] == "x" | is.na(cb[[wave]])]]
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names(data) <- cb$varname[cb[[wave]] == "x" | is.na(cb[[wave]])]
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data
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}
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df1 <- rename_data(dat1, "wave1")
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df2 <- rename_data(dat2, "wave2")
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df3 <- rename_data(dat3, "wave3")
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df4 <- rename_data(dat4, "wave4")
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df5 <- rename_data(dat5, "wave5")
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df6 <- rename_data(dat6, "wave6")
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# identical(dat1$tsk_typ_if_wrtng_gnr_1, df1$tsk_typ_if_wrtng_1)
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save_dataframe <- function(data, wave, path = "../03_cleaned_data/") {
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# as CSV
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write.csv(data,
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paste0(path, "/HMC_", wave, "_cleaned.csv"),
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row.names = FALSE)
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# # as XLSX
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# openxlsx::write.xlsx(data,
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# paste0(path, "/HMC_", wave, "_cleaned.xlsx"),
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# rowNames = FALSE)
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# # as SAV
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# haven::write_sav(data,
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# paste0(path, "/HMC_", wave, "_cleaned.sav"))
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}
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# Save anonymized data frames
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save_dataframe(df1, "wave1")
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save_dataframe(df2, "wave2")
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save_dataframe(df3, "wave3")
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save_dataframe(df4, "wave4")
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save_dataframe(df5, "wave5")
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save_dataframe(df6, "wave6")
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