Removed code for merge and join; deleted some TODOs
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@ -186,7 +186,6 @@ for (art in aws) { # select artwork
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head(dat2[, c("artwork", "event", "trace")], 50)
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tail(dat2[, c("artwork", "event", "trace")], 50)
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# TODO: How to handle popups from glossar???
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rm(aws, i, j, last_event, art)
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@ -246,9 +245,6 @@ table(is.na(dat2[dat2$glossar == 1, "trace"]))
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# How many glossar_files are only associated with one artwork?
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lut[sapply(lut$artwork, length) == 1, "glossar_file"]
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# TODO: Fill in the ones that are associated with one artwork
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# --> Can't come up with something -- maybe ask AK???
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single <- lut[sapply(lut$artwork, length) == 1, "glossar_file"]
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tmp <- subset(dat2, is.na(dat2$trace))$popup
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inside <- unique(tmp[tmp %in% lut[sapply(lut$artwork, length) == 1, "glossar_file"]])
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@ -284,8 +280,6 @@ dat2[14110:14130, ]
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# TODO: Integrate for loop into for loop above
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# TODO: "glossar" entry should be changed to the corresponding artwork
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# TODO: For now: Exclude not matched glossar entries
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df <- subset(dat2, !is.na(dat2$trace))
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@ -434,38 +428,8 @@ dat_openPopup <- openPopup_wide[, c("fileid.start", "fileid.stop", "event",
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rm(num_start, openPopup_wide)
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# Merge all
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# system.time({
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# dat_all <- merge(dat_trans, dat_flipCard, all = TRUE)
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# dat_all <- merge(dat_all, dat_openTopic, all = TRUE)
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# dat_all <- merge(dat_all, dat_openPopup, all = TRUE)
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# })
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#
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# # check
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# nrow(dat_all) == (nrow(dat_trans) + nrow(dat_flipCard) +
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# nrow(dat_openTopic) + nrow(dat_openPopup))
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#
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# dat_all <- dat_all[order(dat_all$date.start), ]
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# rownames(dat_all) <- NULL
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#
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# TODO: from here on NA... WHY??
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# dat_all[19426:19435, ]
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# TODO: Should card maybe also be filled in for "openPopup"?
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# dat_all2 <- dplyr::full_join(dat_trans, dat_flipCard)
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# dat_all2 <- dplyr::full_join(dat_all, dat_openTopic)
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# dat_all2 <- dplyr::full_join(dat_all, dat_openPopup)
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#
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# nrow(dat_all2) == (nrow(dat_trans) + nrow(dat_flipCard) +
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# nrow(dat_openTopic) + nrow(dat_openPopup))
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#
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# dat_all2 <- dat_all2[order(dat_all2$date.start), ]
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# rownames(dat_all2) <- NULL
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# TODO: --> same result - but faster. Need it?
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# --> Would hate to depend on dplyr...
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#' ## Use `rbind()` instead...
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# --> unbeatable in terms of time!
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