138 lines
4.2 KiB
R
138 lines
4.2 KiB
R
library(tidyverse)
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library(stringr)
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library(tidyr)
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library(dplyr)
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library(purrr)
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main_csv <- "~/analysis_data/stale_unifieds/100625_unified_w_affil.csv"
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main_df <- read.csv(main_csv, header = TRUE)
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#filter out existing olmo stuff
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main_df <- main_df |>
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select(-starts_with("olmo"))
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#dedupe Task with changed title and duplicate entries
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first_rows <- main_df |>
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filter(id %in% c(20846, 20847)) |>
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distinct(id, .keep_all = TRUE)
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others <- main_df |>
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filter(!(id %in% c(20846, 20847))) |>
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filter(id != 23366)
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main_df <- bind_rows(others, first_rows)
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desc_info <- main_df %>%
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filter(comment_type == "task_description") %>%
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group_by(TaskPHID) %>%
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ungroup() %>%
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transmute(
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TaskPHID,
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task_desc_author = AuthorPHID,
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task_desc_dateClosed = as.POSIXct(date_closed, origin = "1970-01-01", tz = "UTC")
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)
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#identifying comments in ADAC set
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main_df <- main_df |>
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mutate(created = as.POSIXct(date_created, origin = "1970-01-01", tz = "UTC")) |>
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left_join(desc_info, by = "TaskPHID") |>
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mutate(
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ADAC = as.integer(
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!is.na(task_desc_author) &
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AuthorPHID == task_desc_author &
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(is.na(task_desc_dateClosed) | created < task_desc_dateClosed)
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)
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)
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# add dictionary values
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modal_verb_list <- c("will", "may", "can", "shall", "must",
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"ought", "do", "need", "dare",
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"will not", "may not", "cannot", "shall not",
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"must not", "do not", "don't", "need not",
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"dare not", "won't", "can't")
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modal_regex <- paste0("\\b(", paste(modal_verb_list, collapse = "|"), ")\\b")
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main_df <- main_df |>
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mutate(
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comment_text = dplyr::coalesce(comment_text, ""), # handle NA
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modal_verbs = stringr::str_count(comment_text, stringr::regex(modal_regex, ignore_case = TRUE)),
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log1p_mv = log1p(modal_verbs)
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)
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pca_csv <- "~/analysis_data/102125_constituent_dfs/102025_total_pca_df.csv"
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pca_df <- read.csv(pca_csv, header = TRUE)
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pca_df <- pca_df |>
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select(starts_with("PC"),
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id)
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first_join <- main_df|>
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left_join(
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pca_df,
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by = "id"
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)
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olmo_csv <- "~/analysis_data/102125_constituent_dfs/all_102125_olmo_batched_categorized.csv"
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olmo_df <- read.csv(olmo_csv, header = TRUE)
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olmo_df <- olmo_df |>
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mutate(olmo_cleaned_sentences = cleaned_sentences,
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olmo_sentence_labels = sentence_categories)|>
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select(id, olmo_cleaned_sentences, olmo_sentence_labels)
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second_join <- first_join|>
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left_join(
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olmo_df,
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by = "id"
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)
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#wrangling human labels
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large_human_labels_csv <- "~/analysis_data/102125_constituent_dfs/102025_human_labels.csv"
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large_human_labels_df <- read.csv(large_human_labels_csv, header = TRUE)
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small_human_labels_csv <- "~/analysis_data/102125_constituent_dfs/102125_human_info_sample.csv"
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small_human_labels_df <- read.csv(small_human_labels_csv, header = TRUE)
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#TODO
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# [ x ] collate the two samples into one
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large_human_labels_df <- large_human_labels_df |> select(id, cleaned_sentences, human_label)
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small_human_labels_df <- small_human_labels_df |> select(id, cleaned_sentences, human_label)
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human_labels_df <- rbind(large_human_labels_df, small_human_labels_df)
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# [ x ] aggregate sentence level rows into comment level
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human_labels_reduced <- human_labels_df %>%
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group_by(id) %>%
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summarise(
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cleaned_sentences = list(cleaned_sentences),
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human_labels = list(str_squish(human_label)),
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.groups = "drop"
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)
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# [ x ] merge into unified data set
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third_join <- second_join |>
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left_join(
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human_labels_reduced,
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by="id"
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)
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# [ x ] clean/drop needless fields
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unified_df <- third_join |>
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select(-same_author) |>
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mutate(across(c(human_labels, cleaned_sentences),
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~ {
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x <- as.character(.x)
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x_trim <- str_squish(x)
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ifelse(x_trim == "NULL",
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NA_character_,
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x)
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}))
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# [ x ] verify set
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length(unique(unified_df$TaskPHID))
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length(unique(unified_df$id))
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pulling <- unified_df |>
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filter(id == "24695" | id == "24696")
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pulling <- unified_df |>
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filter(id == "23366" | id == "20846" | id == "20847")
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write.csv(unified_df, "102725_unified.csv", row.names = FALSE)
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