108 lines
3.7 KiB
R
108 lines
3.7 KiB
R
library(tidyverse)
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neurobiber_description_pca_csv <-"~/p2/quest/092325_description_PCA_df.csv"
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neurobiber_description_pca_df <- read.csv(neurobiber_description_pca_csv , header = TRUE)
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neurobiber_subcomment_pca_csv <-"~/p2/quest/092325_subcomment_PCA_df.csv"
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neurobiber_subcomment_pca_df <- read.csv(neurobiber_subcomment_pca_csv , header = TRUE)
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preprocess_comment <- function(message) {
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library(stringr)
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comment_text <- message
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# 1. replace code with CODE
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# Inline code: `...`
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comment_text <- str_replace_all(comment_text, "`[^`]+`", "CODE")
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# Block code: ```...```
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comment_text <- str_replace_all(comment_text, "```[\\s\\S]+?```", "CODE")
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# 2. replace quotes with QUOTE
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lines <- unlist(strsplit(comment_text, "\n"))
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lines <- ifelse(str_detect(str_trim(lines), "^>"), "QUOTE", lines)
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comment_text <- paste(lines, collapse = "\n")
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# 3. replace Gerrit URLs with GERRIT_URL
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gerrit_url_pattern <- "https://gerrit\\.wikimedia\\.org/r/\\d+"
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comment_text <- str_replace_all(comment_text, gerrit_url_pattern, "GERRIT_URL")
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# replace URL with URL
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url_pattern <- "https?://[^\\s]+"
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comment_text <- str_replace_all(comment_text, url_pattern, "URL")
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# 4. replace @screenname with SCREEN_NAME
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cleaned_message <- str_replace_all(comment_text, "(^|\\s)@\\w+", "SCREEN_NAME")
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return(cleaned_message)
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}
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# Add comment_type column to each df
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neurobiber_description_pca_df$comment_type <- "task_description"
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neurobiber_subcomment_pca_df$comment_type <- "subcomment"
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#clean the messages
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neurobiber_description_pca_df$cleaned_comment <- sapply(neurobiber_description_pca_df$text, preprocess_comment)
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neurobiber_subcomment_pca_df$cleaned_comment <- sapply(neurobiber_subcomment_pca_df$text, preprocess_comment)
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# look at correlation between PC1, PC2, and different outcome variables
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library(dplyr)
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description_anova_results <- neurobiber_description_pca_df %>%
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group_by(source) %>%
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group_map(~ summary(aov(PC2 ~ phase, data = .x)), .keep = TRUE)
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description_anova_results
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discussion_anova_results <- neurobiber_subcomment_pca_df %>%
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group_by(source) %>%
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group_map(~ summary(aov(PC2 ~ phase, data = .x)), .keep = TRUE)
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discussion_anova_results
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# look at the representative comments for PC1 and PC2
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top5 <- neurobiber_subcomment_pca_df %>%
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arrange(desc(PC6)) %>%
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slice(300:310) %>%
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pull(cleaned_comment)
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bottom5 <- neurobiber_subcomment_pca_df %>%
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arrange(PC6) %>%
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slice(300:310) %>%
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pull(cleaned_comment)
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cat("Top 300:310 comment_text by PC1 score:\n")
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print(top5)
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cat("\nBottom 300:310 comment_text by PC1 score:\n")
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print(bottom5)
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aggregated_neurobiber_description_pca_df <- neurobiber_description_pca_df |>
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group_by(AuthorWMFAffil, week_index, source, priority, closed_relevance, phase) %>%
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summarise(mean_PC1 = median(PC1),
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mean_PC2 = median(PC2),
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mean_PC3 = median(PC3),
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mean_PC4 = median(PC4),
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mean_PC5 = median(PC5))
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library(scales)
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library(ggplot2)
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affiliationColors <-
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setNames( c('#5da2d8', '#c7756a')
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,c("False", "True"))
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long_df <- aggregated_neurobiber_description_pca_df %>%
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tidyr::pivot_longer(
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cols = starts_with("mean_PC"),
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names_to = "PC",
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values_to = "PC_value"
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)
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unified_df$AuthorWMFAffil <- factor(unified_df$AuthorWMFAffil, levels = c("False", "True"))
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unified_df <- unified_df[order(unified_df$AuthorWMFAffil), ]
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ggplot(neurobiber_description_pca_df, aes(x = PC1, y = PC3, fill = closed_relevance)) +
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geom_point(shape = 21, alpha=0.3, size=2) +
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facet_grid(source ~ phase) +
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scale_fill_viridis_d() + # Or scale_fill_brewer/palette of your choice
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theme_minimal() +
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labs(
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title = "PCs for Task Subcomments (Faceted by Source and Phase)",
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x = "PC1",
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y = "PC3",
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fill = "(tentative affiliation)"
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)
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