govdoc-cr-analysis/quartile_splits.R

contributing_count_filepath <- "/mmfs1/gscratch/comdata/users/mjilg/govdoc-cr-data/final_data/CONTRIBUTING_weekly_count_data.csv"
contributing_count_df = read.csv(contributing_count_filepath, header = TRUE) 

window_num <- 5
contributing_count_df <- contributing_count_df |>
  filter(week_index >= (- window_num) & week_index <= (window_num)) |>
  mutate(scaled_age = scale(age)) |>
  mutate(scaled_age_at_commit = scale(age_at_commit))|>
  mutate(log1p_count = log1p(commit_count))

quantile(contributing_count_df$age / 365, probs = c(0.25, 0.5, 0.75))
quantile(contributing_count_df$age_at_commit / 365, probs = c(0.25, 0.5, 0.75))
aggregate_cccd <- contributing_count_df |>
  group_by(project_id) |>
  summarize(
    avg_weekly_commits = mean(commit_count),
    authors_before = sum(new_author_emails[before_after == 0]),
    authors_after = sum(new_author_emails[before_after == 1]),
  )
quantile(aggregate_cccd$avg_weekly_commits, probs = c(0.25, 0.5, 0.75))
quantile(aggregate_cccd$authors_before, probs = c(0.25, 0.5, 0.75))
quantile(aggregate_cccd$authors_after, probs = c(0.25, 0.5, 0.75))

readme_count_filepath <- "/mmfs1/gscratch/comdata/users/mjilg/govdoc-cr-data/final_data/README_weekly_count_data.csv"
readme_count_df = read.csv(readme_count_filepath, header = TRUE) 

window_num <- 5
readme_count_df <- readme_count_df |>
  filter(week_index >= (- window_num) & week_index <= (window_num)) |>
  mutate(scaled_age = scale(age)) |>
  mutate(scaled_age_at_commit = scale(age_at_commit))|>
  mutate(log1p_count = log1p(commit_count))

quantile(readme_count_df$age / 365, probs = c(0.25, 0.5, 0.75))
quantile(readme_count_df$age_at_commit / 365, probs = c(0.25, 0.5, 0.75))
aggregate_rccd <- readme_count_df |>
  group_by(project_id) |>
  summarize(
    avg_weekly_commits = mean(commit_count),
    authors_before = sum(new_author_emails[before_after == 0]),
    authors_after = sum(new_author_emails[before_after == 1]),
  )
quantile(aggregate_rccd$avg_weekly_commits, probs = c(0.25, 0.5, 0.75))
quantile(aggregate_rccd$authors_before, probs = c(0.25, 0.5, 0.75))
quantile(aggregate_rccd$authors_after, probs = c(0.25, 0.5, 0.75))

readme_readability <- read.csv("text_analysis/020325_README_readability.csv", header=TRUE)
median(readme_readability$mcalpine_eflaw)
median(readme_readability$linsear_write_formula)
median(readme_readability$flesch_reading_ease)

contributing_readability <- read.csv("text_analysis/020125_CONTRIBUTING_readability.csv", header=TRUE)
median(contributing_readability$mcalpine_eflaw)
median(contributing_readability$linsear_write_formula)
median(contributing_readability$flesch_reading_ease)


contributing_topic <- read.csv("text_analysis/020125_CONTRIBUTING_file_topic_distributions.csv", header=TRUE)
quantile(contributing_topic$t0, probs = c(0.25, 0.5, 0.75))
quantile(contributing_topic$t1, probs = c(0.25, 0.5, 0.75))
quantile(contributing_topic$t2, probs = c(0.25, 0.5, 0.75))
quantile(contributing_topic$t3, probs = c(0.25, 0.5, 0.75))
quantile(contributing_topic$t4, probs = c(0.25, 0.5, 0.75))

readme_topic <- read.csv("text_analysis/020325_README_file_topic_distributions.csv", header=TRUE)
quantile(readme_topic$t0, probs = c(0.25, 0.5, 0.75))
quantile(readme_topic$t1, probs = c(0.25, 0.5, 0.75))
quantile(readme_topic$t2, probs = c(0.25, 0.5, 0.75))
quantile(readme_topic$t3, probs = c(0.25, 0.5, 0.75))
quantile(readme_topic$t4, probs = c(0.25, 0.5, 0.75))
quantile(readme_topic$t5, probs = c(0.25, 0.5, 0.75))
quantile(readme_topic$t6, probs = c(0.25, 0.5, 0.75))
quantile(readme_topic$t7, probs = c(0.25, 0.5, 0.75))
quantile(readme_topic$t8, probs = c(0.25, 0.5, 0.75))
quantile(readme_topic$t9, probs = c(0.25, 0.5, 0.75))
quantile(readme_topic$t10, probs = c(0.25, 0.5, 0.75))