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govdoc-cr-analysis/topic-outcome-models/readme_topic_outcome_model.R

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2025-02-03 22:21:27 +00:00
library(dplyr)
library(lubridate)
library(rdd)
library(stringr)
readme_count_data_filepath <- "/mmfs1/gscratch/comdata/users/mjilg/govdoc-cr-data/final_data/README_weekly_count_data.csv"
readme_count_df = read.csv(readme_count_data_filepath, header = TRUE)
readme_topic_dist_filepath <- "text_analysis/020125_README_file_topic_distributions.csv"
readme_topics_df = read.csv(readme_topic_dist_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))
summed_data <- readme_count_df |>
filter(before_after == 1) |>
group_by(project_id) |>
summarise_at(vars(commit_count), list(summed_count=sum))
readme_topics_df <- readme_topics_df |>
mutate(project_id = sapply(str_split(filename, "_hullabaloo_"), `[`, 1)) |>
mutate(project_id = ifelse(filename=="jaraco_keyrings.alt_hullabaloo_README.rst", "jaraco_keyrings.alt", project_id)) |>
mutate(project_id = ifelse(filename=="_vcr_vcr_README.md", "vcr_vcr", project_id))
merged_df <- inner_join(summed_data, readme_topics_df, by="project_id")
merged_df$logged_commits <- log1p(merged_df$summed_count)