94 lines
3.9 KiB
R
94 lines
3.9 KiB
R
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library(stringr)
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library(tidyverse)
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readme_topics_df <- read_csv("../text_analysis/readme_file_topic_distributions.csv")
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colMeans(subset(readme_topics_df, select = -filename))
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readme_df <- read_csv("../final_data/deb_readme_did.csv")
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readme_pop_df <- read_csv("../final_data/deb_readme_pop_change.csv")
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#get the readmeution count
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#some preprocessing and expansion
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col_order <- c("upstream_vcs_link", "age_in_days", "first_commit", "first_commit_dt", "event_gap", "event_date", "event_hash", "before_all_ct", "after_all_ct", "before_mrg_ct", "after_mrg_ct", "before_auth_new", "after_auth_new", "before_commit_new", "after_commit_new")
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readme_df <- readme_df[,col_order]
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readme_df$ct_before_all <- str_split(gsub("[][]","", readme_df$before_all_ct), ", ")
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readme_df$ct_after_all <- str_split(gsub("[][]","", readme_df$after_all_ct), ", ")
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readme_df$ct_before_mrg <- str_split(gsub("[][]","", readme_df$before_mrg_ct), ", ")
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readme_df$ct_after_mrg <- str_split(gsub("[][]","", readme_df$after_mrg_ct), ", ")
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drop <- c("before_all_ct", "before_mrg_ct", "after_all_ct", "after_mrg_ct")
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readme_df = readme_df[,!(names(readme_df) %in% drop)]
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# 2 some expansion needs to happens for each project
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expand_timeseries <- function(project_row) {
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longer <- project_row |>
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pivot_longer(cols = starts_with("ct"),
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names_to = "window",
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values_to = "count") |>
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unnest(count)
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longer$observation_type <- gsub("^.*_", "", longer$window)
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longer <- ddply(longer, "observation_type", transform, week=seq(from=0, by=1, length.out=length(observation_type)))
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longer$count <- as.numeric(longer$count)
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#longer <- longer[which(longer$observation_type == "all"),]
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return(longer)
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}
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expanded_data <- expand_timeseries(readme_df[1,])
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for (i in 2:nrow(readme_df)){
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expanded_data <- rbind(expanded_data, expand_timeseries(readme_df[i,]))
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}
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#filter out the windows of time that we're looking at
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window_num <- 8
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windowed_data <- expanded_data |>
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filter(week >= (27 - window_num) & week <= (27 + window_num)) |>
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mutate(D = ifelse(week > 27, 1, 0))
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summed_data <- windowed_data |>
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filter(D==1) |>
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group_by(upstream_vcs_link) |>
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summarise_at(vars(count), list(summed_count=sum))
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#concat dataframes into central data
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readme_pop_df <- readme_pop_df |>
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mutate(project_name = map_chr(upstream_vcs_link, ~ {
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parts <- str_split(.x, pattern = "/")[[1]]
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if (length(parts) >= 1) {
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parts[length(parts)]
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} else {
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NA_character_
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}
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}))
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readme_topics_df <- readme_topics_df |>
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mutate(project_name = map_chr(filename, ~ {
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parts <- str_split(.x, pattern = "_")[[1]]
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if (length(parts) >= 1) {
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paste(head(parts, -1), collapse="_")
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} else {
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NA_character_
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}
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}))
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readme_total_df <- readme_pop_df |>
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join(readme_topics_df, by="project_name")
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readme_total_df <- readme_total_df|>
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join(summed_data, by="upstream_vcs_link")
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#outcome variable that is number of commits by number of new readmeutors
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readme_total_df$commit_by_contrib = readme_total_df$summed_count *readme_total_df$after_contrib_new
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readme_total_df$logged_outcome = log1p(readme_total_df$commit_by_contrib)
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readme_total_df$logged_contrib = log1p(readme_total_df$after_contrib_new)
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readme_total_df$logged_commits = log1p(readme_total_df$summed_count)
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#running regressions
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library(MASS)
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lm1 <- glm.nb(logged_contrib~ t0+t1+t2+t7+t3 +t6 + t5, data = readme_total_df)
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qqnorm(residuals(lm1))
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summary(lm1)
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#saveRDS(lm1, "0725_topic_contriboutcome_readme.rda")
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contrib_ <- glm.nb(logged_contrib~ t0+t1+t2+t3+ t5 +t6 +t7, data = readme_total_df)
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commit_ <- glm.nb(logged_commits~ t0+t1+t2+t3+ t5 +t6 +t7, data = readme_total_df)
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library(texreg)
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texreg(list(contrib_, commit_), stars=NULL, digits=3, use.packages=FALSE,
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custom.model.names=c( 'Contributions','Commits'),
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custom.coef.names=c('(Intercept)', 'Topic 1', 'Topic 2', 'Topic 3', 'Topic 4', 'Topic 6', 'Topic 7', 'Topic 8'),
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table=FALSE, ci.force = TRUE)
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