78 lines
3.3 KiB
R
78 lines
3.3 KiB
R
#libraries
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library(stringr)
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contrib_df <- read_csv("../final_data/deb_contrib_did.csv")
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contrib_pop_df <- read_csv("../final_data/deb_contrib_pop_change.csv")
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contrib_readability_df <- read_csv('../text_analysis/dwo_readability_contributing.csv')
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#get the contribution 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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contrib_df <- contrib_df[,col_order]
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contrib_df$ct_before_all <- str_split(gsub("[][]","", contrib_df$before_all_ct), ", ")
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contrib_df$ct_after_all <- str_split(gsub("[][]","", contrib_df$after_all_ct), ", ")
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contrib_df$ct_before_mrg <- str_split(gsub("[][]","", contrib_df$before_mrg_ct), ", ")
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contrib_df$ct_after_mrg <- str_split(gsub("[][]","", contrib_df$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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contrib_df = contrib_df[,!(names(contrib_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(contrib_df[1,])
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for (i in 2:nrow(contrib_df)){
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expanded_data <- rbind(expanded_data, expand_timeseries(contrib_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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contrib_pop_df <- contrib_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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contrib_readability_df <- contrib_readability_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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contrib_total_df <- contrib_pop_df |>
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join(contrib_readability_df, by="project_name")
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contrib_total_df <- contrib_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 contributors
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contrib_total_df$commit_by_contrib = contrib_total_df$summed_count * contrib_total_df$after_contrib_new
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contrib_total_df$logged_outcome = log1p(contrib_total_df$commit_by_contrib)
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# test regressions
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library(MASS)
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lm1 <- glm.nb(summed_count ~ reading_time + linsear_write_formula + flesch_reading_ease + mcalpine_eflaw + word_count, data = contrib_total_df)
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qqnorm(residuals(lm1))
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summary(lm1)
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