101 lines
3.9 KiB
R
101 lines
3.9 KiB
R
library(plyr)
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library(tidyverse)
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library(rdd)
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#set wd, read in data
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try(setwd(dirname(rstudioapi::getActiveDocumentContext()$path)))
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readme_df <- read_csv("../final_data/deb_readme_did.csv")
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contributing_df <- read_csv("../final_data/deb_contrib_did.csv")
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#preprocessing for readme_df
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colnames(readme_df) <- c("upstream_vcs_link", "event_date", "event_hash", "before_all_ct", "before_mrg_ct", "after_all_ct", "after_mrg_ct", "before_auth_new", "after_commit_new", "after_auth_new", "before_commit_new")
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col_order <- c("upstream_vcs_link", "event_date", "event_hash", "before_all_ct", "after_all_ct", "before_mrg_ct", "after_mrg_ct", "before_auth_new", "after_auth_new", "before_commit_new", "after_commit_new")
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readme_df <- readme_df[,col_order]
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readme_df$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$after_mrg_ct), ", ")
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drop <- c("before_all_ct", "before_mrg_ct", "after_all_ct", "after_mrg_ct")
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readme_df = readme_df[,!(names(readme_df) %in% drop)]
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#preprocessing for contributing_df
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# test <- readme_df$cnt_before_all
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# as.numeric(unlist(test[1]))
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# test_two <- c()
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# iterator <- 0
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# for (entry in test) {
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# readme_df$cnt_before_all[iterator] <- as.numeric(unlist(entry))
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# print(as.numeric(unlist(entry)))
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# iterator <- iterator + 1
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# }
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# test_two
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#Yes, need to expand the dataframe, but again, for the sake of clarity, do not want to until analysis step
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# https://rpubs.com/phle/r_tutorial_regression_discontinuity_design
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## https://www.rdocumentation.org/packages/lme4/versions/1.1-35.2/topics/lmer
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new_test <- readme_df[9,]
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get_optimal_window <- function(project_row) {
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longer <- project_row |>
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pivot_longer(cols = starts_with("ct"),
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names_to = "window",
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values_to = "count") |>
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unnest(count)
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longer$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$count)
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#this below line makes the code specific to the all-commits data
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longer <- longer[which(longer$observation_type == "all"),]
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result <- tryCatch({
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#Imbens-Kalyanaraman Optimal Bandwidth Calculation
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optimal_bandwidth <- IKbandwidth(longer$week, longer$count, cutpoint = 26, verbose = FALSE, kernel = "triangular")
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return(optimal_bandwidth)
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}, error = function(e){
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#have tested it with multiple different error-values and all medians/means still hover around 8
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return(8)
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})
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}
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#this just gets the optimal bandwith window for each project and then appends to lists
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bandwidths <- c()
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for (i in 1:nrow(readme_df)){
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bandwidth <- get_optimal_window(readme_df[i,])
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bandwidths <- c(bandwidths, bandwidth)
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}
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mean(bandwidths)
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#8.574233
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median(bandwidths) #8.363088
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table(bandwidths)
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#from this, I think setting the bandwidth to 8 weeks, two months, the floor
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# of both the median and mean calculations
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#longer <- longer %>%
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# filter(week >= (26 - window_num) & week <= (26 + window_num))
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#sapply(longer, class)
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#longer$biweekly <- ceiling(longer$week / 2)
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#longer <- longer %>%
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# group_by(window, biweekly, observation_type) %>%
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# summarise(biweekly_count = sum(count, na.rm = TRUE))
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#testing out analysis below
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longer[which(longer$observation_type == "all"),] |>
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ggplot(aes(x = week, y = count)) +
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geom_point() +
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geom_vline(xintercept = 26)
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longer[which(longer$observation_type == "all"),] |>
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mutate(D = ifelse(week >= 26, 1, 0)) |>
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lm(formula = count ~ D * I(week - 26)) |>
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summary()
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longer[which(longer$observation_type == "all"),] |>
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select(count, week) |>
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mutate(D = as.factor(ifelse(week >= 26, 1, 0))) |>
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ggplot(aes(x = week, y = count, color = D)) +
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geom_point() +
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geom_smooth(se = TRUE) +
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geom_vline(xintercept = 26)
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