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govdoc-cr-analysis/mlm/readme_did_prep.R
2025-02-03 14:21:27 -08:00

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1.2 KiB
R

library(tidyverse)
library(dplyr)
library(lubridate)
library(rdd)
readme_df_filepath <- "/mmfs1/gscratch/comdata/users/mjilg/govdoc-cr-data/final_data/README_weekly_count_data.csv"
df = read.csv(readme_df_filepath, header = TRUE)
#filtered_df <- df |>
# filter(!project_id == "letsencrypt_letsencrypt")
#output_filepath <-"/mmfs1/gscratch/comdata/users/mjilg/govdoc-cr-data/final_data/README_weekly_count_data.csv"
#write.csv(filtered_df, output_filepath, row.names = FALSE)
#EDA
var(df$commit_count) # 112.4945
mean(df$commit_count) # 2.431342
median(df$commit_count) # 0
mean(df$age) # 4911.734 days
mean(df$age_at_commit) # 197.296 days
median(df$age) # 4689 days
median(df$age_at_commit) # 0 days
# scale and log-transform
df$scaled_age <- scale(df$age)
df$scaled_age_at_commit <- scale(df$age_at_commit)
df$log1p_count <- log1p(df$commit_count)
#getting IK Bandwidth
get_optimal_bandwidth <- function(df){
IKbandwidth(df$week_index, df$log1p_count, cutpoint = 0, verbose = FALSE, kernel = "triangular")
}
mean_optimal_bandwidth <- df %>%
group_by(project_id) %>%
summarise(optimal_bandwidth = get_optimal_bandwidth(cur_data())) %>%
summarise(mean_optimal_bandwidth = mean(optimal_bandwidth))
#Mean Optimal Bandwidth: 5.44841