38 lines
1.1 KiB
R
38 lines
1.1 KiB
R
library(dplyr)
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library(lubridate)
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library(rdd)
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library(tidyr)
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contributing_df_filepath <- "/mmfs1/gscratch/comdata/users/mjilg/govdoc-cr-data/final_data/metadata/final_0207_CONTRIBUTING_weekly_count_data.csv"
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df = read.csv(contributing_df_filepath, header = TRUE)
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#EDA
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var(df$commit_count) # 349.06
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mean(df$commit_count) # 8.371495
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median(df$commit_count) # 1
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mean(df$age) # 4939.859
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mean(df$age_at_commit) # 2286.772 days
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median(df$age) #4738 says
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median(df$age_at_commit) # 1806 days
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# scale and log-transform
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df$scaled_age <- scale(df$age)
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df$scaled_age_at_commit <- scale(df$age_at_commit)
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df$log1p_count <- log1p(df$commit_count)
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#getting IK Bandwidth
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get_optimal_bandwidth <- function(df){
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bw <- tryCatch({
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IKbandwidth(df$relative_week, df$log1p_count, cutpoint = 0, verbose = FALSE, kernel = "triangular")
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}, error = function(e) {
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NA
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})
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}
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mean_optimal_bandwidth <- df %>%
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group_by(project_id) %>%
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summarise(optimal_bandwidth = get_optimal_bandwidth(cur_data())) %>%
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drop_na(optimal_bandwidth) |>
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summarise(mean_optimal_bandwidth = mean(optimal_bandwidth))
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#5.676423 |