40 lines
1.4 KiB
R
40 lines
1.4 KiB
R
library(dplyr)
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library(lubridate)
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library(rdd)
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library(stringr)
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readme_count_data_filepath <- "/mmfs1/gscratch/comdata/users/mjilg/govdoc-cr-data/final_data/README_weekly_count_data.csv"
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readme_count_df = read.csv(readme_count_data_filepath, header = TRUE)
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readme_topic_dist_filepath <- "text_analysis/020125_README_file_topic_distributions.csv"
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readme_topics_df = read.csv(readme_topic_dist_filepath, header = TRUE)
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window_num <- 5
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readme_count_df <- readme_count_df |>
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filter(week_index >= (- window_num) & week_index <= (window_num)) |>
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mutate(scaled_age = scale(age)) |>
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mutate(scaled_age_at_commit = scale(age_at_commit))|>
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mutate(log1p_count = log1p(commit_count))
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summed_data <- readme_count_df |>
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filter(before_after == 1) |>
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group_by(project_id) |>
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summarise_at(vars(commit_count), list(summed_count=sum))
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readme_topics_df <- readme_topics_df |>
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mutate(project_id = sapply(str_split(filename, "_hullabaloo_"), `[`, 1)) |>
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mutate(project_id = ifelse(filename=="jaraco_keyrings.alt_hullabaloo_README.rst", "jaraco_keyrings.alt", project_id)) |>
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mutate(project_id = ifelse(filename=="_vcr_vcr_README.md", "vcr_vcr", project_id))
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filtered_topics <- readme_topics_df |>
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filter(project_id %in% summed_data$project_id)
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merged_df <- inner_join(summed_data, readme_topics_df, by="project_id")
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multiple_mappings <- merged_df %>%
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group_by(project_id) %>%
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filter(n() > 1) %>%
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ungroup()
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merged_df$logged_commits <- log1p(merged_df$summed_count)
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