adding in longitudinal_text_analysis

commit_activity_analysis/get_weekly_commit_counts.R

@@ -1,45 +0,0 @@
-library(tidyverse)
-# test data directory: /gscratch/comdata/users/mjilg/program_testing/
-# load in the paritioned directories
-library(dplyr)
-library(lubridate)
-
-#for a given file we want to get the count data and produce a csv
-test_file <- "/gscratch/comdata/users/mjilg/program_testing/core_2012-01-01_to_2014-12-31.csv"
-test_dir <- "/gscratch/comdata/users/mjilg/program_testing/"
-
-transform_commit_data <- function(filepath){
-  df = read.csv(filepath, header = TRUE) 
-  dir_path = dirname(filepath)
-  file_name = basename(filepath)
-  
-  # transform the rows of commit data to weekly count data
-  project_name <- sub("_[0-9]{4}-[0-9]{2}-[0-9]{2}_to_[0-9]{4}-[0-9]{2}-[0-9]{2}.csv$", "", file_name)
-  
-  df <- df |>
-    mutate(commit_date = ymd_hms(commit_date)) |>
-    mutate(project_name = project_name)
-  
-  weekly_commits <- df |>
-    mutate(week = floor_date(commit_date, "week")) |>
-    group_by(week, project_name) |>
-    summarise(commit_count = n(), .groups = 'drop')
-  
-  #prepare to save the new, transformed file
-  count_path <- file.path(dir_path, "weekly_counts")
-  count_file_name <- paste0("weeklycount_", file_name)
-  output_file_path <- file.path(count_path, count_file_name)
-  #save and gracefully exit
-  write.csv(weekly_commits, output_file_path, row.names = FALSE)
-  return(weekly_commits)
-}
-
-#then for all files in a directory
-transform_directory_of_commit_data <- function(dir_path) {
-  file_list <- list.files(path = dir_path, pattern = "*.csv", full.names = TRUE)
-  for (filepath in file_list) {
-    transform_commit_data(filepath)
-  }
-}
-
-transform_directory_of_commit_data(test_dir)

text_analysis/longitudinal_analysis.R

@@ -0,0 +1,15 @@
+library(dplyr)
+library(ggplot2)
+phab_data_path <- "/mmfs1/gscratch/comdata/users/mjilg/mw-repo-lifecycles/0205_convo_data/phab_data/visualeditor/0205_ve_phab_comments.csv"
+phab_data <- read.csv(phab_data_path, header=TRUE)
+
+phab_data <- phab_data |>
+  mutate(has_ref = grepl("bots", comment_text)) |>
+  mutate(timestamp = as.POSIXct(date_created, origin = "1970-01-01", tz = "UTC")) |>
+  filter(date_created < 1518232866 & date_created > 1356923678)
+
+
+g <- ggplot(phab_data, aes(x=timestamp, y=has_ref)) +
+  geom_point(alpha = 0.5) + 
+  theme_minimal()
+g