update with cresc info and plotting

mg-govdoc-cr_24092572.out

@@ -1,17 +1,17 @@
 1. SSH tunnel from your workstation using the following command:
 
-   ssh -N -L 8787:n3436:56811 mjilg@klone.hyak.uw.edu
+   ssh -N -L 8787:n3433:51613 mjilg@klone.hyak.uw.edu
 
    and point your web browser to http://localhost:8787
 
 2. log in to RStudio Server using the following credentials:
 
    user: mjilg
-   password: +g73U+bdF4uygmNdsKEt
+   password: ezvnunJrjaDZsvr0nhPR
 
 When done using RStudio Server, terminate the job by:
 
 1. Exit the RStudio Session ("power" button in the top right corner of the RStudio window)
 2. Issue the following command on the login node:
 
-      scancel -f 24085748
+      scancel -f 24092572

mlm/contributing_crescendo_model.R

@@ -2,23 +2,23 @@ library(dplyr)
 library(lubridate)
 library(rdd)
 
-contributing_df_filepath <- "/mmfs1/gscratch/comdata/users/mjilg/govdoc-cr-data/final_data/CONTRIBUTING_weekly_count_data.csv"
+contributing_df_filepath <-  "/mmfs1/gscratch/comdata/users/mjilg/govdoc-cr-data/final_data/metadata/final_0207_CONTRIBUTING_weekly_count_data.csv"
 contributing_df = read.csv(contributing_df_filepath, header = TRUE) 
 
 window_num <- 5
 contributing_df <- contributing_df |>
-  filter(week_index >= (- window_num) & week_index <= (window_num)) |>
+  filter(relative_week >= (- window_num) & relative_week <= (0)) |>
   mutate(scaled_age = scale(age)) |>
   mutate(scaled_age_at_commit = scale(age_at_commit))|>
   mutate(log1p_count = log1p(commit_count)) |>
-  mutate(C = ifelse(week_index > -4 & week_index <= 0, 1,0))
+  mutate(C = ifelse(relative_week > -3 & relative_week <= 0, 1,0))
 
 library(lme4)
 library(optimx)
 library(lattice)
 
-all_gmodel <- glmer.nb(log1p_count ~ C * week_index + scaled_age + scaled_age_at_commit + (C * week_index | project_id),
+all_gmodel <- glmer.nb(log1p_count ~ C * relative_week + scaled_age + scaled_age_at_commit + (C * relative_week | project_id),
                        control=glmerControl(optimizer="bobyqa",
                                             optCtrl=list(maxfun=2e5)), nAGQ=0, data=contributing_df)
 summary(all_gmodel)
-saveRDS(all_gmodel, "020425_contributing_crescendo_model.rda")
+saveRDS(all_gmodel, "020725_contributing_crescendo_model.rda")

quartile_splits.R

@@ -1,9 +1,9 @@
-contributing_count_filepath <- "/mmfs1/gscratch/comdata/users/mjilg/govdoc-cr-data/final_data/CONTRIBUTING_weekly_count_data.csv"
+contributing_count_filepath <-  "/mmfs1/gscratch/comdata/users/mjilg/govdoc-cr-data/final_data/metadata/final_0207_CONTRIBUTING_weekly_count_data.csv"
 contributing_count_df = read.csv(contributing_count_filepath, header = TRUE) 
 
 window_num <- 5
 contributing_count_df <- contributing_count_df |>
-  filter(week_index >= (- window_num) & week_index <= (window_num)) |>
+  filter(relative_week >= (- window_num) & relative_week <= (window_num)) |>
   mutate(scaled_age = scale(age)) |>
   mutate(scaled_age_at_commit = scale(age_at_commit))|>
   mutate(log1p_count = log1p(commit_count))
@@ -21,18 +21,19 @@ quantile(aggregate_cccd$avg_weekly_commits, probs = c(0.25, 0.5, 0.75))
 quantile(aggregate_cccd$authors_before, probs = c(0.25, 0.5, 0.75))
 quantile(aggregate_cccd$authors_after, probs = c(0.25, 0.5, 0.75))
 
-readme_count_filepath <- "/mmfs1/gscratch/comdata/users/mjilg/govdoc-cr-data/final_data/README_weekly_count_data.csv"
+readme_count_filepath <- "/mmfs1/gscratch/comdata/users/mjilg/govdoc-cr-data/final_data/metadata/final_0207_README_weekly_count_data.csv"
 readme_count_df = read.csv(readme_count_filepath, header = TRUE) 
 
 window_num <- 5
 readme_count_df <- readme_count_df |>
-  filter(week_index >= (- window_num) & week_index <= (window_num)) |>
+  filter(relative_week >= (- window_num) & relative_week <= (window_num)) |>
   mutate(scaled_age = scale(age)) |>
   mutate(scaled_age_at_commit = scale(age_at_commit))|>
   mutate(log1p_count = log1p(commit_count))
 
 quantile(readme_count_df$age / 365, probs = c(0.25, 0.5, 0.75))
 quantile(readme_count_df$age_at_commit / 365, probs = c(0.25, 0.5, 0.75))
+sd(readme_count_df$age_at_commit / 365)
 aggregate_rccd <- readme_count_df |>
   group_by(project_id) |>
   summarize(
@@ -44,25 +45,30 @@ quantile(aggregate_rccd$avg_weekly_commits, probs = c(0.25, 0.5, 0.75))
 quantile(aggregate_rccd$authors_before, probs = c(0.25, 0.5, 0.75))
 quantile(aggregate_rccd$authors_after, probs = c(0.25, 0.5, 0.75))
 
-readme_readability <- read.csv("text_analysis/020325_README_readability.csv", header=TRUE)
+readme_readability <- read.csv("text_analysis/020725_README_readability.csv", header=TRUE)
 median(readme_readability$mcalpine_eflaw)
 median(readme_readability$linsear_write_formula)
 median(readme_readability$flesch_reading_ease)
+median(readme_readability$reading_time)
+readme_less_than_10 <- readme_readability |>
+  filter(reading_time < 10)
 
-contributing_readability <- read.csv("text_analysis/020125_CONTRIBUTING_readability.csv", header=TRUE)
+contributing_readability <- read.csv("text_analysis/020725_CONTRIBUTING_readability.csv", header=TRUE)
 median(contributing_readability$mcalpine_eflaw)
 median(contributing_readability$linsear_write_formula)
 median(contributing_readability$flesch_reading_ease)
+median(contributing_readability$reading_time)
+contrib_less_than_10 <- contributing_readability |>
+  filter(reading_time < 10)
 
-
+contributing_topic <- read.csv("text_analysis/020725_CONTRIBUTING_file_topic_distributions.csv", header=TRUE)
-contributing_topic <- read.csv("text_analysis/020125_CONTRIBUTING_file_topic_distributions.csv", header=TRUE)
 quantile(contributing_topic$t0, probs = c(0.25, 0.5, 0.75))
 quantile(contributing_topic$t1, probs = c(0.25, 0.5, 0.75))
 quantile(contributing_topic$t2, probs = c(0.25, 0.5, 0.75))
 quantile(contributing_topic$t3, probs = c(0.25, 0.5, 0.75))
 quantile(contributing_topic$t4, probs = c(0.25, 0.5, 0.75))
 
-readme_topic <- read.csv("text_analysis/020325_README_file_topic_distributions.csv", header=TRUE)
+readme_topic <- read.csv("text_analysis/020725_README_file_topic_distributions.csv", header=TRUE)
 quantile(readme_topic$t0, probs = c(0.25, 0.5, 0.75))
 quantile(readme_topic$t1, probs = c(0.25, 0.5, 0.75))
 quantile(readme_topic$t2, probs = c(0.25, 0.5, 0.75))
@@ -72,8 +78,6 @@ quantile(readme_topic$t5, probs = c(0.25, 0.5, 0.75))
 quantile(readme_topic$t6, probs = c(0.25, 0.5, 0.75))
 quantile(readme_topic$t7, probs = c(0.25, 0.5, 0.75))
 quantile(readme_topic$t8, probs = c(0.25, 0.5, 0.75))
-quantile(readme_topic$t9, probs = c(0.25, 0.5, 0.75))
-quantile(readme_topic$t10, probs = c(0.25, 0.5, 0.75))
 
 
 

text_analysis/checking_overlap.R

@@ -1,4 +1,5 @@
-contributing_manifest <- read.csv("text_analysis/0203_contributing_merged_manifest.csv", header=TRUE)
+contributing_manifest <- read.csv("text_analysis/0207_contributing_merged_manifest.csv", header=TRUE)
-readme_manifest <- read.csv("text_analysis/0203_readme_merged_manifest.csv", header=TRUE)
+readme_manifest <- read.csv("text_analysis/0207_readme_merged_manifest.csv", header=TRUE)
 
 overlap = inner_join(contributing_manifest, readme_manifest, by="repo_id")
+length(overlap)

topic-outcome-models/topic_model_presentation.R

@@ -1,11 +1,11 @@
 library(texreg)
 
-readme_rdd <- readRDS("topic-outcome-models/020325_README_commit_topic_model.rda")
+readme_rdd <- readRDS("topic-outcome-models/020725_README_commit_topic_model.rda")
-contrib_rdd <- readRDS("topic-outcome-models/020325_CONTRIBUTING_commit_topic_model.rda")
+contrib_rdd <- readRDS("topic-outcome-models/020725_CONTRIBUTING_commit_topic_model.rda")
 
 texreg(readme_rdd, stars=NULL, digits=3, use.packages=FALSE, 
        custom.model.names=c( 'README'),
-       custom.coef.names=c('Topic 1', 'Topic 2', 'Topic 3', 'Topic 4', 'Topic 5', 'Topic 6', 'Topic 7', 'Topic 8', 'Topic 9', 'Topic 10', 'Topic 11'), 
+       custom.coef.names=c('Topic 1', 'Topic 2', 'Topic 3', 'Topic 4', 'Topic 5', 'Topic 6', 'Topic 7', 'Topic 8', 'Topic 9'), 
        table=FALSE, ci.force = TRUE)
 
 texreg(contrib_rdd, stars=NULL, digits=3, use.packages=FALSE,