moving to kibo

110124_supp_analysis/contrib_supp_analysis.r

@@ -0,0 +1,51 @@
+#library(tidyverse)
+#library(plyr)
+
+contrib_df <- read_csv("110124_contrib_strict_subset.csv")
+#some preprocessing and expansion 
+col_order <- c("upstream_vcs_link", "age_in_days", "first_commit", "first_commit_dt", "event_gap", "event_date",  "event_hash", "before_all_ct", "after_all_ct", "before_mrg_ct", "after_mrg_ct", "before_auth_new", "after_auth_new", "before_commit_new",  "after_commit_new")
+contrib_df <- contrib_df[,col_order]
+contrib_df$ct_before_all <- str_split(gsub("[][]","", contrib_df$before_all_ct), ", ")
+contrib_df$ct_after_all <- str_split(gsub("[][]","", contrib_df$after_all_ct), ", ")
+contrib_df$ct_before_mrg <- str_split(gsub("[][]","", contrib_df$before_mrg_ct), ", ")
+contrib_df$ct_after_mrg <- str_split(gsub("[][]","", contrib_df$after_mrg_ct), ", ")
+drop <- c("before_all_ct", "before_mrg_ct", "after_all_ct", "after_mrg_ct")
+# 2 some expansion needs to happens for each project 
+expand_timeseries <- function(project_row) {
+  longer <- project_row |> 
+    pivot_longer(cols = starts_with("ct"),
+                 names_to = "window",
+                 values_to = "count") |>
+    unnest(count) 
+  longer$observation_type <- gsub("^.*_", "", longer$window)
+  longer <- ddply(longer, "observation_type", transform, week=seq(from=0, by=1, length.out=length(observation_type)))
+  longer$count <- as.numeric(longer$count)
+  #longer <- longer[which(longer$observation_type == "all"),]
+  return(longer)
+}
+expanded_data <- expand_timeseries(contrib_df[1,])
+for (i in 2:nrow(contrib_df)){
+  expanded_data <- rbind(expanded_data, expand_timeseries(contrib_df[i,]))
+}
+#filter out the windows of time that we're looking at
+window_num <- 8
+windowed_data <- expanded_data |>
+  filter(week >= (27 - window_num) & week <= (27 + window_num)) |>
+  mutate(D = ifelse(week > 27, 1, 0))
+#scale the age numbers and calculate the week offset here
+windowed_data$scaled_project_age <- scale(windowed_data$age_in_days)
+windowed_data$scaled_event_gap <- scale(windowed_data$event_gap)
+windowed_data$week_offset <- windowed_data$week - 27
+#break out the different type of commit actions
+all_actions_data <- windowed_data[which(windowed_data$observation_type == "all"),]
+mrg_actions_data <- windowed_data[which(windowed_data$observation_type == "mrg"),]
+# data is expanded, can look at things now
+all_actions_data$logged_count <- log(all_actions_data$count)
+all_actions_data$log1p_count <- log1p(all_actions_data$count)
+
+#all_gmodel <- glmer.nb(log1p_count ~ D * week_offset + scaled_project_age + scaled_event_gap + (D * week_offset | upstream_vcs_link),
+#              control=glmerControl(optimizer="bobyqa",
+#                                           optCtrl=list(maxfun=2e5)), nAGQ=0, data=all_actions_data)
+#saveRDS(all_gmodel, "0711_contrib_all_01.rda")
+
+#all_residuals <- residuals(all_gmodel)

110124_supp_analysis/readme_supp_analysis.R

@@ -0,0 +1,60 @@
+library(readr)
+library(plyr)
+library(lme4)
+library(stringr)
+library(tidyr)
+
+readme_df <- read_csv("110124_supp_analysis/110124_readme_strict_subset.csv")
+
+col_order <- c("upstream_vcs_link", "age_in_days", "first_commit", "first_commit_dt", "event_gap", "event_date", "event_hash", "before_all_ct", "after_all_ct", "before_mrg_ct", "after_mrg_ct", "before_auth_new", "after_auth_new", "before_commit_new",  "after_commit_new")
+readme_df <- readme_df[,col_order]
+readme_df$ct_before_all <- str_split(gsub("[][]","", readme_df$before_all_ct), ", ")
+readme_df$ct_after_all <- str_split(gsub("[][]","", readme_df$after_all_ct), ", ")
+readme_df$ct_before_mrg <- str_split(gsub("[][]","", readme_df$before_mrg_ct), ", ")
+readme_df$ct_after_mrg <- str_split(gsub("[][]","", readme_df$after_mrg_ct), ", ")
+drop <- c("before_all_ct", "before_mrg_ct", "after_all_ct", "after_mrg_ct")
+
+# 2 some expansion needs to happens for each project 
+expand_timeseries <- function(project_row) {
+  longer <- project_row |> 
+    pivot_longer(cols = starts_with("ct"),
+                 names_to = "window",
+                 values_to = "count") |>
+    unnest(count) 
+  longer$observation_type <- gsub("^.*_", "", longer$window)
+  longer <- ddply(longer, "observation_type", transform, week=seq(from=0, by=1, length.out=length(observation_type)))
+  longer$count <- as.numeric(longer$count)
+  #longer <- longer[which(longer$observation_type == "all"),]
+  return(longer)
+}
+expanded_data <- expand_timeseries(readme_df[1,])
+for (i in 2:nrow(readme_df)){
+  expanded_data <- rbind(expanded_data, expand_timeseries(readme_df[i,]))
+}
+head(expanded_data)
+#filter out the windows of time that we're looking at
+window_num <- 8
+windowed_data <- expanded_data |>
+  filter(week >= (27 - window_num) & week <= (27 + window_num)) |>
+  mutate(D = ifelse(week > 27, 1, 0))
+#scale the age numbers 
+windowed_data$scaled_project_age <- scale(windowed_data$age_in_days)
+windowed_data$scaled_event_gap <- scale(windowed_data$event_gap)
+windowed_data$week_offset <- windowed_data$week - 27
+#break out the different types of commit actions that are studied
+all_actions_data <- windowed_data[which(windowed_data$observation_type == "all"),]
+mrg_actions_data <- windowed_data[which(windowed_data$observation_type == "mrg"),]
+#log the dependent 
+all_actions_data$logged_count <- log(all_actions_data$count)
+all_actions_data$log1p_count <- log1p(all_actions_data$count)
+range(all_actions_data$log1p_count)
+
+
+var(all_actions_data$log1p_count) 
+mean (all_actions_data$log1p_count) 
+sd(all_actions_data$log1p_count)
+median(all_actions_data$log1p_count)
+var(all_actions_data$count) 
+mean (all_actions_data$count) 
+sd (all_actions_data$count)
+median(all_actions_data$count)