diff --git a/R/.Rhistory b/R/.Rhistory
index f715765..08586d1 100644
--- a/R/.Rhistory
+++ b/R/.Rhistory
@@ -1,286 +1,3 @@
-for (i in 1:nrow(readme_df)){
-link <- readme_df[i,]$upstream_vcs_link
-age <- full_df$age_of_project[full_df$upstream_vcs_link == link]
-project <- full_df$project_name[full_df$upstream_vcs_link == link]
-ages <- c(ages, age)
-if (length(project) != 1){
-project
-break
-} else {
-projects <- c(projects, project)
-}
-}
-#set wd, read in data
-try(setwd(dirname(rstudioapi::getActiveDocumentContext()$path)))
-readme_df <- read_csv("../final_data/deb_readme_did.csv")
-contributing_df <- read_csv("../final_data/deb_contrib_did.csv")
-full_df <- read_csv("../final_data/deb_full_data.csv")
-#preprocessing for readme_df
-colnames(readme_df) <- c("upstream_vcs_link", "event_date", "event_hash", "before_all_ct", "before_mrg_ct", "after_all_ct", "after_mrg_ct", "before_auth_new", "after_commit_new", "after_auth_new", "before_commit_new")
-col_order <- c("upstream_vcs_link", "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")
-ages <- c()
-projects <- c()
-for (i in 1:nrow(readme_df)){
-link <- readme_df[i,]$upstream_vcs_link
-age <- full_df$age_of_project[full_df$upstream_vcs_link == link]
-project <- full_df$project_name[full_df$upstream_vcs_link == link]
-ages <- c(ages, age)
-if (length(project) != 1){
-project
-break
-} else {
-projects <- c(projects, project)
-}
-}
-#set wd, read in data
-try(setwd(dirname(rstudioapi::getActiveDocumentContext()$path)))
-readme_df <- read_csv("../final_data/deb_readme_did.csv")
-contributing_df <- read_csv("../final_data/deb_contrib_did.csv")
-full_df <- read_csv("../final_data/deb_full_data.csv")
-#preprocessing for readme_df
-colnames(readme_df) <- c("upstream_vcs_link", "event_date", "event_hash", "before_all_ct", "before_mrg_ct", "after_all_ct", "after_mrg_ct", "before_auth_new", "after_commit_new", "after_auth_new", "before_commit_new")
-col_order <- c("upstream_vcs_link", "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")
-ages <- c()
-projects <- c()
-for (i in 1:nrow(readme_df)){
-link <- readme_df[i,]$upstream_vcs_link
-age <- full_df$age_of_project[full_df$upstream_vcs_link == link]
-project <- full_df$project_name[full_df$upstream_vcs_link == link]
-ages <- c(ages, age)
-if (length(project) != 1){
-project
-break
-} else {
-projects <- c(projects, project)
-}
-}
-#set wd, read in data
-try(setwd(dirname(rstudioapi::getActiveDocumentContext()$path)))
-readme_df <- read_csv("../final_data/deb_readme_did.csv")
-contributing_df <- read_csv("../final_data/deb_contrib_did.csv")
-full_df <- read_csv("../final_data/deb_full_data.csv")
-#preprocessing for readme_df
-colnames(readme_df) <- c("upstream_vcs_link", "event_date", "event_hash", "before_all_ct", "before_mrg_ct", "after_all_ct", "after_mrg_ct", "before_auth_new", "after_commit_new", "after_auth_new", "before_commit_new")
-col_order <- c("upstream_vcs_link", "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")
-ages <- c()
-projects <- c()
-for (i in 1:nrow(readme_df)){
-link <- readme_df[i,]$upstream_vcs_link
-age <- full_df$age_of_project[full_df$upstream_vcs_link == link]
-project <- full_df$project_name[full_df$upstream_vcs_link == link]
-ages <- c(ages, age)
-if (length(project) != 1){
-project
-break
-} else {
-projects <- c(projects, project)
-}
-}
-#set wd, read in data
-try(setwd(dirname(rstudioapi::getActiveDocumentContext()$path)))
-readme_df <- read_csv("../final_data/deb_readme_did.csv")
-contributing_df <- read_csv("../final_data/deb_contrib_did.csv")
-full_df <- read_csv("../final_data/deb_full_data.csv")
-#preprocessing for readme_df
-colnames(readme_df) <- c("upstream_vcs_link", "event_date", "event_hash", "before_all_ct", "before_mrg_ct", "after_all_ct", "after_mrg_ct", "before_auth_new", "after_commit_new", "after_auth_new", "before_commit_new")
-col_order <- c("upstream_vcs_link", "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")
-ages <- c()
-projects <- c()
-for (i in 1:nrow(readme_df)){
-link <- readme_df[i,]$upstream_vcs_link
-age <- full_df$age_of_project[full_df$upstream_vcs_link == link]
-project <- full_df$project_name[full_df$upstream_vcs_link == link]
-ages <- c(ages, age)
-if (length(project) != 1){
-project
-break
-} else {
-projects <- c(projects, project)
-}
-}
-#set wd, read in data
-try(setwd(dirname(rstudioapi::getActiveDocumentContext()$path)))
-readme_df <- read_csv("../final_data/deb_readme_did.csv")
-contributing_df <- read_csv("../final_data/deb_contrib_did.csv")
-full_df <- read_csv("../final_data/deb_full_data.csv")
-#preprocessing for readme_df
-colnames(readme_df) <- c("upstream_vcs_link", "event_date", "event_hash", "before_all_ct", "before_mrg_ct", "after_all_ct", "after_mrg_ct", "before_auth_new", "after_commit_new", "after_auth_new", "before_commit_new")
-col_order <- c("upstream_vcs_link", "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")
-ages <- c()
-projects <- c()
-for (i in 1:nrow(readme_df)){
-link <- readme_df[i,]$upstream_vcs_link
-age <- full_df$age_of_project[full_df$upstream_vcs_link == link]
-project <- full_df$project_name[full_df$upstream_vcs_link == link]
-ages <- c(ages, age)
-if (length(project) != 1){
-project
-break
-} else {
-projects <- c(projects, project)
-}
-}
-length(ages)
-#set wd, read in data
-try(setwd(dirname(rstudioapi::getActiveDocumentContext()$path)))
-readme_df <- read_csv("../final_data/deb_readme_did.csv")
-contributing_df <- read_csv("../final_data/deb_contrib_did.csv")
-full_df <- read_csv("../final_data/deb_full_data.csv")
-#preprocessing for readme_df
-colnames(readme_df) <- c("upstream_vcs_link", "event_date", "event_hash", "before_all_ct", "before_mrg_ct", "after_all_ct", "after_mrg_ct", "before_auth_new", "after_commit_new", "after_auth_new", "before_commit_new")
-col_order <- c("upstream_vcs_link", "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")
-ages <- c()
-projects <- c()
-for (i in 1:nrow(readme_df)){
-link <- readme_df[i,]$upstream_vcs_link
-age <- full_df$age_of_project[full_df$upstream_vcs_link == link]
-project <- full_df$project_name[full_df$upstream_vcs_link == link]
-ages <- c(ages, age)
-if (length(project) != 1){
-project
-break
-} else {
-projects <- c(projects, project)
-}
-}
-#set wd, read in data
-try(setwd(dirname(rstudioapi::getActiveDocumentContext()$path)))
-readme_df <- read_csv("../final_data/deb_readme_did.csv")
-contributing_df <- read_csv("../final_data/deb_contrib_did.csv")
-full_df <- read_csv("../final_data/deb_full_data.csv")
-#preprocessing for readme_df
-colnames(readme_df) <- c("upstream_vcs_link", "event_date", "event_hash", "before_all_ct", "before_mrg_ct", "after_all_ct", "after_mrg_ct", "before_auth_new", "after_commit_new", "after_auth_new", "before_commit_new")
-col_order <- c("upstream_vcs_link", "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")
-ages <- c()
-projects <- c()
-for (i in 1:nrow(readme_df)){
-link <- readme_df[i,]$upstream_vcs_link
-age <- full_df$age_of_project[full_df$upstream_vcs_link == link]
-project <- full_df$project_name[full_df$upstream_vcs_link == link]
-ages <- c(ages, age)
-if (length(project) != 1){
-project
-break
-} else {
-projects <- c(projects, project)
-}
-}
-length(ages)
-readme_df$age_of_project = full_df$age_of_project[full_df$upstream_vcs_link == readme_df$upstream_vcs_link]
-View(readme_df)
-readme_df$age_of_project = ages
-View(readme_df)
-write.csv(readme_df, "deb_readme_data_4_19.csv", row.names=FALSE)
-#preprocessing for readme_df
-colnames(contributing_df) <- c("upstream_vcs_link", "event_date", "event_hash", "before_all_ct", "before_mrg_ct", "after_all_ct", "after_mrg_ct", "before_auth_new", "after_commit_new", "after_auth_new", "before_commit_new")
-col_order <- c("upstream_vcs_link", "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")
-ages <- c()
-projects <- c()
-for (i in 1:nrow(contributing_df)){
-link <- readme_df[i,]$upstream_vcs_link
-age <- full_df$age_of_project[full_df$upstream_vcs_link == link]
-project <- full_df$project_name[full_df$upstream_vcs_link == link]
-ages <- c(ages, age)
-if (length(project) != 1){
-project
-break
-} else {
-projects <- c(projects, project)
-}
-}
-contributing_df$age_of_project = ages
-write.csv(contributing_df, "deb_contributing_data_4_19.csv", row.names=FALSE)
-View(contributing_df)
-View(contributing_df)
-View(readme_df)
-View(contributing_df)
-View(contributing_df)
-contributing_df <- read_csv("../final_data/deb_contrib_did.csv")
-View(contributing_df)
-#preprocessing for readme_df
-colnames(contributing_df) <- c("upstream_vcs_link", "event_date", "event_hash", "before_all_ct", "before_mrg_ct", "after_all_ct", "after_mrg_ct", "before_auth_new", "after_commit_new", "after_auth_new", "before_commit_new")
-ages <- c()
-projects <- c()
-for (i in 1:nrow(contributing_df)){
-link <- contributing_df[i,]$upstream_vcs_link
-age <- full_df$age_of_project[full_df$upstream_vcs_link == link]
-project <- full_df$project_name[full_df$upstream_vcs_link == link]
-ages <- c(ages, age)
-if (length(project) != 1){
-project
-break
-} else {
-projects <- c(projects, project)
-}
-}
-#set wd, read in data
-try(setwd(dirname(rstudioapi::getActiveDocumentContext()$path)))
-readme_df <- read_csv("../final_data/deb_readme_did.csv")
-contributing_df <- read_csv("../final_data/deb_contrib_did.csv")
-full_df <- read_csv("../final_data/deb_full_data.csv")
-#preprocessing for readme_df
-colnames(contributing_df) <- c("upstream_vcs_link", "event_date", "event_hash", "before_all_ct", "before_mrg_ct", "after_all_ct", "after_mrg_ct", "before_auth_new", "after_commit_new", "after_auth_new", "before_commit_new")
-col_order <- c("upstream_vcs_link", "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")
-ages <- c()
-projects <- c()
-for (i in 1:nrow(contributing_df)){
-link <- contributing_df[i,]$upstream_vcs_link
-age <- full_df$age_of_project[full_df$upstream_vcs_link == link]
-project <- full_df$project_name[full_df$upstream_vcs_link == link]
-ages <- c(ages, age)
-if (length(project) != 1){
-project
-break
-} else {
-projects <- c(projects, project)
-}
-}
-#set wd, read in data
-try(setwd(dirname(rstudioapi::getActiveDocumentContext()$path)))
-readme_df <- read_csv("../final_data/deb_readme_did.csv")
-contributing_df <- read_csv("../final_data/deb_contrib_did.csv")
-full_df <- read_csv("../final_data/deb_full_data.csv")
-#preprocessing for readme_df
-colnames(contributing_df) <- c("upstream_vcs_link", "event_date", "event_hash", "before_all_ct", "before_mrg_ct", "after_all_ct", "after_mrg_ct", "before_auth_new", "after_commit_new", "after_auth_new", "before_commit_new")
-ages <- c()
-projects <- c()
-for (i in 1:nrow(contributing_df)){
-link <- contributing_df[i,]$upstream_vcs_link
-age <- full_df$age_of_project[full_df$upstream_vcs_link == link]
-project <- full_df$project_name[full_df$upstream_vcs_link == link]
-ages <- c(ages, age)
-if (length(project) != 1){
-project
-break
-} else {
-projects <- c(projects, project)
-}
-}
-contributing_df$age_of_project = ages
-write.csv(contributing_df, "deb_contributing_data_4_19.csv", row.names=FALSE)
-# 0 loading the readme data in
-try(setwd(dirname(rstudioapi::getActiveDocumentContext()$path)))
-# 0 loading the readme data in
-try(setwd(dirname(rstudioapi::getActiveDocumentContext()$path)))
-readme_df <- read_csv("../final_data/deb_readme_did.csv")
-View(readme_df)
-# 1 preprocessing
-#colnames(readme_df) <- c("upstream_vcs_link", "event_date", "event_hash", "before_all_ct", "before_mrg_ct", "after_all_ct", "after_mrg_ct", "before_auth_new", "after_commit_new", "after_auth_new", "before_commit_new")
-col_order <- c("upstream_vcs_link", "age_of_project", "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), ", ")
-View(readme_df)
-View(readme_df)
-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")
-readme_df = readme_df[,!(names(readme_df) %in% drop)]
-View(readme_df)
-# 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)
@@ -510,3 +227,286 @@ poisson_all_model <- glmer(count ~ D + I(week - 26) + D:I(week - 26) + scaled_pr
 summary(poisson_all_model)
 poisson_residuals <- residuals(poisson_all_model)
 qqnorm(poisson_residuals)
+# this is the file with the lmer multi-level rddAnalysis
+library(tidyverse)
+library(plyr)
+# 0 loading the readme data in
+try(setwd(dirname(rstudioapi::getActiveDocumentContext()$path)))
+readme_df <- read_csv("../final_data/deb_readme_did.csv")
+# 1 preprocessing
+#colnames(readme_df) <- c("upstream_vcs_link", "event_date", "event_hash", "before_all_ct", "before_mrg_ct", "after_all_ct", "after_mrg_ct", "before_auth_new", "after_commit_new", "after_auth_new", "before_commit_new")
+col_order <- c("upstream_vcs_link", "age_of_project", "event_date", "event_hash", "before_all_ct", "after_all_ct", "before_mrg_ct", "after_mrg_ct", "before_auth_new", "after_auth_new", "before_commit_new",  "after_commit_new")
+readme_df <- readme_df[,col_order]
+readme_df$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,]))
+}
+#filter out the windows of time that we're looking at
+window_num <- 8
+expanded_data <- expanded_data |>
+filter(week >= (26 - window_num) & week <= (26 + window_num)) |>
+mutate(D = ifelse(week > 26, 1, 0))
+# this is the file with the lmer multi-level rddAnalysis
+library(tidyverse)
+library(plyr)
+# 0 loading the readme data in
+try(setwd(dirname(rstudioapi::getActiveDocumentContext()$path)))
+readme_df <- read_csv("../final_data/deb_readme_did.csv")
+# 1 preprocessing
+#colnames(readme_df) <- c("upstream_vcs_link", "event_date", "event_hash", "before_all_ct", "before_mrg_ct", "after_all_ct", "after_mrg_ct", "before_auth_new", "after_commit_new", "after_auth_new", "before_commit_new")
+col_order <- c("upstream_vcs_link", "age_of_project", "event_date", "event_hash", "before_all_ct", "after_all_ct", "before_mrg_ct", "after_mrg_ct", "before_auth_new", "after_auth_new", "before_commit_new",  "after_commit_new")
+readme_df <- readme_df[,col_order]
+readme_df$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$after_mrg_ct), ", ")
+drop <- c("before_all_ct", "before_mrg_ct", "after_all_ct", "after_mrg_ct")
+readme_df = readme_df[,!(names(readme_df) %in% drop)]
+# 2 some expansion needs to happens for each project
+expand_timeseries <- function(project_row) {
+longer <- project_row |>
+pivot_longer(cols = starts_with("ct"),
+names_to = "window",
+values_to = "count") |>
+unnest(count)
+longer$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,]))
+}
+#filter out the windows of time that we're looking at
+window_num <- 8
+windowed_data <- expanded_data |>
+filter(week >= (26 - window_num) & week <= (26 + window_num)) |>
+mutate(D = ifelse(week > 26, 1, 0))
+#scale the age numbers
+windowed_data$scaled_project_age <- scale(windowed_data$age_of_project)
+#separate out the cleaning d
+all_actions_data <- windowed_data[which(windowed_data$observation_type == "all"),]
+mrg_actions_data <- windowed_data[which(windowed_data$observation_type == "mrg"),]
+#if I'm reading the residuals right, the poisson is better?
+poisson_all_model <- glmer(count ~ D + I(week - 26) + D:I(week - 26) + scaled_project_age + (1 + D |upstream_vcs_link), data=all_actions_data, family = poisson(link = "log"))
+# 3 rdd in lmer analysis
+# rdd: https://rpubs.com/phle/r_tutorial_regression_discontinuity_design
+# lmer: https://www.youtube.com/watch?v=LzAwEKrn2Mc
+library(lme4)
+#if I'm reading the residuals right, the poisson is better?
+poisson_all_model <- glmer(count ~ D + I(week - 26) + D:I(week - 26) + scaled_project_age + (1 + D |upstream_vcs_link), data=all_actions_data, family = poisson(link = "log"))
+summary(poisson_all_model)
+#logistic regression mixed effects
+log_all_model <- glmer(count ~ D + I(week - 26) + D:I(week - 26) + scaled_project_age + (1 + D |upstream_vcs_link),data=all_actions_data, family = binomial)
+#if I'm reading the residuals right, the poisson is better?
+poisson_all_model <- glmer(scale(count) ~ D + I(week - 26) + D:I(week - 26) + scaled_project_age + (1 + D |upstream_vcs_link), data=all_actions_data, family = poisson(link = "log"))
+#if I'm reading the residuals right, the poisson is better?
+poisson_all_model <- glmer(count ~ D + I(week - 26) + D:I(week - 26) + scaled_project_age + (1 + D |upstream_vcs_link), data=all_actions_data, family = poisson(link = "log"))
+#if I'm reading the residuals right, the poisson is better?
+poisson_all_model <- glmer(count ~ D + I(week - 26) + D:I(week - 26) + scaled_project_age + (1 + D |upstream_vcs_link), data=all_actions_data, family = poisson(link = "log"), control=glmerControl(optimizer="bobyqa"))
+#logistic regression mixed effects
+log_all_model <- glmer(count ~ D + I(week - 26) + D:I(week - 26) + scaled_project_age + (1 + D |upstream_vcs_link),data=all_actions_data, family = binomial)
+qqnorm(poisson_residuals)
+summary(poisson_all_model)
+poisson_residuals <- residuals(poisson_all_model)
+qqnorm(poisson_residuals)
+#logistic regression mixed effects (doesn't work)
+log_all_model <- glmer(count ~ D + I(week - 26) + D:I(week - 26) + scaled_project_age + (1 + upstream_vcs_link |upstream_vcs_link),data=all_actions_data, family = binomial)
+#logistic regression mixed effects (doesn't work)
+log_all_model <- glmer(count ~ D + I(week - 26) + D:I(week - 26) + scaled_project_age + (1 + D |upstream_vcs_link),data=all_actions_data, family = binomial)
+#if I'm reading the residuals right, the poisson is better?
+poisson_all_model <- glmer(count ~ D + I(week - 26) + D:I(week - 26) + scaled_project_age + (1 + upstream_vcs_link |upstream_vcs_link), data=all_actions_data, family = poisson(link = "log"))
+#if I'm reading the residuals right, the poisson is better?
+poisson_all_model <- glmer(count ~ D + I(week - 26) + D:I(week - 26) + scaled_project_age + (1 + D |upstream_vcs_link), data=all_actions_data, family = poisson(link = "log"))
+#if I'm reading the residuals right, the poisson is better?
+poisson_all_model <- glmer(count ~ D + I(week - 26) + D:I(week - 26) + scaled_project_age + (D ||upstream_vcs_link), data=all_actions_data, family = poisson(link = "log"))
+summary(poisson_all_model)
+poisson_residuals <- residuals(poisson_all_model)
+qqnorm(poisson_residuals)
+lmer_all_model <- lmer(count ~ D + I(week - 26) + D:I(week - 26) + scaled_project_age + (D ||upstream_vcs_link), REML=FALSE, data=all_actions_data)
+summary(lmer_all_model)
+lmer_residuals <- residuals(lmer_all_model)
+qqnorm(lmer_residuals)
+#if I'm reading the residuals right, the poisson is better?
+poisson_all_model <- glmer(count ~ D + I(week - 26) + D:I(week - 26) + scaled_project_age + (D ||upstream_vcs_link), data=all_actions_data, family = poisson(link = "log"))
+summary(poisson_all_model)
+poisson_residuals <- residuals(poisson_all_model)
+poisson_residuals <- residuals(poisson_all_model)
+qqnorm(poisson_residuals)
+#if I'm reading the residuals right, the poisson is better?
+poisson_all_model <- glmer(count ~ D + I(week - 26) + D:I(week - 26) + scaled_project_age + (D |upstream_vcs_link), data=all_actions_data, family = poisson(link = "log"))
+summary(poisson_all_model)
+# this is the file with the lmer multi-level rddAnalysis
+library(tidyverse)
+library(plyr)
+# 0 loading the readme data in
+try(setwd(dirname(rstudioapi::getActiveDocumentContext()$path)))
+readme_df <- read_csv("../final_data/deb_readme_did.csv")
+# 1 preprocessing
+#colnames(readme_df) <- c("upstream_vcs_link", "event_date", "event_hash", "before_all_ct", "before_mrg_ct", "after_all_ct", "after_mrg_ct", "before_auth_new", "after_commit_new", "after_auth_new", "before_commit_new")
+col_order <- c("upstream_vcs_link", "age_of_project", "event_date", "event_hash", "before_all_ct", "after_all_ct", "before_mrg_ct", "after_mrg_ct", "before_auth_new", "after_auth_new", "before_commit_new",  "after_commit_new")
+readme_df <- readme_df[,col_order]
+readme_df$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$after_mrg_ct), ", ")
+drop <- c("before_all_ct", "before_mrg_ct", "after_all_ct", "after_mrg_ct")
+readme_df = readme_df[,!(names(readme_df) %in% drop)]
+# 2 some expansion needs to happens for each project
+expand_timeseries <- function(project_row) {
+longer <- project_row |>
+pivot_longer(cols = starts_with("ct"),
+names_to = "window",
+values_to = "count") |>
+unnest(count)
+longer$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,]))
+}
+#filter out the windows of time that we're looking at
+window_num <- 8
+windowed_data <- expanded_data |>
+filter(week >= (26 - window_num) & week <= (26 + window_num)) |>
+mutate(D = ifelse(week > 26, 1, 0))
+#scale the age numbers
+windowed_data$scaled_project_age <- scale(windowed_data$age_of_project)
+#separate out the cleaning d
+all_actions_data <- windowed_data[which(windowed_data$observation_type == "all"),]
+mrg_actions_data <- windowed_data[which(windowed_data$observation_type == "mrg"),]
+#if I'm reading the residuals right, the poisson is better?
+# there's a conversation to be had between whether (D |upstream_vcs_link) or (D || upstream_vcs_link)
+# https://www.bristol.ac.uk/cmm/learning/videos/random-intercepts.html#exvar
+poisson_all_model <- glmer(count ~ D + I(week - 26) + D:I(week - 26) + scaled_project_age + (D |upstream_vcs_link), data=all_actions_data, family = poisson(link = "log"))
+summary(poisson_all_model)
+poisson_test_model <- glmer(count ~ D + I(week - 26) + D:I(week - 26) + scaled_project_age + (1 + D |upstream_vcs_link), data=all_actions_data, family = poisson(link = "log"))
+summary(poisson_test_model)
+summary(poisson_all_model)
+summary(poisson_test_model)
+poisson_test_model <- glmer(count ~ D + I(week - 26) + D:I(week - 26) + scaled_project_age + (D ||upstream_vcs_link), data=all_actions_data, family = poisson(link = "log"))
+summary(poisson_test_model)
+summary(poisson_all_model)
+summary(poisson_test_model)
+summary(poisson_all_model)
+summary(poisson_test_model)
+poisson_testing_model <- glmer(count ~ D + I(week - 26) + D:I(week - 26) + scaled_project_age + (1 | upstream_vcs_link) +  (0 + D |upstream_vcs_link), data=all_actions_data, family = poisson(link = "log"))
+summary(poisson_testing_model)
+#| label: packages
+#| echo: true
+library(tidyverse)
+library(tidytext)
+library(textdata)
+library(textstem)
+library(tidymodels)
+#| label: packages
+#| echo: true
+library(tidyverse)
+library(tidytext)
+library(textdata)
+library(textstem)
+library(tidymodels)
+#| label: data 1
+#| echo: true
+#|
+reviews_df <- read_csv("data/rotten_tomatoes_critic_reviews.csv")
+reviews_df |> head(2) |> kableExtra::kable()
+summary(poisson_test_model)
+summary(poisson_all_model)
+summary(poisson_all_model)
+summary(poisson_test_model)
+#if I'm reading the residuals right, the poisson is better?
+# there's a conversation to be had between whether (D |upstream_vcs_link) or (D || upstream_vcs_link)
+# https://www.bristol.ac.uk/cmm/learning/videos/random-intercepts.html#exvar
+poisson_all_model <- glmer(count ~ D + I(week - 26) + D:I(week - 26) + scaled_project_age + (week |upstream_vcs_link), data=all_actions_data, family = poisson(link = "log"))
+summary(poisson_all_model)
+poisson_residuals <- residuals(poisson_all_model)
+qqnorm(poisson_residuals)
+poisson_test_model <- glmer(count ~ D + I(week - 26) + D:I(week - 26) + scaled_project_age + (week ||upstream_vcs_link), data=all_actions_data, family = poisson(link = "log"))
+summary(poisson_test_model)
+summary(poisson_all_model)
+# https://www.bristol.ac.uk/cmm/learning/videos/random-intercepts.html#exvar
+poisson_all_model <- glmer(count ~ D + I(week - 26) + D:I(week - 26) + scaled_project_age + (1 + week |upstream_vcs_link), data=all_actions_data, family = poisson(link = "log"))
+summary(poisson_all_model)
+poisson_residuals <- residuals(poisson_all_model)
+qqnorm(poisson_residuals)
+# https://www.bristol.ac.uk/cmm/learning/videos/random-intercepts.html#exvar
+poisson_all_model <- glmer(count ~ D + I(week - 26) + D:I(week - 26) + scaled_project_age + (week |upstream_vcs_link), data=all_actions_data, family = poisson(link = "log"))
+summary(poisson_all_model)
+ICC(outcome="count", group="week", data=all_actions_data)
+library(merTools)
+ICC(outcome="count", group="week", data=all_actions_data)
+# this is the file with the lmer multi-level rddAnalysis
+library(tidyverse)
+library(plyr)
+# 0 loading the readme data in
+try(setwd(dirname(rstudioapi::getActiveDocumentContext()$path)))
+readme_df <- read_csv("../final_data/deb_readme_did.csv")
+# 1 preprocessing
+#colnames(readme_df) <- c("upstream_vcs_link", "event_date", "event_hash", "before_all_ct", "before_mrg_ct", "after_all_ct", "after_mrg_ct", "before_auth_new", "after_commit_new", "after_auth_new", "before_commit_new")
+col_order <- c("upstream_vcs_link", "age_of_project", "event_date", "event_hash", "before_all_ct", "after_all_ct", "before_mrg_ct", "after_mrg_ct", "before_auth_new", "after_auth_new", "before_commit_new",  "after_commit_new")
+readme_df <- readme_df[,col_order]
+readme_df$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$after_mrg_ct), ", ")
+drop <- c("before_all_ct", "before_mrg_ct", "after_all_ct", "after_mrg_ct")
+readme_df = readme_df[,!(names(readme_df) %in% drop)]
+# 2 some expansion needs to happens for each project
+expand_timeseries <- function(project_row) {
+longer <- project_row |>
+pivot_longer(cols = starts_with("ct"),
+names_to = "window",
+values_to = "count") |>
+unnest(count)
+longer$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,]))
+}
+#filter out the windows of time that we're looking at
+window_num <- 8
+windowed_data <- expanded_data |>
+filter(week >= (26 - window_num) & week <= (26 + window_num)) |>
+mutate(D = ifelse(week > 26, 1, 0))
+#scale the age numbers
+windowed_data$scaled_project_age <- scale(windowed_data$age_of_project)
+#separate out the cleaning d
+all_actions_data <- windowed_data[which(windowed_data$observation_type == "all"),]
+mrg_actions_data <- windowed_data[which(windowed_data$observation_type == "mrg"),]
+# for visualization, may have to run model for each project and then identify top 5 projects for RDD graphs
+#
+#
+poisson_mrg_model <- glmer(count ~ D + I(week - 26) + D:I(week - 26) + scaled_project_age + (week |upstream_vcs_link), data=mrg_actions_data, family = poisson(link = "log"))
+summary(poisson_mrg_model)
+poisson_mrg_residuals <- residuals(poisson_mrg_model)
+qqnorm(poisson_mrg_residuals)
diff --git a/R/didAnalysis.R b/R/readmeRDDAnalysis.R
similarity index 89%
rename from R/didAnalysis.R
rename to R/readmeRDDAnalysis.R
index 7fdd41a..33a8ae7 100644
--- a/R/didAnalysis.R
+++ b/R/readmeRDDAnalysis.R
@@ -53,18 +53,18 @@ qqplot(all_actions_data$count, y)
 # rdd: https://rpubs.com/phle/r_tutorial_regression_discontinuity_design
 # lmer: https://www.youtube.com/watch?v=LzAwEKrn2Mc
 library(lme4)
-lmer_all_model <- lmer(count ~ D + I(week - 26) + D:I(week - 26) + scaled_project_age + (D ||upstream_vcs_link), REML=FALSE, data=all_actions_data)
-summary(lmer_all_model)
-lmer_residuals <- residuals(lmer_all_model)
-qqnorm(lmer_residuals)
 # https://www.bristol.ac.uk/cmm/learning/videos/random-intercepts.html#exvar
 poisson_all_model <- glmer(count ~ D + I(week - 26) + D:I(week - 26) + scaled_project_age + (week |upstream_vcs_link), data=all_actions_data, family = poisson(link = "log"))
 summary(poisson_all_model)
 poisson_residuals <- residuals(poisson_all_model)
 qqnorm(poisson_residuals)
-# Performance: 
-draft_mrg_model <- lmer(count ~ D * I(week - 26) + scaled_project_age + (1 + D |upstream_vcs_link), REML=FALSE, data=mrg_actions_data)
-summary(draft_mrg_model)
+# for visualization, may have to run model for each project and then identify top 5 projects for RDD graphs 
+#
+# 
+poisson_mrg_model <- glmer(count ~ D + I(week - 26) + D:I(week - 26) + scaled_project_age + (week |upstream_vcs_link), data=mrg_actions_data, family = poisson(link = "log"))
+summary(poisson_mrg_model)
+poisson_mrg_residuals <- residuals(poisson_mrg_model)
+qqnorm(poisson_mrg_residuals)
 # Performance: 
 
 library(merTools)