diff --git a/R/.Rhistory b/R/.Rhistory
index 889f0a7..f715765 100644
--- a/R/.Rhistory
+++ b/R/.Rhistory
@@ -1,88 +1,3 @@
-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$upstream_vcs_link == link]
-ages <- c(ages, age)
-if (length(project) != 1){
-project
-break
-} else {
-projects <- c(projects, project)
-}
-}
-readme_df <- read_csv("../final_data/deb_readme_did.csv")
-contributing_df <- read_csv("../final_data/deb_contrib_did.csv")
-full_df <- read_csv("../final_data/deb_full_data.csv")
-#preprocessing for readme_df
-colnames(readme_df) <- c("upstream_vcs_link", "event_date", "event_hash", "before_all_ct", "before_mrg_ct", "after_all_ct", "after_mrg_ct", "before_auth_new", "after_commit_new", "after_auth_new", "before_commit_new")
-col_order <- c("upstream_vcs_link", "event_date", "event_hash", "before_all_ct", "after_all_ct", "before_mrg_ct", "after_mrg_ct", "before_auth_new", "after_auth_new", "before_commit_new",  "after_commit_new")
-ages <- c()
-projects <- c()
 for (i in 1:nrow(readme_df)){
 link <- readme_df[i,]$upstream_vcs_link
 age <- full_df$age_of_project[full_df$upstream_vcs_link == link]
@@ -510,3 +425,88 @@ poisson_all_model <- glmer(count ~ D + I(week - 26) + D:I(week - 26) + age_of_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$after_mrg_ct), ", ")
+drop <- c("before_all_ct", "before_mrg_ct", "after_all_ct", "after_mrg_ct")
+readme_df = readme_df[,!(names(readme_df) %in% drop)]
+# 2 some expansion needs to happens for each project
+expand_timeseries <- function(project_row) {
+longer <- project_row |>
+pivot_longer(cols = starts_with("ct"),
+names_to = "window",
+values_to = "count") |>
+unnest(count)
+longer$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
+expanded_data <- expanded_data |>
+filter(week >= (26 - window_num) & week <= (26 + window_num)) |>
+mutate(D = ifelse(week > 26, 1, 0))
+#separate out the cleaning d
+all_actions_data <- expanded_data[which(expanded_data$observation_type == "all"),]
+mrg_actions_data <- expanded_data[which(expanded_data$observation_type == "mrg"),]
+#find some EDA to identify which types of models might be the best for this
+mean(all_actions_data$count)
+median(all_actions_data$count)
+#if I'm reading the residuals right, the poisson is better?
+poisson_all_model <- glmer(count ~ D + I(week - 26) + D:I(week - 26) + age_of_project + (1 + D |upstream_vcs_link), data=all_actions_data, family = poisson(link = "log"), nAGQ = 100)
+summary(poisson_all_model)
+# 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) + age_of_project + (1 + D |upstream_vcs_link), data=all_actions_data, family = poisson(link = "log"), nAGQ = 100)
+#if I'm reading the residuals right, the poisson is better?
+poisson_all_model <- glmer(count ~ D + I(week - 26) + D:I(week - 26) + age_of_project + (1 + D |upstream_vcs_link), data=all_actions_data, family = poisson(link = "log"), nAGQ = 100)
+#if I'm reading the residuals right, the poisson is better?
+poisson_all_model <- glmer(count ~ D + I(week - 26) + D:I(week - 26) + age_of_project + (1 + 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)
+#if I'm reading the residuals right, the poisson is better?
+poisson_all_model <- glmer(count ~ D + I(week - 26) + D:I(week - 26) + scale(age_of_project) + (1 + 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)
+qqnorm(poisson_residuals)
+qqnorm(poisson_residuals)
+#scale the age numbers
+expanded_data$scaled_project_age <- scale(expanded_data$age_of_project)
+#separate out the cleaning d
+all_actions_data <- expanded_data[which(expanded_data$observation_type == "all"),]
+mrg_actions_data <- expanded_data[which(expanded_data$observation_type == "mrg"),]
+#find some EDA to identify which types of models might be the best for this
+mean(all_actions_data$count)
+median(all_actions_data$count)
+table(all_actions_data$count)
+var(all_actions_data$count)
+qqnorm(all_actions_data$count)
+y <- qunif(ppoints(length(all_actions_data$count)))
+qqplot(all_actions_data$count, y)
+#if I'm reading the residuals right, the poisson is better?
+poisson_all_model <- glmer(count ~ D + I(week - 26) + D:I(week - 26) + scaled_project_age + (1 + D |upstream_vcs_link), data=all_actions_data, family = poisson(link = "log"))
+summary(poisson_all_model)
+poisson_residuals <- residuals(poisson_all_model)
+qqnorm(poisson_residuals)
diff --git a/R/didAnalysis.R b/R/didAnalysis.R
index 4837d59..eef22e2 100644
--- a/R/didAnalysis.R
+++ b/R/didAnalysis.R
@@ -33,14 +33,14 @@ for (i in 2:nrow(readme_df)){
 }
 #filter out the windows of time that we're looking at
 window_num <- 8
-expanded_data <- expanded_data |>
+windowed_data <- expanded_data |>
   filter(week >= (26 - window_num) & week <= (26 + window_num)) |>
   mutate(D = ifelse(week > 26, 1, 0))
 #scale the age numbers 
-expanded_data$scaled_project_age <- scale(expanded_data$age_of_project)
+windowed_data$scaled_project_age <- scale(windowed_data$age_of_project)
 #separate out the cleaning d
-all_actions_data <- expanded_data[which(expanded_data$observation_type == "all"),]
-mrg_actions_data <- expanded_data[which(expanded_data$observation_type == "mrg"),]
+all_actions_data <- windowed_data[which(windowed_data$observation_type == "all"),]
+mrg_actions_data <- windowed_data[which(windowed_data$observation_type == "mrg"),]
 #find some EDA to identify which types of models might be the best for this
 mean(all_actions_data$count)
 median(all_actions_data$count)
@@ -53,14 +53,13 @@ qqplot(all_actions_data$count, y)
 # rdd: https://rpubs.com/phle/r_tutorial_regression_discontinuity_design
 # lmer: https://www.youtube.com/watch?v=LzAwEKrn2Mc
 library(lme4)
-flat_all_model <- lm(count ~ D + I(week - 26) + D:I(week - 26) + scaled_project_age, REML=FALSE, data=all_actions_data)
-summary(flat_all_model)
-lmer_all_model <- lmer(count ~ D + I(week - 26) + D:I(week - 26) + scaled_project_age + (1 + D |upstream_vcs_link), REML=FALSE, data=all_actions_data)
+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 + (1 + D |upstream_vcs_link), data=all_actions_data, family = poisson(link = "log"))
+# there's a conversation to be had between whether (D |upstream_vcs_link) or (D || upstream_vcs_link)
+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)