clean model and draft duplicate for contrib

R/.Rhistory

@@ -1,90 +1,3 @@
-expanded_sample_data <- expand_timeseries(sampled_data[1,])
-for (i in 2:nrow(sampled_data)){
-expanded_sample_data <- rbind(expanded_sample_data, expand_timeseries(sampled_data[i,]))
-}
-windowed_sample_data <- expanded_sample_data |>
-filter(week >= (26 - window_num) & week <= (26 + window_num)) |>
-mutate(D = ifelse(week > 26, 1, 0))
-windowed_sample_data$scaled_project_age <- scale(windowed_sample_data$age_of_project)
-windowed_sample_data$week_offset <- windowed_sample_data$week - 26
-all_actions_sample_data <- windowed_sample_data[which(windowed_sample_data$observation_type == "all"),]
-#test model
-test_model <- lmer(count ~ D * I(week_offset) + scaled_project_age + (week_offset|upstream_vcs_link),  data=all_actions_sample_data, REML=FALSE)
-summary(test_model)
-#plot results
-p <- ggplot(all_actions_sample_data, aes(x=week_offset, y=count, color=upstream_vcs_link), show.legend = FALSE) +
-geom_point(size=3, show.legend = FALSE) +
-geom_line(aes(y=predict(test_model), group=upstream_vcs_link), show.legend = FALSE) +
-theme_bw()
-p
-#test model
-test_model <- lmer(count ~ D * I(week_offset) + scaled_project_age + (D * I(week_offset)|upstream_vcs_link),  data=all_actions_sample_data, REML=FALSE)
-summary(test_model)
-#plot results
-p <- ggplot(all_actions_sample_data, aes(x=week_offset, y=count, color=upstream_vcs_link), show.legend = FALSE) +
-geom_point(size=3, show.legend = FALSE) +
-geom_line(aes(y=predict(test_model), group=upstream_vcs_link), show.legend = FALSE) +
-theme_bw()
-p
-#test model
-test_model <- lmer(count ~ D * I(week_offset) + scaled_project_age + (week_offset|upstream_vcs_link),  data=all_actions_sample_data, REML=FALSE)
-summary(test_model)
-#plot results
-p <- ggplot(all_actions_sample_data, aes(x=week_offset, y=count, color=upstream_vcs_link), show.legend = FALSE) +
-geom_point(size=3, show.legend = FALSE) +
-geom_line(aes(y=predict(test_model), group=upstream_vcs_link), show.legend = FALSE) +
-theme_bw()
-p
-##
-all_model <- lmer(count ~ D * I(week_offset)+ scaled_project_age + (D * I(week_offset)| upstream_vcs_link), data=all_actions_data, REML=FALSE)
-summary(all_model)
-##
-all_model <- lmer(count ~ D * I(week_offset)+ scaled_project_age + (week_offset| upstream_vcs_link), data=all_actions_data, REML=FALSE)
-summary(all_model)
-##
-all_model <- lmer(count ~ D * I(week_offset)+ scaled_project_age + (D * I(week_offset)| upstream_vcs_link), data=all_actions_data, REML=FALSE)
-summary(all_model)
-#test model
-test_model <- lmer(count ~ D * I(week_offset) + scaled_project_age + (D * I(week_offset)|upstream_vcs_link),  data=all_actions_sample_data, REML=FALSE)
-summary(test_model)
-#plot results
-p <- ggplot(all_actions_sample_data, aes(x=week_offset, y=count, color=upstream_vcs_link), show.legend = FALSE) +
-geom_point(size=3, show.legend = FALSE) +
-geom_line(aes(y=predict(test_model), group=upstream_vcs_link), show.legend = FALSE) +
-theme_bw()
-p
-# 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$observation_type == "all"),]
-return(longer)
-}
-expanded_data <- expand_timeseries(readme_df[1,])
-for (i in 2:nrow(readme_df)){
-expanded_data <- rbind(expanded_data, expand_timeseries(readme_df[i,]))
 }
 #filter out the windows of time that we're looking at
 window_num <- 8
@@ -510,3 +423,90 @@ library(merTools)
 ICC(outcome="count", group="week", data=all_actions_data)
 ICC(outcome="count", group="upstream_vcs_link", data=all_actions_data)
 ICC(outcome="count", group="week", data=all_actions_data)
+# this is the file with the lmer multi-level rddAnalysis
+library(tidyverse)
+library(plyr)
+# 0 loading the readme data in
+try(setwd(dirname(rstudioapi::getActiveDocumentContext()$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$observation_type == "all"),]
+return(longer)
+}
+expanded_data <- expand_timeseries(readme_df[1,])
+for (i in 2:nrow(readme_df)){
+expanded_data <- rbind(expanded_data, expand_timeseries(readme_df[i,]))
+}
+#filter out the windows of time that we're looking at
+window_num <- 8
+windowed_data <- expanded_data |>
+filter(week >= (27 - window_num) & week <= (27 + window_num)) |>
+mutate(D = ifelse(week > 27, 1, 0))
+#scale the age numbers
+windowed_data$scaled_project_age <- scale(windowed_data$age_of_project)
+windowed_data$week_offset <- windowed_data$week - 27
+#separate out the cleaning d
+all_actions_data <- windowed_data[which(windowed_data$observation_type == "all"),]
+mrg_actions_data <- windowed_data[which(windowed_data$observation_type == "mrg"),]
+all_actions_data$logged_count <- log(all_actions_data$count)
+all_actions_data$log1p_count <- log1p(all_actions_data$count)
+# 3 rdd in lmer analysis
+# rdd: https://rpubs.com/phle/r_tutorial_regression_discontinuity_design
+# lmer: https://www.youtube.com/watch?v=LzAwEKrn2Mc
+library(lme4)
+##end of the model testing and plotting section
+all_model <- lmer(log1p_count ~ D * I(week_offset)+ scaled_project_age + (D * I(week_offset)| upstream_vcs_link), data=all_actions_data, REML=FALSE)
+summary(all_model)
+(
+##end of the model testing and plotting section
+all_model <- lmer(log1p_count ~ D * I(week_offset)+ scaled_project_age + (D * I(week_offset)| upstream_vcs_link), data=all_actions_data, REML=FALSE, control = lmerControl(optimizer ="Nelder_Mead"))
+##end of the model testing and plotting section
+all_model <- lmer(log1p_count ~ D * I(week_offset)+ scaled_project_age + (D * I(week_offset)| upstream_vcs_link), data=all_actions_data, REML=FALSE, control = lmerControl(optimizer ="Nelder_Mead"))
+all_model <- lmer(log1p_count ~ D * I(week_offset)+ scaled_project_age + (D * I(week_offset)| upstream_vcs_link), data=all_actions_data, REML=FALSE, control = lmerControl(optimizer="optimx",
+optCtrl=list(method="nlminb")))
+all_model <- lmer(log1p_count ~ D * I(week_offset)+ scaled_project_age + (D * I(week_offset)| upstream_vcs_link), data=all_actions_data, REML=FALSE, control = lmerControl(
+optimizer ='optimx', optCtrl=list(method='L-BFGS-B')))
+summary(all_model)
+all0_model <- lmer(log1p_count ~ D * I(week_offset)+ scaled_project_age + (D * I(week_offset)| upstream_vcs_link), data=all_actions_data, REML=FALSE)
+summary(all0_model)
+all_model <- lmer(log1p_count ~ D * I(week_offset)+ scaled_project_age + (D * I(week_offset)| upstream_vcs_link), data=all_actions_data, REML=FALSE, control = lmerControl(
+optimizer ='optimx', optCtrl=list(method='L-BFGS-B')))
+all_model.ranef <- ranef(all_model)
+str(all_model.ranef)
+head(all_model.ranef)
+all_actions_data$D_effect_quart <- ntile(all_model.ranef$D, 4)
+head(all_model.ranef)
+all_model.ranef <- random.effects(all_model)
+head(as.data.frame(all_model.ranef))
+head(all_model_ranef)
+all_model_ranef <- as.data.frame(ranef(all_model))
+head(all_model_ranef)
+d_effect_ranef_all <- subset(all_model_ranef, term="D")
+d_effect_ranef_all <- all_model_ranef[all_model_ranef$term="D",]
+d_effect_ranef_all <- all_model_ranef[all_model_ranef$term=="D",]
+View(d_effect_ranef_all)
+d_effect_ranef_all$quartile <- ntile(d_effect_ranef_all$condval, 4)
+View(d_effect_ranef_all)
+# mrg behavior for this
+mrg_model <- lmer(log1p_count ~ D * I(week_offset)+ scaled_project_age + (D * I(week_offset)| upstream_vcs_link), data=all_actions_data, REML=FALSE, control = lmerControl(
+optimizer ='optimx', optCtrl=list(method='L-BFGS-B')))
+summary(mrg_model)

R/contribRDDAnalysis.R

@@ -0,0 +1,72 @@
+library(tidyverse)
+library(plyr) 
+#get the contrib data instead
+try(setwd(dirname(rstudioapi::getActiveDocumentContext()$path)))
+contrib_df <- read_csv("../final_data/deb_contrib_did.csv")
+#some preprocessing and expansion 
+col_order <- c("upstream_vcs_link", "age_of_project", "event_date", "event_hash", "before_all_ct", "after_all_ct", "before_mrg_ct", "after_mrg_ct", "before_auth_new", "after_auth_new", "before_commit_new",  "after_commit_new")
+contrib_df <- contrib_df[,col_order]
+contrib_df$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$after_mrg_ct), ", ")
+drop <- c("before_all_ct", "before_mrg_ct", "after_all_ct", "after_mrg_ct")
+contrib_df = contrib_df[,!(names(contrib_df) %in% drop)]
+# 2 some expansion needs to happens for each project 
+expand_timeseries <- function(project_row) {
+  longer <- project_row |> 
+    pivot_longer(cols = starts_with("ct"),
+                 names_to = "window",
+                 values_to = "count") |>
+    unnest(count) 
+  longer$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(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 
+windowed_data$scaled_project_age <- scale(windowed_data$age_of_project)
+windowed_data$week_offset <- windowed_data$week - 27
+#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"),]
+#EDA?
+#TKTK ---------------------
+#imports for models 
+library(lme4)
+library(optimx)
+#models -- TKTK need to be fixed
+all_model <- lmer(log1p_count ~ D * I(week_offset)+ scaled_project_age + (D * I(week_offset)| upstream_vcs_link), data=all_actions_data, REML=FALSE, control = lmerControl(
+  optimizer ='optimx', optCtrl=list(method='L-BFGS-B')))
+summary(all_model)
+#identifying the quartiles of effect for D
+all_model_ranef <- as.data.frame(ranef(all_model))
+d_effect_ranef_all <- all_model_ranef[all_model_ranef$term=="D",]
+d_effect_ranef_all$quartile <- ntile(d_effect_ranef_all$condval, 4)
+#model residuals
+all_residuals <- residuals(all_model)
+qqnorm(all_residuals)
+# mrg behavior for this
+mrg_model <- lmer(log1p_count ~ D * I(week_offset)+ scaled_project_age + (D * I(week_offset)| upstream_vcs_link), data=all_actions_data, REML=FALSE, control = lmerControl(
+  optimizer ='optimx', optCtrl=list(method='L-BFGS-B')))
+summary(mrg_model)
+#identifying the quartiles of effect for D
+mrg_model_ranef <- as.data.frame(ranef(mrg_model))
+d_effect_ranef_mrg <- mrg_model_ranef[mrg_model_ranef$term=="D",]
+d_effect_ranef_mrg$quartile <- ntile(d_effect_ranef_mrg$condval, 4)
+#merge model residuals
+mrg_residuals <- residuals(mrg_model)
+qqnorm(mrg_residuals)
+
+
+

R/readmeRDDAnalysis.R

@@ -57,43 +57,28 @@ all_actions_data$log1p_count <- log1p(all_actions_data$count)
 # lmer: https://www.youtube.com/watch?v=LzAwEKrn2Mc
 library(lme4)
 # https://www.bristol.ac.uk/cmm/learning/videos/random-intercepts.html#exvar
-#making some random data 
-sampled_data <- readme_df[sample(nrow(readme_df), 220), ]
-expanded_sample_data <- expand_timeseries(sampled_data[1,])
-for (i in 2:nrow(sampled_data)){
-  expanded_sample_data <- rbind(expanded_sample_data, expand_timeseries(sampled_data[i,]))
-}
-windowed_sample_data <- expanded_sample_data |>
-  filter(week >= (27 - window_num) & week <= (27 + window_num)) |>
-  mutate(D = ifelse(week > 27, 1, 0))
-windowed_sample_data$scaled_project_age <- scale(windowed_sample_data$age_of_project)
-windowed_sample_data$week_offset <- windowed_sample_data$week - 27
-all_actions_sample_data <- windowed_sample_data[which(windowed_sample_data$observation_type == "all"),]
-all_actions_sample_data$log1p_count <- log1p(all_actions_sample_data$count)
-#test model
-test_model <- lmer(log1p_count ~ D * I(week_offset) + scaled_project_age + (D * I(week_offset)|upstream_vcs_link),  data=all_actions_sample_data, REML=FALSE)
-summary(test_model)
-#plot results
-p <- ggplot(all_actions_sample_data, aes(x=week_offset, y=count, color=upstream_vcs_link), show.legend = FALSE) +
-  geom_point(size=3, show.legend = FALSE) +
-  geom_line(aes(y=predict(test_model)), show.legend = FALSE) +
-  theme_bw() 
-p
-##end of the model testing and plotting section
 library(optimx)
 all_model <- lmer(log1p_count ~ D * I(week_offset)+ scaled_project_age + (D * I(week_offset)| upstream_vcs_link), data=all_actions_data, REML=FALSE, control = lmerControl(
   optimizer ='optimx', optCtrl=list(method='L-BFGS-B')))
 summary(all_model)
-random_effects <- ranef(all_model)
+#identifying the quartiles of effect for D
+all_model_ranef <- as.data.frame(ranef(all_model))
+d_effect_ranef_all <- all_model_ranef[all_model_ranef$term=="D",]
+d_effect_ranef_all$quartile <- ntile(d_effect_ranef_all$condval, 4)
+#model residuals
 all_residuals <- residuals(all_model)
 qqnorm(all_residuals)
-# for visualization, may have to run model for each project and then identify top 5 projects for RDD graphs 
+# mrg behavior for this
-mrg_model <- lmer(count ~ D * I(week_offset)+ scaled_project_age + (D * I(week_offset)| upstream_vcs_link), data=mrg_actions_data, REML=FALSE)
+mrg_model <- lmer(log1p_count ~ D * I(week_offset)+ scaled_project_age + (D * I(week_offset)| upstream_vcs_link), data=all_actions_data, REML=FALSE, control = lmerControl(
+  optimizer ='optimx', optCtrl=list(method='L-BFGS-B')))
 summary(mrg_model)
+#identifying the quartiles of effect for D
+mrg_model_ranef <- as.data.frame(ranef(mrg_model))
+d_effect_ranef_mrg <- mrg_model_ranef[mrg_model_ranef$term=="D",]
+d_effect_ranef_mrg$quartile <- ntile(d_effect_ranef_mrg$condval, 4)
+#merge model residuals
 mrg_residuals <- residuals(mrg_model)
 qqnorm(mrg_residuals)
 # Performance: 
-library(merTools)
+
-ICC(outcome="count", group="week", data=all_actions_data)
-#testing for different types of models