diff --git a/R/.Rhistory b/R/.Rhistory
index 000d063..74b9299 100644
--- a/R/.Rhistory
+++ b/R/.Rhistory
@@ -1,92 +1,3 @@
-}
-#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"),]
-# 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)
-# 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$observation_type == "all"),]
-#test model
-test_model <- lmer(count ~ D * I(week_offset) + scaled_project_age + (D * I(week_offset)|upstream_vcs_link),  data=all_actions_sample_data, REML=FALSE)
-summary(test_model)
-#plot results
-p <- ggplot(all_actions_sample_data, aes(x=week_offset, y=count, color=upstream_vcs_link), show.legend = FALSE) +
-geom_point(size=3, show.legend = FALSE) +
-geom_line(aes(y=predict(test_model), group=upstream_vcs_link), show.legend = FALSE) +
-theme_bw()
-p
-##
-all_model <- lmer(count ~ D * I(week_offset)+ scaled_project_age + (D * I(week_offset)| upstream_vcs_link), data=all_actions_data, REML=FALSE)
-summary(all_model)
-# https://www.bristol.ac.uk/cmm/learning/videos/random-intercepts.html#exvar
-#making some random data
-sampled_data <- readme_df[sample(nrow(readme_df), 220), ]
-expanded_sample_data <- expand_timeseries(sampled_data[1,])
-for (i in 2:nrow(sampled_data)){
-expanded_sample_data <- rbind(expanded_sample_data, expand_timeseries(sampled_data[i,]))
-}
-windowed_sample_data <- expanded_sample_data |>
-filter(week >= (27 - window_num) & week <= (27 + window_num)) |>
-mutate(D = ifelse(week > 27, 1, 0))
-windowed_sample_data$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"),]
-#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
-#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
-# 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)
@@ -510,3 +421,92 @@ View(d_effect_ranef_all)
 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)
+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')))
+#EDA?
+hist(log(all_actions_data$count))
+all_actions_data$logged_count <- log(all_actions_data$count)
+all_actions_data$log1p_count <- log1p(all_actions_data$count)
+#models -- TKTK need to be fixed
+all_model <- lmer(logged_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')))
+#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)
+#models -- TKTK need to be fixed
+all_model <- lmer(log1p_count ~ D * I(week_offset)+ scaled_project_age + (week_offset| upstream_vcs_link), data=all_actions_data, REML=FALSE, control = lmerControl(
+optimizer ='optimx', optCtrl=list(method='L-BFGS-B')))
+summary(all_model)
+#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)
+week_offset
+#models -- TKTK need to be fixed
+all_model <- lmer(log1p_count ~ D * I(week_offset)+ scaled_project_age + (week_offset| upstream_vcs_link), data=all_actions_data, REML=FALSE, control = lmerControl(
+optimizer ='optimx', optCtrl=list(method='L-BFGS-B')))
+summary(all_model)
+# 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')))
+# mrg behavior for this
+mrg_model <- lmer(log1p_count ~ D * I(week_offset)+ scaled_project_age + (week_offset | upstream_vcs_link), data=all_actions_data, REML=FALSE, control = lmerControl(
+optimizer ='optimx', optCtrl=list(method='L-BFGS-B')))
+summary(mrg_model)
+# mrg behavior for this
+mrg_model <- lmer(log1p_count ~ D * I(week_offset)+ scaled_project_age + (week_offset | upstream_vcs_link), data=mrg_actions_data, REML=FALSE, control = lmerControl(
+optimizer ='optimx', optCtrl=list(method='L-BFGS-B')))
+# now for merge
+mrg_actions_data$logged_count <- log(mrg_actions_data$count)
+mrg_actions_data$log1p_count <- log1p(mrg_actions_data$count)
+# mrg behavior for this
+mrg_model <- lmer(log1p_count ~ D * I(week_offset)+ scaled_project_age + (week_offset | upstream_vcs_link), data=mrg_actions_data, REML=FALSE, control = lmerControl(
+optimizer ='optimx', optCtrl=list(method='L-BFGS-B')))
+summary(mrg_model)
diff --git a/R/contribRDDAnalysis.R b/R/contribRDDAnalysis.R
index b0a84af..90d970c 100644
--- a/R/contribRDDAnalysis.R
+++ b/R/contribRDDAnalysis.R
@@ -41,12 +41,18 @@ windowed_data$week_offset <- windowed_data$week - 27
 all_actions_data <- windowed_data[which(windowed_data$observation_type == "all"),]
 mrg_actions_data <- windowed_data[which(windowed_data$observation_type == "mrg"),]
 #EDA?
+hist(log(all_actions_data$count))
+all_actions_data$logged_count <- log(all_actions_data$count)
+all_actions_data$log1p_count <- log1p(all_actions_data$count)
+# now for merge
+mrg_actions_data$logged_count <- log(mrg_actions_data$count)
+mrg_actions_data$log1p_count <- log1p(mrg_actions_data$count)
 #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(
+all_model <- lmer(log1p_count ~ D * I(week_offset)+ scaled_project_age + (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
@@ -57,7 +63,7 @@ d_effect_ranef_all$quartile <- ntile(d_effect_ranef_all$condval, 4)
 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(
+mrg_model <- lmer(log1p_count ~ D * I(week_offset)+ scaled_project_age + (week_offset | upstream_vcs_link), data=mrg_actions_data, REML=FALSE, control = lmerControl(
   optimizer ='optimx', optCtrl=list(method='L-BFGS-B')))
 summary(mrg_model)
 #identifying the quartiles of effect for D