From 153e7b7c1679b575678b163cf9ba47ae0694e1c2 Mon Sep 17 00:00:00 2001 From: mjgaughan Date: Wed, 24 Apr 2024 20:55:56 -0500 Subject: [PATCH] clean model and draft duplicate for contrib --- R/.Rhistory | 174 ++++++++++++++++++++--------------------- R/contribRDDAnalysis.R | 72 +++++++++++++++++ R/readmeRDDAnalysis.R | 43 ++++------ 3 files changed, 173 insertions(+), 116 deletions(-) create mode 100644 R/contribRDDAnalysis.R diff --git a/R/.Rhistory b/R/.Rhistory index 74a2e43..000d063 100644 --- a/R/.Rhistory +++ b/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$condval, 4) +View(d_effect_ranef_all) +# mrg behavior for this +mrg_model <- lmer(log1p_count ~ D * I(week_offset)+ scaled_project_age + (D * I(week_offset)| upstream_vcs_link), data=all_actions_data, REML=FALSE, control = lmerControl( +optimizer ='optimx', optCtrl=list(method='L-BFGS-B'))) +summary(mrg_model) diff --git a/R/contribRDDAnalysis.R b/R/contribRDDAnalysis.R new file mode 100644 index 0000000..b0a84af --- /dev/null +++ b/R/contribRDDAnalysis.R @@ -0,0 +1,72 @@ +library(tidyverse) +library(plyr) +#get the contrib data instead +try(setwd(dirname(rstudioapi::getActiveDocumentContext()$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$condval, 4) +#merge model residuals +mrg_residuals <- residuals(mrg_model) +qqnorm(mrg_residuals) + + + diff --git a/R/readmeRDDAnalysis.R b/R/readmeRDDAnalysis.R index 67ba780..6cf75a1 100644 --- a/R/readmeRDDAnalysis.R +++ b/R/readmeRDDAnalysis.R @@ -57,43 +57,28 @@ all_actions_data$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$condval, 4) +#model residuals all_residuals <- residuals(all_model) qqnorm(all_residuals) -# for visualization, may have to run model for each project and then identify top 5 projects for RDD graphs -mrg_model <- lmer(count ~ D * I(week_offset)+ scaled_project_age + (D * I(week_offset)| upstream_vcs_link), data=mrg_actions_data, REML=FALSE) +# mrg behavior for this +mrg_model <- lmer(log1p_count ~ D * I(week_offset)+ scaled_project_age + (D * I(week_offset)| upstream_vcs_link), data=all_actions_data, REML=FALSE, control = lmerControl( + optimizer ='optimx', optCtrl=list(method='L-BFGS-B'))) summary(mrg_model) +#identifying the quartiles of effect for D +mrg_model_ranef <- as.data.frame(ranef(mrg_model)) +d_effect_ranef_mrg <- mrg_model_ranef[mrg_model_ranef$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) # Performance: -library(merTools) -ICC(outcome="count", group="week", data=all_actions_data) -#testing for different types of models +