513 lines
30 KiB
R
513 lines
30 KiB
R
# need to calculate inter-class correlation coefficient?
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library(merTools)
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ICC(outcome="count", group="upstream_vcs_link", data=all_actions_data)
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ICC(outcome="count", group="week", data=all_actions_data)
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draft_all_model <- lmer(count ~ D * I(week - 26) + age_of_project + (1 + D * I(week - 26) + age_of_project |upstream_vcs_link), REML=FALSE, data=all_actions_data)
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summary(draft_all_model)
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draft_all_model <- lmer(count ~ D * I(week - 26) + age_of_project + (1 |upstream_vcs_link), REML=FALSE, data=all_actions_data)
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summary(draft_all_model)
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describe(all_actions_data)
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hist(all_actions_data$count)
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mean(all_actions_data$count)
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median(all_actions_data$count)
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mean(all_actions_data$count)
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draft_all_model <- lmer(count ~ D * I(week - 26) + age_of_project + (1 |upstream_vcs_link), REML=FALSE, data=all_actions_data)
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summary(draft_all_model)
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draft_all_model <- lmer(count ~ D * I(week - 26) + age_of_project + (1+week|upstream_vcs_link), REML=FALSE, data=all_actions_data)
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summary(draft_all_model)
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draft_all_model <- lmer(count ~ D * I(week - 26) + age_of_project + (1+D * I(week - 26)|upstream_vcs_link), REML=FALSE, data=all_actions_data)
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summary(draft_all_model)
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draft_all_model <- lmer(count ~ D * I(week - 26) + age_of_project + (1+ upstream_vcs_link|upstream_vcs_link), REML=FALSE, data=all_actions_data)
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draft_all_model <- lmer(count ~ (1 | D * I(week - 26) + age_of_project) + (1 |upstream_vcs_link), REML=FALSE, data=all_actions_data)
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draft_all_model <- lmer(count ~ D * I(week - 26) + age_of_project + (1 + I(week - 26) |upstream_vcs_link), REML=FALSE, data=all_actions_data)
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summary(draft_all_model)
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draft_all_model <- lmer(count ~ D * I(week - 26) + age_of_project + (1 + week |upstream_vcs_link), REML=FALSE, data=all_actions_data)
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summary(draft_all_model)
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draft_all_model <- lmer(count ~ D * I(week - 26) + age_of_project + (1 + I(week - 26) |upstream_vcs_link), REML=FALSE, data=all_actions_data)
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summary(draft_all_model)
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draft_all_model <- lmer(count ~ D * I(week - 26) + age_of_project + (1 + D |upstream_vcs_link), REML=FALSE, data=all_actions_data)
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summary(draft_all_model)
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draft_mrg_model <- lmer(count ~ D * I(week - 26) + age_of_project + (1 + D |upstream_vcs_link), REML=FALSE, data=mrg_actions_data)
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summary(draft_mrg_model)
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draft_all_model <- lmer(count ~ D + I(week - 26) + D:I(week - 26) + age_of_project + (1 + D |upstream_vcs_link), REML=FALSE, data=all_actions_data)
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summary(draft_all_model)
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flat_all_model <- lmer(count ~ D + I(week - 26) + D:I(week - 26) + age_of_project, REML=FALSE, data=all_actions_data)
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flat_all_model <- lm(count ~ D + I(week - 26) + D:I(week - 26) + age_of_project, REML=FALSE, data=all_actions_data)
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summary(flat_all_model)
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draft_all_model <- lmer(count ~ D + I(week - 26) + D:I(week - 26) + age_of_project + (1 + D |upstream_vcs_link), REML=FALSE, data=all_actions_data)
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summary(draft_all_model)
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#find some EDA to identify which types of models might be the best for this
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mean(all_actions_data$count)
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median(all_actions_data$count)
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table(all_actions_data$count)
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dist(all_actions_data$count)
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var(all_actions_data$count)
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sd(all_actions_data$count)
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qqplot(all_actions_data$count, all_actions_data$week)
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qqnorm(all_actions_data$count)
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y <- qunif(ppoints(length(all_actions_data$count)))
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qqplot(all_actions_data$count, y)
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qqnorm(all_actions_data$count)
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qqnorm(log(all_actions_data$count)
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qqnorm(log(all_actions_data$count))
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qqnorm(log(all_actions_data$count))
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qqplot(log(all_actions_data$count), y)
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qqnorm(all_actions_data$count)
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qqnorm(root(all_actions_data$count))
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qqnorm(log(all_actions_data$count))
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qqplot(log(all_actions_data$count), y)
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qqplot(all_actions_data$count, y)
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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"))
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summary(poisson_all_model)
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summary(draft_all_model)
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# Performance:
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draft_mrg_model <- lmer(count ~ D * I(week - 26) + age_of_project + (1 + D |upstream_vcs_link), REML=FALSE, data=mrg_actions_data)
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summary(draft_mrg_model)
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lmer_residuals <- residuals(lmer_all_model)
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lmer_all_model <- lmer(count ~ D + I(week - 26) + D:I(week - 26) + age_of_project + (1 + D |upstream_vcs_link), REML=FALSE, data=all_actions_data)
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summary(lmer_all_model)
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lmer_residuals <- residuals(lmer_all_model)
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qqnorm(lmer_residuals)
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poisson_residuals <- residuals(poisson_all_model)
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qqnorm(poisson_residuals)
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summary(poisson_all_model)
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#if I'm reading the residuals right, the poisson is better?
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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)
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summary(poisson_all_model)
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poisson_residuals <- residuals(poisson_all_model)
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qqnorm(poisson_residuals)
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# this is the file with the lmer multi-level rddAnalysis
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library(tidyverse)
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library(plyr)
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# 0 loading the readme data in
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try(setwd(dirname(rstudioapi::getActiveDocumentContext()$path)))
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readme_df <- read_csv("../final_data/deb_readme_did.csv")
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# 1 preprocessing
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#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")
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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")
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readme_df <- readme_df[,col_order]
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readme_df$ct_before_all <- str_split(gsub("[][]","", readme_df$before_all_ct), ", ")
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readme_df$ct_after_all <- str_split(gsub("[][]","", readme_df$after_all_ct), ", ")
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readme_df$ct_before_mrg <- str_split(gsub("[][]","", readme_df$before_mrg_ct), ", ")
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readme_df$ct_after_mrg <- str_split(gsub("[][]","", readme_df$after_mrg_ct), ", ")
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drop <- c("before_all_ct", "before_mrg_ct", "after_all_ct", "after_mrg_ct")
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readme_df = readme_df[,!(names(readme_df) %in% drop)]
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# 2 some expansion needs to happens for each project
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expand_timeseries <- function(project_row) {
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longer <- project_row |>
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pivot_longer(cols = starts_with("ct"),
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names_to = "window",
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values_to = "count") |>
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unnest(count)
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longer$observation_type <- gsub("^.*_", "", longer$window)
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longer <- ddply(longer, "observation_type", transform, week=seq(from=0, by=1, length.out=length(observation_type)))
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longer$count <- as.numeric(longer$count)
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#longer <- longer[which(longer$observation_type == "all"),]
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return(longer)
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}
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expanded_data <- expand_timeseries(readme_df[1,])
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for (i in 2:nrow(readme_df)){
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expanded_data <- rbind(expanded_data, expand_timeseries(readme_df[i,]))
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}
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#filter out the windows of time that we're looking at
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window_num <- 8
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expanded_data <- expanded_data |>
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filter(week >= (26 - window_num) & week <= (26 + window_num)) |>
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mutate(D = ifelse(week > 26, 1, 0))
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#separate out the cleaning d
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all_actions_data <- expanded_data[which(expanded_data$observation_type == "all"),]
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mrg_actions_data <- expanded_data[which(expanded_data$observation_type == "mrg"),]
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#find some EDA to identify which types of models might be the best for this
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mean(all_actions_data$count)
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median(all_actions_data$count)
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#if I'm reading the residuals right, the poisson is better?
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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)
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summary(poisson_all_model)
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# 3 rdd in lmer analysis
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# rdd: https://rpubs.com/phle/r_tutorial_regression_discontinuity_design
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# lmer: https://www.youtube.com/watch?v=LzAwEKrn2Mc
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library(lme4)
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#if I'm reading the residuals right, the poisson is better?
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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)
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#if I'm reading the residuals right, the poisson is better?
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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)
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#if I'm reading the residuals right, the poisson is better?
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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"))
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summary(poisson_all_model)
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poisson_residuals <- residuals(poisson_all_model)
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qqnorm(poisson_residuals)
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#if I'm reading the residuals right, the poisson is better?
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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"))
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summary(poisson_all_model)
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poisson_residuals <- residuals(poisson_all_model)
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qqnorm(poisson_residuals)
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qqnorm(poisson_residuals)
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qqnorm(poisson_residuals)
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#scale the age numbers
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expanded_data$scaled_project_age <- scale(expanded_data$age_of_project)
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#separate out the cleaning d
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all_actions_data <- expanded_data[which(expanded_data$observation_type == "all"),]
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mrg_actions_data <- expanded_data[which(expanded_data$observation_type == "mrg"),]
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#find some EDA to identify which types of models might be the best for this
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mean(all_actions_data$count)
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median(all_actions_data$count)
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table(all_actions_data$count)
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var(all_actions_data$count)
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qqnorm(all_actions_data$count)
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y <- qunif(ppoints(length(all_actions_data$count)))
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qqplot(all_actions_data$count, y)
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#if I'm reading the residuals right, the poisson is better?
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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"))
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summary(poisson_all_model)
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poisson_residuals <- residuals(poisson_all_model)
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qqnorm(poisson_residuals)
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# this is the file with the lmer multi-level rddAnalysis
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library(tidyverse)
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library(plyr)
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# 0 loading the readme data in
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try(setwd(dirname(rstudioapi::getActiveDocumentContext()$path)))
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readme_df <- read_csv("../final_data/deb_readme_did.csv")
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# 1 preprocessing
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#colnames(readme_df) <- c("upstream_vcs_link", "event_date", "event_hash", "before_all_ct", "before_mrg_ct", "after_all_ct", "after_mrg_ct", "before_auth_new", "after_commit_new", "after_auth_new", "before_commit_new")
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col_order <- c("upstream_vcs_link", "age_of_project", "event_date", "event_hash", "before_all_ct", "after_all_ct", "before_mrg_ct", "after_mrg_ct", "before_auth_new", "after_auth_new", "before_commit_new", "after_commit_new")
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readme_df <- readme_df[,col_order]
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readme_df$ct_before_all <- str_split(gsub("[][]","", readme_df$before_all_ct), ", ")
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readme_df$ct_after_all <- str_split(gsub("[][]","", readme_df$after_all_ct), ", ")
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readme_df$ct_before_mrg <- str_split(gsub("[][]","", readme_df$before_mrg_ct), ", ")
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readme_df$ct_after_mrg <- str_split(gsub("[][]","", readme_df$after_mrg_ct), ", ")
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drop <- c("before_all_ct", "before_mrg_ct", "after_all_ct", "after_mrg_ct")
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# 2 some expansion needs to happens for each project
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expand_timeseries <- function(project_row) {
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longer <- project_row |>
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pivot_longer(cols = starts_with("ct"),
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names_to = "window",
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values_to = "count") |>
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unnest(count)
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longer$observation_type <- gsub("^.*_", "", longer$window)
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longer <- ddply(longer, "observation_type", transform, week=seq(from=0, by=1, length.out=length(observation_type)))
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longer$count <- as.numeric(longer$count)
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#longer <- longer[which(longer$observation_type == "all"),]
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return(longer)
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}
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expanded_data <- expand_timeseries(readme_df[1,])
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for (i in 2:nrow(readme_df)){
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expanded_data <- rbind(expanded_data, expand_timeseries(readme_df[i,]))
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}
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#filter out the windows of time that we're looking at
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window_num <- 8
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expanded_data <- expanded_data |>
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filter(week >= (26 - window_num) & week <= (26 + window_num)) |>
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mutate(D = ifelse(week > 26, 1, 0))
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# this is the file with the lmer multi-level rddAnalysis
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library(tidyverse)
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library(plyr)
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# 0 loading the readme data in
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try(setwd(dirname(rstudioapi::getActiveDocumentContext()$path)))
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readme_df <- read_csv("../final_data/deb_readme_did.csv")
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# 1 preprocessing
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#colnames(readme_df) <- c("upstream_vcs_link", "event_date", "event_hash", "before_all_ct", "before_mrg_ct", "after_all_ct", "after_mrg_ct", "before_auth_new", "after_commit_new", "after_auth_new", "before_commit_new")
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col_order <- c("upstream_vcs_link", "age_of_project", "event_date", "event_hash", "before_all_ct", "after_all_ct", "before_mrg_ct", "after_mrg_ct", "before_auth_new", "after_auth_new", "before_commit_new", "after_commit_new")
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readme_df <- readme_df[,col_order]
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readme_df$ct_before_all <- str_split(gsub("[][]","", readme_df$before_all_ct), ", ")
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readme_df$ct_after_all <- str_split(gsub("[][]","", readme_df$after_all_ct), ", ")
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readme_df$ct_before_mrg <- str_split(gsub("[][]","", readme_df$before_mrg_ct), ", ")
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readme_df$ct_after_mrg <- str_split(gsub("[][]","", readme_df$after_mrg_ct), ", ")
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drop <- c("before_all_ct", "before_mrg_ct", "after_all_ct", "after_mrg_ct")
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readme_df = readme_df[,!(names(readme_df) %in% drop)]
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# 2 some expansion needs to happens for each project
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expand_timeseries <- function(project_row) {
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longer <- project_row |>
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pivot_longer(cols = starts_with("ct"),
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names_to = "window",
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values_to = "count") |>
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unnest(count)
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longer$observation_type <- gsub("^.*_", "", longer$window)
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longer <- ddply(longer, "observation_type", transform, week=seq(from=0, by=1, length.out=length(observation_type)))
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longer$count <- as.numeric(longer$count)
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#longer <- longer[which(longer$observation_type == "all"),]
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return(longer)
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}
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expanded_data <- expand_timeseries(readme_df[1,])
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for (i in 2:nrow(readme_df)){
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expanded_data <- rbind(expanded_data, expand_timeseries(readme_df[i,]))
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}
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#filter out the windows of time that we're looking at
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window_num <- 8
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windowed_data <- expanded_data |>
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filter(week >= (26 - window_num) & week <= (26 + window_num)) |>
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mutate(D = ifelse(week > 26, 1, 0))
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#scale the age numbers
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windowed_data$scaled_project_age <- scale(windowed_data$age_of_project)
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#separate out the cleaning d
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all_actions_data <- windowed_data[which(windowed_data$observation_type == "all"),]
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mrg_actions_data <- windowed_data[which(windowed_data$observation_type == "mrg"),]
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#if I'm reading the residuals right, the poisson is better?
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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"))
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# 3 rdd in lmer analysis
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# rdd: https://rpubs.com/phle/r_tutorial_regression_discontinuity_design
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# lmer: https://www.youtube.com/watch?v=LzAwEKrn2Mc
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library(lme4)
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#if I'm reading the residuals right, the poisson is better?
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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"))
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summary(poisson_all_model)
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#logistic regression mixed effects
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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)
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#if I'm reading the residuals right, the poisson is better?
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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"))
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#if I'm reading the residuals right, the poisson is better?
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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"))
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#if I'm reading the residuals right, the poisson is better?
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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"))
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#logistic regression mixed effects
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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)
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qqnorm(poisson_residuals)
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summary(poisson_all_model)
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poisson_residuals <- residuals(poisson_all_model)
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qqnorm(poisson_residuals)
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#logistic regression mixed effects (doesn't work)
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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)
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#logistic regression mixed effects (doesn't work)
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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)
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#if I'm reading the residuals right, the poisson is better?
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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)
|
|
# 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"),]
|
|
# https://www.bristol.ac.uk/cmm/learning/videos/random-intercepts.html#exvar
|
|
# (1 |upstream_vcs_link) or (week | upstream_vcs_link)
|
|
poisson_all_model <- glmer(count ~ (D + I(week - 26) + D:I(week - 26) + scaled_project_age | upstream_vcs_link) + (1 |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)
|
|
# https://www.bristol.ac.uk/cmm/learning/videos/random-intercepts.html#exvar
|
|
# (1 |upstream_vcs_link) or (week | upstream_vcs_link)
|
|
poisson_all_model <- glmer(count ~ (D + I(week - 26) + D:I(week - 26) + scaled_project_age | upstream_vcs_link) + (1 |upstream_vcs_link), data=all_actions_data, family = poisson(link = "log"))
|
|
# https://www.bristol.ac.uk/cmm/learning/videos/random-intercepts.html#exvar
|
|
# (1 |upstream_vcs_link) or (week | upstream_vcs_link)
|
|
poisson_all_model <- glmer(count ~ D + I(week - 26) + D:I(week - 26) + scaled_project_age + (D + I(week - 26) + D:I(week - 26) + scaled_project_age | upstream_vcs_link) + (1 |upstream_vcs_link), data=all_actions_data, family = poisson(link = "log"))
|
|
# https://www.bristol.ac.uk/cmm/learning/videos/random-intercepts.html#exvar
|
|
# (1 |upstream_vcs_link) or (week | upstream_vcs_link)
|
|
poisson_all_model <- glmer(count ~ D + I(week - 26) + D:I(week - 26) + scaled_project_age + (D | upstream_vcs_link) + (1 |upstream_vcs_link), data=all_actions_data, family = poisson(link = "log"))
|
|
summary(poisson_all_model)
|
|
# https://www.bristol.ac.uk/cmm/learning/videos/random-intercepts.html#exvar
|
|
# (D |upstream_vcs_link) or (week | upstream_vcs_link)
|
|
poisson_all_model <- glmer(count ~ D + I(week - 26) + D:I(week - 26) + scaled_project_age + (week | upstream_vcs_link) + (1 |upstream_vcs_link), data=all_actions_data, family = poisson(link = "log"))
|
|
summary(poisson_all_model)
|
|
poisson_residuals <- residuals(poisson_all_model)
|
|
qqnorm(poisson_residuals)
|