513 lines
24 KiB
R
513 lines
24 KiB
R
library(ggplot2)
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data1 <- read_csv('../power_data_111023_mmt.csv',show_col_types = FALSE)
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data2 <- read_csv('../inst_all_packages_full_results.csv')
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data1 <- read_csv('../kk_final_expanded_data_final.csv',show_col_types = FALSE)
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library(readr)
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library(ggplot2)
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library(tidyverse)
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data1 <- read_csv('../kk_final_expanded_data_final.csv',show_col_types = FALSE)
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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 >= (27 - window_num) & week <= (27 + window_num)) |>
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mutate(D = ifelse(week > 27, 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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windowed_data$week_offset <- windowed_data$week - 27
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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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all_actions_data$log1p_count <- log1p(all_actions_data$count)
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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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# https://www.bristol.ac.uk/cmm/learning/videos/random-intercepts.html#exvar
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library(optimx)
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library(lattice)
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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(
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optimizer ='optimx', optCtrl=list(method='L-BFGS-B')))
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#identifying the quartiles of effect for D
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all_model_ranef <- as.data.frame(ranef(all_model, condVar=TRUE))
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View(all_model_ranef)
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d_effect_ranef_all <- all_model_ranef[all_model_ranef$term=="D",]
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#identifying the quartiles of effect for D
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all_model_ranef <- ranef(all_model, condVar=TRUE)
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d_effect_ranef_all <- all_model_ranef[all_model_ranef$term=="D",]
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dotplot(all_model_ranef)
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d_effect_ranef_all <- all_model_ranef['upstream_vcs_link']['D']
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View(all_model_ranef)
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d_effect_ranef_all <- all_model_ranef[upstream_vcs_link,2]
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d_effect_ranef_all <- all_model_ranef['upstream_vcs_link',2]
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d_effect_ranef_all <- all_model_ranef$upstream_vcs_link
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View(d_effect_ranef_all)
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dotplot(all_model_ranef)[["D"]]
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dotplot(all_model_ranef)[["upstream_vcs_link"]]
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dotplot(all_model_ranef)[["upstream_vcs_link"]["D"]]
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dotplot(all_model_ranef)$D
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View(all_model_ranef)
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for (j in 1:nschool) {
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jj <- order(all_model_ranef)[j]
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lines (x=c(j,j),y=c(ranef.lower[jj],ranef.upper[jj]))
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}
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for (j in 1:upstream_vcs_link) {
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jj <- order(all_model_ranef)[j]
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lines (x=c(j,j),y=c(ranef.lower[jj],ranef.upper[jj]))
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}
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View(all_model_ranef)
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df_ranefs <- as.data.frame(all_model_ranef)
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View(df_ranefs)
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#identifying the quartiles of effect for D
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all_model_ranef <- ranef(all_model, condVar=TRUE)$upstream_vcs_link[[2]]
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#identifying the quartiles of effect for D
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all_model_ranef <- ranef(all_model, condVar=TRUE)$upstream_vcs_link
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dotplot(all_model_ranef)
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dotplot(all_model_ranef)
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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 >= (27 - window_num) & week <= (27 + window_num)) |>
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mutate(D = ifelse(week > 27, 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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windowed_data$week_offset <- windowed_data$week - 27
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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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all_actions_data$log1p_count <- log1p(all_actions_data$count)
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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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# https://www.bristol.ac.uk/cmm/learning/videos/random-intercepts.html#exvar
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library(optimx)
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library(lattice)
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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(
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optimizer ='optimx', optCtrl=list(method='L-BFGS-B')))
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#identifying the quartiles of effect for D
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all_model_ranef <- ranef(all_model, condVar=TRUE)
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dotplot(all_model_ranef)$g[1]
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re <- ranef(all_model,postVar = TRUE)
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re$g$'(Intercept)' <- NULL
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re$g$'D:I(week_offset)' <- NULL
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re <- ranef(all_model, condVar=TRUE)
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re$g$'(Intercept)' <- NULL
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re$g$'D:I(week_offset)' <- NULL
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dotplot(re)
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dotplot(re)
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re <- ranef(all_model, condVar=TRUE)
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re$upstream_vcs_link$'(Intercept)' <- NULL
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re$upstream_vcs_link$'D:I(week_offset)' <- NULL
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View(re)
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re$upstream_vcs_link$'I(week_offset)' <- NULL
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dotplot(re)
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View(re)
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View(all_model_ranef)
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dotplot(all_model_ranef)
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#identifying the quartiles of effect for D
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all_model_ranef <- ranef(all_model, condVar=TRUE)
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dotplot(all_model_ranef)
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#identifying the quartiles of effect for D
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all_model_ranef <- ranef(all_model, condVar=TRUE)
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dotplot(all_model_ranef)
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re <- ranef(all_model, condVar=TRUE)
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re$upstream_vcs_link$'(Intercept)' <- NULL
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re$upstream_vcs_link$'D:I(week_offset)' <- NULL
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re$upstream_vcs_link$'I(week_offset)' <- NULL
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dotplot(re)
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dotplot(all_model_ranef)
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# this is the file with the lmer multi-level rddAnalysis
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library(tidyverse)
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library(plyr)
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# 0 loading the readme data in
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try(setwd(dirname(rstudioapi::getActiveDocumentContext()$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$observation_type == "all"),]
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return(longer)
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}
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expanded_data <- expand_timeseries(readme_df[1,])
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for (i in 2:nrow(readme_df)){
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expanded_data <- rbind(expanded_data, expand_timeseries(readme_df[i,]))
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}
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#filter out the windows of time that we're looking at
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window_num <- 8
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windowed_data <- expanded_data |>
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filter(week >= (27 - window_num) & week <= (27 + window_num)) |>
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mutate(D = ifelse(week > 27, 1, 0))
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#scale the age numbers
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windowed_data$scaled_project_age <- scale(windowed_data$age_of_project)
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windowed_data$week_offset <- windowed_data$week - 27
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#separate out the cleaning d
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all_actions_data <- windowed_data[which(windowed_data$observation_type == "all"),]
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mrg_actions_data <- windowed_data[which(windowed_data$observation_type == "mrg"),]
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all_actions_data$log1p_count <- log1p(all_actions_data$count)
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# 3 rdd in lmer analysis
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# rdd: https://rpubs.com/phle/r_tutorial_regression_discontinuity_design
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# lmer: https://www.youtube.com/watch?v=LzAwEKrn2Mc
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library(lme4)
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# https://www.bristol.ac.uk/cmm/learning/videos/random-intercepts.html#exvar
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library(optimx)
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library(lattice)
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all_model <- lmer(log1p_count ~ D * I(week_offset)+ scaled_project_age + (D * I(week_offset)| upstream_vcs_link), data=all_actions_data, REML=FALSE, control = lmerControl(
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optimizer ='optimx', optCtrl=list(method='L-BFGS-B')))
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summary(all_model)
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#identifying the quartiles of effect for D
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all_model_ranef <- ranef(all_model, condVar=TRUE)
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dotplot(all_model_ranef)
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df_ranefs <- as.data.frame(all_model_ranef)
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View(df_ranefs)
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D_df_ranef <- df_ranefs[term == "D"]
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D_df_ranef <- df_ranefs[df_ranefs$term == "D"]
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library(tidyverse)
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library(plyr)
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#get the contrib data instead
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try(setwd(dirname(rstudioapi::getActiveDocumentContext()$path)))
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contrib_df <- read_csv("../final_data/deb_contrib_did.csv")
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#some preprocessing and expansion
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col_order <- c("upstream_vcs_link", "age_of_project", "event_date", "event_hash", "before_all_ct", "after_all_ct", "before_mrg_ct", "after_mrg_ct", "before_auth_new", "after_auth_new", "before_commit_new", "after_commit_new")
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contrib_df <- contrib_df[,col_order]
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contrib_df$ct_before_all <- str_split(gsub("[][]","", contrib_df$before_all_ct), ", ")
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contrib_df$ct_after_all <- str_split(gsub("[][]","", contrib_df$after_all_ct), ", ")
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contrib_df$ct_before_mrg <- str_split(gsub("[][]","", contrib_df$before_mrg_ct), ", ")
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contrib_df$ct_after_mrg <- str_split(gsub("[][]","", contrib_df$after_mrg_ct), ", ")
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drop <- c("before_all_ct", "before_mrg_ct", "after_all_ct", "after_mrg_ct")
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contrib_df = contrib_df[,!(names(contrib_df) %in% drop)]
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# 2 some expansion needs to happens for each project
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expand_timeseries <- function(project_row) {
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longer <- project_row |>
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pivot_longer(cols = starts_with("ct"),
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names_to = "window",
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values_to = "count") |>
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unnest(count)
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longer$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(contrib_df[1,])
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for (i in 2:nrow(contrib_df)){
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expanded_data <- rbind(expanded_data, expand_timeseries(contrib_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 >= (27 - window_num) & week <= (27 + window_num)) |>
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mutate(D = ifelse(week > 27, 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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windowed_data$week_offset <- windowed_data$week - 27
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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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#EDA?
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hist(log(all_actions_data$count))
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all_actions_data$logged_count <- log(all_actions_data$count)
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all_actions_data$log1p_count <- log1p(all_actions_data$count)
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# now for merge
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mrg_actions_data$logged_count <- log(mrg_actions_data$count)
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mrg_actions_data$log1p_count <- log1p(mrg_actions_data$count)
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#TKTK ---------------------
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#imports for models
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library(lme4)
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library(optimx)
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library(lattice)
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#models -- TKTK need to be fixed
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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(
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optimizer ='optimx', optCtrl=list(method='L-BFGS-B')))
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summary(all_model)
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#identifying the quartiles of effect for D
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all_model_ranef <- as.data.frame(ranef(all_model))
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View(all_model_ranef)
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# mrg behavior for this
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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(
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optimizer ='optimx', optCtrl=list(method='L-BFGS-B')))
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summary(mrg_model)
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#identifying the quartiles of effect for D
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mrg_model_ranef <- ranef(mrg_model)
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View(mrg_model_ranef)
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dotplot(mrg_model_ranef)
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#load in data
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contrib_df <- read_csv("../final_data/deb_contrib_pop_change.csv")
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readme_df <- read_csv("../final_data/deb_readme_pop_change.csv")
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View(readme_df)
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|
#some expansion needs to happens for each project
|
|
expand_timeseries <- function(project_row) {
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|
longer <- project_row |>
|
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pivot_longer(cols = ends_with("new"),
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names_to = "window",
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values_to = "count") |>
|
|
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)
|
|
}
|
|
expanded_data <- expand_timeseries(readme_df[1,])
|
|
View(expand_timeseries)
|
|
View(expanded_data)
|
|
longer <- project_row |>
|
|
pivot_longer(cols = ends_with("new"),
|
|
names_to = "window",
|
|
values_to = "count") |>
|
|
unnest(count) |>
|
|
mutate(after_doc = str_detect(window, "after"))
|
|
#some expansion needs to happens for each project
|
|
expand_timeseries <- function(project_row) {
|
|
longer <- project_row |>
|
|
pivot_longer(cols = ends_with("new"),
|
|
names_to = "window",
|
|
values_to = "count") |>
|
|
unnest(count) |>
|
|
mutate(after_doc = str_detect(window, "after"))
|
|
return(longer)
|
|
}
|
|
expanded_data <- expand_timeseries(readme_df[1,])
|
|
longer <- project_row |>
|
|
pivot_longer(cols = ends_with("new"),
|
|
names_to = "window",
|
|
values_to = "count") |>
|
|
unnest(count) |>
|
|
mutate(after_doc = as.numeric(str_detect(window, "after")))
|
|
return(longer)
|
|
#some expansion needs to happens for each project
|
|
expand_timeseries <- function(project_row) {
|
|
longer <- project_row |>
|
|
pivot_longer(cols = ends_with("new"),
|
|
names_to = "window",
|
|
values_to = "count") |>
|
|
unnest(count) |>
|
|
mutate(after_doc = as.numeric(str_detect(window, "after")))
|
|
return(longer)
|
|
}
|
|
expanded_data <- expand_timeseries(readme_df[1,])
|
|
#some expansion needs to happens for each project
|
|
expand_timeseries <- function(project_row) {
|
|
longer <- project_row |>
|
|
pivot_longer(cols = ends_with("new"),
|
|
names_to = "window",
|
|
values_to = "count") |>
|
|
unnest(count) |>
|
|
mutate(after_doc = as.numeric(str_detect(window, "after"))) |>
|
|
mutate(is_collab = as.numeric(str_detect(window, "collab")))
|
|
return(longer)
|
|
}
|
|
expanded_data <- expand_timeseries(readme_df[1,])
|
|
for (i in 2:nrow(readme_df)){
|
|
expanded_readme_data <- rbind(expanded_readme_data, expand_timeseries(readme_df[i,]))
|
|
}
|
|
expanded_readme_data <- expand_timeseries(readme_df[1,])
|
|
for (i in 2:nrow(readme_df)){
|
|
expanded_readme_data <- rbind(expanded_readme_data, expand_timeseries(readme_df[i,]))
|
|
}
|
|
expanded_contrib_data <- expand_timeseries(contrib_df[1,])
|
|
for (i in 2:nrow(contrib_df)){
|
|
expanded_contrib_data <- rbind(expanded_contrib_data, expand_timeseries(contrib_df[i,]))
|
|
}
|
|
View(expanded_contrib_data)
|
|
readme_model <- lmer(count ~ after_doc + (1| upstream_vcs_link), data=expanded_readme_data, REML=FALSE)
|
|
summary(readme_model)
|
|
readme_model <- lmer(count ~ after_doc + ( after_doc | upstream_vcs_link), data=expanded_readme_data, REML=FALSE)
|
|
summary(readme_model)
|
|
contrib_model <- lmer(count ~ after_doc + ( after_doc | upstream_vcs_link), data=expanded_contrib_data, REML=FALSE)
|
|
summary(contrib_model)
|
|
collab_readme_model <- lmer(count ~ after_doc + ( after_doc | upstream_vcs_link), data=collab_pop_readme, REML=FALSE)
|
|
#breaking out the types of population counts
|
|
collab_pop_readme <- expanded_readme_data[which(expanded_readme_data$is_collab == 1),]
|
|
contrib_pop_readme <- expanded_readme_data[which(expanded_readme_data$is_collab == 0),]
|
|
collab_pop_contrib <- expanded_contrib_data[which(expanded_contrib_data$is_collab == 1),]
|
|
contrib_pop_contrib <- expanded_contrib_data[which(expanded_contrib_data$is_collab == 0),]
|
|
collab_readme_model <- lmer(count ~ after_doc + ( after_doc | upstream_vcs_link), data=collab_pop_readme, REML=FALSE)
|
|
collab_readme_model <- lmer(count ~ after_doc + (1| upstream_vcs_link), data=collab_pop_readme, REML=FALSE)
|
|
summary(collab_readme_model)
|
|
contrib_readme_model <- lmer(count ~ after_doc + ( 1| upstream_vcs_link), data=contrib_pop_readme, REML=FALSE)
|
|
summary(contrib_readme_model)
|
|
collab_readme_model <- lmer(count ~ after_doc + (after_doc| upstream_vcs_link), data=collab_pop_readme, REML=FALSE)
|
|
collab_readme_model <- lmer(count ~ after_doc + (1| upstream_vcs_link), data=collab_pop_readme, REML=FALSE)
|
|
summary(collab_readme_model)
|
|
contrib_readme_model <- lmer(count ~ after_doc + ( 1| upstream_vcs_link), data=contrib_pop_readme, REML=FALSE)
|
|
summary(contrib_readme_model)
|
|
collab_contrib_model <- lmer(count ~ after_doc + ( 1 | upstream_vcs_link), data=collab_pop_contrib, REML=FALSE)
|
|
summary(collab_contrib_model)
|
|
contrib_contrib_model <- lmer(count ~ after_doc + ( 1 | upstream_vcs_link), data=contrib_pop_contrib, REML=FALSE)
|
|
summary(contrib_contrib_model)
|
|
expanded_readme_data |>
|
|
ggplot(aes(x = after_doc, y = count, col = is_collab)) +
|
|
geom_point()
|
|
ggplot(aes(x = after_doc, y = count, col = as.factor(is_collab)) +
|
|
expanded_readme_data |>
|
|
ggplot(aes(x = after_doc, y = count, col = as.factor(is_collab))) +
|
|
geom_point()
|
|
expanded_readme_data |>
|
|
expanded_readme_data |>
|
|
ggplot(aes(x = after_doc, y = count, col = as.factor(is_collab))) +
|
|
geom_point()
|
|
expanded_readme_data |>
|
|
ggplot(aes(x = as.factor(after_doc), y = count, col = as.factor(is_collab))) +
|
|
geom_point()
|
|
expanded_readme_data |>
|
|
ggplot(aes(x = as.factor(after_doc), y = scale(count), col = as.factor(is_collab))) +
|
|
geom_point()
|
|
expanded_readme_data$log1pcount <- log1p(expanded_readme_data$count)
|
|
expanded_contrib_data$log1pcount <- log1p(expanded_contrib_data$count)
|
|
expanded_readme_data |>
|
|
ggplot(aes(x = as.factor(after_doc), y = log1pcount, col = as.factor(is_collab))) +
|
|
geom_point()
|
|
expanded_readme_data |>
|
|
ggplot(aes(x = as.factor(after_doc), y = log1pcount, col = as.factor(is_collab))) +
|
|
geom_point() +
|
|
geom_smooth(method = 'lm', se = F)
|
|
expanded_readme_data |>
|
|
ggplot(aes(x = after_doc, y = log1pcount, col = as.factor(is_collab))) +
|
|
geom_point() +
|
|
geom_smooth(method = 'lm', se = F)
|
|
expanded_contrib_data |>
|
|
ggplot(aes(x = after_doc, y = log1pcount, col = as.factor(is_collab))) +
|
|
geom_point() +
|
|
geom_smooth(method = 'lm', se = F)
|
|
expanded_readme_data |>
|
|
ggplot(aes(x = after_doc, y = log1pcount, col = as.factor(is_collab))) +
|
|
geom_point() +
|
|
geom_smooth(method = 'lm', se = F) + geom_jitter()
|
|
expanded_readme_data |>
|
|
ggplot(aes(x = after_doc, y = count, col = as.factor(is_collab))) +
|
|
geom_point() +
|
|
geom_smooth(method = 'lm', se = F) + geom_jitter()
|
|
expanded_readme_data$logcount <- log(expanded_readme_data$count)
|
|
expanded_contrib_data$logcount <- log(expanded_contrib_data$count)
|
|
expanded_readme_data |>
|
|
ggplot(aes(x = after_doc, y = logcount, col = as.factor(is_collab))) +
|
|
geom_point() +
|
|
geom_smooth(method = 'lm', se = F) + geom_jitter()
|
|
expanded_contrib_data |>
|
|
ggplot(aes(x = after_doc, y = log1pcount, col = as.factor(is_collab))) +
|
|
geom_point() +
|
|
geom_smooth(method = 'lm', se = F)
|
|
expanded_contrib_data |>
|
|
ggplot(aes(x = after_doc, y = log1pcount, col = as.factor(is_collab))) +
|
|
geom_point() +
|
|
geom_smooth(method = 'lm', se = F) + geom_jitter()
|
|
expanded_contrib_data |>
|
|
ggplot(aes(x = after_doc, y = count, col = as.factor(is_collab))) +
|
|
geom_point() +
|
|
geom_smooth(method = 'lm', se = F) + geom_jitter()
|
|
expanded_readme_data |>
|
|
ggplot(aes(x = after_doc, y = logcount, col = as.factor(is_collab))) +
|
|
geom_point() +
|
|
geom_smooth(method = 'lm', se = F) + geom_jitter()
|
|
collab_readme_model <- lmer(logcount ~ after_doc + (1| upstream_vcs_link), data=collab_pop_readme, REML=FALSE)
|
|
#breaking out the types of population counts
|
|
collab_pop_readme <- expanded_readme_data[which(expanded_readme_data$is_collab == 1),]
|
|
contrib_pop_readme <- expanded_readme_data[which(expanded_readme_data$is_collab == 0),]
|
|
collab_pop_contrib <- expanded_contrib_data[which(expanded_contrib_data$is_collab == 1),]
|
|
contrib_pop_contrib <- expanded_contrib_data[which(expanded_contrib_data$is_collab == 0),]
|
|
collab_readme_model <- lmer(logcount ~ after_doc + (1| upstream_vcs_link), data=collab_pop_readme, REML=FALSE)
|
|
collab_readme_model <- lmer(log1pcount ~ after_doc + (1| upstream_vcs_link), data=collab_pop_readme, REML=FALSE)
|
|
summary(collab_readme_model)
|
|
contrib_readme_model <- lmer(log1pcount ~ after_doc + ( 1| upstream_vcs_link), data=contrib_pop_readme, REML=FALSE)
|
|
summary(contrib_readme_model)
|
|
collab_contrib_model <- lmer(log1pcount ~ after_doc + ( 1 | upstream_vcs_link), data=collab_pop_contrib, REML=FALSE)
|
|
summary(collab_contrib_model)
|
|
contrib_contrib_model <- lmer(log1pcount ~ after_doc + ( 1 | upstream_vcs_link), data=contrib_pop_contrib, REML=FALSE)
|
|
summary(contrib_contrib_model)
|
|
library(ggplot2)
|
|
expanded_readme_data |>
|
|
ggplot(aes(x = after_doc, y = logcount, col = as.factor(is_collab))) +
|
|
geom_point() +
|
|
geom_smooth(method = 'lm', se = F) + geom_jitter()
|
|
expanded_readme_data |>
|
|
ggplot(aes(x = after_doc, y = log1pcount, col = as.factor(is_collab))) +
|
|
geom_point() +
|
|
geom_smooth(method = 'lm', se = F) + geom_jitter()
|
|
expanded_contrib_data |>
|
|
ggplot(aes(x = after_doc, y = count, col = as.factor(is_collab))) +
|
|
geom_point() +
|
|
geom_smooth(method = 'lm', se = F) + geom_jitter()
|