2024-04-15 14:38:41 +00:00
|
|
|
window_num <- 10
|
|
|
|
|
longer <- longer %>%
|
|
|
|
|
filter(week >= (26 - window_num) & week <= (26 + window_num))
|
2024-04-09 01:20:50 +00:00
|
|
|
#testing out analysis below
|
|
|
|
|
longer[which(longer$observation_type == "all"),] |>
|
|
|
|
|
ggplot(aes(x = week, y = count)) +
|
|
|
|
|
geom_point() +
|
|
|
|
|
geom_vline(xintercept = 26)
|
2024-04-15 14:38:41 +00:00
|
|
|
longer[which(longer$observation_type == "all"),] |>
|
|
|
|
|
mutate(D = ifelse(week >= 26, 1, 0)) |>
|
|
|
|
|
lm(formula = count ~ D * I(week - 26)) |>
|
|
|
|
|
summary()
|
|
|
|
|
longer[which(longer$observation_type == "all"),] |>
|
|
|
|
|
select(count, week) |>
|
|
|
|
|
mutate(D = as.factor(ifelse(week >= 26, 1, 0))) |>
|
|
|
|
|
ggplot(aes(x = week, y = count, color = D)) +
|
|
|
|
|
geom_point() +
|
|
|
|
|
geom_smooth(se = FALSE) +
|
|
|
|
|
geom_vline(xintercept = 26)
|
2024-04-09 01:20:50 +00:00
|
|
|
# test_two <- c()
|
|
|
|
|
# iterator <- 0
|
|
|
|
|
# for (entry in test) {
|
|
|
|
|
# readme_df$cnt_before_all[iterator] <- as.numeric(unlist(entry))
|
|
|
|
|
# print(as.numeric(unlist(entry)))
|
|
|
|
|
# iterator <- iterator + 1
|
|
|
|
|
# }
|
|
|
|
|
# test_two
|
|
|
|
|
#Yes, need to expand the dataframe, but again, for the sake of clarity, do not want to until analysis step
|
2024-04-15 14:38:41 +00:00
|
|
|
# https://rpubs.com/phle/r_tutorial_regression_discontinuity_design
|
|
|
|
|
new_test <- readme_df[697,]
|
2024-04-09 01:20:50 +00:00
|
|
|
longer <- new_test |>
|
|
|
|
|
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)))
|
2024-04-15 14:38:41 +00:00
|
|
|
longer$count <- as.numeric(longer$count)
|
|
|
|
|
window_num <- 27
|
|
|
|
|
longer <- longer %>%
|
|
|
|
|
filter(week >= (26 - window_num) & week <= (26 + window_num))
|
2024-04-09 01:20:50 +00:00
|
|
|
#testing out analysis below
|
|
|
|
|
longer[which(longer$observation_type == "all"),] |>
|
|
|
|
|
ggplot(aes(x = week, y = count)) +
|
|
|
|
|
geom_point() +
|
|
|
|
|
geom_vline(xintercept = 26)
|
2024-04-15 14:38:41 +00:00
|
|
|
longer[which(longer$observation_type == "all"),] |>
|
|
|
|
|
mutate(D = ifelse(week >= 26, 1, 0)) |>
|
|
|
|
|
lm(formula = count ~ D * I(week - 26)) |>
|
|
|
|
|
summary()
|
|
|
|
|
longer[which(longer$observation_type == "all"),] |>
|
|
|
|
|
select(count, week) |>
|
|
|
|
|
mutate(D = as.factor(ifelse(week >= 26, 1, 0))) |>
|
|
|
|
|
ggplot(aes(x = week, y = count, color = D)) +
|
|
|
|
|
geom_point() +
|
|
|
|
|
geom_smooth(se = FALSE) +
|
|
|
|
|
geom_vline(xintercept = 26)
|
|
|
|
|
window_num <- 13
|
|
|
|
|
longer <- longer %>%
|
|
|
|
|
filter(week >= (26 - window_num) & week <= (26 + window_num))
|
|
|
|
|
#testing out analysis below
|
|
|
|
|
longer[which(longer$observation_type == "all"),] |>
|
|
|
|
|
ggplot(aes(x = week, y = count)) +
|
|
|
|
|
geom_point() +
|
|
|
|
|
geom_vline(xintercept = 26)
|
|
|
|
|
longer[which(longer$observation_type == "all"),] |>
|
|
|
|
|
mutate(D = ifelse(week >= 26, 1, 0)) |>
|
|
|
|
|
lm(formula = count ~ D * I(week - 26)) |>
|
|
|
|
|
summary()
|
|
|
|
|
longer[which(longer$observation_type == "all"),] |>
|
|
|
|
|
select(count, week) |>
|
|
|
|
|
mutate(D = as.factor(ifelse(week >= 26, 1, 0))) |>
|
|
|
|
|
ggplot(aes(x = week, y = count, color = D)) +
|
|
|
|
|
geom_point() +
|
|
|
|
|
geom_smooth(se = FALSE) +
|
|
|
|
|
geom_vline(xintercept = 26)
|
|
|
|
|
longer[which(longer$observation_type == "all"),] |>
|
|
|
|
|
select(count, week) |>
|
|
|
|
|
mutate(D = as.factor(ifelse(week >= 26, 1, 0))) |>
|
|
|
|
|
ggplot(aes(x = week, y = count, color = D)) +
|
|
|
|
|
geom_point() +
|
|
|
|
|
geom_smooth(se = TRUE) +
|
|
|
|
|
geom_vline(xintercept = 26)
|
|
|
|
|
#testing out analysis below
|
|
|
|
|
longer[which(longer$observation_type == "all"),] |>
|
|
|
|
|
ggplot(aes(x = week, y = count)) +
|
|
|
|
|
geom_point() +
|
|
|
|
|
geom_vline(xintercept = 25.5)
|
|
|
|
|
longer[which(longer$observation_type == "all"),] |>
|
|
|
|
|
mutate(D = ifelse(week >= 26, 1, 0)) |>
|
|
|
|
|
lm(formula = count ~ D * I(week - 26)) |>
|
|
|
|
|
summary()
|
|
|
|
|
longer[which(longer$observation_type == "all"),] |>
|
|
|
|
|
select(count, week) |>
|
|
|
|
|
mutate(D = as.factor(ifelse(week >= 26, 1, 0))) |>
|
|
|
|
|
ggplot(aes(x = week, y = count, color = D)) +
|
|
|
|
|
geom_point() +
|
|
|
|
|
geom_smooth(se = TRUE) +
|
|
|
|
|
geom_vline(xintercept = 25.5)
|
|
|
|
|
#testing out analysis below
|
|
|
|
|
longer[which(longer$observation_type == "all"),] |>
|
|
|
|
|
ggplot(aes(x = week, y = count)) +
|
|
|
|
|
geom_point() +
|
|
|
|
|
geom_vline(xintercept = 26)
|
|
|
|
|
longer[which(longer$observation_type == "all"),] |>
|
|
|
|
|
mutate(D = ifelse(week >= 26, 1, 0)) |>
|
|
|
|
|
lm(formula = count ~ D * I(week - 26)) |>
|
|
|
|
|
summary()
|
|
|
|
|
longer[which(longer$observation_type == "all"),] |>
|
|
|
|
|
select(count, week) |>
|
|
|
|
|
mutate(D = as.factor(ifelse(week >= 26, 1, 0))) |>
|
|
|
|
|
ggplot(aes(x = week, y = count, color = D)) +
|
|
|
|
|
geom_point() +
|
|
|
|
|
geom_smooth(se = TRUE) +
|
|
|
|
|
geom_vline(xintercept = 26)
|
|
|
|
|
library(rdd-package)
|
|
|
|
|
library(rdd)
|
|
|
|
|
library(rdd)
|
2024-04-09 01:20:50 +00:00
|
|
|
# test_two <- c()
|
|
|
|
|
# iterator <- 0
|
|
|
|
|
# for (entry in test) {
|
|
|
|
|
# readme_df$cnt_before_all[iterator] <- as.numeric(unlist(entry))
|
|
|
|
|
# print(as.numeric(unlist(entry)))
|
|
|
|
|
# iterator <- iterator + 1
|
|
|
|
|
# }
|
|
|
|
|
# test_two
|
|
|
|
|
#Yes, need to expand the dataframe, but again, for the sake of clarity, do not want to until analysis step
|
2024-04-15 14:38:41 +00:00
|
|
|
# https://rpubs.com/phle/r_tutorial_regression_discontinuity_design
|
|
|
|
|
new_test <- readme_df[697,]
|
2024-04-09 01:20:50 +00:00
|
|
|
longer <- new_test |>
|
|
|
|
|
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)))
|
2024-04-15 14:38:41 +00:00
|
|
|
longer$count <- as.numeric(longer$count)
|
|
|
|
|
#longer <- longer %>%
|
|
|
|
|
# filter(week >= (26 - window_num) & week <= (26 + window_num))
|
|
|
|
|
IKbandwidth(longer$week, longer$count, cutpoint = 26, verbose = FALSE, kernel = "triangular")
|
2024-04-09 01:20:50 +00:00
|
|
|
#testing out analysis below
|
|
|
|
|
longer[which(longer$observation_type == "all"),] |>
|
|
|
|
|
ggplot(aes(x = week, y = count)) +
|
|
|
|
|
geom_point() +
|
|
|
|
|
geom_vline(xintercept = 26)
|
2024-04-15 14:38:41 +00:00
|
|
|
longer[which(longer$observation_type == "all"),] |>
|
|
|
|
|
mutate(D = ifelse(week >= 26, 1, 0)) |>
|
|
|
|
|
lm(formula = count ~ D * I(week - 26)) |>
|
|
|
|
|
summary()
|
|
|
|
|
longer[which(longer$observation_type == "all"),] |>
|
|
|
|
|
select(count, week) |>
|
|
|
|
|
mutate(D = as.factor(ifelse(week >= 26, 1, 0))) |>
|
|
|
|
|
ggplot(aes(x = week, y = count, color = D)) +
|
|
|
|
|
geom_point() +
|
|
|
|
|
geom_smooth(se = TRUE) +
|
|
|
|
|
geom_vline(xintercept = 26)
|
2024-04-09 01:20:50 +00:00
|
|
|
# test_two <- c()
|
|
|
|
|
# iterator <- 0
|
|
|
|
|
# for (entry in test) {
|
|
|
|
|
# readme_df$cnt_before_all[iterator] <- as.numeric(unlist(entry))
|
|
|
|
|
# print(as.numeric(unlist(entry)))
|
|
|
|
|
# iterator <- iterator + 1
|
|
|
|
|
# }
|
|
|
|
|
# test_two
|
|
|
|
|
#Yes, need to expand the dataframe, but again, for the sake of clarity, do not want to until analysis step
|
2024-04-15 14:38:41 +00:00
|
|
|
# https://rpubs.com/phle/r_tutorial_regression_discontinuity_design
|
|
|
|
|
new_test <- readme_df[0,]
|
2024-04-09 01:20:50 +00:00
|
|
|
longer <- new_test |>
|
|
|
|
|
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)))
|
2024-04-15 14:38:41 +00:00
|
|
|
longer$count <- as.numeric(longer$count)
|
|
|
|
|
#longer <- longer %>%
|
|
|
|
|
# filter(week >= (26 - window_num) & week <= (26 + window_num))
|
|
|
|
|
IKbandwidth(longer$week, longer$count, cutpoint = 26, verbose = FALSE, kernel = "triangular")
|
2024-04-09 01:20:50 +00:00
|
|
|
# test_two <- c()
|
|
|
|
|
# iterator <- 0
|
|
|
|
|
# for (entry in test) {
|
|
|
|
|
# readme_df$cnt_before_all[iterator] <- as.numeric(unlist(entry))
|
|
|
|
|
# print(as.numeric(unlist(entry)))
|
|
|
|
|
# iterator <- iterator + 1
|
|
|
|
|
# }
|
|
|
|
|
# test_two
|
|
|
|
|
#Yes, need to expand the dataframe, but again, for the sake of clarity, do not want to until analysis step
|
2024-04-15 14:38:41 +00:00
|
|
|
# https://rpubs.com/phle/r_tutorial_regression_discontinuity_design
|
|
|
|
|
new_test <- readme_df[3,]
|
2024-04-09 01:20:50 +00:00
|
|
|
longer <- new_test |>
|
|
|
|
|
pivot_longer(cols = starts_with("ct"),
|
|
|
|
|
names_to = "window",
|
|
|
|
|
values_to = "count") |>
|
|
|
|
|
unnest(count)
|
|
|
|
|
longer$observation_type <- gsub("^.*_", "", longer$window)
|
|
|
|
|
longer <- ddply(longer, "observation_type", transform, week=seq(from=0, by=1, length.out=length(observation_type)))
|
2024-04-15 14:38:41 +00:00
|
|
|
longer$count <- as.numeric(longer$count)
|
|
|
|
|
#longer <- longer %>%
|
|
|
|
|
# filter(week >= (26 - window_num) & week <= (26 + window_num))
|
|
|
|
|
IKbandwidth(longer$week, longer$count, cutpoint = 26, verbose = FALSE, kernel = "triangular")
|
2024-04-09 01:20:50 +00:00
|
|
|
# test_two <- c()
|
|
|
|
|
# iterator <- 0
|
|
|
|
|
# for (entry in test) {
|
|
|
|
|
# readme_df$cnt_before_all[iterator] <- as.numeric(unlist(entry))
|
|
|
|
|
# print(as.numeric(unlist(entry)))
|
|
|
|
|
# iterator <- iterator + 1
|
|
|
|
|
# }
|
|
|
|
|
# test_two
|
|
|
|
|
#Yes, need to expand the dataframe, but again, for the sake of clarity, do not want to until analysis step
|
2024-04-15 14:38:41 +00:00
|
|
|
# https://rpubs.com/phle/r_tutorial_regression_discontinuity_design
|
|
|
|
|
new_test <- readme_df[9,]
|
2024-04-09 01:20:50 +00:00
|
|
|
longer <- new_test |>
|
|
|
|
|
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)
|
2024-04-15 14:38:41 +00:00
|
|
|
#longer <- longer %>%
|
|
|
|
|
# filter(week >= (26 - window_num) & week <= (26 + window_num))
|
|
|
|
|
IKbandwidth(longer$week, longer$count, cutpoint = 26, verbose = FALSE, kernel = "triangular")
|
|
|
|
|
get_optimal_window <- 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)
|
|
|
|
|
optimal_bandwidth <- IKbandwidth(longer$week, longer$count, cutpoint = 26, verbose = FALSE, kernel = "triangular")
|
|
|
|
|
return(optimal_bandwidth)
|
|
|
|
|
}
|
|
|
|
|
bandwidths <- c()
|
|
|
|
|
for (i in 1:nrow(readme_df)){
|
|
|
|
|
bandwidths <- c(bandwidths, get_optimal_window(readme_df[i,]))
|
|
|
|
|
}
|
|
|
|
|
bandwidths
|
|
|
|
|
mean(bandwidths)
|
|
|
|
|
median(bandwidths)
|
|
|
|
|
get_optimal_window <- 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$observation_type == "all"),]
|
|
|
|
|
optimal_bandwidth <- IKbandwidth(longer$week, longer$count, cutpoint = 26, verbose = FALSE, kernel = "triangular")
|
|
|
|
|
return(optimal_bandwidth)
|
|
|
|
|
}
|
|
|
|
|
bandwidths <- c()
|
|
|
|
|
for (i in 1:nrow(readme_df)){
|
|
|
|
|
bandwidths <- c(bandwidths, get_optimal_window(readme_df[i,]))
|
|
|
|
|
}
|
|
|
|
|
mean(bandwidths)
|
|
|
|
|
median(bandwidths)
|
|
|
|
|
bandwidths <- c()
|
|
|
|
|
for (i in 1:nrow(readme_df)){
|
|
|
|
|
bandwidth <- get_optimal_window(readme_df[i,])
|
|
|
|
|
bandwidths <- c(bandwidths, bandwidth)
|
|
|
|
|
}
|
|
|
|
|
mean(bandwidths)
|
|
|
|
|
median(bandwidths)
|
|
|
|
|
get_optimal_window <- function(project_row) {
|
|
|
|
|
longer <- project_row |>
|
|
|
|
|
pivot_longer(cols = starts_with("ct"),
|
|
|
|
|
names_to = "window",
|
|
|
|
|
values_to = "count") |>
|
|
|
|
|
unnest(count)
|
|
|
|
|
longer$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$count)
|
|
|
|
|
#this below line makes the code specific to the all-commits data
|
|
|
|
|
longer <- longer[which(longer$observation_type == "all"),]
|
|
|
|
|
result <- tryCatch({
|
|
|
|
|
optimal_bandwidth <- IKbandwidth(longer$week, longer$count, cutpoint = 26, verbose = FALSE, kernel = "triangular")
|
|
|
|
|
return(optimal_bandwidth)
|
|
|
|
|
}, error = function(e){
|
|
|
|
|
return(8)
|
|
|
|
|
})
|
|
|
|
|
}
|
|
|
|
|
bandwidths <- c()
|
|
|
|
|
for (i in 1:nrow(readme_df)){
|
|
|
|
|
bandwidth <- get_optimal_window(readme_df[i,])
|
|
|
|
|
bandwidths <- c(bandwidths, bandwidth)
|
|
|
|
|
}
|
|
|
|
|
mean(bandwidths)
|
|
|
|
|
median(bandwidths)
|
|
|
|
|
mode(bandwidths)
|
|
|
|
|
table(bandwidths)
|
|
|
|
|
mean(bandwidths) #
|
|
|
|
|
median(bandwidths)
|
|
|
|
|
# this is the file with the lmer multi-level rddAnalysis
|
|
|
|
|
# 0 loading the readme data in
|
|
|
|
|
try(setwd(dirname(rstudioapi::getActiveDocumentContext()$path)))
|
|
|
|
|
readme_df <- read_csv("../final_data/deb_readme_did.csv")
|
|
|
|
|
# 1 preprocessing
|
|
|
|
|
colnames(readme_df) <- c("upstream_vcs_link", "event_date", "event_hash", "before_all_ct", "before_mrg_ct", "after_all_ct", "after_mrg_ct", "before_auth_new", "after_commit_new", "after_auth_new", "before_commit_new")
|
|
|
|
|
col_order <- c("upstream_vcs_link", "event_date", "event_hash", "before_all_ct", "after_all_ct", "before_mrg_ct", "after_mrg_ct", "before_auth_new", "after_auth_new", "before_commit_new", "after_commit_new")
|
|
|
|
|
readme_df <- readme_df[,col_order]
|
|
|
|
|
readme_df$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,]))
|
|
|
|
|
}
|
|
|
|
|
View(expanded_data)
|
|
|
|
|
View(expanded_data)
|
|
|
|
|
View(expanded_data)
|
|
|
|
|
View(expanded_data)
|
|
|
|
|
View(expanded_data)
|
|
|
|
|
get_optimal_window <- 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)
|
|
|
|
|
#this below line makes the code specific to the all-commits data
|
|
|
|
|
longer <- longer[which(longer$observation_type == "all"),]
|
|
|
|
|
result <- tryCatch({
|
|
|
|
|
#Imbens-Kalyanaraman Optimal Bandwidth Calculation
|
|
|
|
|
optimal_bandwidth <- IKbandwidth(longer$week, longer$count, cutpoint = 26, verbose = FALSE, kernel = "triangular")
|
|
|
|
|
return(optimal_bandwidth)
|
|
|
|
|
}, error = function(e){
|
|
|
|
|
return(9)
|
|
|
|
|
})
|
|
|
|
|
}
|
|
|
|
|
#this just gets the optimal bandwith window for each project and then appends to lists
|
|
|
|
|
bandwidths <- c()
|
|
|
|
|
for (i in 1:nrow(readme_df)){
|
|
|
|
|
bandwidth <- get_optimal_window(readme_df[i,])
|
|
|
|
|
bandwidths <- c(bandwidths, bandwidth)
|
|
|
|
|
}
|
|
|
|
|
mean(bandwidths) #8.574233
|
|
|
|
|
median(bandwidths) #8.363088
|
|
|
|
|
table(bandwidths)
|
|
|
|
|
#filter out the timewindows
|
|
|
|
|
window_num <- 8
|
|
|
|
|
expanded_data |>
|
|
|
|
|
filter(week >= (26 - window_num) & week <= (26 + window_num))
|
|
|
|
|
expanded_data |>
|
|
|
|
|
filter(week >= (26 - window_num) & week <= (26 + window_num))
|
|
|
|
|
# 3 rdd in lmer analysis
|
|
|
|
|
library(lme4)
|
|
|
|
|
draft_model <- lmer(count ~ D * I(week - 26) + upstream_vcs_link, data=expanded_data[which(longer$observation_type == "all"),])
|
|
|
|
|
expanded_data |>
|
|
|
|
|
filter(week >= (26 - window_num) & week <= (26 + window_num)) |>
|
|
|
|
|
mutate(D = ifelse(week >= 26, 1, 0))
|
|
|
|
|
# 3 rdd in lmer analysis
|
|
|
|
|
library(lme4)
|
|
|
|
|
draft_model <- lmer(count ~ D * I(week - 26) + upstream_vcs_link, data=expanded_data[which(longer$observation_type == "all"),])
|
|
|
|
|
summary(draft_model)
|
|
|
|
|
View(expanded_data)
|
|
|
|
|
#filter out the timewindows
|
|
|
|
|
window_num <- 8
|
|
|
|
|
expanded_data <- expanded_data |>
|
|
|
|
|
filter(week >= (26 - window_num) & week <= (26 + window_num)) |>
|
|
|
|
|
mutate(D = ifelse(week >= 26, 1, 0))
|
|
|
|
|
draft_model <- lmer(count ~ D * I(week - 26) + upstream_vcs_link, data=expanded_data[which(longer$observation_type == "all"),])
|
|
|
|
|
summary(draft_model)
|
|
|
|
|
draft_model <- lmer(count ~ D * I(week - 26) + upstream_vcs_link, REML=FALSE, data=expanded_data[which(longer$observation_type == "all"),])
|
|
|
|
|
draft_model <- lmer(count ~ D * I(week - 26) + upstream_vcs_link, REML=FALSE, data=expanded_data[which(longer$observation_type == "all"),])
|
|
|
|
|
draft_model <- lmer(count ~ D * I(week - 26) + (1|upstream_vcs_link), REML=FALSE, data=expanded_data[which(longer$observation_type == "all"),])
|
|
|
|
|
summary(draft_model)
|
2024-04-16 00:36:11 +00:00
|
|
|
# this is the file with the lmer multi-level rddAnalysis
|
|
|
|
|
# 0 loading the readme data in
|
|
|
|
|
try(setwd(dirname(rstudioapi::getActiveDocumentContext()$path)))
|
|
|
|
|
readme_df <- read_csv("../final_data/deb_readme_did.csv")
|
|
|
|
|
# this is the file with the lmer multi-level rddAnalysis
|
|
|
|
|
library(tidyverse)
|
|
|
|
|
# 0 loading the readme data in
|
|
|
|
|
try(setwd(dirname(rstudioapi::getActiveDocumentContext()$path)))
|
|
|
|
|
readme_df <- read_csv("../final_data/deb_readme_did.csv")
|
|
|
|
|
# 1 preprocessing
|
|
|
|
|
colnames(readme_df) <- c("upstream_vcs_link", "event_date", "event_hash", "before_all_ct", "before_mrg_ct", "after_all_ct", "after_mrg_ct", "before_auth_new", "after_commit_new", "after_auth_new", "before_commit_new")
|
|
|
|
|
col_order <- c("upstream_vcs_link", "event_date", "event_hash", "before_all_ct", "after_all_ct", "before_mrg_ct", "after_mrg_ct", "before_auth_new", "after_auth_new", "before_commit_new", "after_commit_new")
|
|
|
|
|
readme_df <- readme_df[,col_order]
|
|
|
|
|
readme_df$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,]))
|
|
|
|
|
}
|
|
|
|
|
library(plyr)
|
|
|
|
|
# 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$observation_type == "all"),]
|
|
|
|
|
return(longer)
|
|
|
|
|
}
|
|
|
|
|
expanded_data <- expand_timeseries(readme_df[1,])
|
|
|
|
|
for (i in 2:nrow(readme_df)){
|
|
|
|
|
expanded_data <- rbind(expanded_data, expand_timeseries(readme_df[i,]))
|
|
|
|
|
}
|
|
|
|
|
#filter out the timewindows
|
|
|
|
|
window_num <- 8
|
|
|
|
|
expanded_data <- expanded_data |>
|
|
|
|
|
filter(week >= (26 - window_num) & week <= (26 + window_num)) |>
|
|
|
|
|
mutate(D = ifelse(week >= 26, 1, 0))
|
|
|
|
|
# 3 rdd in lmer analysis
|
|
|
|
|
library(lme4)
|
|
|
|
|
draft_model <- lmer(count ~ D * I(week - 26) + (1|as.factor(upstream_vcs_link)), REML=FALSE, data=expanded_data[which(longer$observation_type == "all"),])
|
|
|
|
|
draft_model <- lmer(count ~ D * I(week - 26) + (1|as.factor(upstream_vcs_link)), REML=FALSE, data=expanded_data[which(expanded_data$observation_type == "all"),])
|
|
|
|
|
draft_model <- lmer(count ~ D * I(week - 26) + (1|upstream_vcs_link), REML=FALSE, data=expanded_data[which(expanded_data$observation_type == "all"),])
|
|
|
|
|
summary(draft_model)
|
2024-04-18 01:09:30 +00:00
|
|
|
# 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()$path)))
|
|
|
|
|
readme_df <- read_csv("../final_data/deb_readme_did.csv")
|
|
|
|
|
# 1 preprocessing
|
|
|
|
|
colnames(readme_df) <- c("upstream_vcs_link", "event_date", "event_hash", "before_all_ct", "before_mrg_ct", "after_all_ct", "after_mrg_ct", "before_auth_new", "after_commit_new", "after_auth_new", "before_commit_new")
|
|
|
|
|
col_order <- c("upstream_vcs_link", "event_date", "event_hash", "before_all_ct", "after_all_ct", "before_mrg_ct", "after_mrg_ct", "before_auth_new", "after_auth_new", "before_commit_new", "after_commit_new")
|
|
|
|
|
readme_df <- readme_df[,col_order]
|
|
|
|
|
readme_df$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 timewindows
|
|
|
|
|
window_num <- 8
|
|
|
|
|
expanded_data <- expanded_data |>
|
|
|
|
|
filter(week >= (26 - window_num) & week <= (26 + window_num)) |>
|
|
|
|
|
mutate(D = ifelse(week >= 26, 1, 0))
|
|
|
|
|
# 3 rdd in lmer analysis
|
|
|
|
|
library(lme4)
|
|
|
|
|
draft_model <- lmer(count ~ D * I(week - 26) + (1|upstream_vcs_link), REML=FALSE, data=expanded_data[which(expanded_data$observation_type == "all"),])
|
|
|
|
|
summary(draft_model)
|
|
|
|
|
expanded_data <- expanded_data |>
|
|
|
|
|
filter(week >= (26 - window_num) & week <= (26 + window_num)) |>
|
|
|
|
|
mutate(D = ifelse(week > 26, 1, 0))
|
|
|
|
|
# 3 rdd in lmer analysis
|
|
|
|
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# rdd: https://rpubs.com/phle/r_tutorial_regression_discontinuity_design
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# lmer: https://www.youtube.com/watch?v=LzAwEKrn2Mc
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library(lme4)
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draft_model <- lmer(count ~ D * I(week - 26) + (1|upstream_vcs_link), REML=FALSE, data=expanded_data[which(expanded_data$observation_type == "all"),])
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summary(draft_model)
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View(expanded_data)
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draft_all_model <- lmer(count ~ D * I(week - 26) + (1|upstream_vcs_link), REML=FALSE, data=expanded_data[which(expanded_data$observation_type == "all"),])
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summary(draft_all_model)
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draft_mrg_model <- lmer(count ~ D * I(week - 26) + (1|upstream_vcs_link), REML=FALSE, data=expanded_data[which(expanded_data$observation_type == "mrg"),])
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summary(draft_mrg_model)
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draft_all_model <- lmer(count ~ D * I(week - 26) + (1|upstream_vcs_link), REML=TRUE, data=expanded_data[which(expanded_data$observation_type == "all"),])
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summary(draft_all_model)
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summary(draft_all_model)
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