library(plyr)
library(tidyverse)


#set wd, read in data
try(setwd(dirname(rstudioapi::getActiveDocumentContext()$path)))
readme_df <- read_csv("../final_data/deb_readme_did.csv")
contributing_df <- read_csv("../final_data/deb_contrib_did.csv")

#preprocessing for readme_df
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)]

#preprocessing for contributing_df


# test <- readme_df$cnt_before_all
# as.numeric(unlist(test[1]))
# test_two <- c()
# iterator <- 0
# for (entry in test) {
#   readme_df$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
new_test <- readme_df[231,]
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)

#sapply(longer, class)

#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)