first draft of mlm rdd
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mjgaughan
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R/.Rhistory
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R/.Rhistory
@ -1,488 +1,51 @@
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pivot_longer(cols = starts_with("cnt"),
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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 <- new_test |>
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pivot_longer(cols = starts_with("cnt"),
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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
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longer <- new_test |>
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pivot_longer(cols = starts_with("cnt"),
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names_to = "window",
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values_to = "count") |>
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unnest(count) |>
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as.numeric(unlist(count))
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longer <- new_test |>
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pivot_longer(cols = starts_with("cnt"),
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names_to = "window",
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values_to = "count") |>
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unnest(count) |>
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unlist(count)
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View(new_longer)
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new_longer
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longer <- new_test |>
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pivot_longer(cols = starts_with("cnt"),
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names_to = "window",
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values_to = "count") |>
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unnest(count) |>
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unlist(count) |>
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as.numeric(count)
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View(new_longer)
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new_longer
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longer <- new_test |>
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pivot_longer(cols = starts_with("cnt"),
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names_to = "window",
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values_to = "count") |>
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unnest(count) |>
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unlist(count) |>
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as.numeric(count)
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longer
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longer <- new_test |>
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pivot_longer(cols = starts_with("cnt"),
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names_to = "window",
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values_to = "count") |>
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unnest(count) |>
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unlist(count)
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longer
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longer <- new_test |>
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pivot_longer(cols = starts_with("cnt"),
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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
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View(longer)
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longer <- new_test |>
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pivot_longer(cols = starts_with("cnt"),
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names_to = "window",
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values_to = "count") |>
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unnest(count) |>
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as.numeric(count)
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longer
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longer <- new_test |>
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pivot_longer(cols = starts_with("cnt"),
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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
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library(tidyverse)
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#set wd, read in data
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try(setwd(dirname(rstudioapi::getActiveDocumentContext()$path)))
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readme_df <- read_csv("../final_data/deb_readme_did.csv")
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#preprocessing for readme_df
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colnames(readme_df) <- c("upstream_vcs_link", "event_date", "event_hash", "before_all_ct", "before_mrg_ct", "after_all_ct", "after_mrg_ct", "before_auth_new", "after_commit_new", "after_auth_new", "before_commit_new")
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col_order <- c("upstream_vcs_link", "event_date", "event_hash", "before_all_ct", "after_all_ct", "before_mrg_ct", "after_mrg_ct", "before_auth_new", "after_auth_new", "before_commit_new", "after_commit_new")
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readme_df <- readme_df[,col_order]
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readme_df$cnt_before_all <- str_split(gsub("[][]","", readme_df$before_all_cnt), ", ")
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readme_df$cnt_after_all <- str_split(gsub("[][]","", readme_df$after_all_cnt), ", ")
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readme_df$cnt_before_mrg <- str_split(gsub("[][]","", readme_df$before_mrg_cnt), ", ")
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readme_df$cnt_after_mrg <- str_split(gsub("[][]","", readme_df$after_mrg_cnt), ", ")
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drop <- c("before_all_ct", "before_mrg_ct", "after_all_ct", "after_mrg_ct")
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library(tidyverse)
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#set wd, read in data
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try(setwd(dirname(rstudioapi::getActiveDocumentContext()$path)))
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readme_df <- read_csv("../final_data/deb_readme_did.csv")
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#preprocessing for readme_df
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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", "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$cnt_before_all <- str_split(gsub("[][]","", readme_df$before_all_cnt), ", ")
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library(tidyverse)
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#set wd, read in data
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try(setwd(dirname(rstudioapi::getActiveDocumentContext()$path)))
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readme_df <- read_csv("../final_data/deb_readme_did.csv")
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contributing_df <- read_csv("../final_data/deb_contrib_did.csv")
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#preprocessing for readme_df
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colnames(readme_df) <- c("upstream_vcs_link", "event_date", "event_hash", "before_all_ct", "before_mrg_ct", "after_all_ct", "after_mrg_ct", "before_auth_new", "after_commit_new", "after_auth_new", "before_commit_new")
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col_order <- c("upstream_vcs_link", "event_date", "event_hash", "before_all_ct", "after_all_ct", "before_mrg_ct", "after_mrg_ct", "before_auth_new", "after_auth_new", "before_commit_new", "after_commit_new")
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readme_df <- readme_df[,col_order]
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readme_df$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$after_mrg_ct), ", ")
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drop <- c("before_all_ct", "before_mrg_ct", "after_all_ct", "after_mrg_ct")
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readme_df = readme_df[,!(names(readme_df) %in% drop)]
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# as.numeric(unlist(test[1]))
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# test_two <- c()
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# iterator <- 0
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# for (entry in test) {
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# readme_df$cnt_before_all[iterator] <- as.numeric(unlist(entry))
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# print(as.numeric(unlist(entry)))
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# iterator <- iterator + 1
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# }
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# test_two
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#Yes, need to expand the dataframe, but again, for the sake of clarity, do not want to until analysis step
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new_test <- head(readme_df, 1)
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longer <- new_test |>
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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
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View(longer)
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library(tidyverse)
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#set wd, read in data
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try(setwd(dirname(rstudioapi::getActiveDocumentContext()$path)))
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readme_df <- read_csv("../final_data/deb_readme_did.csv")
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contributing_df <- read_csv("../final_data/deb_contrib_did.csv")
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#preprocessing for readme_df
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colnames(readme_df) <- c("upstream_vcs_link", "event_date", "event_hash", "before_all_ct", "before_mrg_ct", "after_all_ct", "after_mrg_ct", "before_auth_new", "after_commit_new", "after_auth_new", "before_commit_new")
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col_order <- c("upstream_vcs_link", "event_date", "event_hash", "before_all_ct", "after_all_ct", "before_mrg_ct", "after_mrg_ct", "before_auth_new", "after_auth_new", "before_commit_new", "after_commit_new")
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readme_df <- readme_df[,col_order]
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readme_df$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$after_mrg_ct), ", ")
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drop <- c("before_all_ct", "before_mrg_ct", "after_all_ct", "after_mrg_ct")
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readme_df = readme_df[,!(names(readme_df) %in% drop)]
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# as.numeric(unlist(test[1]))
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# test_two <- c()
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# iterator <- 0
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# for (entry in test) {
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# readme_df$cnt_before_all[iterator] <- as.numeric(unlist(entry))
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# print(as.numeric(unlist(entry)))
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# iterator <- iterator + 1
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# }
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# test_two
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#Yes, need to expand the dataframe, but again, for the sake of clarity, do not want to until analysis step
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new_test <- head(readme_df, 1)
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longer <- new_test |>
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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
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View(longer)
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longer <- ddply(longer, "window", transform, t=seq(from=0, by=1, length.out=length(window)))
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library(plyr)
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longer <- ddply(longer, "window", transform, t=seq(from=0, by=1, length.out=length(window)))
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View(longer)
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library(plyr)
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library(tidyverse)
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#set wd, read in data
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try(setwd(dirname(rstudioapi::getActiveDocumentContext()$path)))
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readme_df <- read_csv("../final_data/deb_readme_did.csv")
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contributing_df <- read_csv("../final_data/deb_contrib_did.csv")
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#preprocessing for readme_df
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colnames(readme_df) <- c("upstream_vcs_link", "event_date", "event_hash", "before_all_ct", "before_mrg_ct", "after_all_ct", "after_mrg_ct", "before_auth_new", "after_commit_new", "after_auth_new", "before_commit_new")
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col_order <- c("upstream_vcs_link", "event_date", "event_hash", "before_all_ct", "after_all_ct", "before_mrg_ct", "after_mrg_ct", "before_auth_new", "after_auth_new", "before_commit_new", "after_commit_new")
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readme_df <- readme_df[,col_order]
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readme_df$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$after_mrg_ct), ", ")
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drop <- c("before_all_ct", "before_mrg_ct", "after_all_ct", "after_mrg_ct")
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readme_df = readme_df[,!(names(readme_df) %in% drop)]
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# as.numeric(unlist(test[1]))
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# test_two <- c()
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# iterator <- 0
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# for (entry in test) {
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# readme_df$cnt_before_all[iterator] <- as.numeric(unlist(entry))
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# print(as.numeric(unlist(entry)))
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# iterator <- iterator + 1
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# }
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# test_two
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#Yes, need to expand the dataframe, but again, for the sake of clarity, do not want to until analysis step
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new_test <- head(readme_df, 1)
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longer <- new_test |>
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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 <- ddply(longer, "window", transform, t=seq(from=0, by=1, length.out=length(window)))
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View(longer)
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longer <- ddply(longer, strsplit("window", split="_")[-1], transform, week=seq(from=0, by=1, length.out=length(window)))
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longer <- ddply(longer, strsplit(window, split="_")[-1], transform, week=seq(from=0, by=1, length.out=length(window)))
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longer <- new_test |>
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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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add_column(rel = gsub("^.*_", "", window))
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longer <- new_test |>
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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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add_column(rel = gsub("^.*_", "", "window"))
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View(longer)
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longer$rel <- gsub("^.*_", "", longer$window)
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View(longer)
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# as.numeric(unlist(test[1]))
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# test_two <- c()
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# iterator <- 0
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# for (entry in test) {
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# readme_df$cnt_before_all[iterator] <- as.numeric(unlist(entry))
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# print(as.numeric(unlist(entry)))
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# iterator <- iterator + 1
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# }
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# test_two
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#Yes, need to expand the dataframe, but again, for the sake of clarity, do not want to until analysis step
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new_test <- head(readme_df, 1)
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new_testr$observation_type <- gsub("^.*_", "", new_test$window)
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# as.numeric(unlist(test[1]))
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# test_two <- c()
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# iterator <- 0
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# for (entry in test) {
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# readme_df$cnt_before_all[iterator] <- as.numeric(unlist(entry))
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# print(as.numeric(unlist(entry)))
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# iterator <- iterator + 1
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# }
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# test_two
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#Yes, need to expand the dataframe, but again, for the sake of clarity, do not want to until analysis step
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new_test <- head(readme_df, 1)
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longer <- new_test |>
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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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View(longer)
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head(longer)
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sapply(longer, class)
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library(plyr)
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library(tidyverse)
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#set wd, read in data
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try(setwd(dirname(rstudioapi::getActiveDocumentContext()$path)))
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readme_df <- read_csv("../final_data/deb_readme_did.csv")
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contributing_df <- read_csv("../final_data/deb_contrib_did.csv")
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#preprocessing for readme_df
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colnames(readme_df) <- c("upstream_vcs_link", "event_date", "event_hash", "before_all_ct", "before_mrg_ct", "after_all_ct", "after_mrg_ct", "before_auth_new", "after_commit_new", "after_auth_new", "before_commit_new")
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col_order <- c("upstream_vcs_link", "event_date", "event_hash", "before_all_ct", "after_all_ct", "before_mrg_ct", "after_mrg_ct", "before_auth_new", "after_auth_new", "before_commit_new", "after_commit_new")
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readme_df <- readme_df[,col_order]
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readme_df$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$after_mrg_ct), ", ")
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drop <- c("before_all_ct", "before_mrg_ct", "after_all_ct", "after_mrg_ct")
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readme_df = readme_df[,!(names(readme_df) %in% drop)]
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# as.numeric(unlist(test[1]))
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# test_two <- c()
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# iterator <- 0
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# for (entry in test) {
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# readme_df$cnt_before_all[iterator] <- as.numeric(unlist(entry))
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# print(as.numeric(unlist(entry)))
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# iterator <- iterator + 1
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# }
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# test_two
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#Yes, need to expand the dataframe, but again, for the sake of clarity, do not want to until analysis step
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new_test <- head(readme_df, 1)
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longer <- new_test |>
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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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View(longer)
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#testing out analysis below
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longer[which(longer$observation_type == all)] |>
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ggplot(aes(x = week, y = count)) +
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geom_point() +
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geom_vline(xintercept = 26)
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#testing out analysis below
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longer[which(longer$observation_type == "all")] |>
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ggplot(aes(x = week, y = count)) +
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geom_point() +
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geom_vline(xintercept = 26)
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window <- 26
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longer <- longer %>%
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filter(week >= (26 - window) & week <= (26 + window))
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window_num <- 26
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longer <- longer %>%
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filter(week >= (26 - window_num) & week <= (26 + window_num))
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#testing out analysis below
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longer[which(longer$observation_type == "all"),] |>
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ggplot(aes(x = week, y = count)) +
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geom_point() +
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geom_vline(xintercept = 26)
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View(readme_df)
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# as.numeric(unlist(test[1]))
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# test_two <- c()
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# iterator <- 0
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# for (entry in test) {
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# readme_df$cnt_before_all[iterator] <- as.numeric(unlist(entry))
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# print(as.numeric(unlist(entry)))
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# iterator <- iterator + 1
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# }
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# test_two
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#Yes, need to expand the dataframe, but again, for the sake of clarity, do not want to until analysis step
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new_test <- readme_df[5,]
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longer <- new_test |>
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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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#testing out analysis below
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longer[which(longer$observation_type == "all"),] |>
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ggplot(aes(x = week, y = count)) +
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geom_point() +
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geom_vline(xintercept = 26)
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View(readme_df)
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# as.numeric(unlist(test[1]))
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# test_two <- c()
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# iterator <- 0
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# for (entry in test) {
|
||||
# readme_df$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[76,]
|
||||
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)))
|
||||
#testing out analysis below
|
||||
longer[which(longer$observation_type == "all"),] |>
|
||||
ggplot(aes(x = week, y = count)) +
|
||||
geom_point() +
|
||||
geom_vline(xintercept = 26)
|
||||
# as.numeric(unlist(test[1]))
|
||||
# test_two <- c()
|
||||
# iterator <- 0
|
||||
# for (entry in test) {
|
||||
# readme_df$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[77,]
|
||||
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)))
|
||||
#testing out analysis below
|
||||
longer[which(longer$observation_type == "all"),] |>
|
||||
ggplot(aes(x = week, y = count)) +
|
||||
geom_point() +
|
||||
geom_vline(xintercept = 26)
|
||||
# as.numeric(unlist(test[1]))
|
||||
# test_two <- c()
|
||||
# iterator <- 0
|
||||
# for (entry in test) {
|
||||
# readme_df$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[143,]
|
||||
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)))
|
||||
#testing out analysis below
|
||||
longer[which(longer$observation_type == "all"),] |>
|
||||
ggplot(aes(x = week, y = count)) +
|
||||
geom_point() +
|
||||
geom_vline(xintercept = 26)
|
||||
# as.numeric(unlist(test[1]))
|
||||
# test_two <- c()
|
||||
# iterator <- 0
|
||||
# for (entry in test) {
|
||||
# readme_df$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[185,]
|
||||
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)))
|
||||
#testing out analysis below
|
||||
longer[which(longer$observation_type == "all"),] |>
|
||||
ggplot(aes(x = week, y = count)) +
|
||||
geom_point() +
|
||||
geom_vline(xintercept = 26)
|
||||
# as.numeric(unlist(test[1]))
|
||||
# test_two <- c()
|
||||
# iterator <- 0
|
||||
# for (entry in test) {
|
||||
# readme_df$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)))
|
||||
#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(count ~ D + I(week - 26)) |>
|
||||
summary()
|
||||
longer[which(longer$observation_type == "all"),] |>
|
||||
mutate(D = ifelse(week >= 26, 1, 0)) |>
|
||||
lm(count ~ D * I(week - 26)) |>
|
||||
summary()
|
||||
longer[which(longer$observation_type == "all"),] |>
|
||||
mutate(D = ifelse(week >= 26, 1, 0)) |>
|
||||
lm(formula = count ~ D * I(week - 26)) |>
|
||||
summary()
|
||||
longer[which(longer$observation_type == "all"),] |>
|
||||
select(count, week) |>
|
||||
mutate(D = ifelse(week >= 26, 1, 0)) |>
|
||||
ggplot(aes(x = week, y = count, color = D)) +
|
||||
geom_point() +
|
||||
geom_smooth(method = "lm")
|
||||
longer[which(longer$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(method = "lm")
|
||||
longer[which(longer$observation_type == "all"),] |>
|
||||
select(count, week) |>
|
||||
mutate(D = as.factor(ifelse(week >= 26, 1, 0))) |>
|
||||
ggplot(aes(x = week, y = count, color = D)) +
|
||||
geom_point() +
|
||||
geom_smooth()
|
||||
longer[which(longer$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()
|
||||
longer[which(longer$observation_type == "all"),] |>
|
||||
select(count, week) |>
|
||||
mutate(D = as.factor(ifelse(week >= 26, 1, 0))) |>
|
||||
ggplot(aes(x = week, y = count, color = D)) +
|
||||
geom_point() +
|
||||
geom_smooth(aes(x = week, y = count, color = D))
|
||||
longer[which(longer$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()
|
||||
sapply(longer, class)
|
||||
geom_smooth(se = FALSE) +
|
||||
geom_vline(xintercept = 26)
|
||||
window_num <- 27
|
||||
longer <- longer %>%
|
||||
filter(week >= (26 - window_num) & week <= (26 + window_num))
|
||||
# test_two <- c()
|
||||
# iterator <- 0
|
||||
# for (entry in test) {
|
||||
# readme_df$cnt_before_all[iterator] <- as.numeric(unlist(entry))
|
||||
# print(as.numeric(unlist(entry)))
|
||||
# iterator <- iterator + 1
|
||||
# }
|
||||
# test_two
|
||||
#Yes, need to expand the dataframe, but again, for the sake of clarity, do not want to until analysis step
|
||||
# https://rpubs.com/phle/r_tutorial_regression_discontinuity_design
|
||||
new_test <- readme_df[450,]
|
||||
longer <- new_test |>
|
||||
pivot_longer(cols = starts_with("ct"),
|
||||
names_to = "window",
|
||||
values_to = "count") |>
|
||||
unnest(count)
|
||||
longer$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$count)
|
||||
sapply(longer, class)
|
||||
window_num <- 27
|
||||
longer <- longer %>%
|
||||
filter(week >= (26 - window_num) & week <= (26 + window_num))
|
||||
#testing out analysis below
|
||||
longer[which(longer$observation_type == "all"),] |>
|
||||
ggplot(aes(x = week, y = count)) +
|
||||
@ -497,16 +60,453 @@ select(count, week) |>
|
||||
mutate(D = as.factor(ifelse(week >= 26, 1, 0))) |>
|
||||
ggplot(aes(x = week, y = count, color = D)) +
|
||||
geom_point() +
|
||||
geom_smooth()
|
||||
geom_smooth(se = FALSE) +
|
||||
geom_vline(xintercept = 26)
|
||||
window_num <- 20
|
||||
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$observation_type == "all"),] |>
|
||||
select(count, week) |>
|
||||
mutate(D = as.factor(ifelse(week >= 26, 1, 0))) |>
|
||||
ggplot(aes(x = week, y = count, color = D)) +
|
||||
geom_point() +
|
||||
geom_smooth(se = False)
|
||||
geom_smooth(se = FALSE) +
|
||||
geom_vline(xintercept = 26)
|
||||
window_num <- 4
|
||||
longer <- longer %>%
|
||||
filter(week >= (26 - window_num) & week <= (26 + window_num))
|
||||
#testing out analysis below
|
||||
longer[which(longer$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_smooth(se = FALSE) +
|
||||
geom_vline(xintercept = 26)
|
||||
window_num <- 10
|
||||
longer <- longer %>%
|
||||
filter(week >= (26 - window_num) & week <= (26 + window_num))
|
||||
# test_two <- c()
|
||||
# iterator <- 0
|
||||
# for (entry in test) {
|
||||
# readme_df$cnt_before_all[iterator] <- as.numeric(unlist(entry))
|
||||
# print(as.numeric(unlist(entry)))
|
||||
# iterator <- iterator + 1
|
||||
# }
|
||||
# test_two
|
||||
#Yes, need to expand the dataframe, but again, for the sake of clarity, do not want to until analysis step
|
||||
# https://rpubs.com/phle/r_tutorial_regression_discontinuity_design
|
||||
new_test <- readme_df[450,]
|
||||
longer <- new_test |>
|
||||
pivot_longer(cols = starts_with("ct"),
|
||||
names_to = "window",
|
||||
values_to = "count") |>
|
||||
unnest(count)
|
||||
longer$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$count)
|
||||
window_num <- 10
|
||||
longer <- longer %>%
|
||||
filter(week >= (26 - window_num) & week <= (26 + window_num))
|
||||
#testing out analysis below
|
||||
longer[which(longer$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)
|
||||
# test_two <- c()
|
||||
# iterator <- 0
|
||||
# for (entry in test) {
|
||||
# readme_df$cnt_before_all[iterator] <- as.numeric(unlist(entry))
|
||||
# print(as.numeric(unlist(entry)))
|
||||
# iterator <- iterator + 1
|
||||
# }
|
||||
# test_two
|
||||
#Yes, need to expand the dataframe, but again, for the sake of clarity, do not want to until analysis step
|
||||
# https://rpubs.com/phle/r_tutorial_regression_discontinuity_design
|
||||
new_test <- readme_df[697,]
|
||||
longer <- new_test |>
|
||||
pivot_longer(cols = starts_with("ct"),
|
||||
names_to = "window",
|
||||
values_to = "count") |>
|
||||
unnest(count)
|
||||
longer$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$count)
|
||||
window_num <- 27
|
||||
longer <- longer %>%
|
||||
filter(week >= (26 - window_num) & week <= (26 + window_num))
|
||||
#testing out analysis below
|
||||
longer[which(longer$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)
|
||||
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)
|
||||
# test_two <- c()
|
||||
# iterator <- 0
|
||||
# for (entry in test) {
|
||||
# readme_df$cnt_before_all[iterator] <- as.numeric(unlist(entry))
|
||||
# print(as.numeric(unlist(entry)))
|
||||
# iterator <- iterator + 1
|
||||
# }
|
||||
# test_two
|
||||
#Yes, need to expand the dataframe, but again, for the sake of clarity, do not want to until analysis step
|
||||
# https://rpubs.com/phle/r_tutorial_regression_discontinuity_design
|
||||
new_test <- readme_df[697,]
|
||||
longer <- new_test |>
|
||||
pivot_longer(cols = starts_with("ct"),
|
||||
names_to = "window",
|
||||
values_to = "count") |>
|
||||
unnest(count)
|
||||
longer$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$count)
|
||||
#longer <- longer %>%
|
||||
# filter(week >= (26 - window_num) & week <= (26 + window_num))
|
||||
IKbandwidth(longer$week, longer$count, cutpoint = 26, verbose = FALSE, kernel = "triangular")
|
||||
#testing out analysis below
|
||||
longer[which(longer$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)
|
||||
# test_two <- c()
|
||||
# iterator <- 0
|
||||
# for (entry in test) {
|
||||
# readme_df$cnt_before_all[iterator] <- as.numeric(unlist(entry))
|
||||
# print(as.numeric(unlist(entry)))
|
||||
# iterator <- iterator + 1
|
||||
# }
|
||||
# test_two
|
||||
#Yes, need to expand the dataframe, but again, for the sake of clarity, do not want to until analysis step
|
||||
# https://rpubs.com/phle/r_tutorial_regression_discontinuity_design
|
||||
new_test <- readme_df[0,]
|
||||
longer <- new_test |>
|
||||
pivot_longer(cols = starts_with("ct"),
|
||||
names_to = "window",
|
||||
values_to = "count") |>
|
||||
unnest(count)
|
||||
longer$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$count)
|
||||
#longer <- longer %>%
|
||||
# filter(week >= (26 - window_num) & week <= (26 + window_num))
|
||||
IKbandwidth(longer$week, longer$count, cutpoint = 26, verbose = FALSE, kernel = "triangular")
|
||||
# test_two <- c()
|
||||
# iterator <- 0
|
||||
# for (entry in test) {
|
||||
# readme_df$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
|
||||
# https://rpubs.com/phle/r_tutorial_regression_discontinuity_design
|
||||
new_test <- readme_df[3,]
|
||||
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)
|
||||
#longer <- longer %>%
|
||||
# filter(week >= (26 - window_num) & week <= (26 + window_num))
|
||||
IKbandwidth(longer$week, longer$count, cutpoint = 26, verbose = FALSE, kernel = "triangular")
|
||||
# test_two <- c()
|
||||
# iterator <- 0
|
||||
# for (entry in test) {
|
||||
# readme_df$cnt_before_all[iterator] <- as.numeric(unlist(entry))
|
||||
# print(as.numeric(unlist(entry)))
|
||||
# iterator <- iterator + 1
|
||||
# }
|
||||
# test_two
|
||||
#Yes, need to expand the dataframe, but again, for the sake of clarity, do not want to until analysis step
|
||||
# https://rpubs.com/phle/r_tutorial_regression_discontinuity_design
|
||||
new_test <- readme_df[9,]
|
||||
longer <- new_test |>
|
||||
pivot_longer(cols = starts_with("ct"),
|
||||
names_to = "window",
|
||||
values_to = "count") |>
|
||||
unnest(count)
|
||||
longer$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$count)
|
||||
#longer <- longer %>%
|
||||
# filter(week >= (26 - window_num) & week <= (26 + window_num))
|
||||
IKbandwidth(longer$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)
|
||||
|
||||
@ -0,0 +1,40 @@
|
||||
# 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,]))
|
||||
}
|
||||
#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(longer$observation_type == "all"),])
|
||||
summary(draft_model)
|
||||
@ -52,19 +52,24 @@ get_optimal_window <- function(project_row) {
|
||||
optimal_bandwidth <- IKbandwidth(longer$week, longer$count, cutpoint = 26, verbose = FALSE, kernel = "triangular")
|
||||
return(optimal_bandwidth)
|
||||
}, error = function(e){
|
||||
#have tested it with multiple different error-values and all medians/means still hover around 8
|
||||
return(8)
|
||||
})
|
||||
}
|
||||
|
||||
#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
|
||||
mean(bandwidths)
|
||||
#8.574233
|
||||
median(bandwidths) #8.363088
|
||||
table(bandwidths)
|
||||
#window_num <- 13
|
||||
#from this, I think setting the bandwidth to 8 weeks, two months, the floor
|
||||
# of both the median and mean calculations
|
||||
|
||||
|
||||
#longer <- longer %>%
|
||||
# filter(week >= (26 - window_num) & week <= (26 + window_num))
|
||||
|
||||
Loading…
Reference in New Issue
Block a user