adding some results and analysis
This commit is contained in:
mjgaughan
parent
979dd9ccc6
commit
b4a55f464e
580
R/.Rhistory
580
R/.Rhistory
@ -1,293 +1,3 @@
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contrib_readme_model <- load(file = "final_models/0623_pop_rm_contrib.rda")
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contrib_readme_model <- load(file = "final_models/0623_pop_rm_contrib.rda")
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contrib_readme_model <- load("final_models/0623_pop_rm_contrib.rda")
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contrib_readme_model <- source("final_models/0623_pop_rm_contrib.rda")
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contrib_readme_model <- readRDS("final_models/0623_pop_rm_contrib.rda")
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contrib_readme_model <- readRDS("final_models/0623_pop_contrib_collab.rda")
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collab_readme_model <- readRDS("final_models/0623_pop_rm_collab.rda")
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texreg(list(collab_readme_model, contrib_readme_model), stars=NULL, digits=2,
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custom.model.names=c( 'collab','contrib.' ),
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custom.coef.names=c('(Intercept)', 'after_introduction'),
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use.packages=FALSE, table=FALSE, ci.force = TRUE)
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library(texreg)
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texreg(list(collab_readme_model, contrib_readme_model), stars=NULL, digits=2,
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custom.model.names=c( 'collab','contrib.' ),
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custom.coef.names=c('(Intercept)', 'after_introduction'),
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use.packages=FALSE, table=FALSE, ci.force = TRUE)
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texreg(list(collab_readme_model, contrib_readme_model), stars=NULL, digits=2,
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custom.model.names=c( 'collab','contrib.' ),
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custom.coef.names=c('(Intercept)', 'after_introduction' 'etc'),
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texreg(list(collab_readme_model, contrib_readme_model), stars=NULL, digits=2,
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custom.model.names=c( 'collab','contrib.' ),
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custom.coef.names=c('(Intercept)', 'after_introduction', 'etc'),
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use.packages=FALSE, table=FALSE, ci.force = TRUE)
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library(tidyverse)
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library(plyr)
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library(stringr)
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try(setwd(dirname(rstudioapi::getActiveDocumentContext()$path)))
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#load in data
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contrib_df <- read_csv("../final_data/deb_contrib_pop_change.csv")
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readme_df <- read_csv("../final_data/deb_readme_pop_change.csv")
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#some expansion needs to happens for each project
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expand_timeseries <- function(project_row) {
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longer <- project_row |>
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pivot_longer(cols = ends_with("new"),
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names_to = "window",
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values_to = "count") |>
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unnest(count) |>
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mutate(after_doc = as.numeric(str_detect(window, "after"))) |>
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mutate(is_collab = as.numeric(str_detect(window, "collab")))
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return(longer)
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}
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expanded_readme_data <- expand_timeseries(readme_df[1,])
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for (i in 2:nrow(readme_df)){
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expanded_readme_data <- rbind(expanded_readme_data, expand_timeseries(readme_df[i,]))
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}
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expanded_contrib_data <- expand_timeseries(contrib_df[1,])
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for (i in 2:nrow(contrib_df)){
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expanded_contrib_data <- rbind(expanded_contrib_data, expand_timeseries(contrib_df[i,]))
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}
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expanded_readme_data$log1pcount <- log1p(expanded_readme_data$count)
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expanded_contrib_data$log1pcount <- log1p(expanded_contrib_data$count)
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expanded_readme_data$logcount <- log(expanded_readme_data$count)
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expanded_contrib_data$logcount <- log(expanded_contrib_data$count)
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#breaking out the types of population counts
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collab_pop_readme <- expanded_readme_data[which(expanded_readme_data$is_collab == 1),]
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contrib_pop_readme <- expanded_readme_data[which(expanded_readme_data$is_collab == 0),]
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collab_pop_contrib <- expanded_contrib_data[which(expanded_contrib_data$is_collab == 1),]
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contrib_pop_contrib <- expanded_contrib_data[which(expanded_contrib_data$is_collab == 0),]
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simple_collab_readme_model <- glm.nb(count ~ as.factor(after_doc), data=collab_pop_readme)
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summary(simple_collab_readme_model)
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anova(simple_collab_readme_model, collab_readme_model)
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summary(collab_readme_model)
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#load in data
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full_df <- read_csv("../final_data/deb_full_data.csv")
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contrib_df <- read_csv("../final_data/deb_contrib_pop_change.csv")
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contrib_df <- merge(full_df, contrib_df, by="upstream_vcs_link")
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View(contrib_df)
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View(contrib_df)
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readme_df <- read_csv("../final_data/deb_readme_pop_change.csv")
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readme_df <- merge(full_df, readme_df, by="upstream_vcs_link")
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# age is calculated against December 11, 2023
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contrib_df |>
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mutate(start_date = as.Date("2023-12-11") - age_of_project)
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View(contrib_df)
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# age is calculated against December 11, 2023
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contrib_df <- contrib_df |>
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mutate(start_date = as.Date("2023-12-11") - age_of_project)
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View(contrib_df)
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View(contrib_df)
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View(readme_df)
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readme_df <- readme_df |>
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mutate(start_date = as.Date("2023-12-11") - age_of_project)
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View(readme_df)
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collab_readme_model_plus <- glmer.nb(log1pcount ~ as.factor(after_doc) + event_date + (after_doc| upstream_vcs_link), data=collab_pop_readme)
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#some expansion needs to happens for each project
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expand_timeseries <- function(project_row) {
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longer <- project_row |>
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pivot_longer(cols = ends_with("new"),
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names_to = "window",
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values_to = "count") |>
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unnest(count) |>
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mutate(after_doc = as.numeric(str_detect(window, "after"))) |>
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mutate(is_collab = as.numeric(str_detect(window, "collab")))
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return(longer)
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}
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expanded_readme_data <- expand_timeseries(readme_df[1,])
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for (i in 2:nrow(readme_df)){
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expanded_readme_data <- rbind(expanded_readme_data, expand_timeseries(readme_df[i,]))
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}
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expanded_contrib_data <- expand_timeseries(contrib_df[1,])
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for (i in 2:nrow(contrib_df)){
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expanded_contrib_data <- rbind(expanded_contrib_data, expand_timeseries(contrib_df[i,]))
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}
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expanded_readme_data$log1pcount <- log1p(expanded_readme_data$count)
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expanded_contrib_data$log1pcount <- log1p(expanded_contrib_data$count)
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expanded_readme_data$logcount <- log(expanded_readme_data$count)
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expanded_contrib_data$logcount <- log(expanded_contrib_data$count)
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#breaking out the types of population counts
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collab_pop_readme <- expanded_readme_data[which(expanded_readme_data$is_collab == 1),]
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contrib_pop_readme <- expanded_readme_data[which(expanded_readme_data$is_collab == 0),]
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collab_pop_contrib <- expanded_contrib_data[which(expanded_contrib_data$is_collab == 1),]
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contrib_pop_contrib <- expanded_contrib_data[which(expanded_contrib_data$is_collab == 0),]
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library(tidyverse)
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library(plyr)
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library(stringr)
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try(setwd(dirname(rstudioapi::getActiveDocumentContext()$path)))
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#load in data
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full_df <- read_csv("../final_data/deb_full_data.csv")
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contrib_df <- read_csv("../final_data/deb_contrib_pop_change.csv")
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View(contrib_df)
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readme_df <- read_csv("../final_data/deb_readme_pop_change.csv")
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contrib_df <- merge(full_df, contrib_df, by="upstream_vcs_link")
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readme_df <- merge(full_df, readme_df, by="upstream_vcs_link")
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# age is calculated against December 11, 2023
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contrib_df <- contrib_df |>
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mutate(start_date = as.Date("2023-12-11") - age_of_project) +
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mutate(event_date_days = as.Date("2024-06-24") - event_date) +
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readme_df <- readme_df |>
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mutate(start_date = as.Date("2023-12-11") - age_of_project)
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# age is calculated against December 11, 2023
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contrib_df <- contrib_df |>
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mutate(start_date = as.Date("2023-12-11") - age_of_project) +
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mutate(event_date_days = as.Date("2024-06-24") - event_date)
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# age is calculated against December 11, 2023
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contrib_df <- contrib_df |>
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mutate(start_date = as.Date("2023-12-11") - age_of_project) +
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mutate(event_date_days = diff.Date(as.Date("2023-12-11"),event_date, units = "days"))
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# age is calculated against December 11, 2023
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contrib_df <- contrib_df |>
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mutate(start_date = as.Date("2023-12-11") - age_of_project) |>
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mutate(event_date_days = diff.Date(as.Date("2023-12-11"),event_date, units = "days"))
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# age is calculated against December 11, 2023
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contrib_df <- contrib_df |>
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mutate(start_date = as.Date("2023-12-11") - age_of_project) |>
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mutate(event_date_days = diff.Date(as.Date("2024-06-24"),event_date, units = "days"))
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# age is calculated against December 11, 2023
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contrib_df <- contrib_df |>
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mutate(start_date = as.Date("2023-12-11") - age_of_project) |>
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mutate(event_date_days = diff.Date(as.Date("2024-06-24"),as.Date(event_date), units = "days"))
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View(contrib_df)
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View(contrib_df)
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# age is calculated against December 11, 2023
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contrib_df <- contrib_df |>
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mutate(start_date = as.Date("2023-12-11") - age_of_project) |>
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mutate(event_date_days = diff.Date(as.Date("2024-06-24"),as.POSIXct(event_date, format = "%Y-%m-%d %H:%M:%S"), units = "days"))
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# age is calculated against December 11, 2023
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contrib_df <- contrib_df |>
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mutate(start_date = as.Date("2023-12-11") - age_of_project) |>
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mutate(event_date_days =
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as.numeric(
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difftime(as.POSIXct("2024-06-24 00:00:00", format = "%Y-%m-%d %H:%M:%S")
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as.POSIXct(event_date, format = "%Y-%m-%d %H:%M:%S"),
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# age is calculated against December 11, 2023
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contrib_df <- contrib_df |>
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mutate(start_date = as.Date("2023-12-11") - age_of_project) |>
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mutate(event_date_days =
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as.numeric(
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difftime(as.POSIXct("2024-06-24 00:00:00", format = "%Y-%m-%d %H:%M:%S"),
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as.POSIXct(event_date, format = "%Y-%m-%d %H:%M:%S"),
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units = "days")))
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View(contrib_df)
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readme_df <- readme_df |>
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mutate(start_date = as.Date("2023-12-11") - age_of_project) |>
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mutate(event_date_days =
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as.numeric(
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difftime(as.POSIXct("2024-06-24 00:00:00", format = "%Y-%m-%d %H:%M:%S"),
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as.POSIXct(event_date, format = "%Y-%m-%d %H:%M:%S"),
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units = "days")))
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#some expansion needs to happens for each project
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expand_timeseries <- function(project_row) {
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longer <- project_row |>
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pivot_longer(cols = ends_with("new"),
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names_to = "window",
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values_to = "count") |>
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unnest(count) |>
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mutate(after_doc = as.numeric(str_detect(window, "after"))) |>
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mutate(is_collab = as.numeric(str_detect(window, "collab")))
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return(longer)
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}
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expanded_readme_data <- expand_timeseries(readme_df[1,])
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for (i in 2:nrow(readme_df)){
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expanded_readme_data <- rbind(expanded_readme_data, expand_timeseries(readme_df[i,]))
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}
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expanded_contrib_data <- expand_timeseries(contrib_df[1,])
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for (i in 2:nrow(contrib_df)){
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expanded_contrib_data <- rbind(expanded_contrib_data, expand_timeseries(contrib_df[i,]))
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}
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expanded_readme_data$log1pcount <- log1p(expanded_readme_data$count)
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expanded_contrib_data$log1pcount <- log1p(expanded_contrib_data$count)
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expanded_readme_data$logcount <- log(expanded_readme_data$count)
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expanded_contrib_data$logcount <- log(expanded_contrib_data$count)
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#breaking out the types of population counts
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collab_pop_readme <- expanded_readme_data[which(expanded_readme_data$is_collab == 1),]
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contrib_pop_readme <- expanded_readme_data[which(expanded_readme_data$is_collab == 0),]
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collab_pop_contrib <- expanded_contrib_data[which(expanded_contrib_data$is_collab == 1),]
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contrib_pop_contrib <- expanded_contrib_data[which(expanded_contrib_data$is_collab == 0),]
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#import models
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library(lme4)
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library(optimx)
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collab_readme_model_plus <- glmer.nb(log1pcount ~ as.factor(after_doc) + event_date_days + (after_doc| upstream_vcs_link), data=collab_pop_readme)
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anova(collab_readme_model_plus, collab_readme_model)
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collab_readme_model <- readRDS("final_models/0623_pop_rm_collab.rda")
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anova(collab_readme_model_plus, collab_readme_model)
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saveRDS(collab_readme_model, "final_models/0623_pop_rm_collab_better.rda")
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summary(collab_readme_model_plus)
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summary(collab_readme_model)
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library(tidyverse)
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#things to get:
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# - delete old age column
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# - normal age, in date
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# - age from today in days
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# - delta between first commit and document in days
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#README Document updates
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#loading in new ages
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####RDD CSV
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first_commit_df <- read_csv("../062424_did_first_commit_readme.csv")
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first_commit_df_2 <- read_csv("../062424_did_first_commit_readme_2.csv")
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first_commit_df <- rbind(first_commit_df, first_commit_df_2)
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# need to first do an rbind with this data and the second file
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# check with the head of the file/size of the file
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old_rdd_readme <- read_csv("../final_data/deb_readme_did.csv")
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old_rdd_readme <- merge(old_rdd_readme, first_commit_df, by="upstream_vcs_link")
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new_rm_data <- old_rdd_readme |>
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select(-age_of_project) |>
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mutate(first_commit_dt = as.POSIXct(first_commit,
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format = "%a %b %d %H:%M:%S %Y %z")) |>
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mutate(age_in_days =
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as.numeric(
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difftime(
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as.POSIXct("2024-06-24 00:00:00", format = "%Y-%m-%d %H:%M:%S"),
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first_commit_dt,
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units = "days"))) |>
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mutate (event_gap =
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as.numeric(
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difftime(
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as.POSIXct(event_date, format = "%Y-%m-%d %H:%M:%S"),
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first_commit_dt,
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units = "days")))
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View(old_rdd_readme)
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new_rm_data <- old_rdd_readme |>
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select(-c(age_of_project)) |>
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mutate(first_commit_dt = as.POSIXct(first_commit,
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format = "%a %b %d %H:%M:%S %Y %z")) |>
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mutate(age_in_days =
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as.numeric(
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difftime(
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as.POSIXct("2024-06-24 00:00:00", format = "%Y-%m-%d %H:%M:%S"),
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first_commit_dt,
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units = "days"))) |>
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mutate (event_gap =
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as.numeric(
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difftime(
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as.POSIXct(event_date, format = "%Y-%m-%d %H:%M:%S"),
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first_commit_dt,
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units = "days")))
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new_rm_data <- old_rdd_readme |>
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mutate(first_commit_dt = as.POSIXct(first_commit,
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format = "%a %b %d %H:%M:%S %Y %z")) |>
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mutate(age_in_days =
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as.numeric(
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difftime(
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as.POSIXct("2024-06-24 00:00:00", format = "%Y-%m-%d %H:%M:%S"),
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first_commit_dt,
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units = "days"))) |>
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mutate (event_gap =
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as.numeric(
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difftime(
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as.POSIXct(event_date, format = "%Y-%m-%d %H:%M:%S"),
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first_commit_dt,
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units = "days")))
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View(new_rm_data)
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new_rm_data <- new_rm_data |>
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select(-age_of_project)
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new_rm_data$age_of_project = NULL
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head(new_rm_data)
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write.csv(new_rm_data, file = "../final_data/deb_readme_did_updated.csv", row.names = FALSE)
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old_pop_readme <- merge(old_pop_readme, first_commit_df, by="upstream_vcs_link")
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####PopChange CSV
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old_pop_readme <- read_csv("../final_data/deb_readme_pop_change.csv")
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old_pop_readme <- merge(old_pop_readme, first_commit_df, by="upstream_vcs_link")
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new_pop_data <- old_pop_readme |>
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mutate(first_commit_dt = as.POSIXct(first_commit,
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format = "%a %b %d %H:%M:%S %Y %z")) |>
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@ -510,3 +220,293 @@ summary(contrib_contrib_model)
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# optCtrl=list(maxfun=2e5)), nAGQ=0, data=all_actions_data)
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all_gmodel <- readRDS("0512_contrib_all.rda")
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summary(all_gmodel)
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#all_gmodel <- glmer.nb(log1p_count ~ D * week_offset + scaled_project_age + scaled_event_gap + (D * week_offset | upstream_vcs_link),
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# control=glmerControl(optimizer="bobyqa",
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# optCtrl=list(maxfun=2e5)), nAGQ=0, data=all_actions_data)
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all_gmodel <- readRDS("0512_contrib_all.rda")
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#identifying the quartiles of effect for D
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test_condvals <- broom.mixed::tidy(all_gmodel, effects = "ran_vals", conf.int = TRUE)
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test_glmer_ranef_D <- test_condvals [which(test_condvals $term == "D"),]
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has_zero <- function(estimate, low, high){
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return(ifelse((low < 0),ifelse((high > 0), 1, 0), 2))
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}
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test_glmer_ranef_D <- test_glmer_ranef_D |>
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mutate(ranef_grouping = has_zero(estimate, conf.low, conf.high)) |>
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mutate(rank = rank(estimate))
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g <- test_glmer_ranef_D |>
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ggplot(aes(x=rank, y=estimate, col = as.factor(ranef_grouping))) +
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geom_linerange(aes(ymin= conf.low, ymax= conf.high)) +
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theme_bw()
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library(tidyverse)
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g <- test_glmer_ranef_D |>
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ggplot(aes(x=rank, y=estimate, col = as.factor(ranef_grouping))) +
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geom_linerange(aes(ymin= conf.low, ymax= conf.high)) +
|
||||
theme_bw()
|
||||
g
|
||||
test_glmer_ranef_D <- test_glmer_ranef_D |>
|
||||
mutate(ranef_grouping = has_zero(estimate, conf.low, conf.high)) |>
|
||||
mutate(rank = rank(estimate))
|
||||
g <- test_glmer_ranef_D |>
|
||||
ggplot(aes(x=rank, y=estimate, col = as.factor(ranef_grouping))) +
|
||||
geom_linerange(aes(ymin= conf.low, ymax= conf.high)) +
|
||||
theme_bw()
|
||||
g
|
||||
library(tidyverse)
|
||||
library(plyr)
|
||||
library(gridExtra)
|
||||
library(ggpubr)
|
||||
# script for the analysis of document readability metrics
|
||||
# readability metrics will be studied controlled by their length
|
||||
# gaughan@u.northwestern.edu
|
||||
# loading in the data
|
||||
try(setwd(dirname(rstudioapi::getActiveDocumentContext()$path)))
|
||||
source("~/Desktop/git/24_deb_gov/R/documentReadabilityAnalysis.R", echo=TRUE)
|
||||
source("~/Desktop/git/24_deb_gov/R/documentReadabilityAnalysis.R", echo=TRUE)
|
||||
source("~/Desktop/git/24_deb_gov/R/documentReadabilityAnalysis.R", echo=TRUE)
|
||||
aggregate(readme_df[, 3:10], list(readme_df$subdir), median)
|
||||
readme_df <- read_csv("../text_analysis/dwo_readability_readme.csv")
|
||||
aggregate(readme_df[, 3:10], list(readme_df$subdir), median)
|
||||
aggregate(contributing_df[, 3:10], list(contributing_df$subdir), median)
|
||||
aggregate(readme_df[, 3:10], list(readme_df$subdir), median)
|
||||
readme_df <- read_csv("../text_analysis/dwo_readability_readme.csv")
|
||||
contributing_df <- read_csv("../text_analysis/dwo_readability_contributing.csv")
|
||||
#getting basic stats for the readme readability
|
||||
median(readme_df$flesch_reading_ease)
|
||||
median(readme_df$linsear_write_formula)
|
||||
readme_rdd <- readRDS("final_models/0624_readme_all_rdd.rda")
|
||||
contrib_rdd <- readRDS("final_models/0624_contrib_all_rdd.rda")
|
||||
textreg(list(readme_rdd, contrib_rdd), stars=NULL, digits=3, use.packages=FALSE, table=FALSE, ci.force = TRUE))
|
||||
textreg(list(readme_rdd, contrib_rdd), stars=NULL, digits=3, use.packages=FALSE, table=FALSE, ci.force = TRUE)
|
||||
reg(list(readme_rdd, contrib_rdd), stars=NULL, digits=3, use.packages=FALSE, table=FALSE, ci.force = TRUE)
|
||||
texreg(list(readme_rdd, contrib_rdd), stars=NULL, digits=3, use.packages=FALSE, table=FALSE, ci.force = TRUE)
|
||||
library(texreg)
|
||||
texreg(list(readme_rdd, contrib_rdd), stars=NULL, digits=3, use.packages=FALSE, table=FALSE, ci.force = TRUE)
|
||||
summary(readme)
|
||||
summary(readme_rdd)
|
||||
texreg(list(readme_rdd, contrib_rdd), stars=NULL, digits=3, use.packages=FALSE,
|
||||
custom.model.names=c( 'README','CONTRIBUTING'),
|
||||
custom.coef.names=c('(Intercept)', 'Indtroduction', 'Week (Time)', 'Project Age', 'Introduction:Week', 'Event Gap'),
|
||||
table=FALSE, ci.force = TRUE)
|
||||
readme_groupings <- read.csv('../final_data/deb_readme_interaction_groupings.csv')
|
||||
contrib_groupings <- read.csv('../final_data/deb_contrib_interaction_groupings.csv')
|
||||
View(readme_groupings)
|
||||
library(tidyverse)
|
||||
readme_g <- readme_groupings |>
|
||||
ggplot(aes(x=rank, y=estimate, col = as.factor(ranef_grouping))) +
|
||||
geom_linerange(aes(ymin= conf.low, ymax= conf.high)) +
|
||||
theme_bw()
|
||||
readme_g
|
||||
subdirColors <-
|
||||
setNames( c('firebrick1', 'forestgreen', 'cornflowerblue')
|
||||
, c(0,1,2) )
|
||||
readme_g <- readme_groupings |>
|
||||
ggplot(aes(x=rank, y=estimate, col = as.factor(ranef_grouping))) +
|
||||
geom_linerange(aes(ymin= conf.low, ymax= conf.high)) +
|
||||
scale_color_manual(values = subdirColors) +
|
||||
theme_bw()
|
||||
readme_g
|
||||
contrib_groupings <- read.csv('../final_data/deb_contrib_interaction_groupings.csv')
|
||||
contrib_g <- contrib_groupings |>
|
||||
ggplot(aes(x=rank, y=estimate, col = as.factor(ranef_grouping))) +
|
||||
geom_linerange(aes(ymin= conf.low, ymax= conf.high)) +
|
||||
scale_color_manual(values = subdirColors) +
|
||||
theme_bw()
|
||||
contrib_g
|
||||
grid.arrange(readme_g, contrib_g, nrow = 1)
|
||||
library(gridExtra)
|
||||
grid.arrange(readme_g, contrib_g, nrow = 1)
|
||||
readme_g <- readme_groupings |>
|
||||
ggplot(aes(x=rank, y=estimate, col = as.factor(ranef_grouping))) +
|
||||
geom_linerange(aes(ymin= conf.low, ymax= conf.high)) +
|
||||
scale_color_manual(values = subdirColors) +
|
||||
guides(fill="none", color="none")+
|
||||
theme_bw()
|
||||
readme_g
|
||||
grid.arrange(readme_g, contrib_g, nrow = 1)
|
||||
grid.arrange(contrib_g, readme_g, nrow = 1)
|
||||
contrib_g <- contrib_groupings |>
|
||||
ggplot(aes(x=rank, y=estimate, col = as.factor(ranef_grouping))) +
|
||||
geom_linerange(aes(ymin= conf.low, ymax= conf.high)) +
|
||||
scale_color_manual(values = subdirColors) +
|
||||
theme_bw() +
|
||||
theme(legend.position = "top")
|
||||
grid.arrange(contrib_g, readme_g, nrow = 1)
|
||||
library(jtools)
|
||||
plot_summs(readme_rdd, contrib_rdd)
|
||||
?plot_summs
|
||||
plot_summs(readme_rdd, contrib_rdd, plot.distributions = TRUE)
|
||||
col_order <- c("upstream_vcs_link", "age_in_days", "first_commit", "first_commit_dt", "event_gap", "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")
|
||||
contrib_df <- contrib_df[,col_order]
|
||||
contrib_df$ct_before_all <- str_split(gsub("[][]","", contrib_df$before_all_ct), ", ")
|
||||
contrib_df <- read_csv('../final_data/deb_contrib_did.csv')
|
||||
col_order <- c("upstream_vcs_link", "age_in_days", "first_commit", "first_commit_dt", "event_gap", "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")
|
||||
contrib_df <- contrib_df[,col_order]
|
||||
contrib_df$ct_before_all <- str_split(gsub("[][]","", contrib_df$before_all_ct), ", ")
|
||||
contrib_df$ct_after_all <- str_split(gsub("[][]","", contrib_df$after_all_ct), ", ")
|
||||
contrib_df$ct_before_mrg <- str_split(gsub("[][]","", contrib_df$before_mrg_ct), ", ")
|
||||
contrib_df$ct_after_mrg <- str_split(gsub("[][]","", contrib_df$after_mrg_ct), ", ")
|
||||
drop <- c("before_all_ct", "before_mrg_ct", "after_all_ct", "after_mrg_ct")
|
||||
contrib_df = contrib_df[,!(names(contrib_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$observation_type == "all"),]
|
||||
return(longer)
|
||||
}
|
||||
expanded_data <- expand_timeseries(contrib_df[1,])
|
||||
library(plyr)
|
||||
contrib_df <- read_csv('../final_data/deb_contrib_did.csv')
|
||||
col_order <- c("upstream_vcs_link", "age_in_days", "first_commit", "first_commit_dt", "event_gap", "event_date", "event_hash", "before_all_ct", "after_all_ct", "before_mrg_ct", "after_mrg_ct", "before_auth_new", "after_auth_new", "before_commit_new", "after_commit_new")
|
||||
contrib_df <- contrib_df[,col_order]
|
||||
contrib_df$ct_before_all <- str_split(gsub("[][]","", contrib_df$before_all_ct), ", ")
|
||||
contrib_df$ct_after_all <- str_split(gsub("[][]","", contrib_df$after_all_ct), ", ")
|
||||
contrib_df$ct_before_mrg <- str_split(gsub("[][]","", contrib_df$before_mrg_ct), ", ")
|
||||
contrib_df$ct_after_mrg <- str_split(gsub("[][]","", contrib_df$after_mrg_ct), ", ")
|
||||
drop <- c("before_all_ct", "before_mrg_ct", "after_all_ct", "after_mrg_ct")
|
||||
contrib_df = contrib_df[,!(names(contrib_df) %in% drop)]
|
||||
# 2 some expansion needs to happens for each project
|
||||
expand_timeseries <- function(project_row) {
|
||||
longer <- project_row |>
|
||||
pivot_longer(cols = starts_with("ct"),
|
||||
names_to = "window",
|
||||
values_to = "count") |>
|
||||
unnest(count)
|
||||
longer$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(contrib_df[1,])
|
||||
for (i in 2:nrow(contrib_df)){
|
||||
expanded_data <- rbind(expanded_data, expand_timeseries(contrib_df[i,]))
|
||||
}
|
||||
View(expand_timeseries)
|
||||
View(expanded_data)
|
||||
window_num <- 8
|
||||
windowed_data <- expanded_data |>
|
||||
filter(week >= (27 - window_num) & week <= (27 + window_num)) |>
|
||||
mutate(D = ifelse(week > 27, 1, 0))
|
||||
windowed_data$week_offset <- windowed_data$week - 27
|
||||
View(windowed_data)
|
||||
time_plot <- expanded_data |>
|
||||
ggplot(aes(x=week_offset, y=count))
|
||||
time_plot
|
||||
time_plot <- windowed_data |>
|
||||
ggplot(aes(x=week_offset, y=count))
|
||||
time_plot
|
||||
time_plot <- windowed_data |>
|
||||
ggplot(aes(x=week_offset, y=count)) +
|
||||
geom_point()
|
||||
time_plot
|
||||
time_plot <- windowed_data |>
|
||||
ggplot(aes(x=week_offset, y=median(count))) +
|
||||
geom_point()
|
||||
time_plot
|
||||
time_plot <- windowed_data |>
|
||||
ggplot(aes(x=week_offset, y=mean(count))) +
|
||||
geom_point()
|
||||
time_plot
|
||||
time_plot <- windowed_data |>
|
||||
ggplot(aes(x=week_offset, y=count)) +
|
||||
geom_point()
|
||||
time_plot
|
||||
all_actions_data <- windowed_data[which(windowed_data$observation_type == "all"),]
|
||||
all_actions_data$log1p_count <- log1p(all_actions_data$count)
|
||||
time_plot <- all_actions_data |>
|
||||
ggplot(aes(x=week_offset, y=log1p_count)) +
|
||||
geom_point()
|
||||
time_plot
|
||||
time_plot <- all_actions_data |>
|
||||
ggplot(aes(x=week_offset, y=log1p_count)) +
|
||||
geom_smooth()+
|
||||
geom_point()
|
||||
time_plot
|
||||
time_plot <- all_actions_data |>
|
||||
ggplot(aes(x=week_offset, y=log1p_count)) +
|
||||
geom_smooth()
|
||||
time_plot
|
||||
time_plot <- all_actions_data |>
|
||||
ggplot(aes(x=week_offset, y=log1p_count)) +
|
||||
geom_smooth() +
|
||||
theme_bw()
|
||||
time_plot
|
||||
windowed_readme_data$week_offset <- windowed_readme_data$week - 27
|
||||
all_actions_readme_data <- windowed_readme_data[which(windowed_readme_data$observation_type == "all"),]
|
||||
source("~/Desktop/git/24_deb_gov/R/gam_plot_documents.R")
|
||||
time_plot
|
||||
time_plot <- all_actions_data |>
|
||||
ggplot(aes(x=week_offset, y=log1p_count, color=factor(document_type))) +
|
||||
geom_smooth() +
|
||||
theme_bw()
|
||||
time_plot
|
||||
View(expanded_readme_data)
|
||||
mean(all_actions_readme_data$event_gap)
|
||||
mean(median$event_gap)
|
||||
median(all_actions_readme_data$event_gap)
|
||||
mean(all_actions_readme_data$event_gap)
|
||||
mean(all_actions_contrib_data$event_gap)
|
||||
median(all_actions_contrib_data$event_gap)
|
||||
time_plot <- all_actions_data |>
|
||||
ggplot(aes(x=week_offset, y=log1p_count, color=factor(document_type))) +
|
||||
geom_smooth() +
|
||||
theme_bw() +
|
||||
theme(legend.position = "top")
|
||||
time_plot
|
||||
time_plot <- all_actions_data |>
|
||||
ggplot(aes(x=week_offset, y=log1p_count, color=factor(document_type))) +
|
||||
geom_smooth() +
|
||||
geom_vline(x=0)
|
||||
theme_bw() +
|
||||
theme(legend.position = "top")
|
||||
time_plot <- all_actions_data |>
|
||||
ggplot(aes(x=week_offset, y=log1p_count, color=factor(document_type))) +
|
||||
geom_smooth() +
|
||||
geom_vline(x=0)+
|
||||
theme_bw() +
|
||||
theme(legend.position = "top")
|
||||
time_plot
|
||||
time_plot <- all_actions_data |>
|
||||
ggplot(aes(x=week_offset, y=log1p_count, color=factor(document_type))) +
|
||||
geom_smooth() +
|
||||
geom_vline(x=0)+
|
||||
theme_bw() +
|
||||
theme(legend.position = "top")
|
||||
time_plot <- all_actions_data |>
|
||||
ggplot(aes(x=week_offset, y=log1p_count, color=factor(document_type))) +
|
||||
geom_smooth() +
|
||||
geom_vline(0)+
|
||||
theme_bw() +
|
||||
theme(legend.position = "top")
|
||||
time_plot
|
||||
time_plot <- all_actions_data |>
|
||||
ggplot(aes(x=week_offset, y=log1p_count, color=factor(document_type))) +
|
||||
geom_smooth() +
|
||||
geom_vline(xintercept = 0)+
|
||||
theme_bw() +
|
||||
theme(legend.position = "top")
|
||||
time_plot
|
||||
#looking at event gap
|
||||
document_event_gap <- ggplot(all_actions_data, aes(x=event_gap, group=as.factor(document_type))) +
|
||||
geom_density(aes(color = as.factor(document_type), fill=as.factor(document_type)), alpha=0.2, position="identity") +
|
||||
theme_bw()
|
||||
document_event_gap
|
||||
#looking at event gap
|
||||
document_event_gap <- ggplot(all_actions_data, aes(x=scale(event_gap), group=as.factor(document_type))) +
|
||||
geom_density(aes(color = as.factor(document_type), fill=as.factor(document_type)), alpha=0.2, position="identity") +
|
||||
theme_bw()
|
||||
document_event_gap
|
||||
#looking at event gap
|
||||
mean(all_actions_readme_data$event_gap)
|
||||
sd(all_actions_readme_data$event_gap)
|
||||
mean(all_actions_contrib_data$event_gap)
|
||||
sd(all_actions_contrib_data$event_gap)
|
||||
mode(all_actions_contrib_data$event_gap)
|
||||
mean(all_actions_contrib_data$event_gap)
|
||||
|
||||
BIN
R/d_working_readability_plot.png
Normal file
BIN
R/d_working_readability_plot.png
Normal file
Binary file not shown.
|
After Width: | Height: | Size: 127 KiB |
@ -7,10 +7,11 @@ library(ggpubr)
|
||||
# gaughan@u.northwestern.edu
|
||||
# loading in the data
|
||||
try(setwd(dirname(rstudioapi::getActiveDocumentContext()$path)))
|
||||
readme_df <- read_csv("../text_analysis/draft_readability_readme.csv")
|
||||
contributing_df <- read_csv("../text_analysis/draft_readability_contributing.csv")
|
||||
readme_df <- read_csv("../text_analysis/dwo_readability_readme.csv")
|
||||
contributing_df <- read_csv("../text_analysis/dwo_readability_contributing.csv")
|
||||
head(readme_df)
|
||||
aggregate(readme_df[, 3:10], list(readme_df$subdir), median)
|
||||
aggregate(contributing_df[, 3:10], list(contributing_df$subdir), median)
|
||||
#getting basic stats for the readme readability
|
||||
median(readme_df$flesch_reading_ease)
|
||||
median(readme_df$linsear_write_formula)
|
||||
|
||||
BIN
R/draft_Rdd_groupings_plot.png
Normal file
BIN
R/draft_Rdd_groupings_plot.png
Normal file
Binary file not shown.
|
After Width: | Height: | Size: 46 KiB |
BIN
R/draft_average_over_time_plot.png
Normal file
BIN
R/draft_average_over_time_plot.png
Normal file
Binary file not shown.
|
After Width: | Height: | Size: 48 KiB |
BIN
R/draft_averages_plot.png
Normal file
BIN
R/draft_averages_plot.png
Normal file
Binary file not shown.
|
After Width: | Height: | Size: 48 KiB |
BIN
R/draft_rdd_ranef_plot.png
Normal file
BIN
R/draft_rdd_ranef_plot.png
Normal file
Binary file not shown.
|
After Width: | Height: | Size: 46 KiB |
BIN
R/dwo_working_readability.png
Normal file
BIN
R/dwo_working_readability.png
Normal file
Binary file not shown.
|
After Width: | Height: | Size: 159 KiB |
70
R/gam_plot_documents.R
Normal file
70
R/gam_plot_documents.R
Normal file
@ -0,0 +1,70 @@
|
||||
#trying to make a time plot showing the over-time shift
|
||||
library(plyr)
|
||||
contrib_df <- read_csv('../final_data/deb_contrib_did.csv')
|
||||
readme_df <- read_csv("../final_data/deb_readme_did.csv")
|
||||
col_order <- c("upstream_vcs_link", "age_in_days", "first_commit", "first_commit_dt", "event_gap", "event_date", "event_hash", "before_all_ct", "after_all_ct", "before_mrg_ct", "after_mrg_ct", "before_auth_new", "after_auth_new", "before_commit_new", "after_commit_new")
|
||||
#first contrib
|
||||
contrib_df <- contrib_df[,col_order]
|
||||
contrib_df$ct_before_all <- str_split(gsub("[][]","", contrib_df$before_all_ct), ", ")
|
||||
contrib_df$ct_after_all <- str_split(gsub("[][]","", contrib_df$after_all_ct), ", ")
|
||||
contrib_df$ct_before_mrg <- str_split(gsub("[][]","", contrib_df$before_mrg_ct), ", ")
|
||||
contrib_df$ct_after_mrg <- str_split(gsub("[][]","", contrib_df$after_mrg_ct), ", ")
|
||||
drop <- c("before_all_ct", "before_mrg_ct", "after_all_ct", "after_mrg_ct")
|
||||
contrib_df = contrib_df[,!(names(contrib_df) %in% drop)]
|
||||
#then readme
|
||||
readme_df <- readme_df[,col_order]
|
||||
readme_df$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_contrib_data <- expand_timeseries(contrib_df[1,])
|
||||
for (i in 2:nrow(contrib_df)){
|
||||
expanded_contrib_data <- rbind(expanded_contrib_data, expand_timeseries(contrib_df[i,]))
|
||||
}
|
||||
expanded_readme_data <- expand_timeseries(readme_df[1,])
|
||||
for (i in 2:nrow(readme_df)){
|
||||
expanded_readme_data <- rbind(expanded_readme_data, expand_timeseries(readme_df[i,]))
|
||||
}
|
||||
window_num <- 8
|
||||
windowed_contrib_data <- expanded_contrib_data |>
|
||||
filter(week >= (27 - window_num) & week <= (27 + window_num)) |>
|
||||
mutate(D = ifelse(week > 27, 1, 0))
|
||||
windowed_readme_data <- expanded_readme_data |>
|
||||
filter(week >= (27 - window_num) & week <= (27 + window_num)) |>
|
||||
mutate(D = ifelse(week > 27, 1, 0))
|
||||
|
||||
windowed_contrib_data$week_offset <- windowed_contrib_data$week - 27
|
||||
all_actions_contrib_data <- windowed_contrib_data[which(windowed_contrib_data$observation_type == "all"),]
|
||||
all_actions_contrib_data$document_type <- rep("contributing", length(all_actions_contrib_data$count))
|
||||
windowed_readme_data$week_offset <- windowed_readme_data$week - 27
|
||||
all_actions_readme_data <- windowed_readme_data[which(windowed_readme_data$observation_type == "all"),]
|
||||
all_actions_readme_data$document_type <- rep("readme", length(all_actions_readme_data$count))
|
||||
all_actions_data <- rbind(all_actions_contrib_data, all_actions_readme_data)
|
||||
all_actions_data$log1p_count <- log1p(all_actions_data$count)
|
||||
time_plot <- all_actions_data |>
|
||||
ggplot(aes(x=week_offset, y=log1p_count, color=factor(document_type))) +
|
||||
geom_smooth() +
|
||||
geom_vline(xintercept = 0)+
|
||||
theme_bw() +
|
||||
theme(legend.position = "top")
|
||||
time_plot
|
||||
#looking at event gap
|
||||
mean(all_actions_readme_data$event_gap)
|
||||
sd(all_actions_readme_data$event_gap)
|
||||
mean(all_actions_contrib_data$event_gap)
|
||||
sd(all_actions_contrib_data$event_gap)
|
||||
42
R/model_presentation.R
Normal file
42
R/model_presentation.R
Normal file
@ -0,0 +1,42 @@
|
||||
library(tidyverse)
|
||||
library(texreg)
|
||||
|
||||
readme_rdd <- readRDS("final_models/0624_readme_all_rdd.rda")
|
||||
contrib_rdd <- readRDS("final_models/0624_contrib_all_rdd.rda")
|
||||
summary(readme_rdd)
|
||||
|
||||
texreg(list(readme_rdd, contrib_rdd), stars=NULL, digits=3, use.packages=FALSE,
|
||||
custom.model.names=c( 'README','CONTRIBUTING'),
|
||||
custom.coef.names=c('(Intercept)', 'Indtroduction', 'Week (Time)', 'Project Age', 'Introduction:Week', 'Event Gap'),
|
||||
table=FALSE, ci.force = TRUE)
|
||||
|
||||
readme_groupings <- read.csv('../final_data/deb_readme_interaction_groupings.csv')
|
||||
contrib_groupings <- read.csv('../final_data/deb_contrib_interaction_groupings.csv')
|
||||
|
||||
subdirColors <-
|
||||
setNames( c('firebrick1', 'forestgreen', 'cornflowerblue')
|
||||
, c(0,1,2) )
|
||||
|
||||
readme_g <- readme_groupings |>
|
||||
ggplot(aes(x=rank, y=estimate, col = as.factor(ranef_grouping))) +
|
||||
geom_linerange(aes(ymin= conf.low, ymax= conf.high)) +
|
||||
scale_color_manual(values = subdirColors) +
|
||||
guides(fill="none", color="none")+
|
||||
theme_bw()
|
||||
readme_g
|
||||
|
||||
|
||||
contrib_g <- contrib_groupings |>
|
||||
ggplot(aes(x=rank, y=estimate, col = as.factor(ranef_grouping))) +
|
||||
geom_linerange(aes(ymin= conf.low, ymax= conf.high)) +
|
||||
scale_color_manual(values = subdirColors) +
|
||||
theme_bw() +
|
||||
theme(legend.position = "top")
|
||||
contrib_g
|
||||
|
||||
library(gridExtra)
|
||||
grid.arrange(contrib_g, readme_g, nrow = 1)
|
||||
library(jtools)
|
||||
plot_summs(readme_rdd, contrib_rdd, plot.distributions = TRUE)
|
||||
?plot_summs
|
||||
|
||||
Loading…
Reference in New Issue
Block a user