mutate(crescendo_limit = ifelse(week_offset < (-4), 0, 1))|>
cor.test(crescendo_limit, count)
cor.test(second_windowed_data$crescendo_limit, second_windowed_data$count)
#checking crescendo of contributions before document publication
#second window
second_windowed_data <- windowed_data |>
filter(week_offset <= 0) |>
mutate(crescendo_limit = ifelse(week_offset < (-2), 0, 1))
cor.test(second_windowed_data$crescendo_limit, second_windowed_data$count)
#checking crescendo of contributions before document publication
#second window
second_windowed_data <- windowed_data |>
filter(week_offset <= 0) |>
mutate(crescendo_limit = ifelse(week_offset < (-1), 0, 1))
cor.test(second_windowed_data$crescendo_limit, second_windowed_data$count)
#checking crescendo of contributions before document publication
#second window
second_windowed_data <- all_actions_data |>
filter(week_offset <= 0) |>
mutate(crescendo_limit = ifelse(week_offset < (-1), 0, 1))
#testing whether there's a correlation between count and the presce
cor.test(second_windowed_data$crescendo_limit, second_windowed_data$count)
library(tidyverse)
library(plyr)
#get the contrib data instead
try(setwd(dirname(rstudioapi::getActiveDocumentContext()$path)))
contrib_df <- read_csv("../final_data/deb_contrib_did.csv")
#some preprocessing and expansion
col_order <- c("upstream_vcs_link", "age_of_project", "event_date", "event_hash", "before_all_ct", "after_all_ct", "before_mrg_ct", "after_mrg_ct", "before_auth_new", "after_auth_new", "before_commit_new",  "after_commit_new")
contrib_df <- contrib_df[,col_order]
contrib_df$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,]))
}
#filter out the windows of time that we're looking at
window_num <- 8
windowed_data <- expanded_data |>
filter(week >= (27 - window_num) & week <= (27 + window_num)) |>
mutate(D = ifelse(week > 27, 1, 0))
#scale the age numbers and calculate the week offset here
windowed_data$scaled_project_age <- scale(windowed_data$age_of_project)
windowed_data$week_offset <- windowed_data$week - 27
#break out the different type of commit actions
all_actions_data <- windowed_data[which(windowed_data$observation_type == "all"),]
mrg_actions_data <- windowed_data[which(windowed_data$observation_type == "mrg"),]
#logging
all_actions_data$logged_count <- log(all_actions_data$count)
all_actions_data$log1p_count <- log1p(all_actions_data$count)
# now for merge
mrg_actions_data$logged_count <- log(mrg_actions_data$count)
mrg_actions_data$log1p_count <- log1p(mrg_actions_data$count)
#checking crescendo of contributions before document publication
#second window
second_windowed_data <- all_actions_data |>
filter(week_offset <= 0) |>
mutate(crescendo_limit = ifelse(week_offset < (-1), 0, 1))
#testing whether there's a correlation between count and the two weeks before the introduction
cor.test(second_windowed_data$crescendo_limit, second_windowed_data$count)
#checking crescendo of contributions before document publication
#second window
second_windowed_data <- all_actions_data |>
filter(week_offset <= 0) |>
mutate(crescendo_limit = ifelse(week_offset < (-3), 0, 1))
#testing whether there's a correlation between count and the two weeks before the introduction
cor.test(second_windowed_data$crescendo_limit, second_windowed_data$count)
#checking crescendo of contributions before document publication
#second window
second_windowed_data <- all_actions_data |>
filter(week_offset <= 0) |>
mutate(crescendo_limit = ifelse(week_offset < (-1), 0, 1))
#testing whether there's a correlation between count and the two weeks before the introduction
cor.test(second_windowed_data$crescendo_limit, second_windowed_data$count)
# this is the file with the lmer multi-level rddAnalysis
library(tidyverse)
library(plyr)
# 0 loading the readme data in
try(setwd(dirname(rstudioapi::getActiveDocumentContext()$path)))
readme_df <- read_csv("../final_data/deb_readme_did.csv")
# 1 preprocessing
#colnames(readme_df) <- c("upstream_vcs_link", "event_date", "event_hash", "before_all_ct", "before_mrg_ct", "after_all_ct", "after_mrg_ct", "before_auth_new", "after_commit_new", "after_auth_new", "before_commit_new")
col_order <- c("upstream_vcs_link", "age_of_project", "event_date", "event_hash", "before_all_ct", "after_all_ct", "before_mrg_ct", "after_mrg_ct", "before_auth_new", "after_auth_new", "before_commit_new",  "after_commit_new")
readme_df <- readme_df[,col_order]
readme_df$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 windows of time that we're looking at
window_num <- 8
windowed_data <- expanded_data |>
filter(week >= (27 - window_num) & week <= (27 + window_num)) |>
mutate(D = ifelse(week > 27, 1, 0))
#scale the age numbers
windowed_data$scaled_project_age <- scale(windowed_data$age_of_project)
windowed_data$week_offset <- windowed_data$week - 27
#break out the different types of commit actions that are studied
all_actions_data <- windowed_data[which(windowed_data$observation_type == "all"),]
mrg_actions_data <- windowed_data[which(windowed_data$observation_type == "mrg"),]
#log the dependent
all_actions_data$logged_count <- log(all_actions_data$count)
all_actions_data$log1p_count <- log1p(all_actions_data$count)
#checking crescendo of contributions before document publication
#second window
second_windowed_data <- all_actions_data |>
filter(week_offset <= 0) |>
mutate(crescendo_limit = ifelse(week_offset < (-1), 0, 1))
#testing whether there's a correlation between count and the two weeks before the introduction
cor.test(second_windowed_data$crescendo_limit, second_windowed_data$count)
lm(count ~ crescendo_limit + week_offset, data = second_windowed_data)
crescendow_huh <- lm(count ~ crescendo_limit + week_offset, data = second_windowed_data)
crescendo_huh <- lm(count ~ crescendo_limit + week_offset, data = second_windowed_data)
summary(crescendo_huh)
#checking crescendo of contributions before document publication
#second window
second_windowed_data <- all_actions_data |>
filter(week_offset <= 0) |>
mutate(crescendo_limit = ifelse(week_offset < (-3), 0, 1))
#testing whether there's a correlation between count and the two weeks before the introduction
cor.test(second_windowed_data$crescendo_limit, second_windowed_data$count)
crescendo_huh <- lm(count ~ crescendo_limit + week_offset, data = second_windowed_data)
summary(crescendo_huh)
library(tidyverse)
library(plyr)
#get the contrib data instead
try(setwd(dirname(rstudioapi::getActiveDocumentContext()$path)))
contrib_df <- read_csv("../final_data/deb_contrib_did.csv")
#some preprocessing and expansion
col_order <- c("upstream_vcs_link", "age_of_project", "event_date", "event_hash", "before_all_ct", "after_all_ct", "before_mrg_ct", "after_mrg_ct", "before_auth_new", "after_auth_new", "before_commit_new",  "after_commit_new")
contrib_df <- contrib_df[,col_order]
contrib_df$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,]))
}
#filter out the windows of time that we're looking at
window_num <- 8
windowed_data <- expanded_data |>
filter(week >= (27 - window_num) & week <= (27 + window_num)) |>
mutate(D = ifelse(week > 27, 1, 0))
#scale the age numbers and calculate the week offset here
windowed_data$scaled_project_age <- scale(windowed_data$age_of_project)
windowed_data$week_offset <- windowed_data$week - 27
#break out the different type of commit actions
all_actions_data <- windowed_data[which(windowed_data$observation_type == "all"),]
mrg_actions_data <- windowed_data[which(windowed_data$observation_type == "mrg"),]
#logging
all_actions_data$logged_count <- log(all_actions_data$count)
all_actions_data$log1p_count <- log1p(all_actions_data$count)
# now for merge
mrg_actions_data$logged_count <- log(mrg_actions_data$count)
mrg_actions_data$log1p_count <- log1p(mrg_actions_data$count)
#checking crescendo of contributions before document publication
#second window
second_windowed_data <- all_actions_data |>
filter(week_offset <= 0) |>
mutate(crescendo_limit = ifelse(week_offset < (-1), 0, 1))
#testing whether there's a correlation between count and the two weeks before the introduction
cor.test(second_windowed_data$crescendo_limit, second_windowed_data$count)
crescendo_huh <- lm(count ~ crescendo_limit + week_offset, data = second_windowed_data)
summary(crescendo_huh)
crescendo_huh <- lm(count ~ crescendo_limit * week_offset, data = second_windowed_data)
summary(crescendo_huh)
# this is the file with the lmer multi-level rddAnalysis
library(tidyverse)
library(plyr)
# 0 loading the readme data in
try(setwd(dirname(rstudioapi::getActiveDocumentContext()$path)))
readme_df <- read_csv("../final_data/deb_readme_did.csv")
# 1 preprocessing
#colnames(readme_df) <- c("upstream_vcs_link", "event_date", "event_hash", "before_all_ct", "before_mrg_ct", "after_all_ct", "after_mrg_ct", "before_auth_new", "after_commit_new", "after_auth_new", "before_commit_new")
col_order <- c("upstream_vcs_link", "age_of_project", "event_date", "event_hash", "before_all_ct", "after_all_ct", "before_mrg_ct", "after_mrg_ct", "before_auth_new", "after_auth_new", "before_commit_new",  "after_commit_new")
readme_df <- readme_df[,col_order]
readme_df$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 windows of time that we're looking at
window_num <- 8
windowed_data <- expanded_data |>
filter(week >= (27 - window_num) & week <= (27 + window_num)) |>
mutate(D = ifelse(week > 27, 1, 0))
#scale the age numbers
windowed_data$scaled_project_age <- scale(windowed_data$age_of_project)
windowed_data$week_offset <- windowed_data$week - 27
#break out the different types of commit actions that are studied
all_actions_data <- windowed_data[which(windowed_data$observation_type == "all"),]
mrg_actions_data <- windowed_data[which(windowed_data$observation_type == "mrg"),]
#log the dependent
all_actions_data$logged_count <- log(all_actions_data$count)
all_actions_data$log1p_count <- log1p(all_actions_data$count)
#checking crescendo of contributions before document publication
#second window
second_windowed_data <- all_actions_data |>
filter(week_offset <= 0) |>
mutate(crescendo_limit = ifelse(week_offset < (-1), 0, 1))
#testing whether there's a correlation between count and the two weeks before the introduction
cor.test(second_windowed_data$crescendo_limit, second_windowed_data$count)
crescendo_huh <- lm(count ~ crescendo_limit * week_offset, data = second_windowed_data)
summary(crescendo_huh)
#checking crescendo of contributions before document publication
#second window
second_windowed_data <- all_actions_data |>
filter(week_offset <= 0) |>
mutate(crescendo_limit = ifelse(week_offset < (-3), 0, 1))
#testing whether there's a correlation between count and the two weeks before the introduction
cor.test(second_windowed_data$crescendo_limit, second_windowed_data$count)
crescendo_huh <- lm(count ~ crescendo_limit * week_offset, data = second_windowed_data)
summary(crescendo_huh)
#checking crescendo of contributions before document publication
#second window
second_windowed_data <- all_actions_data |>
filter(week_offset <= 0) |>
mutate(crescendo_limit = ifelse(week_offset < (-1), 0, 1))
#testing whether there's a correlation between count and the two weeks before the introduction
cor.test(second_windowed_data$crescendo_limit, second_windowed_data$count)
crescendo_huh <- lm(count ~ crescendo_limit * week_offset, data = second_windowed_data)
summary(crescendo_huh)
library(tidyverse)
library(plyr)
# 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()$path)))
readme_df <- read_csv("../text_analysis/draft_readability_readme.csv")
contributing_df <- read_csv("../text_analysis/draft_readability_contributing.csv")
head(readme_df)
readme_df <- readme_df |>
mutate(coef_grouping <- as.factor(subdir))
cor.test(readme_df$coef_grouping, readme_df$flesch_reading_ease)
readme_df <- readme_df |>
mutate(coef_grouping <- as.factor(subdir))
cor.test(readme_df$coef_grouping, readme_df$flesch_reading_ease)
cor(readme_df$coef_grouping, readme_df$flesch_reading_ease)
readme_df <- readme_df |>
mutate(coef_grouping <- as.factor(subdir))
test_lm <- lm(flesch_reading_ease ~ coef_grouping,data=readme_df)
readme_df <- readme_df |>
mutate(coef_grouping <- as.factor(subdir))
test_lm <- lm(flesch_reading_ease ~ coef_grouping,data=readme_df)
test_lm <- lm(flesch_reading_ease ~ subdir,data=readme_df)
summary(test_lm)
test_lm <- lm(flesch_reading_ease ~ as.factor(subdir),data=readme_df)
summary(test_lm)
head(readme_df)
test_lm <- lm(flesch_reading_ease ~ char_count + as.factor(subdir),data=readme_df)
summary(test_lm)
head(readme_df)
test_lm <- lm(linsear_write_formula ~ char_count + as.factor(subdir),data=readme_df)
summary(test_lm)
head(readme_df)
test_lm <- lm(mcalpine_eflaw ~ char_count + as.factor(subdir),data=readme_df)
summary(test_lm)
test_lm <- lm(mcalpine_eflaw ~ word_count + as.factor(subdir),data=readme_df)
summary(test_lm)
aggregate(readme_df[, 3:11], list(readme_df$subdir), mean)
aggregate(readme_df[, 3:10], list(readme_df$subdir), mean)
#readme_df <- readme_df |>
#  mutate(coef_grouping <- as.factor(subdir))
#test_lm <- lm(mcalpine_eflaw ~ word_count + as.factor(subdir),data=readme_df)
#summary(test_lm)
aggregate(contributing_df[, 3:10], list(contributing_df$subdir), mean)
library(tidyverse)
library(plyr)
# 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()$path)))
readme_df <- read_csv("../text_analysis/draft_readability_readme.csv")
contributing_df <- read_csv("../text_analysis/draft_readability_contributing.csv")
head(readme_df)
aggregate(readme_df[, 3:10], list(readme_df$subdir), mean)
aggregate(readme_df[, 3:10], list(readme_df$subdir), median)
#readme_df <- readme_df |>
#  mutate(coef_grouping <- as.factor(subdir))
#test_lm <- lm(mcalpine_eflaw ~ word_count + as.factor(subdir),data=readme_df)
#summary(test_lm)
aggregate(contributing_df[, 3:10], list(contributing_df$subdir), median)
rm(list=ls())
set.seed(424242)
library(readr)
library(ggplot2)
library(tidyverse)
overall_data <- read_csv('../final_data/deb_full_data.csv',show_col_types = FALSE)
overall_data <- read_csv('../final_data/deb_full_data.csv',show_col_types = FALSE)
octo_data <- read_csv('../final_data/deb_octo_data.csv', show_col_types = FALSE)
readme_data <- read_csv("../final_data/deb_readme_roster.csv", show_col_types = FALSE)
overall_data$mmt <- (((overall_data$collaborators * 2)+ overall_data$contributors) / (overall_data$contributors + overall_data$collaborators))
mean(overall_data$mmt)
hist(overall_data$mmt, probability = TRUE)
#the basic stuff for the overall data
overall_data$mmt <- (((overall_data$collaborators * 2)+ overall_data$contributors) / (overall_data$contributors + overall_data$collaborators))
mean(overall_data$mmt)
hist(overall_data$mmt, probability = TRUE)
#some new variables around age
#overall_data$new.age <- as.numeric(cut(overall_data$age_of_project/365, breaks=c(0,7.524197,10.323056,13.649367,17), labels=c(1,2,3,4)))
#table(overall_data$new.age)
#overall_data$new.age.factor <- as.factor(overall_data$new.age)
overall_data$scaled_age <- scale(overall_data$age_of_project)
#model
mmtmodel1 <- lm(underproduction_mean ~ mmt + scaled_age, data=overall_data)
summary(mmtmodel1)
qqnorm(residuals(mmtmodel1))
# below this is the analysis for the octo data
octo_data$new.age <- as.numeric(cut(octo_data$age_of_project/365, breaks=c(0,7.524197,10.323056,13.649367,17), labels=c(1,2,3,4)))
table(octo_data$new.age)
octo_data$new.age.factor <- as.factor(octo_data$new.age)
octo_data$scaled_age <- scale(octo_data$age_of_project)
octo_data$mmt <- (((octo_data$collaborators * 2)+ octo_data$contributors) / (octo_data$contributors + octo_data$collaborators))
mean(octo_data$mmt)
hist(octo_data$mmt)
head(octo_data)
#getting the mmt-equivalent for both issue activity as well as wiki contrib activity
octo_data$issue_mmt <- ((octo_data$issue_contrib_count * 2) + (octo_data$total_contrib - octo_data$issue_contrib_count)) / (octo_data$total_contrib)
octo_data$sqrt_issue_mmt <- sqrt(octo_data$issue_mmt)
#right skewed data, need to transform
octo_data$wiki_mmt <- ((octo_data$wiki_contrib_count * 2) + (octo_data$total_contrib - octo_data$wiki_contrib_count)) / (octo_data$total_contrib)
hist(octo_data$wiki_mmt)
#below are the models for the octo data, there should be analysis for each one
octo_mmtmodel1 <- lm(underproduction_mean ~ mmt + scaled_age + has_readme + has_contrib, data=octo_data)
summary(octo_mmtmodel1)
issue_mmtmodel1 <- lm(underproduction_mean ~ issue_mmt + scaled_age + has_readme + has_contrib, data=octo_data)
summary(issue_mmtmodel1)
qqnorm(residuals(issue_mmtmodel1))
wiki_mmtmodel1 <- lm(underproduction_mean ~ wiki_mmt + scaled_age + has_readme + has_contrib, data=octo_data)
summary(wiki_mmtmodel1)
#getting some of the information in about whether projects have specific files
readme_did_roster <- read_csv("../final_data/deb_readme_did.csv", show_col_types = FALSE)
contrib_did_roster <- read_csv("../final_data/deb_contrib_did.csv", show_col_types = FALSE)
octo_data <- octo_data |>
mutate(has_readme = as.numeric(upstream_vcs_link %in% readme_did_roster$upstream_vcs_link)) |>
mutate(has_contrib = as.numeric(upstream_vcs_link %in% contrib_did_roster$upstream_vcs_link))
overall_data <- overall_data |>
mutate(has_readme = as.numeric(upstream_vcs_link %in% readme_did_roster$upstream_vcs_link)) |>
mutate(has_contrib = as.numeric(upstream_vcs_link %in% contrib_did_roster$upstream_vcs_link))
#below are the models for the octo data, there should be analysis for each one
octo_mmtmodel1 <- lm(underproduction_mean ~ mmt + scaled_age + has_readme + has_contrib, data=octo_data)
summary(octo_mmtmodel1)
issue_mmtmodel1 <- lm(underproduction_mean ~ issue_mmt + scaled_age + has_readme + has_contrib, data=octo_data)
summary(issue_mmtmodel1)
qqnorm(residuals(issue_mmtmodel1))
wiki_mmtmodel1 <- lm(underproduction_mean ~ wiki_mmt + scaled_age + has_readme + has_contrib, data=octo_data)
summary(wiki_mmtmodel1)
qqnorm(residuals(wiki_mmtmodel1))
#these next three are looking at mmt as an outcome of other factors
mmt_outcome_model <- lm(mmt ~ scaled_age + as.factor(has_readme) + as.factor(has_contrib), data = octo_data)
summary(mmt_outcome_model)
library(texreg) #my little "lib"
texreg(list(octo_mmtmodel1, issue_mmtmodel1, wiki_mmtmodel1), stars=NULL, digits=2,
custom.model.names=c( 'M1: MMT','M2: issue contrib.', 'M3: wiki contrib.'  ),
custom.coef.names=c('(Intercept)', 'MMT', 'scaled_age', 'has readme', 'has contrib', 'Issue MMT', 'Wiki MMT'),
use.packages=FALSE, table=FALSE, ci.force = TRUE)
govdoc_issuesmmt <- lm(issue_mmt ~ scaled_age + as.factor(has_readme) + as.factor(has_contrib), data=octo_data)
summary(govdoc_issuesmmt)
View(octo_data)
octo_cleaned <- octo_data[octo_data$issue_mmt != NaN]
octo_cleaned <- octo_data[!is.nan(octo_data$issue_mmt),]
#below are the models for the octo data, there should be analysis for each one
octo_mmtmodel1 <- lm(underproduction_mean ~ mmt + scaled_age + has_readme + has_contrib, data=octo_cleaned)
summary(octo_mmtmodel1)
issue_mmtmodel1 <- lm(underproduction_mean ~ issue_mmt + scaled_age + has_readme + has_contrib, data=octo_cleaned)
summary(issue_mmtmodel1)
qqnorm(residuals(issue_mmtmodel1))
wiki_mmtmodel1 <- lm(underproduction_mean ~ wiki_mmt + scaled_age + has_readme + has_contrib, data=octo_cleaned)
summary(wiki_mmtmodel1)
write.csv(octo_cleaned,"cleaned_octo.csv", row.names = FALSE)
texreg(list(octo_mmtmodel1, issue_mmtmodel1, wiki_mmtmodel1), stars=NULL, digits=2,
custom.model.names=c( 'M1: MMT','M2: issue contrib.', 'M3: wiki contrib.'  ),
custom.coef.names=c('(Intercept)', 'MMT', 'scaled_age', 'has readme', 'has contrib', 'Issue MMT', 'Wiki MMT'),
use.packages=FALSE, table=FALSE, ci.force = TRUE)
rm(list=ls())
set.seed(424242)
library(readr)
library(ggplot2)
library(tidyverse)
#primary analysis for cross-sectional community metrics
overall_data <- read_csv('../final_data/deb_full_data.csv',show_col_types = FALSE)
octo_data <- read_csv('../final_data/deb_octo_data.csv', show_col_types = FALSE)
readme_data <- read_csv("../final_data/deb_readme_roster.csv", show_col_types = FALSE)
contributing_data <- read_csv("../final_data/deb_contribfile_roster.csv", show_col_types = FALSE)
overall_data$mmt <- (((overall_data$collaborators * 2)+ overall_data$contributors) / (overall_data$contributors + overall_data$collaborators))
mean(overall_data$mmt)
#the basic stuff for the overall data
overall_data$mmt <- (((overall_data$collaborators * 2)+ overall_data$contributors) / (overall_data$contributors + overall_data$collaborators))
mean(overall_data$mmt)
hist(overall_data$mmt, probability = TRUE)
#model
mmtmodel1 <- lm(underproduction_mean ~ mmt + scaled_age, data=overall_data)
summary(mmtmodel1)
qqnorm(residuals(mmtmodel1))
#clean octo data
octo_data <- filter(octo_data, total_contrib != 0)
#some new variables around age
#overall_data$new.age <- as.numeric(cut(overall_data$age_of_project/365, breaks=c(0,7.524197,10.323056,13.649367,17), labels=c(1,2,3,4)))
#table(overall_data$new.age)
#overall_data$new.age.factor <- as.factor(overall_data$new.age)
overall_data$scaled_age <- scale(overall_data$age_of_project)
#model
mmtmodel1 <- lm(underproduction_mean ~ mmt + scaled_age, data=overall_data)
table(octo_data$new.age)
octo_data$new.age.factor <- as.factor(octo_data$new.age)
octo_data$scaled_age <- scale(octo_data$age_of_project)
octo_data$mmt <- (((octo_data$collaborators * 2)+ octo_data$contributors) / (octo_data$contributors + octo_data$collaborators))
mean(octo_data$mmt)
hist(octo_data$mmt)
head(octo_data)
#getting the mmt-equivalent for both issue activity as well as wiki contrib activity
octo_data$issue_mmt <- ((octo_data$issue_contrib_count * 2) + (octo_data$total_contrib - octo_data$issue_contrib_count)) / (octo_data$total_contrib)
#right skewed data, need to transform
octo_data$wiki_mmt <- ((octo_data$wiki_contrib_count * 2) + (octo_data$total_contrib - octo_data$wiki_contrib_count)) / (octo_data$total_contrib)
#getting some of the information in about whether projects have specific files
readme_did_roster <- read_csv("../final_data/deb_readme_did.csv", show_col_types = FALSE)
contrib_did_roster <- read_csv("../final_data/deb_contrib_did.csv", show_col_types = FALSE)
octo_data <- octo_data |>
mutate(has_readme = as.numeric(upstream_vcs_link %in% readme_did_roster$upstream_vcs_link)) |>
mutate(has_contrib = as.numeric(upstream_vcs_link %in% contrib_did_roster$upstream_vcs_link))
overall_data <- overall_data |>
mutate(has_readme = as.numeric(upstream_vcs_link %in% readme_did_roster$upstream_vcs_link)) |>
mutate(has_contrib = as.numeric(upstream_vcs_link %in% contrib_did_roster$upstream_vcs_link))
#below are the models for the octo data, there should be analysis for each one
octo_mmtmodel1 <- lm(underproduction_mean ~ mmt + scaled_age + has_readme + has_contrib, data=octo_data)
summary(octo_mmtmodel1)
issue_mmtmodel1 <- lm(underproduction_mean ~ issue_mmt + scaled_age + has_readme + has_contrib, data=octo_data)
summary(issue_mmtmodel1)
qqnorm(residuals(issue_mmtmodel1))
wiki_mmtmodel1 <- lm(underproduction_mean ~ wiki_mmt + scaled_age + has_readme + has_contrib, data=octo_data)
summary(wiki_mmtmodel1)
library(texreg) #my little "lib"
texreg(list(octo_mmtmodel1, issue_mmtmodel1, wiki_mmtmodel1), stars=NULL, digits=2,
custom.model.names=c( 'M1: MMT','M2: issue contrib.', 'M3: wiki contrib.'  ),
custom.coef.names=c('(Intercept)', 'MMT', 'scaled_age', 'has readme', 'has contrib', 'Issue MMT', 'Wiki MMT'),
use.packages=FALSE, table=FALSE, ci.force = TRUE)
#now large MMT model taking into account having contributing or README
mmtmodel2 <- lm(underproduction_mean ~ mmt + scaled_age + has_readme + has_contrib, data=overall_data)
summary(mmtmodel2)
qqnorm(residuals(mmtmodel2))
summary(mmtmodel2)