diff --git a/R/.Rhistory b/R/.Rhistory
index 4db6dfa..9187d92 100644
--- a/R/.Rhistory
+++ b/R/.Rhistory
@@ -1,439 +1,3 @@
-method='lm', formula= y~x, se=FALSE)+
-labs(x="MMT", y="Mean Underproduction Factor", color = "Project Age Group") +
-theme_bw() +
-theme(legend.position = c(0.05, 0.05), legend.justification = c("left", "bottom"))
-g2
-data1$new.age.factor <- factor(data1$new.age, levels=c(1,2,3,4), labels=c("0-9y", "9-12y", "12-15y","15-16y"))
-g2 <- ggplot(data1, aes(x=mmt, y=up.fac.mean)) +
-geom_smooth(mapping = aes(x=mmt, y=up.fac.mean, color=new.age.factor),
-method='lm', formula= y~x, se=FALSE)+
-labs(x="MMT", y="Mean Underproduction Factor", color = "Project Age Group") +
-theme_bw() +
-theme(legend.position = c(0.05, 0.05), legend.justification = c("left", "bottom"))
-g2
-library(readr)
-library(ggplot2)
-library(tidyverse)
-data7 <- read_csv('../final_data/kk_final_octo.csv', show_col_types = FALSE)
-median(data7$underproduction_mean)
-length(which(data7$underproduction_low < 0))
-364 / 3843
-data5 <- read_csv('../kk_final_readme_roster.csv', show_col_types=FALSE)
-data5 <- read_csv('..final_data/kk_final_readme_roster.csv', show_col_types=FALSE)
-data5 <- read_csv('../final_data/kk_final_readme_roster.csv', show_col_types=FALSE)
-length(which(data5$underproduction_low < 0))
-227/2695
-#primary analysis for cross-sectional community metrics
-overall_data <- read_csv('../final_data/deb_full_data.csv',show_col_types = FALSE)
-rm(list=ls())
-set.seed(424242)
-library(readr)
-library(ggplot2)
-library(tidyverse)
-#primary analysis for cross-sectional community metrics
-overall_data <- read_csv('../final_data/deb_full_data.csv',show_col_types = FALSE)
-octo_data <- read_csv('../final_data/deb_octo_data.csv', show_col_types = FALSE)
-overall_data$mmt <- (((oveall_data1$collaborators * 2)+ overall_data$contributors) / (overall_data$contributors + overall_data$collaborators))
-overall_data$mmt <- (((overall_data1$collaborators * 2)+ overall_data$contributors) / (overall_data$contributors + overall_data$collaborators))
-overall_data$mmt <- (((overall_data$collaborators * 2)+ overall_data$contributors) / (overall_data$contributors + overall_data$collaborators))
-mean(overall_data1$mmt)
-mean(overall_data$mmt)
-hist(overall_data$mmt, probability = TRUE)
-overall_data$new.age <- as.numeric(cut(overall_data$age_of_project/365, breaks=c(0,9,12,15,17), labels=c(1,2,3,4)))
-table(data1$new.age)
-table(overall_data$new.age)
-overall_data$new.age.factor <- as.factor(overall_data1$new.age)
-overall_data$new.age.factor <- as.factor(overall_data$new.age)
-hist(overall_data$new.age)
-hist(overall_data$new.age.factor)
-overall_data$new.age <- as.numeric(cut(overall_data$age_of_project/365, breaks=c(0,7,11,13,17), labels=c(1,2,3,4)))
-table(overall_data$new.age)
-overall_data$new.age.factor <- as.factor(overall_data$new.age)
-hist(overall_data$new.age.factor)
-overall_data$new.age <- as.numeric(cut(overall_data$age_of_project/365, breaks=c(0,7,11,13,17), labels=c(1,2,3,4)))
-table(overall_data$new.age)
-overall_data$new.age.factor <- as.factor(overall_data$new.age)
-hist(overall_data$new.age.factor)
-hist(overall_data$new.age)
-overall_data$new.age <- as.numeric(cut(overall_data$age_of_project/365, breaks=c(0,8,11,13,17), labels=c(1,2,3,4)))
-table(overall_data$new.age)
-overall_data$new.age.factor <- as.factor(overall_data$new.age)
-hist(overall_data$new.age)
-overall_data$new.age <- as.numeric(cut(overall_data$age_of_project/365, breaks=c(0,8,11,15,17), labels=c(1,2,3,4)))
-table(overall_data$new.age)
-overall_data$new.age.factor <- as.factor(overall_data$new.age)
-hist(overall_data$new.age)
-overall_data$new.age <- as.numeric(cut(overall_data$age_of_project/365, breaks=c(0,8,11,14,17), labels=c(1,2,3,4)))
-table(overall_data$new.age)
-overall_data$new.age.factor <- as.factor(overall_data$new.age)
-hist(overall_data$new.age)
-overall_data$new.age <- as.numeric(cut(overall_data$age_of_project/365, breaks=c(0,7,11,14,17), labels=c(1,2,3,4)))
-table(overall_data$new.age)
-overall_data$new.age.factor <- as.factor(overall_data$new.age)
-hist(overall_data$new.age)
-overall_data$new.age <- as.numeric(cut(overall_data$age_of_project/365, breaks=c(0,7,10,14,17), labels=c(1,2,3,4)))
-table(overall_data$new.age)
-overall_data$new.age.factor <- as.factor(overall_data$new.age)
-hist(overall_data$new.age)
-overall_data$new.age <- as.numeric(cut(overall_data$age_of_project/365, breaks=c(0,7,10,13,17), labels=c(1,2,3,4)))
-table(overall_data$new.age)
-overall_data$new.age.factor <- as.factor(overall_data$new.age)
-hist(overall_data$new.age)
-data1$new.age <- as.numeric(cut(data1$age_of_project/365, breaks=c(0,9,12,15,17), labels=c(1,2,3,4)))
-age_vector <- overall_data$age_of_project/365
-order(age_vector)
-order(age_vector)
-quartile(age_vector)
-quantile(age_vector)
-overall_data$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)
-hist(overall_data$new.age)
-1159/5105
-1391/5105
-1277/5105
-1276/510
-1276/5105
-#shows the cross-age downward slopes for all underproduction averages in the face of MMT
-g3 <- ggplot(overall_data, aes(x=mmt, y=underproduction_mean)) +
-geom_smooth(mapping = aes(x=mmt, y=underproduction_mean, color=new.age.factor),
-method='lm', formula= y~x) +
-xlab("MMT") +
-ylab("Underproduction Factor") +
-theme_bw()
-g3
-#shows the cross-age downward slopes for all underproduction averages in the face of MMT
-g3 <- ggplot(overall_data, aes(x=mmt, y=underproduction_mean)) +
-geom_point(mapping = aes(color=new.age.factor)) +
-geom_smooth(mapping = aes(x=mmt, y=underproduction_mean, color=new.age.factor),
-method='lm', formula= y~x) +
-xlab("MMT") +
-ylab("Underproduction Factor") +
-theme_bw()
-g3
-#shows the cross-age downward slopes for all underproduction averages in the face of MMT
-g3 <- ggplot(overall_data, aes(x=mmt, y=underproduction_mean)) +
-geom_smooth(mapping = aes(x=mmt, y=underproduction_mean, color=new.age.factor),
-method='lm', formula= y~x) +
-xlab("MMT") +
-ylab("Underproduction Factor") +
-theme_bw()
-g3
-mmtmodel1 <- lm(up.fac.mean ~ mmt + new.age.factor, data=overall_data)
-mmtmodel1 <- lm(underproduction_mean ~ mmt + new.age.factor, data=overall_data)
-summary(mmtmodel1)
-#shows the cross-age downward slopes for all underproduction averages in the face of MMT
-g3 <- ggplot(overall_data, aes(x=mmt, y=underproduction_mean)) +
-geom_smooth(mapping = aes(x=mmt, y=underproduction_mean, color=new.age.factor),
-method='lm', formula= y~x) +
-xlab("MMT") +
-ylab("Underproduction Factor") +
-theme_bw() +
-theme(legend.position = c(0.05, 0.05), legend.justification = c("left", "bottom"))
-g3
-#shows the cross-age downward slopes for all underproduction averages in the face of MMT
-g3 <- ggplot(overall_data, aes(x=mmt, y=underproduction_mean)) +
-geom_smooth(mapping = aes(x=mmt, y=underproduction_mean, color=new.age.factor), formula= y~x) +
-xlab("MMT") +
-ylab("Underproduction Factor") +
-theme_bw() +
-theme(legend.position = c(0.05, 0.05), legend.justification = c("left", "bottom"))
-g3
-g4 <- ggplot(overall_data, aes(x=mmt, y=underproduction_mean)) +
-geom_smooth(mapping = aes(x=mmt, y=underproduction_mean, color=new.age.factor)) +
-xlab("MMT") +
-ylab("Underproduction Factor") +
-theme_bw() +
-theme(legend.position = c(0.05, 0.05), legend.justification = c("left", "bottom"))
-g4
-g4 <- ggplot(overall_data, aes(x=mmt, y=underproduction_mean)) +
-geom_smooth(mapping = aes(x=mmt, y=underproduction_mean, color=new.age.factor)) +
-xlab("MMT") +
-ylab("Underproduction Factor") +
-theme_bw() +
-theme(legend.position = c(0.0, 0.0), legend.justification = c("left", "bottom"))
-g4
-g4 <- ggplot(overall_data, aes(x=mmt, y=underproduction_mean)) +
-geom_smooth(mapping = aes(x=mmt, y=underproduction_mean, color=new.age.factor)) +
-xlab("MMT") +
-ylab("Underproduction Factor") +
-theme_bw() +
-theme(legend.position = c(0.0, 0.0), legend.justification = c("right", "top"))
-g4
-g4 <- ggplot(overall_data, aes(x=mmt, y=underproduction_mean)) +
-geom_smooth(mapping = aes(x=mmt, y=underproduction_mean, color=new.age.factor)) +
-xlab("MMT") +
-ylab("Underproduction Factor") +
-theme_bw() +
-theme(legend.position = c(1.0, 1.0), legend.justification = c("right", "top"))
-g4
-g4 <- ggplot(overall_data, aes(x=mmt, y=underproduction_mean)) +
-geom_smooth(mapping = aes(x=mmt, y=underproduction_mean, color=new.age.factor)) +
-xlab("MMT") +
-ylab("Underproduction Factor") +
-theme_bw() +
-theme(legend.position = c(0.9, 1.0), legend.justification = c("right", "top"))
-g4
-g4 <- ggplot(overall_data, aes(x=mmt, y=underproduction_mean)) +
-geom_smooth(mapping = aes(x=mmt, y=underproduction_mean, color=new.age.factor)) +
-xlab("MMT") +
-ylab("Underproduction Factor") +
-theme_bw() +
-theme(legend.position = c(0.9, 0.9), legend.justification = c("right", "top"))
-g4
-g4 <- ggplot(overall_data, aes(x=mmt, y=underproduction_mean)) +
-geom_smooth(mapping = aes(x=mmt, y=underproduction_mean, color=new.age.factor), se=FALSE) +
-xlab("MMT") +
-ylab("Underproduction Factor") +
-theme_bw() +
-theme(legend.position = c(0.9, 0.9), legend.justification = c("right", "top"))
-g4
-min(overall_data$underproduction_mean)
-max(overall_data$underproduction_mean)
-octo_data <- read_csv('../final_data/deb_octo_data.csv', show_col_types = FALSE)
-octo_data$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)
-999 / 3842
-1139/3842
-955/3842
-747/3842
-octo_data$new.age.factor <- as.factor(octo_data$new.age)
-hist(overall_data$new.age)
-hist(octo_data$new.age)
-octo_data$mmt <- (((octo_data$collaborators * 2)+ octo_data$contributors) / (octo_data$contributors + octo_data$collaborators))
-mean(overall_data$mmt)
-mean(octo_data$mmt)
-hist(octo_data$mmt, probability = TRUE)
-head(octo_data)
-octo_data$issue_mmt <- (((octo_data$issue_contrib_count * 2)+ octo_data$api_contrib_count) / (octo_data$api_contrib_count))
-hist(octo_data$issue_mmt, probability = TRUE)
-octo_data$issue_mmt <- (((octo_data$issue_contrib_count * 2)+ octo_data$api_contrib_count + octo_data$wiki_contrib_count + octo_data$file_contrib_count) / (octo_data$api_contrib_count + + octo_data$wiki_contrib_count + octo_data$issue_contrib_count + octo_data$file_contrib_count))
-hist(octo_data$issue_mmt, probability = TRUE)
-octo_mmtmodel1 <- lm(underproduction_mean ~ mmt + new.age.factor, data=octo_data)
-summary(octo_mmtmodel1)
-issue_mmtmodel1 <- lm(underproduction_mean ~ issue_mmt + new.age.factor, data=octo_data)
-summary(issue_mmtmodel1)
-octo_data$wiki_mmt <- (((octo_data$wiki_contrib_count * 2)+ octo_data$api_contrib_count + octo_data$wiki_contrib_count + octo_data$file_contrib_count) / (octo_data$api_contrib_count + + octo_data$wiki_contrib_count + octo_data$issue_contrib_count + octo_data$file_contrib_count))
-hist(octo_data$wiki_mmt, probability = TRUE)
-wiki_mmtmodel1 <- lm(underproduction_mean ~ issue_mmt + new.age.factor, data=octo_data)
-summary(wiki_mmtmodel1)
-wiki_mmtmodel1 <- lm(underproduction_mean ~ wiki_mmt + new.age.factor, data=octo_data)
-summary(wiki_mmtmodel1)
-texreg(list(mmtmodel1, issue_mmtmodel1, wiki_mmtmodel1), stars=NULL, digits=2,
-custom.model.names=c( 'MMT (Overall Dataset)'),
-custom.coef.names=c('(Intercept)', 'MMT', 'Age-2', 'Age-3', 'Age-4'),
-use.packages=FALSE, table=FALSE, ci.force = TRUE)
-source('powerAnalysis.R') #my little "lib"
-texreg(list(octo_mmtmodel1, issue_mmtmodel1, wiki_mmtmodel1), stars=NULL, digits=2,
-custom.model.names=c( 'M1: augm. formality','M2: MMT', 'M3: milestones'  ),
-custom.coef.names=c('(Intercept)', 'Augmented formality', 'MMT', 'Age-2', 'Age-3', 'Age-4', 'Milestones'),
-use.packages=FALSE, table=FALSE, ci.force = TRUE)
-texreg(list(mmtmodel1, issue_mmtmodel1, wiki_mmtmodel1), stars=NULL, digits=2,
-custom.model.names=c( 'MMT (Overall Dataset)'),
-custom.coef.names=c('(Intercept)', 'MMT', 'Age-2', 'Age-3', 'Age-4'),
-use.packages=FALSE, table=TRUE, ci.force = TRUE)
-readme_data <- read_csv("../final_data/deb_readme_roster.csv", show_col_types = FALSE)
-readme_data <- read_csv("../final_data/deb_readme_roster.csv", show_col_types = FALSE)
-#below here is the analysis for the readme data
-readme_data$new.age <- as.numeric(cut(readme_data$age_of_project/365, breaks=c(0,7.524197,10.323056,13.649367,17), labels=c(1,2,3,4)))
-table(readme_data$new.age)
-readme_data$new.age.factor <- as.factor(readme_data$new.age)
-hist(readme_data$new.age)
-637 / 2694
-676 / 2694
-725 / 2694
-656 / 2694
-contributing_data <- read_csv("../final_data/deb_contribfile_roster.csv", show_col_types = FALSE)
-#below here is the analysis for the contributing.md files
-readme_data$new.age <- as.numeric(cut(readme_data$age_of_project/365, breaks=c(0,7.524197,10.323056,13.649367,17), labels=c(1,2,3,4)))
-table(readme_data$new.age)
-readme_data$new.age.factor <- as.factor(readme_data$new.age)
-#below here is the analysis for the contributing.md files
-contributing_data$new.age <- as.numeric(cut(contributing_data$age_of_project/365, breaks=c(0,7.524197,10.323056,13.649367,17), labels=c(1,2,3,4)))
-table(contributing_data$new.age)
-contributing_data$new.age.factor <- as.factor(contributing_data$new.age)
-hist(contributing_data$new.age)
-76/528
-119 / 528
-171/ 528
-162 / 528
-octo_data <- read_csv('../new_denom_032624.csv', show_col_types = FALSE)
-rm(list=ls())
-set.seed(424242)
-library(readr)
-library(ggplot2)
-library(tidyverse)
-readme_data <- read_csv("../final_data/deb_readme_roster.csv", show_col_types = FALSE)
-octo_data <- read_csv('../new_denom_032624.csv', show_col_types = FALSE)
-# 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)))
-octo_data <- read_csv('../new_denom_032624.csv', show_col_types = FALSE)
-# 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$new.age)
-hist(octo_data$new.age)
-octo_data$mmt <- (((octo_data$collaborators * 2)+ octo_data$contributors) / (octo_data$contributors + octo_data$collaborators))
-mean(octo_data$mmt)
-hist(octo_data$mmt, probability = TRUE)
-head(octo_data)
-#TODO: the counts here aren't unique, need to go back and calculate so that no overlap between the counts
-#i.e. needs to be a total contrib number that is not attached to the high level counts
-octo_data$issue_mmt <- (((octo_data$issue_contrib_count * 2) + (octo_data$total_contrib - octo_data$issue_contrib_count)) / (octo_data$total_contrib))
-hist(octo_data$issue_mmt, probability = TRUE)
-#TODO: the counts here aren't unique, need to go back and calculate so that no overlap between the counts
-#i.e. needs to be a total contrib number that is not attached to the high level counts
-octo_data$issue_mmt <- ((octo_data$issue_contrib_count * 2) + (octo_data$total_contrib - octo_data$issue_contrib_count)) / (octo_data$total_contrib)
-hist(octo_data$issue_mmt, probability = TRUE)
-max(octo_data$issue_mmt)
-max(octo_data$issue_mmt)
-median(octo_data$issue_mmt)
-median(octo_data$issue_mmt)
-min(octo_data$issue_mmt)
-hist(octo_data$total_contrib)
-mean(octo_data$total_contrib)
-median(octo_data$total_contrib)
-median(octo_data$contributors)
-median(octo_data$collaborators)
-median(octo_data$total_contrib)
-#TODO: the counts here aren't unique, need to go back and calculate so that no overlap between the counts
-#i.e. needs to be a total contrib number that is not attached to the high level counts
-octo_data$issue_mmt <- ((octo_data$issue_contrib_count * 2) + (octo_data$total_contrib - octo_data$issue_contrib_count)) / (octo_data$total_contrib)
-hist(octo_data$issue_mmt, probability = TRUE)
-hist(octo_data$issue_mmt)
-octo_data$wiki_mmt <- ((octo_data$wiki_contrib_count * 2) + (octo_data$total_contrib - octo_data$wiki_contrib_count)) / (octo_data$total_contrib)
-hist(octo_data$wiki_mmt)
-min(octo_data$wiki_mmt)
-median(octo_data$wiki_mmt)
-#TODO: the counts here aren't unique, need to go back and calculate so that no overlap between the counts
-#i.e. needs to be a total contrib number that is not attached to the high level counts
-octo_data$issue_mmt <- ((octo_data$issue_contrib_count * 2) + (octo_data$total_contrib - octo_data$issue_contrib_count)) / (octo_data$total_contrib)
-hist(octo_data$issue_mmt)
-octo_data$mmt <- (((octo_data$collaborators * 2)+ octo_data$contributors) / (octo_data$contributors + octo_data$collaborators))
-mean(octo_data$mmt)
-hist(octo_data$mmt)
-median(octo_data$total_contrib)
-#TODO: the counts here aren't unique, need to go back and calculate so that no overlap between the counts
-#i.e. needs to be a total contrib number that is not attached to the high level counts
-octo_data$issue_mmt <- ((octo_data$issue_contrib_count * 2) + (octo_data$total_contrib - octo_data$issue_contrib_count)) / (octo_data$total_contrib)
-hist(octo_data$issue_mmt)
-max(octo_data$issue_mmt)
-maximum(octo_data$issue_mmt)
-octo_data$wiki_mmt <- ((octo_data$wiki_contrib_count * 2) + (octo_data$total_contrib - octo_data$wiki_contrib_count)) / (octo_data$total_contrib)
-hist(octo_data$wiki_mmt)
-median(octo_data$wiki_mmt)
-#below are the models for the octo data, there should be analysis for each one
-octo_mmtmodel1 <- lm(underproduction_mean ~ mmt + new.age.factor, data=octo_data)
-summary(octo_mmtmodel1)
-#TODO: the counts here aren't unique, need to go back and calculate so that no overlap between the counts
-#i.e. needs to be a total contrib number that is not attached to the high level counts
-octo_data$issue_mmt <- ((octo_data$issue_contrib_count * 2) + (octo_data$total_contrib - octo_data$issue_contrib_count)) / (octo_data$total_contrib)
-hist(octo_data$issue_mmt)
-maximum(octo_data$issue_mmt)
-typeof(octo_data$issue_mmt)
-length(octo_data$issue_mmt)
-#TODO: the counts here aren't unique, need to go back and calculate so that no overlap between the counts
-#i.e. needs to be a total contrib number that is not attached to the high level counts
-octo_data$issue_mmt <- ((octo_data$issue_contrib_count * 2) + (octo_data$total_contrib - octo_data$issue_contrib_count)) / (octo_data$total_contrib)
-hist(octo_data$issue_mmt)
-length(octo_data$issue_mmt)
-sum(octo_data$issue_mmt > 2)
-length(octo_data$issue_mmt > 2)
-length(octo_data$issue_mmt > 2.0)
-median(octo_data$wiki_mmt)
-typeof(octo_data$issue_mmt)
-median(octo_data$issue_mmt, na.rm = TRUE)
-median(octo_data$issue_contrib_count)
-octo_data <- na.omit(octo_data$issue_contrib_count)
-median(octo_data$issue_contrib_count)
-octo_data <- read_csv('../new_denom_032624.csv', show_col_types = FALSE)
-# below this is the analysis for the octo data
-octo_data$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)
-hist(octo_data$new.age)
-octo_data$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)
-median(octo_data$issue_contrib_count)
-octo_data <- na.omit(octo_data)
-median(octo_data$issue_contrib_count)
-#TODO: the counts here aren't unique, need to go back and calculate so that no overlap between the counts
-#i.e. needs to be a total contrib number that is not attached to the high level counts
-octo_data$issue_mmt <- ((octo_data$issue_contrib_count * 2) + (octo_data$total_contrib - octo_data$issue_contrib_count)) / (octo_data$total_contrib)
-hist(octo_data$issue_mmt)
-median(octo_data$issue_mmt, na.rm = TRUE)
-length(octo_data$issue_mmt > 2.0)
-length(octo_data$issue_mmt > 2.0)
-length(octo_data$issue_mmt > 2)
-median(octo_data$issue_mmt)
-, na.rm = TRUE
-median(octo_data$issue_mmt, na.rm = TRUE)
-length(octo_data$issue_mmt > 2)
-length(octo_data$issue_mmt > 2)
-length(octo_data$issue_mmt > 2.0)
-max(octo_data$issue_mmt, na.rm = TRUE)
-octo_data$new_mmt <- (((octo_data$collaborators * 2)+ (octo_data$total_contrib - octo_data$collaborators)) / (octo_data$total_contrib))
-hist(octo_data$new_mmt)
-octo_data$mmt <- (((octo_data$collaborators * 2)+ octo_data$contributors) / (octo_data$contributors + octo_data$collaborators))
-mean(octo_data$mmt)
-hist(octo_data$mmt)
-#TODO: there's an issue with calculating this but somehow not an issue with the wiki one
-octo_data$issue_mmt <- ((octo_data$issue_contrib_count * 2) + (octo_data$total_contrib - octo_data$issue_contrib_count)) / (octo_data$total_contrib)
-hist(octo_data$issue_mmt)
-octo_data$wiki_mmt <- ((octo_data$wiki_contrib_count * 2) + (octo_data$total_contrib - octo_data$wiki_contrib_count)) / (octo_data$total_contrib)
-hist(octo_data$wiki_mmt)
-hist(octo_data$issue_mmt)
-length(octo_data$issue_mmt > 2.0)
-octo_data[which(octo_data$issue_contrib_count > octo_data$total_contrib)]
-octo_data[which(octo_data$issue_contrib_count > octo_data$total_contrib),]
-octo_data[which(octo_data$issue_contrib_count > octo_data$total_contrib),]$issue_contrib_count
-octo_data <- read_csv('../new_denom_032624_stripped.csv', show_col_types = FALSE)
-# 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$new.age)
-hist(octo_data$new.age)
-octo_data$mmt <- (((octo_data$collaborators * 2)+ octo_data$contributors) / (octo_data$contributors + octo_data$collaborators))
-mean(octo_data$mmt)
-hist(octo_data$mmt)
-head(octo_data)
-octo_data[which(octo_data$issue_contrib_count > octo_data$total_contrib),]$issue_contrib_count
-octo_data[which(octo_data$issue_contrib_count > octo_data$total_contrib),]
-octo_data[which(octo_data$issue_contrib_count > octo_data$total_contrib),]
-octo_data <- read_csv('../new_denom_032624_stripped.csv', show_col_types = FALSE)
-# 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$new.age)
-hist(octo_data$new.age)
-octo_data$mmt <- (((octo_data$collaborators * 2)+ octo_data$contributors) / (octo_data$contributors + octo_data$collaborators))
-mean(octo_data$mmt)
-hist(octo_data$mmt)
-head(octo_data)
-octo_data <- octo_data[which(octo_data$issue_contrib_count <= octo_data$total_contrib),]
-#TODO: there's an issue with calculating this but somehow not an issue with the wiki one
-octo_data$issue_mmt <- ((octo_data$issue_contrib_count * 2) + (octo_data$total_contrib - octo_data$issue_contrib_count)) / (octo_data$total_contrib)
-hist(octo_data$issue_mmt)
-max(octo_data$issue_mmt, na.rm = TRUE)
-length(octo_data$issue_mmt > 2.0)
-issue_mmtmodel1 <- lm(underproduction_mean ~ issue_mmt + new.age.factor, data=octo_data)
-summary(issue_mmtmodel1)
-wiki_mmtmodel1 <- lm(underproduction_mean ~ wiki_mmt + new.age.factor, data=octo_data)
-summary(wiki_mmtmodel1)
-write.csv(octo_data, "new_octo.csv", row.names = FALSE)
-octo_data <- read_csv('../final_data/deb_octo_data.csv', show_col_types = FALSE)
-qqnorm(octo_data$issue_mmt)
-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)
-median(octo_data$wiki_mmt)
-qqnorm(octo_data$wiki_mmt)
-qqnorm(octo_data$issue_mmt)
-qqnorm(octo_data$wiki_mmt)
-qqnorm(log(octo_data$issue_mmt))
-qqnorm(octo_data$issue_mmt)
-qqnorm(log(octo_data$issue_mmt))
 qqnorm(octo_data$issue_mmt)
 qqnorm(log(octo_data$issue_mmt))
 qqnorm(residuals(octo_data$issue_mmt))
@@ -510,3 +74,439 @@ texreg(list(octo_mmtmodel1, issue_mmtmodel1, wiki_mmtmodel1), stars=NULL, digits
 custom.model.names=c( 'M1: MMT','M2: issue contrib.', 'M3: wiki_contrib.'  ),
 custom.coef.names=c('(Intercept)', 'MMT', 'Issues', 'Age-2', 'Age-3', 'Age-4', 'Wiki'),
 use.packages=FALSE, table=FALSE, ci.force = TRUE)
+glimpse(readme_df)
+library(tidyverse)
+#set wd, read in data
+try(setwd(dirname(rstudioapi::getActiveDocumentContext()$path)))
+readme_df <- read_csv("../final_data/deb_readme_did.csv")
+contributing_df <- read_csv("../final_data/deb_contrib_did.csv")
+glimpse(readme_df)
+head(readme_df)
+colnames(readme_df) <- c("upstream_vcs_link", "event_date", "event_hash", "before_all_cnt", "before_mrg_cnt", "after_all_cnt", "after_mrg_cnt", "before_auth_new", "after_commit_new", "after_auth_new", "before_commit_new")
+glimpse(readme_df)
+col_order <- c("upstream_vcs_link", "event_date", "event_hash", "before_all_cnt", "after_all_cnt", "before_mrg_cnt", "after_mrg_cnt", "before_auth_new", "after_auth_new", "before_commit_new",  "after_commit_new")
+readme_df <- readme_df[,col_order]
+glimpse(readme_df)
+#TODO: turn character type into vector of numbers
+str_split(test, ", ")
+test <- "[0, 0, 0, 0]"
+#TODO: turn character type into vector of numbers
+str_split(test, ", ")
+#TODO: turn character type into vector of numbers
+str_split(gsub("[][]","", test), ", ")
+readme_df %>% add_column(cnt_before_all = str_split(gsub("[][]","", before_all_count), ", "))
+readme_df %>% mutate(cnt_before_all = str_split(gsub("[][]","", before_all_count), ", "))
+readme_df %>% mutate("cnt_before_all" = str_split(gsub("[][]","", "before_all_count"), ", "))
+head(readme_df$before_all_cnt)
+head(readme_df$cnt_before_all)
+readme_df %>% mutate(cnt_before_all = str_split(gsub("[][]","", "before_all_count"), ", "))
+head(readme_df$cnt_before_all)
+View(readme_df)
+View(readme_df)
+readme_df$cnt_before_all
+readme_df %>% mutate(cnt_before_all = str_split(gsub("[][]","", "before_all_count"), ", "))
+str_split(gsub("[][]","", readme_df$before_all_count), ", ")
+#str_split(gsub("[][]","", readme_df$before_all_cnt), ", ")
+readme_df %>% mutate(cnt_before_all = str_split(gsub("[][]","", "before_all_cnt"), ", "))
+readme_df$cnt_before_all
+#str_split(gsub("[][]","", readme_df$before_all_cnt), ", ")
+readme_df %>% mutate("cnt_before_all" = str_split(gsub("[][]","", "before_all_cnt"), ", "))
+readme_df$cnt_before_all
+str_split(gsub("[][]","", readme_df$before_all_cnt), ", ")
+readme_df$cnt_before_all <- str_split(gsub("[][]","", readme_df$before_all_cnt), ", ")
+readme_df$cnt_before_all
+readme_df$cnt_after_all <- str_split(gsub("[][]","", readme_df$after_all_cnt), ", ")
+readme_df$cnt_after_all
+readme_df$cnt_before_mrg <- str_split(gsub("[][]","", readme_df$before_mrg_cnt), ", ")
+readme_df$cnt_before_mrg
+readme_df$cnt_after_mrg <- str_split(gsub("[][]","", readme_df$after_mrg_cnt), ", ")
+readme_df$cnt_after_mrg
+#TODO: figure out if one needs to expand the data into a different dataframe, and if so how
+readme_df <- subset(readme_df, select = -c("before_all_cnt", "before_mrg_cnt", "after_all_cnt", "after_mrg_cnt"))
+drop <- c("before_all_cnt", "before_mrg_cnt", "after_all_cnt", "after_mrg_cnt")
+readme_df = readme_df[,!(names(readme_df) %in% drop)]
+View(readme_df)
+library(tidyverse)
+#set wd, read in data
+try(setwd(dirname(rstudioapi::getActiveDocumentContext()$path)))
+readme_df <- read_csv("../final_data/deb_readme_did.csv")
+colnames(readme_df) <- c("upstream_vcs_link", "event_date", "event_hash", "before_all_cnt", "before_mrg_cnt", "after_all_cnt", "after_mrg_cnt", "before_auth_new", "after_commit_new", "after_auth_new", "before_commit_new")
+col_order <- c("upstream_vcs_link", "event_date", "event_hash", "before_all_cnt", "after_all_cnt", "before_mrg_cnt", "after_mrg_cnt", "before_auth_new", "after_auth_new", "before_commit_new",  "after_commit_new")
+readme_df <- readme_df[,col_order]
+glimpse(readme_df)
+readme_df$cnt_before_all <- str_split(gsub("[][]","", readme_df$before_all_cnt), ", ")
+readme_df$cnt_before_all <- as.numeric(readme_df$cnt_before_all)
+View(readme_df)
+readme_df$cnt_before_all
+type(readme_df$cnt_before_all)
+typeof(readme_df$cnt_before_all)
+typeof(readme_df$cnt_before_all[0])
+readme_df$cnt_before_all <- unlist(str_split(gsub("[][]","", readme_df$before_all_cnt), ", "))
+readme_df$cnt_before_all <- str_split(gsub("[][]","", readme_df$before_all_cnt), ", ")
+typeof(readme_df$cnt_before_all)
+typeof(readme_df$cnt_before_all[[0]])
+typeof(readme_df$cnt_before_all[0])
+sapply(readme_df, class)
+readme_df[,lapply(readme_df, unlist)]
+readme_df[,lapply(readme_df$cnt, unlist)]
+readme_df[,lapply(readme_df$cnt_before_all, unlist)]
+typeof(readme_df$cnt_before_all[0])
+View(readme_df)
+View(readme_df)
+readme_df$cnt_before_all <- as.numeric(str_split(gsub("[][]","", readme_df$before_all_cnt), ", "))
+readme_df$cnt_before_all <- as.numeric(str_split(gsub("[][]","", readme_df$before_all_cnt), ", ")[[1]])
+readme_df$cnt_before_all <- str_split(gsub("[][]","", readme_df$before_all_cnt), ", ")
+typeof(readme_df$cnt_before_all[0])
+typeof(readme_df$cnt_before_all[0][0])
+readme_df$cnt_before_all[0]
+unlist(readme_df$cnt_before_all[0])
+readme_df$cnt_before_all[0]
+readme_df$cnt_before_all
+test <- readme_df$cnt_before_all
+test
+as.numeric(test)
+test[0]
+test[1]
+as.numeric(test[1])
+unlist(test[1])
+as.numeric(unlist(test[1]))
+test2 <- as.numeric(unlist(test))
+test2
+print(entry)
+for (entry in test) {
+print(entry)
+}
+print(as.numeric(unlist(entry)))
+for (entry in test) {
+print(as.numeric(unlist(entry)))
+}
+test_two <- append(test_two, as.numeric(unlist(entry)))
+print(as.numeric(unlist(entry)))
+for (entry in test) {
+test_two <- append(test_two, as.numeric(unlist(entry)))
+print(as.numeric(unlist(entry)))
+}
+test_two <- c()
+for (entry in test) {
+test_two <- append(test_two, as.numeric(unlist(entry)))
+print(as.numeric(unlist(entry)))
+}
+readme_df$cnt_before_all <- as.numeric(readme_df$cnt_before_all)
+test_two
+readme_df$cnt_before_all[iterator] <- as.numeric(unlist(entry))
+iterator <- 0
+for (entry in test) {
+readme_df$cnt_before_all[iterator] <- as.numeric(unlist(entry))
+print(as.numeric(unlist(entry)))
+iterator <- iterator + 1
+}
+View(readme_df)
+library(tidyverse)
+#set wd, read in data
+try(setwd(dirname(rstudioapi::getActiveDocumentContext()$path)))
+readme_df <- read_csv("../final_data/deb_readme_did.csv")
+colnames(readme_df) <- c("upstream_vcs_link", "event_date", "event_hash", "before_all_cnt", "before_mrg_cnt", "after_all_cnt", "after_mrg_cnt", "before_auth_new", "after_commit_new", "after_auth_new", "before_commit_new")
+col_order <- c("upstream_vcs_link", "event_date", "event_hash", "before_all_cnt", "after_all_cnt", "before_mrg_cnt", "after_mrg_cnt", "before_auth_new", "after_auth_new", "before_commit_new",  "after_commit_new")
+readme_df <- readme_df[,col_order]
+glimpse(readme_df)
+head(readme_df)
+#this has to happen on the analysis side of things for a given row, it cannot happen on the storage side
+#this is a conversation of whether or not the data should be saved in terms of
+readme_df$cnt_before_all <- str_split(gsub("[][]","", readme_df$before_all_cnt), ", ")
+# test <- readme_df$cnt_before_all
+# as.numeric(unlist(test[1]))
+# test_two <- c()
+# iterator <- 0
+# for (entry in test) {
+#   readme_df$cnt_before_all[iterator] <- as.numeric(unlist(entry))
+#   print(as.numeric(unlist(entry)))
+#   iterator <- iterator + 1
+# }
+# test_two
+readme_df$cnt_after_all <- str_split(gsub("[][]","", readme_df$after_all_cnt), ", ")
+readme_df$cnt_before_mrg <- str_split(gsub("[][]","", readme_df$before_mrg_cnt), ", ")
+readme_df$cnt_after_mrg <- str_split(gsub("[][]","", readme_df$after_mrg_cnt), ", ")
+drop <- c("before_all_cnt", "before_mrg_cnt", "after_all_cnt", "after_mrg_cnt")
+readme_df = readme_df[,!(names(readme_df) %in% drop)]
+#Yes, need to expand the dataframe, but again, for the sake of clarity, do not want to until analysis step
+new_test <- head(readme_df, 1)
+View(new_test)
+write.csv(readme_df, "r_readme_did.csv", row.names=FALSE)
+# 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 <- head(readme_df, 1)
+View(new_test)
+longer <- new_test |>
+pivot_longer(cols = starts_with("cnt"),
+names_to = "window",
+values_to = "count"))
+longer <- new_test |>
+pivot_longer(cols = starts_with("cnt"),
+names_to = "window",
+values_to = "count")
+longer
+View(longer)
+longer |> unnest(count)
+new_longer <- longer |> unnest(count)
+View(new_longer)
+longer
+View(new_longer)
+longer <- new_test |>
+pivot_longer(cols = starts_with("cnt"),
+names_to = "window",
+values_to = "count") |>
+unnest(as.numeric(unlist(count)))
+longer
+longer <- new_test |>
+pivot_longer(cols = starts_with("cnt"),
+names_to = "window",
+values_to = "count") |>
+unnest(count))
+longer <- new_test |>
+pivot_longer(cols = starts_with("cnt"),
+names_to = "window",
+values_to = "count") |>
+unnest(count)
+longer
+longer <- new_test |>
+pivot_longer(cols = starts_with("cnt"),
+names_to = "window",
+values_to = "count") |>
+unnest(count) |>
+as.numeric(unlist(count))
+longer <- new_test |>
+pivot_longer(cols = starts_with("cnt"),
+names_to = "window",
+values_to = "count") |>
+unnest(count) |>
+unlist(count)
+View(new_longer)
+new_longer
+longer <- new_test |>
+pivot_longer(cols = starts_with("cnt"),
+names_to = "window",
+values_to = "count") |>
+unnest(count) |>
+unlist(count) |>
+as.numeric(count)
+View(new_longer)
+new_longer
+longer <- new_test |>
+pivot_longer(cols = starts_with("cnt"),
+names_to = "window",
+values_to = "count") |>
+unnest(count) |>
+unlist(count) |>
+as.numeric(count)
+longer
+longer <- new_test |>
+pivot_longer(cols = starts_with("cnt"),
+names_to = "window",
+values_to = "count") |>
+unnest(count) |>
+unlist(count)
+longer
+longer <- new_test |>
+pivot_longer(cols = starts_with("cnt"),
+names_to = "window",
+values_to = "count") |>
+unnest(count)
+longer
+View(longer)
+longer <- new_test |>
+pivot_longer(cols = starts_with("cnt"),
+names_to = "window",
+values_to = "count") |>
+unnest(count) |>
+as.numeric(count)
+longer
+longer <- new_test |>
+pivot_longer(cols = starts_with("cnt"),
+names_to = "window",
+values_to = "count") |>
+unnest(count)
+longer
+library(tidyverse)
+#set wd, read in data
+try(setwd(dirname(rstudioapi::getActiveDocumentContext()$path)))
+readme_df <- read_csv("../final_data/deb_readme_did.csv")
+#preprocessing for readme_df
+colnames(readme_df) <- c("upstream_vcs_link", "event_date", "event_hash", "before_all_ct", "before_mrg_ct", "after_all_ct", "after_mrg_ct", "before_auth_new", "after_commit_new", "after_auth_new", "before_commit_new")
+col_order <- c("upstream_vcs_link", "event_date", "event_hash", "before_all_ct", "after_all_ct", "before_mrg_ct", "after_mrg_ct", "before_auth_new", "after_auth_new", "before_commit_new",  "after_commit_new")
+readme_df <- readme_df[,col_order]
+readme_df$cnt_before_all <- str_split(gsub("[][]","", readme_df$before_all_cnt), ", ")
+readme_df$cnt_after_all <- str_split(gsub("[][]","", readme_df$after_all_cnt), ", ")
+readme_df$cnt_before_mrg <- str_split(gsub("[][]","", readme_df$before_mrg_cnt), ", ")
+readme_df$cnt_after_mrg <- str_split(gsub("[][]","", readme_df$after_mrg_cnt), ", ")
+drop <- c("before_all_ct", "before_mrg_ct", "after_all_ct", "after_mrg_ct")
+library(tidyverse)
+#set wd, read in data
+try(setwd(dirname(rstudioapi::getActiveDocumentContext()$path)))
+readme_df <- read_csv("../final_data/deb_readme_did.csv")
+#preprocessing for readme_df
+colnames(readme_df) <- c("upstream_vcs_link", "event_date", "event_hash", "before_all_ct", "before_mrg_ct", "after_all_ct", "after_mrg_ct", "before_auth_new", "after_commit_new", "after_auth_new", "before_commit_new")
+col_order <- c("upstream_vcs_link", "event_date", "event_hash", "before_all_ct", "after_all_ct", "before_mrg_ct", "after_mrg_ct", "before_auth_new", "after_auth_new", "before_commit_new",  "after_commit_new")
+readme_df <- readme_df[,col_order]
+readme_df$cnt_before_all <- str_split(gsub("[][]","", readme_df$before_all_cnt), ", ")
+library(tidyverse)
+#set wd, read in data
+try(setwd(dirname(rstudioapi::getActiveDocumentContext()$path)))
+readme_df <- read_csv("../final_data/deb_readme_did.csv")
+contributing_df <- read_csv("../final_data/deb_contrib_did.csv")
+#preprocessing for readme_df
+colnames(readme_df) <- c("upstream_vcs_link", "event_date", "event_hash", "before_all_ct", "before_mrg_ct", "after_all_ct", "after_mrg_ct", "before_auth_new", "after_commit_new", "after_auth_new", "before_commit_new")
+col_order <- c("upstream_vcs_link", "event_date", "event_hash", "before_all_ct", "after_all_ct", "before_mrg_ct", "after_mrg_ct", "before_auth_new", "after_auth_new", "before_commit_new",  "after_commit_new")
+readme_df <- readme_df[,col_order]
+readme_df$ct_before_all <- str_split(gsub("[][]","", readme_df$before_all_ct), ", ")
+readme_df$ct_after_all <- str_split(gsub("[][]","", readme_df$after_all_ct), ", ")
+readme_df$ct_before_mrg <- str_split(gsub("[][]","", readme_df$before_mrg_ct), ", ")
+readme_df$ct_after_mrg <- str_split(gsub("[][]","", readme_df$after_mrg_ct), ", ")
+drop <- c("before_all_ct", "before_mrg_ct", "after_all_ct", "after_mrg_ct")
+readme_df = readme_df[,!(names(readme_df) %in% drop)]
+# as.numeric(unlist(test[1]))
+# test_two <- c()
+# iterator <- 0
+# for (entry in test) {
+#   readme_df$cnt_before_all[iterator] <- as.numeric(unlist(entry))
+#   print(as.numeric(unlist(entry)))
+#   iterator <- iterator + 1
+# }
+# test_two
+#Yes, need to expand the dataframe, but again, for the sake of clarity, do not want to until analysis step
+new_test <- head(readme_df, 1)
+longer <- new_test |>
+pivot_longer(cols = starts_with("ct"),
+names_to = "window",
+values_to = "count") |>
+unnest(count)
+longer
+View(longer)
+library(tidyverse)
+#set wd, read in data
+try(setwd(dirname(rstudioapi::getActiveDocumentContext()$path)))
+readme_df <- read_csv("../final_data/deb_readme_did.csv")
+contributing_df <- read_csv("../final_data/deb_contrib_did.csv")
+#preprocessing for readme_df
+colnames(readme_df) <- c("upstream_vcs_link", "event_date", "event_hash", "before_all_ct", "before_mrg_ct", "after_all_ct", "after_mrg_ct", "before_auth_new", "after_commit_new", "after_auth_new", "before_commit_new")
+col_order <- c("upstream_vcs_link", "event_date", "event_hash", "before_all_ct", "after_all_ct", "before_mrg_ct", "after_mrg_ct", "before_auth_new", "after_auth_new", "before_commit_new",  "after_commit_new")
+readme_df <- readme_df[,col_order]
+readme_df$ct_before_all <- str_split(gsub("[][]","", readme_df$before_all_ct), ", ")
+readme_df$ct_after_all <- str_split(gsub("[][]","", readme_df$after_all_ct), ", ")
+readme_df$ct_before_mrg <- str_split(gsub("[][]","", readme_df$before_mrg_ct), ", ")
+readme_df$ct_after_mrg <- str_split(gsub("[][]","", readme_df$after_mrg_ct), ", ")
+drop <- c("before_all_ct", "before_mrg_ct", "after_all_ct", "after_mrg_ct")
+readme_df = readme_df[,!(names(readme_df) %in% drop)]
+# as.numeric(unlist(test[1]))
+# test_two <- c()
+# iterator <- 0
+# for (entry in test) {
+#   readme_df$cnt_before_all[iterator] <- as.numeric(unlist(entry))
+#   print(as.numeric(unlist(entry)))
+#   iterator <- iterator + 1
+# }
+# test_two
+#Yes, need to expand the dataframe, but again, for the sake of clarity, do not want to until analysis step
+new_test <- head(readme_df, 1)
+longer <- new_test |>
+pivot_longer(cols = starts_with("ct"),
+names_to = "window",
+values_to = "count") |>
+unnest(count)
+longer
+View(longer)
+longer <- ddply(longer, "window", transform, t=seq(from=0, by=1, length.out=length(window)))
+library(plyr)
+longer <- ddply(longer, "window", transform, t=seq(from=0, by=1, length.out=length(window)))
+View(longer)
+library(plyr)
+library(tidyverse)
+#set wd, read in data
+try(setwd(dirname(rstudioapi::getActiveDocumentContext()$path)))
+readme_df <- read_csv("../final_data/deb_readme_did.csv")
+contributing_df <- read_csv("../final_data/deb_contrib_did.csv")
+#preprocessing for readme_df
+colnames(readme_df) <- c("upstream_vcs_link", "event_date", "event_hash", "before_all_ct", "before_mrg_ct", "after_all_ct", "after_mrg_ct", "before_auth_new", "after_commit_new", "after_auth_new", "before_commit_new")
+col_order <- c("upstream_vcs_link", "event_date", "event_hash", "before_all_ct", "after_all_ct", "before_mrg_ct", "after_mrg_ct", "before_auth_new", "after_auth_new", "before_commit_new",  "after_commit_new")
+readme_df <- readme_df[,col_order]
+readme_df$ct_before_all <- str_split(gsub("[][]","", readme_df$before_all_ct), ", ")
+readme_df$ct_after_all <- str_split(gsub("[][]","", readme_df$after_all_ct), ", ")
+readme_df$ct_before_mrg <- str_split(gsub("[][]","", readme_df$before_mrg_ct), ", ")
+readme_df$ct_after_mrg <- str_split(gsub("[][]","", readme_df$after_mrg_ct), ", ")
+drop <- c("before_all_ct", "before_mrg_ct", "after_all_ct", "after_mrg_ct")
+readme_df = readme_df[,!(names(readme_df) %in% drop)]
+# as.numeric(unlist(test[1]))
+# test_two <- c()
+# iterator <- 0
+# for (entry in test) {
+#   readme_df$cnt_before_all[iterator] <- as.numeric(unlist(entry))
+#   print(as.numeric(unlist(entry)))
+#   iterator <- iterator + 1
+# }
+# test_two
+#Yes, need to expand the dataframe, but again, for the sake of clarity, do not want to until analysis step
+new_test <- head(readme_df, 1)
+longer <- new_test |>
+pivot_longer(cols = starts_with("ct"),
+names_to = "window",
+values_to = "count") |>
+unnest(count)
+longer <- ddply(longer, "window", transform, t=seq(from=0, by=1, length.out=length(window)))
+View(longer)
+longer <- ddply(longer, strsplit("window", split="_")[-1], transform, week=seq(from=0, by=1, length.out=length(window)))
+longer <- ddply(longer, strsplit(window, split="_")[-1], transform, week=seq(from=0, by=1, length.out=length(window)))
+longer <- new_test |>
+pivot_longer(cols = starts_with("ct"),
+names_to = "window",
+values_to = "count") |>
+unnest(count) |>
+add_column(rel = gsub("^.*_", "", window))
+longer <- new_test |>
+pivot_longer(cols = starts_with("ct"),
+names_to = "window",
+values_to = "count") |>
+unnest(count) |>
+add_column(rel = gsub("^.*_", "", "window"))
+View(longer)
+longer$rel <- gsub("^.*_", "", longer$window)
+View(longer)
+# as.numeric(unlist(test[1]))
+# test_two <- c()
+# iterator <- 0
+# for (entry in test) {
+#   readme_df$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 <- head(readme_df, 1)
+new_testr$observation_type <- gsub("^.*_", "", new_test$window)
+# as.numeric(unlist(test[1]))
+# test_two <- c()
+# iterator <- 0
+# for (entry in test) {
+#   readme_df$cnt_before_all[iterator] <- as.numeric(unlist(entry))
+#   print(as.numeric(unlist(entry)))
+#   iterator <- iterator + 1
+# }
+# test_two
+#Yes, need to expand the dataframe, but again, for the sake of clarity, do not want to until analysis step
+new_test <- head(readme_df, 1)
+longer <- new_test |>
+pivot_longer(cols = starts_with("ct"),
+names_to = "window",
+values_to = "count") |>
+unnest(count)
+longer$observation_type <- gsub("^.*_", "", longer$window)
+longer <- ddply(longer, "observation_type", transform, week=seq(from=0, by=1, length.out=length(observation_type)))
+View(longer)
+head(longer)
+sapply(longer, class)
diff --git a/R/didCleaning.R b/R/didCleaning.R
index b9e5d15..2722467 100644
--- a/R/didCleaning.R
+++ b/R/didCleaning.R
@@ -1,5 +1,7 @@
+library(plyr)
 library(tidyverse)
 
+
 #set wd, read in data
 try(setwd(dirname(rstudioapi::getActiveDocumentContext()$path)))
 readme_df <- read_csv("../final_data/deb_readme_did.csv")
@@ -36,4 +38,5 @@ longer <- new_test |>
                names_to = "window",
                values_to = "count") |>
   unnest(count) 
-longer
+longer$observation_type <- gsub("^.*_", "", longer$window)
+longer <- ddply(longer, "observation_type", transform, week=seq(from=0, by=1, length.out=length(observation_type)))