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updates crossectional analyses

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mjgaughan 2024-05-10 17:37:40 -05:00
parent 34fdc67359
commit de600e63a3
1 changed files with 28 additions and 35 deletions
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63
R/GovRiskPower.R
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@ -20,10 +20,11 @@ hist(overall_data$mmt, probability = TRUE)
overall_data$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$new.age)
overall_data$scaled_age <- scale(overall_data$age_of_project)
mmtmodel1 <- lm(underproduction_mean ~ mmt + new.age.factor, data=overall_data)
mmtmodel1 <- lm(underproduction_mean ~ mmt + scaled_age, data=overall_data)
summary(mmtmodel1)
qqnorm(residuals(mmtmodel1))
#shows the cross-age downward slopes for all underproduction averages in the face of MMT
g4 <- ggplot(overall_data, aes(x=mmt, y=underproduction_mean)) +
@ -33,17 +34,14 @@ g4 <- ggplot(overall_data, aes(x=mmt, y=underproduction_mean)) +
theme_bw() +
theme(legend.position = c(0.9, 0.9), legend.justification = c("right", "top"))
g4
texreg(list(mmtmodel1, issue_mmtmodel1, wiki_mmtmodel1), stars=NULL, digits=2,
custom.model.names=c( 'MMT (Overall Dataset)'),
custom.coef.names=c('(Intercept)', 'MMT', 'Age-2', 'Age-3', 'Age-4'),
use.packages=FALSE, table=TRUE, ci.force = TRUE)
#clean octo data
octo_data <- filter(octo_data, total_contrib != 0)
# below this is the analysis for the octo data
octo_data$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$new.age)
octo_data$scaled_age <- scale(octo_data$age_of_project)
length(which(octo_data$underproduction_low < 0))
median(octo_data$underproduction_mean)
@ -53,7 +51,6 @@ hist(octo_data$mmt)
head(octo_data)
#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$total_contrib)
octo_data$sqrt_issue_mmt <- sqrt(octo_data$issue_mmt)
g2 <- ggplot(octo_data, aes(issue_mmt)) + geom_histogram(binwidth = 0.01) + theme_bw()
@ -61,7 +58,6 @@ g2
g1 <- ggplot(octo_data, aes(sqrt_issue_mmt)) + geom_histogram(binwidth = 0.01) + theme_bw()
g1
#right skewed data, need to transform
octo_data$wiki_mmt <- ((octo_data$wiki_contrib_count * 2) + (octo_data$total_contrib - octo_data$wiki_contrib_count)) / (octo_data$total_contrib)
hist(octo_data$wiki_mmt)
g3 <- ggplot(octo_data, aes(wiki_mmt)) + geom_histogram(binwidth = 0.01) + theme_bw()
@ -69,40 +65,37 @@ g3
median(octo_data$wiki_mmt)
qqnorm(octo_data$wiki_mmt)
#left skewed data, need to transform
g4 <- ggplot(octo_data)
g4
typeof(octo_data$wiki_contrib_count)
sum(octo_data$total_contrib == 0)
#below are the models for the octo data, there should be analysis for each one
octo_mmtmodel1 <- lm(underproduction_mean ~ mmt + new.age.factor, data=octo_data)
octo_mmtmodel1 <- lm(underproduction_mean ~ mmt + scaled_age + has_readme + has_contrib, data=octo_data)
summary(octo_mmtmodel1)
issue_mmtmodel1 <- lm(underproduction_mean ~ issue_mmt + new.age.factor, data=octo_data)
issue_mmtmodel1 <- lm(underproduction_mean ~ issue_mmt + scaled_age + has_readme + has_contrib, data=octo_data)
summary(issue_mmtmodel1)
qqnorm(residuals(issue_mmtmodel1))
sqrt_issue_mmtmodel1 <- lm(underproduction_mean ~ sqrt_issue_mmt + new.age.factor, data=octo_data)
summary(sqrt_issue_mmtmodel1)
wiki_mmtmodel1 <- lm(underproduction_mean ~ wiki_mmt + new.age.factor, data=octo_data)
wiki_mmtmodel1 <- lm(underproduction_mean ~ wiki_mmt + scaled_age + has_readme + has_contrib, data=octo_data)
summary(wiki_mmtmodel1)
qqnorm(residuals(wiki_mmtmodel1))
library(texreg) #my little "lib"
texreg(list(octo_mmtmodel1, issue_mmtmodel1, wiki_mmtmodel1), stars=NULL, digits=2,
custom.model.names=c( 'M1: MMT','M2: issue contrib.', 'M3: wiki_contrib.' ),
custom.coef.names=c('(Intercept)', 'MMT', 'Issues', 'Age-2', 'Age-3', 'Age-4', 'Wiki'),
custom.model.names=c( 'M1: MMT','M2: issue contrib.', 'M3: wiki contrib.' ),
custom.coef.names=c('(Intercept)', 'MMT', 'scaled_age', 'has readme', 'has contrib', 'Issue MMT', 'Wiki MMT'),
use.packages=FALSE, table=FALSE, ci.force = TRUE)
#find the overlap between projects with octo data and projects with readmes or contributings
readme_did_roster <- read_csv("../final_data/deb_readme_did.csv", show_col_types = FALSE)
contrib_did_roster <- read_csv("../final_data/deb_contrib_did.csv", show_col_types = FALSE)
octo_data <- octo_data |>
mutate(has_readme = as.numeric(upstream_vcs_link %in% readme_did_roster$upstream_vcs_link)) |>
mutate(has_contrib = as.numeric(upstream_vcs_link %in% contrib_did_roster$upstream_vcs_link))
#below here is the analysis for the readme.md data
readme_data$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$new.age)
#below here is the analysis for the contributing.md files
contributing_data$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$new.age)
cor.test(octo_data$mmt, octo_data$has_readme)
cor.test(octo_data$mmt, octo_data$has_contrib)
cor.test(octo_data$has_readme, octo_data$has_contrib)
issues_expansion <- lm(issue_mmt ~ has_contrib + scaled_age, data=octo_data)
summary(issues_expansion)