137 lines
7.2 KiB
R
137 lines
7.2 KiB
R
rm(list=ls())
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set.seed(424242)
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library(readr)
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library(ggplot2)
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library(tidyverse)
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#primary analysis for cross-sectional community metrics
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overall_data <- read_csv('../final_data/deb_full_data.csv',show_col_types = FALSE)
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octo_data <- read_csv('../final_data/deb_octo_data.csv', show_col_types = FALSE)
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readme_data <- read_csv("../final_data/deb_readme_roster.csv", show_col_types = FALSE)
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contributing_data <- read_csv("../final_data/deb_contribfile_roster.csv", show_col_types = FALSE)
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overall_data$mmt <- (((overall_data$collaborators * 2)+ overall_data$contributors) / (overall_data$contributors + overall_data$collaborators))
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mean(overall_data$mmt)
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hist(overall_data$mmt, probability = TRUE)
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#the basic stuff for the overall data
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overall_data$mmt <- (((overall_data$collaborators * 2)+ overall_data$contributors) / (overall_data$contributors + overall_data$collaborators))
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mean(overall_data$mmt)
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hist(overall_data$mmt, probability = TRUE)
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#some new variables around age
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#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)))
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#table(overall_data$new.age)
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#overall_data$new.age.factor <- as.factor(overall_data$new.age)
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overall_data$scaled_age <- scale(overall_data$age_of_project)
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#model
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mmtmodel1 <- lm(underproduction_mean ~ mmt + scaled_age, data=overall_data)
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summary(mmtmodel1)
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qqnorm(residuals(mmtmodel1))
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#clean octo data
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octo_data <- filter(octo_data, total_contrib != 0)
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# below this is the analysis for the octo data
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octo_data$new.age <- as.numeric(cut(octo_data$age_of_project/365, breaks=c(0,7.524197,10.323056,13.649367,17), labels=c(1,2,3,4)))
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table(octo_data$new.age)
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octo_data$new.age.factor <- as.factor(octo_data$new.age)
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octo_data$scaled_age <- scale(octo_data$age_of_project)
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octo_data$mmt <- (((octo_data$collaborators * 2)+ octo_data$contributors) / (octo_data$contributors + octo_data$collaborators))
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mean(octo_data$mmt)
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hist(octo_data$mmt)
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head(octo_data)
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#getting the mmt-equivalent for both issue activity as well as wiki contrib activity
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octo_data$issue_mmt <- ((octo_data$issue_contrib_count * 2) + (octo_data$total_contrib - octo_data$issue_contrib_count)) / (octo_data$total_contrib)
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octo_data$sqrt_issue_mmt <- sqrt(octo_data$issue_mmt)
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#right skewed data, need to transform
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octo_data$wiki_mmt <- ((octo_data$wiki_contrib_count * 2) + (octo_data$total_contrib - octo_data$wiki_contrib_count)) / (octo_data$total_contrib)
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hist(octo_data$wiki_mmt)
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#getting some of the information in about whether projects have specific files
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readme_did_roster <- read_csv("../final_data/deb_readme_did.csv", show_col_types = FALSE)
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contrib_did_roster <- read_csv("../final_data/deb_contrib_did.csv", show_col_types = FALSE)
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octo_data <- octo_data |>
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mutate(has_readme = as.numeric(upstream_vcs_link %in% readme_did_roster$upstream_vcs_link)) |>
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mutate(has_contrib = as.numeric(upstream_vcs_link %in% contrib_did_roster$upstream_vcs_link))
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overall_data <- overall_data |>
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mutate(has_readme = as.numeric(upstream_vcs_link %in% readme_did_roster$upstream_vcs_link)) |>
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mutate(has_contrib = as.numeric(upstream_vcs_link %in% contrib_did_roster$upstream_vcs_link))
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#below are the models for the octo data, there should be analysis for each one
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octo_mmtmodel1 <- lm(underproduction_mean ~ mmt + scaled_age + has_readme + has_contrib, data=octo_data)
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summary(octo_mmtmodel1)
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issue_mmtmodel1 <- lm(underproduction_mean ~ issue_mmt + scaled_age + has_readme + has_contrib, data=octo_data)
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summary(issue_mmtmodel1)
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qqnorm(residuals(issue_mmtmodel1))
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wiki_mmtmodel1 <- lm(underproduction_mean ~ wiki_mmt + scaled_age + has_readme + has_contrib, data=octo_data)
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summary(wiki_mmtmodel1)
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qqnorm(residuals(wiki_mmtmodel1))
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#these next three are looking at mmt as an outcome of other factors
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mmt_outcome_model <- lm(mmt ~ scaled_age + as.factor(has_readme) + as.factor(has_contrib), data = octo_data)
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summary(mmt_outcome_model)
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all_mmt_outcome_model <- lm(mmt ~ scaled_age + as.factor(has_readme) + as.factor(has_contrib), data = overall_data)
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summary(all_mmt_outcome_model)
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govdoc_issuesmmt <- lm(issue_mmt ~ scaled_age + as.factor(has_readme) + as.factor(has_contrib), data=octo_data)
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summary(govdoc_issuesmmt)
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library(texreg) #my little "lib"
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texreg(list(octo_mmtmodel1, issue_mmtmodel1, wiki_mmtmodel1), stars=NULL, digits=2,
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custom.model.names=c( 'M1: MMT','M2: issue contrib.', 'M3: wiki contrib.' ),
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custom.coef.names=c('(Intercept)', 'MMT', 'scaled_age', 'has readme', 'has contrib', 'Issue MMT', 'Wiki MMT'),
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use.packages=FALSE, table=FALSE, ci.force = TRUE)
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#below here is the analysis for the readme.md data
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cor.test(octo_data$mmt, octo_data$has_readme)
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cor.test(octo_data$mmt, octo_data$has_contrib)
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cor.test(octo_data$has_readme, octo_data$has_contrib)
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#using the groupings and estimates from the ranef coefficients from D as data
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#pulling in the group data for the ranef coefficients
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rm_grouping <- read_csv('051224_readme_grouped.csv',show_col_types = FALSE)
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contrib_grouping <- read_csv('051224_contrib_grouped.csv', show_col_types = FALSE)
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rm_grouping <- rm_grouping |>
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rename(upstream_vcs_link = level)|>
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mutate(factored_group = as.factor(ranef_grouping))
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contrib_grouping <- contrib_grouping |>
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rename(upstream_vcs_link = level) |>
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mutate(factored_group = as.factor(ranef_grouping))
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grouped_rm <- left_join(rm_grouping, overall_data, by="upstream_vcs_link")
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grouped_contrib <- left_join(contrib_grouping, overall_data, by="upstream_vcs_link")
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rm_did <- read_csv('../final_data/deb_readme_did.csv',show_col_types = FALSE)
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contrib_did <- read_csv('../final_data/deb_contrib_did.csv', show_col_types = FALSE)
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grouped_rm <- left_join(grouped_rm, rm_did, by="upstream_vcs_link")
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grouped_contrib <- left_join(grouped_contrib, contrib_did, by="upstream_vcs_link")
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# also looking at how long each project has had a specific governance document
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# calculate in terms of July 6, 2020 (when underproduction metrics were collected)
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library(chron)
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dtparts = t(as.data.frame(strsplit(as.character(grouped_rm$event_date),' ')))
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thetimes = chron(dates=dtparts[,1],times=dtparts[,2], format=c('y-m-d','h:m:s'))
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typeof(thetimes)
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how_long_has_file <- difftime(as.Date("2020-07-06"), as.Date(grouped_rm$event_date), units = "days")
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grouped_rm <- grouped_rm |>
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mutate(formatted_event_time = chron(dates=dtparts[,1],times=dtparts[,2], format=c('y-m-d','h:m:s'))) |>
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mutate(event_delta = difftime(as.chron("2020-07-06"), formatted_event_time, units = "days"))
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#now doing it for the contrib_data
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contrib_dtparts = t(as.data.frame(strsplit(as.character(grouped_contrib$event_date),' ')))
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grouped_contrib <- grouped_contrib |>
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mutate(formatted_event_time = chron(dates=contrib_dtparts[,1],times=contrib_dtparts[,2], format=c('y-m-d','h:m:s'))) |>
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mutate(event_delta = difftime(as.chron("2020-07-06"), formatted_event_time, units = "days"))
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#test with linear model, there should be an interaction between how long the project has had a document and its grouping, no?
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grouping_model_rm <- lm(underproduction_mean ~ event_delta*factored_group + mmt + scaled_age, data=grouped_rm)
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summary(grouping_model_rm)
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qqnorm(residuals(grouping_model_rm))
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grouping_model_contrib <- lm(underproduction_mean ~ event_delta*factored_group + mmt + scaled_age, data=grouped_contrib)
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summary(grouping_model_contrib)
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qqnorm(residuals(grouping_model_contrib))
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