76 lines
3.4 KiB
R
76 lines
3.4 KiB
R
library(tidyverse)
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library(plyr)
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library(stringr)
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try(setwd(dirname(rstudioapi::getActiveDocumentContext()$path)))
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#load in data
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contrib_df <- read_csv("../final_data/deb_contrib_pop_change.csv")
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readme_df <- read_csv("../final_data/deb_readme_pop_change.csv")
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#some expansion needs to happens for each project
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expand_timeseries <- function(project_row) {
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longer <- project_row |>
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pivot_longer(cols = ends_with("new"),
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names_to = "window",
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values_to = "count") |>
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unnest(count) |>
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mutate(after_doc = as.numeric(str_detect(window, "after"))) |>
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mutate(is_collab = as.numeric(str_detect(window, "collab")))
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return(longer)
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}
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expanded_readme_data <- expand_timeseries(readme_df[1,])
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for (i in 2:nrow(readme_df)){
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expanded_readme_data <- rbind(expanded_readme_data, expand_timeseries(readme_df[i,]))
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}
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expanded_contrib_data <- expand_timeseries(contrib_df[1,])
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for (i in 2:nrow(contrib_df)){
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expanded_contrib_data <- rbind(expanded_contrib_data, expand_timeseries(contrib_df[i,]))
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}
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expanded_readme_data$log1pcount <- log1p(expanded_readme_data$count)
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expanded_contrib_data$log1pcount <- log1p(expanded_contrib_data$count)
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expanded_readme_data$logcount <- log(expanded_readme_data$count)
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expanded_contrib_data$logcount <- log(expanded_contrib_data$count)
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#breaking out the types of population counts
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collab_pop_readme <- expanded_readme_data[which(expanded_readme_data$is_collab == 1),]
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contrib_pop_readme <- expanded_readme_data[which(expanded_readme_data$is_collab == 0),]
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collab_pop_contrib <- expanded_contrib_data[which(expanded_contrib_data$is_collab == 1),]
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contrib_pop_contrib <- expanded_contrib_data[which(expanded_contrib_data$is_collab == 0),]
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#import models
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library(lme4)
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library(optimx)
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library(MASS)
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simple_collab_readme_model <- glm.nb(count ~ as.factor(after_doc), data=collab_pop_readme)
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summary(simple_collab_readme_model)
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qqnorm(residuals(simple_collab_readme_model))
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#
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cor.test(collab_pop_readme$count, collab_pop_readme$after_doc)
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# I don't think MLM is the right one
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collab_readme_model <- glmer.nb(log1pcount ~ as.factor(after_doc) + (after_doc| upstream_vcs_link), data=collab_pop_readme)
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summary(collab_readme_model)
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saveRDS(collab_readme_model, "final_models/0623_pop_rm_collab.rda")
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crm_residuals <- residuals(collab_readme_model)
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qqnorm(crm_residuals)
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contrib_readme_model <- glmer.nb(log1pcount ~ after_doc + (after_doc| upstream_vcs_link), data=contrib_pop_readme)
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summary(contrib_readme_model)
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saveRDS(contrib_readme_model, "final_models/0623_pop_rm_contrib.rda")
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#contrib_readme_model <- load("final_models/0510_pop_rm_contrib.rda")
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conrm_residuals <- residuals(contrib_readme_model)
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qqnorm(conrm_residuals)
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collab_contrib_model <- glmer.nb(log1pcount ~ after_doc + (after_doc| upstream_vcs_link), data=collab_pop_contrib)
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summary(collab_contrib_model)
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saveRDS(collab_contrib_model, "final_models/0623_pop_contrib_collab.rda")
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contrib_contrib_model <- glmer.nb(log1pcount ~ after_doc + (after_doc| upstream_vcs_link), data=contrib_pop_contrib)
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summary(contrib_contrib_model)
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saveRDS(contrib_contrib_model, "final_models/0623_pop_contrib_contrib.rda")
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library(ggplot2)
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contrib_pop_readme |>
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ggplot(aes(x = after_doc, y = log1pcount, col = as.factor(after_doc))) +
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geom_violin()
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expanded_readme_data |>
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ggplot(aes(x = after_doc, y = count, col = as.factor(after_doc))) +
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geom_violin()
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expanded_contrib_data |>
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ggplot(aes(x = after_doc, y = count, col = as.factor(after_doc))) +
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geom_violin()
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