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new branch for new sampling

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mgaughan 2024-10-24 15:44:23 -04:00
parent 9ebad53df9
commit 5bc4003a99
2 changed files with 39 additions and 36 deletions
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54
R/popRDDAnalyssis.R
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@ -3,12 +3,8 @@ library(plyr)
library(stringr) library(stringr)
try(setwd(dirname(rstudioapi::getActiveDocumentContext()$path))) try(setwd(dirname(rstudioapi::getActiveDocumentContext()$path)))
#load in data #load in data
full_df <- read_csv("../final_data/deb_full_data.csv")
contrib_df <- read_csv("../final_data/deb_contrib_pop_change.csv") contrib_df <- read_csv("../final_data/deb_contrib_pop_change.csv")
readme_df <- read_csv("../final_data/deb_readme_pop_change.csv") readme_df <- read_csv("../final_data/deb_readme_pop_change.csv")
contrib_df <- merge(full_df, contrib_df, by="upstream_vcs_link")
readme_df <- merge(full_df, readme_df, by="upstream_vcs_link")
# age is calculated against December 11, 2023
#some expansion needs to happens for each project #some expansion needs to happens for each project
expand_timeseries <- function(project_row) { expand_timeseries <- function(project_row) {
longer <- project_row |> longer <- project_row |>
@ -32,9 +28,6 @@ expanded_readme_data$log1pcount <- log1p(expanded_readme_data$count)
expanded_contrib_data$log1pcount <- log1p(expanded_contrib_data$count) expanded_contrib_data$log1pcount <- log1p(expanded_contrib_data$count)
expanded_readme_data$logcount <- log(expanded_readme_data$count) expanded_readme_data$logcount <- log(expanded_readme_data$count)
expanded_contrib_data$logcount <- log(expanded_contrib_data$count) expanded_contrib_data$logcount <- log(expanded_contrib_data$count)
#scale age
expanded_readme_data$scaled_age <- scale(expanded_readme_data$age_in_days)
expanded_contrib_data$scaled_age <- scale(expanded_contrib_data$age_in_days)
#breaking out the types of population counts #breaking out the types of population counts
collab_pop_readme <- expanded_readme_data[which(expanded_readme_data$is_collab == 1),] collab_pop_readme <- expanded_readme_data[which(expanded_readme_data$is_collab == 1),]
contrib_pop_readme <- expanded_readme_data[which(expanded_readme_data$is_collab == 0),] contrib_pop_readme <- expanded_readme_data[which(expanded_readme_data$is_collab == 0),]
@ -43,39 +36,28 @@ contrib_pop_contrib <- expanded_contrib_data[which(expanded_contrib_data$is_coll
#import models #import models
library(lme4) library(lme4)
library(optimx) library(optimx)
library(MASS) collab_readme_model <- glmer.nb(log1pcount ~ after_doc + (after_doc| upstream_vcs_link), data=collab_pop_readme)
#readme docs
simple_collab_readme_model <- glm.nb(log1pcount ~ as.factor(after_doc) + scale(age_in_days), data=collab_pop_readme)
summary(simple_collab_readme_model)
qqnorm(residuals(simple_collab_readme_model))
simple_contrib_readme_model <- glm.nb(log1pcount ~ as.factor(after_doc) + scale(age_in_days), data=collab_pop_readme)
summary(simple_contrib_readme_model)
qqnorm(residuals(simple_contrib_readme_model))
# I don't think MLM is the right one
collab_readme_model <- glmer.nb(log1pcount ~ as.factor(after_doc) + scaled_age + (after_doc| upstream_vcs_link), data=collab_pop_readme)
summary(collab_readme_model) summary(collab_readme_model)
saveRDS(collab_readme_model, "final_models/0624_pop_rm_collab_better.rda") saveRDS(collab_readme_model, "0510_pop_rm_collab.rda")
contrib_readme_model <- glmer.nb(log1pcount ~ as.factor(after_doc) + scaled_age + (after_doc| upstream_vcs_link), data=contrib_pop_readme) crm_residuals <- residuals(collab_readme_model)
qqnorm(crm_residuals)
contrib_readme_model <- glmer.nb(log1pcount ~ after_doc + (after_doc| upstream_vcs_link), data=contrib_pop_readme)
summary(contrib_readme_model) summary(contrib_readme_model)
saveRDS(contrib_readme_model, "final_models/0624_pop_rm_contrib.rda") saveRDS(contrib_readme_model, "0510_pop_rm_contrib.rda")
#contrib_readme_model <- readRDS("final_models/0623_pop_rm_contrib.rda") conrm_residuals <- residuals(contrib_readme_model)
#contributing models are not statistically significant`` qqnorm(conrm_residuals)
library(texreg) collab_contrib_model <- glmer.nb(log1pcount ~ after_doc + (after_doc| upstream_vcs_link), data=collab_pop_contrib)
summary(collab_contrib_model)
texreg(list(collab_readme_model, contrib_readme_model), stars=NULL, digits=2, saveRDS(collab_contrib_model, "0510_pop_contrib_collab.rda")
custom.model.names=c( 'collab','contrib.' ), contrib_contrib_model <- glmer.nb(log1pcount ~ after_doc + (after_doc| upstream_vcs_link), data=contrib_pop_contrib)
custom.coef.names=c('(Intercept)', 'after_introduction', 'etc'), summary(contrib_contrib_model)
use.packages=FALSE, table=FALSE, ci.force = TRUE) saveRDS(contrib_contrib_model, "0510_pop_contrib_contrib.rda")
library(ggplot2) library(ggplot2)
contrib_pop_readme |>
ggplot(aes(x = after_doc, y = log1pcount, col = as.factor(after_doc))) +
geom_violin()
expanded_readme_data |> expanded_readme_data |>
ggplot(aes(x = after_doc, y = count, col = as.factor(after_doc))) + ggplot(aes(x = after_doc, y = log1pcount, col = as.factor(is_collab))) +
geom_violin() geom_point() + geom_jitter()
expanded_contrib_data |> expanded_contrib_data |>
ggplot(aes(x = after_doc, y = count, col = as.factor(after_doc))) + ggplot(aes(x = after_doc, y = count, col = as.factor(is_collab))) +
geom_violin() geom_point() + geom_jitter()

21
sample_good_subset.py Normal file
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@ -0,0 +1,21 @@
import csv
import os
import pandas as pd
def for_readme_files():
ld_csv_path = "final_data/deb_readme_did.csv"
ta_csv_path = "d_readability_readme.csv"
topic_csv_path = "text_analysis/readme_file_topic_distributions.csv"
# criteria for good readme
# longer than half of a pageview
def for_contributing_files():
ld_csv_path = "final_data/deb_contrib_did.csv"
ta_csv_path = "d_readability_contrib.csv"
topic_csv_path = "text_analysis/contrib_file_topic_distributions.csv"
# criteria for good contributing
# longer than half of a pageview