updating topic/outcome relationships
This commit is contained in:
mjgaughan
parent
06c7c922c8
commit
0b3561c70b
390
R/.Rhistory
390
R/.Rhistory
@ -1,198 +1,3 @@
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hist(contrib_df$event_gap)
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median(contrib_df$event_gap)
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1786.431 / 265
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1786.431 / 365
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sd(contrib_df$event_gap)
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sd(contrib_df$event_gap)
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max(readme_df$event_gap)
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#all_gmodel <- glmer.nb(log1p_count ~ D * week_offset + scaled_project_age + scaled_event_gap + (D * week_offset | upstream_vcs_link),
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# control=glmerControl(optimizer="bobyqa",
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# optCtrl=list(maxfun=2e5)), nAGQ=0, data=all_actions_data)
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all_gmodel <- readRDS("0710_contrib_all.rda")
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summary(all_gmodel)
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library(tidyverse)
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library(texreg)
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readme_rdd <- readRDS("final_models/0624_readme_all_rdd.rda")
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contrib_rdd <- readRDS("final_models/0710_contrib_all.rda")
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contrib_rdd <- readRDS("final_models/0710_contrib_all_rdd.rda")
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texreg(list(readme_rdd, contrib_rdd), stars=NULL, digits=3, use.packages=FALSE,
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custom.model.names=c( 'README','CONTRIBUTING'),
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custom.coef.names=c('(Intercept)', 'Indtroduction', 'Week (Time)', 'Project Age', 'Introduction:Week', 'Event Gap'),
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table=FALSE, ci.force = TRUE)
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source("~/Desktop/git/24_deb_gov/R/contribCrescAnalysis.R")
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#all_gmodel <- readRDS("0710_contrib_all.rda")
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summary(all_gmodel)
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saveRDS(all_gmodel, "0710_contrib_cresc.rda")
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range(all_actions_data$log1p_count)
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source("~/Desktop/git/24_deb_gov/R/contribRDDAnalysis.R")
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source("~/Desktop/git/24_deb_gov/R/contribRDDAnalysis.R")
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all_gmodel <- readRDS("0711_contrib_all.rda")
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summary(all_gmodel)
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library(tidyverse)
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library(texreg)
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library(tidyverse)
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library(texreg)
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readme_rdd <- readRDS("final_models/0624_readme_all_rdd.rda")
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contrib_rdd <- readRDS("final_models/0711_contrib_all_rdd.rda")
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summary(readme_rdd)
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texreg(list(readme_rdd, contrib_rdd), stars=NULL, digits=3, use.packages=FALSE,
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custom.model.names=c( 'README','CONTRIBUTING'),
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custom.coef.names=c('(Intercept)', 'Indtroduction', 'Week (Time)', 'Project Age', 'Introduction:Week', 'Event Gap'),
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table=FALSE, ci.force = TRUE)
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contrib_rdd <- readRDS("final_models/0711_contrib_all_rdd.rda")
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contrib_rdd <- readRDS("final_models/0711_contrib_all_rdd.rda")
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texreg(list(readme_rdd, contrib_rdd), stars=NULL, digits=3, use.packages=FALSE,
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custom.model.names=c( 'README','CONTRIBUTING'),
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custom.coef.names=c('(Intercept)', 'Indtroduction', 'Week (Time)', 'Project Age', 'Introduction:Week', 'Event Gap'),
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table=FALSE, ci.force = TRUE)
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texreg(list(readme_rdd, contrib_rdd), stars=NULL, digits=3, use.packages=FALSE,
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custom.model.names=c( 'README','CONTRIBUTING'),
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custom.coef.names=c('(Intercept)', 'Indtroduction', 'Week (Time)', 'Project Age', 'Introduction:Week'),
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table=FALSE, ci.force = TRUE)
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readme_groupings <- read.csv('../final_data/deb_readme_interaction_groupings.csv')
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contrib_groupings <- read.csv('../final_data/0711_contrib_inter_groupings.csv')
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subdirColors <-
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setNames( c('firebrick1', 'forestgreen', 'cornflowerblue')
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, c(0,1,2) )
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readme_g <- readme_groupings |>
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ggplot(aes(x=rank, y=estimate, col = as.factor(ranef_grouping))) +
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geom_linerange(aes(ymin= conf.low, ymax= conf.high)) +
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scale_color_manual(values = subdirColors) +
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guides(fill="none", color="none")+
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theme_bw()
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readme_g
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contrib_g <- contrib_groupings |>
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ggplot(aes(x=rank, y=estimate, col = as.factor(ranef_grouping))) +
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geom_linerange(aes(ymin= conf.low, ymax= conf.high)) +
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scale_color_manual(values = subdirColors) +
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theme_bw() +
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theme(legend.position = "top")
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contrib_g
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library(gridExtra)
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grid.arrange(contrib_g, readme_g, nrow = 1)
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source("~/Desktop/git/24_deb_gov/R/contribRDDAnalysis.R")
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source("~/Desktop/git/24_deb_gov/R/documentReadabilityAnalysis.R")
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contrib_pop_df <- read_csv("../final_data/deb_contrib_pop_change.csv")
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contrib_df <- read_csv("../final_data/deb_contrib_did.csv")
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View(contrib_pop_df)
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contrib_readability_df <- read_csv('../text_analysis/dwo_readability_contributing.csv')
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View(contrib_readability_df)
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View(contrib_pop_df)
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View(contrib_readability_df)
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View(contrib_pop_df)
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View(contrib_readability_df)
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View(contrib_pop_df)
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View(contrib_pop_df)
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View(contrib_df)
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View(contrib_pop_df)
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View(contrib_readability_df)
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View(contrib_pop_df)
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#concat dataframes into central data
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contrib_df_total <- contrib_pop_df |>
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mutate(project_name = str_split(upstream_vcs_link, pattern="/")[-1])
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View(contrib_pop_df)
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View(contrib_readability_df)
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View(contrib_readability_df)
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contrib_df_total <- contrib_readability_df |>
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mutate(project_name = str_split(filename, pattern="_")[-2])
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View(contrib_readability_df)
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contrib_df_total <- contrib_readability_df |>
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mutate(project_name = str_split(filename, pattern="_"))
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View(contrib_df_total)
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contrib_df_total <- contrib_readability_df |>
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mutate(project_name = str_split(filename, pattern="_")[0])
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contrib_df_total <- contrib_readability_df |>
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mutate(project_name = str_split(filename, pattern="_")[1])
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View(contrib_df_total)
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contrib_df_total <- contrib_readability_df |>
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mutate(project_name = str_split(filename, pattern="_")[1] |>
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sapply("[[", 1))
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View(contrib_df_total)
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contrib_df_total <- contrib_readability_df |>
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mutate(project_name = str_split(filename, pattern="_"))
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View(contrib_df_total)
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contrib_df_total <- contrib_readability_df |>
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mutate(project_name_array = str_split(filename, pattern="_")) |>
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mutate(projes_name = project_name_array[1])
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View(contrib_df_total)
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View(contrib_readability_df)
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View(contrib_pop_df)
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#concat dataframes into central data
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contrib_pop_df <- contrib_pop_df %>%
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mutate(first_element = map_chr(upstream_vcs_link, ~ {
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parts <- str_split(.x, pattern = "/")[[1]]
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if (length(parts) >= 1) {
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parts[1] # Extract the first element after splitting
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} else {
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NA_character_
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}
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}))
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View(contrib_pop_df)
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contrib_df_total <- contrib_readability_df |>
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mutate(project_name = map_chr(filename, ~ {
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parts <- str_split(.x, pattern = "_")[[1]]
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if (length(parts) >= 1) {
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parts[1]
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} else {
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NA_character_
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}
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}))
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contrib_df <- read_csv("../final_data/deb_contrib_did.csv")
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contrib_pop_df <- read_csv("../final_data/deb_contrib_pop_change.csv")
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contrib_readability_df <- read_csv('../text_analysis/dwo_readability_contributing.csv')
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contrib_df_total <- contrib_readability_df |>
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mutate(project_name = map_chr(filename, ~ {
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parts <- str_split(.x, pattern = "_")[[1]]
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if (length(parts) >= 1) {
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parts[1]
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} else {
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NA_character_
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}
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}))
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View(contrib_df_total)
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contrib_pop_df <- contrib_pop_df |>
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mutate(project_name = map_chr(upstream_vcs_link, ~ {
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parts <- str_split(.x, pattern = "/")[[1]]
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if (length(parts) >= 1) {
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parts[-1]
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} else {
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NA_character_
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}
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}))
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parts[length(parts)]
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contrib_pop_df <- contrib_pop_df |>
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mutate(project_name = map_chr(upstream_vcs_link, ~ {
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parts <- str_split(.x, pattern = "/")[[1]]
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if (length(parts) >= 1) {
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parts[length(parts)]
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} else {
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NA_character_
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}
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}))
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View(contrib_pop_df)
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source("~/Desktop/git/24_deb_gov/R/docChar_outcomes.R")
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source("~/Desktop/git/24_deb_gov/R/docChar_outcomes.R")
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contrib_total_df <- contrib_pop_df |>
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left_join(contrib_readability_df, by="project_name")
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View(contrib_total_df)
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# test regressions
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lm1 <- glm.nb(after_contrib_new ~ word_count, data = contrib_total_df)
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# test regressions
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library(MASS)
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lm1 <- glm.nb(after_contrib_new ~ word_count, data = contrib_total_df)
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summary(lm1)
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View(contrib_total_df)
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contrib_total_df <- contrib_pop_df |>
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join(contrib_readability_df, by="project_name")
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View(contrib_total_df)
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View(contrib_readability_df)
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qqnorm(residuals(lm1))
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source("~/Desktop/git/24_deb_gov/R/docChar_outcomes.R")
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lm1 <- glm.nb(after_contrib_new ~ linsear_write, data = contrib_total_df)
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lm1 <- glm.nb(after_contrib_new ~ linsear, data = contrib_total_df)
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View(contrib_total_df)
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lm1 <- glm.nb(after_contrib_new ~ linsear_write_formula, data = contrib_total_df)
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qqnorm(residuals(lm1))
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summary(lm1)
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summary(lm1)
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lm1 <- glm.nb(after_contrib_new ~ reading_time, data = contrib_total_df)
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lm1 <- glm.nb(after_contrib_new ~ reading_time, data = contrib_total_df)
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qqnorm(residuals(lm1))
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qqnorm(residuals(lm1))
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@ -510,3 +315,198 @@ summary(lm1)
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lm1 <- glm.nb(summed_count ~ reading_time + linsear_write_formula + flesch_reading_ease + mcalpine_eflaw + word_count, data = contrib_total_df)
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lm1 <- glm.nb(summed_count ~ reading_time + linsear_write_formula + flesch_reading_ease + mcalpine_eflaw + word_count, data = contrib_total_df)
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qqnorm(residuals(lm1))
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qqnorm(residuals(lm1))
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summary(lm1)
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summary(lm1)
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contrib_topics_df <- read_csv("../text_analysis/contrib_file_topic_distributions.csv")
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library(tidyverse)
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contrib_topics_df <- read_csv("../text_analysis/contrib_file_topic_distributions.csv")
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View(contrib_topics_df)
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source("~/Desktop/git/24_deb_gov/R/contrib_topic_outcomes.R")
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source("~/Desktop/git/24_deb_gov/R/contrib_topic_outcomes.R")
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source("~/Desktop/git/24_deb_gov/R/contrib_docChar_outcomes.R")
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source("~/Desktop/git/24_deb_gov/R/contrib_topic_outcomes.R")
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source("~/Desktop/git/24_deb_gov/R/contrib_topic_outcomes.R")
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source("~/Desktop/git/24_deb_gov/R/contrib_topic_outcomes.R")
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lm1 <- glm.nb(summed_count ~ t0 + t1 + t2 + t3, data = contrib_total_df)
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#running regressions
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library(MASS)
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lm1 <- glm.nb(summed_count ~ t0 + t1 + t2 + t3, data = contrib_total_df)
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source("~/Desktop/git/24_deb_gov/R/contrib_topic_outcomes.R")
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library(stringr)
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library(plyr)
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contrib_topics_df <- read_csv("../text_analysis/contrib_file_topic_distributions.csv")
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contrib_df <- read_csv("../final_data/deb_contrib_did.csv")
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contrib_pop_df <- read_csv("../final_data/deb_contrib_pop_change.csv")
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#get the contribution count
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#some preprocessing and expansion
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col_order <- c("upstream_vcs_link", "age_in_days", "first_commit", "first_commit_dt", "event_gap", "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")
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contrib_df <- contrib_df[,col_order]
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contrib_df$ct_before_all <- str_split(gsub("[][]","", contrib_df$before_all_ct), ", ")
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contrib_df$ct_after_all <- str_split(gsub("[][]","", contrib_df$after_all_ct), ", ")
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contrib_df$ct_before_mrg <- str_split(gsub("[][]","", contrib_df$before_mrg_ct), ", ")
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contrib_df$ct_after_mrg <- str_split(gsub("[][]","", contrib_df$after_mrg_ct), ", ")
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drop <- c("before_all_ct", "before_mrg_ct", "after_all_ct", "after_mrg_ct")
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contrib_df = contrib_df[,!(names(contrib_df) %in% drop)]
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# 2 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 = starts_with("ct"),
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names_to = "window",
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values_to = "count") |>
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unnest(count)
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longer$observation_type <- gsub("^.*_", "", longer$window)
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longer <- ddply(longer, "observation_type", transform, week=seq(from=0, by=1, length.out=length(observation_type)))
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longer$count <- as.numeric(longer$count)
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#longer <- longer[which(longer$observation_type == "all"),]
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return(longer)
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}
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expanded_data <- expand_timeseries(contrib_df[1,])
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for (i in 2:nrow(contrib_df)){
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expanded_data <- rbind(expanded_data, expand_timeseries(contrib_df[i,]))
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}
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#filter out the windows of time that we're looking at
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window_num <- 8
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windowed_data <- expanded_data |>
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filter(week >= (27 - window_num) & week <= (27 + window_num)) |>
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mutate(D = ifelse(week > 27, 1, 0))
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summed_data <- windowed_data |>
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filter(D==1) |>
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group_by(upstream_vcs_link) |>
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summarise_at(vars(count), list(summed_count=sum))
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#concat dataframes into central data
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contrib_pop_df <- contrib_pop_df |>
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mutate(project_name = map_chr(upstream_vcs_link, ~ {
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parts <- str_split(.x, pattern = "/")[[1]]
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if (length(parts) >= 1) {
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parts[length(parts)]
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} else {
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NA_character_
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}
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}))
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contrib_topic_df <- contrib_topic_df |>
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mutate(project_name = map_chr(filename, ~ {
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parts <- str_split(.x, pattern = "_")[[1]]
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if (length(parts) >= 1) {
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paste(head(parts, -1), collapse="_")
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} else {
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NA_character_
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}
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}))
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contrib_topics_df <- contrib_topics_df |>
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mutate(project_name = map_chr(filename, ~ {
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parts <- str_split(.x, pattern = "_")[[1]]
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if (length(parts) >= 1) {
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paste(head(parts, -1), collapse="_")
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} else {
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NA_character_
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}
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}))
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contrib_total_df <- contrib_pop_df |>
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join(contrib_topics_df, by="project_name")
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contrib_total_df <- contrib_total_df|>
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join(summed_data, by="upstream_vcs_link")
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#outcome variable that is number of commits by number of new contributors
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contrib_total_df$commit_by_contrib = contrib_total_df$summed_count * contrib_total_df$after_contrib_new
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contrib_total_df$logged_outcome = log1p(contrib_total_df$commit_by_contrib)
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||||||
|
#running regressions
|
||||||
|
library(MASS)
|
||||||
|
lm1 <- glm.nb(summed_count ~ t0 + t1 + t2 + t3, data = contrib_total_df)
|
||||||
|
qqnorm(residuals(lm1))
|
||||||
|
summary(lm1)
|
||||||
|
View(contrib_total_df)
|
||||||
|
lm1 <- glm.nb(summed_count ~ t3, data = contrib_total_df)
|
||||||
|
qqnorm(residuals(lm1))
|
||||||
|
summary(lm1)
|
||||||
|
lm1 <- glm.nb(summed_count ~ t3 + t2 + t1, data = contrib_total_df)
|
||||||
|
qqnorm(residuals(lm1))
|
||||||
|
summary(lm1)
|
||||||
|
lm1 <- glm.nb(summed_count ~ t3 + t2 + t1 + t0, data = contrib_total_df)
|
||||||
|
qqnorm(residuals(lm1))
|
||||||
|
summary(lm1)
|
||||||
|
lm1 <- glm.nb(summed_count ~ t0 + t1 + t2 + t3, data = contrib_total_df)
|
||||||
|
qqnorm(residuals(lm1))
|
||||||
|
summary(lm1)
|
||||||
|
lm1 <- lm(summed_count ~ t0 + t1 + t2 + t3, data = contrib_total_df)
|
||||||
|
qqnorm(residuals(lm1))
|
||||||
|
summary(lm1)
|
||||||
|
lm1 <- glm.nb(summed_count ~ t0 + t1 + t2 + t3, data = contrib_total_df)
|
||||||
|
qqnorm(residuals(lm1))
|
||||||
|
summary(lm1)
|
||||||
|
lm1 <- glm.nb(summed_count ~ t1 + t2 + t3, data = contrib_total_df)
|
||||||
|
qqnorm(residuals(lm1))
|
||||||
|
summary(lm1)
|
||||||
|
lm1 <- glm.nb(summed_count ~ t3, data = contrib_total_df)
|
||||||
|
qqnorm(residuals(lm1))
|
||||||
|
summary(lm1)
|
||||||
|
lm1 <- glm.nb(commit_by_contrib ~ t3, data = contrib_total_df)
|
||||||
|
qqnorm(residuals(lm1))
|
||||||
|
summary(lm1)
|
||||||
|
lm1 <- glm.nb(commit_by_contrib ~ t1 + t2 +t3, data = contrib_total_df)
|
||||||
|
qqnorm(residuals(lm1))
|
||||||
|
summary(lm1)
|
||||||
|
lm1 <- glm.nb(commit_by_contrib ~ t3, data = contrib_total_df)
|
||||||
|
qqnorm(residuals(lm1))
|
||||||
|
summary(lm1)
|
||||||
|
lm1 <- glm.nb(commit_by_contrib ~ t2, data = contrib_total_df)
|
||||||
|
qqnorm(residuals(lm1))
|
||||||
|
summary(lm1)
|
||||||
|
lm1 <- glm.nb(commit_by_contrib ~ t1, data = contrib_total_df)
|
||||||
|
qqnorm(residuals(lm1))
|
||||||
|
summary(lm1)
|
||||||
|
lm1 <- glm.nb(commit_by_contrib ~ t3, data = contrib_total_df)
|
||||||
|
qqnorm(residuals(lm1))
|
||||||
|
summary(lm1)
|
||||||
|
source("~/Desktop/git/24_deb_gov/R/readme_topic_outcomes.R")
|
||||||
|
#outcome variable that is number of commits by number of new readmeutors
|
||||||
|
readme_total_df$commit_by_contrib = readme_total_df$summed_count *readme_total_df$after_contrib_new
|
||||||
|
readme_total_df$logged_outcome = log1p(readme_total_df$commit_by_readme)
|
||||||
|
#running regressions
|
||||||
|
library(MASS)
|
||||||
|
lm1 <- glm.nb(commit_by_readme ~ t3, data = readme_total_df)
|
||||||
|
lm1 <- glm.nb(commit_by_contrib ~ t3, data = readme_total_df)
|
||||||
|
qqnorm(residuals(lm1))
|
||||||
|
summary(lm1)
|
||||||
|
lm1 <- glm.nb(commit_by_contrib ~ t0, data = readme_total_df)
|
||||||
|
qqnorm(residuals(lm1))
|
||||||
|
summary(lm1)
|
||||||
|
lm1 <- glm.nb(commit_by_contrib ~ t1, data = readme_total_df)
|
||||||
|
qqnorm(residuals(lm1))
|
||||||
|
summary(lm1)
|
||||||
|
lm1 <- glm.nb(commit_by_contrib ~ t7, data = readme_total_df)
|
||||||
|
qqnorm(residuals(lm1))
|
||||||
|
summary(lm1)
|
||||||
|
lm1 <- glm.nb(commit_by_contrib ~ t0+t1+t7, data = readme_total_df)
|
||||||
|
qqnorm(residuals(lm1))
|
||||||
|
summary(lm1)
|
||||||
|
lm1 <- glm.nb(commit_by_contrib ~ t0+t1+t2+t7, data = readme_total_df)
|
||||||
|
qqnorm(residuals(lm1))
|
||||||
|
summary(lm1)
|
||||||
|
lm1 <- glm.nb(commit_by_contrib ~ t0+t1+t2+t7+t3, data = readme_total_df)
|
||||||
|
qqnorm(residuals(lm1))
|
||||||
|
summary(lm1)
|
||||||
|
lm1 <- glm.nb(commit_by_contrib ~ t0+t1+t2+t7+t3 + t4, data = readme_total_df)
|
||||||
|
qqnorm(residuals(lm1))
|
||||||
|
summary(lm1)
|
||||||
|
lm1 <- glm.nb(commit_by_contrib ~ t0+t1+t2+t7+t3 +t4 + t5 + t6, data = readme_total_df)
|
||||||
|
qqnorm(residuals(lm1))
|
||||||
|
summary(lm1)
|
||||||
|
lm1 <- glm.nb(commit_by_contrib ~ t0+t1+t2+t7+t3 +t4 + t5, data = readme_total_df)
|
||||||
|
qqnorm(residuals(lm1))
|
||||||
|
summary(lm1)
|
||||||
|
source("~/Desktop/git/24_deb_gov/R/contrib_topic_outcomes.R")
|
||||||
|
lm1 <- glm.nb(commit_by_contrib ~ t3, data = contrib_total_df)
|
||||||
|
qqnorm(residuals(lm1))
|
||||||
|
summary(lm1)
|
||||||
|
lm1 <- glm.nb(commit_by_contrib ~ t0+ t1+ t3, data = contrib_total_df)
|
||||||
|
qqnorm(residuals(lm1))
|
||||||
|
summary(lm1)
|
||||||
|
lm1 <- glm.nb(commit_by_contrib ~ t0+ t1+ t2+ t3, data = contrib_total_df)
|
||||||
|
qqnorm(residuals(lm1))
|
||||||
|
summary(lm1)
|
||||||
|
lm1 <- glm.nb(commit_by_contrib ~ t1+ t2+ t3, data = contrib_total_df)
|
||||||
|
qqnorm(residuals(lm1))
|
||||||
|
summary(lm1)
|
||||||
|
lm1 <- glm.nb(commit_by_contrib ~ t1+ t3, data = contrib_total_df)
|
||||||
|
qqnorm(residuals(lm1))
|
||||||
|
summary(lm1)
|
||||||
|
lm1 <- glm.nb(commit_by_contrib ~ t2+ t3, data = contrib_total_df)
|
||||||
|
qqnorm(residuals(lm1))
|
||||||
|
summary(lm1)
|
||||||
|
|||||||
80
R/contrib_topic_outcomes.R
Normal file
80
R/contrib_topic_outcomes.R
Normal file
@ -0,0 +1,80 @@
|
|||||||
|
|
||||||
|
library(stringr)
|
||||||
|
library(plyr)
|
||||||
|
contrib_topics_df <- read_csv("../text_analysis/contrib_file_topic_distributions.csv")
|
||||||
|
contrib_df <- read_csv("../final_data/deb_contrib_did.csv")
|
||||||
|
contrib_pop_df <- read_csv("../final_data/deb_contrib_pop_change.csv")
|
||||||
|
|
||||||
|
#get the contribution count
|
||||||
|
#some preprocessing and expansion
|
||||||
|
col_order <- c("upstream_vcs_link", "age_in_days", "first_commit", "first_commit_dt", "event_gap", "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")
|
||||||
|
contrib_df <- contrib_df[,col_order]
|
||||||
|
contrib_df$ct_before_all <- str_split(gsub("[][]","", contrib_df$before_all_ct), ", ")
|
||||||
|
contrib_df$ct_after_all <- str_split(gsub("[][]","", contrib_df$after_all_ct), ", ")
|
||||||
|
contrib_df$ct_before_mrg <- str_split(gsub("[][]","", contrib_df$before_mrg_ct), ", ")
|
||||||
|
contrib_df$ct_after_mrg <- str_split(gsub("[][]","", contrib_df$after_mrg_ct), ", ")
|
||||||
|
drop <- c("before_all_ct", "before_mrg_ct", "after_all_ct", "after_mrg_ct")
|
||||||
|
contrib_df = contrib_df[,!(names(contrib_df) %in% drop)]
|
||||||
|
# 2 some expansion needs to happens for each project
|
||||||
|
expand_timeseries <- function(project_row) {
|
||||||
|
longer <- project_row |>
|
||||||
|
pivot_longer(cols = starts_with("ct"),
|
||||||
|
names_to = "window",
|
||||||
|
values_to = "count") |>
|
||||||
|
unnest(count)
|
||||||
|
longer$observation_type <- gsub("^.*_", "", longer$window)
|
||||||
|
longer <- ddply(longer, "observation_type", transform, week=seq(from=0, by=1, length.out=length(observation_type)))
|
||||||
|
longer$count <- as.numeric(longer$count)
|
||||||
|
#longer <- longer[which(longer$observation_type == "all"),]
|
||||||
|
return(longer)
|
||||||
|
}
|
||||||
|
expanded_data <- expand_timeseries(contrib_df[1,])
|
||||||
|
for (i in 2:nrow(contrib_df)){
|
||||||
|
expanded_data <- rbind(expanded_data, expand_timeseries(contrib_df[i,]))
|
||||||
|
}
|
||||||
|
#filter out the windows of time that we're looking at
|
||||||
|
window_num <- 8
|
||||||
|
windowed_data <- expanded_data |>
|
||||||
|
filter(week >= (27 - window_num) & week <= (27 + window_num)) |>
|
||||||
|
mutate(D = ifelse(week > 27, 1, 0))
|
||||||
|
|
||||||
|
summed_data <- windowed_data |>
|
||||||
|
filter(D==1) |>
|
||||||
|
group_by(upstream_vcs_link) |>
|
||||||
|
summarise_at(vars(count), list(summed_count=sum))
|
||||||
|
|
||||||
|
#concat dataframes into central data
|
||||||
|
contrib_pop_df <- contrib_pop_df |>
|
||||||
|
mutate(project_name = map_chr(upstream_vcs_link, ~ {
|
||||||
|
parts <- str_split(.x, pattern = "/")[[1]]
|
||||||
|
if (length(parts) >= 1) {
|
||||||
|
parts[length(parts)]
|
||||||
|
} else {
|
||||||
|
NA_character_
|
||||||
|
}
|
||||||
|
}))
|
||||||
|
|
||||||
|
contrib_topics_df <- contrib_topics_df |>
|
||||||
|
mutate(project_name = map_chr(filename, ~ {
|
||||||
|
parts <- str_split(.x, pattern = "_")[[1]]
|
||||||
|
if (length(parts) >= 1) {
|
||||||
|
paste(head(parts, -1), collapse="_")
|
||||||
|
} else {
|
||||||
|
NA_character_
|
||||||
|
}
|
||||||
|
}))
|
||||||
|
|
||||||
|
contrib_total_df <- contrib_pop_df |>
|
||||||
|
join(contrib_topics_df, by="project_name")
|
||||||
|
|
||||||
|
contrib_total_df <- contrib_total_df|>
|
||||||
|
join(summed_data, by="upstream_vcs_link")
|
||||||
|
|
||||||
|
#outcome variable that is number of commits by number of new contributors
|
||||||
|
contrib_total_df$commit_by_contrib = contrib_total_df$summed_count * contrib_total_df$after_contrib_new
|
||||||
|
contrib_total_df$logged_outcome = log1p(contrib_total_df$commit_by_contrib)
|
||||||
|
#running regressions
|
||||||
|
library(MASS)
|
||||||
|
lm1 <- glm.nb(commit_by_contrib ~ t2+ t3, data = contrib_total_df)
|
||||||
|
qqnorm(residuals(lm1))
|
||||||
|
summary(lm1)
|
||||||
@ -99,3 +99,5 @@ library(MASS)
|
|||||||
lm1 <- glm.nb(logged_outcome~ reading_time + linsear_write_formula + flesch_reading_ease + mcalpine_eflaw + word_count, data = readme_total_df)
|
lm1 <- glm.nb(logged_outcome~ reading_time + linsear_write_formula + flesch_reading_ease + mcalpine_eflaw + word_count, data = readme_total_df)
|
||||||
qqnorm(residuals(lm1))
|
qqnorm(residuals(lm1))
|
||||||
summary(lm1)
|
summary(lm1)
|
||||||
|
|
||||||
|
|
||||||
|
|||||||
80
R/readme_topic_outcomes.R
Normal file
80
R/readme_topic_outcomes.R
Normal file
@ -0,0 +1,80 @@
|
|||||||
|
|
||||||
|
library(stringr)
|
||||||
|
library(plyr)
|
||||||
|
readme_topics_df <- read_csv("../text_analysis/readme_file_topic_distributions.csv")
|
||||||
|
readme_df <- read_csv("../final_data/deb_readme_did.csv")
|
||||||
|
readme_pop_df <- read_csv("../final_data/deb_readme_pop_change.csv")
|
||||||
|
|
||||||
|
#get the readmeution count
|
||||||
|
#some preprocessing and expansion
|
||||||
|
col_order <- c("upstream_vcs_link", "age_in_days", "first_commit", "first_commit_dt", "event_gap", "event_date", "event_hash", "before_all_ct", "after_all_ct", "before_mrg_ct", "after_mrg_ct", "before_auth_new", "after_auth_new", "before_commit_new", "after_commit_new")
|
||||||
|
readme_df <- readme_df[,col_order]
|
||||||
|
readme_df$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$after_mrg_ct), ", ")
|
||||||
|
drop <- c("before_all_ct", "before_mrg_ct", "after_all_ct", "after_mrg_ct")
|
||||||
|
readme_df = readme_df[,!(names(readme_df) %in% drop)]
|
||||||
|
# 2 some expansion needs to happens for each project
|
||||||
|
expand_timeseries <- function(project_row) {
|
||||||
|
longer <- project_row |>
|
||||||
|
pivot_longer(cols = starts_with("ct"),
|
||||||
|
names_to = "window",
|
||||||
|
values_to = "count") |>
|
||||||
|
unnest(count)
|
||||||
|
longer$observation_type <- gsub("^.*_", "", longer$window)
|
||||||
|
longer <- ddply(longer, "observation_type", transform, week=seq(from=0, by=1, length.out=length(observation_type)))
|
||||||
|
longer$count <- as.numeric(longer$count)
|
||||||
|
#longer <- longer[which(longer$observation_type == "all"),]
|
||||||
|
return(longer)
|
||||||
|
}
|
||||||
|
expanded_data <- expand_timeseries(readme_df[1,])
|
||||||
|
for (i in 2:nrow(readme_df)){
|
||||||
|
expanded_data <- rbind(expanded_data, expand_timeseries(readme_df[i,]))
|
||||||
|
}
|
||||||
|
#filter out the windows of time that we're looking at
|
||||||
|
window_num <- 8
|
||||||
|
windowed_data <- expanded_data |>
|
||||||
|
filter(week >= (27 - window_num) & week <= (27 + window_num)) |>
|
||||||
|
mutate(D = ifelse(week > 27, 1, 0))
|
||||||
|
|
||||||
|
summed_data <- windowed_data |>
|
||||||
|
filter(D==1) |>
|
||||||
|
group_by(upstream_vcs_link) |>
|
||||||
|
summarise_at(vars(count), list(summed_count=sum))
|
||||||
|
|
||||||
|
#concat dataframes into central data
|
||||||
|
readme_pop_df <- readme_pop_df |>
|
||||||
|
mutate(project_name = map_chr(upstream_vcs_link, ~ {
|
||||||
|
parts <- str_split(.x, pattern = "/")[[1]]
|
||||||
|
if (length(parts) >= 1) {
|
||||||
|
parts[length(parts)]
|
||||||
|
} else {
|
||||||
|
NA_character_
|
||||||
|
}
|
||||||
|
}))
|
||||||
|
|
||||||
|
readme_topics_df <- readme_topics_df |>
|
||||||
|
mutate(project_name = map_chr(filename, ~ {
|
||||||
|
parts <- str_split(.x, pattern = "_")[[1]]
|
||||||
|
if (length(parts) >= 1) {
|
||||||
|
paste(head(parts, -1), collapse="_")
|
||||||
|
} else {
|
||||||
|
NA_character_
|
||||||
|
}
|
||||||
|
}))
|
||||||
|
|
||||||
|
readme_total_df <- readme_pop_df |>
|
||||||
|
join(readme_topics_df, by="project_name")
|
||||||
|
|
||||||
|
readme_total_df <- readme_total_df|>
|
||||||
|
join(summed_data, by="upstream_vcs_link")
|
||||||
|
|
||||||
|
#outcome variable that is number of commits by number of new readmeutors
|
||||||
|
readme_total_df$commit_by_contrib = readme_total_df$summed_count *readme_total_df$after_contrib_new
|
||||||
|
readme_total_df$logged_outcome = log1p(readme_total_df$commit_by_readme)
|
||||||
|
#running regressions
|
||||||
|
library(MASS)
|
||||||
|
lm1 <- glm.nb(commit_by_contrib ~ t0+t1+t2+t7+t3 +t4 + t5, data = readme_total_df)
|
||||||
|
qqnorm(residuals(lm1))
|
||||||
|
summary(lm1)
|
||||||
Loading…
Reference in New Issue
Block a user