library(ggplot2)
data1 <- read_csv('../power_data_111023_mmt.csv',show_col_types = FALSE)
data2 <- read_csv('../inst_all_packages_full_results.csv')
data1 <- read_csv('../kk_final_expanded_data_final.csv',show_col_types = FALSE)
library(readr)
library(ggplot2)
library(tidyverse)
data1 <- read_csv('../kk_final_expanded_data_final.csv',show_col_types = FALSE)
# this is the file with the lmer multi-level rddAnalysis
library(tidyverse)
library(plyr)
# 0 loading the readme data in
try(setwd(dirname(rstudioapi::getActiveDocumentContext()$path)))
readme_df <- read_csv("../final_data/deb_readme_did.csv")
# 1 preprocessing
#colnames(readme_df) <- c("upstream_vcs_link", "event_date", "event_hash", "before_all_ct", "before_mrg_ct", "after_all_ct", "after_mrg_ct", "before_auth_new", "after_commit_new", "after_auth_new", "before_commit_new")
col_order <- c("upstream_vcs_link", "age_of_project", "event_date", "event_hash", "before_all_ct", "after_all_ct", "before_mrg_ct", "after_mrg_ct", "before_auth_new", "after_auth_new", "before_commit_new",  "after_commit_new")
readme_df <- readme_df[,col_order]
readme_df$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))
#scale the age numbers
windowed_data$scaled_project_age <- scale(windowed_data$age_of_project)
windowed_data$week_offset <- windowed_data$week - 27
#separate out the cleaning d
all_actions_data <- windowed_data[which(windowed_data$observation_type == "all"),]
mrg_actions_data <- windowed_data[which(windowed_data$observation_type == "mrg"),]
all_actions_data$log1p_count <- log1p(all_actions_data$count)
# 3 rdd in lmer analysis
# rdd: https://rpubs.com/phle/r_tutorial_regression_discontinuity_design
# lmer: https://www.youtube.com/watch?v=LzAwEKrn2Mc
library(lme4)
# https://www.bristol.ac.uk/cmm/learning/videos/random-intercepts.html#exvar
library(optimx)
library(lattice)
all_model <- lmer(log1p_count ~ D * I(week_offset)+ scaled_project_age + (D * I(week_offset)| upstream_vcs_link), data=all_actions_data, REML=FALSE, control = lmerControl(
optimizer ='optimx', optCtrl=list(method='L-BFGS-B')))
#identifying the quartiles of effect for D
all_model_ranef <- as.data.frame(ranef(all_model, condVar=TRUE))
View(all_model_ranef)
d_effect_ranef_all <- all_model_ranef[all_model_ranef$term=="D",]
#identifying the quartiles of effect for D
all_model_ranef <- ranef(all_model, condVar=TRUE)
d_effect_ranef_all <- all_model_ranef[all_model_ranef$term=="D",]
dotplot(all_model_ranef)
d_effect_ranef_all <- all_model_ranef['upstream_vcs_link']['D']
View(all_model_ranef)
d_effect_ranef_all <- all_model_ranef[upstream_vcs_link,2]
d_effect_ranef_all <- all_model_ranef['upstream_vcs_link',2]
d_effect_ranef_all <- all_model_ranef$upstream_vcs_link
View(d_effect_ranef_all)
dotplot(all_model_ranef)[["D"]]
dotplot(all_model_ranef)[["upstream_vcs_link"]]
dotplot(all_model_ranef)[["upstream_vcs_link"]["D"]]
dotplot(all_model_ranef)$D
View(all_model_ranef)
for (j in 1:nschool) {
jj <- order(all_model_ranef)[j]
lines (x=c(j,j),y=c(ranef.lower[jj],ranef.upper[jj]))
}
for (j in 1:upstream_vcs_link) {
jj <- order(all_model_ranef)[j]
lines (x=c(j,j),y=c(ranef.lower[jj],ranef.upper[jj]))
}
View(all_model_ranef)
df_ranefs <- as.data.frame(all_model_ranef)
View(df_ranefs)
#identifying the quartiles of effect for D
all_model_ranef <- ranef(all_model, condVar=TRUE)$upstream_vcs_link[[2]]
#identifying the quartiles of effect for D
all_model_ranef <- ranef(all_model, condVar=TRUE)$upstream_vcs_link
dotplot(all_model_ranef)
dotplot(all_model_ranef)
# this is the file with the lmer multi-level rddAnalysis
library(tidyverse)
library(plyr)
# 0 loading the readme data in
try(setwd(dirname(rstudioapi::getActiveDocumentContext()$path)))
readme_df <- read_csv("../final_data/deb_readme_did.csv")
# 1 preprocessing
#colnames(readme_df) <- c("upstream_vcs_link", "event_date", "event_hash", "before_all_ct", "before_mrg_ct", "after_all_ct", "after_mrg_ct", "before_auth_new", "after_commit_new", "after_auth_new", "before_commit_new")
col_order <- c("upstream_vcs_link", "age_of_project", "event_date", "event_hash", "before_all_ct", "after_all_ct", "before_mrg_ct", "after_mrg_ct", "before_auth_new", "after_auth_new", "before_commit_new",  "after_commit_new")
readme_df <- readme_df[,col_order]
readme_df$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))
#scale the age numbers
windowed_data$scaled_project_age <- scale(windowed_data$age_of_project)
windowed_data$week_offset <- windowed_data$week - 27
#separate out the cleaning d
all_actions_data <- windowed_data[which(windowed_data$observation_type == "all"),]
mrg_actions_data <- windowed_data[which(windowed_data$observation_type == "mrg"),]
all_actions_data$log1p_count <- log1p(all_actions_data$count)
# 3 rdd in lmer analysis
# rdd: https://rpubs.com/phle/r_tutorial_regression_discontinuity_design
# lmer: https://www.youtube.com/watch?v=LzAwEKrn2Mc
library(lme4)
# https://www.bristol.ac.uk/cmm/learning/videos/random-intercepts.html#exvar
library(optimx)
library(lattice)
all_model <- lmer(log1p_count ~ D * I(week_offset)+ scaled_project_age + (D * I(week_offset)| upstream_vcs_link), data=all_actions_data, REML=FALSE, control = lmerControl(
optimizer ='optimx', optCtrl=list(method='L-BFGS-B')))
#identifying the quartiles of effect for D
all_model_ranef <- ranef(all_model, condVar=TRUE)
dotplot(all_model_ranef)$g[1]
re <- ranef(all_model,postVar = TRUE)
re$g$'(Intercept)' <- NULL
re$g$'D:I(week_offset)' <- NULL
re <- ranef(all_model, condVar=TRUE)
re$g$'(Intercept)' <- NULL
re$g$'D:I(week_offset)' <- NULL
dotplot(re)
dotplot(re)
re <- ranef(all_model, condVar=TRUE)
re$upstream_vcs_link$'(Intercept)' <- NULL
re$upstream_vcs_link$'D:I(week_offset)' <- NULL
View(re)
re$upstream_vcs_link$'I(week_offset)' <- NULL
dotplot(re)
View(re)
View(all_model_ranef)
dotplot(all_model_ranef)
#identifying the quartiles of effect for D
all_model_ranef <- ranef(all_model, condVar=TRUE)
dotplot(all_model_ranef)
#identifying the quartiles of effect for D
all_model_ranef <- ranef(all_model, condVar=TRUE)
dotplot(all_model_ranef)
re <- ranef(all_model, condVar=TRUE)
re$upstream_vcs_link$'(Intercept)' <- NULL
re$upstream_vcs_link$'D:I(week_offset)' <- NULL
re$upstream_vcs_link$'I(week_offset)' <- NULL
dotplot(re)
dotplot(all_model_ranef)
# this is the file with the lmer multi-level rddAnalysis
library(tidyverse)
library(plyr)
# 0 loading the readme data in
try(setwd(dirname(rstudioapi::getActiveDocumentContext()$path)))
readme_df <- read_csv("../final_data/deb_readme_did.csv")
# 1 preprocessing
#colnames(readme_df) <- c("upstream_vcs_link", "event_date", "event_hash", "before_all_ct", "before_mrg_ct", "after_all_ct", "after_mrg_ct", "before_auth_new", "after_commit_new", "after_auth_new", "before_commit_new")
col_order <- c("upstream_vcs_link", "age_of_project", "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$observation_type == "all"),]
return(longer)
}
expanded_data <- expand_timeseries(readme_df[1,])
for (i in 2:nrow(readme_df)){
expanded_data <- rbind(expanded_data, expand_timeseries(readme_df[i,]))
}
#filter out the windows of time that we're looking at
window_num <- 8
windowed_data <- expanded_data |>
filter(week >= (27 - window_num) & week <= (27 + window_num)) |>
mutate(D = ifelse(week > 27, 1, 0))
#scale the age numbers
windowed_data$scaled_project_age <- scale(windowed_data$age_of_project)
windowed_data$week_offset <- windowed_data$week - 27
#separate out the cleaning d
all_actions_data <- windowed_data[which(windowed_data$observation_type == "all"),]
mrg_actions_data <- windowed_data[which(windowed_data$observation_type == "mrg"),]
all_actions_data$log1p_count <- log1p(all_actions_data$count)
# 3 rdd in lmer analysis
# rdd: https://rpubs.com/phle/r_tutorial_regression_discontinuity_design
# lmer: https://www.youtube.com/watch?v=LzAwEKrn2Mc
library(lme4)
# https://www.bristol.ac.uk/cmm/learning/videos/random-intercepts.html#exvar
library(optimx)
library(lattice)
all_model <- lmer(log1p_count ~ D * I(week_offset)+ scaled_project_age + (D * I(week_offset)| upstream_vcs_link), data=all_actions_data, REML=FALSE, control = lmerControl(
optimizer ='optimx', optCtrl=list(method='L-BFGS-B')))
summary(all_model)
#identifying the quartiles of effect for D
all_model_ranef <- ranef(all_model, condVar=TRUE)
dotplot(all_model_ranef)
df_ranefs <- as.data.frame(all_model_ranef)
View(df_ranefs)
D_df_ranef <- df_ranefs[term == "D"]
D_df_ranef <- df_ranefs[df_ranefs$term == "D"]
library(tidyverse)
library(plyr)
#get the contrib data instead
try(setwd(dirname(rstudioapi::getActiveDocumentContext()$path)))
contrib_df <- read_csv("../final_data/deb_contrib_did.csv")
#some preprocessing and expansion
col_order <- c("upstream_vcs_link", "age_of_project", "event_date", "event_hash", "before_all_ct", "after_all_ct", "before_mrg_ct", "after_mrg_ct", "before_auth_new", "after_auth_new", "before_commit_new",  "after_commit_new")
contrib_df <- contrib_df[,col_order]
contrib_df$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))
#scale the age numbers
windowed_data$scaled_project_age <- scale(windowed_data$age_of_project)
windowed_data$week_offset <- windowed_data$week - 27
#separate out the cleaning d
all_actions_data <- windowed_data[which(windowed_data$observation_type == "all"),]
mrg_actions_data <- windowed_data[which(windowed_data$observation_type == "mrg"),]
#EDA?
hist(log(all_actions_data$count))
all_actions_data$logged_count <- log(all_actions_data$count)
all_actions_data$log1p_count <- log1p(all_actions_data$count)
# now for merge
mrg_actions_data$logged_count <- log(mrg_actions_data$count)
mrg_actions_data$log1p_count <- log1p(mrg_actions_data$count)
#TKTK ---------------------
#imports for models
library(lme4)
library(optimx)
library(lattice)
#models -- TKTK need to be fixed
all_model <- lmer(log1p_count ~ D * I(week_offset)+ scaled_project_age + (week_offset| upstream_vcs_link), data=all_actions_data, REML=FALSE, control = lmerControl(
optimizer ='optimx', optCtrl=list(method='L-BFGS-B')))
summary(all_model)
#identifying the quartiles of effect for D
all_model_ranef <- as.data.frame(ranef(all_model))
View(all_model_ranef)
# mrg behavior for this
mrg_model <- lmer(log1p_count ~ D * I(week_offset)+ scaled_project_age + (week_offset | upstream_vcs_link), data=mrg_actions_data, REML=FALSE, control = lmerControl(
optimizer ='optimx', optCtrl=list(method='L-BFGS-B')))
summary(mrg_model)
#identifying the quartiles of effect for D
mrg_model_ranef <- ranef(mrg_model)
View(mrg_model_ranef)
dotplot(mrg_model_ranef)
#load in data
contrib_df <- read_csv("../final_data/deb_contrib_pop_change.csv")
readme_df <- read_csv("../final_data/deb_readme_pop_change.csv")
View(readme_df)
#some expansion needs to happens for each project
expand_timeseries <- function(project_row) {
longer <- project_row |>
pivot_longer(cols = ends_with("new"),
names_to = "window",
values_to = "count") |>
unnest(count)
#longer$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,])
View(expand_timeseries)
View(expanded_data)
longer <- project_row |>
pivot_longer(cols = ends_with("new"),
names_to = "window",
values_to = "count") |>
unnest(count) |>
mutate(after_doc = str_detect(window, "after"))
#some expansion needs to happens for each project
expand_timeseries <- function(project_row) {
longer <- project_row |>
pivot_longer(cols = ends_with("new"),
names_to = "window",
values_to = "count") |>
unnest(count) |>
mutate(after_doc = str_detect(window, "after"))
return(longer)
}
expanded_data <- expand_timeseries(readme_df[1,])
longer <- project_row |>
pivot_longer(cols = ends_with("new"),
names_to = "window",
values_to = "count") |>
unnest(count) |>
mutate(after_doc = as.numeric(str_detect(window, "after")))
return(longer)
#some expansion needs to happens for each project
expand_timeseries <- function(project_row) {
longer <- project_row |>
pivot_longer(cols = ends_with("new"),
names_to = "window",
values_to = "count") |>
unnest(count) |>
mutate(after_doc = as.numeric(str_detect(window, "after")))
return(longer)
}
expanded_data <- expand_timeseries(readme_df[1,])
#some expansion needs to happens for each project
expand_timeseries <- function(project_row) {
longer <- project_row |>
pivot_longer(cols = ends_with("new"),
names_to = "window",
values_to = "count") |>
unnest(count) |>
mutate(after_doc = as.numeric(str_detect(window, "after"))) |>
mutate(is_collab = as.numeric(str_detect(window, "collab")))
return(longer)
}
expanded_data <- expand_timeseries(readme_df[1,])
for (i in 2:nrow(readme_df)){
expanded_readme_data <- rbind(expanded_readme_data, expand_timeseries(readme_df[i,]))
}
expanded_readme_data <- expand_timeseries(readme_df[1,])
for (i in 2:nrow(readme_df)){
expanded_readme_data <- rbind(expanded_readme_data, expand_timeseries(readme_df[i,]))
}
expanded_contrib_data <- expand_timeseries(contrib_df[1,])
for (i in 2:nrow(contrib_df)){
expanded_contrib_data <- rbind(expanded_contrib_data, expand_timeseries(contrib_df[i,]))
}
View(expanded_contrib_data)
readme_model <- lmer(count ~ after_doc + (1| upstream_vcs_link), data=expanded_readme_data, REML=FALSE)
summary(readme_model)
readme_model <- lmer(count ~ after_doc + ( after_doc | upstream_vcs_link), data=expanded_readme_data, REML=FALSE)
summary(readme_model)
contrib_model <- lmer(count ~ after_doc + ( after_doc | upstream_vcs_link), data=expanded_contrib_data, REML=FALSE)
summary(contrib_model)
collab_readme_model <- lmer(count ~ after_doc + ( after_doc | upstream_vcs_link), data=collab_pop_readme, REML=FALSE)
#breaking out the types of population counts
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),]
collab_pop_contrib <- expanded_contrib_data[which(expanded_contrib_data$is_collab == 1),]
contrib_pop_contrib <- expanded_contrib_data[which(expanded_contrib_data$is_collab == 0),]
collab_readme_model <- lmer(count ~ after_doc + ( after_doc | upstream_vcs_link), data=collab_pop_readme, REML=FALSE)
collab_readme_model <- lmer(count ~ after_doc + (1| upstream_vcs_link), data=collab_pop_readme, REML=FALSE)
summary(collab_readme_model)
contrib_readme_model <- lmer(count ~ after_doc + ( 1| upstream_vcs_link), data=contrib_pop_readme, REML=FALSE)
summary(contrib_readme_model)
collab_readme_model <- lmer(count ~ after_doc + (after_doc| upstream_vcs_link), data=collab_pop_readme, REML=FALSE)
collab_readme_model <- lmer(count ~ after_doc + (1| upstream_vcs_link), data=collab_pop_readme, REML=FALSE)
summary(collab_readme_model)
contrib_readme_model <- lmer(count ~ after_doc + ( 1| upstream_vcs_link), data=contrib_pop_readme, REML=FALSE)
summary(contrib_readme_model)
collab_contrib_model <- lmer(count ~ after_doc + ( 1 | upstream_vcs_link), data=collab_pop_contrib, REML=FALSE)
summary(collab_contrib_model)
contrib_contrib_model <- lmer(count ~ after_doc + ( 1 | upstream_vcs_link), data=contrib_pop_contrib, REML=FALSE)
summary(contrib_contrib_model)
expanded_readme_data |>
ggplot(aes(x = after_doc, y = count, col = is_collab)) +
geom_point()
ggplot(aes(x = after_doc, y = count, col = as.factor(is_collab)) +
expanded_readme_data |>
ggplot(aes(x = after_doc, y = count, col = as.factor(is_collab))) +
geom_point()
expanded_readme_data |>
expanded_readme_data |>
ggplot(aes(x = after_doc, y = count, col = as.factor(is_collab))) +
geom_point()
expanded_readme_data |>
ggplot(aes(x = as.factor(after_doc), y = count, col = as.factor(is_collab))) +
geom_point()
expanded_readme_data |>
ggplot(aes(x = as.factor(after_doc), y = scale(count), col = as.factor(is_collab))) +
geom_point()
expanded_readme_data$log1pcount <- log1p(expanded_readme_data$count)
expanded_contrib_data$log1pcount <- log1p(expanded_contrib_data$count)
expanded_readme_data |>
ggplot(aes(x = as.factor(after_doc), y = log1pcount, col = as.factor(is_collab))) +
geom_point()
expanded_readme_data |>
ggplot(aes(x = as.factor(after_doc), y = log1pcount, col = as.factor(is_collab))) +
geom_point() +
geom_smooth(method = 'lm', se = F)
expanded_readme_data |>
ggplot(aes(x = after_doc, y = log1pcount, col = as.factor(is_collab))) +
geom_point() +
geom_smooth(method = 'lm', se = F)
expanded_contrib_data |>
ggplot(aes(x = after_doc, y = log1pcount, col = as.factor(is_collab))) +
geom_point() +
geom_smooth(method = 'lm', se = F)
expanded_readme_data |>
ggplot(aes(x = after_doc, y = log1pcount, col = as.factor(is_collab))) +
geom_point() +
geom_smooth(method = 'lm', se = F) + geom_jitter()
expanded_readme_data |>
ggplot(aes(x = after_doc, y = count, col = as.factor(is_collab))) +
geom_point() +
geom_smooth(method = 'lm', se = F) + geom_jitter()
expanded_readme_data$logcount <- log(expanded_readme_data$count)
expanded_contrib_data$logcount <- log(expanded_contrib_data$count)
expanded_readme_data |>
ggplot(aes(x = after_doc, y = logcount, col = as.factor(is_collab))) +
geom_point() +
geom_smooth(method = 'lm', se = F) + geom_jitter()
expanded_contrib_data |>
ggplot(aes(x = after_doc, y = log1pcount, col = as.factor(is_collab))) +
geom_point() +
geom_smooth(method = 'lm', se = F)
expanded_contrib_data |>
ggplot(aes(x = after_doc, y = log1pcount, col = as.factor(is_collab))) +
geom_point() +
geom_smooth(method = 'lm', se = F) + geom_jitter()
expanded_contrib_data |>
ggplot(aes(x = after_doc, y = count, col = as.factor(is_collab))) +
geom_point() +
geom_smooth(method = 'lm', se = F) + geom_jitter()
expanded_readme_data |>
ggplot(aes(x = after_doc, y = logcount, col = as.factor(is_collab))) +
geom_point() +
geom_smooth(method = 'lm', se = F) + geom_jitter()
collab_readme_model <- lmer(logcount ~ after_doc + (1| upstream_vcs_link), data=collab_pop_readme, REML=FALSE)
#breaking out the types of population counts
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),]
collab_pop_contrib <- expanded_contrib_data[which(expanded_contrib_data$is_collab == 1),]
contrib_pop_contrib <- expanded_contrib_data[which(expanded_contrib_data$is_collab == 0),]
collab_readme_model <- lmer(logcount ~ after_doc + (1| upstream_vcs_link), data=collab_pop_readme, REML=FALSE)
collab_readme_model <- lmer(log1pcount ~ after_doc + (1| upstream_vcs_link), data=collab_pop_readme, REML=FALSE)
summary(collab_readme_model)
contrib_readme_model <- lmer(log1pcount ~ after_doc + ( 1| upstream_vcs_link), data=contrib_pop_readme, REML=FALSE)
summary(contrib_readme_model)
collab_contrib_model <- lmer(log1pcount ~ after_doc + ( 1 | upstream_vcs_link), data=collab_pop_contrib, REML=FALSE)
summary(collab_contrib_model)
contrib_contrib_model <- lmer(log1pcount ~ after_doc + ( 1 | upstream_vcs_link), data=contrib_pop_contrib, REML=FALSE)
summary(contrib_contrib_model)
library(ggplot2)
expanded_readme_data |>
ggplot(aes(x = after_doc, y = logcount, col = as.factor(is_collab))) +
geom_point() +
geom_smooth(method = 'lm', se = F) + geom_jitter()
expanded_readme_data |>
ggplot(aes(x = after_doc, y = log1pcount, col = as.factor(is_collab))) +
geom_point() +
geom_smooth(method = 'lm', se = F) + geom_jitter()
expanded_contrib_data |>
ggplot(aes(x = after_doc, y = count, col = as.factor(is_collab))) +
geom_point() +
geom_smooth(method = 'lm', se = F) + geom_jitter()