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main.Rmd
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### Generate Table 3 in the paper
```{r}
library(e1071)
library(readr)
library(lme4)
library(MuMIn)
library(car)
library(lmtest)
library(sandwich)
year_string = '2015'
data_join = read_csv(sprintf('./data/author_project_join_com_Python_year_before_%s_sample_factor_0.3_true_random.csv', year_string))
model_individual = glm(join_flag~
+ log(social_strength + 1)
+ technical_contain
, data = data_join
,family = "binomial")
summary(model_individual)
coeftest(model_individual, vcov. = vcovCL(model_individual, cluster = ~author+woc, type = "HC0"))
# cluster the standard coefficient at the author and project level
# vif(model_individual)
summary(model_individual)
AIC(model_individual)
BIC(model_individual)
r.squaredGLMM(model_individual)
model_individual_competition = glm(join_flag~
+ log(social_strength + 1)
+ technical_contain
+ log(woc_within_one_hop_count + 1)
+ social_strength_percentile
+ technical_contain_percentile
, data = data_join
,family = "binomial")
summary(model_individual_competition)
coeftest(model_individual_competition, vcov. = vcovCL(model_individual_competition, cluster = ~author+woc, type = "HC0"))
vif(model_individual_competition)
summary(model_individual_competition)
anova(model_individual_competition)
r.squaredGLMM(model_individual_competition)
library(stargazer)
stargazer(model_individual, model_individual_competition, omit = c('Constant'), digits = 2, omit.stat = c('ser', 'll', 'bic'), single.row = TRUE, star.cutoffs = c(.05, .01, .001), star.char = c("*", "**", "***"), covariate.labels = c('Social strength (log)', 'Technical similarity', 'Number of competing projects (log)','Social strength percentile', 'Technical similarity percentile'))
```
### Generate Table 4 in the paper, step 1, data process
```{r}
library(car)
library(lme4)
library(MuMIn)
library(relaimpo)
library(readr)
year_string = '2020'
author_woc_threshold = '10' # change to 100 for validation purposes
data_survival = read_csv(sprintf('./data/regre_survival_analysis_com_Python_survival_year_before_2022_period_year_at_%s_author_threshold_%s_woc_threshold_%s.csv', year_string,author_woc_threshold,author_woc_threshold))
data_basic_network = read_csv(sprintf('./data/regre_basic_network_metrics_com_Python_survival_year_before_2022_period_year_at_%s_author_threshold_%s_woc_threshold_%s.csv', year_string,author_woc_threshold,author_woc_threshold))
data_chara = read_csv(sprintf('./data/regre_individual_chara_com_Python_survival_year_before_2022_period_year_at_%s_author_threshold_%s_woc_threshold_%s.csv', year_string,author_woc_threshold,author_woc_threshold))
data_merge = merge(data_survival, data_basic_network,by = 'woc')
data_merge = merge(data_merge, data_chara,by = 'woc')
hist(log(data_merge$project_age+1))
hist(log(data_merge$all_dev_alltime_count+1))
hist(log(data_merge$all_dev_recent_count+1))
hist(log(data_merge$all_past_commit_count+1))
hist(log(data_merge$recent_past_commit_count+1))
hist(log(data_merge$all_has_embedding_social_within_one_hop_count+1))
hist(log(data_merge$new_attracted_author_count_one_year_all+1))
hist(log(data_merge$new_attracted_author_count_one_year_has_embedding+1))
data_merge$only_social_value_transformed = log(data_merge$only_social_value+1)
data_merge$only_technical_value_transformed = log(data_merge$only_technical_value + 1)
data_merge$individual_value_transformed = log(data_merge$individual_value + 1)
data_merge$only_competition_value_transformed = log(data_merge$only_competition_value + 1)
data_merge$all_value_transformed = log(data_merge$all_value_agg + 1)
hist(data_merge$only_social_value_transformed)
hist(data_merge$only_technical_value_transformed)
hist(data_merge$individual_value_transformed)
hist(data_merge$all_value_transformed)
if (year_string == '2015'){
data_merge_no_outlier = subset(data_merge, project_age > exp(2) & project_age < exp(9)
& all_dev_alltime_count < exp(6)
& all_dev_recent_count < exp(4)
& all_past_commit_count < exp(10)
& all_past_commit_count > exp(2)
& recent_past_commit_count < exp(8)
& new_attracted_author_count_one_year_all < exp(3)
& new_attracted_author_count_one_year_has_embedding < exp(2)
& only_social_value_transformed < 1
& only_technical_value_transformed < 1
& individual_value_transformed < 1
& all_value_transformed < 2
)
}else if(year_string == '2016'){
data_merge_no_outlier = subset(data_merge, project_age > exp(2) & project_age < exp(9)
& all_dev_alltime_count < exp(6)
& all_dev_recent_count < exp(4)
& all_past_commit_count < exp(10)
& recent_past_commit_count < exp(8)
& all_has_embedding_social_within_one_hop_count < exp(9)
& new_attracted_author_count_one_year_all < exp(4)
& new_attracted_author_count_one_year_has_embedding < exp(3)
& individual_value_transformed < 1
& all_value_transformed < 2
)
}else if(year_string == '2017'){
data_merge_no_outlier = subset(data_merge, project_age > exp(2) & project_age < exp(9)
& all_dev_alltime_count < exp(6)
& all_dev_recent_count < exp(4)
& all_past_commit_count < exp(10)
& all_past_commit_count > exp(2)
& recent_past_commit_count < exp(10)
& all_has_embedding_social_within_one_hop_count < exp(9)
& new_attracted_author_count_one_year_all < exp(4)
& new_attracted_author_count_one_year_has_embedding < exp(3)
& individual_value_transformed < 1
& all_value_transformed < 1
)
}else if(year_string == '2018'){
data_merge_no_outlier = subset(data_merge, project_age > exp(2) & project_age < exp(9)
& all_dev_alltime_count < exp(6)
& all_dev_recent_count < exp(4)
& all_past_commit_count < exp(10)
& all_past_commit_count > exp(2)
& recent_past_commit_count < exp(8)
& all_has_embedding_social_within_one_hop_count < exp(8)
& new_attracted_author_count_one_year_all < exp(4)
& new_attracted_author_count_one_year_has_embedding < exp(3)
& individual_value_transformed < 1
& all_value_transformed < 1
)
}else if(year_string == '2019'){
data_merge_no_outlier = subset(data_merge, project_age > exp(2) & project_age < exp(9)
& all_dev_alltime_count < exp(6)
& all_dev_recent_count < exp(4)
& all_past_commit_count < exp(10)
& all_past_commit_count > exp(2)
& recent_past_commit_count < exp(8)
& all_has_embedding_social_within_one_hop_count < exp(8)
& new_attracted_author_count_one_year_all < exp(4)
& new_attracted_author_count_one_year_has_embedding < exp(3)
& individual_value_transformed < 1
& all_value_transformed < 1
)
}else if(year_string == '2020'){
data_merge_no_outlier = subset(data_merge, project_age > exp(2) & project_age < exp(8.5)
& all_dev_alltime_count < exp(6)
& all_dev_recent_count < exp(4)
& all_past_commit_count < exp(10)
& all_past_commit_count > exp(2)
& recent_past_commit_count < exp(8)
& all_has_embedding_social_within_one_hop_count < exp(8)
& new_attracted_author_count_one_year_all < exp(4)
& new_attracted_author_count_one_year_has_embedding < exp(2)
& individual_value_transformed < 1
& all_value_transformed < 1
)
}else if(year_string == '2014'){
data_merge_no_outlier = subset(data_merge, project_age > exp(2) & project_age < exp(8.5)
& all_dev_alltime_count < exp(6)
& all_dev_recent_count < exp(4)
& all_past_commit_count < exp(10)
& all_past_commit_count > exp(2)
& recent_past_commit_count < exp(8)
& all_has_embedding_social_within_one_hop_count < exp(8)
& new_attracted_author_count_one_year_all < exp(4)
& new_attracted_author_count_one_year_has_embedding < exp(2)
& individual_value_transformed < 1
& all_value_transformed < 1
)
}
```
### Generate Table 4 in the paper, step 2, main model
```{r}
regression_no_environment = lm(log(new_attracted_author_count_one_year_has_embedding + 1)~
log(project_age + 1)
+ log(all_dev_alltime_count + 1)
+ log(all_dev_recent_count + 1)
+ log(all_past_commit_count + 1)
+ log(recent_past_commit_count + 1)
+ as.logical(woc_has_readme_flag)
+ as.logical(woc_has_license_flag)
,data = data_merge_no_outlier)
vif(regression_no_environment)
summary(regression_no_environment)
regression_basic = lm(log(new_attracted_author_count_one_year_has_embedding + 1)~
log(project_age + 1)
+ log(all_dev_alltime_count + 1)
+ log(all_dev_recent_count + 1)
+ log(all_past_commit_count + 1)
+ log(recent_past_commit_count + 1)
+ as.logical(woc_has_readme_flag)
+ as.logical(woc_has_license_flag)
+ log(all_has_embedding_social_within_one_hop_count + 1)
,data = data_merge_no_outlier)
vif(regression_basic)
summary(regression_basic)
regression_individual = lm(log(new_attracted_author_count_one_year_has_embedding + 1)~
log(project_age + 1)
+ log(all_dev_alltime_count + 1)
+ log(all_dev_recent_count + 1)
+ log(all_past_commit_count + 1)
+ log(recent_past_commit_count + 1)
+ as.logical(woc_has_readme_flag)
+ as.logical(woc_has_license_flag)
+ log(all_has_embedding_social_within_one_hop_count + 1)
+ individual_value_transformed
,data = data_merge_no_outlier)
vif(regression_individual)
summary(regression_individual)
regression_full = lm(log(new_attracted_author_count_one_year_has_embedding + 1)~
log(project_age + 1)
+ log(all_dev_alltime_count + 1)
+ log(all_dev_recent_count + 1)
+ log(all_past_commit_count + 1)
+ log(recent_past_commit_count + 1)
+ as.logical(woc_has_readme_flag)
+ as.logical(woc_has_license_flag)
+ log(all_has_embedding_social_within_one_hop_count + 1)
+ all_value_transformed
,data = data_merge_no_outlier)
vif(regression_full)
summary(regression_full)
library(stargazer)
stargazer(regression_no_environment, regression_basic, regression_individual, regression_full, omit = c('Constant'), digits = 2, omit.stat = c('ser', 'f', 'rsq'), single.row = TRUE, star.cutoffs = c(.05, .01, .001), star.char = c("*", "**", "***"),covariate.labels = c('Project age (log)', 'Project total developer size (log)', 'Project recent developer size (log)', 'Project total commits (log)', 'Project recent commits (log)', 'Has readme', 'Has license', 'Labor pool size (log)','Effective size, no-competing variables (log)', 'Effective size, full variables (log)'))
```
### Negative bionminal regression (as a validation)
```{r}
library(pscl)
regression_no_environment_nb = glm.nb(new_attracted_author_count_one_year_has_embedding~
log(project_age + 1)
+ log(all_dev_alltime_count + 1)
+ log(all_dev_recent_count + 1)
+ log(all_past_commit_count + 1)
+ log(recent_past_commit_count + 1)
+ as.logical(woc_has_readme_flag)
+ as.logical(woc_has_license_flag)
,data = data_merge_no_outlier)
summary(regression_no_environment_nb)
regression_basic_nb = glm.nb(new_attracted_author_count_one_year_has_embedding~
log(project_age + 1)
+ log(all_dev_alltime_count + 1)
+ log(all_dev_recent_count + 1)
+ log(all_past_commit_count + 1)
+ log(recent_past_commit_count + 1)
+ as.logical(woc_has_readme_flag)
+ as.logical(woc_has_license_flag)
+ log(all_has_embedding_social_within_one_hop_count + 1)
,data = data_merge_no_outlier)
summary(regression_basic_nb)
regression_individual_nb = glm.nb(new_attracted_author_count_one_year_has_embedding~
log(project_age + 1)
+ log(all_dev_alltime_count + 1)
+ log(all_dev_recent_count + 1)
+ log(all_past_commit_count + 1)
+ log(recent_past_commit_count + 1)
+ as.logical(woc_has_readme_flag)
+ as.logical(woc_has_license_flag)
+ log(all_has_embedding_social_within_one_hop_count + 1)
+ individual_value_transformed
,data = data_merge_no_outlier)
summary(regression_individual_nb)
regression_full_nb = glm.nb(new_attracted_author_count_one_year_has_embedding~
log(project_age + 1)
+ log(all_dev_alltime_count + 1)
+ log(all_dev_recent_count + 1)
+ log(all_past_commit_count + 1)
+ log(recent_past_commit_count + 1)
+ as.logical(woc_has_readme_flag)
+ as.logical(woc_has_license_flag)
+ log(all_has_embedding_social_within_one_hop_count + 1)
+ all_value_transformed
,data = data_merge_no_outlier)
summary(regression_full_nb)
```
### OLS with outcome variable being the number of all attracted developers (as a validation)
```{r}
regression_no_environment_allnew = lm(log(new_attracted_author_count_one_year_all + 1)~
log(project_age + 1)
+ log(all_dev_alltime_count + 1)
+ log(all_dev_recent_count + 1)
+ log(all_past_commit_count + 1)
+ log(recent_past_commit_count + 1)
+ as.logical(woc_has_readme_flag)
+ as.logical(woc_has_license_flag)
,data = data_merge_no_outlier)
vif(regression_no_environment_allnew)
summary(regression_no_environment_allnew)
regression_basic_allnew = lm(log(new_attracted_author_count_one_year_all + 1)~
log(project_age + 1)
+ log(all_dev_alltime_count + 1)
+ log(all_dev_recent_count + 1)
+ log(all_past_commit_count + 1)
+ log(recent_past_commit_count + 1)
+ as.logical(woc_has_readme_flag)
+ as.logical(woc_has_license_flag)
+ log(all_has_embedding_social_within_one_hop_count + 1)
,data = data_merge_no_outlier)
vif(regression_basic_allnew)
summary(regression_basic_allnew)
regression_individual_allnew = lm(log(new_attracted_author_count_one_year_all + 1)~
log(project_age + 1)
+ log(all_dev_alltime_count + 1)
+ log(all_dev_recent_count + 1)
+ log(all_past_commit_count + 1)
+ log(recent_past_commit_count + 1)
+ as.logical(woc_has_readme_flag)
+ as.logical(woc_has_license_flag)
+ log(all_has_embedding_social_within_one_hop_count + 1)
+ individual_value_transformed
,data = data_merge_no_outlier)
vif(regression_individual_allnew)
summary(regression_individual_allnew)
regression_full_allnew = lm(log(new_attracted_author_count_one_year_all + 1)~
log(project_age + 1)
+ log(all_dev_alltime_count + 1)
+ log(all_dev_recent_count + 1)
+ log(all_past_commit_count + 1)
+ log(recent_past_commit_count + 1)
+ as.logical(woc_has_readme_flag)
+ as.logical(woc_has_license_flag)
+ log(all_has_embedding_social_within_one_hop_count + 1)
+ all_value_transformed
,data = data_merge_no_outlier)
vif(regression_full_allnew)
summary(regression_full_allnew)
```
### Generate Figure 2 in the paper
```{r}
devtools::install_github("tidyverse/ggplot2")
install.packages("ggchicklet", # Install & load ggchicklet package
repos = "https://cinc.rud.is")
library(ggplot2)
library(ggchicklet)
library(ggtext)
library(dplyr)
library(forcats)
library(grid)
dat <- dat <- data.frame(
Year = c("2015", "2015", "2015", "2015", "2016", "2016", "2016", "2016",
"2017", "2017", "2017", "2017", "2018", "2018", "2018", "2018",
"2019", "2019", "2019", "2019", "2020", "2020", "2020", "2020"),
Distance = c("one hop", "two hops", "three hops", "four or more hops",
"one hop", "two hops", "three hops", "four or more hops",
"one hop", "two hops", "three hops", "four or more hops",
"one hop", "two hops", "three hops", "four or more hops",
"one hop", "two hops", "three hops", "four or more hops",
"one hop", "two hops", "three hops", "four or more hops"),
Ratio = c(0.22862372, 0.10178636, 0.04668942, 0.62290050,
0.21098826, 0.09316911, 0.04100798, 0.65483464,
0.20751380, 0.09704588, 0.04186916, 0.65357116,
0.20052488, 0.07807841, 0.03820754, 0.68318918,
0.19417531, 0.07017961, 0.03118590, 0.70445918,
0.19970166, 0.07903111, 0.03087086, 0.69039636),
stringsAsFactors = FALSE)
# refactor levels
dat <- dat %>% group_by(Year) %>% mutate(Percent = Ratio/sum(Ratio)) %>% ungroup() %>%
mutate(Year = fct_relevel(
Year,
rev(c("2015", "2016", "2017", "2018", "2019", "2020")))
) %>%
mutate(Distance = fct_relevel(
Distance,
c("four or more hops", "three hops", "two hops", "one hop"))
)
# keep trailing zeroes and add "min" to first bar value labels
dat$Label <- as.character(sprintf("%.2f", dat$Ratio))
# generate plot
gg <- ggplot(dat, aes(Year, Percent, fill = Distance, label = Label)) +
geom_chicklet(width = 1,radius = unit(6,units = "pt"), position = ggplot2::position_stack(reverse = FALSE)) +
geom_text(size = 4, fontface= "bold", position = position_stack(vjust = 0.5)) +
scale_y_continuous(limits = c(0,1),expand = c(0, 0)) + coord_flip() +
theme_minimal() +
theme(
legend.position = "top",
plot.title = element_markdown(hjust =0.5),
plot.subtitle = element_markdown(hjust = 0.5),
plot.caption = element_markdown(hjust = 0, size = 11, margin = unit(c(0, 0, 0, 0), "cm"), color = "#718c9e"),
legend.text = element_markdown(size = 11),
axis.text = element_text(face = "bold", size = 11),
axis.text.x = element_blank(),
axis.title.y = element_markdown(hjust = 0, size = 20, margin = unit(c(0, 0, 0, 0), "cm"), color = "#718c9e"),
panel.grid = element_blank(),
axis.title.x = element_markdown(
halign = 0,
margin = margin(2, 0, 15, 0),
fill = "transparent"
)
) +
scale_fill_manual(
name = NULL,
values = c(`one hop` = "#4477AA", `two hops` = "#66CCEE", `three hops` = "#228833", `four or more hops` = "#CCBB44"),
labels = c(
`one hop` = "<strong>one hop</strong>",
`two hops` = "<strong>two hops</strong>",
`three hops` = "<strong>three hops</strong>",
`four or more hops` = "<strong>four or more hops</strong> (or not connected)"),
guide = guide_legend(reverse = TRUE)
) +
labs(x = NULL, fill = NULL, y = NULL)
# '2015' = "#4477AA", '2016' = "#66CCEE",'2017' = "#228833", '2018' = "#CCBB44", '2019' = '#EE6677', '2020' = '#AA3377'
gg
alignTitles <- function(ggplot, title = NULL, subtitle = NULL, caption = NULL) {
# grab the saved ggplot2 object
g <- ggplotGrob(ggplot)
# find the object which provides the plot information for the title, subtitle, and caption
if(!is.null(title)) {
g$layout[which(g$layout$name == "title"),]$l <- title
}
if(!is.null(subtitle)) {
g$layout[which(g$layout$name == "subtitle"),]$l <- subtitle
}
if(!is.null(caption)) {
g$layout[which(g$layout$name == "caption"),]$l <- caption
}
g
}
# align caption to y axis value labels
gg2 <- alignTitles(gg, caption = 2)
grid.draw(gg2)
# add arrow
x <- rev(c(0.25, 0.25, 0.28, 0.28))
y <- rev(c(0.2, 0.15, 0.15, 0.15))
grid.bezier(x, y, gp=gpar(lwd=1.5, fill="black"),
arrow=arrow(type="open",length = unit(0.1, "inches")))
ggsave('./figure/new_contributors.pdf',width = 7, height = 3, dpi = 150)
```
### Generate Figure 5 in the paper
```{r}
library(ggplot2)
library(ggpubr)
data_prediction_individual = read_csv('./data/Manual_performance_aggregate.csv')
scaleFUN <- function(x) sprintf("%.2f", x)
title_font = 26
axis_text_size = 16
line_size = 1.5
point_size = 5
g_precision = ggplot(data_prediction_individual, aes(x=year)) +
geom_point(aes(y = Individual_model_Precision, colour = "No-Competition Model"), size = point_size, shape = 18) +
geom_point(aes(y = Full_model_Precision, colour ="Full Model"), size = point_size, shape = 18) +
geom_line(aes(y = Individual_model_Precision, colour = "No-Competition Model"), size = line_size) +
geom_line(aes(y = Full_model_Precision, colour ="Full Model"), size = line_size) +
scale_colour_manual("",
breaks = c("No-Competition Model", "Full Model"),
values = c("orange", "red"))+
ylab("Precision")+
xlab('Year') +
ggtitle("Precision")+
theme(plot.title = element_text(hjust = 0.5,size = title_font),
legend.text = element_text(size=20),
axis.text=element_text(size=axis_text_size),
axis.title=element_text(size=20),
panel.background = element_blank(),
axis.line = element_line(colour = "black"))+
scale_y_continuous(limits = c(0,NA), labels=scaleFUN)+
scale_x_continuous(n.breaks=7)
g_recall = ggplot(data_prediction_individual, aes(x=year)) +
geom_point(aes(y = Individual_model_Recall, colour = "No-Competition Model"), size = point_size, shape = 18) +
geom_point(aes(y = Full_model_Recall, colour ="Full Model"), size = point_size, shape = 18) +
geom_line(aes(y = Individual_model_Recall, colour = "No-Competition Model"), size = line_size) +
geom_line(aes(y = Full_model_Recall, colour ="Full Model"), size = line_size) +
scale_colour_manual("",
breaks = c("No-Competition Model", "Full Model"),
values = c("orange", "red"))+
ylab("Recall")+
xlab('Year') +
ggtitle("Recall")+
theme(plot.title = element_text(hjust = 0.5,size = title_font),
legend.text = element_text(size=20),
axis.text=element_text(size=axis_text_size),
axis.title=element_text(size=20),
panel.background = element_blank(),
axis.line = element_line(colour = "black"))+
scale_y_continuous(limits = c(0,NA), labels=scaleFUN)+
scale_x_continuous(n.breaks=7)
g_F1 = ggplot(data_prediction_individual, aes(x=year)) +
geom_point(aes(y = Individual_model_F1, colour = "No-Competition Model"), size = point_size, shape = 18) +
geom_point(aes(y = Full_model_F1, colour ="Full Model"), size = point_size, shape = 18) +
geom_line(aes(y = Individual_model_F1, colour = "No-Competition Model"), size = line_size) +
geom_line(aes(y = Full_model_F1, colour ="Full Model"), size = line_size) +
scale_colour_manual("",
breaks = c("No-Competition Model", "Full Model"),
values = c("orange", "red"))+
ylab("F1 Score")+
xlab('Year') +
ggtitle("F1 Score")+
theme(plot.title = element_text(hjust = 0.5,size = title_font),
legend.text = element_text(size=20),
axis.text=element_text(size=axis_text_size),
axis.title=element_text(size=20),
panel.background = element_blank(),
axis.line = element_line(colour = "black"))+
scale_y_continuous(limits = c(0,NA), labels=scaleFUN)+
scale_x_continuous(n.breaks=7)
ggarrange(g_precision, g_recall, g_F1, ncol=3, nrow=1, common.legend = TRUE, legend="bottom")
ggsave(file="./figure/precision_recall_f1.pdf", width=17, height=5, dpi=300)
```
### Generate Figure 6 in the paper
```{r}
library(ggplot2)
scaleFUN <- function(x) sprintf("%.4f", x)
data_merge_no_outlier_has_license = subset(data_merge_no_outlier, woc_has_license_flag == TRUE)
ggplot(data_merge_no_outlier_has_license, aes(x = as.factor(new_attracted_author_count_one_year_has_embedding), y = all_value_agg)) +
geom_violin()+
stat_summary(fun=mean, color="red")+
xlab('Number of attracted new contributors (with at least ten valid commits)')+
ylab('Effective labor pool size, full variables (log)')+
scale_y_continuous(trans='log2', labels=scaleFUN)
```