In CorrelAid/projectutils: "Small Utility Functions Managing CorrelAid Projects"

knitr::opts_chunk$set(echo = FALSE)
knitr::opts_chunk$set(warning = FALSE)

library(tidyverse)
library(tidyverse)
library(patchwork)
library(correltools)
PROJECT_ID <- params$project_id
ANON_PATH <- params$anon_path
appl <- readr::read_csv(ANON_PATH)

Report metadata:

time generated: r format(Sys.time(), '%Y-%m-%d, %H:%m:%S UTC%z')
applications included: r nrow(appl)

Introduction

Helpful links for team selection committee

Team selection criteria and best practices
Explanation of the experience scale ("Beginner", "User", "Advanced", "Expert")
Explanation of the team roles

Project roles by gender

gender_labels <- c(female = 'female', male = 'male', non_binary = 'nonbinary', not_disclosed = 'not disclosed')
roles_labels <- c(team_lead = 'Team lead', team_member = 'Team member', team_trainee = 'Team trainee')

theme_set(correltools::theme_correlaid(base_size = 12))

#determine y axis limit for all plots
lim_y <- max(appl %>% count(project_role) %>% pull(n) %>% max(),
             appl %>% count(gender) %>% pull(n) %>% max())

gender <- ggplot(appl, aes(x = gender, fill = gender))+
  geom_bar()+
  correltools::scale_fill_correlaid_d(labels = gender_labels)+
  scale_x_discrete(labels = gender_labels)+
  theme(axis.title.x = element_blank(),
        axis.title.y = element_blank())+
  scale_y_continuous(limits = c(0, lim_y), breaks = seq(0, lim_y, 2))+
  labs(fill = 'Gender')

roles <- ggplot(appl, aes(x = project_role))+
  geom_bar()+
  scale_x_discrete(labels = roles_labels)+
  theme(axis.title.x = element_blank(),
        axis.title.y = element_blank())+
  scale_y_continuous(limits = c(0, lim_y), breaks = seq(0, lim_y, 2))

roles_by_gender <- ggplot(appl, aes(x = project_role, fill = gender))+
  geom_bar(position = position_dodge2(preserve = "single", padding = 0))+
  correltools::scale_fill_correlaid_d(labels = gender_labels)+
  scale_x_discrete(labels = roles_labels)+
  theme(axis.title.x = element_blank(),
        axis.title.y = element_blank())+
  scale_y_continuous(limits = c(0, lim_y), breaks = seq(0, lim_y, 2))+
  labs(fill = 'Gender')


( (gender + roles) / roles_by_gender) +
  plot_layout(guides = 'collect') +
  plot_annotation(title = 'Number of applications',
                  subtitle = 'By gender, by role, and by gender and role')

female <- appl %>% 
  filter(gender == "female") %>% 
  arrange(project_role) %>% 
  pull(applicant_id)

res <- lapply(female, function(x) {
  params <- list(applicant_id = x, anon_path = ANON_PATH)
  knitr::knit_child(
    'zzz_template_application_single.Rmd', envir = environment(), quiet = TRUE
  )
})

cat(unlist(res), sep = '\n')

male <- appl %>% 
  filter(gender == "male") %>% 
  arrange(applicant_id) %>% 
  pull(applicant_id)
res <- lapply(male, function(x) {
  params <- list(applicant_id = x, anon_path = ANON_PATH)
  knitr::knit_child(
    'zzz_template_application_single.Rmd', envir = environment(), quiet = TRUE
  )
})
cat(unlist(res), sep = '\n')

div_nonbinary <- appl %>% 
  filter(gender == 'non_binary') %>% 
  arrange(applicant_id) %>% 
  pull(applicant_id)

res <- lapply(div_nonbinary, function(x) {
  params <- list(applicant_id = x, anon_path = ANON_PATH)
  knitr::knit_child(
    'zzz_template_application_single.Rmd', envir = environment(), quiet = TRUE
  )
})

cat(unlist(res), sep = '\n')

not_disclosed <- appl %>% 
  filter(gender == 'not_disclosed') %>% 
  arrange(applicant_id) %>% 
  pull(applicant_id)

res <- lapply(not_disclosed, function(x) {
  params <- list(applicant_id = x, anon_path = ANON_PATH)
  knitr::knit_child(
    'zzz_template_application_single.Rmd', envir = environment(), quiet = TRUE
  )
})

cat(unlist(res), sep = '\n')