team_selection.R

library(kbtbr)
library(readr)
library(stringr)
library(dplyr)
library(tidyr)
library(janitor)
source("utils.R") # custom functions
# please refer to the readme for what the project id is
# this value should be used as a label within the kobo form - for both questions
PROJECT_ID <- "2022-04-LAU" # REPLACE THIS EXAMPLE VALUE 
PROJECT_FOLDER <- here::here("projects/", PROJECT_ID)
# create folder for project
if (!dir.exists(PROJECT_FOLDER)) {
  dir.create(PROJECT_FOLDER)
}

# kobo instance
kobo <- kbtbr::Kobo$new("kobo.correlaid.org")
all_surveys <- kobo$get_surveys()

# get survey id
survey_id <- all_surveys %>% 
  filter(name == "Applications for CorrelAid Projects") %>% 
  pull(uid)

applications <- kobo$get_submissions(survey_id)


# Kobotoolbox handles data very "wide format-y", so we have to do quite a bit of data wrangling
# this is code from the clean_kobo function in projectutils: https://github.com/CorrelAid/projectutils/blob/cd118871ae5d50c5116fd86935aa11e95a4edf25/R/applications.R#L29 
# it is copied here to allow for easier modifications to the code 

# rename 
applications <- applications %>% dplyr::rename(applicant_id = `_id`, 
                                         motivation_why_involved = motivation_why)

# if the gender self identification variable does not exist, then create it but put NA
if (!"gender_self_identification" %in% colnames(applications)) {
  applications$gender_self_identification <- NA
}

# people can apply to multiple projects at once, data is not stored in separate rows by KoboToolbox
# --> pull "applied to" information into separate rows and data frame
project_ids_df <- applications %>% 
  dplyr::select(applicant_id, project_id) %>% 
  dplyr::mutate(applied_to = project_id) %>% 
  tidyr::separate_rows(project_id, sep = " ") %>% 
  dplyr::mutate(project_id = unify_project_id_formats(project_id)) %>% 
  dplyr::distinct()

# project role: each project has its own column --> make into long data frame
project_roles_df <- applications %>% 
  dplyr::select(applicant_id, dplyr::starts_with("project_role")) %>% 
  tidyr::pivot_longer(dplyr::starts_with("project_role"), names_to = "project_id_unclean", values_to = "project_role") %>%
  dplyr::mutate(project_id = project_id_unclean %>% 
                  extract_ids_from_kobo_columnnames()) %>%
  dplyr::filter(project_role != "DNA") %>% 
  dplyr::distinct() %>% 
  dplyr::select(-project_id_unclean)

# personal informaton and skills
# select variables and rename columns 
personal_info_df <- applications %>% 
  dplyr::select(
  applicant_id,
  dplyr::starts_with("gender"),
  first_name,
  last_name,
  email = email_address,
  german_skills,
  dplyr::starts_with("rating"),
  dplyr::starts_with("motivation"),
  consent_privacy_policy,
  dplyr::starts_with("past_")
) %>% 
  dplyr::distinct() %>%
  janitor::clean_names() %>% 
  dplyr::rename_with(
    ~ stringr::str_replace_all(.x,
                               "rating_technologies_tools", "skills"),
    dplyr::starts_with("rating_technologies_tools")
  ) %>%
  dplyr::rename_with( ~ stringr::str_replace_all(.x, "rating_",
                                                 ""),
                      dplyr::starts_with("rating"))

# gender 
personal_info_df <-
  personal_info_df %>% dplyr::mutate(gender = dplyr::if_else(gender ==
                                                               "self_identification", NA_character_, gender))
personal_info_df$gender <- dplyr::coalesce(personal_info_df$gender,
                                           personal_info_df$gender_self_identification)

# join the data frames
cleaned_df <- project_ids_df %>% 
  dplyr::left_join(project_roles_df, by = c("applicant_id", "project_id")) %>% 
  dplyr::left_join(personal_info_df, by = "applicant_id")

# now finally filter for our project!
cleaned_df_filtered <- cleaned_df %>% dplyr::filter(project_id == .env$PROJECT_ID)

if (nrow(cleaned_df_filtered) == 0) {
  usethis::ui_warn(glue::glue("No applicants present after filtering for {project_id}. Did you specify the PROJECT_ID in the correct format?"))
}

cleaned_df_filtered <- cleaned_df_filtered %>%
  dplyr::select(
    applicant_id,
    gender,
    email,
    dplyr::ends_with("name"),
    dplyr::starts_with("project"),
    applied_to,
    dplyr::starts_with("past"),
    dplyr::starts_with("skills"),
    dplyr::starts_with("techniques"),
    dplyr::starts_with("topics"),
    dplyr::everything()
  )

# anonmyize and save
appl_anon <- cleaned_df_filtered %>% 
  select(-email, -first_name, -last_name)

anon_path <- here::here(PROJECT_FOLDER, "applications_anon.csv")
appl_anon %>% readr::write_csv(anon_path)

# mapping from email / name to applicant_id
mapping <- cleaned_df_filtered %>% 
  select(applicant_id, email, first_name, last_name)
mapping_path <- here::here(PROJECT_FOLDER, "mapping.csv")
mapping %>% readr::write_csv(mapping_path)

# google sheets upload 
gs_main_table <- cleaned_df_filtered %>% 
  dplyr::select(project_id, applicant_id, gender, applied_as = project_role, past_applications)
gs_main_table_path <- here::here(PROJECT_FOLDER, "google_sheets_main_table.csv")
gs_main_table %>% readr::write_csv(gs_main_table_path)


# knit report 
rmarkdown::render(here::here("templates/template_applications_report.Rmd"),
                  output_dir = PROJECT_FOLDER,
                  params = list(project_id = PROJECT_ID, anon_path = anon_path))