A script that uses purrr to automate the wrangling and cleaning of economic data

econ_data_wrangle_clean.R

library(tidyverse)
library(rio)
library(rvest)
library(janitor)


# Rcode to go and fetch country codes
country_codes <- read_html("http://web.stanford.edu/~chadj/countrycodes6.3") %>% 
  html_text() %>% 
  str_extract_all("[A-Z]{3}") %>% 
  unlist() %>% 
  .[order(.)]

# Save the country codes to a .csv file that we will always have
country_codes %>% 
  {tibble(codes = .)} %>% 
  write_csv(path = here::here("data/country_codes.csv"))

# create a little function we want to utilize in our map()
read_and_clean <- function(country_code = "USA"){
  dat_url <- paste0("http://www.stanford.edu/~chadj/snapshots/", country_code, ".xls")
  import(dat_url, skip = 9) %>% 
    clean_names() %>% 
    na.omit() %>% 
    filter(population != "NaN") %>% 
    mutate_all(as.numeric) %>% 
    mutate(country = country_code)
}

# We want the function to run despite running into errors
possibly_read_and_clean <- possibly(read_and_clean, otherwise = NULL)

# Remove all our NULLS and turn it to one large data.frame
final_clean_dat <- purrr::map(country_codes, ~ possibly_read_and_clean(.x)) %>% 
  compact() %>% 
  map_df(bind_rows)

# Save and store this data.frame
write_csv(final_clean_dat, here::here("data/final_gdp_data.csv"))