data-raw/corruption/corruption.R

In wilkelab/practicalgg: Practical ggplot2

library(here)
library(dplyr)
library(tidyr)
library(readr)
library(countrycode)

df_cpi <- read_delim(
  here("data-raw", "corruption", "CPI_raw.txt"),
  delim = "\t", col_types = "iccccccc"
)

df_cpi %>% select(-`2016 Rank`) %>%
  gather(year, score, `2016 Score`:`2012 Score`) %>%
  extract(year, "year", regex = "(.+) Score") %>%
  mutate(
    year = as.numeric(year),
    score = as.numeric(score),
    iso3c = countrycode(Country, "country.name", "iso3c")
  ) %>%
  rename(
    country = Country,
    region = Region,
    cpi = score
  ) -> df_cpi_tidy


df_hdi <- read_csv(
  here("data-raw", "corruption", "HDI_raw.csv"),
  col_types = "icd-d-d-d-d-d-d-d-d-d-d-d-d-d-d-d-d-d-d-d-d-d-d-d-d-d-", 
  locale = locale(encoding = "ISO-8859-1")
)

df_hdi %>% select(-`HDI Rank (2015)`) %>%
  gather(year, score, `1990`:`2015`) %>%
  mutate(
    year = as.numeric(year),
    iso3c = countrycode(Country, "country.name", "iso3c")
  ) %>%
  rename(
    hdi = score
  ) %>% 
  select(-Country) -> df_hdi_tidy

corruption <- left_join(df_cpi_tidy, df_hdi_tidy, by = c("iso3c", "year")) %>%
  filter(year != 2016)
usethis::use_data(corruption, overwrite = TRUE)