README.md

whoville

R build
status

The goal of whoville is to provide a package country reference data published by the World Health Organization, United Nations and World Bank easily accessible in R. At the moment, this is a reference dataset and set of functions to work with country codes and names, allowing easy conversion between names and codes as well as easy access to region codes, WHO member status, and other published country metadata. This is used to assist the work of the WHO’s Division of Data, Analytics, and Delivery for Impact’s Global Programme of Work 13.

Functions

Some functions are built to help you turn names or country codes into ISO3 codes:

Other functions encourage a tidy R workflow where ISO3 codes are used as the unique identifier for each country:

All of these functions are built on top of the countries data frame also exported with the package and developed off of public datasets provided by the World Health Organization and United Nations. Details available through ?countries.

Installation

You can install whoville from Github with:

devtools::install_github("caldwellst/whoville")

Usage

If we have an unclean data frame with country names, we can use names_to_codes() to match these to ISO3 codes. The function matches the names vector across all possible names found in the countries data frame. ISO3 codes for exact matches are always returned, but the user has specific options for non-exact matches. They can be fuzzy matched (the default), always made NA, or require user input to confirm fuzzy matching results. Fuzzy matches always return a message to the user on the confirmed match. More details available through ?names_to_codes.

library(whoville)

names_to_code(c("Venezuela", "Arentina", "afghanist"))
#> 'arentina' has no exact match. Closest name found was 'argentina'.
#> 'afghanist' has no exact match. Closest name found was 'afghanistan'.
#> [1] "VEN" "ARG" "AFG"

Since these functions are vectorized, we can easily use them in a normal workflow, especially if we’re using the tidyverse. Below, we can clean up our tidy names and get the correct UN region and income group for our countries, as well as its name in Chinese:

library(dplyr)
df <- data.frame(c_names = c("Venezuela", "Arentina", "afghanist"))

df %>%
  mutate(iso3 = names_to_code(c_names),
         un_region = iso3_to_regions(iso3, region = "un_region"),
         wb_ig = iso3_to_regions(iso3, region = "wb_ig"),
         name_zh = iso3_to_names(iso3, org = "un", language = "zh"))
#>     c_names iso3 un_region wb_ig                name_zh
#> 1 Venezuela  VEN        19   UMC 委内瑞拉玻利瓦尔共和国
#> 2  Arentina  ARG        19   UMC                 阿根廷
#> 3 afghanist  AFG       142   LIC                 阿富汗


caldwellst/whotilities documentation built on April 8, 2021, 3:52 a.m.