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An R Package for Importing Data from Our World in Data

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piersyork/owidR

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owidR

CRAN downloads CRAN status R-CMD-check

This package acts as an interface to Our World in Data datasets, allowing for an easy way to search through data used in over 3,000 charts and load them into the R environment.

Installation

To install from CRAN:

install.packages("owidR")

To install the development version from GitHub:

devtools::install_github("piersyork/owidR")

Using the package

The main function in owidR is owid(), which takes a chart id and returns a data.table of the corresponding OWID dataset. To search for chart ids you can use owid_search() to list all the chart ids that match a keyword or regular expression.

Example

Lets use the core functions to get data on how human rights have changed over time. First by searching for charts on human rights.

library(owidR)

owid_search("human rights")
##      chart_id                                                                   
## [1,] "human-rights-index-vs-electoral-democracy-index"                          
## [2,] "cases-of-killed-human-rights-defenders-journalists-trade-unionists"       
## [3,] "countries-with-independent-national-human-rights-institution"             
## [4,] "distribution-human-rights-index-vdem"                                     
## [5,] "human-rights-index-vdem"                                                  
## [6,] "human-rights-index-population-weighted"                                   
## [7,] "human-rights-index-vs-gdp-per-capita"                                     
## [8,] "share-countries-accredited-independent-national-human-rights-institutions"
##      title                                                                              
## [1,] "Human rights index vs. electoral democracy index"                                 
## [2,] "Confirmed killings of human rights defenders, journalists and trade unionists"    
## [3,] "Countries with accredited independent national human rights institutions"         
## [4,] "Distribution of human rights index"                                               
## [5,] "Human rights index"                                                               
## [6,] "Human rights index"                                                               
## [7,] "Human rights index vs. GDP per capita"                                            
## [8,] "Share of countries with accredited independent national human rights institutions"

Let’s use the v-dem human rights index dataset.

rights <- owid("human-rights-index-vdem")

rights
## Key: <entity, code, year>
##             entity   code  year civ_libs_vdem_owid civ_libs_vdem_high_owid
##             <char> <char> <int>              <num>                   <num>
##     1: Afghanistan    AFG  1789              0.125                   0.169
##     2: Afghanistan    AFG  1790              0.125                   0.169
##     3: Afghanistan    AFG  1791              0.125                   0.169
##     4: Afghanistan    AFG  1792              0.125                   0.169
##     5: Afghanistan    AFG  1793              0.125                   0.169
##    ---                                                                    
## 33373:    Zimbabwe    ZWE  2018              0.428                   0.473
## 33374:    Zimbabwe    ZWE  2019              0.403                   0.456
## 33375:    Zimbabwe    ZWE  2020              0.413                   0.469
## 33376:    Zimbabwe    ZWE  2021              0.395                   0.443
## 33377:    Zimbabwe    ZWE  2022              0.388                   0.432
##        civ_libs_vdem_low_owid
##                         <num>
##     1:                  0.089
##     2:                  0.089
##     3:                  0.089
##     4:                  0.089
##     5:                  0.089
##    ---                       
## 33373:                  0.381
## 33374:                  0.361
## 33375:                  0.373
## 33376:                  0.344
## 33377:                  0.331

ggplot2 makes it easy to visualise our data.

library(ggplot2)
library(dplyr)

rights |> 
  filter(entity %in% c("United Kingdom", "France", "United States")) |> 
  ggplot(aes(year, civ_libs_vdem_owid, colour = entity)) +
  geom_line()

COVID-19 Data

You can quickly download world covid-19 data, including vaccination rates, using owid_covid().

covid <- owid_covid()

str(covid)
## Classes 'owid', 'data.table' and 'data.frame':   351765 obs. of  67 variables:
##  $ iso_code                                  : chr  "AFG" "AFG" "AFG" "AFG" ...
##  $ continent                                 : chr  "Asia" "Asia" "Asia" "Asia" ...
##  $ location                                  : chr  "Afghanistan" "Afghanistan" "Afghanistan" "Afghanistan" ...
##  $ date                                      : IDate, format: "2020-01-03" "2020-01-04" ...
##  $ total_cases                               : num  NA NA NA NA NA NA NA NA NA NA ...
##  $ new_cases                                 : num  0 0 0 0 0 0 0 0 0 0 ...
##  $ new_cases_smoothed                        : num  NA NA NA NA NA 0 0 0 0 0 ...
##  $ total_deaths                              : num  NA NA NA NA NA NA NA NA NA NA ...
##  $ new_deaths                                : num  0 0 0 0 0 0 0 0 0 0 ...
##  $ new_deaths_smoothed                       : num  NA NA NA NA NA 0 0 0 0 0 ...
##  $ total_cases_per_million                   : num  NA NA NA NA NA NA NA NA NA NA ...
##  $ new_cases_per_million                     : num  0 0 0 0 0 0 0 0 0 0 ...
##  $ new_cases_smoothed_per_million            : num  NA NA NA NA NA 0 0 0 0 0 ...
##  $ total_deaths_per_million                  : num  NA NA NA NA NA NA NA NA NA NA ...
##  $ new_deaths_per_million                    : num  0 0 0 0 0 0 0 0 0 0 ...
##  $ new_deaths_smoothed_per_million           : num  NA NA NA NA NA 0 0 0 0 0 ...
##  $ reproduction_rate                         : num  NA NA NA NA NA NA NA NA NA NA ...
##  $ icu_patients                              : num  NA NA NA NA NA NA NA NA NA NA ...
##  $ icu_patients_per_million                  : num  NA NA NA NA NA NA NA NA NA NA ...
##  $ hosp_patients                             : num  NA NA NA NA NA NA NA NA NA NA ...
##  $ hosp_patients_per_million                 : num  NA NA NA NA NA NA NA NA NA NA ...
##  $ weekly_icu_admissions                     : num  NA NA NA NA NA NA NA NA NA NA ...
##  $ weekly_icu_admissions_per_million         : num  NA NA NA NA NA NA NA NA NA NA ...
##  $ weekly_hosp_admissions                    : num  NA NA NA NA NA NA NA NA NA NA ...
##  $ weekly_hosp_admissions_per_million        : num  NA NA NA NA NA NA NA NA NA NA ...
##  $ total_tests                               : num  NA NA NA NA NA NA NA NA NA NA ...
##  $ new_tests                                 : num  NA NA NA NA NA NA NA NA NA NA ...
##  $ total_tests_per_thousand                  : num  NA NA NA NA NA NA NA NA NA NA ...
##  $ new_tests_per_thousand                    : num  NA NA NA NA NA NA NA NA NA NA ...
##  $ new_tests_smoothed                        : num  NA NA NA NA NA NA NA NA NA NA ...
##  $ new_tests_smoothed_per_thousand           : num  NA NA NA NA NA NA NA NA NA NA ...
##  $ positive_rate                             : num  NA NA NA NA NA NA NA NA NA NA ...
##  $ tests_per_case                            : num  NA NA NA NA NA NA NA NA NA NA ...
##  $ tests_units                               : chr  "" "" "" "" ...
##  $ total_vaccinations                        : num  NA NA NA NA NA NA NA NA NA NA ...
##  $ people_vaccinated                         : num  NA NA NA NA NA NA NA NA NA NA ...
##  $ people_fully_vaccinated                   : num  NA NA NA NA NA NA NA NA NA NA ...
##  $ total_boosters                            : num  NA NA NA NA NA NA NA NA NA NA ...
##  $ new_vaccinations                          : num  NA NA NA NA NA NA NA NA NA NA ...
##  $ new_vaccinations_smoothed                 : num  NA NA NA NA NA NA NA NA NA NA ...
##  $ total_vaccinations_per_hundred            : num  NA NA NA NA NA NA NA NA NA NA ...
##  $ people_vaccinated_per_hundred             : num  NA NA NA NA NA NA NA NA NA NA ...
##  $ people_fully_vaccinated_per_hundred       : num  NA NA NA NA NA NA NA NA NA NA ...
##  $ total_boosters_per_hundred                : num  NA NA NA NA NA NA NA NA NA NA ...
##  $ new_vaccinations_smoothed_per_million     : num  NA NA NA NA NA NA NA NA NA NA ...
##  $ new_people_vaccinated_smoothed            : num  NA NA NA NA NA NA NA NA NA NA ...
##  $ new_people_vaccinated_smoothed_per_hundred: num  NA NA NA NA NA NA NA NA NA NA ...
##  $ stringency_index                          : num  0 0 0 0 0 0 0 0 0 0 ...
##  $ population_density                        : num  54.4 54.4 54.4 54.4 54.4 ...
##  $ median_age                                : num  18.6 18.6 18.6 18.6 18.6 18.6 18.6 18.6 18.6 18.6 ...
##  $ aged_65_older                             : num  2.58 2.58 2.58 2.58 2.58 ...
##  $ aged_70_older                             : num  1.34 1.34 1.34 1.34 1.34 ...
##  $ gdp_per_capita                            : num  1804 1804 1804 1804 1804 ...
##  $ extreme_poverty                           : num  NA NA NA NA NA NA NA NA NA NA ...
##  $ cardiovasc_death_rate                     : num  597 597 597 597 597 ...
##  $ diabetes_prevalence                       : num  9.59 9.59 9.59 9.59 9.59 9.59 9.59 9.59 9.59 9.59 ...
##  $ female_smokers                            : num  NA NA NA NA NA NA NA NA NA NA ...
##  $ male_smokers                              : num  NA NA NA NA NA NA NA NA NA NA ...
##  $ handwashing_facilities                    : num  37.7 37.7 37.7 37.7 37.7 ...
##  $ hospital_beds_per_thousand                : num  0.5 0.5 0.5 0.5 0.5 0.5 0.5 0.5 0.5 0.5 ...
##  $ life_expectancy                           : num  64.8 64.8 64.8 64.8 64.8 ...
##  $ human_development_index                   : num  0.511 0.511 0.511 0.511 0.511 0.511 0.511 0.511 0.511 0.511 ...
##  $ population                                : num  41128772 41128772 41128772 41128772 41128772 ...
##  $ excess_mortality_cumulative_absolute      : num  NA NA NA NA NA NA NA NA NA NA ...
##  $ excess_mortality_cumulative               : num  NA NA NA NA NA NA NA NA NA NA ...
##  $ excess_mortality                          : num  NA NA NA NA NA NA NA NA NA NA ...
##  $ excess_mortality_cumulative_per_million   : num  NA NA NA NA NA NA NA NA NA NA ...
##  - attr(*, ".internal.selfref")=<externalptr>

To-do

  • Add function to load multiple country datasets into one dataframe
  • Add caching of data (inc. backend)
  • Remove interactive plotting to reduce dependencies
  • Create way to import owid explorers

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An R Package for Importing Data from Our World in Data

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