owidR

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 tibble (dataframe) 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")
##      titles                                                                                                                                        
## [1,] "Human rights protection vs. liberal democracy"                                                                                               
## [2,] "Countries with National Human Rights Institutions in compliance with the Paris Principles"                                                   
## [3,] "Human rights protection"                                                                                                                     
## [4,] "Human rights protection vs. GDP per capita"                                                                                                  
## [5,] "Proportion of countries that applied for accreditation as independent National Human Rights Institutions in compliance with Paris Principles"
##      chart_id                                                      
## [1,] "human-rights-protection-vs-liberal-democracy"                
## [2,] "countries-in-compliance-with-paris-principles"               
## [3,] "human-rights-protection"                                     
## [4,] "human-rights-protection-vs-gdp-per-capita"                   
## [5,] "countries-that-applied-for-accreditation-in-paris-principles"

Let’s use the human rights protection dataset.

rights <- owid("human-rights-protection")

rights
## # A tibble: 11,273 × 4
##    entity      code   year `Human rights protection`
##  * <chr>       <chr> <int>                     <dbl>
##  1 Afghanistan AFG    1946                     0.829
##  2 Afghanistan AFG    1947                     0.878
##  3 Afghanistan AFG    1948                     0.935
##  4 Afghanistan AFG    1949                     0.966
##  5 Afghanistan AFG    1950                     1.01 
##  6 Afghanistan AFG    1951                     1.09 
##  7 Afghanistan AFG    1952                     1.13 
##  8 Afghanistan AFG    1953                     1.18 
##  9 Afghanistan AFG    1954                     1.22 
## 10 Afghanistan AFG    1955                     1.22 
## # … with 11,263 more rows

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, `Human rights protection`, colour = entity)) +
  geom_line()

COVID-19 Data

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

covid <- owid_covid()

covid
## # A tibble: 218,713 × 67
##    iso_code continent locat…¹ date       total…² new_c…³ new_c…⁴ total…⁵ new_d…⁶
##    <chr>    <chr>     <chr>   <date>       <dbl>   <dbl>   <dbl>   <dbl>   <dbl>
##  1 AFG      Asia      Afghan… 2020-02-24       5       5  NA          NA      NA
##  2 AFG      Asia      Afghan… 2020-02-25       5       0  NA          NA      NA
##  3 AFG      Asia      Afghan… 2020-02-26       5       0  NA          NA      NA
##  4 AFG      Asia      Afghan… 2020-02-27       5       0  NA          NA      NA
##  5 AFG      Asia      Afghan… 2020-02-28       5       0  NA          NA      NA
##  6 AFG      Asia      Afghan… 2020-02-29       5       0   0.714      NA      NA
##  7 AFG      Asia      Afghan… 2020-03-01       5       0   0.714      NA      NA
##  8 AFG      Asia      Afghan… 2020-03-02       5       0   0          NA      NA
##  9 AFG      Asia      Afghan… 2020-03-03       5       0   0          NA      NA
## 10 AFG      Asia      Afghan… 2020-03-04       5       0   0          NA      NA
## # … with 218,703 more rows, 58 more variables: new_deaths_smoothed <dbl>,
## #   total_cases_per_million <dbl>, new_cases_per_million <dbl>,
## #   new_cases_smoothed_per_million <dbl>, total_deaths_per_million <dbl>,
## #   new_deaths_per_million <dbl>, new_deaths_smoothed_per_million <dbl>,
## #   reproduction_rate <dbl>, icu_patients <dbl>,
## #   icu_patients_per_million <dbl>, hosp_patients <dbl>,
## #   hosp_patients_per_million <dbl>, weekly_icu_admissions <dbl>, …

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

edomt / owidR