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 rowsggplot2 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