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.
To install from CRAN:
install.packages("owidR")To install the development version from GitHub:
devtools::install_github("piersyork/owidR")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.
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 Score vs. Political regime type"
## [2,] "Political regime type vs. Human Rights Score"
## [3,] "Countries with National Human Rights Institutions in compliance with the Paris Principles"
## [4,] "Human Rights Score vs. GDP per capita"
## [5,] "Human Rights Scores"
## [6,] "Human Rights Violations"
## [7,] "Proportion of countries that applied for accreditation as independent National Human Rights Institutions in compliance with Paris Principles"
## chart_id
## [1,] "human-rights-score-vs-political-regime-type"
## [2,] "political-regime-type-vs-human-rights-score"
## [3,] "countries-in-compliance-with-paris-principles"
## [4,] "human-rights-score-vs-gdp-per-capita"
## [5,] "human-rights-scores"
## [6,] "human-rights-violations"
## [7,] "countries-that-applied-for-accreditation-in-paris-principles"Let’s use the human rights scores dataset.
rights <- owid("human-rights-scores")
rights
## # A tibble: 11,717 × 4
## entity code year `Human Rights Score (Schnakenberg & Fariss, 2014; F…`
## * <chr> <chr> <int> <dbl>
## 1 Afghanistan AFG 1946 0.690
## 2 Afghanistan AFG 1947 0.740
## 3 Afghanistan AFG 1948 0.787
## 4 Afghanistan AFG 1949 0.817
## 5 Afghanistan AFG 1950 0.851
## 6 Afghanistan AFG 1951 0.909
## 7 Afghanistan AFG 1952 0.938
## 8 Afghanistan AFG 1953 0.988
## 9 Afghanistan AFG 1954 1.01
## 10 Afghanistan AFG 1955 1.01
## # … with 11,707 more rowsowid_plot() makes it easy to visualise an owid dataset, plotting the
first value column of an owid dataset. By default the mean score across
all countries is plotted.
owid_plot(rights)
Use summarise = FALSE to show individual countries instead of the mean
score. Unless a vector of entities is specified using the filter
argument 9 random entities will be plotted. If the data is not a
time-series then a bar chart of the entities values will be plotted.
owid_plot(rights, summarise = FALSE, filter = c("North Korea", "South Korea", "France", "United Kingdom", "United States"))
owid_map() makes it easy to create a choropleth world map of datasets
that contain country level data. The Entities of the owid data must be
country names. By default the most recent year will be plotted, use the
year argument to plot a different year.
owid_map(rights)
You can quickly download world covid-19 data, including vaccination
rates, using owid_covid().
covid <- owid_covid()
covid
## # A tibble: 161,553 × 67
## iso_code continent location date total_cases new_cases new_cases_smoot…
## <chr> <chr> <chr> <date> <dbl> <dbl> <dbl>
## 1 AFG Asia Afghani… 2020-02-24 5 5 NA
## 2 AFG Asia Afghani… 2020-02-25 5 0 NA
## 3 AFG Asia Afghani… 2020-02-26 5 0 NA
## 4 AFG Asia Afghani… 2020-02-27 5 0 NA
## 5 AFG Asia Afghani… 2020-02-28 5 0 NA
## 6 AFG Asia Afghani… 2020-02-29 5 0 0.714
## 7 AFG Asia Afghani… 2020-03-01 5 0 0.714
## 8 AFG Asia Afghani… 2020-03-02 5 0 0
## 9 AFG Asia Afghani… 2020-03-03 5 0 0
## 10 AFG Asia Afghani… 2020-03-04 5 0 0
## # … with 161,543 more rows, and 60 more variables: total_deaths <dbl>,
## # new_deaths <dbl>, 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>, …