This function is used to execute a query to clio-infra, and returns a data.frame containing the required variables, countries and years specified by the user.
Usage
clio_get(
variables,
countries,
from,
to,
list = FALSE,
mergetype = dplyr::full_join
)Arguments
- variables
The variables you want to obtain (provided to you by
Clio::clio_overview())- countries
If left empty, all countries.
- from
Start year
- to
End year
- list
Defaults to FALSE. If TRUE, returns a list of each variable
- mergetype
Defaults to full_join. Can be set to inner_join, outer_join, left_join, etc.
Examples
clio_get(c("infant mortality", "zinc production"))
#> Joining with `by = join_by(ccode, country.name, year)`
#> # A tibble: 14,570 × 5
#> ccode country.name year `Infant Mortality` `Zinc Production`
#> <dbl> <chr> <dbl> <dbl> <dbl>
#> 1 191 Croatia 1810 175 NA
#> 2 246 Finland 1810 200. 0
#> 3 826 United Kingdom 1810 141 0
#> 4 40 Austria 1820 188. 0
#> 5 191 Croatia 1820 150 NA
#> 6 246 Finland 1820 198. 0
#> 7 250 France 1820 182 0
#> 8 528 Netherlands 1820 179 NA
#> 9 826 United Kingdom 1820 153 0
#> 10 40 Austria 1830 251. 0
#> # ℹ 14,560 more rows
clio_get(c("biodiversity - naturalness", "xecutive Constraints (XCONST)"),
from = 1850, to = 1900,
countries = c("Armenia", "Azerbaijan"))
#> Joining with `by = join_by(ccode, country.name, year)`
#> # A tibble: 12 × 5
#> ccode country.name year `Biodiversity - naturalness` Executive Constraints…¹
#> <dbl> <chr> <dbl> <dbl> <dbl>
#> 1 51 Armenia 1850 0.903 NA
#> 2 31 Azerbaijan 1850 0.908 NA
#> 3 51 Armenia 1860 0.899 NA
#> 4 31 Azerbaijan 1860 0.900 NA
#> 5 51 Armenia 1870 0.896 NA
#> 6 31 Azerbaijan 1870 0.892 NA
#> 7 51 Armenia 1880 0.892 NA
#> 8 31 Azerbaijan 1880 0.883 NA
#> 9 51 Armenia 1890 0.888 NA
#> 10 31 Azerbaijan 1890 0.873 NA
#> 11 51 Armenia 1900 0.884 NA
#> 12 31 Azerbaijan 1900 0.863 NA
#> # ℹ abbreviated name: ¹`Executive Constraints (XCONST)`
clio_get(c("Zinc production", "Gold production"),
from = 1800, to = 1920,
countries = c("Botswana", "Zimbabwe",
mergetype = dplyr::inner_join))
#> Joining with `by = join_by(ccode, country.name, year)`
#> # A tibble: 242 × 5
#> ccode country.name year `Zinc Production` `Gold Production`
#> <dbl> <chr> <dbl> <dbl> <dbl>
#> 1 72 Botswana 1800 NA 0
#> 2 716 Zimbabwe 1800 NA 0
#> 3 72 Botswana 1801 NA 0
#> 4 716 Zimbabwe 1801 NA 0
#> 5 72 Botswana 1802 NA 0
#> 6 716 Zimbabwe 1802 NA 0
#> 7 72 Botswana 1803 NA 0
#> 8 716 Zimbabwe 1803 NA 0
#> 9 72 Botswana 1804 NA 0
#> 10 716 Zimbabwe 1804 NA 0
#> # ℹ 232 more rows