This function allows you to enter (one or more of the available) category names as an arguments.
Examples
clio_get_cat("finanz", list = FALSE, from = 1800, to = 1900)
#> Joining with `by = join_by(ccode, country.name, year)`
#> Joining with `by = join_by(ccode, country.name, year)`
#> Joining with `by = join_by(ccode, country.name, year)`
#> Joining with `by = join_by(ccode, country.name, year)`
#> # A tibble: 7,074 × 8
#> ccode country.name year `Exchange Rates to UK Pound` Exchange Rates to US …¹
#> <dbl> <chr> <dbl> <dbl> <dbl>
#> 1 40 Austria 1800 10.1 NA
#> 2 124 Canada 1800 1.08 NA
#> 3 156 China 1800 3.64 NA
#> 4 208 Denmark 1800 5.09 NA
#> 5 276 Germany 1800 11.9 3.00
#> 6 372 Ireland 1800 1.11 NA
#> 7 380 Italy 1800 5.24 NA
#> 8 428 Latvia 1800 3.78 NA
#> 9 528 Netherlands 1800 11.3 2.61
#> 10 616 Poland 1800 21.3 NA
#> # ℹ 7,064 more rows
#> # ℹ abbreviated name: ¹`Exchange Rates to US Dollar`
#> # ℹ 3 more variables: `Gold Standard` <dbl>,
#> # `Long-Term Government Bond Yield` <dbl>,
#> # `Total Gross Central Government Debt as a Percentage of GDP` <dbl>
clio_get_cat(c("agriculture", "environment"),
countries = "Netherlands",
mergetype = dplyr::inner_join, from = 1850, to = 1900)
#> Joining with `by = join_by(ccode, country.name, year)`
#> Joining with `by = join_by(ccode, country.name, year)`
#> Joining with `by = join_by(ccode, country.name, year)`
#> Joining with `by = join_by(ccode, country.name, year)`
#> Joining with `by = join_by(ccode, country.name, year)`
#> Joining with `by = join_by(ccode, country.name, year)`
#> Joining with `by = join_by(ccode, country.name, year)`
#> Joining with `by = join_by(ccode, country.name, year)`
#> Joining with `by = join_by(ccode, country.name, year)`
#> Joining with `by = join_by(ccode, country.name, year)`
#> Joining with `by = join_by(ccode, country.name, year)`
#> Joining with `by = join_by(ccode, country.name, year)`
#> Joining with `by = join_by(ccode, country.name, year)`
#> Joining with `by = join_by(ccode, country.name, year)`
#> Joining with `by = join_by(ccode, country.name, year)`
#> Joining with `by = join_by(ccode, country.name, year)`
#> # A tibble: 6 × 20
#> ccode country.name year `Cattle per Capita` `Cropland per Capita`
#> <dbl> <chr> <dbl> <dbl> <dbl>
#> 1 528 Netherlands 1850 0.397 0.235
#> 2 528 Netherlands 1860 0.386 0.229
#> 3 528 Netherlands 1870 0.389 0.223
#> 4 528 Netherlands 1880 0.362 0.217
#> 5 528 Netherlands 1890 0.335 0.211
#> 6 528 Netherlands 1900 0.320 0.204
#> # ℹ 15 more variables: `Goats per Capita` <dbl>, `Pasture per Capita` <dbl>,
#> # `Pigs per Capita` <dbl>, `Sheep per Capita` <dbl>, `Total Cattle` <dbl>,
#> # `Total Cropland` <dbl>, `Total Number of Goats` <dbl>,
#> # `Total Number of Pigs` <dbl>, `Total Number of Sheep` <dbl>,
#> # `Total Pasture` <dbl>, `Biodiversity - naturalness` <dbl>,
#> # `CO2 Emissions per Capita` <dbl>, `SO2 Emissions per Capita` <dbl>,
#> # `Total CO2 Emissions` <dbl>, `Total SO2 Emissions` <dbl>
clio_get_cat("Produzioni", from = 1700, list = FALSE, mergetype = dplyr::inner_join)
#> Joining with `by = join_by(ccode, country.name, year)`
#> Joining with `by = join_by(ccode, country.name, year)`
#> Joining with `by = join_by(ccode, country.name, year)`
#> Joining with `by = join_by(ccode, country.name, year)`
#> Joining with `by = join_by(ccode, country.name, year)`
#> Joining with `by = join_by(ccode, country.name, year)`
#> Joining with `by = join_by(ccode, country.name, year)`
#> Joining with `by = join_by(ccode, country.name, year)`
#> Joining with `by = join_by(ccode, country.name, year)`
#> Joining with `by = join_by(ccode, country.name, year)`
#> Joining with `by = join_by(ccode, country.name, year)`
#> # A tibble: 1,197 × 15
#> ccode country.name year `Aluminium Production` `Bauxite Production`
#> <dbl> <chr> <dbl> <dbl> <dbl>
#> 1 36 Australia 1880 0 0
#> 2 76 Brazil 1880 0 0
#> 3 156 China 1880 0 0
#> 4 356 India 1880 0 0
#> 5 364 Iran 1880 0 0
#> 6 398 Kazakhstan 1880 0 0
#> 7 643 Russia 1880 0 0
#> 8 724 Spain 1880 0 0
#> 9 840 United States 1880 0 0
#> 10 36 Australia 1881 0 0
#> # ℹ 1,187 more rows
#> # ℹ 10 more variables: `Copper Production` <dbl>, `Gold Production` <dbl>,
#> # `Iron Ore Production` <dbl>, `Lead Production` <dbl>,
#> # `Manganese Production` <dbl>, `Nickel Production` <dbl>,
#> # `Silver Production` <dbl>, `Tin Production` <dbl>,
#> # `Tungsten Production` <dbl>, `Zinc Production` <dbl>
clio_get(c("Tin Production", "income inequality"),
from = 1800,
countries = c("Netherlands", "Russia"))
#> Joining with `by = join_by(ccode, country.name, year)`
#> # A tibble: 225 × 5
#> ccode country.name year `Tin Production` `Income Inequality`
#> <dbl> <chr> <dbl> <dbl> <dbl>
#> 1 643 Russia 1800 0 NA
#> 2 643 Russia 1801 0 NA
#> 3 643 Russia 1802 0 NA
#> 4 643 Russia 1803 0 NA
#> 5 643 Russia 1804 0 NA
#> 6 643 Russia 1805 0 NA
#> 7 643 Russia 1806 0 NA
#> 8 643 Russia 1807 0 NA
#> 9 643 Russia 1808 0 NA
#> 10 643 Russia 1809 0 NA
#> # ℹ 215 more rows
clio_get_cat("labor relation")
#> Joining with `by = join_by(ccode, country.name, year)`
#> Joining with `by = join_by(ccode, country.name, year)`
#> Joining with `by = join_by(ccode, country.name, year)`
#> # A tibble: 6,357 × 7
#> ccode country.name year Number of Days Lost in Lab…¹ Number of Labour Dis…²
#> <dbl> <chr> <dbl> <dbl> <dbl>
#> 1 32 Argentina 1927 363492 56
#> 2 36 Australia 1927 1713581 441
#> 3 40 Austria 1927 686560 216
#> 4 56 Belgium 1927 1658836 186
#> 5 100 Bulgaria 1927 57196 23
#> 6 124 Canada 1927 152570 74
#> 7 156 China 1927 7622029 117
#> 8 208 Denmark 1927 119000 17
#> 9 233 Estonia 1927 3067 5
#> 10 246 Finland 1927 1528182 79
#> # ℹ 6,347 more rows
#> # ℹ abbreviated names: ¹`Number of Days Lost in Labour Disputes`,
#> # ²`Number of Labour Disputes`
#> # ℹ 2 more variables: `Number of Workers Involved in Labour Disputes` <dbl>,
#> # `Working week in manufacturing` <dbl>