Add Correlates of War state system codes to your data with Gleditsch-Ward state codes.
Source:R/add_ccode_to_gw.R
add_ccode_to_gw.Rdadd_ccode_to_gw() allows you to match, as well as one can, Correlates of War system membership data
with Gleditsch-Ward system data.
Value
add_ccode_to_gw() takes a (dyad-year, leader-year, leader-dyad-year, state-year) data frame that already has Gleditsch-Ward
state system codes and adds their corollary Correlates of War codes.
Details
The data-raw directory on the project's Github contains
more information about the underlying data that assists in merging in
these codes.
The user will invariably need to be careful and ask why they want these data included. The issue here is that both have a different composition and the merging process will not (and cannot) be perfect. We can note that a case like Gran Colombia is not too difficult to handle (i.e. CoW does not have this entity and none of the splinter states conflict with CoW's coding). However, there is greater weirdness with a case like the unification of West Germany and East Germany. Herein, Correlates of War treats the unification as the reappearance of the original Germany whereas Gleditsch-Ward treat the unification as an incorporation of East Germany into West Germany. The script will not create state-year or dyad-year duplicates for the Gleditsch-Ward codes. The size of the original data remain unchanged. However, there will be some year duplicates for various Correlates of War codes (prominently Serbia and Yugoslavia in 2006). Use with care. You can also use the countrycode package. Whether you use this function or the countrycode package, do not do this kind of merging without assessing the output.
Examples
# \donttest{
# just call `library(tidyverse)` at the top of the your script
library(magrittr)
create_dyadyears(system = "gw") %>% add_ccode_to_gw()
#> Joining with `by = join_by(gwcode1, gwcode2, year)`
#> # A tibble: 2,089,826 × 5
#> gwcode1 gwcode2 year ccode1 ccode2
#> <dbl> <dbl> <dbl> <dbl> <dbl>
#> 1 2 20 1867 2 NA
#> 2 2 20 1868 2 NA
#> 3 2 20 1869 2 NA
#> 4 2 20 1870 2 NA
#> 5 2 20 1871 2 NA
#> 6 2 20 1872 2 NA
#> 7 2 20 1873 2 NA
#> 8 2 20 1874 2 NA
#> 9 2 20 1875 2 NA
#> 10 2 20 1876 2 NA
#> # ℹ 2,089,816 more rows
create_stateyears(system = 'gw') %>% add_ccode_to_gw()
#> Joining with `by = join_by(gwcode, year)`
#> # A tibble: 18,637 × 4
#> gwcode statename year ccode
#> <dbl> <chr> <dbl> <dbl>
#> 1 2 United States of America 1816 2
#> 2 2 United States of America 1817 2
#> 3 2 United States of America 1818 2
#> 4 2 United States of America 1819 2
#> 5 2 United States of America 1820 2
#> 6 2 United States of America 1821 2
#> 7 2 United States of America 1822 2
#> 8 2 United States of America 1823 2
#> 9 2 United States of America 1824 2
#> 10 2 United States of America 1825 2
#> # ℹ 18,627 more rows
# }