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add_ccode_to_gw() allows you to match, as well as one can, Correlates of War system membership data with Gleditsch-Ward system data.

Usage

add_ccode_to_gw(data)

Arguments

data

a data frame with appropriate peacesciencer attributes

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.

Author

Steven V. Miller

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
# }