Add Correlates of War major power information to a data frame

add_cow_majors() allows you to add Correlates of War major power variables to a dyad-year, leader-year, leader dyad-year, or state-year data frame.

Usage

add_cow_majors(data, mry = TRUE)

Arguments

data

a data frame with appropriate peacesciencer attributes

mry

logical, defaults to TRUE. If TRUE, the data carry forward the identity of the major powers to the most recently concluded calendar year. If FALSE, the panel honors the right bound of the data's temporal domain and creates NAs for observations past it.

Value

add_cow_majors() takes a data frame and adds information about major power status for the given state or dyad in that year. If the data are dyad-year (or leader dyad-year), the function returns two columns for whether the first state (i.e. ccode1) or the second state (i.e. ccode2) are major powers in the given year, according to the Correlates of War. 1 = is a major power. 0 = is not a major power. If the data are state-year (or leader-year), the functions returns just one column (cowmaj) for whether the state was a major power in a given state-year.

Details

Be mindful that the data are fundamentally state-year and that extensions to leader-level data should be understood as approximations for leaders in a given state-year.

The mry argument works on an informal assumption that the composition of the major powers are unchanged since the most recent data update. It simply carries forward the most recent observation from the end of the data and assumes there are no new major powers to note.

References

Correlates of War Project. 2017. "State System Membership List, v2016." Online, https://correlatesofwar.org/data-sets/state-system-membership/

Author

Steven V. Miller

Examples

# just call `library(tidyverse)` at the top of the your script
library(magrittr)

cow_ddy %>% add_cow_majors()
#> # A tibble: 2,214,930 × 5
#>    ccode1 ccode2  year cowmaj1 cowmaj2
#>     <dbl>  <dbl> <int>   <dbl>   <dbl>
#>  1      2     20  1920       1       0
#>  2      2     20  1921       1       0
#>  3      2     20  1922       1       0
#>  4      2     20  1923       1       0
#>  5      2     20  1924       1       0
#>  6      2     20  1925       1       0
#>  7      2     20  1926       1       0
#>  8      2     20  1927       1       0
#>  9      2     20  1928       1       0
#> 10      2     20  1929       1       0
#> # ℹ 2,214,920 more rows