Add Correlates of War war data to dyad-year or state-year data frame.
Source:R/add_cow_wars.R
add_cow_wars.Rdadd_cow_wars() allows you to Correlates of War data to a
dyad-year or state-year data frame
Arguments
- data
a data frame with appropriate peacesciencer attributes
- type
the type of war you want to add. Options include "inter" or "intra".
- intratype
the types of armed conflicts the user wants to consider, specified as a character vector. Options include "local issues" and "central control". Applicable only if
typeis "intra".
Value
add_cow_wars() takes a dyad-year or state-year data frame and
returns information about wars from either the inter-state or intra-state war
data set from the Correlates of War. The function works for state-year data
when the user wants information about extra-state wars or intra-state wars.
The function works for dyad-year data when the user wants information about
inter-state wars.
Details
Intra-state war data are coerced into true state-year data by first selecting the duplicate state-years on unique onsets, then whichever war was the deadliest. The inter-state war data work functionally the same way.
On intra-state wars: the primary_state is used to identify the government
principally fighting the domestic non-state actor over central control over
local issues. Internationalized civil wars are included in the data, but not
for outside actors that intervene on behalf of the government or rebel group.
Extra-state war functionality is not available right now as I try to figure out the demand for its use.
References
Dixon, Jeffrey, and Meredith Sarkees. 2016. A Guide to Intra-State Wars: An Examination of Civil Wars, 1816-2014. Thousand Oaks, CA: Sage.
Sarkees, Meredith Reid, and Frank Wheldon Wayman. 2010. Resort to War: A Data Guide to Inter-State, Extra-State, Intra-State, and Non-State Wars, 1816-2007. Washington DC: CQ Press.
Examples
# \donttest{
# just call `library(tidyverse)` at the top of the your script
library(magrittr)
create_stateyears(system = "cow") %>%
add_cow_wars(type = "intra", intratype = "central control")
#> Joining with `by = join_by(ccode, year)`
#> # A tibble: 17,121 × 13
#> ccode statenme year warnum warname wartype cowintraonset cowintraongoing
#> <dbl> <chr> <int> <dbl> <chr> <chr> <dbl> <dbl>
#> 1 2 United Stat… 1816 NA NA NA 0 0
#> 2 2 United Stat… 1817 NA NA NA 0 0
#> 3 2 United Stat… 1818 NA NA NA 0 0
#> 4 2 United Stat… 1819 NA NA NA 0 0
#> 5 2 United Stat… 1820 NA NA NA 0 0
#> 6 2 United Stat… 1821 NA NA NA 0 0
#> 7 2 United Stat… 1822 NA NA NA 0 0
#> 8 2 United Stat… 1823 NA NA NA 0 0
#> 9 2 United Stat… 1824 NA NA NA 0 0
#> 10 2 United Stat… 1825 NA NA NA 0 0
#> # ℹ 17,111 more rows
#> # ℹ 5 more variables: intnl <dbl>, outcome <dbl>, sideadeaths <dbl>,
#> # sidebdeaths <dbl>, intrawarnums <chr>
create_stateyears(system = "cow") %>%
add_cow_wars(type = "intra", intratype = "local issues")
#> Joining with `by = join_by(ccode, year)`
#> # A tibble: 17,121 × 13
#> ccode statenme year warnum warname wartype cowintraonset cowintraongoing
#> <dbl> <chr> <int> <dbl> <chr> <chr> <dbl> <dbl>
#> 1 2 United Stat… 1816 NA NA NA 0 0
#> 2 2 United Stat… 1817 NA NA NA 0 0
#> 3 2 United Stat… 1818 NA NA NA 0 0
#> 4 2 United Stat… 1819 NA NA NA 0 0
#> 5 2 United Stat… 1820 NA NA NA 0 0
#> 6 2 United Stat… 1821 NA NA NA 0 0
#> 7 2 United Stat… 1822 NA NA NA 0 0
#> 8 2 United Stat… 1823 NA NA NA 0 0
#> 9 2 United Stat… 1824 NA NA NA 0 0
#> 10 2 United Stat… 1825 NA NA NA 0 0
#> # ℹ 17,111 more rows
#> # ℹ 5 more variables: intnl <dbl>, outcome <dbl>, sideadeaths <dbl>,
#> # sidebdeaths <dbl>, intrawarnums <chr>
cow_ddy %>% add_cow_wars(type = "inter")
#> Joining with `by = join_by(ccode1, ccode2, year)`
#> # A tibble: 2,139,270 × 14
#> ccode1 ccode2 year cowinterongoing cowinteronset sidea1 sidea2 initiator1
#> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
#> 1 2 20 1920 0 0 NA NA NA
#> 2 2 20 1921 0 0 NA NA NA
#> 3 2 20 1922 0 0 NA NA NA
#> 4 2 20 1923 0 0 NA NA NA
#> 5 2 20 1924 0 0 NA NA NA
#> 6 2 20 1925 0 0 NA NA NA
#> 7 2 20 1926 0 0 NA NA NA
#> 8 2 20 1927 0 0 NA NA NA
#> 9 2 20 1928 0 0 NA NA NA
#> 10 2 20 1929 0 0 NA NA NA
#> # ℹ 2,139,260 more rows
#> # ℹ 6 more variables: initiator2 <dbl>, outcome1 <dbl>, outcome2 <dbl>,
#> # batdeath1 <dbl>, batdeath2 <dbl>, resume <dbl>
# }