Filter dyad-year data to include just politically relevant dyads
Source:R/filter_prd.R
filter_prd.Rdfilter_prd() filters a dyad-year data frame to just those that
are "politically relevant." This is useful for discarding unnecessary (and unwanted)
observations that just consume space in memory.
Value
filter_prd() takes a dyad-year data frame, assuming it has columns for
major power status and contiguity type, calculates whether the dyad is "politically
relevant", and subsets the data frame to just those observations.
Details
"Political relevance" can be calculated a few ways. Right now, the function considers only "direct" contiguity and Correlates of War major power status. You can employ maximalist definitions of "direct contiguity" to focus on just the land-contiguous. This function is inclusive of any type of contiguity relationship.
As of version 0.5, filter_prd() is a shortcut for add_contiguity()
and/or add_cow_majors() if the function is executed in the absence of the data needed to create
politically relevant dyads. See the example below for what this means.
References
Weede, Erich. 1976. "Overwhelming preponderance as a pacifying condition among contiguous Asian dyads." Journal of Conflict Resolution 20: 395-411.
Lemke, Douglas and William Reed. 2001. "The Relevance of Politically Relevant Dyads." Journal of Conflict Resolution 45(1): 126-144.
Examples
# \donttest{
# just call `library(tidyverse)` at the top of the your script
library(magrittr)
A <- cow_ddy %>% add_contiguity() %>% add_cow_majors() %>% filter_prd()
#> Joining with `by = join_by(ccode1, ccode2, year)`
A
#> # A tibble: 246,302 × 7
#> ccode1 ccode2 year conttype cowmaj1 cowmaj2 prd
#> <dbl> <dbl> <int> <dbl> <dbl> <dbl> <dbl>
#> 1 2 20 1920 1 1 0 1
#> 2 2 20 1921 1 1 0 1
#> 3 2 20 1922 1 1 0 1
#> 4 2 20 1923 1 1 0 1
#> 5 2 20 1924 1 1 0 1
#> 6 2 20 1925 1 1 0 1
#> 7 2 20 1926 1 1 0 1
#> 8 2 20 1927 1 1 0 1
#> 9 2 20 1928 1 1 0 1
#> 10 2 20 1929 1 1 0 1
#> # ℹ 246,292 more rows
# you can also use it as a shortcut for the other functions required
# to calculate politically relevant dyads.
B <- cow_ddy %>% filter_prd()
#> Joining with `by = join_by(ccode1, ccode2, year)`
B
#> # A tibble: 246,302 × 7
#> ccode1 ccode2 year conttype cowmaj1 cowmaj2 prd
#> <dbl> <dbl> <int> <dbl> <dbl> <dbl> <dbl>
#> 1 2 20 1920 1 1 0 1
#> 2 2 20 1921 1 1 0 1
#> 3 2 20 1922 1 1 0 1
#> 4 2 20 1923 1 1 0 1
#> 5 2 20 1924 1 1 0 1
#> 6 2 20 1925 1 1 0 1
#> 7 2 20 1926 1 1 0 1
#> 8 2 20 1927 1 1 0 1
#> 9 2 20 1928 1 1 0 1
#> 10 2 20 1929 1 1 0 1
#> # ℹ 246,292 more rows
identical(A,B)
#> [1] TRUE
# }