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whittle_conflicts_jds() is in a class of do-it-yourself functions for coercing (i.e. "whittling") conflict-year data with cross-sectional units to unique conflict-year data by cross-sectional unit. The inspiration here is clearly the problem of whittling dyadic dispute-year data into true dyad-year data (like in the Gibler-Miller-Little conflict data). This particular function will just drop something, as a kind of nuclear option.

Usage

whittle_conflicts_jds(data)

wc_jds(...)

Arguments

data

a data frame with a declared conflict attribute type.

...

optional, only to make the shortcut work

Value

whittle_conflicts_jds() takes a dyad-year data frame or leader-dyad-year data frame with a declared conflict attribute type and, grouping by the dyad and year, returns just those observations that have the lowest start month.

Details

Dyads are capable of having multiple disputes in a given year, which can create a problem for merging into a complete dyad-year data frame. Consider the case of France and Italy in 1860, which had three separate dispute onsets that year (MID#0112, MID#0113, MID#0306), as illustrative of the problem. The default process in peacesciencer employs several rules to whittle down these duplicate dyad-years for merging into a dyad-year data frame. These are available in add_cow_mids() and add_gml_mids().

This really should be the absolute last exclusion rules a researcher uses. It's a "nuclear option", if you will. Assuming you've run other case exclusion rules to isolate onsets and severe disputes, what remains at the end should be duplicates that are functionally equivalent observations. Your data cannot have duplicates, and these remaining observations are basically the same. Therefore, just drop something.

wc_jds() is a simple, less wordy, shortcut for the same function.

References

Miller, Steven V. 2021. "How peacesciencer Coerces Dispute-Year Data into Dyad-Year Data". URL: http://svmiller.com/peacesciencer/articles/coerce-dispute-year-dyad-year.html

Author

Steven V. Miller

Examples


# \donttest{
# just call `library(tidyverse)` at the top of the your script
library(magrittr)
gml_dirdisp %>% whittle_conflicts_onsets() %>% whittle_conflicts_jds()
#> # A tibble: 9,284 × 40
#>    dispnum ccode1 ccode2  year midongoing midonset sidea1 sidea2 revstate1
#>      <dbl>  <dbl>  <dbl> <dbl>      <dbl>    <dbl>  <dbl>  <dbl>     <dbl>
#>  1    2968      2     20  1979          1        1      0      1         0
#>  2    3900      2     20  1989          1        1      0      1         0
#>  3    3972      2     20  1991          1        1      1      0         1
#>  4    4183      2     20  1997          1        1      0      1         0
#>  5    1665      2     40  1921          1        1      1      0         1
#>  6    1677      2     40  1933          1        1      1      0         1
#>  7    1677      2     40  1934          1        0      1      0         1
#>  8     246      2     40  1960          1        1      1      0         1
#>  9     246      2     40  1961          1        0      1      0         1
#> 10      61      2     40  1962          1        1      1      0         1
#> # … with 9,274 more rows, and 31 more variables: revstate2 <dbl>,
#> #   revtype11 <dbl>, revtype12 <dbl>, revtype21 <dbl>, revtype22 <dbl>,
#> #   fatality1 <dbl>, fatality2 <dbl>, fatalpre1 <dbl>, fatalpre2 <dbl>,
#> #   hiact1 <dbl>, hiact2 <dbl>, hostlev1 <dbl>, hostlev2 <dbl>, orig1 <dbl>,
#> #   orig2 <dbl>, hiact <dbl>, hostlev <dbl>, mindur <dbl>, maxdur <dbl>,
#> #   outcome <dbl>, settle <dbl>, fatality <dbl>, fatalpre <dbl>, stmon <dbl>,
#> #   endmon <dbl>, recip <dbl>, numa <dbl>, numb <dbl>, ongo2010 <dbl>, …

cow_mid_dirdisps %>% whittle_conflicts_onsets() %>% whittle_conflicts_jds()
#> # A tibble: 10,234 × 19
#>    dispnum ccode1 ccode2  year dispongoing disponset sidea1 sidea2 fatality1
#>      <dbl>  <dbl>  <dbl> <dbl>       <dbl>     <dbl>  <dbl>  <dbl>     <dbl>
#>  1    2968      2     20  1979           1         1      0      1         0
#>  2    3900      2     20  1989           1         1      0      1         0
#>  3    3972      2     20  1991           1         1      1      0         0
#>  4    4183      2     20  1997           1         1      0      1         0
#>  5    1665      2     40  1921           1         1      1      0         0
#>  6    1677      2     40  1933           1         1      1      0         0
#>  7    1677      2     40  1934           1         0      1      0         0
#>  8     246      2     40  1960           1         1      0      1         0
#>  9     246      2     40  1961           1         0      0      1         0
#> 10      61      2     40  1962           1         1      1      0         0
#> # … with 10,224 more rows, and 10 more variables: fatality2 <dbl>,
#> #   fatalpre1 <dbl>, fatalpre2 <dbl>, hiact1 <dbl>, hiact2 <dbl>,
#> #   hostlev1 <dbl>, hostlev2 <dbl>, orig1 <dbl>, orig2 <dbl>, duplicated <dbl>


# }