R/whittle_conflicts_reciprocation.R

In peacesciencer: Tools and Data for Quantitative Peace Science Research

#' Whittle Duplicate Conflict-Years by Conflict Reciprocation
#'
#' @description \code{whittle_conflicts_reciprocation()} 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
#' keep the observations that are reciprocated (i.e. have militarized actions
#' on both sides of the conflict).
#'
#' @return \code{whittle_conflicts_reciprocation()} 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 militarized actions on both sides of the conflict. This will not
#' eliminate all duplicates, far from it, but it's a sensible cut later into
#' the procedure (after whittling onsets in \code{whittle_conflicts_onsets()}
#' the extent to which dispute-level reciprocation is a heuristic for
#' dispute-level severity/importance (after some other considerations).
#'
#' @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 \pkg{peacesciencer}
#' employs several rules to whittle down these duplicate dyad-years for
#' merging into a dyad-year data frame. These are available in
#' \code{add_cow_mids()} and \code{add_gml_mids()}.
#'
#' Scholars are free to use this as a heuristic for whittling conflict-year
#' data to be coerced into true dyad-year data, but I would be remiss if I
#' did not offer a caveat about the reciprocation variable in inter-state
#' dispute data. Namely, it is noisy and is not doing what scholars often
#' think it's doing in the inter-state dispute data. Reciprocation is observed
#' only when there is a militarized action on both sides of the conflict.
#' By definition, someone on Side A will have a militarized action. Not every
#' state on Side B does. However, scholars should *not* interpret that as
#' the absence of militarized responses. In a forthcoming article in
#' *Journal of Conflict Resolution*, Doug Gibler and I make the case that
#' reciprocation isn't a useful variable to maintain at all because it
#' can only invite errors (as is often the case in the CoW-MID data) and
#' will obscure the fact that states that are attacked by another side
#' routinely fight back. On many occasions, they also successfully repel
#' the attack. Scholars who uncritically use this variable, certainly for
#' hypothesis-testing on audience costs, are borrowing trouble with
#' this measure.
#'
#' \code{wc_recip()} is a simple, less wordy, shortcut for the same function.
#'
#' @author Steven V. Miller
#'
#' @param data a data frame with a declared conflict attribute type.
#' @param ... optional, only to make the shortcut work
#'
#' @references
#'
#' Miller, Steven V. 2021. "How {peacesciencer} Coerces Dispute-Year Data into Dyad-Year Data".
#' URL: \url{http://svmiller.com/peacesciencer/articles/coerce-dispute-year-dyad-year.html}
#'
#' @name whittle_conflicts_reciprocation
#'
#' @examples
#'
#' \donttest{
#' # just call `library(tidyverse)` at the top of the your script
#' library(magrittr)
#' gml_dirdisp %>% whittle_conflicts_onsets() %>% whittle_conflicts_reciprocation()
#'
#' cow_mid_dirdisps %>% whittle_conflicts_onsets() %>% whittle_conflicts_reciprocation()
#'
#'
#' }
#'


whittle_conflicts_reciprocation <- function(data) {


  if(is.null(attributes(data)$ps_conflict_type)) {

    stop("The 'whittle' class of functions in {peacesciencer} only works on conflict data available in the package.")
  }

  if (length(attributes(data)$ps_data_type) > 0 && attributes(data)$ps_data_type == "dyad_year" &&  attributes(data)$ps_conflict_type == "gml") {

    attr_ps_data_type <- attributes(data)$ps_data_type
    attr_ps_system <- attributes(data)$ps_system
    attr_ps_conflict_type <- attributes(data)$ps_conflict_type

    data %>%
      arrange(.data$ccode1, .data$ccode2, .data$year) %>%
      group_by(.data$ccode1, .data$ccode2, .data$year) %>%
      mutate(duplicated = ifelse(n() > 1, 1, 0)) -> data


      data %>%
        group_by(.data$ccode1, .data$ccode2, .data$year, .data$duplicated) %>%
        # keep highest reciprocateds
        filter(.data$recip == max(.data$recip)) %>%
        arrange(.data$ccode1, .data$ccode2, .data$year) %>%
        # practice safe group_by()
        ungroup() %>%
        select(-.data$duplicated) -> data


    attr(data, "ps_data_type") <- attr_ps_data_type
    attr(data, "ps_system") <-  attr_ps_system
    attr(data, "ps_conflict_type") <-  attr_ps_conflict_type


  } else if (length(attributes(data)$ps_data_type) > 0 && attributes(data)$ps_data_type == "dyad_year" &&  attributes(data)$ps_conflict_type == "cow-mid") {

    attr_ps_data_type <- attributes(data)$ps_data_type
    attr_ps_system <- attributes(data)$ps_system
    attr_ps_conflict_type <- attributes(data)$ps_conflict_type


    data %>%
      left_join(., cow_mid_disps %>% select(.data$dispnum, .data$recip)) %>%
      arrange(.data$ccode1, .data$ccode2, .data$year) %>%
      group_by(.data$ccode1, .data$ccode2, .data$year) %>%
      mutate(duplicated = ifelse(n() > 1, 1, 0)) -> data

    data %>%
      group_by(.data$ccode1, .data$ccode2, .data$year, .data$duplicated) %>%
      # keep highest reciprocateds
      filter(.data$recip == max(.data$recip)) %>%
      arrange(.data$ccode1, .data$ccode2, .data$year) %>%
      # practice safe group_by()
      ungroup() %>%
      select(-.data$duplicated) -> data


    attr(data, "ps_data_type") <- attr_ps_data_type
    attr(data, "ps_system") <-  attr_ps_system
    attr(data, "ps_conflict_type") <-  attr_ps_conflict_type



  } else if(length(attributes(data)$ps_data_type) > 0 && attributes(data)$ps_data_type == "leader_dyad_year" &&  attributes(data)$ps_conflict_type == "gml") {


    data[ , c('styear', 'stmon', 'settle', 'fatality', 'mindur', 'maxdur', 'hiact', 'hostlev', 'recip', 'outcome')] <- list(NULL)

    attr_ps_data_type <- attributes(data)$ps_data_type
    attr_ps_system <- attributes(data)$ps_system
    attr_ps_conflict_type <- attributes(data)$ps_conflict_type

    data %>%
      left_join(., gml_mid_disps ) -> hold_this

    hold_this %>%
      arrange(.data$ccode1, .data$obsid1, .data$ccode2, .data$obsid2, .data$year) %>%
      group_by(.data$ccode1, .data$obsid1, .data$ccode2, .data$obsid2, .data$year) %>%
      mutate(duplicated = ifelse(n() > 1, 1, 0)) -> data

    data %>%
      group_by(.data$ccode1, .data$obsid1, .data$ccode2, .data$obsid2, .data$year, .data$duplicated) %>%
      # keep highest reciprocateds
      filter(.data$recip == max(.data$recip)) %>%
      arrange(.data$ccode1, .data$obsid1, .data$ccode2, .data$obsid2, .data$year) %>%
      # practice safe group_by()
      ungroup() %>%
      select(-.data$duplicated) -> data

    data[ , c('styear', 'stmon', 'settle', 'fatality', 'mindur', 'maxdur', 'hiact', 'hostlev', 'recip', 'outcome')] <- list(NULL)

    attr(data, "ps_data_type") <- attr_ps_data_type
    attr(data, "ps_system") <-  attr_ps_system
    attr(data, "ps_conflict_type") <-  attr_ps_conflict_type

  } else {
    stop("whittle_conflicts_reciprocation() doesn't recognize the data supplied to it.")
  }

  return(data)
}


#' @rdname whittle_conflicts_reciprocation
#' @export

wc_recip <- function(...) peacesciencer::whittle_conflicts_reciprocation(...)