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Function to retrieve customized multilateral military exercise data

Source: R/get_exercises.R
get_exercises.Rd

get_exercises() generates a customized data frame containing exercise-country-year observations of multilateral military exercises. Users can subset the data by participating country, year, exercise duration, geographic location, exercise name, the domain(s) of the exercise (e.g., air, land, sea), the mission focus (warfighting, humanitarian, peacekeeping), and the number of participating countries.

Usage

get_exercises(
  country = NULL,
  startyear = NULL,
  endyear = NULL,
  min_duration = NULL,
  max_duration = NULL,
  location = NULL,
  exercise_name = NULL,
  domain = NULL,
  focus = NULL,
  min_participants = NULL,
  max_participants = NULL
)

Arguments

country

The Gleditsch and Ward (G&W) numeric country code or country name for the participating country or countries to include. Numeric input is matched exactly against the gwcode column. Character input is matched against the country column using a case-insensitive grepl fuzzy match, so partial names are accepted (e.g., "korea" returns both Koreas). Multiple values can be supplied as a vector. The default is NULL, which returns all participating countries.

startyear

The first year for the series. The default is set to the minimum year in the currently published data.

endyear

The last year for the series. The default is the maximum year in the currently published data.

min_duration

Numeric. Minimum exercise duration in days (inclusive). Default is NULL (no minimum filter).

max_duration

Numeric. Maximum exercise duration in days (inclusive). Default is NULL (no maximum filter).

location

Character. A string or vector of strings used to subset exercises by geographic location. Matched against the Location column with a case-insensitive grepl fuzzy match. Default is NULL.

exercise_name

Character. A string or vector of strings used to subset exercises by name. Matched against both the Ex_Name and Series_Name columns with a case-insensitive grepl fuzzy match (e.g., "cobra" matches "Cobra Gold"). Default is NULL.

domain

Character. A string or vector of strings indicating one or more exercise domains (warfighting environments) to include. Accepted values are "air", "land", "sea", "amphibious", and "cyber". Matching is case-insensitive. An exercise is returned if it is flagged for any of the supplied domains (logical OR). Default is NULL, which returns all domains.

focus

Character. A string or vector of strings indicating one or more mission focuses to include. Accepted values are "warfighting", "humanitarian", and "peacekeeping". Matching is case-insensitive. An exercise is returned if it is flagged for any of the supplied focuses (logical OR). Default is NULL, which returns all mission focuses.

min_participants

Numeric. Minimum number of participating countries in the exercise (inclusive). Default is NULL (no minimum filter).

max_participants

Numeric. Maximum number of participating countries in the exercise (inclusive). Default is NULL (no maximum filter).

Value

get_exercises() returns a data frame containing exercise-country-year observations of multilateral military exercises that match the specified filter criteria.

References

D'Orazio, Vito; Galambos, Kevin, 2021, "Multinational Military Exercises, 1980-2010", https://doi.org/10.7910/DVN/KHFODX, Harvard Dataverse, V1.

Gleditsch, Kristian S., and Michael D. Ward. 1999. "Interstate System Membership: A Revised List of the Independent States since 1816." International Interactions 25(4): 393-413.

Author

Michael E. Flynn

Examples


if (FALSE) { # \dontrun{
library(tidyverse)
library(troopdata)

# Pull all exercises that include South Korea between 2000 and 2015.
korea_exercises <- get_exercises(country = "korea",
                                 startyear = 2000,
                                 endyear = 2015)

# Pull all naval and amphibious exercises lasting at least 5 days.
sea_exercises <- get_exercises(domain = c("sea", "amphibious"),
                               min_duration = 5)

# Pull all "Cobra Gold" exercises in Thailand.
cobra_gold <- get_exercises(exercise_name = "cobra gold",
                            location = "thailand")

# Pull large-scale humanitarian exercises (10 or more participants).
large_hadr <- get_exercises(focus = "humanitarian",
                            min_participants = 10)
} # }