Extract the joint membership of a pair of countries across IGOs

Dyadic version of the data. The unit of observation is a dyad of countries. It provides a summary of the joint memberships of two countries across IGOs over time.

Usage

igo_dyadic(country1, country2, year = 1816:2014, ioname = NULL)

Source

Codebook Version 3 IGO Data for full reference.

Arguments

country1, country2: A state of vector of states to be compared. It could be any valid name or code of a state as specified on states2016.
year: Year to be assessed, an integer or an array of year.
ioname: Optional. ioname or vector of ioname corresponding to the IGOs to be assessed. If NULL (the default), all IGOs would be extracted. See codes on igo_search().

Value

A coded data.frame representing the years and country dyad (rows) and the IGOs selected (columns). See Details.

Details

This function tries to replicate the information contained in the original file distributed by The Correlates of War Project (dyadic_format3.dta). That file is not included in this package due to its size.

The result is a data.frame containing the common years of the states selected via country1, country2, year by rows.

An additional column dyadid, computed as (1000*ccode1)+ccode2 is provided in order to identify relationships.

For each IGO selected via ioname (or all if the default option has been used) a column (using lowercase ioname as identifier) is provided with the following code system:

Category	Numerical Value
No Joint Membership	0
Joint Full Membership	1
Missing data	-9
State Not System Member	-1

See igo_recode_dyadic() section for an easy way to recode the numerical values into factors.

If one state in an IGO is a full member but the other is an associate member or observer, that IGO is not coded as a joint membership.

Differences with the original dataset

There are some differences on the results provided by this function and the original dataset on some IGOs regarding the "Missing Data" (-9) and "State Not System Member" (-1). However it is not clear how to fully replicate those values.

See Codebook Version 3 IGO Data

References

Pevehouse, J. C., Nordstrom, T., McManus, R. W., & Jamison, A. S. (2020). Tracking organizations in the world: The Correlates of War IGO Version 3.0 datasets. Journal of Peace Research, 57(3), 492–503. doi:10.1177/0022343319881175 .

Examples

usa_esp <- igo_dyadic("USA", "Spain")
nrow(usa_esp)
#> [1] 199
ncol(usa_esp)
#> [1] 546

dplyr::tibble(usa_esp)
#> # A tibble: 199 × 546
#>    dyadid ccode1 stateabb1 statenme1    state1 ccode2 stateabb2 statenme2 state2
#>     <dbl>  <int> <chr>     <chr>        <chr>   <int> <chr>     <chr>     <chr> 
#>  1   2002      2 USA       United Stat… usa       230 SPN       Spain     spain 
#>  2   2002      2 USA       United Stat… usa       230 SPN       Spain     spain 
#>  3   2002      2 USA       United Stat… usa       230 SPN       Spain     spain 
#>  4   2002      2 USA       United Stat… usa       230 SPN       Spain     spain 
#>  5   2002      2 USA       United Stat… usa       230 SPN       Spain     spain 
#>  6   2002      2 USA       United Stat… usa       230 SPN       Spain     spain 
#>  7   2002      2 USA       United Stat… usa       230 SPN       Spain     spain 
#>  8   2002      2 USA       United Stat… usa       230 SPN       Spain     spain 
#>  9   2002      2 USA       United Stat… usa       230 SPN       Spain     spain 
#> 10   2002      2 USA       United Stat… usa       230 SPN       Spain     spain 
#> # ℹ 189 more rows
#> # ℹ 537 more variables: year <dbl>, ccode <dbl>, state <dbl>, aaaid <dbl>,
#> #   aacb <dbl>, aalco <dbl>, aaro <dbl>, aata <dbl>, aatpo <dbl>, abeda <dbl>,
#> #   abepseac <dbl>, acc <dbl>, acct <dbl>, acdt <dbl>, aci <dbl>, acml <dbl>,
#> #   acp <dbl>, acpeu <dbl>, acs <dbl>, acso <dbl>, acssrb <dbl>, acu <dbl>,
#> #   acwl <dbl>, afesd <dbl>, afeximb <dbl>, afgec <dbl>, afpu <dbl>,
#> #   afrand <dbl>, afristat <dbl>, afspc <dbl>, afte <dbl>, agc <dbl>, …

# Using custom parameters
custom <- igo_dyadic(
  country1 = c("France", "Germany"), country2 = c("Sweden", "Austria"),
  year = 1992:1993, ioname = "EU"
)

dplyr::glimpse(custom)
#> Rows: 8
#> Columns: 11
#> $ dyadid    <dbl> 220220, 220220, 255255, 255255, 220220, 220220, 255255, 2552…
#> $ ccode1    <int> 220, 220, 255, 255, 220, 220, 255, 255
#> $ stateabb1 <chr> "FRN", "FRN", "GMY", "GMY", "FRN", "FRN", "GMY", "GMY"
#> $ statenme1 <chr> "France", "France", "Germany", "Germany", "France", "France"…
#> $ state1    <chr> "france", "france", "germany", "germany", "france", "france"…
#> $ ccode2    <int> 380, 380, 380, 380, 305, 305, 305, 305
#> $ stateabb2 <chr> "SWD", "SWD", "SWD", "SWD", "AUS", "AUS", "AUS", "AUS"
#> $ statenme2 <chr> "Sweden", "Sweden", "Sweden", "Sweden", "Austria", "Austria"…
#> $ state2    <chr> "sweden", "sweden", "sweden", "sweden", "austria", "austria"…
#> $ year      <dbl> 1992, 1993, 1992, 1993, 1992, 1993, 1992, 1993
#> $ eu        <dbl> -1, 0, -1, 0, -1, 0, -1, 0