Many Data on State and State-Like Actors in the International System • manystates

manystates is a package within the many universe of packages useful for working with states and state-like entities. It contains an ensemble of datasets on states and state-like entities in the world, including information on states’ beginning and, where applicable, end dates, and geographical and other characteristics. Please also check out {manydata} for more information about the other packages and tools to handle data from the many universe of packages.

In addition, the package includes several unique functions to help you work with state data, including code_states() to identify states within texts, and generate_states() to create fictional state names. The package is geared towards global governance research, but can also be used by anyone interested in state actors across time.

How to install

It is easy to install and start analysing global governance data in R. Simply install the core package, manydata, as follows, and then you can discover, install and update various ‘many packages’ from the console.

# install.packages("manydata") # install the core package
manydata::call_packages() # this prints a list of the publicly available data packages currently available
# manydata::call_packages("manystates") # this downloads and installs the named package

Included data

Once you have installed the package, you can access the primary datacube in the package, states, as follows. You can see that currently the datacube combines three datasets: Gleditsch and Ward’s (1999) state list, the International System(s) Dataset (ISD) by Griffiths and Butcher (2013), and the GGO state list developed by the PANARCHIC team for this package.

manydata::describe_datacube(manystates::states)

## [1] "The `manystates::states` datacube is a list containing 3 datasets: ISD, GW, and GGO"

manystates::states

## $ISD
## # A tibble: 499 × 36
##    stateID StateName       Begin End   StateNameAlt Latitude Longitude StartType
##    <chr>   <chr>           <mda> <mda> <chr>        <chr>    <chr>         <dbl>
##  1 DEU     Germany         0006… 1945… Prussia      52 31 1… 13 24 18…         3
##  2 KOR     Korea           0676… 1905… Koryo, Chos… 37 34 0… 126 58 0…         4
##  3 JPN     Japan           0710… 1945… <NA>         35 41 2… 139 41 3…         1
##  4 CHI     Chien Khouang,… 0800… 1831… <NA>         19 19 2… 103 21 3…         2
##  5 KHM     Cambodia        0802… 1863… Khmer Kingd… 11 34 1… 104 55 1…         1
##  6 FRA     France          0843… 1942… <NA>         48 51 2… 2 21 8 E          3
##  7 DNK     Denmark         0950… 1940… <NA>         55 40 3… 12 34 6 E         1
##  8 GBR     United Kingdom  1066… 2016… <NA>         51 30 2… 0 7 39 W          1
##  9 TON     Tonga           1100… 1900… Kingdom Of … 21 8 0 S 175 12 0…         4
## 10 OWO     Owo             1150… 1893… <NA>         7 11 0 N 5 35 0 E          1
## # ℹ 489 more rows
## # ℹ 28 more variables: EndType <dbl>, cowID <chr>, cowNR <dbl>,
## #   ISD_Category <dbl>, Region <dbl>, Start_Am <dbl>, EStart_Am <dbl>,
## #   Declare <dbl>, DecDate <chr>, Population <chr>, PopDate <dbl>, PopAm <dbl>,
## #   PopulationHigh <dbl>, PopulationLow <dbl>, StartType_Am <dbl>,
## #   StartSettle <dbl>, End_Am <dbl>, EndType_Am <dbl>, EndSettle <dbl>,
## #   Sovereignty_Am <dbl>, EuroDip <dbl>, Borders <dbl>, Borders_Am <dbl>, …
## 
## $GW
## # A tibble: 216 × 7
##    stateID StateName       Begin        End          StateNameAlt cowID cowNR
##    <chr>   <chr>           <mdate>      <mdate>      <chr>        <chr> <chr>
##  1 AFG     Afghanistan     ..1816-01-01 1888-12-30   <NA>         AFG   700  
##  2 AUH     Austria-Hungary ..1816-01-01 1918-11-13   <NA>         AUH   300  
##  3 BAD     Baden           ..1816-01-01 1871-01-17   <NA>         BAD   267  
##  4 BAV     Bavaria         ..1816-01-01 1871-01-17   <NA>         BAV   245  
##  5 CHE     Switzerland     ..1816-01-01 2017-12-31.. <NA>         SWZ   225  
##  6 CHN     China           ..1816-01-01 2017-12-31.. <NA>         CHN   710  
##  7 DEU     Germany         ..1816-01-01 1945-05-07   Prussia      GMY   255  
##  8 DNK     Denmark         ..1816-01-01 2017-12-31.. <NA>         DEN   390  
##  9 DZA     Algeria         ..1816-01-01 1830-07-05   <NA>         ALG   615  
## 10 ESP     Spain           ..1816-01-01 2017-12-31.. <NA>         SPN   230  
## # ℹ 206 more rows
## 
## $GGO
## # A tibble: 409 × 13
##    stateID StateName  Capital Begin End   Latitude Longitude Region StateNameAlt
##    <chr>   <chr>      <chr>   <mda> <mda>    <dbl>     <dbl> <chr>  <chr>       
##  1 ITA     Italy      Rome    -075… 9999…     41.9    12.5   Weste… <NA>        
##  2 SMR     San Marino San Ma… 0301… 9999…     43.9    12.4   South… <NA>        
##  3 BGR     Bulgaria   Sofia   0681… 1018…     42.7    23.3   South… <NA>        
##  4 ABK     Abkhazia   Anakop… 0778… 1008…     43.0    41.0   Weste… Kingdom of …
##  5 DNK     Denmark    Copenh… 0793… 9999…     55.7    12.6   North… <NA>        
##  6 FRA     France     Paris   0843… 9999…     48.9     2.35  Weste… <NA>        
##  7 HRV     Croatia    Zagreb  0879… 1102…     45.8    16.0   South… <NA>        
##  8 CHM     Chamba     Chamba  0920… 1846…     32.6    76.1   South… <NA>        
##  9 GBR     United Ki… London  0927… 9999…     51.5    -0.128 Weste… <NA>        
## 10 DRV     Annam      Hue     0939… 1883…     16.5   108.    South… <NA>        
## # ℹ 399 more rows
## # ℹ 4 more variables: CapitalAlt <chr>, Coder <chr>, Comments <chr>,
## #   Source <chr>

Working with ensembles of related data has many advantages for robust analysis. For more information on how to work with ensembles of data, please see the manydata package website.

Dataset citations

While some ‘many packages’ can and do include novel data, much of what they offer involves standing on the shoulders of giants. ‘many packages’ endeavour to be as transparent as possible about where data comes from, how it has been coded and/or relabelled, and who has done the work. As such, we make it easy to cite the datasets you use by listing the official references using the function above, as well as the package providers for their work assembling the data by using the function below.

For the Gleditsch and Ward (GW) dataset:

Gleditsch, Kristian S., and Michael D. Ward. 1999. “A revised list of independent states since the congress of Vienna.” International Interactions 25: 393-413.

For the International System(s) Dataset (ISD):

Butcher, Charles R., and Ryan D. Griffiths. 2020. “States and their international relations since 1816: introducing version 2 of the International System(s) Dataset (ISD)”. International Interactions, 46(2), 291–308.

Included functions

In addition to the datasets, the package also includes two main functions that help you work with state data.

code_states()

code_states() is useful for identifying states or state-like entities within text data. This is a non-trivial task, as state names can vary widely in their spelling, abbreviation, and context, and there are many potential ambiguities (e.g., “Korea” could refer to North or South Korea). The function uses a comprehensive library of contemporary and historical state names and their variants to identify states in a given text. It returns a data frame with the identified states and their corresponding ISO 3166 alpha-3 codes or standardised names.

It can handle both single strings and vectors of strings. By default it will identify the first state mentioned in each text, but it is possible to search for multiple states by setting the max_count argument to a value greater than 1.

manystates::code_states(c("The event took place in the United Kingdom.", 
            "Switzerland and New Zealand agreed that...",
            "One of the oldest princely states is Chamba."),
            max_count = 2)

## [1] "GBR"       "{CHE,NZL}" "CHM"

The function can currently discern 500 different states or state-like entities. This is likely to increase in future releases, but is already 2.2 times more than the next best package, states, for instance.

generate_states()

Additionally, there is a seemingly whimsical function that generates a number of random state names. This function is inspired by observed state names, but is not intended to reflect any actual states. It is primarily designed for use in class exercises when a number of novel state names are required. It can also be used to generate fictional state names for creative writing or games.

manystates::generate_states(3)

It is based on a Markov model of syllable patterns found in real state names. Your mileage with the results of this function may vary, but it should get more realistic as we add more state names to the underlying library, and in the meantime it might even be instructive for classroom exercises.

Feedback on either of these functions is most welcome.

Development

Funding details

Development on this package has been funded by the Swiss National Science Foundation (SNSF) Grant Number 188976: “Power and Networks and the Rate of Change in Institutional Complexes” (PANARCHIC). If you find the package useful, please cite the package.

Contributing

If you have already developed a dataset salient to this package, please reach out by flagging this as an issue for us, or by forking, further developing the package yourself, and opening a pull request so that your data can be made available at the next opportunity.