The AppendIDs package provides researchers using cross-sectional and cross-sectional time-series (CSTS) country data with tools to standardize and merge datasets from sources using differing country-coding schemes. Researchers in international relations and comparative politics confront merging problems when combining datasets which rely on differing country-coding methods, such as Gleditch-Ward numbers, Correlates of War (COW) country codes, and International Financial Statistics (IFS) codes. Variations in naming and coding conventions across datasets, particularly for countries which may be coded under different names in the same year, can result in duplicated or missing observations which can affect empirical analyses.
The package's core function, append_ids(), can be used to apply standardized Gleditsch-Ward country names and identifiers to datasets with country-year and dyadic country-year observations. These new identifiers, along with their corresponding values in the Correlates of War coding conventions, are appended to the provided dataset automatically.
The function also informs the user of duplicated country-year observations. The AppendIDs function can be used along with the countrycode package to label and merge datasets without uniform coding schemes, or to merge datasets from differing coding conventions.
We rely on Gleditch-Ward naming conventions to provide a standardized coding criteria for country-year and dyadic country-year observations, enabling researchers to easily merge datasets from different sources using alternative coding conventions such as COW and IFS codes. Gleditch-Ward (Gleditsch and Ward 1999) conventions are selected as their coding criteria provide temporal continuity over country name changes, such as between observations of the "German Federal Republic" prior to 1990 and "Germany" in the post-1990 period.
The AppendIDs package is used to maintain the International Political Economy (IPE) Data Resource (Graham and Tucker 2016), which provides researchers in international relations and comparative politics with a unified dataset of important political and economic variables from 97 of the most commonly used datasets in the IPE literature.
This package can be installed via the devtools package. To install the devtools package, run the following code:
install.packages("devtools")Then, after, you can install the AppendIDs package by running the following codes:
devtools::install_github("alcocer-jj/AppendIDs")or
library(devtools)
install_github("alcocer-jj/AppendIDs")The AppendIDs package provides several functions: append_ids(), append_suffix(), cy_append_ids(), cy_append_suffix(), dyad_append_ids(), and dyad_append_suffix().
The append_ids() function standardizes a dataframe with country-year observations by creating new country identifer columns based on Gleditsch-Ward (GW) country-year observations.
A master dataframe of Gleditch-Ward country-year observations is used to standardize country-year identifiers across observations, allowing for merging of datasets from sources using different country name coding schemes.
In addition to GW country names and country codes (gwno), adds additional columns with other potential country-year identifiers including Correlates of War (COW) and International Finanical Statistics (IFS) codes.
new_data <- append_ids(data, breaks = F) The append_suffix() function appends a designated character string ("suffix") to all variables in a given monadic or dyadic country-year dataframe except for identifying column variables.
Identifying column variables include country names, identifiers, and abbreviations, and year. This function is designed to allow researchers to append suffixes to differentiate between variables in a combined dataset based on their dataset of origin.
For example, "COW" could be appended as a suffix to variables from the Correlates of War dataset.
new_data <- append_suffix(data, suffix = "_COW")The cy_append_ids() function standardizes a dataframe with monadic country-year observations by creating a "country" column based on Gleditsch-Ward (GW) country-year observations.
A master dataframe of Gleditch-Ward country-year observations is used to standardize country-year identifiers across observations, allowing for merging of datasets from sources using different country name coding schemes.
In addition to GW country names and country codes (gwno), the function adds additional columns with other potential country-year identifiers including Correlates of War (COW) and International Finanical Statistics (IFS) codes.
The supplied dataframe must include columns designating each observation's country identifier and the year of observation. The Dataframe must also include single observations for each country-year without duplicated observations.
data_cy <- cy_append_ids(data, breaks=T)The cy_append_suffix() function appends a designated character string ("suffix") to all variables in a given monadic country-year dataframe except for identifying column variables.
The identifying column variables include country names, identifiers, and abbreviations, and year. This function is designed to allow researchers to append suffixes to differentiate between variables in a combined dataset based on their dataset of origin.
cy_append_suffix("IR of Afghanistan", "Afghanistan")The dyad_append_ids() function standardizes a dataframe with dyadic country-year observations by creating "repcountry" and "parcountry" columns based on Gleditsch-Ward (GW) country-year observations.
A master dataframe of Gleditch-Ward country-year observations is used to standardize country-year identifiers across observations, allowing for merging of datasets from sources using different country name coding schemes.
In addition to GW country names and country codes (gwno), adds additional columns with other potential country-year identifiers including Correlates of War (COW) and International Finanical Statistics (IFS) codes.
The supplied dataframe must include columns designating observations for each dyad member's country identifier and the year of observation. The Dataframe must also include single observations for each repcountry-parcountry-year without duplicated observations.
data_dyad <- dyad_append_ids(data, breaks = F)The dyad_append_suffix() function appends a designated character string ("suffix") to all variables in a given dyadic dataframe except for identifying column variables. Identifying column variables include country names, identifiers, and abbreviations, and year.
This function is designed to allow researchers to append suffixes to differentiate between variables in a combined dataset based on their dataset of origin.
dyad_append_suffix("IR of Afghanistan", "Afghanistan")Jacob R. Tucker built the initial version of the AppendIDs function. Miriam Barnum made significant improvements to the structure and performance of this function, including enabling it to be more efficiently updated and extending it to work more smoothly with dyad-year data. Therese Anders also contributed revisions to the code at a variety of junctures. Jose J. Alcocer, Stephen Schick, and Gabriel Solis completed the work to enable the AppendIDs function to be released as a publicly available R package. Benjamin A.T. Graham initiated the creation of the AppendIDs function and guided its development throughout. Jose J. Alcocer (alcocer@usc.edu) is the corresponding author for this package.
We would like to thank many years of research assistants in the USC Security and Political Economy (SPEC) Lab for their contributions to the AppendIDs function, especially the adding of additional usual country spellings and abbreviations. Particular thanks to Anisha Chinwalla, Min Haeng Cho, Taylor Dufour, Robert Huang, Kaitlyn King, Xinru Ma, Rohit Madan, Kartik Mathur, Suzie Mulesky, Jihyun Shin, Patrick Vossler, Xueyuan (Sherry) Wang, Grace Xu, and Madeline Zheng.
Gleditsch, Kristian Skrede, and Michael D. Ward. 1999. "Interstate System Membership: A Revised List of the Independent States since 1816." International Interactions 25(4): 393-413.
Graham, Benjamin A.T., and Jacob R. Tucker. 2016. "The International Political Economy Data Resource." International Studies Perspectives 17(3): 255-270.
This package is released under the MIT License. Please see the LICENSE file for more information.