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{manydata} is the central package in the many packages universe aimed at collecting, connecting, and correcting network data across issue-domains of global governance. To assist users in doing so, {manydata} contains functions that enable users to download and manipulate data easily.

Why manydata?

{manydata} offers users access to all of the tested data in the various ‘many packages’ available, for use in analyses of global governance and beyond. A special feature of the ‘many packages’ is that it is not ‘opinionated’ - instead of offering a single, supposedly authoritative version of global governance events, the packages in the many packages universe gather well-regarded datasets in each issue-domain into three-dimensional ‘datacubes’. The chief advantage of this for global governance researchers is that it enables a quick and easy way to check the robustness of their results using different formulations of the study population or concept specification. The ‘datacube’ structure has a specific coding system for the variables across the datasets. For more details, please see the vignette.

Downloading and installing manydata

The easiest way to install {manydata} is directly from CRAN.


The development version of the package {manydata} can also be downloaded from GitHub.

# install.packages("remotes")

Available ‘many’ packages

{manydata} connects users to other packages that help fill global governance researchers’ data needs. The get_packages() function can be used to discover the ‘many packages’ currently available.


Please see the website for more information about how to use {manydata}.

Visualising ‘many’ databases

Once ‘many’ data packages are downloaded, {manydata} helps users visualize the relationship between matched observations across datasets within a database. Database profiling functions return confirmed, unique, missing, conflicting, or majority values in all (non-ID) variables in the datasets for a ‘many’ package database.

db_plot(database = emperors, key = "ID", variable = "all", category = "all")
#> There were 116 matched observations by ID variable across datasets in database.

Consolidating ‘many’ databases

{manydata} also contains flexible methods for consolidating ‘many’ package database into a single dataset with some combination of the rows, columns, as well as for how to resolve conflicts for observations across datasets.

consolidate(database = emperors, rows = "every", cols = "every",
            resolve = "coalesce", key = "ID")
#> There were 116 matched observations by ID variable across datasets in database.

#> # A tibble: 41 × 3
#>    ID             Beg         End        
#>    <chr>          <mdate>     <mdate>    
#>  1 Aemilian       0253-08-15~ 0253-10-15~
#>  2 Augustus       -0026-01-16 0014-08-19 
#>  3 Aurelian       0270-09-15  0275-09-15 
#>  4 Balbinus       0238-04-22  0238-07-29 
#>  5 Caracalla      0198        0217-04-08 
#>  6 Carinus        0283-08-01~ 0285-08-01~
#>  7 Carus          0282-10-01~ 0283-08-01~
#>  8 Claudius       0041-01-25  0054-10-13 
#>  9 Commodus       0177        0192-12-31 
#> 10 Constantine II 0337-05-22  0340-01-01 
#> # … with 31 more rows

Cheat Sheet

{manydata} contains several other functions to help global governance researchers. For a quick overview, please also check the package cheat sheet.

Contributing to the many packages universe

For more information for developers and data contributors to ‘many packages’, please see {manypkgs} the website.