manyenviron is a data package in the many universe of packages. It currently includes an ensemble of datasets on international environmental agreements, and states’ membership or other relationships to those agreements.

Please also check out {manydata} for more information about the other packages in the many universe.

## How to install

We’ve made it easier than ever 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.

manydata::get_packages() # this prints a list of the publicly available data packages currently available
## # A tibble: 7 × 6
##   name        full_name
##   <chr>       <chr>
## 1 manydata    globalgov/manydata
## 2 manyenviron globalgov/manyenviron
## 3 manyhealth  globalgov/manyhealth
## 4 manypkgs    globalgov/manypkgs
## 5 manystates  globalgov/manystates
## 7 messydates  globalgov/messydates
##   description
##   <chr>
## 1 An R portal for ensembled global governance data
## 2 R Package for ensembled data on environmental agreements
## 3 An R package for ensembled data on international health organisations
## 4 Support for creating new manyverse packages
## 5 An R package for ensembled data on sovereign states
## 6 An R package for ensembled data on trade agreements
## 7 An R package for ISO's Extended Date/Time Format (EDTF)
##   installed latest updated
##   <chr>     <chr>  <date>
## 1 0.7.5     0.7.5  2022-06-07
## 2 0.1.3     0.1.2  2022-03-16
## 3 0.1.1     0.1.1  2022-02-15
## 4 0.2.2     0.2.2  2022-07-21
## 5 0.1.1     0.0.6  2021-12-06
## 6 0.1.2     0.1.2  2022-07-14
## 7 0.3.1     0.3.1  2022-07-21
#manydata::get_packages("manyenviron") # this downloads and installs the named package

## Data included

Once you have installed the package, you can see the different databases and datasets included in the {manyenviron} package using the following function.

manydata::data_contrast("manyenviron")

## agreements :
##        Unique ID Missing Data Rows Columns        Beg        End
## IEADB          0       2.72 % 3666      10 1351-08-01         NA
## GNEVAR         0      50.64 % 7273      14 1351-08-01 1371-07-31
## ECOLEX         0       5.32 % 2174      10 1868-10-17         NA
## CIESIN         0          0 %  666       7 1868-01-01         NA
## HEIDI          0       0.34 % 2280       7 1900-05-11         NA
##                                                   URL
## GNEVAR                                             NA
## ECOLEX     https://www.ecolex.org/result/?type=treaty
## CIESIN       https://sedac.ciesin.columbia.edu/entri/
## HEIDI  https://www.chaire-epi.ulaval.ca/en/data/heidi
##
## memberships :
##            Unique ID Missing Data  Rows Columns        Beg End
## ECOLEX_MEM         0      22.47 % 25003      10 1192-06-12  NA
## GNEVAR_MEM         0       26.4 % 35671      13 1192-06-12  NA
## IEADB_MEM          0      13.94 % 15466      12 1901-02-22  NA
## TFDD_MEM           0       1.77 %  2118       8 1900-01-03  NA
##                                                             URL
## ECOLEX_MEM           https://www.ecolex.org/result/?type=treaty
## GNEVAR_MEM                                                   NA
## TFDD_MEM   https://transboundarywaters.science.oregonstate.edu/
##
## organizations :
##     Unique ID Missing Data  Rows Columns        Beg End
## MIA         0         25 %    78       4 1831-01-01  NA
## YIO         0       26.8 % 75115       4 1997-01-01  NA
##                                                             URL
## MIA https://garymarks.web.unc.edu/data/international-authority/
## YIO                                        https://uia.org/ybio
##
## references :
##            Unique ID Missing Data Rows Columns Beg End URL
## ECOLEX_REF         0          0 % 1164       3  NA  NA  NA
##
## regimes :
##     Unique ID Missing Data Rows Columns        Beg  End
## IRD         0       33.7 %   92       7 1946-01-01 -Inf
##                                                                                                             URL
## IRD https://direct.mit.edu/glep/article-abstract/6/3/121/14360/The-International-Regimes-Database-Designing-and
##
## texts :
##            Unique ID Missing Data Rows Columns        Beg End URL
## GNEVAR_TXT         0      38.03 % 6377       8 1351-08-01  NA  NA

Working with an ensemble of related data has many advantages for robust analysis. Just take a look at our vignettes here.

## The many packages universe

The many universe of packages is aimed at collecting, connecting, and correcting network data across issue-domains of global governance.

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 relabeled, and who has done the work. As such, we make it easy to cite both the particular datasets you use by listing the official references in the function above, as well as the package providers for their work assembling the data by using the function below.

citation("manyenviron")

##
## To cite manyenviron in publications use:
##
##   J. Hollway. Environmental agreements for manydata. 2021.
##
## A BibTeX entry for LaTeX users is
##
##   @Manual{,
##     title = {Manyenviron: Environmental agreements for manydata},
##     author = {James Hollway},
##     year = {2021},
##     url = {https://github.com/globalgov/manyenviron},
##   }

## Contributing

{manypkgs} also makes it easy to contribute in lots of different ways.

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 used easily.

If you have collected or developed other data that may not be best for this package, but could be useful within the wider universe, {manypkgs} includes a number of functions that make it easy to create a new “many” package and populate with clean, consistent global governance data.

If you have any other ideas about how this package or the manydata universe more broadly might better facilitate your empirical analysis, we’d be very happy to hear from you.