Authors: Noam Ross, Evan A. Eskew and Mauricio Vargas
citesdb is an R package to conveniently analyze the full CITES shipment-level wildlife trade database, available at https://trade.cites.org/. This data consists of over 40 years and 20 million records of reported shipments of wildlife and wildlife products subject to oversight under the Convention on International Trade in Endangered Species of Wild Fauna and Flora. The source data are maintained by the UN Environment World Conservation Monitoring Centre.
Install the citesdb package with this command:
devtools::install_github("ropensci/citesdb")Note that since citesdb installs a source dependency from GitHub, you will need package build tools.
When you first load the package, you will see a message like this:
library(citesdb)
#> Local CITES database empty or corrupt. Download with cites_db_download()
Not to worry, just do as it says and run cites_db_download(). This
will fetch the most recent database from online, an approximately 158 MB
download. It will expand to over 1 GB in the local database. During the
download and database building, up to 3.5 GB of disk space may be used
temporarily.
Once you fetch the data, you can connect to the database with the
cites_db() command. The cites_shipments() command loads a remote
tibble that is backed by the database but is not loaded into R. You
can use this command to analyze CITES data without ever loading it into
memory, gathering your results with the dplyr function collect().
For example:
library(citesdb)
library(dplyr)
start <- Sys.time()
cites_shipments() %>%
group_by(Year) %>%
summarize(n_records = n()) %>%
arrange(desc(Year)) %>%
collect()
#> # A tibble: 45 x 2
#> Year n_records
#> <int> <dbl>
#> 1 2019 12610
#> 2 2018 1143044
#> 3 2017 1246684
#> 4 2016 1293178
#> 5 2015 1299183
#> 6 2014 1109877
#> 7 2013 1127377
#> 8 2012 1096664
#> 9 2011 950148
#> 10 2010 894115
#> # … with 35 more rows
stop <- Sys.time()(Note that running collect() on all of cites_shipments() will load
a >3 GB data frame into memory!)
The back-end database, duckdb, is very fast and powerful, making analyses on such large data quite snappy using normal desktops and laptops. Here’s the timing of the above query, which processes over 20 million records:
stop - start
#> Time difference of 0.4658868 secsIf you are using a recent version of RStudio interactively, loading the CITES package also brings up a browsable pane in the “Connections” tab that lets you explore and preview the database, as well as interact with it directly via SQL commands.
If you don’t need any of the bells and whistles of this package, you can
download the raw data as a single compressed TSV file from the releases
page, or as a .zip file
of many CSV files from the original source at
https://trade.cites.org/.
The package database also contains tables of field metadata, codes used,
and CITES countries. This information comes from “A guide to using the
CITES Trade
Database”,
on the CITES website. Convenience functions cites_metadata(),
cites_codes(), and cites_parties() access this information:
head(cites_metadata())
#> # A tibble: 6 x 2
#> variable description
#> <chr> <chr>
#> 1 Year year in which trade occurred
#> 2 Appendix CITES Appendix of taxon concerned
#> 3 Taxon scientific name of animal or plant concerned
#> 4 Class scientific name of animal or plant concerned
#> 5 Order scientific name of animal or plant concerned
#> 6 Family scientific name of animal or plant concerned
head(cites_codes())
#> # A tibble: 6 x 3
#> field code description
#> <chr> <chr> <chr>
#> 1 Purpose B Breeding in captivity or artificial propagation
#> 2 Purpose E Educational
#> 3 Purpose G Botanical garden
#> 4 Purpose H Hunting trophy
#> 5 Purpose L Law enforcement / judicial / forensic
#> 6 Purpose M Medical (including biomedical research)
head(cites_parties())
#> # A tibble: 6 x 6
#> country code former_code non_ISO_code date data_source
#> <chr> <chr> <lgl> <lgl> <chr> <chr>
#> 1 Afghanistan AF FALSE FALSE 1986-01-28 'A guide to using the CITES Trade Database', Version 8, Anne…
#> 2 Africa XF FALSE TRUE <NA> 'A guide to using the CITES Trade Database', Version 8, Anne…
#> 3 Åland Islands AX FALSE FALSE <NA> 'A guide to using the CITES Trade Database', Version 8, Anne…
#> 4 Albania AL FALSE FALSE 2003-09-25 'A guide to using the CITES Trade Database', Version 8, Anne…
#> 5 Algeria DZ FALSE FALSE 1984-02-21 'A guide to using the CITES Trade Database', Version 8, Anne…
#> 6 American Samoa AS FALSE FALSE <NA> 'A guide to using the CITES Trade Database', Version 8, Anne…More information on the release of shipment-level CITES data can be
found in the ?guidance help file.
The rcites package provides access to the Speciesplus/CITES Checklist API, which includes metadata about species and their protected status through time.
If you use citesdb in a publication, please cite both the package and source data:
Ross, Noam, Evan A. Eskew, and Nicolas Ray. 2019. citesdb: An R package to support analysis of CITES Trade Database shipment-level data. Journal of Open Source Software, 4(37), 1483, https://doi.org/10.21105/joss.01483
UNEP-WCMC (Comps.) 2019. Full CITES Trade Database Download. Version 2019.2. CITES Secretariat, Geneva, Switzerland. Compiled by UNEP-WCMC, Cambridge, UK. Available at: https://trade.cites.org.
Have feedback or want to contribute? Great! Please take a look at the contributing guidelines before filing an issue or pull request.
Please note that this project is released with a Contributor Code of Conduct. By participating in this project you agree to abide by its terms.
