Authors: Noam Ross and Evan A. Eskew

citesdb is an R package to conveniently analyze the full CITES shipment-level wildlife trade database, available at 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:


Note that since citesdb installs a source dependency from GitHub, you will need package build tools.


Getting the data

When you first load the package, you will see a message like this:

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.

Using the database

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:


start <- Sys.time()

cites_shipments() %>%
  group_by(Year) %>%
  summarize(n_records = n()) %>%
  arrange(desc(Year)) %>%
#> # A tibble: 44 x 2
#>     Year n_records
#>    <int>     <dbl>
#>  1  2018      1326
#>  2  2017   1015719
#>  3  2016   1262632
#>  4  2015   1296532
#>  5  2014   1109872
#>  6  2013   1127363
#>  7  2012   1096645
#>  8  2011    950144
#>  9  2010    894011
#> 10  2009    908669
#> # … with 34 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, MonetDB, 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 0.841418 secs

If 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


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:

#> # 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

#> # 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)

#> # 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.


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: A high-performance database of shipment-level CITES trade data. R package v0.2.0. EcoHealth Alliance: New York, NY. doi:10.5281/zenodo.2630836

UNEP-WCMC (Comps.) 2019. Full CITES Trade Database Download. Version 2019.2. CITES Secretariat, Geneva, Switzerland. Compiled by UNEP-WCMC, Cambridge, UK. Available at:


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.

Created by EcoHealth Alliance