Lifecycle: experimental CRAN status R-CMD-check

The goal of dafishr is to provide an easy way to download Vessel Monitoring System (VMS) and analyse data from the Mexican Fishery Commission available at Datos Abiertos initiative searching for “Sistema de Monitoreo Satelital Embarcaciones” in Spanish. Within the package you can find tools that allows you to download VMS data, wrangle and clean raw data, and analyse tracks.

The VMS stands for Vessel Monitoring System, which is adopted on industrial fishing vessels to monitor fishing activity. These data are very important to understand the fishing activity within a country, its dynamics in time and space, and to monitor the activity within Marine Protected Areas (MPAs). Along with data from VMS we also provide layers that are used to clean and map the information. We are currently working on a scientific manuscript which will be related to this work that is currently under review.

You can follow the instruction below using a sample dataset that comes along with the package, or you can use the function on data you can download yourself by using the vms_download() function. See ?vms_data for details on its usage.


You can install dafishr with:

# install.packages("devtools")

If you haven’t devtools package previously installed just delete the comment # from the code above and run both lines.

Where to start

You can start using dafishr suit of functions using the sample_dataset provided with this package, or you can download your own raw-data files using the vms_download() function. Further details are explained in the documentation vignette for this package. You can see the suit of data and functions available within the package here.


This package follows the tidyverse programming style and depends on several package of the family that will be downloaded automatically once installed. Some functions can be applied to a more general object, but these are specifically built for the format of the raw data of the VMS form CONAPESCA (Mexican Fishery Commission). Therefore, these package focused mostly on that format to help user analyse and report data.

How to contribute

The workflow provided here is a work in progress and there are probably some errors we haven’t spotted or considered up to now. If you feel you can contribute to this effort feel free to do so by creating a pull request. If you are an undergrad and you which to help or develop scientific projects using this data you are welcome to contact us. Please, find contact information of the main author here, or via twitter.