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.
install.packages("dafishr")You can install development version of dafishr with:
# install.packages("devtools")
devtools::install_github("CBMC-GCMP/dafishr")If you haven’t devtools package previously installed
just delete the comment # from the code above and run both lines.
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.
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.