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RMAVIS

Generic badge Project Status: Active – The project has reached a stable, usable state and is being actively developed. DOI status License: CC BY 4.0

Overview

The R Modular Analysis of Vegetation Information System (RMAVIS) R Shiny web application is a tool to assist in the assignment of vegetation plot sample data to GB National Vegetation Classification units, with additional exploratory analyses.

We will update and improve RMAVIS over time in response to user feedback and hope it acts as a useful tool for the GB ecology and conservation community.

Structure

There are 5 main sections in RMAVIS at present.

Home - Provides outlining information.

Application - The core of RMAVIS, containing the actual application.

NVC Lookup - Contains a searchable table, which can be used to retrieve the full name of an NVC community using an NVC community or sub-community code.

Documentation - Provides a more detailed overview of the underlying methods, data sources, and usage instructions.

News - Contains the release log and additional news regarding the development and maintenance of RMAVIS.

Privacy - A privacy notice.

Running Locally

In addition to accessing RMAVIS via the web RMAVIS can be run on your personal device and installed as an R package.

To run RMAVIS locally take the following steps.

  1. Ensure R >= 4.4 is installed.
  2. Install RMAVIS using remotes::install_github("NERC-CEH/RMAVIS")
  3. Run install.packages("tinytex") then tinytex::install_tinytex() to install a minimal LaTeX distribution. This is required to generate a pdf report. Note that this is only required if you do not have a LaTeX distribution already installed.
  4. From the R terminal run RMAVIS::runApp().

If you wish to use the NVC-matching functions outside of the RMAVIS app, these are also made available through the RMAVIS package.

Reccomended Citation

To reference RMAVIS please cite the JOSS paper as follows:

Marshall et al., (2024). RMAVIS v1.0: a Shiny application for the analysis of vegetation survey data and assignment to GB NVC communities. Journal of Open Source Software, 9(100), 6682, https://doi.org/10.21105/joss.06682

Feedback

To report a bug please submit a Github issue here, fill out the feedback form here, or send an email to Zeke Marshall.

Acknowledgements

The development of this app was partly supported by the UK‐SCAPE programme delivering National Capability (NE/R016429/1) funded by the Natural Environment Research Council.

We would like to thank Lindsay Maskell, Lucy Ridding, Barry Jobson, Colin Conroy, Andy McMullen, John Handley, Michael Tso, Simon Rolph, Cristina Martin Hernandez, and George Linney for testing RMAVIS.

We would also like to thank Rob Marrs for his ongoing collaboration with the development of NVC assignment methodologies and the University of Liverpool for their ongoing support.