Spatial Reproducible Reporting

Description

R Shiny App to select search area(s) and generate reproducible reports (R Markdown) in support of the project "Reproducible Reports for Species at Risk: Development of a tool to produce consistent reports on Species at Risk within Predicted Exposure Zones (PEZ) for a variety of threats and activities, focusing on Maritimes Region as a pilot".

How to use the App

Installation

Set up Revn:

Make sure that RTools is installed.

install.packages("devtools")
install.packages("renv")
renv::activate()
renv::equip() # loads needed installation libs
renv::restore() # loads all needed packages (may take 15+ mins the first time)

Adding a package:

install.packages("new_package")
renv::snapshot()

After adding a new package, commit the updated renv.lock file to source control.

Download necessary data

After making sure that you have the necessary permissions to access the data, run:

source(here::here("app/R/CopyRData.R"))
copy_rdata_files()

Unit Tests:

Once Renv is setup and data has been loaded, running testthat::test_local() should pass all unit tests.

Run the application

library(shiny)
runApp("app")

Demo

See demo.webm.

Repo structure

config.R contains key configuration parameters, including the region, file paths, and the list of Open Data records checked by the github action;
app includes all the files needed to run the application;
app/www includes image (e.g. logos), the .css file, and report preview once generated;
app/data contains the local versions of all data used in running the application.
app/R includes the R files to be loaded (it loads packages and functions);
app/Rmd includes the base .Rmd files for each report section;
app/output includes all outputs once generated: selected geometries as a .geojson and the report in all format (the different section are also added);
dataprocessing contains the common preprocessing functions to prepare data for use in the application, as well as the scripts used in github actions;
sections includes the rmarkdown and preprocessing script for each subsection of the report;
sections/template contains a simple template with basic functionality;
tests/testthat contains the unit test scripts that are run when test_local() is called.

Collaborative Workflow

How to contribute to the project

Branch from master (e.g. from master create and checkout a new branch: git checkout -b my_dev_branch or in RStudio)
Do work in branch, e.g. addressing some issue.
Add and commit changes.
Routinely Push work to the remote version of my_dev_branch: git push -u origin my_dev_branch
Routinely Pull (i.e. git fetch + git merge) from origin/master to deal with any conflicts git pull origin master
Once the original issue is fixed, ensure the report can still be generated through the app and that the test suite passes: testthat::test_local()
If everything still runs correctly, make a pull request from github website to merge my_dev_branch into master.
Other developers will then respond to the request and test out the code in their dev branches to make sure there are no issues.
Once all lights are green, the pull request will be accepted and the new code merged into master branch.

remi-daigle/shinySpatialApp