The goal of user2017.geodataviz is to privide a comprehensive overview of the options available in the R language for Geospatial data visualization. This was presented at useR! 2017 as a tutorial titled Geospatial visualization using R.

This tutorial covers ...

• R Packages for Spatial Analysis in R

• Data Structures for Spatial Data in R

• Operations Supported on Spatial Data

• Visualizing Spatial Data...

  • Using Base Graphics
  • Using ggplot2 and helper packages
  • Using shiny for dynamic mapping
  • Using leaflet and related packages for interactive maps.
  • Using specialized packages such as tmap, choroplethr, ggmap, plotly, highcharter etc.

I will add more and more samples as time progresses so keep a tab on this repository from time to time.

The tutorial overview, the slides, and the R Notebooks can be found here.

The easiest option is using a docker image built specifically for this tutorial.

• Install docker, and docker toolbox (only for Windows and Mac).

• Either using the kitematic GUI tool or command line pull the bhaskarvk/rgeodataviz image.
  Command line: docker pull bhaskarvk/rgeodataviz

For the interested the docker image was built on top of rocker.

This tutorial is also an R package which enables you to pull in all the packages needed to run this tutorial. You can install all the needed packages simply by installing this package.

First start by cloning this git repository, or better yet fork this repository on github and pull your fork. That way you can push changes to your fork.

git clone https://github.com/bhaskarvk/user2017.geodataviz
# OR git pull <your forked repository>
cd user2017.geodataviz

Next start R in this directory, and install this package and all its dependencies.

if(!require("devtools")) {
  install.packages("devtools")
}
devtools::install(dependencies="Suggests")

That should pull in all the packages required, but be aware of the following points...

• The docker image has some more extra packages that were not used in the tutorial but available for you to use.
• The list of packages is quite big so installation can be slow.
• Some packages have dependencies on external libraries which you will need to install, otherwise the packages won't compile if being built from source. The docker image comes with these packages pre-installed.

If you are using docker, then run the following command.

docker run -d -p 8787:8787 --name rgeodataviz bhaskarvk/rgeodataviz

This will start the docker container and you can connect to the Rstudio session using the URL 'http://localhost:8787', the userid/password are rstudio/rstudio.

If you are running in a local environment you can simply open the user2017.geodataviz.Rproj file from the user2017.geodataviz directory which you checked-out using git above.

To stop and subsequently start the container again use

docker stop rgeodataviz
docker start rgeodataviz

Occasionally I will be pushing updates to the docker image. If you want the latest docker image you will have to destroy the existing container, pull the latest image and rerun the container.

docker stop rgeodataviz
docker container rm rgeodataviz
docker pull bhaskarvk/rgeodataviz
docker run -d -p 8787:8787 --name rgeodataviz bhaskarvk/rgeodataviz

Destroying the container (docker container rm) will delete any data or files you may have changed, so back them up somewhere.

The tutorial material is in two directories inst/presentations and inst/notebooks. The presentations directory contains RMarkdowns used to generate the slides, and the notebooks directory contains many code samples in R Notebook format for you to play with. You can knit these files directly from withing RStudio.

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.