It has been possible for some years to launch a web map from within R. A number of packages for doing this are available, including:
- RgoogleMaps, an interface to the Google Maps api
- leafletR, an early package for creating Leaflet maps with R
- rCharts, which can be used as a basis for webmaps
- leaflet is supported by RStudio, who have a track strong track record of creating amazing R packages
- leaflet appears to provide the simplest, fastest way to host interactive maps online in R, requiring only 2 lines of code for one web map! (as you’ll see below)
- leaflet is shiny. Shiny in the literal sense of the word (a new and fresh approach to web mapping in R) but also in the sense that it works well with the R package shiny.
I recently delivered a workshop on a practical introduction to shiny, an R package that enables development, testing and deployment of interactive web applications. Delivered at the University of Sydney’s Institute for Transport and Logistics Studies (ITLS), it was designed for people who are a) fairly new to R (which can seem intimidating) and b) completely new to shiny.
This article provides resources for people wanting to apply shiny to real-world applications and some context which explains the motivations behind running the workshop. The pdf tutorial, example code to create and modify your own apps and a place to contribute to this free teaching resource is available at the following GitHub repository: github.com/Robinlovelace/learning-shiny
Today I’m presenting some work I’ve been doing on spatial microsimulation to the University of Wollongong, who are hosting me for the day. A flyer for the event can be found here and my slides are up online.
This article was originally published in Geoinformatics magazine.
R is well known as a powerful, extensible and relatively fast statistical programming language and open software project with a command line interface (CLI). What is less well known is that R also has cutting edge spatial packages that allow it to behave as a fully featured Geographical Information System in the full sense of the word. In fact, some of cutting edge algorithms for image processing and spatial statistics are implemented in R before any other widely available software product (Bivand et al. 2013). Sophisticated techniques such as geographically weighted regression and spatial interaction models can be custom built around your spatial data in R. But R also works as a general purpose GIS, with mature functions for performing all established techniques of spatial analysis such as spatial selections, buffers and clipping. What is unique about R is that all these capabilities are found in a single programme: R provides a truly integrated modelling environment.
I’m presenting today on “Verification of big data for estimating retail flows”.
Slides from this presentation can be found here.
What I'm up to+ Please click here for more detailed up-to-date info on my precious times of freedom!
Search this site!
Quote of the day
Some interesting links
The internet is awash with detritus. In the name of navigating the maze, these links highlight some more enlightened online content.
George Monbiot: investigative journalism at its best
R-Bloggers: a site endevouring to make statistics accessible and fun
Tom's bike trip: there are many 'bike trip' sites out there; this is one of the best
My presentations on Speaker Deck
TGRG website: the Transport Geography group I'm involved with
MASS profile: academic profile at Leeds
Powerstar Youtube channel: check out my videos
R-Bloggers feed: posts about R