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.
This post is based on the free and open source Creating-maps-in-R teaching resource for introducing R as a command-line GIS.
R is well known as an language ideally suited for data processing, statistics and modelling. R has a number of spatial packages, allowing analyses that would require hundreds of lines of code in other languages to be implemented with relative ease. Geographically weighted regression, analysis of space-time data and raster processing are three niche areas where R outperform much of the competition, thanks to community contributions such as spgwr, spacetime and the wonderfully straightforward raster packages.
What seems to be less well known is that R performs well as a self standing Geographical Information System (GIS) in its own right. Everyday tasks such as reading and writing geographical data formats, reprojecting, joining, subsetting and overlaying spatial objects can be easy and intuitive in R, once you understand the slightly specialist data formats and syntax of spatial R objects and functions. These basic operations are the basic foundations of GIS. Mastering them will make much more advanced operations much easier. Based on the saying ‘master walking before trying to run’, this mini tutorial demonstrates how to load and plot a simple geographical object in R, illustrating that the ease with which continuous and binned choropleth map color schemes can be created using ggmap, an extension of the popular ggplot2 graphics package. Crucially, we will also see how to join spatial and non spatial datasets, resulting in a map of where the Conservative party succeeded and failed in gaining council seats in the 2014 local elections.
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