Introduction

This course is brought to you the Consumer Data Research Centre (CDRC), a project based at the University of Leeds and UCL. It is funded by the ESRC’s (Big Data Network)

Packages

pkgs <- c("rgdal", # (can be tricky)
  "rgeos",
  "ggmap",
  "tmap",)
install.packages(pkgs)

Course agenda

During this course we will cover these topics

• Introducing R as a GIS: Introduction and example with London (Part I handout), 09:00 - 11:00 am
• The structure of spatial objects in R (Part II) 11:15 - lunch
• Loading and interrogating spatial data (Part III) Day 1 14:00 - 15:15
• Visualising spatial datasets (Part III) Day 1 15:30 - 11:00 Day 2

Day 2

• Acquiring external data with R Day 2 11:15 - lunch

After that: you choose!

• Point pattern analysis and spatial interpolation
• Geographical models in R
• Webmaps

A bit about R

• Named after S and its creators
• De-facto standard for advanced statistical analysis
• A programming language in its own right
• The power of the command line
• Used by an increasing number of organisations

Why R?

• Performace: stable, light and fast
• Support network
• documentation, community, developers
• Reproducibility
• anyone anywhere can reproduce results
• enables dissemination (RPubs, RMarkdown, .RPres) - this presentation is a .Rmd file!
• Versatility: unified solution to almost any numerical problem, graphical capabilities
• Ethics removes economic barrier to statistics, is open and democratic

R is up and coming I

Source: r4stats.com

II - Increasing popularity in academia

Source: r4stats.com

III - R vs Python

Source: Data Camp

IV - employment market

Source: revolution analytics

Why R for spatial data?

• Extensive and rapidly expanding spatial packages
• Interface with ‘conventional’ GIS software
• The advantages of the command-line:

“With the advent of “modern” GIS software, most people want to point and click their way through life. That’s good, but there is a tremendous amount of flexibility and power waiting for you with the command line. Many times you can do something on the command line in a fraction of the time you can do it with a GUI (Sherman 2008, p. 283)

Why R for spatial data II

It can take data in a wide range of formats. E.g. MySQL database dump gives you this:

LINESTRING(-1.81 52.55,-1.81 52.55, … ) - solved:

ps <- as.list(ps) # make a list
for(i in 1:length(ps)){
  ps[[i]] <- gsub("LINESTRING\\(", "", ps[[i]])
  ps[[i]] <- gsub("\\)", "", ps[[i]])
  ps[[i]] <- gsub(" ", ",", ps[[i]])
  ps[[i]] <- matrix(ps[[i]], ncol=2, byrow=T)
  ps[[i]] <- Line(ps[[i]])
}

And if you get stuck? Just ask!

Example: I could not load a MultiFeature GeoJSON. So I asked: http://stackoverflow.com/q/29066198/1694378

Visualisation

• R’s visualisation capabilities have evolved over time
• Used to create plots in the best academic journals
• ggplot2 has revolutionised the visualisation of quantitative information in R, and (possibly) overall
• Thus there are different camps with different preferences when it comes to maps in R

Why focus on visualisation?

If you cannot visualise your data, it is very difficult to understand your data. Conversely, visualisation will greatly aid in communicating your results.

Human beings are remarkably adept at discerning relationships from visual representations. A well-crafted graph can help you make meaningful comparisons among thousands of pieces of information, extracting patterns not easily found through other methods. … Data analysts need to look at their data, and this is one area where R shines. (Kabacoff, 2009, p. 45).

Maps, the ‘base graphics’ way

Source: Cheshire and Lovelace (2014) - available online

The ‘ggplot2’ way (source: This tutorial!)

R in the wild 1: Maps of all census variables for local authorities

R in the wild 2: Global shipping routes in the late 1700s

Source: R-Bloggers

R in the wild 3: The national propensity to cycle tool (NPCT)

See https://github.com/npct/pct-shiny

R in the wild 4: Mapping bicycle crashes in West Yorkshire I. | Source: Lovelace, Roberts and Kellar (2015)

Source: Lovelace, Roberts and Kellar (2015)

R in the wild 4: Mapping bicycle crashes in West Yorkshire II

R in the wild 5: Infographic of housing project finances

Flexibility of ggplot2 - see robinlovelace.net

Getting up-and-running for the tutorial

Before progressing further: Any questions?

Course materials are all available online from a GitHub repository. Click “Download ZIP” to download all the test data, ready to procede.

The main document to accompany this tutorial is a pdf within the main repository. This is to be made available for free worldwide - any comments/corrections welcome.