• An introduction to R and applications (Lecture ~30 minutes)
  • R in context (see theWiki page), R for Data Science

• Spatial data and the 'tidyverse' (~30 minutes)

• Practical: Attribute data operations (Practical ~120 minutes)
  • See Chapter 3 of Geocomputation with R
  • And 17 exercises at the end
• Advanced challenge (optional): Cycle infrastructure, short car journeys and green space in a city of your choice (30 minute)
  • Go to http://www.pct.bike/, go to Region Data and download data
  • Make some maps and summarise the data data
  • Where are the most short car trips in the region?
  • What % of routes pass within 500 m of a park?

• R for Data Science: http://r4ds.had.co.nz/
• An introduction to stplanr (R vignette): https://cran.r-project.org/web/packages/stplanr/vignettes/introducing-stplanr.html
• stplanr: a package for transport planning: https://cran.r-project.org/web/packages/stplanr/vignettes/stplanr-paper.html
• Geocomputation with R (work in progress book): http://robinlovelace.net/geocompr/
• An introduction to GitHub for learning and research: https://github.com/jfiksel/github-classroom-for-students
• A blog about the bikedata package (also see dodgr/stplanr): https://ropensci.org/blog/2017/10/17/bikedata/
• Want more data access? See here: http://robinlovelace.net/geocompr/read-write.html

The course website/wiki is github.com/ITSLeeds/R4TS

• Course overview: in the courses folder
• Slides: Available from rpubs.com/robinlovelace and the slides folder
• Printed material:
  • Introduction to Visualising Spatial Data with R
  • Simple Features for Geographic Data: an Introduction
  • stplanr: A package for Transport Planning (stplanr-paper for short, available from github.com/ropensci/stplanr), a detailed account of how to use spatial data in R as part of a transport planning workflow.

R is an extremely flexible and (with the help of add-on packages) powerful language.

As datasets have grown, so has the importance of efficient computing.

• R was designed for efficiency - lots of 'leg-work' done in C
• R is conducive to cloud and parallel computing
• Easy to parallelise

• Vital for communication
• Increasingly interactive

5 * 5

1 + 4 * 5

4 * 5 ^ 2

In R:

• Everything that exists is an object
• Everything that happens is a function

E.g., load a data object and find its dimension:

data(mpg, package = "ggplot2") # load the object mpg
dim(mpg) # use a function (dim) to do something with it

## [1] 234  11

• R is object-orientated
• Objects persist in memory for the duration of the session
• Objects are usually assigned by the <- or = operator (usually identical)
• Any names can be give to R objects, except special characters like \
• Exercise: what do these commands do?

a = 1
b = 2
c = "c"
x_thingy = 4

a + b
a * b
a + c
a / x_thingy

ls()

## [1] "a"        "b"        "c"        "mpg"      "x_thingy"

x <- x_thingy
rm(x_thingy)
x

## [1] 4

ls()

## [1] "a"   "b"   "c"   "mpg" "x"

"R, at its heart, is a functional programming (FP) language. This means that it provides many tools for the creation and manipulation of functions."

• All functions have brackets (to run)
• The arguments of the functions go in the brackets

replicate(n = 3, expr = x)

## [1] 4 4 4

exp(x)

## [1] 54.59815

x = 1:9
y = x^2
plot(x = x, y =y) 

• Open RStudio and have a look around
• Create a new project
• Create a new R Script: pass code to the console with Ctl-Enter
• Use R as a calculator: what is:
• Explore each of the 'panes'
• Find and write down some useful shortcuts (Alt-Shift-K on Windows/Linux)

# Assignment of x
x <- 5
x

## [1] 5

# A trick to print x
(x <- 5)

• Is it part of a longer story that will ultimately be shared? (use script files)
• Is it just playing that does not need to be stored? (use the console)

You can switch effortlessly between them with Ctl+1 and Ctl+2.

• Be warned: easy to overwrite data

x = 5 # the same as x <- 5
(x = x + 1)

## [1] 6

• With so many packages, simply choosing the right package can be hard work!
• Fortunately there are well-known ways to decide on which package to use (see section 4.4 of Efficient R Programming):
  • Is it mature?
  • Is it actively developed?
  • Is it well documented?
  • Is it well used?
• It's worth spending the time thinking about this: can save you hours later on

• As described in Chapter 10 of Efficient R Programming, one of the most important R programming skills is learning how to learn
• A great advantage of R is its strong community
• R's internal documentation is terse but good - try this:

?plot
vignette("plotexample")

• It's worth being aware of different help forums:
  • stackoverflow.com/ and related sites such as gis.stackexchange.com/
  • Email lists such as r-sig-geo
  • Increasingly: GitHub issue
  • Good old fashioned email

• Think about the kind of data and analysis you'll be using R for
• Which packages have you be using to date? (if any)
• Find 3 packages that could help: think about pros and cons
• What are the alternatives to R for this work?