This course teaches two skill-sets that are fundamental in modern transport research: programming and data analytics, with a focus on spatial data. Combining these enables powerful transport planning and analysis workflows for tackling a wide range of problems, including:

• How to effectively handle large transport datasets?
• Where to locate new transport infrastructure?
• How to develop automated and reproducible transport planning workflows?
• How can increasingly available datasets on air quality, traffic and active travel be used to inform policy?
• How to visualise results in an attractive and potentially on-line and interactive manner?

• Learn about the 'R ecosystem' for transport
• Understand spatial data classes
• Read, process and save a wide variety of datasets
• Spatial data and the tidyverse
• R for air quality data

• Working with origin-destination (OD) data
• Converting origin-destination data to lines and routes
• Access and use of OSM data from R
• Route networks
• Visualising transport data

• The course website/wiki is github.com/ITSLeeds/R4TA
• Geocomputation with R (Lovelace, Nowosad, and Muenchow 2018)
  • Chapter 2 on spatial data classes (printed)
  • Chapter 7 on transport applications (printed)
  • Chapters 3 + 6 on the tidyverse + data I/O
• Efficient R Programming (Gillespie and Lovelace 2016)
• stplanr: A package for Transport Planning (stplanr-paper)

R is an extremely flexible language. Huge package ecosystem.

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

"R" > "Python" # ?

## [1] TRUE

R has a unique syntax (R Core Team 2017)

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.

Other shortcuts:

• Magic Tab button
• Pressing Up in console
• See Alt-Shift-K for more

• Be warned: easy to overwrite data

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

## [1] 6

• Warning when using =

system.time({x_big = (1:1e7)^2})
system.time({x_big = 1:1e7})

• Warning when using <-

x < -5
x <-5
library(tidyverse)
5 %>% 
  sin(.) %>% 
  cos(.) -> res
res = cos(sin(5))

• 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?