• An introduction to R based on Collin Gillespie's Introduction to R (13:30 - 14:00)
• Practical: Working through the Introduction to R booklet (14:00 - 15:00)
• Demonstration of working with transport data in R (15:00 - 15:30)
• Practical: Working through R for Spatial Planning and Analysis (15:30 - 16:30)
• Additional practical: Do the question and answer challenges in the nclRintroduction package
• Advanced practical: reproducing the results in the stplanr-paper vignette.

See pct.bike

• Developed by statisticians Ross Ihaka and Robert Gentleman
• 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

• 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

Source: r4stats.com

Source: Data Camp

jobs

Source: revolution analytics

• 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

5 * 5

1 + 4 * 5

4 * 5 ^ 2

sin(90)

sin(0.5 * pi)

a <- 1
b <- 2
c <- "c"
x_thingy <- 4

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

ls()

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

x <- x_thingy
rm(x_thingy)
x

## [1] 4

ls()

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

• Complex outputs can be created with simple inputs

t <- 1:1000 / 10 
x <- sin(t) * exp(t * - 0.01)
y <- sin(pi / 2 + t / 3) * exp(t * -0.01)
plot(x, y, "lines")

• 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:

\[ \pi * 9.15^2 \]

• Explore each of the 'panes'
• Find and write down some useful shortcuts (Alt-Shift-K on Windows/Linux)

In R:

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

# Assignment of x
x <- 5
x

## [1] 5

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

(y <- x)

sin(x)

## [1] -0.9589243

exp(x)

## [1] 148.4132

factorial(x)

## [1] 120

sinx <- sin(x)

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

## [1] 6

x <- c(1, 2, 5)
x

## [1] 1 2 5

x^2

## [1]  1  4 25

x + 2

## [1] 3 4 7

x + rev(x)

x <- c(1, 2, 5)
for(i in x){
  print(i^2)
}

## [1] 1
## [1] 4
## [1] 25

Creating a new vector based on x

for(i in 1:length(x)){
  if(i == 1) x2 <- x[i]^2
  else x2 <- c(x2, x[i]^2)
}
x2

## [1]  1  4 25

R has a hierarchy of data classes, tending to the lowest:

• Binary
• Integer (numeric)
• Double (numeric)
• Character

a <- TRUE
b <- 1:5
c <- pi
d <- "Hello Leeds"

class(a)
class(b)
class(c)
class(d)

ab <- c(a, b)
ab

## [1] 1 1 2 3 4 5

class(ab)

## [1] "integer"

class(c(a, b))

## [1] "integer"

class(c(a, c))

## [1] "numeric"

class(c(b, d))

## [1] "character"

x <- 1:5
y <- 2:6
plot(x, y)

x <- seq(1,2, by = 0.2)
length(x)

## [1] 6

x <- seq(1, 2, length.out = 5)
length(x)

## [1] 5

• Work through Colin Gillespie's Introduction to R booklet

• Optional: install the R package

install.packages("drat")
drat::addRepo("rcourses")
install.packages("nclRintroduction", type="source")
library(nclRintroduction)
vignette(package = "nclRintroduction", "practical1")

• Let's see how R can be used to analyse transport data:

library(stplanr)

## Loading required package: sp

data("routes_fast")
plot(routes_fast)

• See the source code here: https://github.com/ITSLeeds/SSPA/blob/master/r4transport.Rmd
• Experiment with the content

if(!require(devtools))
  install.packages("devtools")
if(!require(nclRintroduction))
  devtools::install_github("rcourses/nclRintroduction", build_vignettes = T)
library(nclRintroduction)
vignette(package = "nclRintroduction")

• Reproduce the work documented in the stplanr-paper vignette

vignette("stplanr-paper")

• Experiment with the example datasets - can you make different versions of the plots?