This code has been kindly contributed by Robin Edwards (from UCL CASA).

There are many useful introductory guides out there to R, but below is the kind of thing I now wish I’d been given when I first started using it – something with simple logically-progressive examples and minimal explanatory text. Copy the text below into a new script in R and run line-by-line to give a quick intro to many of R’s most basic principles and functionality. You can also download a text file with it here. It is by no means comprehensive, even at the most basic level, but still I hope someone finds it useful. You may want to look at RStudio as it is more user-friendly.

## A CRASH COURSE IN [R] PROGRAMMING

## Robin Edwards (geotheory.co.uk), March 2013

## In RStudio run through line-by-line using Ctrl + Enter

# basic R environmental functions

x=3.14159; y=’hello world’; z=TRUE # create some objects. In RStudio they’ll appear in ‘Workspace’

ls() # list the objects in the Workspace

print(y) # print information to R ‘Console’

rm(y) # remove an object

rm(list=ls()) # remove all

getwd() # find current working directory

setwd(“/Users/robinedwards/Documents”) # set working directory as preferred

print ( “R ignores the ‘white-space’ in command syntax” )

# use ‘?’ for help on any R function (if its library is loaded in the session)

?max

??csv # search for a text string in R documentation library

library(help=utils) # get help on a particular package (list its functions)

# ‘str’ is a powerful tool for investigating the underlying structure of any R object

str(max)

# CREATING AND MANIPULATING R OBJECTS

# assigning values to variables

n = 5 # is possible but

n 5 -> n

rm(n)

# R objects can be of various data types, but probably most common are ‘numeric’ and ‘character’

( num ( char <- ‘any text string’ )

# create a VECTOR (array) using the ‘c()’ concatenate function

( vec

# a vector series

( vec

# R vectors can be accessed in various ways using [ ] brackets

vec[3]

vec[3:6]

vec[ c(1,3,8) ]

vec[vec > 15]

# check a vector contains a value

5 %in% vec

12 %in% vec

# finding first index position of a matching value/sting

( x = c(‘one’, ‘five’, ‘two’, 3, ‘two’) )

match(‘two’, x)

match(c(‘two’,’five’), x)

# a MATRIX is a 2D vector (essentially a vector of vectors) of matching data type

( matrx = matrix(1:15, 3, 5) )

( matrx dim(matrx) print(matrx)

t(matrx) # a matrix can be easily transposed

# an ARRAY is a generic vector but with more flexibiity. A 1D array is the same as a normal vector,

# and a 2D array is like a matrix. But arrays can store data with ‘n’ dimensions:

( arry

# Using square brackets on arrays

arry[12] # a single criterion (argument) selects the array’s n’th record

arry[3,1,2] # or use multiple arguments that reflect the array’s dimensionality

arry[,,2]

arry[,1,]

# a DATA.FRAME is like a matrix, but accomodates fields (columns) with different data types

(df

# They can be viewed easily

View(df)

# examine their internal stucture

str(df)

# data interrogation with square brackets

df[1,]

df[2:3,]

df[,1]

df[2,1]

# data.frame and matrix objects can have field (column) and record (row) names

dimnames(df)

colnames(df)

names(df) # not for matrix objects

row.names(df)

# interrogate data.frames by field name using the ‘$’ operator. the result is a simple vector

df$name

df$name[2]

# names can be reassigned

names(df) row.names(df) print(df)

# check dimensions of vector/matrix/array/data.frame objects

length(vec)

dim(df)

dim(arry)

nrow(df)

ncol(df)

# R has various inbuilt data.frame datasets used to illustrate how functions operate e.g.

data()

InsectSprays # this guide makes use of these datasets

warpbreaks

# examine contents

head(InsectSprays) # list the top records of a vector / matrix / d.f.

tail(InsectSprays, n=3) # bottom the 3

summary(InsectSprays) # summarise a data vector

# aggregate() is a powerful function for summarising categorical data

aggregate(InsectSprays$count, by=list(InsectSprays$spray), FUN=mean)

sumInsects names(sumInsects) print(sumInsects)

# subset/apply filter to a data.frame

warpbreaks[warpbreaks$wool==’A’,] # by 1 condition

warpbreaks[warpbreaks$tension %in% c(‘L’,’M’),] # multiple conditions

# adding entries is possible (if a bit tricky)

(newrow (warpbreaks

# but LISTS are better at this

lst = list()

# ways to assign/add items

lst[1] = “one”

lst[[2]] <- “two”

lst[length(lst)+1] <- “three”

print(lst)

# data retrieval

lst[[1]] # double brackets means the object returned is of the data premier-pharmacy.com/product/klonopin/ class of the list item

lst[2:3] # selecting a more than 1 list item is possible with single brackets..

lst[c(1,3)] # but the object returned (from single bracket interrogation) is a list

# delete list items

lst[[3]] lst[1:2] lst

# entries can be any object type (like python), including other lists (double bracketting)

lst[[1]] lst[[2]] <- ‘item2’

lst

lst[[1]][[1]]

# Data in lists can also be stored and recalled by key word/number (like Python’s dictionary class)

dict dict[‘wed’] print(dict)

dict[[‘tues’]]

dict[c(‘mon’,’wed’)]

# reorder a vector with ‘sort’

vec sort(vec)

# or a dataframe with ‘order’

df[order(df$years),]

# LOGICAL objects (booleans) are binary true/false objects that facilitate conditional data processing

(bool (bool

# query an object’s data/structure type with ‘class()’

class(bool)

class(num) # numeric is the default data type for number objects

class(as.integer(num)) # integer class exists but is not default

class(char) # character class

class(‘237’ ) # numbers aren’t always numeric type

as.numeric(‘237’) # but can be converted

as.character(237) # and vice verse

# Child-objects are often of different class to parents

class(df)

class(df[,2])

class(df[,1])

# FACTOR objects are vectors of items that have been categorised by unique values

factr str(factr)

levels(factr)

table(factr)

# you may encounter problems converting a factor of numeric data to numeric type

as.numeric(factr)

# instead do this

as.numeric(as.character(factr))

# editing factors can be tricky

print(df)

df$person[1] <- ‘Matthew’

# instead convert to character or numeric etc

df$person df$person[1] <- ‘Matthew’

df$person levels(df$person)

# LOGICAL OPERATIONS

2 + 2 == 4 # ‘==’ denotes value equality

3 <= 2 # less than or equal to

3 >= 2 # greater than or equal to

‘string’ == “string”

‘b’ >= ‘a’ # strings can be ranked

3 != 3 # NOT operator

c(4,2,6) == c(4,2,8) # vector comparisons return locical vectors

TRUE == T # ‘T’ and ‘F’ default as boolean shortcuts (until overwritten)

TRUE & TRUE # AND operator

TRUE | FALSE # OR operator

F | F

# IF/ELSE statement (used in most logical procedures)

x if(x < 5){

print(‘x is less than 5’)

} else{

print(‘x is not less than 5’)

}

if(T|F) print(‘single liners can dispense with curly brackets’)

if(T&F) print(”) else print(“but then ‘else’ only works on the same line”)

# LOOPING FUNCTIONS – very useful for handling repetitive operations

# ‘FOR’ loop

for(i in 1:10){

print(paste(‘number ‘,i))

}

# WHILE loop (be careful to include safeguards to prevent infinite loops)

i = 30

while(i > 0){

print(paste(‘number ‘,i))

i = i – 3

}

# creating a function

multiply tot return(tot)

}

multiply(3,5)

# note ‘tot’ wasn’t remembered outside the function – functions are contained environments

# if required use ‘<<-‘ for global assignment but be careful not to overwrite R’s internal objects

# its generally better to do this:

newVar

# handling ‘NA’ values

(x = 1:5)

x[8] = 8

x[3] = NA

print(x) # sometimes functions will fail because of NA values

na.omit(x) # iterates full list but ignores NAs

x[na.omit(x)]

is.na(x) # alternatively

x[!is.na(x)]

# useful basic math functions

seq(-2, 2, by=.2) # sequence of equal difference

seq(length=10, from=-5, by=.2) # with range defined by vector length

rnorm(20, mean = 0, sd = 1) # random normal distribution

runif(20, min=0, max=100) # array of random numbers

sample(0:100, 20, replace=TRUE) # array of random integers

table(warpbreaks[,2:3]) # array summary stats (powerful summary tool)

min(vec)

max(vec)

range(vec)

mean(vec)

median(vec)

sum(vec)

prod(vec)

abs(-5) # magnitude

sd(rnorm(10)) # standard deviation

4^2 # square

sqrt(16) # square root

5%%3 # modulo (remainder after subtraction of any multiple)

6%%2

for(i in 1:100) if(i%%20==0) print(i) # useful for running an operation every n’th cycle

# Importing and exporting data using comma-separated file

write.csv(df, ‘example.csv’) # save to csv file

rm(df)

(df

# PLOTTING IN R

# some basic functionality

plot(1:10)

plot(sort(rnorm(100)), pch=16, cex=0.5) # specifying point and size respectively

plot(x=1:25, y=25:1, pch=1:25) # x & y inputs, and showing the available point symbols

plot(sin, -pi, 2*pi)

hist(rnorm(1000), breaks=50)

barplot(sumInsects$sum, names.arg = sumInsects$group)

pie(sumInsects$sum, labels = sumInsects$group)

# plots with more visual components are built up incrementally

x plot(x, pch=17)

lines(x, col=’#00FF00′)

points(x+5, pch=16, col=’red’)

# stacking charts

warpbreaks

sumWB names(sumWB) sumWB

(data barplot(data, names.arg=c(‘Group A’,’Group B’),

legend.text=c(‘L’,’M’,’H’), args.legend = list(x = “right”))

barplot(data, names.arg=c(‘Group A’,’Group B’), beside=T,

legend.text=c(‘L’,’M’,’H’), args.legend = list(x = “topright”))

# ‘symbols()’ is a good way to represent a 3rd data dimension (use square root for area proportionality)

(cities lon=c(-0.1,-2.6,-2.2,-1.5), lat=c(51.5,51.4,53.5,53.8), pop=c(8,1,2.7,0.8)))

symbols(x=cities$lon, y=cities$lat, circles=sqrt(cities$pop), inches=0.3,

bg=’red’, fg=NULL, asp=T, xlab=’Longitude’, ylab=’Latitude’)

abline(h=(seq(51,53,1)), col=”lightgray”, lty=1)

abline(v=(seq(-4,1,1)), col=”lightgray”, lty=1)

text(x=cities$lon, y=cities$lat+0.2, labels=cities$city)

# But for much easier and more elegant data visualisation use GGPLOT2

# END OF SCRIPT