1 R Syntax

Key syntax and operators.

1.1 Basic operators

Action	Operator	Example
Subtract	-	`5 - 4` = 1
Add	+	`5 + 4` = 9
Multiply	*	`5 * 4` = 20
Divide	/	`5 / 4` = 1.25
Raise to the power	^	`5 ^ 4` = 625
Modulus	%%	`9 %% 4` = 1
Integer division	%/%	`9 %/% 4` = 2
Basic sequence	:	`1:3` = 1, 2, 3

1.2 Comparison operators

Action	Operator	Example
Less than	<	`5 < 5` = FALSE
Less than or equal to	<=	`5 <= 5` = TRUE
Greater than	>	`5 > 5` = FALSE
Greater than or equal to	>=	`5 >= 5` = TRUE
Equal	all.equal()	`all.equal(0.5 - 0.3,0.3 - 0.1)` is TRUE
Exactly equal	==	`(0.5 - 0.3) == (0.3 - 0.1)` is FALSE, 2 == 2 is TRUE
Not equal	!=	`(0.5 - 0.3) != (0.3 - 0.1)` is TRUE, 2 != 2 is FALSE

1.3 States

States	Representation
True	`TRUE` `1`
False	`FALSE` `0`
Empty	`NULL`
Unknown	`NA`
Not a number e.g. `0/0`	`NaN`
Infinite e.g. `1/0`	`Inf`

1.4 Logical operators

Action	Operator	Example
Not	!	`!TRUE` is FALSE
And	&	`TRUE & FALSE` is FALSE, `c(TRUE,TRUE) & c(FALSE,TRUE)` is FALSE, TRUE
Or	`\|`	`TRUE \| FALSE` is TRUE, `c(TRUE,FALSE) \| c(FALSE,FALSE)` is TRUE, FALSE
Xor	xor()	`xor(TRUE,FALSE)` is TRUE
Bitwise And	&&	`c(TRUE,TRUE) && c(FALSE,TRUE)` is FALSE
Bitwise Or	`\|\|`	`c(TRUE,FALSE) \|\| c(FALSE,FALSE)` is TRUE
In	%in%	`"Red" %in% c("Blue","Red")` is TRUE
Not in	!( x %in% y)	`!("Red" %in% c("Blue","Red"))` = FALSE

1.5 Control constructs

Type	Implementation	Example
If	if(condition) {dosomething}	`if(TRUE) { 2 }` is 2
If else	if(condition) {do something} else {do something different} or ifelse(condition, do something, do something else)	`if(FALSE) { 2 } else { 3 }` is 3 `ifelse(FALSE, 2, 3)` is 3
For loop	for(i in seq) {dosomething} or foreach(i=1:3) %do% {something}	`for(i in 1:3) {print(i)}` is 1, 2, 3
While loop	while(condition) {do something }	`a<-0 ; while(a<3){a<-a+1} ; a` is 3
Switch	switch(value, …)	`switch(2, "a", "b")` is b
Case	memisc::cases(…)	`cases("pi<3"=pi<3, "pi=3"=pi==3,"pi>3"=pi>3)` is pi>3

NB: If you find yourself using a loop, there’s probably a better, faster solution

1.6 Assignment operators

Action	Operator	Example
Create / update a variable	<-	`a <- 10`

NB: There are others you could use, but this is the best practice

1.7 Accessors

Action	Operator	Example
Use public function from package	::	`memisc::cases()`
Use private function from package	:::	`optiRum:::pounds_format()`
Get a component e.g a data.frame column	$	`iris$Sepal.Length`
Extract a property from a class	`@`	Won’t be used in this course
Refer to positions in a data.frame or vector	[ ]	`iris[5:10,1]`
Refer to item in a list	[[ ]]	`list(iris=iris,mtcars=mtcars)[["iris"]]`

1.8 Meta-operators

Action	Operator	Example
Comment	#	`# This is my comment`
Help	?	`?data.table`
Identifier	`	`1`<-2

1.9 Exercises

Create a variable called Scores that holds the numbers 1 to 10
Return whether each number in Scores is an even number
Get the help text for the function mean
Perform a test to see if R variable names are case sensitive

1.10 Answers

#1
Scores<-1:10
#2
(Scores %% 2) ==0

##  [1] FALSE  TRUE FALSE  TRUE FALSE  TRUE FALSE  TRUE FALSE  TRUE

#3
#?mean
#4
scores

## Error in eval(expr, envir, enclos): object 'scores' not found

2 R Data types & structures

A brief introduction to data structures and types

2.1 Data types

These are the core data types. There are additonal ones like dates with timestamps(POSIXct and POSIXlt) and ordered factors

Data type	Example
Integer	1
Logical	TRUE
Numeric	1.1
String / character	“Red”
Factor (enumerated string)	“Amber” or 2 in c(“Red”,“Amber”,“Green”)
Complex	i
Date	“2016-05-19”

2.2 Data structures

These are the out of the box data structures. There are other data structure that use these as the basis like data.table or a time-series object.

Data type	Info	Construction example(s)
Vector	A 1D set of values of the same data type	c(1,“a”) , 1:3 , LETTERS
Matrix	A 2D set of values of the same data type	matrix(LETTERS,nrow=13, ncol=2) , rbind(1:5,2:6)
Array	An nD set of values of the same data type	array(LETTERS, c(13,2))
Data.frame	A 2D set of values of different data types	data.frame(a=1:26, b=LETTERS)
List	A collection of objects of various data types	list(vector=c(1,“a”), df=data.frame(a=1:6))
Classes	A class is like a formalised list and can also contain functions i.e. methods	Won’t be covered in this class

2.3 Exercises

Make a variable called ID containing the numbers 1 to 52
Make a variable called category holding 52 randomly selected letters (using sample)
Make a variable called records that is a data.frame containing ID and category

2.4 Answers

#1
ID<-1:52
#2
category<-sample(LETTERS,52, replace=TRUE)
#3
records<-data.frame(ID,category)
head(records)

##   ID category
## 1  1        L
## 2  2        W
## 3  3        L
## 4  4        K
## 5  5        I
## 6  6        I

3 R Functions

3.1 Using functions

Use an argument’s position for well known functions e.g. sum(iris$Sepal.Width)
Use argument names for less well known functions (or less common arguments) e.g. seq(from=1, to=5, by=1)
Use argument names to call arguments out of order e.g.seq(by=1, from=1, to=5 )
Get help for a function that’s in a loaded package with ? e.g. ?mean
Get help for a function that’s not loaded with ?? e.g. ??adist
Search for text or a function in the help with help.search() e.g. help.search("concat")
Get the definition of a function by calling it without brackets e.g. Sys.Date

3.2 Functions can …

have named arguments e.g. ifelse(test, yes, no)
take any number of arguments e.g. mean(...)
take a combination of named and unlimited arguments e.g. seq.int(from, to, by, length.out, along.with, ...)
act as operators e.g. +,%%,%>%
work as you’d would normally expect e.g. mean(iris$Sepal.Width)
return functions e.g. scales::gradient_n_pal
have side-effects like making a plot and also returning some data

3.3 Basic function writing

Functions are created with the syntax function(args){code}
Functions do not need to be stored to be used
Store a function with myfunc<-function(args){code}
Functions will usually output whatever object was last called but not stored f<-function(){1}; f()
Use the return() function to output something specific f<-function(){return(1)}; f()
Make an argument optional by providing it with a default value or NULL

myfunc<-function(arg1, arg2=NULL){
print(arg1)
if(!is.null(arg2)) return(arg2)
}

3.4 Exercises

Use the function cut() to produce split floor(rnorm(n = 100, mean=50,sd=20)) into 5 bins with numbers representing the bins they were allocated
Write a function that says “Hello world” or says hello to any string or number passed in (use paste)

3.5 Answers

#1
nums<-floor(rnorm(n = 100, mean=50,sd=20))
bins<-cut(nums, breaks=5, labels=FALSE)

#2
hello<-function(x=NULL){
  if(is.null(x)) x<-"world"
  res<-paste0("Hello ", x, "!")
  return(res)
}
hello()

## [1] "Hello world!"

hello("Dave")

## [1] "Hello Dave!"

hello(c("Dave","Jane", "7"))

## [1] "Hello Dave!" "Hello Jane!" "Hello 7!"

Getting started with R

Steph Locke

2016-05-19