r-cheatsheet.md

R Programming Basics CheatSheet

Table of Contents

• Vector
  • Creating Vectors
  • Vector Functions
  • Selecting Vector Elements
• Getting Help
  • Accessing the Help Files
  • More About An Object
• Working Directory
• Using Libraries
• Programming
  • For Loop
  • While Loop
  • If Statements
  • Functions
• Conditions
• Reading and Writing Data
• Data Type Conversions
• Matrixes
  • Selecting Segments from a Matrix
  • Matrix Functions
• Strings
• Factors
• Math Functions
• Lists
• Statistics
• Variable Assignment
  • The Environment
• Data Frames
  • Understanding a data frame
  • List subsetting
  • Matrix subsetting
  • Data Frame Functions
• Distributions
• Plotting

Vector

Creating Vectors

Initialization	Vector	Definition
c(2,4,6)	2 4 6	Join elements into a vector
2:6	2 3 4 5 6	An Integer Sequence
seq(2, 3, by=0.5)	2.0 2.5 3.0	A complex sequence
rep(1:2, times=3)	1 2 1 2 1 2	Repeat a vector
rep(1:2, each=3)	1 1 1 2 2 2	Repeat elements of a Vector

Vector Functions

sort(x)
#Returns x sorted.
    
rev(x)
#Returns x reverse.

table(x)
#See count of values.

unique(x)
#See unique Values.

Selecting Vector Elements

By Position

Example	Definition
x[4]	The fourth element.
x[-4]	All but the fourth.
x[2:4]	Elements two to four.
x[-(2:4)]	All elements except two to four.
x[c(1:5)]	Elements one and five.

By Value

Example	Definition
x[x == 10]	Elements which are equal to 10.
x[x < 0]	All elements less than zero.
x[x %in% c(1, 2, 5)]	Elements in the set 1, 2, 5.

Named Vectors

Example	Definition
x[‘apple’]	Element with name ‘apple’.

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Getting Help

Accessing the help files

?mean 
#Get help of a particular function. 

help.search(‘weighted mean’) 
#Search the help files for a word or phrase. 

help(package = ‘dplyr’) 
#Find help for a package.

More about an object

str(iris) 
#Get a summary of an object’s structure. 

class(iris) 
#Find the class an object belongs to.

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Working Directory

getwd() 
#Find the current working directory (where inputs are found and outputs are sent).

setwd(‘C://file/path’) 
#Change the current working directory

Use projects in RStudio to set the working directory to the folder you are working in.

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Using Libraries

install.packages(‘dplyr’) 
#Download and install a package from CRAN. 

library(dplyr) 
#Load the package into the session, making all its functions available to use. 

dplyr::select 
#Use a particular function from a package. data(iris) Load a built-in dataset into the environment

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Programming

For Loop

for (variable in sequence)
{
 Do something
}

#Example:
for (i in 1:4)
{
 j <- i + 10 
 print(j) 
}

While Loop

while (condition)
{
 Do something
}

#Example:
while (i < 5)
{
 print(i) 
 i <- i + 1
}

If statements

if (condition)
{ 
Do something 
} 
else 
{ 
Do something different 
}

#Example:
if (i > 3)
{ 
print(‘Yes’) 
} 
else 
{ 
print(‘No’) 
}

Functions

function_name <- function(var)
{
Do something 
return(new_variable) 
}

#Example:
square <- function(x)
{ 
squared <- x*x 
return(squared) 
}

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Conditions

Condition	Definition
a == b	Are equal
a != b	Not equal
a > b	Greater than
a < b	Less than
a >= b	Greater than or equal to
a <= b	Less than or equal to
is.na(a)	Is missing
is.null(a)	Is null

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Reading and Writing Data

Input	Output	Description
df <- read.table(‘file.txt’)	write.table(df, ‘file.txt’)	Read and write a delimited text file.
df <- read.csv(‘file.csv’)	write.csv(df, ‘file.csv’)	Read and write a comma separated value file. This is a special case of read.table/ write.table.
load(‘file.RData’)	save(df, file = ’file.Rdata’)	Read and write an R data file, a file type special for R.

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Data Type Conversions

Converting between common data types in R. Can always go from a higher value in the table to a lower value.

Data Type	Variables	Definition
as.logical	TRUE, FALSE, FALSE	Boolean values (TRUE or FALSE).
as.numeric	1, 0, 0	Integers or floating point numbers.
as.character	'1', '0', '0'	Character strings. Generally preferred to factors.
as.factor	'1', '0', '1', levels: '1', '0'	Character strings with preset levels. Needed for some statistical models.

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Matrixes

Selecting Segments from a Matrix

m <- matrix(x, nrow = 3, ncol = 3) 
#Create a matrix from x.

---

m[2, ] - Select a row

O	O	O

---

m[ ,1] - Select a row

O
O
O

---

m[2,3] - Select an element

		O

---

Matrix Functions

t(m)
#Transpose 

m %*% n 
#Matrix Multiplication 

solve(m, n) 
#Find x in: m * x = n

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Strings

paste(x, y, sep = ' ') 
#Join multiple vectors together. 

paste(x, collapse = ' ') 
#Join elements of a vector together. 

grep(pattern, x) 
#Find regular expression matches in x. 

gsub(pattern, replace, x) 
#Replace matches in x with a string. 

toupper(x) 
#Convert to uppercase. 

tolower(x) 
#Convert to lowercase. 

nchar(x) 
#Number of characters in a string

Also see the stringr library

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Factors

factor(x) 
#Turn a vector into a factor. Can set the levels of the factor and the order.

cut(x, breaks = 4) 
#Turn a numeric vector into a factor but ‘cutting’ into sections.

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Math Functions

log(x) 			- 	Natural log. 
sum(x) 			- 	Sum. 
exp(x) 			- 	Exponential. 
mean(x) 		- 	Mean. 
max(x) 			- 	Largest element. 
median(x) 		-	Median. 
min(x) 			- 	Smallest element. 
quantile(x) 	- 	Percentage quantiles. 
round(x, n) 	- 	Round to n decimal places. 
rank(x) 		- 	Rank of elements. 
signif(x, n) 	-	Round to n significant figures. 
var(x) 			-	The variance. 
cor(x, y) 		- 	Correlation. 
sd(x) 			- 	The standard deviation.

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Lists

l <- list(x = 1:5, y = c('a', 'b')) 
#A list is collection of elements which can be of different types.

---

l[[2]]
#Second element of l.

l[1]
#New list with only the first element.

l$x
#Element named x.

l['y']
#New list with only element named y.

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Statistics

Model	Definition.
lm(x ~ y, data=df)	Linear model.
glm(x ~ y, data=df)	Generalised linear model.
summary( )	Get more detailed information out a model.

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Variable Assignment

> a <- 'apple'
> a 
[1] 'apple'

The Environment

Function	Definition
ls()	List all variables in the environment.
rm()	Remove x from the environment.
rm(list = ls()	Remove all variables from the environment.

You can use the environment panel in RStudio to browse variables in your environment.

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Data Frames

df <- data.frame(x = 1:3, y = c('a', 'b', 'c')) 
#A special case of a list where all elements are the same length.

X	Y
1	a
2	b
3	c

Understanding a data frame

View(df)
#See the full data frame. 

head(df) 
#See the first 6 rows.

List subsetting

df$x

O
O
O
O

df[[2]]

	O
	O
	O
	O

Matrix subsetting

df[ ,2]

	O
	O
	O
	O

df[2, ]

	O
	O
	O
	O

Data Frame Functions

nrow(df) 
#Number of rows. 

ncol(df) 
#Number of columns. 

dim(df) 
#Number of columns and rows.

Also see the dplyr library.

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Distributions

	Random Variates	Density Function	Cumulative Distribution	Quantile
Normal	rnorm	dnorm	cnorm	qnorm
Poison	rpois	dpois	cpois	qpois
Binomlal	rbinom	dbinom	cbinom	qbinom
Uniform	runif	dunif	cunif	qunif

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Plotting

plot(x) 
#Values of x in order.

plot(x, y) 
#Values of x against y

hist(x)
#Histogram of x.

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