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layout: ../../layouts/CheatSheet.astro title: "R Language Cheatsheet"

Synopsis

R is a programming language and free software environment for statistical computing and graphics supported by the R Foundation for Statistical Computing. The R language is widely used among statisticians and data miners for developing statistical software and data analysis.

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’.

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.

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.

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

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)
}

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

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.

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.

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

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

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.

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.

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.

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.

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.

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.

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

Plotting

plot(x)
#Values of x in order.

enter image description here plot(x, y) #Values of x against y enter link description here hist(x) #Histogram of x. enter image description here