R Cheatsheet

This Cheatsheet is taken Directly From Base R Cheat Sheet

Getting Help

Accessing the help files

# Get help of a particular function.
?mean
# Search the help files for a word or phrase.
help.search(‘weighted mean’)
# Find help for a package. 
help(package = ‘dplyr’)

More about an object

# Get a summary of an object’s structure.
str(iris)
# Find the class an object belongs to.
class(iris)

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.

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.

Vectors

Creating Vectors


`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)
# Return x sorted.
rev(x)
# Return x reversed.
table(x)
# See counts of values.
unique(x)
# See unique values.

Selecting Vector Elements

By Position

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

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

x[‘apple’] Element with name ‘apple’.

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)
}

Reading and Writing Data

Input	Ouput	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 and write.table.
`load(‘file.RData’`)	`save(df, file = ’file.Rdata’)`	Read and write an R data file, a file type special for R.

Conditions


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

Data Types


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

Maths 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

Variable Assignment

a <- 'apple'
a

output

[1] 'apple'

The Environment

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

Matrixes

m <- matrix(x, nrow = 3, ncol = 3) # Create a matrix from x.
m[2, ] #  Select a row
m[ , 1] #  Select a column
m[2, 3] #  Select an element
t(m) # Transpose
m %*% n#  Matrix Multiplication
solve(m, n) # Find x in: m * x = n

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.

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.
df$x # grab column of name x
df[[2]] # grab second column values
df[ , 2] # grab second column
df[2, ] # grab second row
df[2, 2] # grab the value in the second column and second row
nrow(df) # number of rows
ncol(df) # number of columns
dim(df) # number of rows and number of columns
cbind # bind by columns
rbind # bind by rows

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.

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.

Statistics

lm(x ~ y, data=df) # Linear model.
glm(x ~ y, data=df) # Generalised linear model.
summary # Get more detailed information out a model.
t.test(x, y) # Preform a t-test for difference between means.
pairwise.t.test # Preform a t-test for paired data.
prop.test # Test for a difference between proportions.
aov # Analysis of variance.

Distributions

	Random Variates	Density Function	Cumulative Distribution	Quantile
Normal	`rnorm`	`dnorm`	`pnorm`	`qnorm`
Poison	`rpois`	`dpois`	`ppois`	`qpois`
Binomial	`rbinom`	`dbinom`	`pbinom`	`qbinom`
Uniform	`runif`	`dunif`	`punif`	`qunif`