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R Programming Language Cheatsheet

A comprehensive cheat sheet for the R programming language, covering data structures, syntax, data manipulation, statistical analysis, and common functions.

Data Structures

Vectors

Definition

A one-dimensional array of elements of the same data type.

Creating Vectors

 
c(element1, element2, ...)
vector(mode = "numeric", length = 5)
seq(from = 1, to = 10, by = 2)
rep(x = 1:3, times = 2)

Accessing Elements

 
vector[index]
vector[c(index1, index2)]
vector[start:end]

Common Functions

 
length(vector)
is.vector(object)
as.vector(object)

Example

 
my_vector <- c(1, 2, 3, 4, 5)
print(my_vector[3]) # Output: 3

Matrices

Definition

A two-dimensional array of elements of the same data type.

Creating Matrices

 
matrix(data, nrow, ncol, byrow = FALSE, dimnames = NULL)

Accessing Elements

 
matrix[row, column]
matrix[row, ] # Entire row
matrix[, column] # Entire column

Common Functions

 
row(matrix)
col(matrix)
dim(matrix)
is.matrix(object)
as.matrix(object)

Example

 
my_matrix <- matrix(1:9, nrow = 3, ncol = 3)
print(my_matrix[2, 3]) # Output: 5

Lists

Definition

An ordered collection of elements, which can be of different data types.

Creating Lists

 
list(element1, element2, ...)
list(name1 = element1, name2 = element2, ...)

Accessing Elements

 
list[[index]]
list$name

Common Functions

 
length(list)
is.list(object)
as.list(object)
names(list)

Example

 
my_list <- list(name = "John", age = 30, grades = c(85, 90, 92))
print(my_list$age) # Output: 30

Data Frames

Definition

A table-like structure with columns of potentially different data types.

Creating Data Frames

 
data.frame(col1 = vector1, col2 = vector2, ...)
read.csv("file.csv")

Accessing Elements

 
dataframe$column
dataframe[row, column]
dataframe[row, ]
dataframe[, column]

Common Functions

 
row(dataframe)
col(dataframe)
dim(dataframe)
names(dataframe)
str(dataframe)
summary(dataframe)

Example

 
my_df <- data.frame(name = c("Alice", "Bob"), age = c(25, 30))
print(my_df$name) # Output: "Alice" "Bob"

Syntax and Basic Operations

Operators

Arithmetic

+, -, *, /, ^ (exponentiation), %% (modulo), %/% (integer division)

Relational

>, <, >=, <=, == (equal to), != (not equal to)

Logical

& (AND), | (OR), ! (NOT)

Assignment

<-, =, <<- (global assignment)

Example

 
x <- 10
y <- 5
z <- x + y # z is now 15

Control Flow

if Statement

 
if (condition) {
  # Code to execute if condition is TRUE
}

if…else Statement

 
if (condition) {
  # Code to execute if condition is TRUE
} else {
  # Code to execute if condition is FALSE
}

for Loop

 
for (variable in sequence) {
  # Code to execute for each element in the sequence
}

while Loop

 
while (condition) {
  # Code to execute while condition is TRUE
}

Example

 
for (i in 1:5) {
  print(i)
}

Functions

Definition

Reusable blocks of code that perform a specific task.

Defining a Function

 
function_name <- function(argument1, argument2, ...) {
  # Function body
  return(value)
}

Calling a Function

 
function_name(value1, value2, ...)

Example

 
add <- function(x, y) {
  return(x + y)
}
result <- add(3, 5) # result is now 8

Data Manipulation

dplyr Package

Description

A powerful package for data manipulation.

Key Functions

filter(): Filter rows based on conditions.
select(): Select columns.
arrange(): Arrange rows in order.
mutate(): Add new columns or modify existing ones.
summarize(): Compute summary statistics.
group_by(): Group data by one or more variables.

Example

 
library(dplyr)
df <- data.frame(group = c("A", "A", "B", "B"), value = c(10, 15, 20, 25))
df %>% group_by(group) %>% summarize(mean_value = mean(value))

tidyr Package

Description

A package for tidying data.

Key Functions

gather(): Convert wide format to long format.
spread(): Convert long format to wide format.
separate(): Separate one column into multiple columns.
unite(): Unite multiple columns into one.

Example

 
library(tidyr)
df <- data.frame(id = 1:2, var1 = c(10, 15), var2 = c(20, 25))
gather(df, key = "variable", value = "value", var1, var2)

Data Subsetting

Using Indices

 
data[rows, columns]

Using Logical Vectors

 
data[logical_vector, ]

Using subset() function

 
subset(data, condition)

Example

 
df <- data.frame(id = 1:5, value = c(10, 15, 20, 25, 30))
df[df$value > 15, ]

Statistical Analysis

Descriptive Statistics

Functions

mean(x): Mean of vector x.
median(x): Median of vector x.
sd(x): Standard deviation of vector x.
var(x): Variance of vector x.
quantile(x, probs): Quantiles of vector x.
summary(x): Summary statistics of vector x.

Example

 
x <- c(1, 2, 3, 4, 5)
mean(x) # Output: 3
sd(x) # Output: 1.581139
summary(x)

Hypothesis Testing

t-tests

t.test(x, y, alternative = "two.sided", mu = 0, paired = FALSE, var.equal = FALSE, conf.level = 0.95)

  • x, y: Numeric vectors.
  • alternative: Type of test (“two.sided”, “less”, “greater”).
  • mu: Null hypothesis value.
  • paired: TRUE for paired t-test.
  • var.equal: TRUE for equal variances.

Chi-squared Test

chisq.test(x, y, correct = TRUE)

  • x, y: Numeric vectors or matrices.
  • correct: Apply Yates’ continuity correction.

Example

 
x <- rnorm(50, mean = 10, sd = 2)
y <- rnorm(50, mean = 12, sd = 2)
t.test(x, y)

Linear Regression

Function

lm(formula, data)

  • formula: Model formula (e.g., y ~ x).
  • data: Data frame.

Example

 
df <- data.frame(x = 1:10, y = 2*(1:10) + rnorm(10))
model <- lm(y ~ x, data = df)
summary(model)
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