R

📈 Welcome to the R Section! Whether you’re brand new to R or aiming to sharpen your advanced skills, this section has something for you. It’s organized into three levels—R Basics, Intermediate, and Advanced—each carefully designed to build your programming and data analysis expertise. From your first print() statement to writing efficient code with functions and advanced data wrangling, you’ll develop the confidence to solve real-world problems using R.

Installation

Please refer to the Data Science Setup to make sure you are up and running!

Summary Table

Summary table of the R section.

Basics

Concept	Example	Description
numeric / double	3.14, 10	Numbers (with or without decimals), stored as double
integer	10L	Whole numbers with L suffix
character	"GW"	Text (strings)
logical	TRUE, FALSE	Boolean values
raw	charToRaw("Tyler")	Binary data in raw byte format
NULL	NULL	Represents no value
Assignment	x <- 10, y = 3.14	Assign value to a variable
Naming	value, .value, Value	Variable names must follow rules and are case-sensitive
Reassignment	x <- x + 1	Update variable value
Multiple Assignment	a <- b <- c <- 0	Assign same value to multiple variables
Dynamic Typing	x <- 5 then x <- "five"	Variables can change type
vector	c(95, 88, 90)	One-dimensional, same type
list	list(name = "Intro", code = 6101)	Collection of different types
matrix	matrix(c(1, 2, 3, 4), nrow = 2)	2D, same type structure
data.frame	data.frame(name = "Alice", score = 95)	Tabular structure with columns of possibly mixed types
factor	factor(c("male", "female"))	Categorical data with levels

Intermediate

Concept	Example	Description
One-Based Indexing	vec[1]	First element is at index 1
Negative Indexing	vec[-1]	Excludes the first element
Multi-Level Indexing	lst$name[1], substr(x, 1, 1)	Access nested or character-level elements
Slice Range	vec[2:4]	Inclusive range from index 2 to 4
Slice to End	vec[3:length(vec)]	Elements from index 3 to end
Full Slice	vec[]	All elements
Index Out of Bounds	vec[10]	Returns NA if index exceeds length
Mixed Positive/Negative	vec[c(-1, 2)]	Error if mixing negative and positive indices
if Statement	if (x > 0) { ... }	Runs block if condition is TRUE
else Statement	else { ... }	Runs if if condition is FALSE
else if Statement	else if (x == 0) { ... }	Adds more conditions
Logical Operators	&, |, !	Combine or negate logical conditions
Nested Conditionals	if (...) { if (...) { ... }}	Place one if inside another
for Loop	for (i in 1:5) { ... }	Iterate over values in a vector
Loop over Characters	for (i in seq_len(nchar(str)))	Index through a string
Loop over List	for (name in names(list)) { ... }	Iterate over names and values in a list
while Loop	while (x > 0) { ... }	Continue loop while condition is TRUE
break	if (x == target) break	Exit loop early
next	if (x == skip) next	Skip current iteration
Infinite Loop	while (TRUE) { ... }	Runs forever unless manually stopped

Advanced

Concept	Example	Description
Basic function	greet <- function() { print("Hi") }	Define a reusable block of code
Parameters	function(name) { print(name) }	Accept input values into the function
Return values	return(x + y)	Send a result back to the caller
Default values	function(name = "Guest")	Provide fallback values for parameters
Named arguments	fun(b = 2, a = 1)	Pass arguments by name in any order
Variable arguments	function(...)	Accept any number of inputs
Access ...	args <- list(...)	Convert ... to a list of named arguments
Multiple return values	return(list(sum = x + y, diff = x - y))	Return a list to simulate multiple return values
Missing required argument	fun(x) → fun()	Error if required arg isn’t provided
Missing parentheses	function(x (syntax error)	Missing closing ) breaks the function
Load a package	library(ggplot2)	Make all functions in a package available
Conditional loading	if (require(pkg)) { ... }	Load only if available; returns TRUE/FALSE
Aliased access	dplyr::filter()	Use function without loading full package
Load custom script	source("utils.R")	Run code from another R file
Access dataset	data(mtcars)	Load built-in dataset
View top rows	head(mtcars)	Show first 6 rows
Summary stats	summary(df)	Get mean, median, min, max, etc.
Handle NA	na.omit(df)	Remove missing data
Impute mean	df$var[is.na(var)] <- mean(var, na.rm=TRUE)	Replace NAs with average
Normalize	(x - min(x)) / (max(x) - min(x))	Rescale between 0 and 1
Scale (standardize)	scale(df)	Mean = 0, SD = 1
Scatter plot	ggplot(df, aes(x, y)) + geom_point()	Basic scatterplot using ggplot2
Histogram	geom_histogram(binwidth = 2)	Show frequency distribution
Import error	library(badpkg)	Error if package doesn’t exist
Function not loaded	select(df) without library(dplyr)	Error if function isn’t available in namespace