R Basics

Author

Affiliation

Julianne Clina, PhD

University of Kansas Medical Center

Published

May 28, 2026

1 R and Positron

1.1 What is R?

R is a programming language designed for:

statistical analysis
data visualization
reproducible research
data science workflows

1.2 What is Positron?

Positron is a modern development environment created by Posit for working with R and Python.

Compared to traditional RStudio, Positron offers:

a cleaner interface
modern editor features
AI-assisted coding tools
multi-language support
improved performance

R is the language. Positron/RStudio is the workspace where you write and run code.

Tip

See this tutorial on Installing R and Positron if you need help prior to the first day of R-LAB.

2 Components of an IDE

Component	Description
Console	Run R commands and view errors
Source	Write and save scripts
Environment	View objects and datasets
Output	Display plots and tables
Files	Manage project files
Packages	Install and load packages
Help	Access documentation
Terminal	Run system commands

2.1 IDE Layout

3 Console vs Script Files

3.1 Console

The Console runs commands immediately.

Use it for:

quick calculations
testing code
exploring objects

Example:

2 + 2

3.2 Script Files

Scripts let you save and organize code.

Benefits include:

reproducibility
easier debugging
collaboration
cleaner workflows

Most analyses should be written in scripts instead of directly in the Console.

my_number <- 2 + 2

4 Running Code

Common shortcuts:

Action	Windows	Mac
Run line/code	Ctrl + Enter	Cmd + Enter
Save file	Ctrl + S	Cmd + S
Find	Ctrl + F	Cmd + F

You can run:

one line
selected code
entire scripts

5 Projects and Directories

5.1 Why Use Projects?

Projects help:

keep files organized
separate analyses
improve reproducibility
simplify collaboration

5.2 What is a Working Directory?

The working directory is the default folder where R reads and saves files.

When you open a project, the working directory is automatically set to that project folder.

6 Creating Objects

R stores information in objects using the assignment operator <-.

age <- 25
study_group <- "control"
height_inches <- 68

6.1 Naming Tips

Good variable names are:

descriptive
lowercase
consistent

Recommended style:

blood_pressure
participant_age
study_group

Avoid vague names like:

x
data1
thing

7 Packages and Functions

7.1 What is a Package?

A package is a collection of functions, datasets, and tools designed for a specific purpose.

7.2 What is a Function?

Functions:

take input
perform an action
return output

A package is like a toolbox. A function is a single tool inside the toolbox.

8 tidyverse Packages

Package	Purpose	Example Functions
dplyr	Data wrangling	`filter()`, `mutate()`
tidyr	Data organization	`pivot_longer()`
ggplot2	Visualization	`geom_point()`
readr	Importing data	`read_csv()`

9 Installing vs Loading

Installing only happens once:

install.packages("ggplot2")

Loading happens every session:

library(ggplot2)

10 Using Functions in R

Functions require inputs called arguments.

Use:

help(mean)

or:

?mean

to access documentation.

11 Error Messages

Errors are a normal part of programming.

Common causes include:

misspelled object names
missing parentheses
missing packages
incorrect data types

Learning to read error messages carefully is an essential R skill.

Example:

mean(c(1, 2, "3"))

12 Updating Packages

12.1 Why Update?

Updating software helps:

fix bugs
improve performance
add new features
improve package compatibility

12.2 Update Packages

update.packages(ask = FALSE)

Install an individual package:

install.packages("package_name")

13 Atomic Data Types

13.1 Common Data Types

Type	Example	Typical Use
double	`34.2`	BMI, blood pressure
integer	`15L`	IDs, counts
character	`"placebo"`	Labels, groups
logical	`TRUE`	Conditions

Integers and doubles are both numeric types.

# Double
bmi <- 24.8
class(bmi)

# Integer
id <- 3L
class(id)

# Character
treatment_arm <- "placebo"
class(treatment_arm)

# Logical
has_hypertension <- FALSE
class(has_hypertension)

14 Why Data Types Matter

Functions behave differently depending on data type.

num1 <- 5
num1 + 2

num2 <- "5"
num2 + 2

15 Vectors

Vectors store multiple values of the same type.

v1 <- c(1:5)

v2 <- c("cat", "house", "bunny")

v3 <- c(v1, v2)
v3

16 Factors

Factors are used for categorical variables.

bloodtype_factor <- factor(c("A", "B", "AB", "O"))

height_factor <- factor(
  c("short", "medium", "tall"),
  ordered = TRUE,
  levels = c("short", "medium", "tall")
)

17 Lists

Lists can store mixed data types.

my_list <- list(
  name = "Bill",
  age = 35,
  scores = c(92, 87, 95)
)

my_list

18 Data Frames vs Tibbles

18.1 Data Frames

Base R structure
Allows partial matching
May automatically convert types

18.2 Tibbles

Part of the tidyverse
Cleaner printing
Requires exact column names
Safer behavior

library(tibble)

tb <- tibble(
  age = c(25, 30, 45),
  average_height = c(55, 62, 50)
)

tb

19 GitHub and CRAN

Source	Purpose	Install Method
CRAN	Stable releases	`install.packages()`
GitHub	Development versions	`devtools::install_github()`

19.1 Install from CRAN

install.packages("ggplot2")

19.2 Install from GitHub

install.packages("devtools")

devtools::install_github("bhelsel/RLAB")

20 Forking vs Cloning

Forking	Cloning
Copies repo to GitHub account	Copies repo locally
Used for contributing	Used for working locally

Typically you fork first, then clone your fork.

21 Reproducible Research

One major goal of R programming is reproducibility.

A reproducible project allows someone else to:

run the same code
obtain the same results
understand the workflow

Good practices include:

using projects
organizing files
saving scripts
commenting code

22 What is Quarto?

Quarto combines:

code
text
figures
results

into a single document.

Quarto can create:

HTML pages
PDFs
presentations
websites

This document was created using Quarto.

23 Free Learning

Cheat Sheets

https://r4ds.hadley.nz/
https://www.youtube.com/@RProgramming101
https://www.youtube.com/@EquitableEquations
https://data-flair.training/blogs/r-tutorials-home/
https://www.coursera.org/learn/r-programming