b a r s l i n k l i n k f l a t Skip to content
Texas A&M University
  • Back to TAMIDS Homepage
Texas A&M Logo
Texas A&M Institute of Data ScienceTraining & Professional Development
  1. Biomedical Data Science
    • Data Science Primer
    • Python Primer
    • R Primer
  2. Professional Training
Texas A&M University
Texas A&M Logo
Texas A&M Institute of Data ScienceTraining & Professional Development
Biomedical Data Science Professional Training
  • Back to TAMIDS Homepage
Search
Data Science Primer
Python Primer
R Primer

R Primer

This primer comprises a sequence of instructional videos and R notebooks covering how to install R and several integrated development environments on various computer systems,a an introductory guide to the R language.

The table of contents below provides links to open the notebooks in Google Colab (from which they can also be downloaded), and to open the videos in the TAMIDS YouTube channel.

This primer was created by Matt Hielsberg for the Texas A&M Institute of Data Science

  1. Home
  2. R Primer

Quick Start Guide for Learning R Without Any Software Installation

  • Obtain a Google account if you don’t already have one; this is needed to run Google Colab. When opening each notebook, you may need to agree to run the content when prompted by Colab.
  • Watch the background video and open the accompanying notebook in Colab (as listed also in Section 1.1 below) for some basic information on the R language and a list of further resources.
  • Skip down below to the R Language Guide and start at Chapter 4

Learning Objectives For This R Primer

After completing this primer you should be able to do the following

  • Create and manage R projects using the R console, RStudio, RStudio Cloud, Google Colab, and Jupyter Notebook
  • Use the built-in help system to find details on functions, packages and data sets
  • Write and evaluate expressions using variable names
  • Understand name resolution and conflicts
  • Use basic data structures including vectors, lists, factors and data frames, among others.
  • Understand and use various control flow mechanisms, and how to replace looping structures using apply
  • Write functions, understand argument lists, use named and unnamed arguments including default values
  • Understand and utilize short-circuit and lazy evaluation
  • Find, install and load packages from CRAN
  • Read/Write basic custom data files
  • Visualize data using ggplot and other plotting tools
Chapter 1

Background and Installation

R Systems and Installation Guide
Section 1.1

Background and Installation

  • Notebook/Colab
  • Video (4:01)
Section 1.2.1

Installation: Introduction

  • Notebook/Colab
  • Video (3:19)
Section 1.2.2

Installation: Google Collaboratory

  • Video (3:23)
Section 1.2.3

Installation: RStudio Cloud

  • Video (2:30)
Section 1.2.4

Installation: R

  • MacOS – Video (1:59)
  • Ubuntu – Video (3:19)
  • Windows 10 – Video (1:59)
Section 1.2.5

Installation: Jupyter

  • MacOS – Video (5:09)
  • Ubuntu – Video (4:12)
  • Windows 10 – Video (2:15)
Section 1.2.6

Installation: IRKernel

  • MacOS – Video (4:02)
  • Ubuntu – Video (2:04)
  • Windows 10 – Video (2:02)
Section 1.2.7

Installation: Anaconda

  • MacOS – Video (2:07)
  • Ubuntu – Video (3:07)
  • Windows 10 – Video (2:04)
Section 1.2.8

Installation: RStudio

  • MacOS – Video (1:47)
  • Ubuntu – Video (2:28)
  • Windows 10 – Video (1:30)
Chapter 2

Getting Started with R Interpreter

R Systems and Installation Guide
Section 2.1

The R Interpreter

  • Notebook/Colab
  • Video (4:02)
Section 2.2

Getting Started With Scripts

  • Notebook/Colab
  • Video (4:13)
Section 2.3

Guidelines/Good Practices

  • Notebook/Colab
  • Video (5:10)
Chapter 3

Getting Started with Integrated Development Environments (IDEs)

R Systems and Installation Guide
Section 3.1

Jupyter Notebook

  • Notebook/Colab
  • Video (10:10)
Section 3.2

Google Colab

  • Notebook/Colab
  • Video 1 (7:31)
  • Video 2 (5:53)
Section 3.3

RStudio Desktop

  • Notebook/Colab
  • Video (14:02)
Chapter 4

Introduction

R Language Guide
Section 4.1

Getting Help in R

  • Notebook/Colab
  • Video 1 (4:02)
  • Video 2 (2:53)
Section 4.2

Calculator

  • Notebook/Colab
  • Video 1 (4:13)
  • Video 2 (2:30)
Section 4.3

Variables, Assignments
& Statements

  • Notebook/Colab
  • Video 1 (5:10)
  • Video 2 (3:27)
  • Video 3 (2:21)
  • Video 4 (5:25)
Chapter 5

Data Structures

R Language Guide
Section 5.1

Vectors, Sequences, and Indexing

  • Notebook/Colab
  • Video 1 (9:45)
  • Video 2 (6:50)
  • Video 3 (11:32)
Section 5.2

Character Vectors and Strings

  • Notebook/Colab
  • Video (9:03)
Section 5.3

Lists

  • Notebook/Colab
  • Video (6:28)
Section 5.4

Matrices

  • Notebook/Colab
  • Video 1 (9:19)
  • Video 2 (8:59)
Section 5.5

Factors

  • Notebook/Colab
  • Video (6:04)
Section 5.6

Data Frames

  • Notebook/Colab
  • Video 1 (9:23)
  • Video 2 (5:45)
  • Video 3 (7:29)
Chapter 6

Control Flow

R Language Guide
Section 6.1

Control Flow

  • Notebook/Colab
  • Video 1 (9:43)
  • Video 2 (12:30)
Section 6.2

Functions

  • Notebook/Colab
  • Video 1 (13:15)
  • Video 2 (8:41)
  • Video 3 (9:37)
Section 6.3

Apply

  • Notebook/Colab
  • Video (16:44)
Chapter 7

Packages

R Language Guide
Section 7.1

Packages

  • Notebook/Colab
  • Video (14:09)
Chapter 8

Data

R Language Guide
Section 8.1

Data

  • Notebook/Colab
  • Video 1 (13:40)
  • Video 2 (13:47)
  • Video 3 (10:27)
On This Page
  • Quick Start Guide for Learning R Without Any Software Installation
  • Learning Objectives For This R Primer
  • Background and Installation
  • Getting Started with R Interpreter
  • Getting Started with Integrated Development Environments (IDEs)
  • Introduction
  • Data Structures
  • Control Flow
  • Packages
  • Data
  • Quick Start Guide for Learning R Without Any Software Installation
  • Learning Objectives For This R Primer
  • Background and Installation
  • Getting Started with R Interpreter
  • Getting Started with Integrated Development Environments (IDEs)
  • Introduction
  • Data Structures
  • Control Flow
  • Packages
  • Data
Texas A&M Logo
Texas A&M Institute of Data ScienceTraining & Professional Development
  • © 2024 Texas A&M University
  • Site Policies
  • Accessibility