Introduction to Data Analysis Using R workshop

1. Overview

R is a popular programming language for statistical computing and graphics. This 3-day workshop focuses on the basic concepts and features of R that will serve as building blocks for data analysis tasks. The workshop is taught using RStudio in the Interactive Data Analytics Service (IDAS).

2. Prerequisites

No experience with R or RStudio is required. A basic understanding of general statistical concepts, as typically taught in the first college-level statistics course, may be helpful for attendees to follow the sections on statistics. (See the workshop agenda below.)

3. Eligibility

This workshop is available to current University of Iowa members only. 

4. How to register

Click here then log in with your HawkID and password. Click “Register now” at the bottom of the page to register. After registering successfully, an automated email with a Zoom link will be sent to your University of Iowa email. Registration will close at 11 am on Monday, April 4, 2022.

5. Additional information         

If you have any questions, please see the workshop FAQs or contact research-computing@uiowa.edu.

6. Workshop agenda

This workshop is taught in 3 sessions over 3 days. Each session builds on the previous ones. Participants are encouraged to attend all sessions in order to learn the complete contents of the workshop.

Again, note that we start by learning the basic concepts and features of R that will be relevant for data analysis tasks later. If you are already familiar with the concepts below, please see the workshop FAQs for a list of additional, free learning resources.

Tentative topics to be covered:

Day 1

• Brief overview of R and RStudio
• RStudio interface - the Console, Environment, and Files panes
• Getting help in R
• Getting and setting the working directory
• Working with a script file
• Built-in functions
• Variable assignment
• Vectors

Day 2

• Basic descriptive statistics
• Booleans
• Comparison statements
• Indexing
• Packages
• Built-in datasets
• Reading and writing to CSV files
• Data Frames

Day 3:

• Conditional statements
• Data transformations
• (selected topics as time permits) General statistics