Statistics With R

A 2-Day Seminar Taught by Andrew Miles, Ph.D.

To see a sample of the course materials, click here.


R is a free and open-source package for statistical analysis that is widely used in the social, health, physical, and computational sciences. Researchers gravitate to R because it is powerful, flexible, and has excellent graphics capabilities. It also has a large and rapidly growing community of users.    

This course is designed as an introduction to R for those who are looking to use R for applied statistical tasks. Topics include data coding and management as well how to perform basic descriptive, bivariate, and multivariate analyses. We will also address the fundamentals of programming in R, using plots to explore data, and how R can simplify the process of exporting the results from statistical analyses. Time permitting, we can also discuss other topics of interest to course participants. To be clear, this course does not teach the principles of data management or statistical analysis. Instead, it assumes prior knowledge of these topics and focuses on explaining how they can be implemented in R.

There is no way to cover all the possible uses of R in a single course, so an important theme will be helping participants understand the fundamentals of how R “thinks” so that they can begin to use R independently. For this reason, the course focuses on basic R functions and practical issues like interpreting output and getting help. After this course, participants will be well-equipped to tailor R to the sort of work they do.

This course is thoroughly hands-on. Participants are encouraged to write code along with the instructor, and to participate in the carefully-designed exercises that will be interspersed throughout the two days. By the end of the course, participants can expect to log more than a dozen hours of guided practice coding in R.


COMPUTING

To participate in the hands-on exercises, you are strongly encouraged to bring a laptop computer with the most recent version of R installed. Participants are also encouraged to download and install RStudio, a front-end for R that makes it easier to work with. This software is free and available for Windows, Mac, and Linux platforms.


WHO SHOULD ATTEND?

This course is for anyone who wants to learn R. No prior knowledge of R is assumed. However, participants should have prior experience with data management, and a basic understanding of fundamental bivariate and multivariate statistics including linear regression and the generalized linear model.


LOCATION, Format, and MATERIALS

The class will meet from 9 am to 5 pm each day with a 1-hour lunch break at the Courtyard by Marriott Chicago Downtown Magnificent Mile, 165 E Ontario St, Chicago, IL 60611.

Participants receive a bound manual containing detailed lecture notes (with equations and graphics), examples of computer printout, and many other useful features. This book frees participants from the distracting task of note taking.


Registration and lodging

The fee of $995 includes all course materials. The early registration fee of $895 is available until May 6.

Refund Policy

If you cancel your registration at least two weeks before the course is scheduled to begin, you are entitled to a full refund (minus a processing fee of $50). 

Lodging Reservation Instructions

A block of guest rooms has been reserved at the Courtyard by Marriott Chicago Downtown Magnificent Mile, 165 E Ontario St, Chicago, IL 60611, where the seminar takes place, at a special rate of $229. In order to make reservations, click here. For guaranteed rate and availability, you must reserve your room no later than Monday, May 6, 2019.

We also recommend going directly to the hotel’s website or checking other online hotel sites. Pricing varies and you may be able to secure a better rate. 


SEMINAR OUTLINE

  • Basic Structure of R
  • Data Basics
    • Basic Data Structures in R
    • Examining Objects
    • Importing and Exporting Data
    • Merging Data
    • Sorting Data
  • Cleaning Up the Workspace
  • Functions, Help Files, and Nesting
  • Examining Data
    • Attaching Objects and R’s Search Path
    • Descriptive Statistics
    • Exploratory Data Plots
  • Data Coding
    • Logical Operators
    • Recoding Data
    • Coding Missing Values
  • Bivariate Analyses
    • Analyses of Continuous Variables
    • Analyses of Categorical Variables
  • Multivariate Analyses
    • Linear Models
    • Generalized Linear Models
  • Additional Topics
    • Control Structures
    • Writing Functions
    • Exporting Results
  • Addendum: Getting Help