Online R Training Courses
These multi-week online courses are presented by expert instructors from Statistics.com.
Contact Revolution Analytics for pricing and scheduling information.
Introduction to R: Data Handling
This course will provide a basic introduction to R, and its use in organizing and exploring data. The emphasis is on understanding and working with fundamental R data structures and we will introduce some basic R programming techniques. Once you've completed this course you'll be able to enter, save, retrieve, manipulate, and summarize data using R; you will also have the proper foundation to build your programming skills in R and take advantage of the full power of R.
Please note the related course, Introduction to R: Statistical Analysis (below), which is suitable for those needing to get up to speed very quickly in certain standard statistical analysis routines, without a systematic introduction to programming.
Introduction to R: Statistical Analysis
After completing this course, students will be able to use R to summarize and graph data, calculate confidence intervals, test hypotheses, assess goodness-of-fit, and perform linear regression. This is a course to learn R through your existing knowledge of basic statistics -- and for teachers who wish to use R in teaching introductory statistics. A solid understanding of statistical concepts is assumed, although familiarity with R is not required.
Please note the related course, Introduction to R: Data Handling (above), which provides an introduction to programming in R.
R Graphics
The aim of this course is to teach how to produce statistical plots of data using the R language and environment for statistical computing and graphics. R's graphics capabilities are particularly suited to producing publication-quality plots, so this course is very useful for anyone who needs to produce articles, reports or Web sites containing plots.
We will cover the creation of standard plots such as scatterplots, bar plots, histograms, and boxplots and we will spend some time on the underlying model used to produce plots in R so that you can extensively customize these plots. We will also look at producing Trellis plots in R and how to customize them. Finally, we will introduce the grid graphics system and look at producing unique plots from the ground up using basic components.
Participants should have a basic familiarity with R, such as that provided by the Introduction to R courses listed above.
R Modeling
In this course, you will learn how to use R to build statistical models and use them to analyze data. Multiple regression is covered first followed by logistic regression. The generalized linear model is then introduced and shown to include multiple regression and logistic regression as special cases. The Poisson model for count data will be introduced and the concept of overdispersion described. You will then learn how to analyse longitudinal data, first using relatively straightforward graphics and simple inferential approaches. This will be followed by describing mixed-effects models and the generalized estimating approach for such data. The emphasis in the course is how to use R to fit the models listed and how to interpret the R output, rather than the theoretical background of the models. Some knowledge of linear models is required.
R Programming
The aim of the course is to give statistical analysts the skills to work with a variety of data types and data sources in R. You'll also learn some techniques for programming "in-the-large" -- when you are trying to provide a suite of functions to flexibly solve a large class of problems. In particular, you'll learn more about functions, environments and closures, and the basics of object oriented-programming.
Participants should have a basic familiarity with R, such as that provided by the Introduction to R courses listed above.
The ggplot2 R Graphs Package
In this course, participants will learn how to use the ggplot R package to make, format, label and adjust graphs using R. The ggplot2 Project, created by Dr. Hadley Wickham, is named after the term "Grammar of Graphics," which was coined by Leland Wilkinson, the creator of Systat. This "grammar of graphics" is a system of describing and organizing the fundamental components of a graph and the process of creating a graph. Using ggplot2, participants will learn how to design and implement graphs in an efficient, elegant and systematic manner, following principles of general good graphing practice.
While ggplot2 is a mini-language specifically tailored for producing graphics, you will need some familiarity with data handling in R before taking this course. For this, we recommend you take the Introduction to R: Data Handling course described above.
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