by Joseph Rickert
Early October: somewhere the leaves are turning brilliant colors, temperatures are cooling down and that back to school feeling is in the air. And for more people than ever before, it is going to seem to be a good time to commit to really learning R. I have some suggestions for R courses below, but first: What does it mean to learn R anyway? My take is that the answer depends on a person's circumstances and motivation.
I find the following graphic to be helpful in sorting things out.
The X axis is time on Malcolm Gladwell's "Outliers" scale. His idea is that it takes 10,000 hours of real effort to master anything, R, Python or Rock and Roll Guitar. The Y axis lists increasingly difficult R tasks, and the arrows within the plot area are labels increasingly proficient types of R users.
The point I want to make here is that a significant amount of very productive R work happens in the area around the red ellipse. So, while their is no avoiding "10,000" hours of hard work to become an R Jedi knight, a curious and motivated person can master enough R to accomplish his/her programming goals with a more modest commitment. There are three main reasons for this:
R's functional programming style is very well suited for statistical modeling, data visualization and data science tasks
The 7,000+ packages available in the R ecosystem provide tens of thousands of functions that make it possible to accomplish quite a bit without having to write much code
Numerous, high quality books and online material devoted to teaching statistical theory and data science with R
If you have some background in some area of statistics or data science a viable strategy for learning R is to identify a resource that works for you and just jump into the middle of things, picking up R as you go along.
The lists below link to courses that can either start you on a formal programming path, or help you become a productive R user in a particular application area. Some of the courses are "live events" that you take with a cohort of students, others are set up for self study.
The courses devoted to teaching R as a programming language are
The Data Scientist’s toolbox
R Programming
Introduction to R Programming
Introduction to R
R Programing - Introduction 1
Introduction a la programacion estadistica con R
O’Reilly Code School
The first two courses above are from Coursera's Data Science Specialization sequence. Taught by Roger Peng, Jeff Leek and Brian Caffo they are probably the gold standard for MOOC R courses. I am a little late with this post. The Data Scientists's toolbox started this past Monday but there is still time to catch up. The third course, Introduction to R Programming, is a relatively new edX course from Microsoft's online offerings that is getting great reviews. The fourth course on the list a solid introduction to R from DataCamp. R Programming - Introduction 1 is a beginner's introduction to R taught by Paul Murrell or Tal Galili. Next listed, is a Spanish language introduction to R from Coursera and O'Reilly's interactive Code School course.
These next three lists contain courses from DataCamp and statistics.com and online resources from R Studio that introduce more advanced features of R by buildng on basic R programming skills. Note that the final course on the DataCamp list introduces Big Data features of Revolution R Enterprise which is available in the Azure Marketplace.
DataCamp
Intermediate R
Data Visualization in R with ggvis
Data Manipulation with dplyr
Data Analysis in R, the data.table Way
Reporting with R Markdown
Big data Analysis with Revolution R Enterprise
statistics.com
R Programming Intro 2
R Programming Advanced
R Programming Interm
R Graphics
R ggplot2
RStudio
R Studio Online Learning
Introduction to Data Science with R video workshop
This next section lists courses from the major MOOCs, and non-MOOCs DataCamp and statistics.com that use R to teach various quantitative disciplines
Coursera Courses
Data Analysis and Statistical Inference
Developing Data Products
Exploratory Data Analysis
Getting and Cleaning Data
Introduction to Computational Finance and Financial Econometrics
Measuring Causal Effects in the Social Sciences
Regression Models
Reproducible Research
Statistical Inference
Statistics One
edX Courses
Data Analysis for Life Sciences 1: Statistics and R
Data Analysis for life Sciences 2: Introduction to Linear Models and Matrix Algebra
Data Analysis for life Sciences 6: High-performance Computing for Reproducible Genomics
Explore Statistics with R
Sabermetrics 101: Introduction to Baseball Analytics
Udacity Course
Sabermetrics 101: Introduction to Baseball Analytics
DataCamp
Introduction to Machine Learning
A Hands-On Introduction to Statistics with R
statistics.com
Bayesian - R
Data Mining - R
Mapping in R
R Modeling
R Statistics
Finally, here are a couple of google apps and Swirl, a new platform for teaching and learning R that may be useful for learning on the go.
R instructor
R Programming
Swirl
It's time to "go back to school" and make some headway against those 10,000 hours.