Here is a list of resources that will be helpful as you progress through the Johns Hopkins University Data Science Specialization's R Programming course. One of the challenges presented by this course is that it depends on the hacker mentality that the professors introduced in the Data Scientist's Toolbox -- you have to spend some time figuring out the resources beyond the lectures and slides to help solve the problems.
In 2017, Data Science Central published a list of 100 Free Tutorials for R. The list is divided into two categories: one focused on Learning R, and another where the focus is on data science topics but including R tutorials.
Beyond resources that aren't directly related to the JHU Data Science Specialization, past students have developed a large amount of content to guide others through the course, starting with the R Programming page on the Data Science Specialization github site.
For assignment 1, Derek Franks' Tutorial for those struggling with Programming Assignment 1 is very helpful, along with my articles:
- Strategy for the Programming Assignments,
- Forms of the Extract Operator,
- Breaking Down Pollutantmean,
- Assignment 1: Common Mistakes - Weighted vs. Unweighted Means,
- Assignment 1: Common Mistakes - complete("specdata",332:1) fails, and
- Assignment 1: A More Elegant Solution
For assignment 2, DanieleP's Tutorial for those Struggling with Programming Assignment 2 is helpful, along with my articles makeCacheMatrix as an Object, and R Objects, S Objects, and Lexical Scoping, which references an extended version of the Lexical Scoping lecture from a 2003 JHU Biostatistics class. Given all of the challenges people have in understanding this assignment, I have also posted a complete walkthrough of the makeVector() function in the article Demystifying makeVector().
Many students struggle with use of Github for this assignment. Git and Github are covered in The Data Scientist's Toolbox, and there is a lot of content explaining how to use Git and Github on the Data Science Specializtion Community Site's Data Scientist's Toolbox page.
Finally, for assignment 3, take a look at DanieleP's Tutorial for those Struggling with Programming Assignment 3, along with my articles Functions to Sort Data Frames, and Forms of the Extract Operator.
A list of other R resources is available at References for R Programming. If you have a background in SAS, the transition to R can be challenging. I've posted a number of articles on this topic, starting with R Onboarding for SAS Users. Finally, if you're stuck, make sure you use the Discussion Forums to get help. A significant part of the learning process in online courses occurs on the Discussion Forums. The more you invest in them, the more you'll learn.
One of the best ways to learn about R is to read code written by other people. Stackoverflow.com is a great place to look for example code. Here we list some answers to basic R questions that I have posted to Stackoverflow.com over the past few years that illustrate essential R concepts to solve various problems.
- What happens inside the curly braces?
- What is the meaning of $ in an R function?
- What is the difference between the workspace and environments?
- How do I read .txt files from a subdirectory in R?
- How do I repeatedly shuffle a deck of cards in R?
All of the articles I've written to support R Programming are located at R Programming Articles.
last updated 5 April 2020