To search for answers before posting your own query, make sure to use the tag. Stack Overflow has long been a programmers' go-to source for asking questions it has an active R community. This is another interactive option, but on your own system, with several courses to choose from that were designed for the platform. I've heard some good things about the R package swirl. The Introduction to R course, estimated to take four hours, is free. The platform features an R cloud implementation, so students can do exercises and get immediate feedback to see if their code is correct. Interactive learning company Datacamp offers a few free classes, although most require a monthly or yearly paid subscription. An earlier PDF version has been updated to include both base R and the stringr package, and also has sections on regular expressions. If you are interested in using R to manipulate character strings, check out Gaston Sanchez's free online book Handling Strings With R. (Disclaimer: I'm writing an R book for publisher Taylor & Francis due out late this year or early 2019.)
Teach Yourself R is the broadest of the three, ranging from discussions of R class systems to the Shiny Web framework. R for Everyone is a smaller volume that's focused a bit more on statistics, with sections on topics like T-Tests, ANOVA, Poisson regression and survival analysis. Lander and Sams Teach Yourself R in 24 Hours by three Mango Solutions consultants. I can recommend at least two other general books for expanding a beginner's knowledge: R for Everyone by Jared P. (An independent Slack community launched last year for people working through the book.) These tracks are not meant to be exhaustive, but instead are designed to help you become productive in the minimum amount of time, based on your experience level.R for Data Science is available for free online as well as in paperback from Amazon. We have created three tracks to help learners navigate the R ecosystem. Yet, when RStudio asks students about their biggest challenges in learning R, respondents overwhelmingly answer that survey question with another question: where should they begin? Whether you are just beginning R or have many years of data science experience, R offers a plethora of choice.
However, after more than 25 years of development, the R ecosystem can seem overwhelming to newcomers. All these features help you think about problems as a data scientist, while supporting fluent interaction between your brain and the computer. R is not just a programming language, but it is also an interactive ecosystem including a runtime, libraries, development environments, and extensions. We think R is a great place to start your data science journey because it is an environment designed for data science.
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