My ggplot2 cheat sheet is a sortable table, searchable by tasks like coloring by category or rotating x-axis labels. LinkedIn and Google+ also have fairly active R groups where questions are regularly asked and answered. While it's tough to use Twitter to get coding help, it can be a good place to ask questions such a,s "Does anyone know of a package that will.". There are a lot of channels in that Slack, so it helps to read up on what each is for so you know where best to post your query. The R for Data Science Slack community mentioned above is also a good place to ask questions. Responses tend be a bit less harsh than at Stack Overflow for newbies making rookie errors. There's also a category for general questions. RStudio launched its own community, which is geared toward issues surrounding RStudio-created packages and other RStudio software. There are other, more specific R-related tags there, too, such as, and. 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.) R for Data Science is available for free online as well as in paperback from Amazon.
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