R resources (free courses, books, tutorials, & cheat sheets) Below is a list of free books, tutorials, packages, cheat sheets, and other material to learn programming in R and improve your workflow.
There are separate overviews for more specific R tricks, Python resources, SQL resources, and general Data Science, Machine Learning, & Statistics resources.If you have additions, please comment below or contact me! Cheatsheets - RStudio. Advanced R Environments, data structures, functions, subsetting and more, by Arianne Colton and Sean Chen.
Updated February 2016. Download Base R Vectors, matrices, lists, data frames, functions and more in base R, by Mhairi McNeill. Download bayesplot. DIY – cheat sheets. I found recently, that in addition to a great list of cheatsheets designed by RStudio, one can also download a template for new cheatsheets from RStudio Cheat Sheets webpage.
With this template you can design your own cheatsheet, and submit it to the collection of Contributed Cheatsheets (Garrett Grolemund will help to improve the submission if needed). Working on a new cheatsheet is pretty enjoying. You need to squeeze selected important stuff into a quite small surface like one or two pages. Lots of fun. I did it for eurostat and survminer packages (cheatsheets below). Related. R Cheat Sheets. R Cheat Sheet. RSeek.org R-project Search Engine. Search for "natural" usage of a function across all CRAN packages. The Guerilla Guide to R.
Update: Okay.
I’ve uploaded a new template and things seem to be fine now. Update: I am aware the table of contents is not being displayed in bullet form as I intended. The web template I’m using seems to be buggy. It also seems to think this page is in Indonesian…Working on it! Table of Contents: Useful tutorials. There are some tools that I use regularly, and I would like my research students and post-docs to learn them too.
Here are some great online tutorials that might help. ggplot tutorial from Winston ChangWriting an R package from Karl BromanRmarkdown from RStudioShiny from RStudiogit/github guide from Karl Bromanminimal make tutorial from Karl Broman Related Posts: Twotorials by anthony damico.
Google Dev R Video Lectures. I got this Google Developers R Programming Video Lectures from Stephen's blog - Getting Genetics Done.
Very useful R tutorial for beginner! Short and efficient. Here is what I learned after watching the lectures: stop() and warning() function I was asked this question during a job interview. stop('message') will print out the error message and stop the function. warning('message') will print out the error message but continue the function. Learning R. Complete beginner: read the 12-page A (very) short introduction to R by Torfs & Brauer.
Beginner / intermediate: read R for Data Science, by Hadley Wickham. It teaches both fundamental data science concepts and the most modern way of using R (with the tidyr, dplyr, purr and ggplot2 packages). The book should be published in late 2016, but most of the first chapters are already freely available online in draft form. Advanced: read Advanced R, by Hadley Wickham. It is very accessible, and will provide you with an in-depth understanding of the R language. NHS-R Community - Promoting the use of R in the NHS. DataScience+ Learn R programming for data science. 8 page guide to starting with R. LearnR4free. Data Science and R: how do I start? On learning to learn From open house to home ownership I had a couple of amazing Chemistry professors in undergrad, and I will never forget how one of them phrased learning: The first time you encounter a piece of information is like going to an open house.
You don’t know if you’re going to rent that house, buy that house, let alone if you’re even going to like that house. The next few times you encounter that same information is like renting a house — you’re committed for a relatively short amount of time, but you’re not necessarily in it for the long haul. Once you’ve really learned something, you’ve bought the house.
Are you fluent in R? How to choose a project to practice data science. The one thing you need to master data science. Curb your imposterism, start meta-learning. Recently, there has been a lot of attention for the imposter syndrome.
Even seasoned programmers admit they suffer from feelings of anxiety and low self-esteem. Some share their personal stories, which can be comforting for those suffering in silence. I focus on a method that helped me grow confidence in recent years. It is a simple, yet very effective way to deal with being overwhelmed by the many things a data scientis can acquaint him or herself with. Two Faces of the Imposter Demon I think imposterism can be broken into two, related, entities. You can’t have it all The field of data science is incredibly broad. On Becoming a Wizard: Strategies for Keeping Up as a New Developer. I graduated with my computer science degree just over a year ago.
I had learned what I thought was a lot about backend, “heavyweight” C-based development, and I assumed I’d be using those skills on the job, while learning new things that were unique to the consulting trade. I had a few acquaintances who were web developers, and I’d come to think of web development as a lightweight version of what I’d learned in school. This turned out not to be the case. When I started at Atomic, I found myself assigned to four different web projects over the course of a year.
Learning web development in 2016 turned out to be incredibly overwhelming, especially for someone who had been taught to use the C++ STL and not much else. Top 10 tips to get started with R. R-gotchas. Vectorization in R: Why? Here are my notes from a recent talk I gave on vectorization at a Davis R Users’ Group meeting. Thanks to Vince Buffalo, John Myles White, and Hadley Wickham for their input as I was preparing this. Feedback welcome! 21 R navigation tools. Navigation gets you from where you are to where you want to be.
Speaking of navigation, you can jump to selected sections of this post: Navigation; R-bloggers; Task views; Rdocumentation.org; sos package; ?? ; apropos; ls; methods; getAnywhere; :::; find; args; grep; %in%; str; getwd; file.choose; Spyglass summary; browser; See also. Overview. Creating your personal, portable R code library with GitHub. As I discussed in a previous post, I have a few helper functions I’ve created that I commonly use in my work. Until recently, I manually included these functions at the start of my R scripts by either the tried and true copy-and-paste method, or by extracting them from a local file with the <code>source()</code> function. The former approach has the benefit of keeping the helper code inextricably attached to the main script, but it adds a good bit of code to wade through.
The latter approach keeps the code cleaner, but requires that whoever is running the code always has access to the sourced file and that it is always in the same relative path – and that makes sharing or moving code more difficult. Customize .Rprofile and Keep Your Workspace Clean. Guide to Speeding Up R Code. This is an overview of tools for speeding up your R code that I wrote for the Davis R Users’ Group.
First, Ask “Why?” It’s customary to quote Donald Knuth at this point, but instead I’ll quote my twitter buddy Ted Hart to illustrate a point: I’m just going to say it.I like for loops in #Rstats, makes my code readable.All you [a-z]*ply snobs can shove it! — Ted Hart (@DistribEcology) March 12, 2013 Code optimization is a matter is a matter of personal taste and priorities. Memory usage · Advanced R. A solid understanding of R’s memory management will help you predict how much memory you’ll need for a given task and help you to make the most of the memory you have.
It can even help you write faster code because accidental copies are a major cause of slow code. The goal of this chapter is to help you understand the basics of memory management in R, moving from individual objects to functions to larger blocks of code. Along the way, you’ll learn about some common myths, such as that you need to call gc() to free up memory, or that for loops are always slow.
Outline. Random Data Sets Quickly. Random Data Sets Quickly This post will discuss a recent GitHub package I’m working on, wakefield to generate random data sets. The post is broken into the following sections: Demo 1.1 Random Variable Functions 1.2 Random Data Frames 1.3 Missing Values 1.4 Default Data SetFuture DirectionGetting Involved. How do you you determine the namespace of a function?
Promises and Closures in R. Lobstr: Understanding complex R objects with tools similar to str() How do I open R help about a symbol or punctuation mark like ( parenthesis or [ bracket? Where to get help with your R question? How to ask for R help. By Stephen Weller, Senior Support Engineer at Revolution Analytics, and Joseph Rickert. Getting help in R: do as I say, not as I've done. “Ever tried.
Ever failed. How to make a great R reproducible example. Minimal reproducible examples. Minimal reproducible examples. So you’ve been asked to make a reprex. Introduction. How to read an R help page. Using the help page for a function in R. Codefinch: An RStudio Addin for Tweeting Your Code. The tidyverse style guide. Google’s R Style Guide. R is a high-level programming language used primarily for statistical computing and graphics. Style - Hadley. Good coding style is like using correct punctuation.
You can manage without it, but it sure makes things easier to read. As with styles of punctuation, there are many possible variations. StyleO. An Intro to formatR. You can install formatR from CRAN, or XRAN if you want to test the latest development version: install.packages("formatR", repos = " Or check out the Github repository and install from source if you know what this means. This page is always based on the development version. Robin Lovelace - Consistent naming conventions in R. Rules for Naming Objects in R. Function Naming Conventions and Best Practice - Package development - RStudio Community.
Today in #rstats things I should have figured out sooner: automatic text wrapping in the @rstudio script editor is a thing! For all your obnoxiously long comment reminders to yourself...… RStudio Primers. Dataquest: Learn R for Data Analysis. Wharton Moneyball Academy. R for Journalists. Education.rstudio. Data Science in a Box. RBootcamp · basics of the tidyverse. Reconlearn. Teacups, Giraffes and Statistics. Quantargo - Intro to R. Introverse: Resources for Beginners Learning R and the Tidyverse. R for Stata Users. R for MATLAB users. Translating between R and SQL: the basics. SPSS 2 R: Materials · R-Ladies. TranslateR your SPSS® script automatically to R. Blue Sky Statistics. Jamovi. Rj Editor – Analyse your data with R in jamovi.