The development of the bookdown package from RStudio in the summer of 2016 has facilitated greatly the ability of educators to create open-source materials for their students to use. Wiki authoring with RStudio + Bookdown? Add a text title to css div custom block in Bookdown. RStudio is an integrated development environment (IDE) for R, a programming language for statistical computing and graphics. It is available in two formats: RStudio Desktop is a regular desktop application while RStudio Server runs on a remote server and allows accessing RStudio using a web browser.
Even once you are an expert at R code development, learning some topics in depth will both help you develop better code and share it more effectively with others.
Dive into the foundations of R. Most R users are not programmers, and therefore much of their R code is not as readable, fast, or efficient as it could be. The second edition of Hadley Wickham’s book, Advanced R(2019) (available for free online and as an O’Reilly paperback from Amazon), unlocks many of the secrets behind how R works the way it does, and gives you new strategies for solving diverse problems. Tally erp latest version free download. You may also want to bookmark Advanced R Solutions, which provides worked solutions to the exercises in this book.
Learn how to extend R. While its content sometime overlaps with Hadley’s Advanced R, the R Core Team offers Writing R Extensions (electronic version) on CRAN. This book is particularly useful if you wish to add your own C and C++ routines to R, but also has unique information on debugging, the R API, and runtime profiling that is difficult to find anywhere else.
Build your own packages. R packages allow you to share your functions with other R programmers in a modular and easy-to-integrate way. The above-mentioned Writing R Extensions documents how to write packages, but you may find Hadley Wickham’s R Packages book (2015) (electronic here (1st edition) and an O’Reilly paperback (1st edition) from Amazon here) a bit easier to read and more step-by-step. The second edition of the R Packages book is currently in progress, written by Hadley and co-author Jenny Bryan. You can read the in-progress book for free online. If you want your R code to reach the widest possible population of developers, you’ll want to know how to build packages.
Use Python in your analyses. Anyone who insists you must choose between R and Python for doing data science is creating a false choice. R offers an easy way to incorporate Python code in the reticulate package. Sean Lopp’s webinar, R, RStudio 1.2 & Python—a love story, demonstrates how easily developers can integrate Python code into their R workflows and walks through the development of a reticulated Shiny app.
Try your hand at Tensorflow for deep learning. RStudio hosts a web site dedicated to R and Tensorflow at tensorflow.rstudio.com, where you can learn how to use deep learning in your analyses.J.J. Allaire and Francois Chollet have published a Deep Learning with R book(2018). You can hear J.J. describe the philosophy behind the system in his rstudio::conf 2019 video. For an quick overview of resources for how to get started with deep learning in R, read Sigrid Keydana’s article and subscribe to the Tensorflow for R blog.
Communicate with R Markdown. The R Markdown family of packages enables you to create and share beautiful data science products like books, blogs, websites, and presentations. Experiment with packages like bookdown, distill, and blogdown; each extends R Markdown to help you publish polished websites for sharing your work. If you want to present analyses or visualizations made with R code, consider making your slides with R Markdown using the xaringan package. If you’ve built an R package, the pkgdown package makes it quick and relatively painless to build a website for your package, using all the work you’ve already done to write and document your functions.
Allaire, JJ, Rich Iannone, and Yihui Xie. 2019. Distill: ’R Markdown’ Format for Scientific and Technical Writing. https://github.com/rstudio/distill.
Chollet, François, and J. J. Allaire. 2018. Deep Learning with R. Manning Publications. Autocad for windows 7 64 bit free download with crack.
Ushey, Kevin, JJ Allaire, and Yuan Tang. 2019. Reticulate: Interface to ’Python’. https://CRAN.R-project.org/package=reticulate.
Wickham, Hadley. 2015. R Packages: Organize, Test, Document, and Share Your Code. O’Reilly Media, Inc. https://r-pkgs.org/.
———. 2019. Advanced R, Second Edition. Chapman; Hall/CRC. https://adv-r.hadley.nz/.
Wickham, Hadley, and Jay Hesselberth. 2018. Pkgdown: Make Static Html Documentation for a Package. https://CRAN.R-project.org/package=pkgdown.
Xie, Yihui. 2019a. Blogdown: Create Blogs and Websites with R Markdown. https://github.com/rstudio/blogdown.
———. 2019b. Bookdown: Authoring Books and Technical Documents with R Markdown. https://github.com/rstudio/bookdown.
———. 2019c. Xaringan: Presentation Ninja. https://github.com/yihui/xaringan.
3.1 What is R?
In Chapter 2, I discussed many of the reasons why you should begin doing your analyses (especially those of the data type) using R. If you skipped over that chapter in the hopes of just diving in to learning about R, I suggest you go back and read it over carefully. As you begin building fluency in working with R, it is especially important to review that introductory chapter from time to time.
3.1.1 R beginnings
R was developed by a group of statisticians who wanted an open-source alternative to the costly proprietary options that were (and still are) popular. Because it was created by statisticians (instead of computer scientists), R has some quirky aspects to it that take some time to get used to. We’ll see that many packages have been developed to help with this, and these days, you don’t need an advanced degree in statistics to work with R.
Getting back to the development of R… R was created by Ross Ihaka and Robert Gentleman in New Zealand at the University of Auckland. It is a spin-off of the S programming language and was named partly after the first names of its developers (as you can see from the emphasis above). The beginning ideas for creating R came in 1992, and the first version of R was released in 1994. You can find much more about the background of R, its features, and its connections to the S language on Wikipedia.
3.1.2 R packages
I first learned to use R as a graduate student at Northern Arizona University from Dr. Philip Turk in 2007. At the time, I never thought that R could have exploded in users as we have seen since 2011. I never would have thought that students taking an introductory statistics course would be encouraged to learn to use R.
In 2007, R was still largely an esoteric and tricky language used by statisticians to do analyses. Getting used to the syntax for producing plots and working with data was especially tricky for those with little to no programming experience. So what has changed since 2007 about learning R?
I believe one of the biggest developments has been the creation of packages to make R easier to work with for newbies. Packages are add-ons created by users of R to increase the functionality of the base R installation. Packages created by Hadley Wickham and others recently have greatly expanded the capabilities of R, while also working to make beginning with R simpler. As of April 2017, more than 10,400 packages were available on common R repositories.1
Rstudio Bookdown
Another great development is the graphical user interface called RStudio and a package developed by RStudio, Inc. called rmarkdown. We will discuss rmarkdown (also referred to as R Markdown) in a Chapter 4, and will now focus on discussing RStudio.