The License Agreement forms a legally binding contract between you and Google in relation to your use of the SDK.ġ.2 "Android" means the Android software stack for devices, as made available under the Android Open Source Project, which is located at the following URL:, as updated from time to time.ġ.3 A "compatible implementation" means any Android device that (i) complies with the Android Compatibility Definition document, which can be found at the Android compatibility website () and which may be updated from time to time and (ii) successfully passes the Android Compatibility Test Suite (CTS).ġ.4 "Google" means Google LLC, organized under the laws of the State of Delaware, USA, and operating under the laws of the USA with principal place of business at 1600 Amphitheatre Parkway, Mountain View, CA 94043, USA.Ģ.1 In order to use the SDK, you must first agree to the License Agreement. 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.This is the Android Software Development Kit License Agreementġ.1 The Android Software Development Kit (referred to in the License Agreement as the "SDK" and specifically including the Android system files, packaged APIs, and Google APIs add-ons) is licensed to you subject to the terms of the License Agreement. If you want to present analyses or visualizations made with R code, consider making your slides with R Markdown using the xaringan package.
Experiment with packages like bookdown, distill, and blogdown each extends R Markdown to help you publish polished websites for sharing your work. The R Markdown family of packages enables you to create and share beautiful data science products like books, blogs, websites, and presentations.
#R studio com how to
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.Ĭommunicate with R Markdown. describe the philosophy behind the system in his rstudio::conf 2019 video. Allaire and Francois Chollet have published a Deep Learning with R book ( 2018). RStudio hosts a web site dedicated to R and Tensorflow at, where you can learn how to use deep learning in your analyses. Try your hand at Tensorflow for deep learning.
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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.
R offers an easy way to incorporate Python code in the reticulate package. Anyone who insists you must choose between R and Python for doing data science is creating a false choice. If you want your R code to reach the widest possible population of developers, you’ll want to know how to build packages.
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You can read the in-progress book for free online. The second edition of the R Packages book is currently in progress, written by Hadley and co-author Jenny Bryan. 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. R packages allow you to share your functions with other R programmers in a modular and easy-to-integrate way. 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.īuild your own packages. While its content sometime overlaps with Hadley’s Advanced R, the R Core Team offers Writing R Extensions ( electronic version) on CRAN. You may also want to bookmark Advanced R Solutions, which provides worked solutions to the exercises in this book.
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. Most R users are not programmers, and therefore much of their R code is not as readable, fast, or efficient as it could be. 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.ĭive into the foundations of R.
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