Neuware - There are many excellent R resources for visualization, data science, and package development. Hundreds of scattered vignettes, web pages, and forums explain how to use R in particular domains. But little has been written on how to simply make R work effectively until now. This hands-on book teaches novices and experienced R users how to write efficient R code.

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Detalles bibliogr�ficos

T�tulo

Efficient R Programming : A Practical Guide to Smarter Programming

Editor

O'reilly Media Jan 2017

Fecha de publicaci�n

2017

Idioma

Ingl�s

ISBN 10

1491950781

ISBN 13

9781491950784

Encuadernaci�n

Taschenbuch

Estado

Neu

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Sinopsis

There are many excellent R resources for visualization, data science, and package development. Hundreds of scattered vignettes, web pages, and forums explain how to use R in particular domains. But little has been written on how to simply make R work effectively--until now. This hands-on book teaches novices and experienced R users how to write efficient R code.

Drawing on years of experience teaching R courses, authors Colin Gillespie and Robin Lovelace provide practical advice on a range of topics--from optimizing the set-up of RStudio to leveraging C++--that make this book a useful addition to any R user's bookshelf. Academics, business users, and programmers from a wide range of backgrounds stand to benefit from the guidance in Efficient R Programming.

Get advice for setting up an R programming environment
Explore general programming concepts and R coding techniques
Understand the ingredients of an efficient R workflow
Learn how to efficiently read and write data in R
Dive into data carpentry--the vital skill for cleaning raw data
Optimize your code with profiling, standard tricks, and other methods
Determine your hardware capabilities for handling R computation
Maximize the benefits of collaborative R programming
Accelerate your transition from R hacker to R programmer

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Colin Gillespie is Senior lecturer (Associate professor) at Newcastle University, UK. He has been running R courses for over five years at a variety of levels, ranging from beginners to advanced programming. During his academic career, he has also been employed as an external consultant at Shell, Burberry, Yorkshire Bank, KPMG, and Tesco Bank. His research interests are high performance statistical computing and Bayesian statistics. Robin Lovelace is a Research Fellow in the Leeds Institute for Data Analytics, which specializes in the handling of large data sets. Robin has five years using R for academic research and three years teaching R at all levels. Robin developed the popular tutorial "Introduction to visualizing spatial data in R" and is working to publish "Spatial microsimulation with R" (CRC Press). Robin has used R on mission-critical contracts, including the creation of a nationally scalable interactive online mapping tool for the UK's Department for Transport (DfT).

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