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

Author: Colin Gillespie, Robin Lovelace
Binding Type: Paperback
Publisher: O'Reilly Media
Published: 01/31/2017
Pages: 219
Weight: 0.7lbs
Size: 9.10h x 6.90w x 0.40d
ISBN: 9781491950784

About the Author

Colin Gillespie is Senior lecturer (Associate professor) at Newcastle University, UK. His research interests are high-performance computing and Bayesian statistics. He is regularly employed as a consultant by Jumping Rivers and has been teaching R since 2005.

Robin Lovelace is a researcher at the Leeds Institute for Transport Studies (ITS) and the Leeds Institute for Data Analytics (LIDA). Robin has many years using R for academic research and has taught numerous R courses at all levels. He has developed a number of popular R resources, including Introduction to Visualising Spatial Data in R and Spatial Microsimulation with R (Lovelace and Dumont 2016). These skills have been applied on a number of projects with real-world applications, including the Propensity to Cycle Tool, a nationally scalable interactive online mapping application and the stplanr package.