Efficient R Programming - by Colin Gillespie & Robin Lovelace (Paperback)

Book Synopsis 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

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.