Efficient R Programming - by Colin Gillespie & Robin Lovelace (Paperback)
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<p/><br></br><p><b> Book Synopsis </b></p></br></br><p>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.</p><p>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.</p><ul><li>Get advice for setting up an R programming environment</li><li>Explore general programming concepts and R coding techniques</li><li>Understand the ingredients of an efficient R workflow</li><li>Learn how to efficiently read and write data in R</li><li>Dive into data carpentry--the vital skill for cleaning raw data</li><li>Optimize your code with profiling, standard tricks, and other methods</li><li>Determine your hardware capabilities for handling R computation</li><li>Maximize the benefits of collaborative R programming</li><li>Accelerate your transition from R hacker to R programmer</li></ul><p/><br></br><p><b> About the Author </b></p></br></br><p>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.</p><p>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.</p>
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