Efficient R Programming: A Practical Guide to Smarter Programming | 誠品線上

Efficient R Programming: A Practical Guide to Smarter Programming

內容簡介

內容簡介 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 environmentExplore general programming concepts and R coding techniquesUnderstand the ingredients of an efficient R workflowLearn how to efficiently read and write data in RDive into data carpentry—the vital skill for cleaning raw dataOptimize your code with profiling, standard tricks, and other methodsDetermine your hardware capabilities for handling R computationMaximize the benefits of collaborative R programmingAccelerate your transition from R hacker to R programmer

作者介紹

作者介紹 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.

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