BLAS and alternative R interpreters: looks at ways to make R fasterĮfficient programming is more than a series of tips: there is no substitute for in-depth understanding.
RStudio: an integrated development environment (IDE) to boost your programming productivity.R start-up: how and why to adjust your.R version: how to keep your base R installation and packages up-to-date.R and the operating systems: system monitoring on Linux, Mac and Windows.By the end of this chapter you should understand how to set-up your computer and R installation for optimal efficiency. That’s why we cover them at this early stage (hardware is covered in Chapter 8). Understanding and at times changing these set-up options can have many knock-on benefits. It explores how the operating system, R version, startup files and IDE can make your R work faster. This chapter describes the set-up that will enable a productive workflow. 9.3.4 Branches, forks, pulls and clonesĪn efficient computer set-up is analogous to a well-tuned vehicle.9.1 Top 5 tips for efficient collaboration.8.5 Operating systems: 32-bit or 64-bit.7.5.1 Parallel versions of apply functions.7.4 Example: Optimising the move_square() function.7.1 Top 5 tips for efficient performance.6.4 Efficient data processing with dplyr.6.3.2 Split joint variables with separate().6.3.1 Make wide tables long with pivot_longer().6.3 Tidying data with tidyr and regular expressions.6.1 Top 5 tips for efficient data carpentry.5.4.1 Native binary formats: Rdata or Rds?.5.3.1 Differences between fread() and read_csv().4.5.1 Dynamic documents with R Markdown.Informative output: message() and cat().3.1 Top 5 tips for efficient programming.2.6.3 Useful BLAS/benchmarking resources.2.6.1 Testing performance gains from BLAS.2.6 BLAS and alternative R interpreters.2.3.4 Installing R packages with dependencies.2.2.1 Operating system and resource monitoring.2.1 Top 5 tips for an efficient R set-up.1.5.2 Consistent style and code conventions.
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