This is especially common with Unix-based systems (Linux and Mac). Sometimes external package dependencies must be installed manually (i.e. not using install.packages()). 2īeyond the \(32\) vs \(64\) bit issue (covered in the next chapter) and process forking (covered in Chapter 7), another OS-related issue to consider is external dependencies: programs that R packages depend on. Minor differences aside, R’s computational efficiency is broadly the same across different operating systems. However, to help remember the key messages buried within the detail of this book, each chapter from now on contains a ‘top 5 tips’ section, after the pre-requisites.Ģ.2.1 Operating system and resource monitoring #NETDRIVE REMOVE SPEED LIMITATION SERIES#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. #NETDRIVE REMOVE SPEED LIMITATION HOW TO#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. #NETDRIVE REMOVE SPEED LIMITATION CODE#1.5.2 Consistent style and code conventions.1.5 Cross-transferable skills for efficiency.1.1 Who this book is for and how to use it.
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