This means that it’s relatively easy to make most R code much faster, as we’ll see in the following chapters.īefore we examine some of the slower parts of the R-language and GNU-R, we need to learn a little about benchmarking so that we can give our intuitions about performance a concrete foundation. Most people use R to understand data: it’s more important to get an answer quickly than to develop a system that will work in a wide variety of situations.
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Few R users have any formal training in programming or software development. In alternative implementations, I discuss some of the promising new implementations of R, and describe one important technique they use to make R code run faster.īeyond performance limitations due to design and implementation, it has to be said that a lot of R code is slow simply because it’s poorly written. While it’s hard to know exactly how much faster a better implementation could be, a >10x improvement in speed seems achievable. In Implementation performance, I discuss why GNU-R is currently far from the theoretical maximum, and why improvements in performance happen so slowly. In Language performance, I discuss some of the ways in which the design of the R-language imposes fundamental constraints on R’s speed. Nevertheless, the distinction between R-language and GNU-R is still useful: poor performance due to the language is hard to fix without breaking existing code fixing poor performance due to the implementation is easier. This is in contrast to other languages, like C++ and javascript, that make a clear distinction between language and implementation by laying out formal specifications that describe in minute detail how every aspect of the language should work. The R-language is mostly defined in terms of how GNU-R works. While there is the R language definition, it is informal and incomplete. The distinction between R-language and GNU-R is a bit murky because the R-language is not formally defined. I’ll call that implementation GNU-R to distinguish it from R-language, and from the other implementations I’ll discuss later in the chapter. The most popular implementation is the one from. The implementation is concrete: it reads R code and computes a result. The R-language is abstract: it defines what R code means and how it should work. To understand R’s performance, it helps to think about R as both a language and as an implementation of that language. Let’s get started by learning more about why R is slow. In R’s C interface, you’ll learn a little about R’s C internals. To really understand the performance of built-in base functions, you’ll need to learn a little bit about R’s C API. Rcpp will teach you the absolute minimum you need to know about C++ so you can write fast code using the Rcpp package. In Memory, you’ll learn about how R uses memory, and how garbage collection and copy-on-modify affect performance and memory usage.įor really high-performance code, you can move outside of R and use another programming language. First you figure what’s slow, and then you apply some general techniques to make the slow parts faster.
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In Profiling, you’ll learn how to systematically make your code faster. The following four chapters will give you the skills to improve the speed of your code when you need to:
In this chapter, you’ll learn about some of the trade-offs that R has made, valuing flexibility over performance.
The goal of this part of the book is to give you a deeper understanding of R’s performance characteristics. While R is slow compared to other programming languages, for most purposes, it’s fast enough. It was not designed to make life easier for your computer. R was purposely designed to make data analysis and statistics easier for you to do. You’re reading the first edition of Advanced R for the latest version see the second edition.
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