利用R进行高性能编程：解决大数据分析难题

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"R High Performance Programming 由 Aloysius Lim 和 William Tjhi 编著，这是一本关于提升R语言编程效率的原版PDF书籍，旨在帮助读者解决R语言在处理大数据时面临的性能问题。" R语言是当前数据科学领域广泛使用的工具，尤其在数据处理、统计分析方面表现突出。随着数据的重要性日益增加，企业和科研人员都需要有效的方法来处理大量数据。R语言起源于学术界，但现在已经渗透到各行各业，并在全球范围内广泛应用。《R High Performance Programming》这本书深入探讨了如何克服R语言在性能上的困难，提供了一系列激动人心的技术和解决方案。作者Aloysius Lim和William Tjhi通过本书分享他们的专业知识，旨在帮助读者提高R语言代码的运行速度和效率，以应对大数据时代的挑战。书中可能涵盖的内容可能包括： 1. 内存管理和优化：讨论如何有效地管理R中的数据结构，避免内存泄漏，以及如何使用向量化操作减少计算时间。 2. 并行计算：介绍如何利用多核处理器和分布式计算框架，如`parallel`包或`snow`包，进行并行化任务，从而显著提升计算速度。 3. C/C++接口：讲解如何使用`Rcpp`等库将R代码与C++结合，以利用编译型语言的高效性。 4. 数据输入/输出优化：讨论快速读写大文件的策略，如使用`data.table`包或`fread/fwrite`函数。 5. 包的选取和使用：推荐并解释适用于高性能计算的R包，如`data.table`、`dplyr`、`tidyverse`等。 6. 性能测试和基准：介绍如何使用`microbenchmark`和`rbenchmark`等工具来度量和比较代码的执行效率。 7. 算法优化：讨论更高效的算法实现，以减少计算时间和资源消耗。 8. 非标准评估（NSE）的注意事项：解释在使用如`dplyr`等包时，理解非标准评估的重要性，以及如何避免它可能导致的性能问题。 9. 调优技巧：提供关于代码重构和优化的一般原则，以提高代码的运行效率。 10. GPU编程：如果涉及，可能会涵盖如何利用GPU进行加速计算，如使用`cuda`或`OpenCL`相关的R包。这本书对于那些希望充分利用R语言处理大规模数据的开发者和数据科学家来说是一份宝贵的资源。通过学习和应用书中的技巧，读者可以提升R代码的性能，使其在大数据时代更加得心应手。同时，出版商Packt Publishing提醒，尽管他们尽力确保书中的信息准确，但不承担因使用本书内容而产生的直接或间接损害的责任。

Understanding R's

Performance – Why Are R

Programs Sometimes Slow?

R is a great tool used for statistical analysis and data processing. When it was rst

developed in 1993, it was designed as a tool that would teach data analysis courses.

Because it is so easy to use, it became more and more popular over the next 20 years,

not only in academia, but also in government and industry. R is also an open source

tool, so its users can use it for free and contribute new statistical packages to the

R public repository called the Comprehensive R Archive Network (CRAN). As

the CRAN library became richer with more than 6,000 well-documented and

ready-to-use packages at the time of writing this book, the attractiveness of

R increased even further. In these 20 years, the volume of data being created,

transmitted, stored, and analyzed, by organizations and individuals alike, has also

grown exponentially. R programmers who need to process and analyze the ever

growing volume of data sometimes nd that R's performance suffers under such

heavy loads. Why does R sometimes not perform well, and how can we overcome

its performance limitations? This book examines the factors behind R's performance

and offers a variety of techniques to improve the performance of R programs, for

example, optimizing memory usage, performing computations in parallel, or even

tapping the computing power of external data processing systems.

Before we can nd the solutions to R's performance problems, we need to

understand what makes R perform poorly in certain situations. This chapter

kicks off our exploration of the high-performance R programming by taking a

peek under the hood to understand how R is designed, and how its design can

limit the performance of R programs.

Chapter 1

[ 13 ]

This is what makes R such a dynamic and interactive programming language. You

can type R statements into the R console and get results immediately because the R

interpreter parses and evaluates the code right away. The downside of this approach

is that R code runs relatively slow because it is reinterpreted every time you run it,

even when it has not changed.

Contrast this with a compiled language such as C or Fortran. When you work with a

compiled language, you compile your source code into the machine code before you

execute it. This makes compiled languages less interactive because the compilation

step can take several minutes for large programs, even when you have made just a

tiny change to the code. On the other hand, once the code has been compiled, it runs

very quickly on the CPU since it is already in the computer's native language.

Due to R being an interpreted language, every time you run an R program, the

CPU is busy doing two things: interpreting your code and executing the instructions

contained in it. Therefore, the CPU's speed can limit the performance of R programs.

We will learn how to overcome CPU limitations in chapters 3 to 5.

R is single-threaded

Another way in which R is CPU limited is that, by default, it runs only on a single

thread on the CPU. It does not matter if you install R on a powerful server with 64

CPU cores, R will only use one of them. For example, nding the sum of a numeric

vector is an operation that can be made to run in parallel in the CPU quite easily. If

there are four CPU cores available, each core can be given roughly one quarter of

the data to process. Each core computes the subtotal of the chunk of data it is given,

and the four subtotals are then added up to nd the total sum of the whole dataset.

However in R, the sum() function runs serially, processing the entire dataset on one

CPU core. In fact, many Big Data operations are of a similar nature to the summation

example here, with the same task running independently on many subsets of data.

In such a scenario, performing the operation sequentially would be an underuse of

today's mostly parallel computing architectures. In Chapter 8, Multiplying Performance

with Parallel Computing, we will learn how to write parallel programs in R to

overcome this limitation.

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