In this chapter, you learned when performance may or may not matter, how to measure the performance of R code, how to use profiling tools to identify the slowest part of code, and why such code can be slow. Then, we introduced the most important ways to boost the code performance: using built-in functions if possible, taking advantage of vectorization, using the byte-code compiler, using parallel computing, writing code in C++ via Rcpp, and using multi-threading techniques in C++. High-performance computing is quite an advanced topic, and there's still a lot more to learn if you want to apply it in practice. This chapter demonstrates that using R does not always mean slow code. Instead, we can achieve high performance if we want.
In the next chapter, we will introduce another useful topic: web scraping. To scrape data from webpages, we need to understand how web pages are structured and how to extract data from their source code. You will learn the basic idea and representation of...
