To “re-iterate” something is to repeat something. In ordinary language, proceeding in an “iterative” fashion typically means to proceed step-by-step. In computer science, the notion of iteration is an essential concept that implies that some code is executed repeatedly. However, doing something more than once only makes sense if some aspect of the computed code or answer can change. Thus, when repeating code, there typically is a value or object (e.g., some variable or data) that changes between consecutive iterations. This change can be a counter or index variable that indicates the current repetition or points to some particular location in data.
Whereas people easily get bored or distracted when doing the same thing over and over again, computers usually do not mind and are really good at it. Thus, whenever a procedure or process requires many repetitions, computers are faster and more reliable than people.
After working through this chapter, you should be able to:
- understand the notion of iteration in computer programming,
forloops to iterate over sequences of a known length,
whileloops to iterate over sequences of an unknown length,
- use the
apply()family of functions of base R to replace some loops,
- use the
map()family of functions of purrr to replace some loops.
12.1.2 Loops vs. functions
The traditional way to address issues of iteration is by repeating code in
for loops. Such loops can include an arbitrary amount of code (i.e., a few lines or an entire library of programs) and execute it repeatedly.
However, the previous Chapter 11 has taught us that Functions provide another way of enclosing code: Instead of writing a loop, we could encapsulate the required code in a new function and then call this function repeatedly.
As R is a functional programming language, it provides sophisticated ways of applying functions repeatedly to data structures.
As we focus on the essentials in this book, we will primarily focus on
while loops, and only briefly introduce the base R
apply() (R Core Team, 2020) and purrr’s
map() family of functions (Henry & Wickham, 2020).
12.1.3 Data used
To illustrate the notion of iteration in various type of loops, we are using various toy datasets (from the ds4psy package) in this chapter.
For instance, the dataset
tb includes some information on 100 ficticious people and
pi_100k contains the first 100.000 digits of pi.
12.1.4 Getting ready
This chapter formerly assumed that you have read and worked through Chapter 21: Iteration of the r4ds textbook (Wickham & Grolemund, 2017). It now can be read by itself, but reading Chapter 21 of r4ds is still recommended.
Please do the following to get started:
Structure your document by inserting headings and empty lines between different parts. Here’s an example how your initial file could look:
Create an initial code chunk below the header of your
.Rmdfile that loads the R packages of the tidyverse (and see Section F.3.3 if you want to get rid of the messages and warnings of this chunk in your HTML output).
Save your file (e.g., as
12_iteration.Rmdin the R folder of your current project) and remember saving and knitting it regularly as you keep adding content to it.
Henry, L., & Wickham, H. (2020). purrr: Functional programming tools. Retrieved from https://CRAN.R-project.org/package=purrr
R Core Team. (2020). R base: A language and environment for statistical computing. Retrieved from https://www.R-project.org
Wickham, H., & Grolemund, G. (2017). R for data science: Import, tidy, transform, visualize, and model data. Retrieved from http://r4ds.had.co.nz