One part of reducing duplication is writing functions. Functions allow you to identify repeated patterns of code and extract them out into independent pieces that you can reuse and easily update as code changes. Iteration helps you when you need to do the same thing to multiple inputs: repeating the same operation on different columns, or on different datasets. (Generally, you won't need to use explicit iteration to deal with different subsets of your data: in most cases the implicit iteration in dplyr will take care of that problem for you.)
In this chapter you'll learn about two important iteration paradigms: imperative programming and functional programming, and the machinary each provides. On the imperative side you have things like for loops and while loops, which are a great place to start because they make iteration very explicit, so it's obvious what's happening. However, for loops are quite verbose, and include quite a bit of book-keeping code, that is duplicated for every for loop. Functional programming (FP) offers tools to extract out this duplicated code, so each common for loop pattern gets its own function. Once you master the vocabulary of FP, you can solve many common iteration problems with less code, more ease, and fewer errors.
Some people will tell you to avoid for loops because they are slow. They're wrong! (Well at least they're rather out of date, for loops haven't been slow for many years). The chief benefits of using FP functions like `lapply()` or `purrr::map()` is that they are more expressive and make code both easier to write and easier to read.
In later chapters you'll learn how to apply these iterating ideas when modelling. You can often use multiple simple models to help understand a complex dataset, or you might have multiple models because you're bootstrapping or cross-validating. The techniques you'll learn in this chapter will be invaluable.
The goal of using purrr functions instead of for loops is to allow you break common list manipulation challenges into independent pieces:
1. How can you solve the problem for a single element of the list? Once
you've solved that problem, purrr takes care of generalising your
solution to every element in the list.
1. If you're solving a complex problem, how can you break it down into
bite sized pieces that allow you to advance one small step towards a
solution? With purrr, you get lots of small pieces that you can
compose together with the pipe.
This structure makes it easier to solve new problems. It also makes it easier to understand your solutions to old problems when you re-read your old code.
That's all there is to the for loop! Now is a good time to practice creating some basic (and not so basic) for loops using the exercises below. Then we'll move on some variations of the for loop that help you solve other problems that will crop up in practice.
### Exercises
1. Write for loops to:
1. Compute the mean of every column in the `mtcars`.
1. Determine the type of each column in `nycflights13::flights`.
1. Compute the number of unique values in each column of `iris`.
1. Convert the song "99 bottles of beer on the wall" to a function.
Generalise to any number of any vessel containing any liquid on
any surface.
1. Convert the nursery rhyme "ten in the bed" to a function. Generalise
it to any number of people in any sleeping structure.
1. It's common to see for loops that don't preallocate the output and instead
increase the length of a vector at each step:
```{r, eval = FALSE}
output <- vector("integer", 0)
for (i in seq_along(x)) {
output <- c(output, lengths(x[[i]]))
}
output
```
How does this affect performance?
## For loop variations
Once you have the basic for loop under your belt, there are some variations on a theme that you should be aware of. These variations are important regardless of how you do iteration, so don't forget about them once you've master the FP techniques you'll learn about in the next section.
1. Modifying an existing object, instead of creating a new object.
1. Looping over names or values, instead of indices.
1. Handling outputs of unknown length.
1. Handling sequences of unknown length.
### Modifying an existing object
Sometimes you want to use a for loop to modify an existing object. For example, remember our challenge from [functions]. We wanted to rescale every column in a data frame:
Typically you'll be modifying a list or data frame with this sort of loop, so remember to use `[[`, not `[`. You might have spotted that I used `[[` in all my for loops: I think it's safer to use the subsetting operator that will work in all circumstances (and it makes it clear than I'm working with a single value each time).
There are three basic ways to loop over a vector. So far I've shown you the most general: looping over the numeric indices with `for (i in seq_along(xs))`, and extracting the value with `x[[i]]`. There are two other forms:
1. Loop over the elements: `for (x in xs)`. This is most useful if you only
care about side-effects, liking plotting or saving a file, because it's
Sometimes you might know now how long the output will be. For example, imagine you want to simulate some random vectors of random lengths. You might be tempted to solve this problem by progressively growing the vector:
But this type of is not very efficient because in each iteration, R has to copy all the data from the previous iterations. In technical terms you get "quadratic" ($O(n^2)$) behaviour which means that a loop with three times as many elements would take nine times ($3^2$) as long to run.
Sometimes you don't even know how long the input sequence should run for. This is common when doing simulations. For example, you might want to loop until you get three heads in a row. You can't do that sort of iteration with the for loop. Instead, you can use a while loop.
A while loop is more general than a for loop, because you can rewrite any for loop as a while loop, but you can't rewrite every while loop as a for loop:
I mention while loops briefly, because I hardly ever use them. They're most often used for simulation, which is outside the scope of this book. However, it is good to know they exist, if you encounter a problem where the number of iterations is not known in advance.
For loops are not as important in R as they are in other languages because R is a functional programming language. This means that it's possible to wrap up for loops in a function, and call that function instead of using the for loop directly.
But then you think it'd also be helpful to be able to compute the median, and the standard deviation, so you copy and paste your `col_mean()` function and replace the `mean()` with `median()` and `sd()`:
Uh oh! You've copied-and-pasted this code twice, so it's time to think about how to generalise it. Notice that most of code is for-loop boilerplate and it's hard to see the one thing (`mean()`, `median()`, `sd()`) that is different between the functions.
You've reduced the chance of bugs (because you now have 1/3 less code), and made it easy to generalise to new situations.
We can do exactly the same thing with `col_mean()`, `col_median()` and `col_sd()`. We can add an argument that supplies the function to apply to each column:
The idea of passing a function to another function is extremely powerful idea, and it's one of the reasons that R is called a functional programming language. It might take you a while to wrap your head around the idea, but it's worth the investment. In the rest of the chapter, you'll learn about and use the __purrr__ package, which provides functions that eliminate the need for many common for loops. The apply family of functions in base R (`apply()`, `lapply()`, `tapply()`, etc) solve a similar problem, but purrr is more consistent and thus is easier to learn.
The pattern of looping over a vector, doing something to each element and saving the results is so common that the purrr package provides a family of functions to do it for you. There is one function for each type of vector:
Each function takes a vector as input, applies a function to each piece, and then returns a new vector that's the same length (and has the same names) as the input. The type of the vector is determined by the suffix to the map function.
Once you master these functions, you'll find it takes much less time to solve iteration problems. But you should never feel bad about using a for loop instead of a map function. The map functions are a step up a tower of abstraction, and it can take a long time to get your head around how they work. The important thing is that you solve the problem that you're working on, not write the most concise and elegant code (although that's definitely something you want to strive towards!).
We can use these functions to perform the same computations as the last for loop. Those summary functions returned doubles, so we need to use `map_dbl()`:
Compared to using a for loop, focus is on the operation being performed (i.e. `mean()`, `median()`, `sd()`), not the book-keeping required to loop over every element and store the output. This is even more apparent if we use the pipe:
There are a few shortcuts that you can use with `.f` in order to save a little typing. Imagine you want to fit a linear model to each group in a dataset. The following toy example splits up the `mtcars` dataset into three pieces (one for each value of cylinder) and fits the same linear model to each piece:
When you're looking at many models, you might want to extract a summary statistic like the $R^2$. To do that we need to first run `summary()` and then extract the component called `r.squared`. We could do that using the shorthand for anonymous functions:
```{r}
models %>%
map(summary) %>%
map_dbl(~.$r.squared)
```
But extracting named components is a common operation, so purrr provides an even shorter shortcut: you can use a string.
```{r}
models %>%
map(summary) %>%
map_dbl("r.squared")
```
You can also use a numeric vector to select elements by position:
I focus on purrr functions here because they have more consistent names and arguments, helpful shortcuts, and in a future release will provide easy parallelism and progress bars.
When you use the map functions to repeat many operations, the chances are much higher that one of those operations will fail. When this happens, you'll get an error message, and no output. This is annoying: why does one failure prevent you from accessing all the other successes? How do you ensure that one bad apple doesn't ruin the whole barrel?
In this section you'll learn how to deal this situation with a new function: `safely()`. `safely()` is an adverb: it takes a function (a verb) and returns a modified version. In this case, the modified function will never throw an error. Instead, it always returns a list with two elements:
1. `result` is the original result. If there was an error, this will be `NULL`.
1. `error` is an error object. If the operation was successful this will be
`NULL`.
(You might be familiar with the `try()` function in base R. It's similar, but because it sometimes returns the original result and it sometimes returns an error object it's more difficult to work with.)
Let's illustrate this with a simple example: `log()`:
```{r}
safe_log <- safely(log)
str(safe_log(10))
str(safe_log("a"))
```
When the function succeeds the `result` element contains the result and the `error` element is `NULL`. When the function fails, the `result` element is `NULL` and the `error` element contains an error object.
This would be easier to work with if we had two lists: one of all the errors and one of all the output. That's easy to get with `purrr::transpose()` (you'll learn more about `transpose()` in [transpose])
It's up to you how to deal with the errors, but typically you'll either look at the values of `x` where `y` is an error, or work with the values of `y` that are ok:
So far we've mapped along a single list. But often you have multiple related lists that you need iterate along in parallel. That's the job of the `map2()` and `pmap()` functions.
Note that the arguments that vary for each call come before the function name, and arguments that are the same for every function call come afterwards.
Like `map()`, `map2()` is just a wrapper around a for loop:
```{r}
map2 <- function(x, y, f, ...) {
out <- vector("list", length(x))
for (i in seq_along(x)) {
out[[i]] <- f(x[[i]], y[[i]], ...)
}
out
}
```
You could also imagine `map3()`, `map4()`, `map5()`, `map6()` etc, but that would get tedious quickly. Instead, purrr provides `pmap()` which takes a list of arguments. You might use that if you wanted to vary the mean, standard deviation, and number of samples:
If you don't name the elements of list, `pmap()` will use positional matching when calling the function. That's a little fragile, and makes the code harder to read, so it's better to name the arguments:
As soon as your code gets complicated, I think a data frame is a good approach because it ensures that each column has a name and is the same length as all the other columns.
We'll come back to this idea in [hierarchy], and again when we explore the intersection of dplyr, purrr, and model fitting.
The first argument is a list of functions or character vector of function names. The second argument is a list of lists giving the arguments that vary for each function. The subsequent arguments are passed on to every function.
Walk is an alternative to map that you use when you want to call a function for its side effects, rather than for its return value. You typically do this because you want to render output to the screen or save files to disk - the important thing is the action, not the return value. Here's a very simple example:
```{r}
x <- list(1, "a", 3)
x %>%
walk(print)
```
`walk()` is generally not that useful compared to `walk2()` or `pwalk()`. For example, if you had a list of plots and a vector of file names, you could use `pwalk()` to save each file to the corresponding location on disk:
Purrr provides a number of other functions that abstract over other types of for loops. You'll use them less frequently than the map functions, but they're useful to have in your back pocket. The goal here is to briefly illustrate each function so hopefully it will come to mind if you see a similar problem in the future. Then you can go look up the documentation for more details.
Sometimes you have a complex list that you want to reduce to a simple list by repeatedly applying a function that reduces two inputs to a single input. This useful if you want to apply a two-table dplyr verb to multiple tables. For example, you might have a list of data frames, and you want to reduce to a single data frame by joining the elements together
```{r}
dfs <- list(
age = tibble::data_frame(name = "John", age = 30),
sex = tibble::data_frame(name = c("John", "Mary"), sex = c("M", "F")),
trt = tibble::data_frame(name = "Mary", treatment = "A")
The reduce function takes a "binary" function (i.e. a function with two primary inputs), and applies it repeatedly to a list until there is only a single element left.
