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# Functions

One of the best ways to improve your reach as a data scientist is to write functions. Functions allow you to automate common tasks. Writing a function has three big advantages over using copy-and-paste:

1.  You drastically reduce the chances of making incidental mistakes when
    you copy and paste.
    
1.  As requirements change, you only need to update code in one place, instead
    of many.
    
1.  You can give a function an evocative name that makes your code easier to 
    understand.

Writing good functions is a lifetime journey. Even after using R for many years we still learn new techniques and better ways of approaching old problems. The goal of this chapter is not to master every esoteric detail of functions but to get you started with some pragmatic advice that you can start using right away.

As well as practical advice for writing functions, this chapter also gives you some suggestions for how to style your code. Good coding style is like using correct punctuation. You can manage without it, but it sure makes things easier to read. As with styles of punctuation, there are many possible variations. Here we present the style we use in our code, but the most important thing is to be consistent.

## When should you write a function?

You should consider writing a function whenever you've copied and pasted a block of code more than twice (i.e. you now have three copies of the same code). For example, take a look at this code. What does it do?

```{r}
df <- data.frame(
  a = rnorm(10),
  b = rnorm(10),
  c = rnorm(10),
  d = rnorm(10)
)

df$a <- (df$a - min(df$a, na.rm = TRUE)) / 
  (max(df$a, na.rm = TRUE) - min(df$a, na.rm = TRUE))
df$b <- (df$b - min(df$b, na.rm = TRUE)) / 
  (max(df$a, na.rm = TRUE) - min(df$b, na.rm = TRUE))
df$c <- (df$c - min(df$c, na.rm = TRUE)) / 
  (max(df$c, na.rm = TRUE) - min(df$c, na.rm = TRUE))
df$d <- (df$d - min(df$d, na.rm = TRUE)) / 
  (max(df$d, na.rm = TRUE) - min(df$d, na.rm = TRUE))
```

You might be able to puzzle out that this rescales each column to have a range from 0 to 1. But did you spot the mistake? I made an error when copying-and-pasting the code for `df$b`: I forgot to change an `a` to a `b`. Extracting repeated code out into a function is a good idea because it prevents you from making this type of mistake.

To write a function you need to first analyse the code. How many inputs does it have?

```{r, eval = FALSE}
(df$a - min(df$a, na.rm = TRUE)) /
  (max(df$a, na.rm = TRUE) - min(df$a, na.rm = TRUE))
```

This code only has one input: `df$a`. (You might wonder if that `TRUE` is also an input: you can explore why it's not in the exercise below). To make the single input more clear, it's a good idea to rewrite the code using temporary variables with a general name. Here this function only takes one vector of input, so I'll call it `x`:

```{r}
x <- 1:10
(x - min(x, na.rm = TRUE)) / (max(x, na.rm = TRUE) - min(x, na.rm = TRUE))
```

There is some duplication in this code. We're computing the range of the data three times, but it makes sense to do it in one step:

```{r}
rng <- range(x, na.rm = TRUE)
(x - rng[1]) / (rng[2] - rng[1])
```

Pulling out intermediate calculations into named variables is a good practice because it makes it more clear what the code is doing. Now that I've simplified the code, and checked that it still works, I can turn it into a function:

```{r}
rescale01 <- function(x) {
  rng <- range(x, na.rm = TRUE)
  (x - rng[1]) / (rng[2] - rng[1])
}
rescale01(c(0, 5, 10))
```

There are three key steps to creating a new function:

1.  You need to pick a __name__ for the function. Here I've used `rescale01` 
    because this function rescales a vector to lie between 0 and 1.

1.  You list the inputs, or __arguments__, to the function inside `function`.
    Here we have just one argument. If we had more the call would look like
    `function(x, y, z)`.

1.  You place the __body__ of the function inside a `{` block immediately
    following `function`.

Note the overall process: I only made the function after I'd figured out how to make it work with a simple input. It's easier to start with working code and turn it into a function; it's harder to create a function and then try to make it work.

Now that we have `rescale01()` we can use that to simplify the original example:

```{r}
df$a <- rescale01(df$a)
df$b <- rescale01(df$b)
df$c <- rescale01(df$c)
df$d <- rescale01(df$d)
```

Compared to the original, this code is easier to understand. We've also eliminated one class of copy-and-paste errors. There is, however, still quite a bit of duplication since we're doing the same thing to multiple columns. You'll learn how to eliminate that duplication in the next chapter.

### Practice

1.  Why is `TRUE` not a parameter to `rescale01()`? What would happen if
    `x` contained a missing value, and `na.rm` was `FALSE`?

1.  Practice turning the following code snippets into functions. Think about 
    what each function does. What would you call it? How many arguments does it
    need? Can you rewrite it to be more expressive or less duplicative?

    ```{r, eval = FALSE}
    mean(is.na(x))
    
    x / sum(x, na.rm = TRUE)
    
    sd(x, na.rm = TRUE) / mean(x, na.rm = TRUE)
    ```

1.  Follow <http://nicercode.github.io/intro/writing-functions.html> to 
    write your own functions to compute the variance and skew of a vector.

1.  Implement a `fizzbuzz` function. It take a single number as input. If
    the number is divisible by three, return "fizz". If it's divisible by
    five return "buzz". If it's divisible by three and five, return "fizzbuzz".
    Otherwise, return the number. Make sure you first write working code, 
    before you create the function.
    
1.  Write `both_na()`, a function that takes two vectors of the same length 
    and returns the number of positions that have an `NA` in both vectors.

1.  What do the following functions do? Why are they useful even though they
    are so short?
    
    ```{r}
    is_directory <- function(x) file.info(x)$isdir
    is_readable <- function(x) file.access(x, 4) == 0
    ```

1.  Read the [complete lyrics](https://en.wikipedia.org/wiki/Little_Bunny_Foo_Foo) 
    to "Little Bunny Foo". There's a lot of duplication in this song. 
    Extend the initial piping example to recreate the complete song, and use 
    functions to reduce the duplication.

## Functions are for humans and computers

It's important to remember that functions are not just for the computer, but are also for humans. R doesn't care what your function is called, or what comments it contains, but these are important for human readers. This section discusses some things that you should bear in mind when writing functions that humans can understand.

The name of a function is important. Ideally the name of your function will be short, but clearly evoke what the function does. However, it's hard to come up with concise names, and autocomplete makes it easy to type long names, so it's better to err on the side of clear descriptions, rather than short names. There are a few exceptions to this rule: the handful of very common, very short names. It's worth memorising these:

* `x`, `y`, `z`: vectors.
* `df`: a data frame.
* `i`, `j`: numeric indices (typically rows and columns).
* `n`: length, or number of rows.
* `p`: number of columns.

Generally, function names should be verbs, and arguments should be nouns. There are some exceptions: nouns are ok if the function computes a very well known noun (i.e. `mean()` is better than `compute_mean()`), or accessing some property of an object (i.e. `coef()` is better than `get_coefficients()`). A good sign that a noun might be a better choice is if you're using a very broad verb like get, or compute, or calculate, or determine. Use your best judgement and don't be afraid to rename a function if you later figure out a better name.

If your function name is composed of multiple words, I recommend using "snake\_case", where each word is lower case and separated by an underscore. camelCase is a popular alternative alternative, but be consistent: pick one or the other and stick with it. R itself is not very consistent, but there's nothing you can do about that. Make sure you don't fall into the same trap by making your code as consistent as possible.

If you have a family of functions that do similar things, make sure they have consistent names and arguments. Use a common prefix to indicate that they are connected. That's better than a common suffix because autocomplete allows you to type the prefix and see all the members of the family.

```{r, eval = FALSE}
# Good
input_select
input_checkbox
input_text

# Not so good
select_input
checkbox_input
text_input
```

Where possible, avoid using names of common existing functions and variables. It's impossible to do in general because so many good names are already taken by other packages, but avoiding the most common names from base R will avoid confusion:

```{r, eval = FALSE}
# Don't do this!
T <- FALSE
c <- 10
mean <- function(x) sum(x)
```

Use comments, lines starting with `#`, to explain the "why" of your code. You generally should avoid comments that explain the "what" or the "how". If you can't understand what the code does from reading it, you should think about how to rewrite it to be more clear. Do you need to add some intermediate variables with useful names? Do you need to break out a subcomponent of a large function so you can name it? However, your code can never capture the reasoning behind your decisions: why did you choose this approach instead of an alternative? What else did you try that didn't work? It's a great idea to capture that sort of thinking in a comment.

Another important use of comments is to break up your file into easily readable chunks. Use long lines of `-` and `=` to make it easy to spot the breaks. RStudio even provides a keyboard shortcut for this: Cmd/Ctrl + Shift + R. 

```{r, eval = FALSE}
# Load data --------------------------------------

# Plot data --------------------------------------
```

### Exercises

1.  Read the source code for each of the following three functions, puzzle out
    what they do, and then brainstorm good names.
    
    ```{r}
    f1 <- function(string, prefix) {
      substr(string, 1, nchar(prefix)) == prefix
    }
    f2 <- function(x) {
      if (length(x) <= 1) return(NULL)
      x[-length(x)]
    }
    f3 <- function(x, y) {
      rep(y, length.out = length(x))
    }
    ```
    
1.  Take a function that you've written recently and spend 5 minutes 
    brainstorming a better name for it and its arguments.

1.  Compare and contrast `rnorm()` and `MASS::mvrnorm()`. How could you make
    them more consistent? 
    
1.  Make a case for why `normr()`, `normd()` etc would be better than
    `rnorm()`, `dnorm()`. Make a case for the opposite.

## Conditional execution

An `if` statement allows you to conditionally execute code. It looks like this:

```{r, eval = FALSE}
if (condition) {
  # code executed when condition is TRUE
} else {
  # code executed when condition is FALSE
}
```

To get help on `if` you need to surround it in backticks: `` ?`if` ``.

Here's a simple function that uses an if statement. The goal of this function is to return a logical vector describing whether or not each element of a vector is named.

```{r}
has_name <- function(x) {
  nms <- names(x)
  if (is.null(nms)) {
    rep(FALSE, length(x))
  } else {
    !is.na(nms) & nms != ""
  }
}
```

This takes advantage of the standard rules of function return values: a function returns the last value that was computed. Here it will be one of the two if branches.

### Conditions

The `condition` should be either a single `TRUE` or a single `FALSE`. If it's a vector you'll get a warning message, if it's an `NA`, you'll get an error. Watch out for these messages in your own code:

```{r, error = TRUE}
if (c(TRUE, FALSE)) {

}

if (NA) {
  
}
```

You can use `||` (or) and `&&` (and) to combine multiple logical expressions. These operators are "short-circuiting": as soon as `||` sees the first `TRUE` it returns `TRUE` without computing anything else. As soon as `&&` sees the first `FALSE` it returns `FALSE`. 

You should never use `|` or `&` in an `if` statement: these are vectorised operations that apply to multiple values. If you do have a logical vector, you can use `any()` or `all()` to collapse it to a single value.

### If styles

Squiggly brackets are always optional (for both `if` and `function`), but I recommend using them because it makes it easier to see the hierarchy in your code. An opening curly brace should never go on its own line and should always be followed by a new line. A closing curly brace should always go on its own line, unless it's followed by `else`. Always indent the code inside curly braces.

```{r, eval = FALSE}
# Good
if (y < 0 && debug) {
  message("Y is negative")
}

if (y == 0) {
  log(x)
} else {
  y ^ x
}

# Bad
if (y < 0 && debug)
message("Y is negative")

if (y == 0) {
  log(x)
} 
else {
  y ^ x
}
```

It's ok to drop the curly braces if you have a very short `if `statement that can fit on one line:

```{r}
y <- 10
x <- if (y < 20) "Too low" else "Too high"
```

I recommend this only for very brief `if` statements. Otherwise, the full form is easier to read:

```{r}
if (y < 20) {
  x <- "Too low" 
} else {
  x <- "Too high"
}
```

### Multiple conditions

You can chain multiple if statements together:

```{r, eval = FALSE}
if (this) {
  # do that
} else if (that) {
  # do something else
} else {
  # 
}
```

If you find that you have a very long series of chained `if` statements, you should consider rewriting. One useful technique is the `switch()` function. It allows you to evaluate selected code based on position or name.

```{r}
function(x, y, op) {
  switch(op,
    plus = x + y,
    minus = x - y,
    times = x * y,
    divide = x / y,
    stop("Unknown op!")
  )
}
```

Another useful function that can often eliminate long chains of `if` statements is `cut()`. It's used to discretise continuous variables.

Note that neither `if` nor `switch()` is vectorised: they work with a single value at a time. 

### Exercises

1.  What's the difference between `if` and `ifelse()`? Carefully read the help
    and construct three examples that illustrate the key differences.

1.  Write a greeting function that says "good morning", "good afternoon",
    or "good evening", depending on the time of day. (Hint: use a time
    argument that defaults to `lubridate::now()`. That will make it 
    easier to test your function.)

1.  How could you use `cut()` to simplify this set of nested if-else statements?

    ```{r, eval = FALSE}
    if (temp <= 0) {
      "freezing"
    } else if (temp <= 10) {
      "cold"
    } else if (temp <= 20) {
      "cool"
    } else if (temp <= 30) {
      "warm"
    } else {
      "hot"
    }
    ```
    
    How would you change the call to `cut()` if I'd used `<` instead of `<=`?

1.  What happens if you use `switch()` with numeric values?

1.  What does this `switch()` call do?

    ```{r}
    switch(x, 
      a = ,
      b = "ab",
      c = ,
      d = "cd"
    )
    ```

## Function arguments

The arguments to a function typically fall into two broad sets: one set supplies the data to compute on, and the other supplies arguments that controls the details of the computation. For example:

* In `log()`, the data is `x`, and the detail is the `base` of the logarithm.

* In `mean()`, the data is `x`, and the details are the `trim` and how to 
  handle missing values (`na.rm`).

* In `t.test()`, the data is `x` and `y`, and the details of the test are
  `alternative`, `mu`, `paired`, `var.equal`, and `conf.level`.
  
* In `paste()` you can supply any number of strings to `...`, and the details
  of the concatenation are controlled by `sep` and `collapse`.
  
Generally, data arguments should come first. Detail arguments should go on the end, and usually should have default values. You specify a default value in the same way you call a function with a named argument:

```{r}
# Compute standard error of a mean using normal approximation
mean_se <- function(x, conf = 0.95) {
  se <- sd(x) / sqrt(length(x))
  mean(x) + se * qnorm(c(1 - conf, conf))
}

x <- runif(100)
mean_se(x)
mean_se(x, 0.99)
```

The default value should almost always be the most common value. There are a few exceptions to do with safety. For example, it makes sense for `na.rm` to default to `FALSE` because missing values are important. Even though `na.rm = TRUE` is what you usually put in your code, it's a bad idea to silently ignore missing values by default.

When you call a function, typically you can omit the names for the data arguments (because they are used so commonly). If you override the default value of a detail argument, you should use the full name:

```{r, eval = FALSE}
# Good
mean(1:10, na.rm = TRUE)

# Bad
mean(x = 1:10, , FALSE)
mean(, TRUE, x = c(1:10, NA))
```

You can refer to an argument by its unique prefix (e.g. `mean(x, n = TRUE)`), but this is generally best avoided given the possibilities for confusion.

Notice that when you call a function, you should place a space around `=` in function calls, and always put a space after a comma, not before (just like in regular English). Using whitespace makes it easier to skim the function for the important components.

```{r, eval = FALSE}
# Good
average <- mean(feet / 12 + inches, na.rm = TRUE)

# Bad
average<-mean(feet/12+inches,na.rm=TRUE)
```

### Dot dot dot

There's one special argument you need to know about: `...`, pronounced dot-dot-dot. This captures any other arguments not otherwise matched. It's useful because you can then send those `...` on to another function. This is a useful catch-all if your function primarily wraps another function.

For example, I commonly create these helper functions that wrap around `paste()`:

```{r}
commas <- function(...) paste0(..., collapse = ", ")
commas(letters[1:10])

rule <- function(..., pad = "-") {
  title <- paste0(...)
  width <- getOption("width") - nchar(title) - 5
  cat(title, " ", paste(rep(pad, width, collapse = "")), "\n", sep = "")
}
rule("Important output")
```

Here `...` lets me forward on any arguments that I don't want to deal with to `paste()`. It's a very convenient technique. But it does came at a price: any misspelled arguments will not raise an error. This makes it easy for typos to go unnoticed:

```{r}
x <- c(1, 2)
sum(x, na.mr = TRUE)
```

If you just want to get the values of the `...`, use `list(...)`.

### Lazy evaluation

Arguments in R are lazily evaluated: they're not computed until they're needed. That means if they're never used, they're never called. This is an important property of R as a programming language, but is generally not important for data analysis. You can read more about lazy evaluation at <http://adv-r.had.co.nz/Functions.html#lazy-evaluation>

### Exercises

1.  What does `commas(letters, collapse = "-")` do? Why?

1.  It'd be nice if you supply multiple characters to the `pad` argument, e.g.
    `rule("Title", pad = "-+")`. Why doesn't this currently work? How could you
    fix it?
    
1.  What does the `trim` argument to `mean()` do? When might you use it?

1.  The default value for the `method` argument to `cor()` is 
    `c("pearson", "kendall", "spearman")`. What does that mean? What 
    value is used by default?

## Return values

The value returned by the function is the last statement it evaluates. You can explicitly return early from a function with `return()`. I think it's best to save the use of `return()` to signal that you can return early with a simpler solution. For example, you might write an if statement like this:

```{r, eval = FALSE}
f <- function() {
  if (x) {
    # Do 
    # something
    # that
    # takes
    # many
    # lines
    # to
    # express
  } else {
    # return something short
  }
}
```

But if the first block is very long, by the time you get to the else, you've forgotten what's going on. One way to rewrite it is to use an early return for the simple case:

```{r, eval = FALSE}

f <- function() {
  if (!x) {
    return(something_short)
  }

  # Do 
  # something
  # that
  # takes
  # many
  # lines
  # to
  # express
}
```

This tends to make the code easier to understand, because you don't need quite so much context to understand it.

### Invisible values

Some functions return "invisible" values. These are not printed by default but can be saved to a variable:

```{r}
f <- function() {
  invisible(42)
}

f()

x <- f()
x
```

You can also force printing by surrounding the call in parentheses:

```{r}
(f())
```

Invisible values are mostly used when your function is called primarily for its side-effects (e.g. printing, plotting, or saving a file). It's nice to be able pipe such functions together, so it's good practice to invisibly return the first argument. This allows you to do things like:

```{r, eval = FALSE}
library(readr)

mtcars %>% 
  write_csv("mtcars.csv") %>% 
  write_tsv("mtcars.tsv")
```

## Environment

The environment of a function controls how R finds the value associated with a name. For example, take this function:

```{r}
f <- function(x) {
  x + y
} 
```

In many programming languages, this would be an error, because `y` is not defined inside the function. In R, this is valid code because R uses rules called lexical scoping to determine the value associated with a name. Since `y` is not defined inside the function, R will look where the function was defined:

```{r}
y <- 100
f(10)

y <- 1000
f(10)
```

This behaviour seems like a recipe for bugs, and indeed you should avoid creating functions like this deliberately, but by and large it doesn't cause too many problems (especially if you regularly restart R to get to a clean slate). The advantage of this behaviour is that from a language standpoint it allows R to be very consistent. Every name is looked up using the same set of rules. For `f()` that includes the behaviour of two things that you might not expect: `{` and `+`.

This allows you to do devious things like:

```{r}
`+` <- function(x, y) {
  if (runif(1) < 0.1) {
    sum(x, y)
  } else {
    sum(x, y) * 1.1
  }
}
table(replicate(1000, 1 + 2))
rm(`+`)
```

This is a common phenomenon in R. R gives you a lot of control. You can do many things that are not possible in other programming languages. You can things that 99% of the time extremely ill-advised (like overriding how addition works!), but this power and flexibility is what makes tools like ggplot2 and dplyr possible. Learning how to make good use of this flexibility is beyond the scope of this book, but you can read about in "Advanced R".

## Errors

```{r}
try_require <- function(package, fun) {
  if (requireNamespace(package, quietly = TRUE)) {
    library(package, character.only = TRUE)
    return(invisible())
  }

  stop("Package `", package, "` required for `", fun , "`.\n",
    "Please install and try again.", call. = FALSE)
}
```

Another place where it's useful to throw errors is when the inputs to the function are the wrong type.  It's a good idea to throw an error early.

`stopifnot()`.

## Non-standard evaluation

One challenge with writing functions is that many of the functions you've used in this book use non-standard evaluation to minimise typing. This makes these functions great for interactive use, but it does make it more challenging to program with them, because you need to use more advanced techniques. For example, imagine you'd written the following duplicated code across a handful of data analysis projects:

```{r, eval = FALSE}
mtcars %>% 
  group_by(cyl) %>% 
  summarise(mean = mean(mpg, na.rm = TRUE), n = n()) %>% 
  filter(n > 10) %>% 
  arrange(desc(mean))

ggplot2::diamonds %>% 
  group_by(cut) %>% 
  summarise(mean = mean(price, na.rm = TRUE), n = n()) %>% 
  filter(n > 10) %>% 
  arrange(desc(mean))

nycflights13::planes %>% 
  group_by(model) %>% 
  summarise(mean = mean(year, na.rm = TRUE), n = n()) %>% 
  filter(n > 100) %>% 
  arrange(desc(mean))
```

You'd like to be able to write a function with arguments data frame, group and variable so you could rewrite the above code as:

```{r, eval = FALSE}
mtcars %>% 
  mean_by(cyl, mpg, n = 10)

ggplot2::diamonds %>% 
  mean_by(cut, price, n = 10)

nycflights13::planes %>% 
  mean_by(model, year, n = 100)
```

Unfortunately the obvious approach doesn't work:

```{r}
mean_by <- function(data, group_var, mean_var, n = 10) {
  data %>% 
    group_by(group_var) %>% 
    summarise(mean = mean(mean_var, na.rm = TRUE), n = n()) %>% 
    filter(n > 100) %>% 
    arrange(desc(mean))
}
```

This fails because it tells dplyr to group by `group_var` and compute the mean of `mean_var` neither of which exist in the data frame. 

Writing reusable functions for ggplot2 poses a similar problem because `aes(group_var, mean_var)` would look for variables called `group_var` and `mean_var`. It's really only been in the last couple of months that I fully understood this problem, so there aren't currently any great (or general) solutions. However, now that I've understood the problem I think there will be some systematic solutions in the near future.
