functions

Chapter 15

General format for writing a function

name <- function(arguments) {
  body
}

We’ll discuss 3 types of functions:

1. Vector

2. Data

3. Plotting

Vector Functions

Motivation for functions n <- function(a) {

1. Create 4 vectors, each containing 5 random numbers with mean = 2, and standard deviation = 3.
2. Normalize each vector to have min = 0 and max = 1.
3. Notice any inefficency?
4. Write a function to normalize a vector and use it.
5. Use the function inside mutate like this:

n <- function(a) { rng <- range(a, na.rm = TRUE) (a - rng[1])/(rng[2] - rng[1]) }

# using a function within mutate
df |> mutate(
  a = rescale01(a),
  b = rescale01(b),
  c = rescale01(c),
  d = rescale01(d),
)

We can use across() to make the above even more efficient

How to compute the mean of many columns

library(tidyverse)
# get the mean of columns a - d.
df <- tibble(
  a = rnorm(10),
  b = rnorm(10),
  c = rnorm(10),
  d = rnorm(10)
)

print(df)

# A tibble: 10 × 4
         a       b       c      d
     <dbl>   <dbl>   <dbl>  <dbl>
 1 -1.00    1.30    0.867  -0.964
 2 -0.458  -1.27   -0.0498 -0.636
 3  0.984  -1.13   -0.583  -3.57 
 4  1.46   -0.172   0.496   2.31 
 5  0.0556  0.767   0.385   1.27 
 6 -1.95   -1.22    0.249   0.842
 7 -2.08   -0.0279  1.60    0.358
 8 -0.652   0.442   0.159   0.616
 9 -1.02    1.39   -0.125   0.929
10 -0.794   1.14   -0.193  -0.104

df |> summarize(across(a:d, mean))

# A tibble: 1 × 4
       a     b     c     d
   <dbl> <dbl> <dbl> <dbl>
1 -0.545 0.121 0.280 0.105

How to compute the median of numeric columns

glimpse(diamonds)

Rows: 53,940
Columns: 10
$ carat   <dbl> 0.23, 0.21, 0.23, 0.29, 0.31, 0.24, 0.24, 0.26, 0.22, 0.23, 0.…
$ cut     <ord> Ideal, Premium, Good, Premium, Good, Very Good, Very Good, Ver…
$ color   <ord> E, E, E, I, J, J, I, H, E, H, J, J, F, J, E, E, I, J, J, J, I,…
$ clarity <ord> SI2, SI1, VS1, VS2, SI2, VVS2, VVS1, SI1, VS2, VS1, SI1, VS1, …
$ depth   <dbl> 61.5, 59.8, 56.9, 62.4, 63.3, 62.8, 62.3, 61.9, 65.1, 59.4, 64…
$ table   <dbl> 55, 61, 65, 58, 58, 57, 57, 55, 61, 61, 55, 56, 61, 54, 62, 58…
$ price   <int> 326, 326, 327, 334, 335, 336, 336, 337, 337, 338, 339, 340, 34…
$ x       <dbl> 3.95, 3.89, 4.05, 4.20, 4.34, 3.94, 3.95, 4.07, 3.87, 4.00, 4.…
$ y       <dbl> 3.98, 3.84, 4.07, 4.23, 4.35, 3.96, 3.98, 4.11, 3.78, 4.05, 4.…
$ z       <dbl> 2.43, 2.31, 2.31, 2.63, 2.75, 2.48, 2.47, 2.53, 2.49, 2.39, 2.…

diamonds |> summarize(across(where(is.numeric), median))

# A tibble: 1 × 7
  carat depth table price     x     y     z
  <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
1   0.7  61.8    57  2401   5.7  5.71  3.53

The range function

Use the range of a vector to minimize redundancy

rescale01 <- function(x) {
  rng <- range(x, na.rm = TRUE)
  (x - rng[1]) / (rng[2] - rng[1])
}

The z-score

z_score <- function(x) {
  (x - mean(x, na.rm = TRUE)) / sd(x, na.rm = TRUE)
}

Clamp

What happens if you remove the .default line?

clamp <- function(x, min, max) {
  case_when(
    x < min ~ min,
    x > max ~ max,
    .default = x
  )
}

clamp(1:10, min = 3, max = 7)

 [1] 3 3 3 4 5 6 7 7 7 7

A function on strings

# notice what is gettings assigned TO
first_upper <- function(x) {
  str_sub(x, 1, 1) <- str_to_upper(str_sub(x, 1, 1))
  x
}

first_upper("hello")

[1] "Hello"

A string stripper

# the order matters in the str_remove_all's below
clean_number <- function(x) {
  is_pct <- str_detect(x, "%")
  num <- x |> 
    str_remove_all("%") |> 
    str_remove_all(",") |> 
    str_remove_all(fixed("$")) |> 
    as.numeric()
  if_else(is_pct, num / 100, num)
}

clean_number("$12,300")

[1] 12300

clean_number("45%")

[1] 0.45

Summarize functions

Here’s a somewhat advanced version. Observe the importance of the :=

f <- function(x,cat_var, num_var){
    x |> group_by({{cat_var}}) |> 
        summarize("{{num_var}}" := mean({{num_var}}))
}