Functions

Warmup activity

Work on the activity (handout) with a neighbor, then we will discuss as a class

Warmup

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

diamonds_new <- diamonds |>
  mutate(carat_z = z_score(carat))

What does this code do?

Warmup

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

diamonds_new <- diamonds |>
  mutate(carat_z = z_score(carat))

The diamonds dataset has 53940 rows and 10 columns. What will be the dimensions of the diamonds_new dataset?

Warmup

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

diamonds_new <- diamonds |>
  mutate(carat_z = z_score(carat))

glimpse(diamonds_new)

Rows: 53,940
Columns: 11
$ 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.…
$ carat_z <dbl> -1.198157, -1.240350, -1.198157, -1.071577, -1.029384, -1.1770…

Functions

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

Functions

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

diamonds_new <- diamonds |>
  mutate(carat_z = z_score(carat))

R for Data Science calls the z_score function a “mutate” function. Why?

Another challenge

What are the individual points on the right of the boxplot?

Another challenge: identifying outliers

How do we identify outliers when constructing a boxplot?

Identifying outliers

We wish to write a function that we can use to identify outliers in numeric variables.

What should we name the function?

Identifying outliers

We wish to write a function that we can use to identify outliers in numeric variables.

What should the input to the function be?

Identifying outliers

find_outliers <- function(x) {
  
}

What needs to happen inside the function?

Identifying outliers

find_outliers <- function(x) {
  
}

(Switch to R Studio)

Identifying outliers

diamonds |>
  mutate(carat_outliers = find_outliers(carat)) |>
  filter(carat_outliers) |>
  pull(carat) |>
  head()

[1] 2.06 2.14 2.15 2.22 2.01 2.01

Counting outliers

find_outliers <- function(x) {
  q1 <- quantile(x, 0.25)
  q3 <- quantile(x, 0.75)
  iqr <- q3 - q1
  (x > q3 + 1.5*iqr) | (x < q1 - 1.5*iqr)
}

What if we want to count the number of outliers?

Counting outliers

find_outliers <- function(x) {
  q1 <- quantile(x, 0.25)
  q3 <- quantile(x, 0.75)
  iqr <- q3 - q1
  (x > q3 + 1.5*iqr) | (x < q1 - 1.5*iqr)
}

count_outliers <- function(x) {
  sum(find_outliers(x))
}

Counting outliers

diamonds |>
  summarize(carat_outliers = count_outliers(carat))

# A tibble: 1 × 1
  carat_outliers
           <int>
1           1889

R for Data Science would call the count_outliers function a “summarize” function. Why?

Counting outliers

diamonds |>
  summarize(carat_outliers = count_outliers(carat))

# A tibble: 1 × 1
  carat_outliers
           <int>
1           1889

What if I want to count outliers for multiple variables?

Counting outliers

diamonds |>
  summarize(across(c(carat, depth, price),
                   list("outliers" = count_outliers)))

# A tibble: 1 × 3
  carat_outliers depth_outliers price_outliers
           <int>          <int>          <int>
1           1889           2545           3540

Class activity

https://sta279-f25.github.io/class_activities/ca_09.html

• Work with a neighbor on the class activity
• At the end of class, submit your work as an HTML file on Canvas (one per group, list all your names)

For next time, read:

• Chapter 25.3 in R for Data Science