Functions

Day 16

Prof Emily Kurtz

Carleton College
Stat 220 - Spring 2026

Today

Writing Functions
Conditional Execution

Warm up

What does the mutate code below do?

df

# A tibble: 5 × 4
       a       b     c       d
   <dbl>   <dbl> <dbl>   <dbl>
1  1.08  -1.46   0.243  0.239 
2  1.02   0.0941 0.431 -0.0970
3 -1.83   0.310  1.38  -0.841 
4 -0.164 -0.769  0.276 -1.01  
5 -0.287  0.129  0.908  0.121

df |> mutate(
  a = (a - mean(a, na.rm = TRUE)) / 
    sd(a, na.rm = TRUE),
  b = (b - mean(b, na.rm = TRUE)) / 
    sd(b, na.rm = TRUE),
  c = (c - mean(a, na.rm = TRUE)) / 
    sd(c, na.rm = TRUE),
  d = (d - mean(d, na.rm = TRUE)) / 
    sd(d, na.rm = TRUE)
)

Rescaling variables

Standardizing/rescaling/normalizing variables is often an essential first step in predictive modeling

Example:

Set of digitized breast cancer image features
Each row in the data set represents an image of a tumor sample, including the diagnosis (benign or malignant) and several other measurements (nucleus texture, perimeter, area, etc.)
Diagnosis for each image was conducted by physicians.

# A tibble: 569 × 12
        ID Class Radius Texture Perimeter  Area Smoothness Compactness Concavity
     <dbl> <chr>  <dbl>   <dbl>     <dbl> <dbl>      <dbl>       <dbl>     <dbl>
 1  8.42e5 M       18.0    10.4     123.  1001      0.118       0.278     0.300 
 2  8.43e5 M       20.6    17.8     133.  1326      0.0847      0.0786    0.0869
 3  8.43e7 M       19.7    21.2     130   1203      0.110       0.160     0.197 
 4  8.43e7 M       11.4    20.4      77.6  386.     0.142       0.284     0.241 
 5  8.44e7 M       20.3    14.3     135.  1297      0.100       0.133     0.198 
 6  8.44e5 M       12.4    15.7      82.6  477.     0.128       0.17      0.158 
 7  8.44e5 M       18.2    20.0     120.  1040      0.0946      0.109     0.113 
 8  8.45e7 M       13.7    20.8      90.2  578.     0.119       0.164     0.0937
 9  8.45e5 M       13      21.8      87.5  520.     0.127       0.193     0.186 
10  8.45e7 M       12.5    24.0      84.0  476.     0.119       0.240     0.227 
11  8.46e5 M       16.0    23.2     103.   798.     0.0821      0.0667    0.0330
12  8.46e7 M       15.8    17.9     104.   781      0.0971      0.129     0.0995
13  8.46e5 M       19.2    24.8     132.  1123      0.0974      0.246     0.206 
14  8.46e5 M       15.8    24.0     104.   783.     0.0840      0.100     0.0994
15  8.47e7 M       13.7    22.6      93.6  578.     0.113       0.229     0.213 
16  8.48e7 M       14.5    27.5      96.7  659.     0.114       0.160     0.164 
# ℹ 553 more rows
# ℹ 3 more variables: Concave_Points <dbl>, Symmetry <dbl>,
#   Fractal_Dimension <dbl>

Standardizing variables

Goal: standardize the 10 quantitative variables in the data set

Approach: subtract sample mean, divide by sample standard deviation

scaled_cancer <- unscaled_cancer %>%
  mutate(
    Radius = (Radius - mean(Radius)) / sd(Radius),
    Texture = (Texture - mean(Texture)) / sd(Texture),
    Perimeter = (Perimeter - mean(Perimeter)) / sd(Perimeter),
    Area = (Area - mean(Area)) / sd(Area),
    Smoothness = (Smoothness - mean(Smoothness)) / sd(Smoothness),
    Compactness = (Compactness - mean(Compactness)) / sd(Compactness),
    Concavity = (Concavity - mean(Concavity)) / sd(Concavity),
    Concave_Points = (Concave_Points - mean(Concave_Points)) / sd(Concave_Points),
    Symmetry = (Symmetry - mean(Symmetry)) / sd(Symmetry),
    Fractal_Dimension = (Fractal_Dimension - mean(Fractal_Dimension)) / sd(Fractal_Dimension) 
  )

Don’t repeat yourself (DRY)

We’ve copied, pasted, and edited the same code chunk many times
That’s a lot of work when the only change in the code is the variable name!

scaled_cancer <- unscaled_cancer %>%
  mutate(
    Radius = (Radius - mean(Radius)) / sd(Radius), 
    Texture = (Texture - mean(Texture)) / sd(Texture), 
    Perimeter = (Perimeter - mean(Perimeter)) / sd(Perimeter), 
    Area = (Area - mean(Area)) / sd(Area), 
    Smoothness = (Smoothness - mean(Smoothness)) / sd(Smoothness), 
    Compactness = (Compactness - mean(Compactness)) / sd(Compactness),
    Concavity = (Concavity - mean(Concavity)) / sd(Concavity), 
    Concave_Points = (Concave_Points - mean(Concave_Points)) / sd(Concave_Points), 
    Symmetry = (Symmetry - mean(Symmetry)) / sd(Symmetry), 
    Fractal_Dimension = (Fractal_Dimension - mean(Fractal_Dimension)) / sd(Fractal_Dimension) 
  )

You should consider writing a function
whenever you’ve copied and pasted a
block of code more than twice

—Hadley Wickham

Why functions?

Automate common tasks in a more powerful and more general way than copy-and-pasting

You can give a function an evocative name that makes your code easier to understand.
As requirements change, you only need to update code in one place, instead of many.
You eliminate the chance of making incidental mistakes when you copy and paste (i.e. updating a variable name in one place, but not in another).

Down the line — Improve your reach as a data scientist by writing functions (and packages!) that others use

Anatomy of a function

A short but informative name, preferably a verb

calc_this <- function() { 

}

Anatomy of a function

A short but informative name, preferably a verb
Arguments of the function inside function()

calc_this <- function(..arguments..) { 

}

Anatomy of a function

A short but informative name, preferably a verb
Arguments of the function inside function()
Place the code you have developed in body of the function, a { block that immediately follows function(...).

calc_this <- function(..arguments..) { 
  # do stuff with arguments 
  # last result will be returned 
}

Building `standardize`

A short but informative name, preferably a verb

standardize <- function() { 

}

Building `standardize`

A short but informative name, preferably a verb
Arguments of the function inside function()

standardize <- function(x) { 

}

Building `standardize`

A short but informative name, preferably a verb
Arguments of the function inside function()
Place the code you have developed in body of the function, a { block that immediately follows function(...).

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

Does it work?

Unstandardized Compactness

head(unscaled_cancer$Compactness)

[1] 0.27760 0.07864 0.15990 0.28390 0.13280 0.17000

Standardized Compactness from copy and paste mutate() code

head(scaled_cancer$Compactness)

[1]  3.2806281 -0.4866435  1.0519999  3.3999174  0.5388663  1.2432416

Standardized Compactness from created standardize() function

std_compact <- standardize(unscaled_cancer$Compactness)
head(std_compact)

[1]  3.2806281 -0.4866435  1.0519999  3.3999174  0.5388663  1.2432416

Your turn

Turn the following code snippets into functions. Think about what each function does before you begin (think SI vs. imperial units), and be sure to give each function an informative name.

x * 0.0022
x / sum(x, na.rm = TRUE)

You can test your functions on variables from the palmerpenguins::penguins dataset (e.g. my_func(penguins$body_mass_g)).

Return values

The value returned by the function is usually the last statement it evaluates

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

You can force a function to return early using return().

This is often paired with conditional execution (more later).

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

Adding arguments

We can include na.rm as an argument to give the user control over it

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

std_mass <- standardize(penguins$body_mass_g, na.rm = TRUE)
head(std_mass)

[1] -0.5633167 -0.5009690 -1.1867934         NA -0.9374027 -0.6880121

std_mass <- standardize(penguins$body_mass_g, na.rm = FALSE)
head(std_mass)

[1] NA NA NA NA NA NA

Setting defaults

Unless a default value is set for an argument, R will require a value to be specified in the function call

std_compact <- standardize(penguins$body_mass_g)

 Error in mean(x, na.rm = na.rm) : 
  argument "na.rm" is missing, with no default

Setting defaults

To set a default, set the value in the function definition

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

std_mass <- standardize(penguins$body_mass_g)
std_mass

  [1] -0.563316704 -0.500969030 -1.186793445           NA -0.937402749
  [6] -0.688012052 -0.719185889  0.590115266 -0.906228912  0.060160036
 [11] -1.124445771 -0.625664378 -1.249141119 -0.500969030  0.247203059
 [16] -0.625664378 -0.937402749  0.371898407 -1.093271934 -0.002187638
 [21] -0.999750423 -0.750359726 -0.500969030 -0.313926008 -0.500969030
 [26] -0.500969030 -0.812707400 -1.249141119 -1.311488793 -0.313926008
 [31] -1.186793445 -0.376273682 -1.124445771 -0.376273682 -1.093271934
 [36] -0.064535312 -0.313926008 -0.812707400 -1.124445771  0.558941429
 [41] -1.311488793 -0.376273682 -1.373836467  0.247203059 -1.498531815
 [46]  0.496593755 -0.968576586 -1.529705652 -0.937402749 -0.064535312
 [51] -0.875055074  0.122507710 -0.937402749 -0.189230660 -1.623227163
 [56] -0.625664378 -0.812707400 -0.500969030 -1.685574837 -0.563316704
 [61] -1.311488793  0.247203059 -0.750359726 -0.189230660 -1.685574837
 [66] -0.313926008 -1.062098097 -0.126882986 -1.436184141  0.309550733
 [71] -0.750359726 -0.376273682 -0.812707400 -0.064535312 -0.625664378
 [76]  0.060160036 -0.625664378 -0.376273682 -0.812707400 -0.251578334
 [81] -1.249141119  0.621289103 -0.500969030 -0.002187638 -1.062098097
 [86] -0.812707400 -0.500969030 -0.875055074 -0.313926008 -0.750359726
 [91] -0.812707400  0.122507710 -0.999750423  0.309550733 -1.124445771
 [96]  0.122507710 -0.625664378  0.184855384 -1.623227163 -0.126882986
[101] -0.594490541  0.652462940 -1.405010304  0.060160036 -1.592053326
[106] -0.812707400 -0.563316704 -0.376273682 -1.280314956  0.714810614
[111] -0.469795193  0.496593755 -1.249141119  0.091333873 -0.376273682
[116] -0.158056823 -1.623227163 -0.532142867 -1.062098097 -1.093271934
[121] -1.311488793 -0.875055074 -0.937402749 -0.407447519 -1.436184141
[126] -0.251578334 -1.155619608  0.122507710 -1.436184141 -0.251578334
[131] -1.093271934 -0.875055074 -0.875055074  0.340724570 -0.968576586
[136] -0.376273682 -1.280314956 -0.282752171 -0.999750423  0.060160036
[141] -0.999750423 -0.906228912 -1.436184141 -0.594490541 -1.498531815
[146] -0.688012052  0.060160036 -0.906228912 -0.937402749 -0.563316704
[151] -0.625664378 -0.251578334  0.371898407  1.868242584  0.309550733
[156]  1.868242584  1.494156540  0.434246081  0.745984451  1.244765843
[161]  0.247203059  1.182418169  0.558941429  1.681199562  0.558941429
[166]  2.055285606 -0.002187638  2.055285606 -0.064535312  2.616414673
[171]  0.745984451  1.431808866  1.868242584  0.995375147  0.247203059
[176]  1.057722821  0.995375147  1.120070495 -0.126882986  1.805894910
[181]  0.496593755  1.681199562  1.307113518  0.621289103  1.057722821
[186]  2.304676302  1.182418169  1.494156540  0.933027473  1.307113518
[191]  0.184855384  1.431808866 -0.313926008  1.868242584  0.122507710
[196]  0.683636777  1.681199562  0.870679799 -0.002187638  1.494156540
[201]  1.120070495  1.369461192  0.808332125  1.369461192  0.247203059
[206]  0.995375147  0.870679799  1.057722821  0.122507710  0.995375147
[211]  0.309550733  1.681199562 -0.002187638  1.369461192  0.247203059
[216]  1.805894910  0.621289103  1.868242584  0.558941429  1.992937932
[221]  0.621289103  1.681199562  0.683636777  0.995375147  1.120070495
[226]  1.244765843  0.621289103  1.992937932  0.496593755  2.242328628
[231]  0.683636777  2.179980954  0.527767592  1.556504214  0.652462940
[236]  1.431808866  0.683636777  1.743547236  0.496593755  1.369461192
[241]  0.839505962  1.681199562  0.933027473  1.494156540  0.683636777
[246]  1.805894910  0.808332125  1.244765843  0.901853636  0.839505962
[251]  0.527767592  1.307113518  0.808332125  1.743547236  0.964201310
[256]  1.618851888  0.652462940  1.618851888  0.621289103  1.618851888
[261]  0.465419918  1.618851888  0.995375147  2.179980954  0.558941429
[266]  1.618851888  0.216029222  2.055285606  0.839505962  2.242328628
[271]  0.901853636           NA  0.808332125  1.930590258  1.244765843
[276]  1.494156540 -0.875055074 -0.376273682 -0.688012052 -0.843881237
[281] -0.594490541 -0.313926008 -1.186793445 -0.563316704 -0.064535312
[286] -0.625664378 -0.500969030 -0.532142867 -0.625664378 -0.189230660
[291] -0.781533563 -0.189230660 -1.124445771 -0.625664378 -0.937402749
[296]  0.247203059 -0.750359726 -0.999750423 -1.623227163 -0.500969030
[301] -1.124445771 -0.064535312 -0.999750423 -0.500969030 -0.625664378
[306]  0.434246081 -1.249141119  0.122507710 -1.062098097 -0.126882986
[311] -0.750359726 -0.376273682 -0.438621356  0.745984451 -1.872617859
[316]  0.371898407 -0.313926008 -0.688012052 -0.812707400 -0.875055074
[321] -0.656838215  0.309550733 -0.999750423  0.122507710 -1.186793445
[326] -0.656838215 -1.093271934 -0.313926008 -0.750359726 -0.189230660
[331] -1.062098097 -0.937402749 -1.186793445 -0.189230660 -0.500969030
[336] -0.843881237 -0.313926008 -0.688012052 -0.688012052 -0.251578334
[341] -0.999750423 -0.532142867 -0.126882986 -0.532142867

Your turn

Write a function called column_mean that takes a data set and column name (as a string) as inputs and returns the column mean as output. (Hint: access the column using [[)
You should also include a na.rm argument and set the default to TRUE so that NAs are removed from the calculation by default.
Test your function on the mtcars data set.

column_mean(mtcars, "cyl")

[1] 6.1875

Plotting functions

p1 = unscaled_cancer %>%
  ggplot(aes(x = Radius, fill = Class)) + 
  geom_histogram(col = "white", bins = 20, alpha = .7) 

p2 = unscaled_cancer %>%
  ggplot(aes(x = Texture, fill = Class)) + 
  geom_histogram(col = "white", bins = 20, alpha = .7) 

p3 = unscaled_cancer %>%
  ggplot(aes(x = Perimeter, fill = Class)) + 
  geom_histogram(col = "white", bins = 20, alpha = .7) 

p4 = unscaled_cancer %>%
  ggplot(aes(x = Area, fill = Class)) + 
  geom_histogram(col = "white", bins = 20, alpha = .7) 

(p1 + p2)/(p3 + p4)

Custom `histogram()` function

histogram <- function(df, var, bins = 20) {
  df %>%
    ggplot(aes(x = var, fill = Class)) + 
    geom_histogram(col = "white", bins = 20, alpha = .7) 
}

histogram(unscaled_cancer, Radius)

Error in `geom_histogram()`:
! Problem while computing aesthetics.
ℹ Error occurred in the 1st layer.
Caused by error:
! object 'Radius' not found

🤗 embracing 🤗

This issue arises in {dplyr} and {ggplot} functions because they use a special kind of evaluation, which allows us to refer to our variable names directly when writing code with their functions.

To write our own functions that use these packages, we use 🤗 embracing 🤗 (wrap the variable in braces). This tells R to use the value stored inside the argument, not the argument as the literal variable name.

Ex:

count(my_variable) looks for a column literally named my_variable

count({{my_variable}}) looks for the column name stored inside my_variable

`histogram()` function

histogram <- function(df, var, bins = 20) {
  df %>%
    ggplot(aes(x = {{var}}, fill = Class)) + 
    geom_histogram(col = "white", bins = 20, alpha = .7) 
}

histogram(unscaled_cancer, Radius)

Your turn

Write a plotting function called scatterplot() that makes a scatterplot of any two quantitative variables, coloring the points by a 3rd categorical variable.

Test your function with the following examples:

scatterplot(unscaled_cancer, 
            Radius, 
            Texture, 
            Class)

scatterplot(penguins, 
            bill_length_mm, 
            bill_depth_mm, 
            species)

Functions with conditional execution

Sometimes, we want our function to do different things depending on the arguments we feed it.
Example: a histobar function that makes a histogram if the variable is quantitative and a barchart if the variable is categorical.

histobar(unscaled_cancer, Radius)

histobar(unscaled_cancer, Class)

Conditional execution

The if() statement allows us to control which statements are executed.

if(condition) {
  # commands when TRUE
}

if(condition) {
  # commands when TRUE
} else {
  # commands when FALSE
}

Conditional execution

A basic example

x <- 5
if (x > 5) {
  x <- x + 1
}
x

[1] 5

x <- 6
if (x > 5) {
  x <- x + 1
}
x

[1] 7

Conditional execution

Another basic example

x <- 5
if (x > 5) {
  x <- x + 1
} else {
  x <- x - 1
}

x

[1] 4

`histobar`

histobar <- function(df, var){
  if(is.numeric(class({{var}}))){
    ggplot(df, aes(x = {{var}})) + 
      geom_histogram(col = "white")
  }
  else {
    ggplot(df, aes(y = {{var}})) + 
      geom_bar()
  }
}

histobar(unscaled_cancer, Radius)

Error: object 'Radius' not found

`histobar`

{var} is a column in a data frame/tibble, but is.numeric only works on vectors

histobar <- function(df, var){
  if(is.numeric(class({{var}}))){
    ggplot(df, aes(x = {{var}})) + 
      geom_histogram(col = "white")
  }
  else {
    ggplot(df, aes(y = {{var}})) + 
      geom_bar()
  }
}

`histobar`

pull() works like a $

histobar <- function(df, var){
  if(is.numeric(df %>% pull({{var}}))){
    ggplot(df, aes(x = {{var}})) + 
      geom_histogram(col = "white")
  }
  else {
    ggplot(df, aes(y = {{var}})) + 
      geom_bar()
  }
}

Testing `histobar`

histobar(unscaled_cancer, Radius)

Testing `histobar`

histobar(unscaled_cancer, Class)

Testing `histobar`

histobar(palmerpenguins::penguins, species)

Testing `histobar`

histobar(palmerpenguins::penguins, flipper_length_mm)

Your turn

Edit your scatterplot function to include an argument called draw_line. If draw_line is TRUE, your function should add a line of best fit to your scatterplot. Test your function with the following examples

scatterplot(unscaled_cancer, 
            Radius, 
            Texture, 
            Class, 
            draw_line = FALSE)

scatterplot(palmerpenguins::penguins, 
            bill_length_mm, 
            bill_depth_mm, 
            species, 
            draw_line = TRUE)

Functions

Today

Warm up

Rescaling variables

Example:

Standardizing variables

Don’t repeat yourself (DRY)

Why functions?

Anatomy of a function

Anatomy of a function

Anatomy of a function

Building standardize

Building standardize

Building standardize

Does it work?

Your turn

Return values

Adding arguments

Setting defaults

Setting defaults

Your turn

Plotting functions

Custom histogram() function

🤗 embracing 🤗

histogram() function

Your turn

Functions with conditional execution

Conditional execution

Conditional execution

Conditional execution

histobar

histobar

histobar

Testing histobar

Testing histobar

Testing histobar

Testing histobar

Your turn

Building `standardize`

Building `standardize`

Building `standardize`

Custom `histogram()` function

`histogram()` function

`histobar`

`histobar`

`histobar`

Testing `histobar`

Testing `histobar`

Testing `histobar`

Testing `histobar`