Control Flow

Grayson White

Math 241
Week 9 | Spring 2026

Annoucements

DataFest 2026 @ Willamette
- Great opportunity to meet folks and show off your data science skills!

Project 1 Timeline Updates
- Final product and Data Scientist Statement due Sunday April 5th at 10pm instead of Friday April 3rd at 10pm.
- Group member feedback form is now due Tuesday April 7th at 10pm instead of Sunday April 5th at 10pm.

AI Policy

Post Spring Break: Big Picture Goals

Three days: Core R programming skills: control flow, functions, iteration (loops and functional programming)
- Problem set 5 on this material
Two days on ethics and AI
- Problem set 6 on this material
Three days on writing your own R packages
- Project 2 will be centrally focused on writing your own R package
- We’ll have a project work day during the last week of class
Last day of class: Building your data science profile: Github and personal professional website

Post Spring Break: Assignments / deadlines

Project 1
- Final product and Data Scientist Statement due Sunday April 5th at 10pm
- Group member feedback form due Tuesday April 7th at 10pm

Problem Set 5
- Released Tuesday April 7th at 9am, due Wednesday April 15th at 10pm

Problem Set 6
- Released Tuesday April 14th at 9am, due Wednesday April 21st at 10pm

Project 2
- Released Tuesday April 21st, Due Wednesday May 13th at noon

Today: Control Flow

Control Flow

The flow of execution of R code.
So far, our primary rule: Place your code in the order that you want R to evaluate it.

Incorrect order:

crash_data <- read_csv("data/pdx_crash_2018_page1.csv")

Error in `read_csv()`:
! could not find function "read_csv"

library(tidyverse)

Correct order:

library(tidyverse)
crash_data <- read_csv("data/pdx_crash_2018_page1.csv")

Control Flow

Can also get R to conditionally run code blocks or run code blocks multiple times.
Control structures allow you to control the flow of execution.
Main tools we will use to control the flow:
- if() and else()
- stop() and stopifnot()
- for() loops

die_roll <- sample(1:6, size = 1)
if(die_roll == 6) {
    print("Biggest number!")
  } else {
    print(die_roll)
  }

[1] 1

for(i in 1:6){
  print(i * 10)
}

[1] 10
[1] 20
[1] 30
[1] 40
[1] 50
[1] 60

Come Back to Logical Operators

To control the flow, we need to be really comfortable with logical operators.

Or operators:

a <- c(TRUE, FALSE, FALSE, TRUE)
b <- c(TRUE, FALSE)

a | b

[1]  TRUE FALSE  TRUE  TRUE

a || b

Error in `a || b`:
! 'length = 4' in coercion to 'logical(1)'

xor(a, b)

[1] FALSE FALSE  TRUE  TRUE

Which is vectorized?
What is recycling again?
What is going on with ||?

Logical Operators

And operators:

a <- c(TRUE, FALSE, FALSE, TRUE)
b <- c(TRUE, FALSE)

a & b

[1]  TRUE FALSE FALSE FALSE

a && b

Error in `a && b`:
! 'length = 4' in coercion to 'logical(1)'

Which is vectorized?
What is recycling again?
What is going on with &&?

Element-wise comparison (`&` and `|`) vs. single-element comparison (`&&` and `||`)

Element-wise comparison:

a <- c(TRUE, FALSE, FALSE, TRUE)
b <- c(TRUE, FALSE)
a & b

[1]  TRUE FALSE FALSE FALSE

a | b

[1]  TRUE FALSE  TRUE  TRUE

Single-element comparison:

a && b

Error in `a && b`:
! 'length = 4' in coercion to 'logical(1)'

a || b

Error in `a || b`:
! 'length = 4' in coercion to 'logical(1)'

a <- TRUE
b <- FALSE
a && b

[1] FALSE

a || b

[1] TRUE

More exploration of && and || in our activity today
For further discussion of the differences, see here

Logical Operators

Not operator:

a <- c(TRUE, FALSE, FALSE, TRUE)
b <- c(TRUE, FALSE)

!a

[1] FALSE  TRUE  TRUE FALSE

!a & b

[1] FALSE FALSE  TRUE FALSE

!(a & b)

[1] FALSE  TRUE  TRUE  TRUE

Comparison Operators

x <- c(1, 3, 5)
y <- c(1, 4, 2)
z <- c(3, 2)


x < y
x <= y
x > y
x >= y

x == y
x != y
x != z
z %in% x
x %in% z

Comparison Operators

x <- c(1, 3, 5)
y <- c(1, 4, 2)
z <- c(3, 2)


x < y

[1] FALSE  TRUE FALSE

x <= y

[1]  TRUE  TRUE FALSE

x > y

[1] FALSE FALSE  TRUE

x >= y

[1]  TRUE FALSE  TRUE

x == y

[1]  TRUE FALSE FALSE

x != y

[1] FALSE  TRUE  TRUE

x != z

[1] TRUE TRUE TRUE

z %in% x

[1]  TRUE FALSE

x %in% z

[1] FALSE  TRUE FALSE

Operators are just functions with funky notation
Typically, we use operators with infix notation:

x == y

[1]  TRUE FALSE FALSE

However, we can use prefix notation just like other functions we are used to:

`==`(x, y)

[1]  TRUE FALSE FALSE

Note the use of backticks.
This works for all operators!

Conditional Control Flow

R will conditionally execute code via if statements.

The basic one-line form is:

if (condition) true_action
if (condition) true_action else false_action

The basic multi-line form is:

if (condition) {
  true_action
}

if (condition) {
  true_action
} else {
  false_action
}

Conditional Control Flow

R will conditionally execute code via if statements.

x <- c(1, 3, 5)

if (3 %in% x){
  print("Vector contains 3.")
}

[1] "Vector contains 3."

if (4 %in% x){
  print("Vector contains 4.")
}

if (4 %in% x){
  print("Vector contains 4.")
} else {
  print("Vector does not contain 4.")
}

[1] "Vector does not contain 4."

Conditional Control Flow

if() is not vectorized!

x <- c(1, 3, 5)

if (x == 1){
  print("Vector contains 1.")
}

Error in `if (x == 1) ...`:
! the condition has length > 1

if (x == 3){
  print("Vector contains 3.")
}

Error in `if (x == 3) ...`:
! the condition has length > 1

Alternative condition?

Conditional Control Flow

if() is not vectorized! Alternative condition?

if (x %in% 3){
  print("Vector contains 3.")
}

Conditional Control Flow

if() is not vectorized! Alternative condition?

if (x %in% 3){
  print("Vector contains 3.")
}

Error in `if (x %in% 3) ...`:
! the condition has length > 1

Conditional Control Flow

if() is not vectorized! Alternative condition?

if (x %in% 3){
  print("Vector contains 3.")
}

Error in `if (x %in% 3) ...`:
! the condition has length > 1

if (3 %in% x){
  print("Vector contains 3.")
}

[1] "Vector contains 3."

Collapsing Logical Vectors

Because if is not vectorized, it is often helpful to collapse logical vectors to a single value.

Enter: any() and all()

x <- c(1, 3, 5)
any(x == 1)

[1] TRUE

if (any(x == 1)){
  print("Vector contains 1.")
}

[1] "Vector contains 1."

if (any(x == 3)){
  print("Vector contains 3.")
}

[1] "Vector contains 3."

all(x <= 4)

[1] FALSE

all(x <= 5)

[1] TRUE

Continuing the Conditional Executions

x = 3

if (x < 0) {
  print("x is negative")
} else if (x > 0) {
  print("x is positive")
} else {
  print("x is zero")
}

[1] "x is positive"

x = 0

if (x < 0) {
  print("x is negative")
} else if (x > 0) {
  print("x is positive")
} else {
  print("x is zero")
}

[1] "x is zero"

Saving Results

height_in <- 82
if (height_in >= 84) {
    "Center"
  } else if (height_in >= 78) {
    "Forward"
  } else if (height_in >= 75) {
    "Shooting Guard"
  } else {
    "Point Guard"
  }

[1] "Forward"

if (height_in >= 84) {
    position <- "Center"
  } else if (height_in >= 78) {
    position <- "Forward"
  } else if (height_in >= 75) {
    position <- "Shooting Guard"
  } else {
    position <- "Point Guard"
  }
position

[1] "Forward"

Vectorized `if() else()`

ifelse() runs operations on an entire vector.

height_in <- c(82, 74, 87)
position <- ifelse(height_in >= 84,
                   "Center",
                   "not Center")
position

[1] "not Center" "not Center" "Center"

case_when() provides a more flexible alternative.

position <- case_when(
  height_in >= 84 ~ "Center",
  height_in >= 78 ~ "Forward",
  height_in >= 75 ~ "Shooting Guard",
  TRUE ~ "Point Guard"
)
position

[1] "Forward"     "Point Guard" "Center"

Error Checking

Often want to validate user inputs or function assumptions.
- Will see this in action next time when discussing functions.
If an input is incorrect or an assumption isn’t met, then want to report the error and stop the code.

height_in <- c(82, 74, 87)
stopifnot(is.character(height_in))

Error:
! is.character(height_in) is not TRUE

stopifnot(is.numeric(height_in))

Want a more helpful error message!

Error Checking

Alternative for more informative error messages:

Check inputs with if()
And then stop() if they are wrong.

height_in <- c(82, 74, 87)
 if(!is.character(height_in)) {
    stop('This function only works for character inputs.\n',
         'You have provided an object of class: ', class(x))
 }

Error:
! This function only works for character inputs.
You have provided an object of class: numeric

Control Flow Recap

The flow of execution of R code.
Order still matters.
But now we can:
- Run subsets of the code with if() and else().
- Check for errors with stop().
Very helpful for building functions (Wednesday)
Will add in for() loops and other iteration methods next week.
Also need to weave in best practices.
- Lots of ways to do something. Which is best?

Annoucements

Post Spring Break: Big Picture Goals

Post Spring Break: Assignments / deadlines

Today: Control Flow

Control Flow

Control Flow

Come Back to Logical Operators

Logical Operators

Element-wise comparison (`&` and `|`) vs. single-element comparison (`&&` and `||`)

Logical Operators

Comparison Operators

Comparison Operators

Sidebar: Operators as functions

Conditional Control Flow

Conditional Control Flow

Conditional Control Flow

Conditional Control Flow

Conditional Control Flow

Conditional Control Flow

Collapsing Logical Vectors

Continuing the Conditional Executions

Saving Results

Vectorized `if() else()`

Error Checking

Error Checking

Control Flow Recap

Activity Time

Annoucements

Post Spring Break: Big Picture Goals

Post Spring Break: Assignments / deadlines

Today: Control Flow

Control Flow

Control Flow

Come Back to Logical Operators

Logical Operators

Element-wise comparison (& and |) vs. single-element comparison (&& and ||)

Logical Operators

Comparison Operators

Comparison Operators

Sidebar: Operators as functions

Conditional Control Flow

Conditional Control Flow

Conditional Control Flow

Conditional Control Flow

Conditional Control Flow

Conditional Control Flow

Collapsing Logical Vectors

Continuing the Conditional Executions

Saving Results

Vectorized if() else()

Error Checking

Error Checking

Control Flow Recap

Activity Time

Element-wise comparison (`&` and `|`) vs. single-element comparison (`&&` and `||`)

Vectorized `if() else()`