Topics: Conditional Statements with if, else, and else if
These slides were last updated on July 29, 2025
Up to this point, our R code has been running line by line following in a fixed sequence.
Now we ask: Can our code be dynamic vs fixed?
For Example: Can it skip some lines and run others depending on a condition? Or can it repeat itself when needed?
Yes, with control structures!
Control structures let us write code that can:
Make decisions
→ with conditional statements (if, if...else, etc.)
Repeat actions
→ with loops (for, while)
Conditional statements and loops — that is, control structures — let us control the flow of execution. They allow our code to respond dynamically to different situations, rather than always running in the same fixed order like we’ve done so far.
Conditional statements let our code make decisions: the code checks conditions, evaluates them, and chooses what to do.
Said more formally, we use logical tests to determine which code should run:
Single test with if... else
Multiple tests with if... else if... else
Nested tests by placing one if inside another
Vectorized tests with ifelse() (or if_else() in tidyverse) to apply a condition to each element of a vector
Download today’s class materials from the course website.
Open the file called warm-up.R.
Before running the code: look over the syntax and try to predict what the code will output
Run the code and compare it to your prediction.
Be ready to share at least one question about what the code.
Use if...else when your code needs to test one condition and take one of two possible actions, depending on whether the condition is TRUE or FALSE.
Syntax:
if (condition) {
# action performed when condition is TRUE
action1
} else {
# action performed when condition is FALSE
action2
}Example:
Another Example: What’s the output of this code?
Use if...else if...else when your code needs to test multiple conditions and take different actions depending on which condition is TRUE.
Syntax:
if (condition1) {
# action performed when condition 1 is TRUE
action1
} else if (condition2) {
# action performed when condition 2 is TRUE
action2
} else {
# action performed when conditions 1 and 2 are FALSE
action3
}Example:
Conditional statements are evaluated in order so the sequence of your code matters:
TRUE, it runs that block and skips the restAnother Example with multiple else if statements:
# Take user input
temperature <- as.integer(readline(prompt = "Enter today's temperature in Celsius: "))
# Determine weather based on temperature
if (temperature >= 30) {
weather <- "Hot"
} else if (temperature >= 20) {
weather <- "Cool"
} else if (temperature >= 10) {
weather <- "Breezy"
} else {
weather <- "Freezing"
}
print(weather)temperature >= 10 before temperature >= 20 in the code and run it with temperature 25else if statement to further refine the code: If the temperature is between 0 and 10 (inclusive of 0, exclusive of 10), set the weather to “Cold”. Hint: use the & operator to combine two conditions.Once done, share your code here.
Use nested if...else statements when your code needs to check conditions within conditions. This allows you to make a second decision only after a first condition is met.
Example (note the %% returns the remainder after division):
Same example, but this checks also the condition x <- 0:
Use ifelse() to apply a condition to each element of a vector. This is called a “vectorized test”, and fits well R’s vector-based design.
Syntax:
ifelse (condition to be evaluated,
action performed when condition is TRUE,
action performed when condition is FALSE)Example:
Another example: What is the output of this code?
How ifelse() works:
The input to ifelse() is often a vector, not just a single value, so the output is also a vector. The code evaluates each element of the vector, applying the specified action for each case.
This works because ifelse() supports vectorized operations: operations directly applied on entire vectors, rather than looping through individual elements one-by-one.
Example: What is the output of this code?
In the previous example, we only had two possible categories: “Admitted” and “Rejected”
What happens to our code if we add a third one, say “Waitlisted”?
qualify <- tibble("Athlet" = c("Noah", "Julio", "Nick", "Maria"),
"Scores" = c(32, 37, 28, 30))
qualify %>%
mutate(Status = ifelse(Scores > 35, # first condition
"Admitted", # if TRUE
ifelse(Scores >= 30, # second condition (only if first is FALSE)
"Waitlisted", # if TRUE
"Rejected"))) # if both FALSENested ifelse() statements work, but they make the code harder to read.
Compare the previous code with this code that does not use them:
qualify <- tibble("Athlet" = c("Noah", "Julio", "Nick", "Maria"),
"Scores" = c(32, 37, 28, 30))
qualify %>%
mutate(Status = "Rejected") %>% # default label
mutate(Status = ifelse(Scores >= 30, "Waitlisted", Status)) %>% # update if condition met
mutate(Status = ifelse(Scores > 35, "Admitted", Status)) # update if higher condition metTip
If you have multiple conditions (more than two):
Write several separate mutate() + ifelse() steps, as shown on this slide, rather than nesting them, as shown on the previous slide
When possible, use case_when() instead of ifelse() for better readability
People often use ifelse() to recode variables in a dataframe. It’s a good choice when the recoding can be done with a single ifelse(), as shown in this example.
Example: the variable decisionDirection takes four values (1 conservative, 2 liberal, 3 unspecifiable, and NA). Recode it to take three values (0 conservative, 1 liberal, and NA for both NA and unspecifiable)
Note there is also a tidyverse version of the base R ifelse() function!
The tidyverse version is called if_else() https://dplyr.tidyverse.org/reference/if_else.html
The two are similar, hence I introduced only on here, pick the one you prefer.
You can fine more practice exercises in today’s class materials (downloaded from the website)
if, else, and else ififelse() and if_else()Click on the icon bottom-right corner > Tools > PDF Export Mode > Print as a Pdf