If Else Conditions in R (with Examples)

This tutorial explains various ways to apply IF ELSE conditional statements in R, along with examples.

There are the following two ways to handle conditional statements in R.

• ifelse() function
• if-else statement

Syntax : ifelse() function

Below is the syntax of the ifelse() function in R. It works similar to MS Excel IF function.

ifelse(condition, value if condition is true, value if condition is false)

Example

my_variable <- c(-2, 0, 5, -3, 2, 6)
result <- ifelse(my_variable > 0, "Positive", "Non-Positive")

Output

[1] "Non-Positive" "Non-Positive" "Positive"     "Non-Positive"
[5] "Positive"     "Positive"    

Syntax : if-else statement

Below is the syntax of the if-else statement in R.

if (condition1) {
  # Code to be executed if condition1 is TRUE
} else if (condition2) {
  # Code to be executed if condition2 is TRUE and condition1 is FALSE
} else {
  # Code to be executed if both condition1 and condition2 are FALSE
}

Example

my_variable <- -7
if (my_variable > 0) {
  print("it is positive.")
} else if (my_variable < 0) {
  print("it is negative.")
} else {
  print("it is zero.")
}

Output

[1] "it is negative."  

Table of Contents

---

ifelse() Function

In this section, we will cover ifelse() Function in detail.

Examples : ifelse Function

Below are examples showing the application of the ifelse() function in R.

Sample Data

Let's create a sample data to show how to perform IF ELSE function. This data frame would be used further in examples.

x1	x2	x3
1	129	A
3	178	B
5	140	C
7	186	D
9	191	E
11	104	F
13	150	G
15	183	H
17	151	I
19	142	J

Run the syntax below to generate the above table in R.

set.seed(123)
mydata = data.frame(x1 = seq(1,20,by=2),
                    x2 = sample(100:200,10,FALSE),
                    x3 = LETTERS[1:10])

Example 1 : Simple ifelse Function

Suppose you are asked to create a binary variable - 1 or 0 based on the variable 'x2'. If value of a variable 'x2' is greater than 150, assign 1 else 0.

mydata$x4 = ifelse(mydata$x2>150,1,0)

In this case, it creates a variable x4 on the same data frame 'mydata'. The output is shown in the image below -

ifelse : Output

Create variable in a new data frame Suppose you need to add the above created binary variable in a new data frame. You can do it by using the code below -

x = ifelse(mydata$x2>150,1,0)
newdata = cbind(x,mydata)

The cbind() is used to combine two vectors, matrices or data frames by columns.

Apply ifelse() on Character Variables If variable 'x3' contains character values - 'A', 'D', the variable 'x1' should be multiplied by 2. Otherwise it should be multiplied by 3.

mydata$y = ifelse(mydata$x3 %in% c("A","D") ,mydata$x1*2,mydata$x1*3)

The output is shown in the table below

x1 x2 x3 y
1 129 A 2
3 178 B 9
5 140 C 15
7 186 D 14
9 191 E 27
11 104 F 33
13 150 G 39
15 183 H 45
17 151 I 51
19 142 J 57

Example 2 : Nested ifelse Function in R

Multiple ifelse functions can be written similarly to excel's If function. In this case, we are telling R to multiply variable x1 by 2 if variable x3 contains values 'A' 'B'. If values are 'C' 'D', multiply it by 3. Else multiply it by 4.

mydata$y = ifelse(mydata$x3 %in% c("A","B") ,mydata$x1*2,
                  ifelse(mydata$x3 %in% c("C","D"), mydata$x1*3,
                         mydata$x1*4))

Do you hate specifying data frame multiple times with each variable?

You can use with() function to avoid mentioning data frame each time. It makes writing R code faster.

mydata$y = with(mydata, ifelse(x3 %in% c("A","B") , x1*2,
                  ifelse(x3 %in% c("C","D"), x1*3, x1*4)))

Let's dive into the important points regarding the ifelse function, which is commonly used to solve real-world data problems.

How to treat missing values in ifelse Function?

In R, missing values are denoted by the special value NA (Not Available).

Incorrect Method

x = NA
ifelse(x==NA,1,0)

Result : NA
It should have returned 1.
Correct Method

x = NA
ifelse(is.na(x),1,0)

Result : 1
The is.na() function tests whether a value is NA or not.

How to use OR and AND operators in ifelse Function

The & symbol is used to perform AND conditions

ifelse(mydata$x1<10 & mydata$x2>150,1,0)

Result : 0 1 0 1 1 0 0 0 0 0

The | symbol is used to perform OR conditions

ifelse(mydata$x1<10 | mydata$x2>150,1,0)

Result : 1 1 1 1 1 0 0 1 1 0

How to combine summary and ifelse functions?

In this example, we can counting the number of records where the condition meets. The condition is x1<10 and x2>150.

sum(ifelse(mydata$x1<10 | mydata$x2>150,1,0))

Result : 7

If Else Statement

There is one more way to define conditional statement in R i.e. if-else statement. This style of writing If-Else is mostly used when we use conditional statements in loop and R functions. In other words, it is used when we need to perform various actions based on a condition.

Examples : If..Else If..Else Statements

The code below would return the value 0 because the condition k > 100 is false.

k = 99
if(k > 100) 1 else 0

Result : 0

How to handle multiple conditions?

The following code sets a variable k to 100. It checks if k is greater than 100. Since it's not, it checks if k is less than 100 which is also false. So it prints "Equal to 100".

k = 100
if(k > 100){
print("Greater than 100")
} else if (k < 100){
print("Less than 100")
} else {
print ("Equal to 100")
}

Result : "Equal to 100"

ifelse function vs If-Else Statement

Below are the main differences between the ifelse function and the if-else statement in R.

1. Usage: ifelse() function is commonly used when applying a condition to an entire vector or column of data. whereas, if-else statement is typically used when dealing with more complex conditional logic.
2. Vectorized vs Scalar: The ifelse() function can work with entire vectors or columns of data at once, while the if-else statement operates on individual values.
3. Output Length: The ifelse() function returns a vector with the same length as the input. On the other hand, the if-else statement returns a single value or executes blocks of code.

If Else in Popular R Packages

Other than base R, there are functions available in packages for If Else conditions.

dplyr: If Else

Below is the syntax of if_else( ) function of dplyr package in R.

if_else(condition, value if condition is true, value if condition is false, value if NA)

Example 1: Using if_else() to create a new column.

The following program creates a new column called "SpeciesType" based on the condition that if the "Species" column is equal to "setosa", the corresponding "SpeciesType" value is set to "Type A". Otherwise it's set to "Type B".

iris <- iris %>%
  mutate(SpeciesType = if_else(Species == "setosa", "Type A", "Type B"))
  
unique(iris$SpeciesType)  

Result :

[1] "Type A" "Type B"

Example 2: Checks whether a value is a multiple of 2

library(dplyr)
x=c(1,NA,2,3)
if_else(x%%2==0, "Multiple of 2", "Not a multiple of 2", "Missing")

Result :

Not a multiple of 2
Missing
Multiple of 2
Not a multiple of 2

The %% symbol returns remainder after a value is divided by divisor. In this case, first element 1 is divided by 2.

sqldf: If Else

We can write SQL query in R using sqldf package. In SQL, If Else statement is defined in CASE WHEN statement.

df=data.frame(var1=c(2,NA,3,4,5))
library(sqldf)
sqldf(
  "SELECT *,
  CASE WHEN (var1%2)=0  THEN 'Multiple of 2'
  WHEN  var1 is NULL  THEN 'Missing'
  ELSE 'Not a multiple of 2'
  END AS new_variable
  FROM df"
)

Output

  var1        new_variable
1    2       Multiple of 2
2   NA             Missing
3    3 Not a multiple of 2
4    4       Multiple of 2
5    5 Not a multiple of 2

Operators

Below is a list of operators frequently used in if-else conditions in R.

1. Comparison Operators:
   • ==: Equal to
   • !=: Not equal to
   • <: Less than
   • >: Greater than
   • <=: Less than or equal to
   • >=: Greater than or equal to
2. Logical Operators:
   • &: Logical AND
   • |: Logical OR
   • !: Logical NOT
3. Compound Operator : %in%: Multiple OR conditions. Checks if a value is in a vector or set

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Deepanshu founded ListenData with a simple objective - Make analytics easy to understand and follow. He has over 10 years of experience in data science. During his tenure, he worked with global clients in various domains like Banking, Insurance, Private Equity, Telecom and HR.

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