How to Handle NaN Values in R (With Examples)


In R, NaN stands for Not a Number.

Typically NaN values occur when you attempt to perform some calculation that results in an invalid result.

For example, dividing by zero or calculating the log of a negative number both produce NaN values:

#attempt to divide by zero
0 / 0

[1] NaN

#attempt to calculate log of negative value
log(-12)

[1] NaN

Note that NaN values are different from NA values, which simply represent missing values.

You can use the following methods to handle NaN values in R:

#identify positions in vector with NaN values
which(is.nan(x))

#count total NaN values in vector
sum(is.nan(x)) 

#remove NaN values in vector
x_new <- x[!is.nan(x)]

#replace NaN values in vector
x[is.nan(x)] <- 0 

The following examples show how to use each of these methods in practice.

Example 1: Identify Positions in Vector with NaN Values

The following code shows how to identify the positions in a vector that contain NaN values:

#create vector with some NaN values
x <- c(1, NaN, 12, NaN, 50, 30)

#identify positions with NaN values
which(is.nan(x))

[1] 2 4

From the output we can see that the elements in positions 2 and 4 in the vector are NaN values.

Example 2: Count Total NaN Values in Vector

The following code shows how to count the total number of NaN values in a vector in R:

#create vector with some NaN values
x <- c(1, NaN, 12, NaN, 50, 30)

#identify positions with NaN values
sum(is.nan(x))

[1] 2

From the output we can see that there are 2 total NaN values in the vector.

Example 3: Remove NaN Values in Vector

The following code shows how to create a new vector that has the NaN values removed from the original vector:

#create vector with some NaN values
x <- c(1, NaN, 12, NaN, 50, 30)

#define new vector with NaN values removed
x_new <- x[!is.nan(x)]

#view new vector
x_new

[1]  1 12 50 30

Notice that both NaN values have been removed from the vector.

Example 4: Replace NaN Values in Vector

The following code shows how to replace NaN values in a vector with zeros:

#create vector with some NaN values
x <- c(1, NaN, 12, NaN, 50, 30)

#replace NaN values with zero
x[is.nan(x)] <- 0

#view updated vector
x

[1]  1  0 12  0 50 30

Notice that both NaN values have been replaced by zeros in the vector.

Additional Resources

The following tutorials explain how to perform other common tasks in R:

How to Interpolate Missing Values in R
How to Find and Count Missing Values in R
How to Use “Is Not NA” in R

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