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Notice that both NaN values have been replaced by zeros in the vector.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