What is NaN ?
NaN stands for “Not a Number.” It’s used to show when a math operation doesn’t give a meaningful result. For example, if we try to divide zero by zero or take the square root of a negative number, we’ll get NaN.
# Perform a division by zero operation to generate NaN
result <- 0 / 0
# Check if the result is NaN
print("Is the result NaN?")
is_nan <- is.nan(result)
print(is_nan)
# Attempt to calculate the square root of a negative number to generate NaN
result <- sqrt(-1)
# Check if the result is NaN
print("Is the result NaN?")
is_nan <- is.nan(result)
print(is_nan)
Output:
[1] "Is the result NaN?"
[1] TRUE
Warning message:
In sqrt(-1) : NaNs produced
[1] "Is the result NaN?"
[1] TRUE
What is the difference between NA and NAN in R?
R Programming Language is a super popular programming language for analyzing data. Lots of data scientists, statisticians, and researchers love using it because it’s so versatile and has lots of tools to help them out. But sometimes, figuring out all the little details can’t be easy. One thing that often confuses people is understanding the difference between NA and NaN. They might look similar, but they’re actually used for different things in R.