Missing values in R are denoted by NA or NaN. NA stands for "Not Available" and NaN stands for "Not a Number". Missing values can occur for a variety of reasons, such as when a data point is not recorded or when a value is unknown.
The `is.na()` function can be used to test objects if they are NA. The `is.nan()` function can be used to test for NaN. Both functions return a logical vector indicating which elements are NA or NaN, respectively.
NA values have a class also, so there are integer NA, character NA, etc. This means that you can have a vector of NA values that are all of the same class, or you can have a vector of NA values that are of different classes.
A NaN value is also NA, but the converse is not true. This means that if an object is NA, it is not necessarily NaN, but if an object is NaN, it is always NA.
Here is an example of how to use the `is.na()` and `is.nan()` functions:
x <- c(1, 2, NA, 10, 3)
# Check if any elements of x are NA
is.na(x)
# [1] FALSE FALSE TRUE FALSE FALSE
# Check if any elements of x are NaN
is.nan(x)
# [1] FALSE FALSE FALSE FALSE FALSE
# Create a vector with both NA and NaN values
x <- c(1, 2, NaN, NA, 4)
# Check if any elements of x are NA
is.na(x)
# [1] FALSE FALSE TRUE TRUE FALSE
# Check if any elements of x are NaN
is.nan(x)
# [1] FALSE FALSE TRUE FALSE FALSE
