• NaN (“Not a Number”) means 0/0
• NA (“Not Available”) is generally interpreted as a missing value and has various forms – NA_integer_, NA_real_, etc. 
• Therefore, NaN ≠ NA and there is a need for NaN and NA.
• is.na() returns TRUE for both NA and NaN, however is.nan() return TRUE for NaN (0/0) and FALSE for NA.
• NULL represents that the value in question simply does not exist, rather than being existent but unknown.

is.na(x) # returns TRUE of x is missing

y <- c(1,2,3,NA)

is.na(y) # returns a vector (F F F T)

 

x <- c(1,2,NA,3)

mean(x) # returns NA

mean(x, na.rm=TRUE) # returns 2

 

The function na.omit() returns the object with listwise deletion of missing values.

# create new dataset without missing data

newdata <- na.omit(mydata)

 

They are not supposed to give the same result. Consider this example:

exdf<-data.frame(a=c(1,NA,5),b=c(3,2,2)) # a b #1 1 3 #2 NA 2 #3 5 2 colMeans(exdf,na.rm=TRUE) ## remove only "NA" # a b #3.000000 2.333333 colMeans(na.omit(exdf)) ## remove "NA 2" # a b #3.0 2.5

Why is this? In the first case, the mean of column b is calculated through (3+2+2)/3. In the second case, the second row is removed in its entirety (also the value of b which is not-NA and therefore considered in the first case) by na.omit and so the b mean is just (3+2)/2.

 

• REF:
• http://www.cookbook-r.com/Basics/Working_with_NULL_NA_and_NaN/
• http://stackoverflow.com/questions/7031127/data-frames-and-is-nan
• http://www.r-bloggers.com/difference-between-na-and-nan-in-r/
• http://help.scilab.org/docs/5.5.2/en_US/isnan.html
• http://www.quantlego.com/howto/special-missing-values-in-r/