First, let's look at the missing codes for different languages:
Languages
|
Missing code
|
Explanation or examples
|
R |
NA |
NA stands for Not Available |
Python |
nan |
import scipy as sp misingCode=sp.nan
|
Jullia |
missing |
julia> missing + 5 missing |
Octave |
NaN |
Same for MATLAB as well |
Table 3.7: Missing codes for R, Python, Julia, and Octave
For R, the missing code is NA. Here are several functions we could use to remove those missing observations, shown in an example:
> head(na_example,20) [1] 2 1 3 2 1 3 1 4 3 2 2 NA 2 2 1 4 NA 1 1 2 > length(na_example) [1] 1000 > x<-na.exclude(na_example) > length(x) [1] 855 > head(x,20) [1] 2 1 3 2 1 3 1 4 3 2 2 2 2 1 4 1 1 2 1 2
In the previous example, we removed 145 missing values by using the R function called na.exclude(). We could...