[R] Summary of variables with NA, empty

[David Winsemius]

On Oct 23, 2012, at 11:17 AM, Lopez, Dan wrote:

> Hi,
> 
> Is there a function I can use on my dataframe to give me a concise summary of variables that are NA,blank,etc? Basically all Null values, Empty strings, white space, blank values. Ideally it would look something like the below:
> 
> # it should only includes the fields with NAs, blanks, etc. Added bonus would be to include column Index.
> #Valid Records = records that are not NA, blank,etc
> #ColIndex - what place is column in the original dataframe...1,2,3, ...xth
> 
>                Valid Records  Null (NA?)        Empty String      White Space       Blank Value        ColIndex

Would a "Valid Record" be defined by grep([^ ], column)? ... i.e. has a non-space character in it
What is a "ColIndex"?
How is an "Empty String" different than "White Space" or a "Blank Value"



> Var1                       52        8                                                                        2
> Var2                       40           20                                           10                           10                                           3
> Var3                       58                                                           2                                                                              20
> ..
> 

I generally use describe from package:Hmisc. There are other versions of describe in other packages. It's not going to classify items composed entirely of a varying number of spaces and other non-character items like tabs as a single group. And it's unclear what you will use as an operational definition to separate blanks and white-space. You will probably need to code that yourself. You might want to look at the code for Hmisc::describe as a starting point.


> I now there is summary() but I am not sure if that always displays NAs and blanks especially with factor variables that have several levels (lumps them in 'Other' when I run the entire dataframe).


> In these instances I can run the individual field separately and see all levels but that would be inefficient to do for a dataframe with over 50 variables.

How were you going to "run the individual field"? If you show us code, there might be more rapid progress. It would probably be very easy to turn that into a function that could then be "run" with `lapply`.
> 
> 
-- 

David Winsemius, MD
Alameda, CA, USA