To highlight: "Basically all Null values" is a meaningless phrase in R. ?Null ?NA ?NaN have **very specific meanings** in R and have nothing to do with the various sorts of whitespace characters that David mentions (spaces, tabs...). If you wish to use R, you **must** understand the distinctions (the Intro to R tutorial discusses some of this -- have you read it?).
There is functionality to test for these sorts of things (is.na, is.null, etc). You need to put in the effort to learn about this if you mean to use R in any serious way, as these will occur in either data I/O (NA's) or data manipulation (e.g. 0/0) -- Bert On Tue, Oct 23, 2012 at 2:44 PM, David Winsemius <dwinsem...@comcast.net> wrote: > > 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 > > ______________________________________________ > R-help@r-project.org mailing list > https://stat.ethz.ch/mailman/listinfo/r-help > PLEASE do read the posting guide http://www.R-project.org/posting-guide.html > and provide commented, minimal, self-contained, reproducible code. -- Bert Gunter Genentech Nonclinical Biostatistics Internal Contact Info: Phone: 467-7374 Website: http://pharmadevelopment.roche.com/index/pdb/pdb-functional-groups/pdb-biostatistics/pdb-ncb-home.htm ______________________________________________ R-help@r-project.org mailing list https://stat.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide http://www.R-project.org/posting-guide.html and provide commented, minimal, self-contained, reproducible code.
