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Data Import to R

6 messages · Ramesh Gautam, David Winsemius, PIKAL Petr +3 more

Original
Ramesh Gautam
# 
While importing .csv files into R, all data are converted to factor-by
default. But, how can I preserve the original format of the data like
numeric to numeric, integer to integer, character to character etc while
importing from csv to R environment.

I tried several ways, no thing helps. I used 'stringsAsFactor = FALSE'
command, it did convert all data to characters. But, I wanted to preserve
the numeric data to be in integer or double.

Any idea? examples?

Thanks
David Winsemius
#
On Nov 13, 2014, at 4:28 PM, Ramesh Gautam wrote:

            

      
      
      
    

        
Use colClasses. And if that hint is not enough then post an example to work with.

            

      
      
      
    

        

      
      
    

        
David Winsemius
Alameda, CA, USA

  
  


  


      

    

    

      
      

        


  

        

      PIKAL Petr
    

    

      
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Hi

Your original numeric data probably contain something which prevents read.* to accept them as numeric (decimal point, white space)

what is result of

str(imported.data)

Petr Pikal

            

      
      
      
    

        
Your original numeric data probably contain something which prevents read.* to accept them as numeric (decimal point, white space)

What is result of str(imported.data)

Example of data is up to you, we do not have them.

Cheers
Petr

            

      
      
      
    

        

      
      
    

        
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      theo
    

    

      
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i think you can set: options(stringsAsFactor=FALSE)  which will apply
globally or use read.csv(..., stringsAsFactor=FALSE) when imporing.
have a look at the documentation ?read.csv
good luck.

        
On 11/14/2014 09:44 AM, David Winsemius wrote:

            

      
      
      
    

        

      
      
    

            

      
      
      
    

        

      
      
    

  
  


  


      

    

    

      
      

        


  

        

      MacQueen, Don
    

    

      
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Petr is almost certainly correct. A further suggestion:

Continue to import using stringsAsFactors = FALSE

On one of the columns that should be numeric, use as.numeric(), find the
NA's in the result of that, and then look at those rows of the data. There
will be something there that is non-numeric. By any chance, does your data
use a comma instead of a decimal point (see the "dec" argument to
read.table)?

  
    

      
      
    

  


  


      

    

    

      
      

        


  

        

      Jeff Newmiller
    

    

      
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If your data uses a special marker such as "--" or "n/a" to indicate not available then once you have identified those markers (using any method, though Don's procedure below is what I use) then you can specify them with the na.strings parameter to read.csv. (See the help for read.table for many other parameters you can also give to read.csv.) Once those special values are specified, then you should get numeric columns just fine.
---------------------------------------------------------------------------
Jeff Newmiller                        The     .....       .....  Go Live...
DCN:<jdnewmil at dcn.davis.ca.us>        Basics: ##.#.       ##.#.  Live Go...
                                      Live:   OO#.. Dead: OO#..  Playing
Research Engineer (Solar/Batteries            O.O#.       #.O#.  with
/Software/Embedded Controllers)               .OO#.       .OO#.  rocks...1k
--------------------------------------------------------------------------- 
Sent from my phone. Please excuse my brevity.

        
On November 14, 2014 11:11:20 AM PST, "MacQueen, Don" <macqueen1 at llnl.gov> wrote: