R help - Apr 2011 - integer and floating-point storage

I note that "current implementations of R use 32-bit integers for integer 
vectors," but I am working with large arrays that contain integers from 0 
to 3, so they could be stored as unsigned 8-bit integers.  Can R do this? 
(FYI -- This is for storing minor-allele counts for genetic studies. 
There are 0, 1 or 2 minor alleles and 3 would represent missing.)

It is theoretically possible to store such data with four integers per 
byte.  This is what PLINK (GPL license) does in its binary (.bed) pedigree 
format:

http://pngu.mgh.harvard.edu/~purcell/plink/data.shtml#ped

That might be too much to hope for.  ;-)

I think that the R system uses double-precision floating point numbers by 
default.  When I impute minor-allele counts, I get posterior expected 
values ranging from 0 to 2 (called dosages).  The imputation isn't very 
precise, so it would be fine to store such data using one or two bytes. 
(The values are used as regressors and small changes would have minimal 
impact on results.)  I could use unsigned 8-bit integers (0 to 255), 
probably using only 0 to 254 so that 1 and 2 could be represented with 
perfect precision as 127/127 and 254/127 (but I would do regression on the 
integer values).  Or I could use 16 bits, doubling memory load and 
improving precision.  It would be convenient if R could work with 
half-precision floating-point numbers (binary16):

http://en.wikipedia.org/wiki/Half_precision_floating-point_format

Can R do that?

If not, is anyone interested in working on developing some of these 
features in R?  We have GPL code from PLINK and Octave that might help a 
lot.

http://www.gnu.org/software/octave/doc/interpreter/Integer-Data-Types.html

Best,

Mike

--
Michael B. Miller, Ph.D.
Bioinformatics Specialist
Minnesota Center for Twin and Family Research
Department of Psychology
University of Minnesota

Hi Mike,

There are some facilities for storing and manipulating small (2 bit) 
integers. See here:

http://cran.r-project.org/web/packages/ff/index.html

-Matt

On 04/14/2011 01:20 PM, Mike Miller wrote:
> I note that "current implementations of R use 32-bit integers for
> integer vectors," but I am working with large arrays that contain
> integers from 0 to 3, so they could be stored as unsigned 8-bit
> integers. Can R do this? (FYI -- This is for storing minor-allele counts
> for genetic studies. There are 0, 1 or 2 minor alleles and 3 would
> represent missing.)
>
> It is theoretically possible to store such data with four integers per
> byte. This is what PLINK (GPL license) does in its binary (.bed)
> pedigree format:
>
> http://pngu.mgh.harvard.edu/~purcell/plink/data.shtml#ped
>
> That might be too much to hope for. ;-)
>
> I think that the R system uses double-precision floating point numbers
> by default. When I impute minor-allele counts, I get posterior expected
> values ranging from 0 to 2 (called dosages). The imputation isn't very
> precise, so it would be fine to store such data using one or two bytes.
> (The values are used as regressors and small changes would have minimal
> impact on results.) I could use unsigned 8-bit integers (0 to 255),
> probably using only 0 to 254 so that 1 and 2 could be represented with
> perfect precision as 127/127 and 254/127 (but I would do regression on
> the integer values). Or I could use 16 bits, doubling memory load and
> improving precision. It would be convenient if R could work with
> half-precision floating-point numbers (binary16):
>
> http://en.wikipedia.org/wiki/Half_precision_floating-point_format
>
> Can R do that?
>
> If not, is anyone interested in working on developing some of these
> features in R? We have GPL code from PLINK and Octave that might help a
> lot.
>
> http://www.gnu.org/software/octave/doc/interpreter/Integer-Data-Types.html
>
> Best,
>
> Mike
>
> --
> Michael B. Miller, Ph.D.
> Bioinformatics Specialist
> Minnesota Center for Twin and Family Research
> Department of Psychology
> University of Minnesota
>
> ______________________________________________
> R-help at r-project.org mailing list
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> PLEASE do read the posting guide
> http://www.R-project.org/posting-guide.html
> and provide commented, minimal, self-contained, reproducible code.
>

-- 
Matthew S Shotwell   Assistant Professor           School of Medicine
                      Department of Biostatistics   Vanderbilt University