[STATSGRASS] Re: Connecting to databases on Windows using R

[Craig Aumann]

Well I'm curious to know what R on Windows actually does with the
following commands:

install.packages(contriburl="http://www.bioconductor.org/repository/devel/package/Source/","Rdbi")
install.packages(contriburl="http://www.bioconductor.org/repository/devel/package/Source/","RdbiPgSQL")

Does it successully install the packages, or complain loudly?


On Thu, 2005-03-17 at 13:30, Markus Neteler wrote:
> Hi 
> 
> (for me) it's strange that Rdbi doesn't appear on CRAN but
> I was already notified once not to discuss Rdbi questions on 
> the R Mailing lists... (so better to ask here :-)
> 
> Anyway, I have compiled some usage instructions here:
>  http://grass.itc.it/statsgrass/r_and_dbms.html
> which should be more or less still valid. 
> 
> Maybe a bit helpful, also comparing speed of Rdbi and RODBC.
> However, it doesn't cover MS-Win installation...
> 
> Best
> 
>  Markus
> 
> On Thu, Mar 17, 2005 at 11:33:02AM -0700, Craig Aumann wrote:
> > See Teague's question below.  As a linux person, I don't know the answer
> > to this so am forward this message to the list.  
> > 
> > Having looked also at  http://www.bioconductor.org/ - which links off of
> > the main R web page, I see a Windows version of Rdbi (click "What is
> > Bioconductor"  and scroll down till you find the heading: "Database
> > Interaction").  I'm seeing a RDBI for Windows, but no windows version of
> > RdbiPgsql which is a problem! 
> > 
> > Have you tried sending a message to the R-Help list about this - how do
> > windows users access PGSQL databases from R?  (R website -> mailing
> > lists.)  
> > 
> > On the CRAN site, just searching databases, I saw the following:
> > DBI, RMySQL, RODBC, ROracle. Not sure if windows versions exist for any
> > of these.   
> > 
> > Also have a look at the links in
> > http://cran.r-project.org/doc/manuals/R-data.html .  
> > 
> > Afraid that's all I know about attaching to Databases on Windows - which
> > ain't much I'm afraid. 
> > 
> > Cheers!
> > Craig
> > 
> > 
> > 
> > 
> > 
> > 
> > On Thu, 2005-03-17 at 10:40, Teague O'Mara wrote:
> > > Hi Craig.  I came back across this email from awhile ago.  I'm attempting to
> > > look at cluster of points in GRASS and have been using the script that Michael
> > > Barton wrote, but I'd like to look at a few other things and it seems that using
> > > R and my GRASS data will be much easier. I have been using R for basic
> > > statistics, but am far from being fluent with it.  If you have a moment, can you
> > > help me out with some R issues?  I'm using an XP machine with R 2.0.1.
> > > 
> > > I'm having a problem downloading and installing Rdbi and RdbiPgSQL.  I can't
> > > find these two sets, or any SQL interface that will download from CRAN.  I was
> > > able to download these directly from sourcforge (tar.gz format), but I'm not
> > > sure what to do with them now.  Is it an issue with this version of R?
> > > 
> > > Thanks for any help you can offer.
> > > 
> > > Cheers--
> > > Teague
> > > 
> > > 
> > > 
> > > Teague O'Mara
> > > Department of Anthropology
> > > Arizona State University
> > > Box 872402
> > > Tempe, AZ 85287-2402
> > > 480.965.6213
> > > 
> > > 
> > > Quoting Craig Aumann <caumann at ualberta.ca>:
> > > 
> > > > In glancing at your script, I wondered why you didn't just import the
> > > > stuff into a statistical package like R and then use all the
> > > > clustering
> > > > routines it has.    See www.r-project.org
> > > > 
> > > > 
> > > > 
> > > > 
> > > > ## Key commands in R are:
> > > > 
> > > > ## Load the packages to access the database, in particular the PgSQL
> > > > ##
> > > > database
> > > > require(Rdbi)
> > > > require(RdbiPgSQL)
> > > > 
> > > > ## Load some clustering routines
> > > > 
> > > > require(cluster)
> > > > require(mclust)
> > > > 
> > > > 
> > > > ### Connect to the database and read in a points data set "jogis"
> > > > ## and read in the dataset "wells_att".  
> > > > 
> > > > 
> > > > conn <- dbConnect(PgSQL(), dbname="jogis", user="caumann")
> > > > wells <- dbReadTable(conn, "wells_att")
> > > > dbDisconnect(conn)
> > > > 
> > > > ## Now you can apply any clustering or kernel smoothing technique you
> > > > want to the dataset.  
> > > > 
> > > > If this is of any help, let me know and I can provide more details.  
> > > > Cheers!
> > > > Craig
> > > > 
> > > > 
> > > 
> > > 
> > > 
> > 
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