Stochastic Nonsense

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Querying Databases From R on a Mac

I use a mac, currently running OS 10.6 / Snow Leopard, and I’d like to query our greenplum / postgres database from R. This used to work with R 2.9, but I unfortunately had to upgrade R, and R 2.10 on the mac is a 64 bit app. So, I want to use [...]

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Querying Postgres or Greenplum from R on a Mac

So, I’m using snow leopard, and I want to query our postgres / greenplum database.
First things first: I’m familiar with the RODBC package on CRAN. This installs fine, since it’s a binary package. I also installed the ODBC Administrator app that you have to download from apple here . Now all [...]

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Querying Databases in R, on Mac OS

Unfortunately, it appears with the recent release of 10.6 / Snow Leopard Apple has removed the ODBC Administrator Tool from the OS. It can still be downloaded from Apple.

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Querying Databases in R

One of the first things you’ll want to do in R is set it up to talk to databases. The easiest way to do this is using ODBC, via package RODBC.
To get the package, run

> install.packages(RODBC)

Once you have RODBC installed, you call it in R as follows. But it’s very simple: a bit [...]

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R Dates – Recovering and Converting From Integers

One problem with R is that dates (class Date) are internally stored as integer numbers of days elapsed since 1 January 1970 and R sometimes loses the dateness of the variables and thinks of it only as an integer. So in the first line, we take the range of dates present in our data, [...]

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Removing Extra Column of Data from CSVs in R — R Tip

When R writes a csv file, you get an extra column of data as such:

> s
> plot(x=s$x, y=s$y )
>
> write.csv(x=s, file=’s0.csv’ )

When you peek in the csv file, you see this:

blog earl$ head s0.csv
“”,”x”,”y”
“1″,1,8.29164186026901
“2″,2,2.83956938423216
“3″,3,7.43510165950283
“4″,4,6.38210728997365
“5″,5,9.29241271456704
“6″,6,6.13102467032149
“7″,7,5.03747826907784
“8″,8,1.83257902506739
“9″,9,9.62789378128946
blog earl$

What is that first column? It’s actually pretty obvious in this example, but if you’ve sorted your data frame [...]