[R] R newbie: logical subsets

[Joshua Tokle]

This is exactly what I needed -- thanks for your help Greg and Gabor.

I'm looking forward to replacing a dozen stored procedures, temp tables, 
and database calls with a one page R script.

Josh

On Wed, 12 Jul 2006, Greg Snow wrote:

> Gabor, your solution does not take into account the groups.  How about
> something like:
>
> iris2 <- iris
> iris2$m <- ave(iris2$Sepal.Length, iris2$Species)
> iris2$s <- ave(iris2$Sepal.Length, iris2$Species, FUN=sd)
>
> iris2 <- transform(iris2, z= (Sepal.Length-m)/s)
>
> iris2.2 <- subset(iris2, abs(z) < 2)
>
> aggregate(iris2.2, list(iris2.2$Species), FUN=mean)
>
>
>
> -- 
> Gregory (Greg) L. Snow Ph.D.
> Statistical Data Center
> Intermountain Healthcare
> greg.snow at intermountainmail.org
> (801) 408-8111
>
>
> -----Original Message-----
> From: r-help-bounces at stat.math.ethz.ch
> [mailto:r-help-bounces at stat.math.ethz.ch] On Behalf Of Gabor
> Grothendieck
> Sent: Tuesday, July 11, 2006 1:06 PM
> To: Joshua Tokle
> Cc: r-help at stat.math.ethz.ch
> Subject: Re: [R] R newbie: logical subsets
>
> Try this, using the built in anscombe data set:
>
> anscombe[!rowSums(abs(scale(anscombe)) > 2),]
>
>
>
> On 7/11/06, Joshua Tokle <jtokle at math.washington.edu> wrote:
>> Hello!  I'm a newcomer to R hoping to replace some convoluted database
>
>> code with an R script.  Unfortunately, I haven't been able to figure
>> out how to implement the following logic.
>>
>> Essentially, we have a database of transactions that are coded with a
>> geographic locale and a type.  These are being loaded into a
>> data.frame with named variables city, type, and price.  E.g.,
>> trans$city and all that.
>>
>> We want to calculate mean prices by city and type, AFTER excluding
>> outliers.  That is, we want to calculate the mean price in 3 steps:
>>
>> 1. calculate a mean and standard deviation by city and type over all
>> transactions 2. create a subset of the original data frame, excluding
>> transactions that differ from the relevant mean by more than 2
>> standard deviations 3. calculate a final mean by city and type based
>> on this subset.
>>
>> I'm stuck on step 2.  I would like to do something like the following:
>>
>> fs <- list(factor(trans$city), factor(trans$type)) means <-
>> tapply(trans$price, fs, mean) stdevs <- tapply(trans$price, fs, sd)
>>
>> filter <- abs(trans$price - means[trans$city, trans$type]) <
>>             2*stdevs[trans$city, trans$type]
>>
>> sub <- subset(trans, filter)
>>
>> The above code doesn't work.  What's the correct way to do this?
>>
>> Thanks,
>> Josh
>>
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