Hi, Michael,
Seems like all you need is aggregate and rbind:
x <- aggregate(saw.aggr.data["value"],
saw.aggr.data[c("conversion.type", "filteredID", "metric")],
sum)
x$bucketID <- "combined"
y <- rbind(saw.aggr.data, x)
Is this what you need?
--sundar
On Mon, Oct 12, 2009 at 6:09 AM, Michael Pearmain <mpearm...@google.com> wrote:
> Hi All,
>
> I have a questions about associative list mappings in R, and if they are
> possible?
>
> I have data in the form show below, and want to make a new 'bucket' variable
> called combined. Which is the sum of the control and the exposed metric
> values
> This combined variable is a many to many matching as values only appear in
> the file if they have a value > 0.
>
> conversion.type filteredID bucketID Metric Value
> counter true control a 1
> counter true control b 1
> counter true control c 2
> counter true control d 3
>
> counter true exposed a 4
> counter true exposed e 1
>
> ASIDE:
>
> At the minute i read the data into my file and and then create all the
> 'missing' row values
> (in this case,
> counter true control e 0
> counter true exposed b 0
> counter true exposed c 0
> counter true exposed d 0)
>
>
> and then run a sort on the data, and count the number of times control
> appears, and then use this as an index matcher.
>
> saw.aggr.data <- [order(saw.aggr.data$bucketID, saw.aggr.data$metric), ]
> no.of.metrics <- length(saw.aggr.data$bucketID[grep("control",
> saw.aggr.data$bucketID)])
>
> for (i in (1:no.of.metrics)) {
> assign(paste("combined", as.character(saw.aggr.data$metric[i])),
> (saw.aggr.data$value[i] + saw.aggr.data$value[i + no.of.metrics]))
> }
>
> This does what i want it to but is very very weak and could be open to large
> errors, ( error handling currently via grepping the names of the metric[i]
> == name of metric [i + no.of.metrics])
>
> Is there a more powerful way of doing this using some kind of list mapping?
> I've looked at the older threads in this area and it looks like something
> that should be possible but i can't figure out how to do this?
> Ideally i'd like a final dataset / list that is of the following form.
>
> conversion.type filteredID bucketID Metric Value
> counter true control a 1
> counter true control b 1
> counter true control c 2
> counter true control d 3
>
> counter true exposed a 4
> counter true exposed e 1
> counter true combined a 5
> counter true combined b 1
> counter true combined c 2
> counter true combined d 3
> counter true combined e 1
>
> So i dont have to create the dummy variables.
>
> does this make sense?
>
> Many thanks in advance
>
> Mike
>
>
>
> --
> Michael Pearmain
> "I abhor averages. I like the individual case. A man may have six meals
> one day and none the next, making an average of three meals per day, but
> that is not a good way to live. ~Louis D. Brandeis"
>
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>
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