Here are two different ways with your data as the data.frame xx
# Basic R
aggregate(xx$Tip, list(xx$Time), sum)
# Using the rshape package
library(reshape2)
yy <- melt(xx, id=c("Time"), measure.vars=c("Tip"))
dcast(yy , Time ~ variable , sum)
--- On Thu, 6/9/11, Trying To learn again <tryingtolearnag...@gmail.com> wrote:
> From: Trying To learn again <tryingtolearnag...@gmail.com>
> Subject: [R] Item analysis
> To: r-help@r-project.org
> Received: Thursday, June 9, 2011, 4:41 PM
> Hi all,
>
> For several reasons I have no used CARN R in months....I
> have an idea and I
> want to retry to learn CRAN R.
>
> I know I need to formulate more "intelligent" questions but
> I will expose
> and if someone can help me I would be very gratefull I
> promise to try to
> learn again....
>
> The question I have a data file like this:
>
> Date, Time, Tip
> 13/11/2008,23:16:00,432
> 13/01/2009,23:17:00,633
> 13/11/2009,23:16:00,134
> 13/12/2009,23:17:00,234
> 13/01/2010,23:16:00,111
>
> I want to make an statistic (the sum of tip) but to extract
> one sum by each
> different minute in Time, so you see, in this easy example
> I will have a
> final table with two items:
>
> Item Sum
> 16 (432+134+111)
> 17 (633+234)
>
> Of course in my file (is bigger) I have minutes from 0 to
> 59.
>
> Would it be too much difficult if I want this analysis but
> on table for each
> day (indentified in the column Date) of the week. So you
> see on
> 13/11/2008 was thursday so you go each day and sum if
> this day is thrusday
> from 0 to 59 minutes...
>
> MAny thanks for all, each tip you give me I will send
> thousands of
> thanks¡¡¡¡¡
>
> [[alternative HTML version deleted]]
>
>
> -----Inline Attachment Follows-----
>
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