Group several variables and apply a function to the group
?aggregate should do it
aggregate(df$x,list(df$comn, df$mi), sd)
There are other ways of course
Using the reshape2 package
library(reshape2)
x1 <- melt(df, id=c("comn", "mi"))
dcast(x1, comn + mi ~ variable, sd)
--- On Sun, 12/4/11, Aur?lien PHILIPPOT <aurelien.philippot at gmail.com> wrote:
From: Aur?lien PHILIPPOT <aurelien.philippot at gmail.com>
Subject: [R] Group several variables and apply a function to the group
To: R-help at r-project.org
Received: Sunday, December 4, 2011, 3:32 PM
Dear R-experts,
I am struggling with the following problem, and I am
looking for advice
from more experienced R-users: I have a data frame with 2
identifying
variables (comn and mi), and an output variable (x). comn
is a variable for
a company and mi is a variable for a month.
comn<-c("abc", "abc", "abc", "abc", "abc", "abc", "xyz",
"xyz","xyz", "xyz")
mi<- c("1", "1","1", "2", "2", "2", "1", "1", "3", "3")
x<- c("-0.0031", "0.0009", "-0.007", "0.1929","0.0087",
"0.099","-0.089",
"0.005", "-0.0078", "0.67" )
df<- data.frame(comn=comn, mi=mi, x=x)
For each company, within a particular month, I would like
to compute the
standard deviation of x: for example, for abc, I would like
to compute the
sd of x for month1 (when mi=1) and for month2 (when mi=2).
In other languages (Stata for instance), I would create a
grouping variable
(group comnn and mi) and then, apply the sd function for
each group.
However, I don't find an elegant way to do the same in R:
I was thinking about the following: I could subset my data
frame by mi and
create one file per month, and then make a loop and in each
file, use a
"by" operator for each comn. I am sure it would work, but I
feel that it
would be like killing an ant with a tank.
I was wondering if anyone knew a more straightforward way
to implement that
kind of operation?
Thanks a lot,
Best,
Aurelien
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