On Mar 27, 2010, at 6:53 AM, Dennis Murphy wrote:
Hi:
Does this do what you want?
# Create some fake data...
df <- data.frame(id = factor(rep(c('cell1', 'cell2'), each = 10)),
cond = factor(rep(rep(c('A', 'B'), each = 5), 2)),
time = round(rnorm(20, 350, 10), 2))
# Create a function to subtract each element of a vector from its mean
f <- function(x) x - mean(x)
A function, ave, already exists in base R for calculating means
within groups. Subtraction from the time variable is straightforward:
> df$dev <- df$time - ave(df$time, df$id, df$cond)
> df$dev
[1] -1.346 -8.586 -2.366 2.714 9.584 -12.108 13.052 0.742
-7.438
[10] 5.752 1.434 10.854 0.514 -21.166 8.364 4.128 -4.502
-5.602
[19] -4.322 10.298
Although the default for ave() is the mean function, other functions
can be used with the FUN= argument.
--
David.
# Load the plyr package, which contains the function ddply():
library(plyr)
df2 <- ddply(df, .(id, cond), transform, dev = f(time))
# output
df2
id cond time dev
1 cell1 A 353.01 7.226
2 cell1 A 351.06 5.276
3 cell1 A 343.59 -2.194
4 cell1 A 341.50 -4.284
5 cell1 A 339.76 -6.024
6 cell1 B 351.18 0.644
7 cell1 B 340.53 -10.006
8 cell1 B 345.09 -5.446
9 cell1 B 347.44 -3.096
10 cell1 B 368.44 17.904
11 cell2 A 343.48 -3.776
12 cell2 A 352.35 5.094
13 cell2 A 350.78 3.524
14 cell2 A 340.38 -6.876
15 cell2 A 349.29 2.034
16 cell2 B 364.45 15.524
17 cell2 B 354.52 5.594
18 cell2 B 350.41 1.484
19 cell2 B 345.78 -3.146
20 cell2 B 329.47 -19.456
# cell means
with(df, aggregate(time, list(id = id, cond = cond), mean))
id cond x
1 cell1 A 345.784
2 cell2 A 347.256
3 cell1 B 350.536
4 cell2 B 348.926
HTH,
Dennis
On Fri, Mar 26, 2010 at 1:31 PM, Dgnn <sharkbrain...@gmail.com> wrote:
I have a data frame containing the results of time measurements
taken from
several cells. Each cell was measured in conditions A and B, and
there are
an arbitrary number of measurements in each condition. I am trying to
calculate the difference of each measurement from the mean of a
given cell
in a given condition without relying on loops.
my.df
id cond time
1 cell1 A 343.5
2 cell1 A 355.2
...
768 cell1 B 454.0
...
2106 cell2 A 433.9
...
as a first approach I tried:
mews<-aggregate(my.df$time, list(cond=data$id, id=data$cond), mean)
id cond time
cell1 A 352
cell1 B 446
cell2 A 244
cell2 B ...
I then tried to use %in% to match id and cond of mews with my.df,
but I
haven't been able to get it to work.
Am I on the right track? What are some other solutions?
Thanks for any help.
jason
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______________________________________________
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PLEASE do read the posting guide http://www.R-project.org/posting-guide.html
and provide commented, minimal, self-contained, reproducible code.
David Winsemius, MD
West Hartford, CT
______________________________________________
R-help@r-project.org mailing list
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PLEASE do read the posting guide http://www.R-project.org/posting-guide.html
and provide commented, minimal, self-contained, reproducible code.
