> df <- read.table(textConnection('Experiment_id Treament_type Value
+ 12345 "control" 3
+ 12345 "full treatment" 4
+ 12345 "full treatment" 5
+ 12345 "partial treatment" 4
+ 10000 "control" 1'), header = TRUE)
> df
Experiment_id Treament_type Value
1 12345 control 3
2 12345 full treatment 4
3 12345 full treatment 5
4 12345 partial treatment 4
5 10000 control 1
> tapply(df$Value, df$Treatment_type, mean)
Error in as.vector(x, mode) : invalid 'mode' argument
# Hey, cut me some slack, I speeled treatment correctly
# Try again:
> tapply(df$Value, df$Treament_type, mean)
control full treatment partial treatment
2.0 4.5 4.0
--
David Winsemius
On Apr 2, 2009, at 12:54 PM, haettulegur wrote:
I have a data frame that looks something like...
Column 1 is an experiment_id, Column 2 is the type of treatment
("control",
"full treatment", or "partial treatment"), and Column 3 is a value.
Experiment_id Treament_type Value
12345 "control" 3
12345 "full treatment" 4
12345 "full treatment" 5
12345 "partial treatment" 4
10000 "control" 1
What I want to do is this:
For each experiment_id, compute the mean "control" value, the mean
"full
treatment" value, and the mean "partial treatment" value.
Is there an easy way to do this?
(Also, for each experiment_id, I then want to find
1) "mean full treatment value" - "mean control value"
2) "mean partial treatment value" - "mean control value".)
Thanks!
--
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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
Heritage Laboratories
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.
