> From: David L Carlson [dcarl...@tamu.edu]
>This overwrites the data so you might want to create a copy first.
>
> example <- data.frame(V1=c(3, -1), V2=c(-2, 4), V3=c(4, 1))
> tf <- ifelse(example<0, TRUE, FALSE)
> example[tf] <- NA
> apply(example, 1,
on
Associate Professor of Anthropology
Texas A&M University
College Station, TX 77843-4352
> -Original Message-
> From: r-help-boun...@r-project.org [mailto:r-help-bounces@r-
> project.org] On Behalf Of Andrea Sica
> Sent: Monday, May 14, 2012 11:32 AM
> To: r-help@r-project
Thank you all. Really!
I have used the following function:
apply(dfrm, 1, function(x) mean(x[x>=0]) )
Someone of you even gave me a few interesting explanations
about why to use it.
Still thank you all.
Andrea
On Mon, May 14, 2012 at 6:52 PM, David Winsemius wrote:
>
> On May 14, 2012, at 12:
On May 14, 2012, at 12:32 PM, Andrea Sica wrote:
Dear all,
I am sure it won't be difficult for you!!
I need to calculate the average among variables for the single units
of my
dataset.
But, while doing it, I need to do not consider some values.
To better explain, think like there are two un
This was actually discussed about a week and a half ago with many good
solutions offered, but I think the most idiomatic would be something
like this:
apply(dataset, 1, function(x) mean(x[x>0]))
The reasons I like it:
i) It uses the apply function to do the same operation row-wise
(that's what t
Dear all,
I am sure it won't be difficult for you!!
I need to calculate the average among variables for the single units of my
dataset.
But, while doing it, I need to do not consider some values.
To better explain, think like there are two units and three variables:
V1V2 V3
[1] 3
On 2011-01-10 07:38, David A. wrote:
Dear list, havig the following matrix
"Value" "Class"
13.001
12.801
11.781
11.702
11.612
11.952
11.552
12.403
11.401
12.271
12.493
11.394
11.804
12.393
12.723
12.183
11.643
11.504
12.81
Dear list, havig the following matrix
"Value" "Class"
13.001
12.801
11.781
11.702
11.612
11.952
11.552
12.403
11.401
12.271
12.493
11.394
11.804
12.393
12.723
12.183
11.643
11.504
12.814
11.314
11.952
12.652
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