If your data is in a matrix named "orgdata" :
newvar <- apply(orgdata , 1, function(arow, if (all(arow=='Yes'))
'Yes' else 'No'
newdata <- cbind(orgdata, newvar)
finaloutcome <- newdata[ newvar=='Yes',]
The key to this is the apply() function.
I might have missed some parentheses...
There are other ways; this is just one. I might think of a simpler
one if I gave it more time...
-Don
At 4:40 PM -0800 2/26/10, wookie1976 wrote:
I am new to R, but have been using SAS for years. In this transition period,
I am finding myself pulling my hair out to do some of the simplest things.
An example of this is that I need to generate a new variable based on the
outcome of several existing variables in a data row. In other words, if the
variable in all three existing columns are "Yes", then then the new variable
should also be "Yes", however if any one of the three existing variables is
a "No", then then new variable should be a "No". I would then use that new
variable as an exclusion for data in a new or existing dataset (i.e., if
NewVariable = "No" then delete):
Take this:
Column1, Column2, Column3
Yes, Yes, Yes
Yes, No, Yes
No, No, No
No, Yes, No
Yes, Yes, No
Generate this:
Column1, Column2, Column3, NewVariable1
Yes, Yes, Yes, Yes
Yes, No, Yes, No
No, No, No, No
No, Yes, No, No
Yes, Yes, No, No
And end up with this:
Column1, Column2, Column3, NewVariable1
Yes, Yes, Yes, Yes
Any suggestions on how to efficiently do this in either the existing or a
new dataset?
Thanks,
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--
--------------------------------------
Don MacQueen
Environmental Protection Department
Lawrence Livermore National Laboratory
Livermore, CA, USA
925-423-1062
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