On 2010-03-05 18:00, Alastair wrote:
Hi,
I'm new to R and I've run into a problem that I'm not really sure how to
express properly in the language. I've got a data table that I've read from
a file containing some simple information about the performance of 4
algorithms. The columns are the name of the algorithm, the problem instance
and the resulting score on that problem (if it wasn't solved I mark that
with NA).
solver instance result
A prob1 40
B prob1 NA
C prob1 39
D prob1 35
A prob2 100
B prob2 50
C prob2 NA
D prob2 NA
A prob3 75
B prob3 80
C prob3 60
D prob3 70
A prob4 80
B prob4 NA
C prob4 85
D prob4 75
I've managed to read in the data as follows:
data<- table.read("./test.txt", header = TRUE, colClasses =
c("factor","factor","numeric"), na.strings = c("NA"))
and I've got a nice barchart via lattice
library(lattice)
barchart(result ~ instance, group = solver, data = data)
What I want to try and calculate (and plot somehow) is
a) What percentage of the instances each solver can solve
and b) What percentage of the instances a solver returns a better score than
solver A for that particular problem.
These don't seem like particularly ambitious requirements, but I still don't
really know where to start. Any pointers would be most appreciated.
(I doubt that table.read(....) worked for you.)
For (a), use tapply():
with(data, tapply(result, solver, function(x) sum(!is.na(x))))
# A B C D
# 4 2 3 3
For (b), you could either use reshape() to transform to wide format
or generate an appropriate matrix; in either case, follow with apply():
m <- with(data, tapply(result, list(instance, solver), function(x) x))
m
apply(m[,-1], 2, function(x) sum(x > m[, 1], na.rm=TRUE))
# B C D
# 1 1 0
-Peter Ehlers
Thanks,
Alastair
--
Peter Ehlers
University of Calgary
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