On Oct 17, 2011, at 9:45 PM, David Wolfskill wrote:
> Sorry about the odd terminology, but I suspect that my intent might be
> completely missed had I used "aggregate" or "classify"
(each of which
> appears to have some rather special meanings in statistical analysis
> and
> modeling).
>
> I have some data about software builds; one of the characteristics of
> each is the name of the branch.
>
> A colleague has generated some fairly interesting graphs from the
> data,
> but he's treating each unique branch as if it were a separate factor.
>
> Last I checked, I had 276 unique branches, but these could be
> aggregated, classified, or "lumped" into about 8 - 10 categories;
I
> believe it would be useful and helpful for me to be able to do
> precisely
> that.
>
> A facility that could work for this purpose (that that we use in our
> "continuous build" driver) is the Bourne shell "case"
statement.
> Such a
> construct might look like:
>
> case branch in
> trunk) factor="trunk"; continue;;
> IB*) factor="IB"; continue;;
> DEV*) factor="DEV"; continue;;
> PVT*) factor="PVT"; continue;;
> RELEASE*) factor="RELEASE"; continue;;
> *) factor="UNK"; continue;;
> esac
>
> Which would assign one of 6 values to "factor" depending on the
> value of
> "branch" -- using "UNK" as a default if nothing else
matched.
>
> Mind, the patterns there are "Shell Patterns"
("globs"), not regular
> expressions.
>
> I've looked at R functions match(), pmatch(), charmatch(), and
> switch();
> while each looks as it it might be coercable to get the result I want,
> it also looks to require iteration over the thousands of entries I
> have
> -- as well as using the functions in question in a fairly
"unnatural"
> way.
>
> I could also write my own function that iterates over the entries,
> generating factors from the branch names -- but I can't help but think
> that what I'm trying to do can't be so uncommon that someone
hasn't
> already written a function to do what I'm trying to do. And I'd
> really
> rather avoid "re-inventing the wheel," here.
Here's a loopless lumping of random letters with an "other" value
.
There better ways, but my efforts with match and switch came to
naught. "pmatch" returns a numeric vector that selects the group.
> x <- sample(letters[1:10], 50, replace =TRUE)
>
c("abc","abc","abc","def","def","def","ghi","ghi","ghi",
"j")
[pmatch(x, letters[1:10], duplicates.ok=TRUE, nomatch=10)]
[1] "ghi" "ghi" "ghi" "ghi"
"ghi" "def" "def" "ghi" "def"
"abc"
"abc" "j" "def" "def" "ghi"
[16] "abc" "j" "def" "ghi"
"abc" "ghi" "abc" "abc" "abc"
"abc" "abc"
"abc" "ghi" "def" "abc"
[31] "ghi" "def" "ghi" "def"
"abc" "ghi" "ghi" "j" "abc"
"def" "abc"
"ghi" "abc" "def" "def"
[46] "def" "j" "ghi" "def"
"def"
Classifying 5 million letters in about a second:
> x <- sample(letters[1:10], 5000000, replace =TRUE)
> system.time( v <-
c("abc","abc","abc","def","def","def","ghi","ghi","ghi",
"j")
[pmatch(x, letters[1:10], duplicates.ok=TRUE, nomatch=10)] )
user system elapsed
0.858 0.208 1.062
The same strategy (indexing to return a set membership) can be used
with findInterval.
--
David Winsemius, MD
Heritage Laboratories
West Hartford, CT