On 9/10/2010 12:40 PM, Davis, Brian wrote:
I'm trying to find a more elegant way of doing this. What I'm trying
to accomplish is to count the frequency of letters (major / minor
alleles) in a string grouped by the factor levels in another column
of my data frame.
Ex.
DF<-data.frame(c("CC", "CC", NA, "CG", "GG", "GC"), c("L", "U", "L", "U", "L",
NA))
colnames(DF)<-c("X", "Y")
DF
X Y
1 CC L
2 CC U
3<NA> L
4 CG U
5 GG L
6 GC<NA>
I have an ugly solution, which works if you know the factor levels of Y in
advance.
ans<-rbind(table(unlist(strsplit(as.character(DF[DF[ ,'Y'] == 'L', 1]), ""))),
+ table(unlist(strsplit(as.character(DF[DF[ ,'Y'] == 'U', 1]), ""))))
rownames(ans)<-c("L", "U")
ans
C G
L 2 2
U 3 1
I've played with table, xtab, tabulate, aggregate, tapply, etc but
haven't found a combination that gives a more general solution to
this problem.
Any ideas?
Brian
You are almost there. The "plyr" package gets you the rest of the way.
You already have something that will, for a group of cases with the
same "Y" value, tabulate the "X" values the way you want. ddply will
split the dataframe up by "Y" values and run that on each part.
library("plyr")
tab <- ddply(DF, .(Y),
function(x) {table(unlist(strsplit(as.character(x$X),"")))})
tab
# Y C G
#1 L 2 2
#2 U 3 1
#3 <NA> 1 1
It is almost what you asked for. If you really want it as a matrix with
named rows:
tab2 <- as.matrix(tab[,-1])
rownames(tab2) <- tab[,1]
It still has an entry for the NA value of "Y", but that can be filtered
as whatever step you like.
--
Brian S. Diggs, PhD
Senior Research Associate, Department of Surgery
Oregon Health & Science University
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