I'm sorry, Rob, but that code is dense enough and formatted badly
enough that it's hard to dig through.
You may want to try the data.table package. The development version on
R-forge is pretty fast for grouping operations like this. I'm not sure
if this is what you're really after. It's hard to tell from your
example.
Compare some speeds:
> dat <- data.frame(D=sample(32000:33000, 666000,T),
+ Fid=sample(1:10,666000,T),
+ A=sample(1:5,666000,T))
>
> ####### one of your examples
> system.time(ret <- fedb.ddplyWrapper2(dat, c("D", "Fid"),
+ function(x) c(sum(x[,"A"], na.rm=T),
sum(x[,"A"], na.rm=T))))
user system elapsed
21.78 14.42 36.35
>
>
> ####### data.table
> install.packages("data.table",repos="http://R-Forge.R-project.org";)
> library(data.table)
> dt <- as.data.table(dat)
> system.time(ret2 <- dt[, sum(A, na.rm=T), by = "D,Fid"])
user system elapsed
0.27 0.00 0.28
>
>
> ####### plyr for comparison, too
> library(plyr)
> system.time(ret3 <- ddply(dat, .(D,Fid), function(x) sum(x$A, na.rm=T)))
user system elapsed
28.94 12.16 41.23
> head(ret)
[,1] [,2]
1 175 175
2 222 222
3 221 221
4 134 134
5 253 253
6 194 194
> head(ret2)
D Fid V1
[1,] 32000 1 228
[2,] 32000 2 209
[3,] 32000 3 182
[4,] 32000 4 180
[5,] 32000 5 181
[6,] 32000 6 222
> head(ret3)
D Fid V1
1 32000 1 175
2 32000 2 222
3 32000 3 221
4 32000 4 134
5 32000 5 253
6 32000 6 194
- Tom
On Fri, Feb 26, 2010 at 2:58 PM, Rob Forler <rfor...@uchicago.edu> wrote:
> So I have a function that does lapply's for me based on dimension. Currently
> only works for length(pivotColumns)=2 because I haven't fixed the rbinds. I
> have two versions. One runs WAYYY faster than the other. And I'm not sure
> why.
>
> Fast Version:
>
> fedb.ddplyWrapper2Fast <- function(data, pivotColumns, listNameFunctions,
> ...){
> lapplyFunctionRecurse <- function(cdata, level=1, ...){
> if(level==1){
>
> return(lapply(split(seq(nrow(cdata)),cdata[,pivotColumns[level]], drop=T),
> function(x) lapplyFunctionRecurse(x, level+1, ...)))
> } else if (level==length(pivotColumns)) {
> #
> return(lapply(split(cdata,data[cdata,pivotColumns[level]], drop=T),
> function(x, ...) listNameFunctions(data[x,], ...)))
> return(lapply(split(cdata,data[cdata,pivotColumns[level]],
> drop=T), function(x, ...) c(data[cdata[1],pivotColumns[2]],
> data[cdata[1],pivotColumns[1]], sum(data[cdata,"A"], na.rm=T),
> sum(data[cdata,"A"], na.rm=T))))
> } else {
> return(lapply(split(cdata,data[cdata,pivotColumns[level]],
> drop=T), function(x) lapplyFunctionRecurse(x, level+1, ...)))
> }
> }
> result = lapplyFunctionRecurse(data, ...)
> matrix2 <- do.call('rbind', lapply(result, function(x)
> do.call('rbind',x)))
> return(matrix2)
> }
>
>
> dat <- data.frame(D=sample(32000:33000, 666000,
> T),Fid=sample(1:10,666000,T), A=sample(1:5,666000,T))
>> temp = proc.time(); ret = fedb.ddplyWrapper2(dat, c("D", "Fid"),
> function(x) c(sum(x[,"A"], na.rm=T), sum(x[,"A"], na.rm=T)));
> proc.time()-temp
> user system elapsed
> 4.616 0.006 4.630
> #note in thie case the anonymous function I pass in isn't used because I
> hardcode the function into the lapply.
>
> approx 4 seconds
>
> This runs very fast. This runs very slow:
>
> fedb.ddplyWrapper2 <- function(data, pivotColumns, listNameFunctions, ...){
> lapplyFunctionRecurse <- function(cdata, level=1, ...){
> if(level==1){
>
> return(lapply(split(seq(nrow(cdata)),cdata[,pivotColumns[level]], drop=T),
> function(x) lapplyFunctionRecurse(x, level+1, ...)))
> } else if (level==length(pivotColumns)) {
> #this line is different. it essentially calls the function you
> pass in
> return(lapply(split(cdata,data[cdata,pivotColumns[level]],
> drop=T), function(x, ...) listNameFunctions(data[x,], ...)))
> } else {
> return(lapply(split(cdata,data[cdata,pivotColumns[level]],
> drop=T), function(x) lapplyFunctionRecurse(x, level+1, ...)))
> }
> }
> result = lapplyFunctionRecurse(data, ...)
> matrix2 <- do.call('rbind', lapply(result, function(x)
> do.call('rbind',x)))
> return(matrix2)
> }
>
> dat <- data.frame(D=sample(32000:33000, 666000,
> T),Fid=sample(1:10,666000,T), A=sample(1:5,666000,T))
>> temp = proc.time(); ret = fedb.ddplyWrapper2(dat, c("D", "Fid"),
> function(x) c(sum(x[,"A"], na.rm=T), sum(x[,"A"], na.rm=T)));
> proc.time()-temp
> user system elapsed
> 16.346 65.059 81.680
>
> head(ret3)
D Fid V1
1 32000 1 175
2 32000 2 222
3 32000 3 221
4 32000 4 134
5 32000 5 253
6 32000 6 194
>
>
> Can anyone explain to me why there is a 4x time difference? I don't want to
> have to hardcore into the recursion function, but if I have to I will.
>
> Thanks,
> Rob
>
> [[alternative HTML version deleted]]
>
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>
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