On 09/16/2015 04:41 PM, Bert Gunter wrote:
Yes! Chuck's use of mapply is exactly the split/combine strategy I was
looking for. In retrospect, exactly how one should think about it.
Many thanks to all for a constructive discussion .
-- Bert
Bert Gunter
Use mapply like this on large problems:
unsplit(
mapply(
function(x,z) eval( x, list( y=z )),
expression( A=y*2, B=y+3, C=sqrt(y) ),
split( dat$Flow, dat$ASB ),
SIMPLIFY=FALSE),
dat$ASB)
Chuck
Is there any reason not to use data.table for this purpose, especially
if efficiency is of concern?
---
# load data.table and microbenchmark
library(data.table)
library(microbenchmark)
#
# prepare data
DF <- data.frame(
ASB = rep_len(factor(LETTERS[1:3]), 3e5),
Flow = rnorm(3e5)^2)
DT <- as.data.table(DF)
DT[, ASB := as.character(ASB)]
#
# define functions
#
# Chuck's version
fnSplit <- function(dat) {
unsplit(
mapply(
function(x,z) eval( x, list( y=z )),
expression( A=y*2, B=y+3, C=sqrt(y) ),
split( dat$Flow, dat$ASB ),
SIMPLIFY=FALSE),
dat$ASB)
}
#
# data.table-way (IMHO, much easier to read)
fnDataTable <- function(dat) {
dat[,
result :=
if (.BY == "A") {
2 * Flow
} else if (.BY == "B") {
3 + Flow
} else if (.BY == "C") {
sqrt(Flow)
},
by = ASB]
}
#
# benchmark
#
microbenchmark(fnSplit(DF), fnDataTable(DT))
identical(fnSplit(DF), fnDataTable(DT)[, result])
---
Actually, in Chuck's version the unsplit() part is slow. If the order is
not of concern (e.g., DF is reordered before calling fnSplit), fnSplit
is comparable to the DT-version.
Denes
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