I'm trying to add columns to my data.table that essentially append a cumulative frequency table for each group that is aggregated. Unfortunately, my current solution is about ten times slower than I had hoped.
Here is what I'm using (apologies for the ugly one-liner):
DT[, c("bin1","bin2","bin3","bin4") := as.list(cumsum(hist(colx,c(lbound,bound1,bound2, bound3,ubound),plot=FALSE)$counts)), by=category]
If the bin boundaries are set at 0,25,50,75,100
, I would like my table to look like:
id category colx bin1 bin2 bin3 bin4
1 a 5 1 2 2 3
2 a 30 1 2 2 3
3 b 21 1 2 3 4
4 c 62 0 1 3 3
5 b 36 1 2 3 4
6 a 92 1 2 2 3
7 c 60 0 1 3 3
8 b 79 1 2 3 4
9 b 54 1 2 3 4
10 c 27 0 1 3 3
In the actual dataset I'm grouping using 4 different columns and there are millions of rows and unique groups. When I try a simpler function, such as sum
, it takes an acceptable amount of time to do the calculation. Is there any way to significantly speed up the counting process?
Okay, here's one way (here I use data.table v1.9.3
). Remove the by=.EACHI
if you're using versions <= 1.9.2
.
dt[, ival := findInterval(colx, seq(0, 100, by=25), rightmost.closed=TRUE)]
setkey(dt, category, ival)
ans <- dt[CJ(unique(category), unique(ival)), .N, allow.cartesian=TRUE, by=.EACHI]
ans[, N := cumsum(N), by="category"][, bin := "bin"]
ans <- dcast.data.table(ans, category ~ bin+ival, value.var="N")
ans <- dt[ans][, ival := NULL]
id category colx bin_1 bin_2 bin_3 bin_4
1: 1 a 5 1 2 2 3
2: 2 a 30 1 2 2 3
3: 6 a 92 1 2 2 3
4: 3 b 21 1 2 3 4
5: 5 b 36 1 2 3 4
6: 9 b 54 1 2 3 4
7: 8 b 79 1 2 3 4
8: 10 c 27 0 1 3 3
9: 4 c 62 0 1 3 3
10: 7 c 60 0 1 3 3
I generate here a data.table with 20 million rows and a total of 1-million groups with 2 grouping columns (instead of 4 as you state in your question).
K = 1e3L
N = 20e6L
sim_data <- function(K, N) {
set.seed(1L)
ff <- function(K, N) sample(paste0("V", 1:K), N, TRUE)
data.table(x=ff(K,N), y=ff(K,N), val=sample(1:100, N, TRUE))
}
dt <- sim_data(K, N)
method1 <- function(x) {
dt[, ival := findInterval(val, seq(0, 100, by=25), rightmost.closed=TRUE)]
setkey(dt, x, y, ival)
ans <- dt[CJ(unique(x), unique(y), unique(ival)), .N, allow.cartesian=TRUE, by=.EACHI]
ans[, N := cumsum(N), by="x,y"][, bin := "bin"]
ans <- dcast.data.table(ans, x+y ~ bin+ival, value.var="N")
ans <- dt[ans][, ival := NULL]
}
system.time(ans1 <- method1(dt))
# user system elapsed
# 13.148 2.778 16.209
I hope this is faster than your original solution and scales well for your real data dimensions.
Update: Here's another version using data.table's
rolling joins instead of findInterval from base. We've to modify the intervals slightly so that the rolling join finds the right match.
dt <- sim_data(K, N)
method2 <- function(x) {
ivals = seq(24L, 100L, by=25L)
ivals[length(ivals)] = 100L
setkey(dt, x,y,val)
dt[, ival := seq_len(.N), by="x,y"]
ans <- dt[CJ(unique(x), unique(y), ivals), roll=TRUE, mult="last"][is.na(ival), ival := 0L][, bin := "bin"]
ans <- dcast.data.table(ans, x+y~bin+val, value.var="ival")
dt[, ival := NULL]
ans2 <- dt[ans]
}
system.time(ans2 <- method2(dt))
# user system elapsed
# 12.538 2.649 16.079
## check if both methods give identical results:
setkey(ans1, x,y,val)
setnames(ans2, copy(names(ans1)))
setkey(ans2, x,y,val)
identical(ans1, ans2) # [1] TRUE
Edit: Some explanation on why OP's is very time consuming:
A huge reason, I suspect, for the difference in runtime between these solutions and hist
is that both the answers here are vectorised (written entirely in C and will work on the whole data set directly), where as hist
is a S3 method (which'll take time for dispatch to the .default
method and added to that, it's written in R. So, basically you're executing about a million times hist
, a function in R, where as the other two vectorised solutions are calling it once in C (no need to call for every group here).
And since that's the most complex part of your question, it obviously slows things down.
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