条件和上的组向量 [英] Group vector on conditional sum
问题描述
我想根据小于或等于 n
的元素总和对向量进行分组.假设如下,
I want to group a vector based on the sum of the elements being less than or equal to n
. Assume the following,
set.seed(1)
x <- sample(10, 20, replace = TRUE)
#[1] 3 4 6 10 3 9 10 7 7 1 3 2 7 4 8 5 8 10 4 8
#Where,
n = 15
预期输出是对值进行分组,而它们的总和 <= 15,即
The expected output would be to group values while their sum is <= 15, i.e.
y <- c(1, 1, 1, 2, 2, 3, 4, 5 ,5, 5, 6, 6, 6, 7, 7, 8, 8, 9, 9, 10)
如您所见,总和永远不会大于 15,
As you can see the sum is never greater than 15,
sapply(split(x, y), sum)
# 1 2 3 4 5 6 7 8 9 10
#13 13 9 10 15 12 12 13 14 8
注意:我将在庞大的数据集(通常 > 150 - 200GB)上运行它,因此效率是必须的.
NOTE: I will be running this on huge datasets (usually > 150 - 200GB) so efficiency is a must.
我尝试过并接近但失败的方法是,
A method that I tried and comes close but fails is,
as.integer(cut(cumsum(x), breaks = seq(0, max(cumsum(x)) + 15, 15)))
#[1] 1 1 1 2 2 3 3 4 4 4 5 5 5 6 6 6 7 8 8 8
推荐答案
Here is my Rcpp
-solution (close to Khashaa 的 解决方案,但有点短/精简),因为您说速度很重要,Rcpp
可能是要走的路:
Here is my Rcpp
-solution (close to Khashaa's solution but a bit shorter/stripped down), because you said speed was important, Rcpp
is probably the way to go:
# create the data
set.seed(1)
x <- sample(10, 20, replace = TRUE)
y <- c(1, 1, 1, 2, 2, 3, 4, 5 ,5, 5, 6, 6, 6, 7, 7, 8, 8, 9, 9, 10)
# create the Rcpp function
library(Rcpp)
cppFunction('
IntegerVector sotosGroup(NumericVector x, int cutoff) {
IntegerVector groupVec (x.size());
int group = 1;
double runSum = 0;
for (int i = 0; i < x.size(); i++) {
runSum += x[i];
if (runSum > cutoff) {
group++;
runSum = x[i];
}
groupVec[i] = group;
}
return groupVec;
}
')
# use the function as usual
y_cpp <- sotosGroup(x, 15)
sapply(split(x, y_cpp), sum)
#> 1 2 3 4 5 6 7 8 9 10
#> 13 13 9 10 15 12 12 13 14 8
all.equal(y, y_cpp)
#> [1] TRUE
万一有人需要被速度说服:
In case anyone needs to be convinced by the speed:
# Speed Benchmarks
library(data.table)
library(microbenchmark)
dt <- data.table(x)
frank <- function(DT, n = 15) {
DT[, xc := cumsum(x)]
b = DT[.(shift(xc, fill=0) + n + 1), on=.(xc), roll=-Inf, which=TRUE]
z = 1; res = z
while (!is.na(z))
res <- c(res, z <- b[z])
DT[, g := cumsum(.I %in% res)][]
}
microbenchmark(
frank(dt),
sotosGroup(x, 15),
times = 100
)
#> Unit: microseconds
#> expr min lq mean median uq max neval cld
#> frank(dt) 1720.589 1831.320 2148.83096 1878.0725 1981.576 13728.830 100 b
#> sotosGroup(x, 15) 2.595 3.962 6.47038 7.5035 8.290 11.579 100 a
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