条件总和上的组矢量

Question

我想基于元素之和小于或等于对矢量进行分组n.假设如下,

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

预期输出将是组值,而它们的总和<= 15,即

y <- c(1, 1, 1, 2, 2, 3, 4, 5 ,5, 5, 6, 6, 6, 7, 7, 8, 8, 9, 9, 10)

如你所见,总和绝不会超过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:我将在大型数据集(通常> 150 - 200GB)上运行它,因此效率是必须的.

我试过并接近但失败的方法是,

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

Answer 1

这是我的Rcpp解决方案（接近Khashaa 的解决方案，但更短/精简），因为你说速度很重要，Rcpp可能是要走的路：

# 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

如果有人需要相信速度：

# 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