R按条件累计总和并重置 [英] R cumulative sum by condition with reset

问题描述

我在 data.frame 中有一个数字向量，如下所示.

I have a vector of numbers in a data.frame such as below.

df <- data.frame(a = c(1,2,3,4,2,3,4,5,8,9,10,1,2,1))

我需要创建一个新列，该列提供比其前任更大的条目的运行计数.结果列向量应该是这样的:

I need to create a new column which gives a running count of entries that are greater than their predecessor. The resulting column vector should be this:

0,1,2,3,0,1,2,3,4,5,6,0,1,0

我的尝试是创建一个差异的标志"列来标记值何时更大.

My attempt is to create a "flag" column of diffs to mark when the values are greater.

df$flag <- c(0,diff(df$a)>0)
> df$flag
 [1] 0 1 1 1 0 1 1 1 1 1 1 0 1 0

然后我可以应用一些 dplyr group/sum 魔法来几乎得到正确的答案，除了当 flag == 0 时总和不会重置:

Then I can apply some dplyr group/sum magic to almost get the right answer, except that the sum doesn't reset when flag == 0:

df %>% group_by(flag) %>% mutate(run=cumsum(flag))

    a flag run
1   1    0   0
2   2    1   1
3   3    1   2
4   4    1   3
5   2    0   0
6   3    1   4
7   4    1   5
8   5    1   6
9   8    1   7
10  9    1   8
11 10    1   9
12  1    0   0
13  2    1  10
14  1    0   0

我不想求助于 for() 循环，因为我有几个这样的运行总和要计算一个 data.frame 中的几十万行.

I don't want to have to resort to a for() loop because I have several of these running sums to compute with several hundred thousand rows in a data.frame.

推荐答案

这是 ave 的一种方式:

ave(df$a, cumsum(c(F, diff(df$a) < 0)), FUN=seq_along) - 1
 [1] 0 1 2 3 0 1 2 3 4 5 6 0 1 0

我们可以得到一个按 diff(df$a) < 分组的运行计数.0.哪些是向量中小于其前辈的位置.我们添加 c(F, ..) 来占第一个位置.该向量的累积总和创建了一个用于分组的索引.函数 ave 可以对该索引执行函数，我们使用 seq_along 进行运行计数.但是因为它从 1 开始，所以我们减去一个 ave(...) - 1 以从零开始.

We can get a running count grouped by diff(df$a) < 0. Which are the positions in the vector that are less than their predecessors. We add c(F, ..) to account for the first position. The cumulative sum of that vector creates an index for grouping. The function ave can carry out a function on that index, we use seq_along for a running count. But since it starts at 1, we subtract by one ave(...) - 1 to start from zero.

使用 dplyr 的类似方法:

library(dplyr)
df %>% 
  group_by(cumsum(c(FALSE, diff(a) < 0))) %>% 
  mutate(row_number() - 1)

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