dplyr会覆盖组中除值的首次出现以外的所有值 [英] dplyr override all but the first occurrences of a value within a group

问题描述

我有一个分组的data_frame，带有 tag列，取值 0和 1。在每个组中，我需要找到第一个出现的 1并将所有剩余出现的值更改为 0。有没有一种方法可以在dplyr中实现？

I have a grouped data_frame with a "tag" column taking on values "0" and "1". In each group, I need to find the first occurrence of "1" and change all the remaining occurrences to "0". Is there a way to achieve it in dplyr?

例如，让我们获取 iris数据，然后添加额外的 tag列：

For example, let's take "iris" data and let's add the extra "tag" column:

data(iris)
set.seed(1)
iris$tag <- sample( c(0, 1), 150, replace = TRUE, prob = c(0.8, 0.2))
giris <- iris %>% group_by(Species)

在 giris中，在 setosa组中，我只保留第一个出现的 1（即第四行），并将其余的设置为 0 。似乎有点像戴口罩之类的东西。

In "giris", in the "setosa" group I need to keep only the first occurrence of "1" (i.e. in 4th row) and set the remaining ones to "0". This seems a bit like applying a mask or something...

有没有办法做到这一点？我一直在尝试与和重复，但没有成功。我一直在考虑只过滤 1，然后保留它们，然后再加入其余的数据集，但这似乎很尴尬，尤其是对于12GB的数据集。

Is there a way to do it? I have been experimenting with "which" and "duplicated" but I did not succeed. I have been thinking about filtering the "1"s only, keeping them, then joining with the remaining set, but this seems awkward, especially for a 12GB data set.

推荐答案

一个dplyr选项：

mutate(giris, newcol = as.integer(tag & cumsum(tag) == 1))

或

mutate(giris, newcol = as.integer(tag & !duplicated(tag)))

或者使用data.table，相同的方法，但通过引用进行修改：

Or using data.table, same approach, but modify by reference:

library(data.table)
setDT(giris)
giris[, newcol := as.integer(tag & cumsum(tag) == 1), by = Species]

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