dplyr 使用条件值进行变异 [英] dplyr mutate with conditional values

问题描述

在包含四列的大型数据框(myfile")中，我必须添加第五列，其中的值基于前四列有条件.

In a large dataframe ("myfile") with four columns I have to add a fifth column with values conditionally based on the first four columns.

首选 dplyr 和 mutate 的答案，主要是因为它在大型数据集中的速度.

Prefer answers with dplyr and mutate, mainly because of its speed in large datasets.

我的数据框如下所示:

  V1 V2 V3 V4
1  1  2  3  5
2  2  4  4  1
3  1  4  1  1
4  4  5  1  3
5  5  5  5  4
...

第五列 (V5) 的值基于一些条件规则:

The values of the fifth column (V5) are based on some conditional rules:

if (V1==1 & V2!=4) {
  V5 <- 1
} else if (V2==4 & V3!=1) {
  V5 <- 2
} else {
  V5 <- 0
}

现在我想使用 mutate 函数在所有行上使用这些规则(以避免慢循环).像这样的东西(是的，我知道这样不行！):

Now I want to use the mutate function to use these rules on all rows (to avoid slow loops). Something like this (and yes, I know it doesn't work this way!):

myfile <- mutate(myfile, if (V1==1 & V2!=4){V5 = 1}
    else if (V2==4 & V3!=1){V5 = 2}
    else {V5 = 0})

这应该是结果:

  V1 V2 V3 V4 V5
1  1  2  3  5  1
2  2  4  4  1  2
3  1  4  1  1  0
4  4  5  1  3  0
5  5  5  5  4  0

如何在 dplyr 中做到这一点?

How to do this in dplyr?

推荐答案

试试这个:

myfile %>% mutate(V5 = (V1 == 1 & V2 != 4) + 2 * (V2 == 4 & V3 != 1))

给予:

  V1 V2 V3 V4 V5
1  1  2  3  5  1
2  2  4  4  1  2
3  1  4  1  1  0
4  4  5  1  3  0
5  5  5  5  4  0

或者这个:

myfile %>% mutate(V5 = ifelse(V1 == 1 & V2 != 4, 1, ifelse(V2 == 4 & V3 != 1, 2, 0)))

给予:

  V1 V2 V3 V4 V5
1  1  2  3  5  1
2  2  4  4  1  2
3  1  4  1  1  0
4  4  5  1  3  0
5  5  5  5  4  0

注意

建议您为数据框取一个更好的名称.myfile 使它看起来好像包含一个文件名.

Note

Suggest you get a better name for your data frame. myfile makes it seem as if it holds a file name.

上面使用了这个输入:

myfile <- 
structure(list(V1 = c(1L, 2L, 1L, 4L, 5L), V2 = c(2L, 4L, 4L, 
5L, 5L), V3 = c(3L, 4L, 1L, 1L, 5L), V4 = c(5L, 1L, 1L, 3L, 4L
)), .Names = c("V1", "V2", "V3", "V4"), class = "data.frame", row.names = c("1", 
"2", "3", "4", "5"))

更新 1 由于最初发布的 dplyr 已将 %.% 更改为 %>%，因此相应地修改了答案.

Update 1 Since originally posted dplyr has changed %.% to %>% so have modified answer accordingly.

更新 2 dplyr 现在有 case_when，它提供了另一种解决方案:

Update 2 dplyr now has case_when which provides another solution:

myfile %>% 
       mutate(V5 = case_when(V1 == 1 & V2 != 4 ~ 1, 
                             V2 == 4 & V3 != 1 ~ 2,
                             TRUE ~ 0))

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