使用 dplyr 将函数应用于表的每一行? [英] Applying a function to every row of a table using dplyr?

问题描述

在使用 plyr 时，我经常发现将 adply 用于必须应用于每一行的标量函数.

When working with plyr I often found it useful to use adply for scalar functions that I have to apply to each and every row.

例如

data(iris)
library(plyr)
head(
     adply(iris, 1, transform , Max.Len= max(Sepal.Length,Petal.Length))
    )
  Sepal.Length Sepal.Width Petal.Length Petal.Width Species Max.Len
1          5.1         3.5          1.4         0.2  setosa     5.1
2          4.9         3.0          1.4         0.2  setosa     4.9
3          4.7         3.2          1.3         0.2  setosa     4.7
4          4.6         3.1          1.5         0.2  setosa     4.6
5          5.0         3.6          1.4         0.2  setosa     5.0
6          5.4         3.9          1.7         0.4  setosa     5.4

现在我更多地使用 dplyr，我想知道是否有一种整洁/自然的方法来做到这一点?因为这不是我想要的:

Now I'm using dplyr more, I'm wondering if there is a tidy/natural way to do this? As this is NOT what I want:

library(dplyr)
head(
     mutate(iris, Max.Len= max(Sepal.Length,Petal.Length))
    )
  Sepal.Length Sepal.Width Petal.Length Petal.Width Species Max.Len
1          5.1         3.5          1.4         0.2  setosa     7.9
2          4.9         3.0          1.4         0.2  setosa     7.9
3          4.7         3.2          1.3         0.2  setosa     7.9
4          4.6         3.1          1.5         0.2  setosa     7.9
5          5.0         3.6          1.4         0.2  setosa     7.9
6          5.4         3.9          1.7         0.4  setosa     7.9

推荐答案

从 dplyr 0.2(我认为)rowwise() 开始实现，所以这个问题的答案变成了:

As of dplyr 0.2 (I think) rowwise() is implemented, so the answer to this problem becomes:

iris %>% 
  rowwise() %>% 
  mutate(Max.Len= max(Sepal.Length,Petal.Length))

非rowwise替代

五年 (!) 之后，这个答案仍然获得了大量流量.自从给出它以来，越来越不推荐 rowwise，尽管很多人似乎觉得它很直观.帮自己一个忙，通过 Jenny Bryan 的 R 中面向行的工作流与 tidyverse 材料以很好地处理这个主题.

Non rowwise alternative

Five years (!) later this answer still gets a lot of traffic. Since it was given, rowwise is increasingly not recommended, although lots of people seem to find it intuitive. Do yourself a favour and go through Jenny Bryan's Row-oriented workflows in R with the tidyverse material to get a good handle on this topic.

我发现的最直接的方法是基于 Hadley 使用 pmap 的示例之一:

The most straightforward way I have found is based on one of Hadley's examples using pmap:

iris %>% 
  mutate(Max.Len= purrr::pmap_dbl(list(Sepal.Length, Petal.Length), max))

使用这种方法，您可以为 pmap 中的函数 (.f) 提供任意数量的参数.

Using this approach, you can give an arbitrary number of arguments to the function (.f) inside pmap.

pmap 是一个很好的概念方法，因为它反映了这样一个事实，即当您进行行明智的操作时，您实际上是在处理来自向量列表(数据帧中的列)的元组.

pmap is a good conceptual approach because it reflects the fact that when you're doing row wise operations you're actually working with tuples from a list of vectors (the columns in a dataframe).

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