列子集上的dplyr mutate(所有这些列上的一个函数合并) [英] dplyr mutate on column subset (one function on all these columns combined)

问题描述

我有一个数据框，其中包含一些信息和一些度量.为了进行测量，我想计算马哈拉诺比斯距离，但我并没有采用干净的dplyr方法.我想要类似的东西:

I have a dataframe with some info and some measurement. For the measurement, I want to calculate the mahalanobis distance, but I don't get to a clean dplyr-approach. I would like to have something like:

library(anomalyDetection)

test<-data.frame(id=LETTERS[1:10],
                 A = rnorm(10,0,2),
                 B = rnorm(10,5,3))

test<-test%>%
  mutate(MD = mahalanobis_distance(.%>%dplyr::select(one_of(c("A","B")))))

我知道以下方法可行:

test<-test%>%
  mutate(MD = mahalanobis_distance(test%>%dplyr::select(one_of(c("A","B")))))

但是如果在mutate-call之前还有其他步骤，则会导致崩溃:

but that breaks down if there are some other step preceding the mutate-call:

test<-test%>%
  mutate(group = id %in% c(LETTERS[1:5]))%>%
  group_by(group)%>%
  mutate(MD = mahalanobis_distance(test%>%dplyr::select(one_of(c("A","B")))))

推荐答案

我们可以基于逻辑向量执行 split ，然后使用 map_df 创建"MD"通过对拆分数据集应用 mahalanobis_distance 列

We can do a split based on the logical vector, then with map_df create the 'MD' column by applying the mahalanobis_distance on the split dataset

library(purrr)
library(dplyr)
library(anomalyDetection)

test %>%
    split(.$id %in% LETTERS[1:5]) %>%  
    map_df(~mutate(., MD = mahalanobis_distance(.[-1])))
#  id          A          B        MD
#1   F -0.7829759 4.22808758 2.9007659
#2   G  2.4246532 5.96043439 1.3520245
#3   H -4.8649537 4.95510794 3.0842137
#4   I  1.2221836 5.36154775 0.2921482
#5   J  0.6995204 5.63616864 0.3708477
#6   A  1.2374543 5.17288708 1.4382259
#7   B -2.7815555 0.06437452 2.1244313
#8   C -2.2160242 2.74747556 0.5088291
#9   D  0.8561507 2.70631852 1.5174367
#10  E -1.6427978 6.23758354 2.4110771

注意:在OP的帖子中创建数据集时没有种子集

NOTE: There was no seed set while creating the dataset in the OP's post

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