Question

I am having a dataset that has a variable called individuals with many options and it comes like that. I have observations for a given Day on different individuals (Individual_ID)

The different options of individuals look like this: Individual_ID("Adele", "Fitz", "Abba").... these would belong to a group that is Group=A Individual_ID("Noir", "Rouge", "Bleue").... these would belong to a group called Group=B

In some instances, the individuals from different groups, can get mixed, so we have something like this Individual_ID("Adele", "Rouge", "Bleue")... so this would represent a mixed-group,

I would like to create a variable called GroupingID that can be either GroupA, GroupB, or MixedGroup For that I do not precise that all individuals of the group are present, but instead, that the representation of the individuals is neat or not neat with respect to their group.

为了考虑混合群体,涉及不同群体至少两个个人的任何组合都足够了。

难道有人能解释我如何运用一种条件和(或)相互交织的条件来形成一种可变的集团?

这里我的数据如何看待

Date      IndividualsObserved    
1/1/2016   Abba,Adele
2/1/2016   Adele,Fitz
3/1/2016   Fitz,Rouge,Noir
4/1/2016   Fitz,Adele,Abba
5/1/2016   Rouge,Noir,Bleue
6/1/2016   Rouge,Abba,Fitz

(不同个人在个人服务一栏的每个切身边)

So I would like to have a grouping category that is able to discern whether the grouping is neat (only one group identity, or whether the grouping is composed by a mixed of individuals from different groups). It would be something like this (GroupingID)

Date      IndividualsObserved   GroupingID
1/1/2016   Abba,Adele           GroupA
2/1/2016   Adele,Fitz           GroupA
3/1/2016   Fitz,Rouge,Noir      MixedGrouping
4/1/2016   Fitz,Adele,Abba      GroupA
5/1/2016   Rouge,Noir,Bleue     GroupB
6/1/2016   Rouge,Abba,Fitz      MixedGrouping
7/1/2016   Noir,Bleue,Abba      MixedGrouping

我试图这样做,但并不奏效:

  mutate(GroupingID = case_when(IndividualsObserved %in% c("Adele","Abba", "Fitz") ~ "GroupA",
                                IndividualsObserved %in% c("Noir","Bleue", "Rouge") ~ "GroupB",
                                TRUE ~ ToCheck))

谨请你就如何利用相互交错的选择来处理这一问题提出看法。

I Trial using dplyr function mut

Answer 1

步骤:

Create a named list for the groups
Split each Individuals row into a list, giving us a list column
For each row in the new list column, check if any of the names are in groups A and B. If both, then mixed, A then A, B then B, neither then None.

library(tidyverse)

groups <- list("A" = c("Adele", "Fitz", "Abba"),
               "B" = c("Rouge", "Noir", "Bleue"))

df |>
  mutate(IndividualsObserved = str_split(IndividualsObserved, ","),
         Group = map_chr(IndividualsObserved, (x) {
            a <- any(x %in% groups$A)
            b <- any(x %in% groups$B)
            case_when(a & b ~ "MixedGrouping",
                      a ~ "GroupA",
                      b ~ "GroupB",
                      TRUE ~ "None")}))

产出:

      Date IndividualsObserved         Group
1 1/1/2016         Abba, Adele        GroupA
2 2/1/2016         Adele, Fitz        GroupA
3 3/1/2016   Fitz, Rouge, Noir MixedGrouping
4 4/1/2016   Fitz, Adele, Abba        GroupA
5 5/1/2016  Rouge, Noir, Bleue        GroupB
6 6/1/2016   Rouge, Abba, Fitz MixedGrouping
7 7/1/2016   Noir, Bleue, Abba MixedGrouping

你可以采取许多其他方式,例如,为各群体及其相应个人提供一个数据框架,将每个人分别列入<代码>df。我认为这是最直截了当的。

Data:

df <- read.table(text= 
"Date      IndividualsObserved
1/1/2016   Abba,Adele
2/1/2016   Adele,Fitz
3/1/2016   Fitz,Rouge,Noir
4/1/2016   Fitz,Adele,Abba
5/1/2016   Rouge,Noir,Bleue
6/1/2016   Rouge,Abba,Fitz
7/1/2016   Noir,Bleue,Abba", header = T)

友情链接