r语言 - 使用 dplyr "group_by"创建组，然后使用 stringr 查找组之间的差异

Question

使用下面的示例，我想按CaseWorker，然后是Client分组数据框，然后确定每个Client组中的"Task"中的任务列表是否与"Task2"中的任务列表相同。

我会很高兴有一个简单的真或假，或者更好的是，如果每个任务是在"Task2"，但不是"任务"可以提取和显示在一个新的列或数据框。

所以基本上我需要确保"Task"one_answers"Task2"为每个单独的客户端包含相同的条目。

如果可能的话，我想坚持使用Dplyr和Stringr，或者至少留在Tidyverse中。我认为有一些方法可以使用"group_by"one_answers"str_detect"或其他一些Stringr功能以一种优雅的方式实现这一点。

CaseWorker<-c("John","John","John","John","John","John","Melanie","Melanie","Melanie","Melanie","Melanie","Melanie")
Client<-c("Chris","Chris","Chris","Tom","Tom","Tom","Valerie","Valerie","Valerie","Tim","Tim","Tim")
Task<-c("Feed cat","Make dinner","Iron shirt","Make dinner","Do homework","Make lunch","Make dinner","Feed cat","Buy groceries","Do homework","Iron shirt","Make lunch")
Task2<-c("Feed cat","Make dinner","Iron shirt","Make dinner","Do homework","Feed cat","Make dinner","Feed cat","Iron shirt","Do homework","Iron shirt","Make lunch")
Df<-data.frame(CaseWorker,Client,Task,Task2)

Answer 1

看看这是不是你想要的。

首先，查看Task是否与Task2匹配。如果不是，返回Task2作为一个新变量。我将其存储到一个新的数据帧df2

df2 <- Df %>% 
    mutate(match = Task == Task2,
           non_match = ifelse(!match, Task2, "")) 
df2
#    CaseWorker  Client          Task       Task2 match  non_match
# 1        John   Chris      Feed cat    Feed cat  TRUE           
# 2        John   Chris   Make dinner Make dinner  TRUE           
# 3        John   Chris    Iron shirt  Iron shirt  TRUE           
# 4        John     Tom   Make dinner Make dinner  TRUE           
# 5        John     Tom   Do homework Do homework  TRUE           
# 6        John     Tom    Make lunch    Feed cat FALSE   Feed cat
# 7     Melanie Valerie   Make dinner Make dinner  TRUE           
# 8     Melanie Valerie      Feed cat    Feed cat  TRUE           
# 9     Melanie Valerie Buy groceries  Iron shirt FALSE Iron shirt
# 10    Melanie     Tim   Do homework Do homework  TRUE           
# 11    Melanie     Tim    Iron shirt  Iron shirt  TRUE           
# 12    Melanie     Tim    Make lunch  Make lunch  TRUE           

然后summarise结果，看看是否个别CaseWorker/Client对匹配所有条目。

df2 %>% 
   group_by(CaseWorker, Client) %>% 
   summarise(n = n(),
             matches = sum(match),
             all_match = n == matches)
#   CaseWorker  Client     n matches all_match
#        <chr>   <chr> <int>   <int>     <lgl>
# 1       John   Chris     3       3      TRUE
# 2       John     Tom     3       2     FALSE
# 3    Melanie     Tim     3       3      TRUE
# 4    Melanie Valerie     3       2     FALSE

如果你需要原始数据集中的all_match变量，你当然可以将它合并回你的数据帧。

Answer 2

您可以简单地通过dplyr和使用%in%来做到这一点

Df %>% 
  group_by(CaseWorker,Client) %>% 
  mutate(Check = Task %in% Task2) 

这取决于精确的大小写匹配，如果你担心，你可以这样做:

 Df %>% 
  group_by(CaseWorker,Client) %>% 
  rowwise() %>% 
  mutate(Check = grepl(Task, Task2, ignore.case = TRUE)) 

，但您必须在更改之前使用row，以绕过grepl(或大多数R函数)的矢量化性质

Answer 3

如果您想使用字符串包。

Df %>% 
     group_by(CaseWorker,Client) %>% 
     mutate(Check=str_detect(as.character(Task),as.character(Task2))

Answer 4

这可能只是我误解了这个问题，但我认为如果你想要的只是Task不匹配Task2的记录，那么你可能会把这个问题复杂化。

> Df[which(Df$Task != Df$Task2),]
===  ==========  =======  =============  ==========
    CaseWorker  Client   Task           Task2     
===  ==========  =======  =============  ==========
6    John        Tom      Make lunch     Feed cat  
9    Melanie     Valerie  Buy groceries  Iron shirt
===  ==========  =======  =============  ==========