Question

如果可能的话，我想使用dplyr和stringr，或者至少保持在Tidyverse中以实现以下目的：

我需要通过CaseWorker和Client对数据进行分组，并比较＆＃34;任务＆＃34;和＆＃34;任务2＆＃34;找到＆＃34; Task2＆＃34;中的所有类别不在＆＃34;任务＆＃34;中，以及＆＃34;任务2＆＃34;的相关总时间。类别。

＆＃34;任务＆＃34;可以有不属于＆＃34;任务2＆＃34;的类别，所以我只对在＆＃34;任务2＆＃34;中找到类别感兴趣。那些不在＆＃34;任务＆＃34;。能够创建新列以显示＆＃34; Task2＆＃34;中的特定条目将会很棒。而不是＆＃34;任务＆＃34;以及相关的＆＃34;时间＆＃34;值。

最终结果应该为客户Chris显示四个新列，一个用于＆＃34; Iron shirt＆＃34;以及相关的＃34; Time＆＃34; 45，以及＆＃34; Do workwork＆＃34;和＃34;时间＆＃34;客户Eric将有两个新专栏，一个用于＆＃34; Iron Shirt＆＃34;和相关时间为12的一个。

 CaseWorker<-c("John","John","John","John","John","John","John","John",
"John","Kim","Kim")

  Client<-c("Chris","Chris","Chris","Chris","Chris","Chris","Chris","Chris","Chris","Eric","Eric")

Task<-c("Feed cat","Feed cat","Feed cat","Make dinner","Make dinner","Make dinner","Buy groceries","Buy groceries","Buy groceries","Do homework","Do homework")

Task2<-c("Feed cat","Iron shirt","Iron shirt","Do homework","Do homework","Do homework","Make dinner","Feed cat","Feed cat","Do homework","Iron shirt")

Time<-c(20,34,11,10,5,6,55,30,20,10,12)

Df<-data.frame(CaseWorker,Client,Task,Task2,Time)

Answer 1

我们可以尝试

library(dplyr)
library(tidyr)
Df %>% 
   group_by(CaseWorker, Client) %>%
   filter(Task2 %in% setdiff(Task2, Task)) %>% 
   group_by(Task2, add=TRUE) %>% 
   summarise(Time = sum(Time)) %>% 
   spread(Task2, Time)
#   CaseWorker Client `Do homework` `Iron shirt`
#*     <fctr> <fctr>         <dbl>        <dbl>
#1       John  Chris            21           45
#2        Kim   Eric            NA           12

使用Dplyr＆＃34; group_by＆＃34;创建群组然后使用Stringr或Set Operations查找组之间的差异

1 个答案: