通过参考类似的列名将多列与Tidyr的联合使用

Z时代
2024-01-10
分类：问答

library(tidyr) 
library(dplyr) 
library(tidyverse)

下面是简单数据框的代码。我有一些混乱的数据，导出的列因子类别分布在不同的列中。通过参考类似的列名将多列与Tidyr的联合使用

Client<-c("Client1","Client2","Client3","Client4","Client5") 
Sex_M<-c("Male","NA","Male","NA","Male") 
Sex_F<-c(" ","Female"," ","Female"," ") 
Satisfaction_Satisfied<-c("Satisfied"," "," ","Satisfied","Satisfied") 
Satisfaction_VerySatisfied<-c(" ","VerySatisfied","VerySatisfied"," "," ") 
CommunicationType_Email<-c("Email"," "," ","Email","Email") 
CommunicationType_Phone<-c(" ","Phone ","Phone "," "," ") 
DF<-data_frame(Client,Sex_M,Sex_F,Satisfaction_Satisfied,Satisfaction_VerySatisfied,CommunicationType_Email,CommunicationType_Phone)

我想用tidyr的“团结”将这些类别重新组合成单列。

DF<-DF%>%unite(Sat,Satisfaction_Satisfied,Satisfaction_VerySatisfied,sep=" ")%>% 
unite(Sex,Sex_M,Sex_F,sep=" ")

不过，我必须写多个“团结”行，我觉得这违反了三次规则，所以必须有一种方法，使这更容易，尤其是因为我真正的数据包含几十个需要列合并。是否有一种方法可以使用“统一”一次，但不知何故指的是匹配列名，以便所有相似的列名（例如，包含“Sex”为“Sex_M”和“Sex_F”，以及“CommunicationType”为“CommunicationType_Email”和“CommunicationType_Phone”）与上面的公式结合？

我也在想一个允许我输入列名的函数，但这对我来说太难了，因为它涉及复杂的标准评估。

回答：

我们可以使用unite

library(tidyverse) 
DF %>% 
    unite(Sat, matches("^Sat"))

对于多个的情况下，也许

gather(DF, Var, Val, -Client, na.rm = TRUE) %>% 
     separate(Var, into = c("Var1", "Var2")) %>% 
     group_by(Client, Var1) %>% 
     summarise(Val = paste(Val[!(is.na(Val)|Val=="")], collapse="_")) %>% 
     spread(Var1, Val) 
# Client CommunicationType Satisfaction Sex 
#* <chr>    <chr>   <chr> <chr> 
#1 Client1    Email  Satisfied Male 
#2 Client2    Phone VerySatisfied Female 
#3 Client3    Phone VerySatisfied Male 
#4 Client4    Email  Satisfied Female 
#5 Client5    Email  Satisfied Male

回答：

是这样的吗？如果你有很多列。

result<-with(new.env(),{ 
    Client<-c("Client1","Client2","Client3","Client4","Client5") 
    Sex_M<-c("Male","NA","Male","NA","Male") 
    Sex_F<-c(" ","Female"," ","Female"," ") 
    Satisfaction_Satisfied<-c("Satisfied"," "," ","Satisfied","Satisfied") 
    Satisfaction_VerySatisfied<-c(" ","VerySatisfied","VerySatisfied"," "," ") 
    CommunicationType_Email<-c("Email"," "," ","Email","Email") 
    CommunicationType_Phone<-c(" ","Phone ","Phone "," "," ") 
    x<-ls() 
    categories<-unique(sub("(.*)_(.*)", "\\1", x)) 
    df<-setNames(data.frame(lapply(x, function(y) get(y))), x) 
    for(nm in categories){ 
    df<-unite_(df, nm, x[contains(vars = x, match = nm)]) 
    } 
    return(df) 
}) 
Client CommunicationType Satisfaction  Sex 
1 Client1   Email_  Satisfied_  _Male 
2 Client2   _Phone _VerySatisfied Female_NA 
3 Client3   _Phone _VerySatisfied  _Male 
4 Client4   Email_  Satisfied_ Female_NA 
5 Client5   Email_  Satisfied_  _Male