通过 id 重塑并保持其他变量不变答案

【问题标题】：Reshape by id and keep other variables fixed通过 id 重塑并保持其他变量不变
【发布时间】：2020-04-24 07:00:54
【问题描述】：

我想要做的是重新排序数据在列中的数据，但保留其余变量

       c1<- c("ID","Location", "Year","Gender", "MoneySpent", "MoneyWithCreditCard")
         c2<- c(1,"EEUU",2007,"M",1500,400)
         c3<- c(1,"EEUU",2008,"M",3900,0)
         c4<- c(1,"EEUU",2009,"M",0,100)
         c5<- c(2,"Germany",2007,"F",0,1000)
         c6<- c(2,"Germany",2008,"F",4000,500)
         c7<- c(2,"Germany",2009,"F",700,0)
         c8<- c(2,"Germany",2010,"F",0,50)
         Df<-data.frame(rbind(c2,c3,c4,c5,c6,c7,c8))
         colnames(Df)<-c1   

#   ID Location Year Gender MoneySpent   MoneyWithCreditCard   TypeofHome
#c2  1     EEUU 2007      M       1500        400                House
#c3  1     EEUU 2008      M       3900         0                 House
#c4  1     EEUU 2009      M          0        100                House
#c5  2  Germany 2007      F          0        1000               Department
#c6  2  Germany 2008      F       4000        500                Department
#c7  2  Germany 2009      F        700         0                 Department
#c8  2  Germany 2010      F          0         50                Department

我需要的结果是这个：

# ID Location Gender TypeofHome MS.2007 MS.2008 MS.2009 MS.2010 MWC.2007 MWC.2008 MWC.2009 MWC.2010
# 1   EEUU      M      House     1500    3900      0      NA      400       0        100     NA
# 2   Germany   F      Department 0      4000     700      0     1000      500       0       50

哪一个是更好的解决方案？谢谢顺便说一句！

【问题讨论】：

您能否检查您的rbind 代码，因为它与您显示的不同
检查！ @akrun
我认为更新中的列名将是 MoneyWithCreditCard 而不是 CreditCard。我更新了你的帖子
"哪一个是更好的解决方案？"哪一个？您尝试过什么，其他长到宽的重塑帖子没有奏效怎么办？我会看看新的tidyr::pivot_wider，它采用多个值列
使用data.table：library(data.table); setDT(Df); dcast(Df, ID + Location + Gender ~ Year, value.var = c("MoneySpent", "MoneyWithCreditCard"))

标签： r pivot reshape

【解决方案1】：

这会重命名原始数据中的列，因此只需要一个数据透视表：

library(dplyr)
library(tidyr)

Df %>%
  rename(MS = MoneySpent, MWC = CreditCard) %>%
  pivot_wider(names_from = c("Year"),
              values_from = c("MS", "MWC"))
# # A tibble: 2 x 11
#   ID    Location Gender MS_2007 MS_2008 MS_2009 MS_2010 MWC_2007 MWC_2008 MWC_2009 MWC_2010
#   <fct> <fct>    <fct>  <fct>   <fct>   <fct>   <fct>   <fct>    <fct>    <fct>    <fct>   
# 1 1     EEUU     M      1500    3900    0       NA      400      0        100      NA      
# 2 2     Germany  F      0       4000    700     0       1000     500      0        50

【讨论】：

【解决方案2】：

这是pivot_longer 和pivot_wider 的选项。我们首先在“Money”列上使用pivot_longer 重塑为“long”格式，通过根据列名附加“MC”或“MWC”来更改“Year”，然后将pivot_wider 转换为“wide”格式

library(dplyr)
library(tidyr)
library(stringr)
Df %>% 
    pivot_longer(cols = starts_with("Money")) %>% 
    mutate(Year = case_when(name == "MoneySpent" ~ str_c("MS.", Year),
                            TRUE ~ str_c("MWC.", Year))) %>% 
    select(-name) %>%
    pivot_wider(names_from = Year, values_from = value)
#ID Location Gender TypeofHome MS.2007 MWC.2007 MS.2008 MWC.2008 MS.2009 MWC.2009 MS.2010 MWC.2010
#1  1     EEUU      M      House    1500      400    3900        0       0      100      NA       NA
#2  2  Germany      F Department       0     1000    4000      500     700        0       0       50

或使用rename_at

Df %>% 
   rename_at(vars(matches("Money")), ~ str_remove_all(., "[a-z]+")) %>% 
   pivot_wider(names_from = Year, values_from = starts_with("M"))
# ID Location Gender MS_2007 MS_2008 MS_2009 MS_2010 MWCC_2007 MWCC_2008 MWCC_2009 MWCC_2010
#1  1     EEUU      M    1500    3900       0    <NA>       400         0       100      <NA>
#2  2  Germany      F       0    4000     700       0      1000       500         0        50

【讨论】：

【解决方案3】：

您可能想尝试使用 base R 中的 reshape。

reshape(setNames(Df, c(names(Df)[1:4], "MS", "MWS", "TypeOfHome")), 
        idvar=c("ID", "Location", "Gender", "TypeOfHome"),
        timevar="Year", direction="wide")
#   ID Location Gender TypeOfHome MS.2007 MWS.2007 MS.2008 MWS.2008 MS.2009 MWS.2009 MS.2010 MWS.2010
# 1  1     EEUU      M      House    1500      400    3900        0       0      100    <NA>     <NA>
# 4  2  Germany      F Appartment       0     1000    4000      500     700        0       0       50

【讨论】：