在reshape（）或stack（）中重复列

Question

我有这个df ：

df = data.frame(Meaning = c('Tax', 'Internet', 'Tax', 'Phone', 'Tax', 'Car'),
            Code = c(4656, 6152, 4656, 6150, 4656, 6151),
            Total = c(0.73, 4.4, 1.33, 8, 1.67, 10),
            Tax = c(0.73, NA, 1.33, NA, 1.67, NA),
            Subtotal = c(NA, 3.67, NA, 6.67, NA, 8.33),
             stringsAsFactors = FALSE)

> df
Meaning   Code   Total   Tax    Subtotal
Tax       4656   0.73    0.73   NA
Internet  6152   4.40    NA     3.67
Tax       4656   1.33    1.33   NA
Phone     6150   8.00    NA     6.67
Tax       4656   1.67    1.67   NA
Car       6151   10.00   NA     8.33

我想使用reshape()或stack来获取另一个data.frame如下所示：

Code    Meaning   Category   Price
6152    Internet   Total      4.4
6152    Internet   Subtotal   3.67
4656    Tax        Subtotal   0.73
6150    Phone      Total      8
6150    Phone      Subtotal   6.67
4656    Tax        Subtotal   1.33
6151    Car        Total      10
6151    Car        Subtotal   8.33
4656    Tax        Subtotal   1.67

其中Category显示来自df的列（ Total或Subtotal ），而Price按以下方式显示：在df显示的Total ， Subtotal ， Tax 。

到目前为止，我尝试使用： cbind(df[1:2], stack(lapply(df[-c(1:2)], as.character)))

但它检索：

Meaning   Code values      ind
Tax       4656   0.73    Total
Internet  6152    4.4    Total
Tax       4656   1.33    Total
Phone     6150      8    Total
Tax       4656   1.67    Total
Car       6151     10    Total
Tax       4656   0.73      Tax
Internet  6152   <NA>      Tax
Tax       4656   1.33      Tax
Phone     6150   <NA>      Tax
Tax       4656   1.67      Tax
Car       6151   <NA>      Tax
Tax       4656   <NA> Subtotal
Internet  6152   3.67 Subtotal
Tax       4656   <NA> Subtotal
Phone     6150   6.67 Subtotal
Tax       4656   <NA> Subtotal
Car       6151   8.33 Subtotal

有任何想法吗？

注意 ：我已经尝试了所有这些答案，但是由于我的df有一些不NA所以解决方案不起作用。 答案1 ， 答案2 ， 答案3

Answer 1

这看起来正确吗？

  library(tidyverse)

df %>% 
      gather(Total, Tax, Subtotal, key="key",  value="value") %>% 
      arrange(Code)

    Meaning Code      key value
1       Tax 4656    Total  0.73
2       Tax 4656    Total  1.33
3       Tax 4656    Total  1.67
4       Tax 4656      Tax  0.73
5       Tax 4656      Tax  1.33
6       Tax 4656      Tax  1.67
7       Tax 4656 Subtotal    NA
8       Tax 4656 Subtotal    NA
9       Tax 4656 Subtotal    NA
10    Phone 6150    Total  8.00
11    Phone 6150      Tax    NA
12    Phone 6150 Subtotal  6.67
13      Car 6151    Total 10.00
14      Car 6151      Tax    NA
15      Car 6151 Subtotal  8.33
16 Internet 6152    Total  4.40
17 Internet 6152      Tax    NA
18 Internet 6152 Subtotal  3.67

Answer 2

我更喜欢使用data.table的melt函数：

library(data.table)

melt(df, 
     id.vars = c('Meaning', 'Code'), 
     variable.name = 'Category', 
     value.name = 'Price')

    Meaning Code Category price
1       Tax 4656    Total  0.73
2  Internet 6152    Total  4.40
3       Tax 4656    Total  1.33
4     Phone 6150    Total  8.00
5       Tax 4656    Total  1.67
6       Car 6151    Total 10.00
7       Tax 4656      Tax  0.73
8  Internet 6152      Tax    NA
9       Tax 4656      Tax  1.33
10    Phone 6150      Tax    NA
11      Tax 4656      Tax  1.67
12      Car 6151      Tax    NA
13      Tax 4656 Subtotal    NA
14 Internet 6152 Subtotal  3.67
15      Tax 4656 Subtotal    NA
16    Phone 6150 Subtotal  6.67
17      Tax 4656 Subtotal    NA
18      Car 6151 Subtotal  8.33