如何清理此数据框

Question

我的数据具有以以下格式存储的变量：

             V2                             V3
1 Price :  33,990          Size : 16, 17 & 18.5"
2 Price :  30,830      Size : 13, 16, 18 & 19.5"
3 Price :  48,560             Sizes : 21 & 21.5"
4 Price :  33,790 Size : 17.5, 18.5, 19.5 & 21.5
5 Price :  37,990       Size : 17.5, 18.5 & 19.5
6 Price :  43,690      Size : 17.5, 18.5 & 19.5"

我需要的变量是Price 和Size 等等。 R 中将这些原始数据转换为如下格式的最简洁方法是什么：

            Price        Size
1          33,990       16, 17 & 18.5"
2          30,830       13, 16, 18 & 19.5"
3          48,560       21 & 21.5"
4          33,790       17.5, 18.5, 19.5 & 21.5
5          37,990       17.5, 18.5 & 19.5
6          43,690       17.5, 18.5 & 19.5"

此外，第三行的变量名称拼写错误为Sizes，而不是Size。我该如何处理这个问题，因为还有其他变量有相同的错误？

编辑： 我不能使用特定于列的策略（例如使用gsub()），因为给定列中的变量不一致。具体来说，

                                           V20
1                        Grips : Bontrager SSR
2                  Headset : 1-1/8" threadless
3                                             
4          Brakeset : Tektro alloy linear-pull
5            Brakeset : HL 280 mechanical disc
6 Brakeset : Tektro M290 hydraulic disc brakes

V20 列有 3 个唯一变量，Grips、Headset、Brakeset 和一个空白。整洁的数据框应如下所示：

           Grips        Headset              Brakeset
1   Bontrager SSR       NA                   NA
2              NA       1-1/8" threadless    NA
3              NA       NA                   NA
4              NA       NA                   Tektro alloy linear-pull
5              NA       NA                   HL 280 mechanical disc
6              NA       NA                   Tektro M290 hydraulic disc brakes

这是一种过度简化，因为我假设 Brakeset 对前 3 行没有任何价值。这可能是也可能不是，因为值可能存储在不同的列中。如果特定行对给定变量没有值，则将使用 NA。我希望问题很清楚。

Answer 1

library(tidyr)
# convert = T automatically converts to integer/numeric
df$Price <- separate(df, Price, into = c("x","y"), sep = ":", convert = T)[,2]
df$Size  <- separate(df, Size, into = c("x","y"), sep = ":")[,2]

---

# with gsub()
# irrespective of what is appearing before ":", gsub() shall take care of it
df$Price <- trimws(gsub(".*\\:", "",df$Price)) # this should work

---

# I'm using the below data to explain. This is obtained after using separate() once.
df1
          x                                  y
1    Grips                       Bontrager SSR
2    Grips                       Bontrager SSR
3  Headset                    1-1/8 threadless
4 Brakeset   Tektro M290 hydraulic disc brakes

# need to add a unique key to the data
> df1[["id"]] <- 1:nrow(df1)
> df1
          x                                  y id
1    Grips                       Bontrager SSR  1
2    Grips                       Bontrager SSR  2
3  Headset                    1-1/8 threadless  3
4 Brakeset   Tektro M290 hydraulic disc brakes  4

# using spread() from tidyr package
> spread(df1, x, y)
  id                          Brakeset          Grips           Headset 
1  1                               <NA>  Bontrager SSR              <NA>
2  2                               <NA>  Bontrager SSR              <NA>
3  3                               <NA>           <NA>  1-1/8 threadless
4  4  Tektro M290 hydraulic disc brakes           <NA>              <NA>

Answer 2

一种可能的替代方法：

# create a list of the needed columnnames for the desired dataframe
nameslist <- lapply(mydf, function(x) unique(trimws(gsub('\\:.*', '', x[x != '']))))

# create a new dataframe with this list
mydf2 <- mydf[, rep(names(mydf), lengths(nameslist))]
names(mydf2) <- unlist(nameslist)

# create an array index of which values need to be included
idx <- mapply(function(x,y) grepl(x, y), x = names(mydf2), y = mydf2)

# replace the other values with 'NA'
mydf2[!idx] <- NA

# use gsub to remove everything before ':'
mydf2[] <- lapply(mydf2, function(x) trimws(gsub('.*\\:', '', x)))

给出：

   Price                    Size         Grips          Headset                          Brakeset
1 33,990           16, 17 & 18.5 Bontrager SSR             <NA>                              <NA>
2 30,830       13, 16, 18 & 19.5          <NA> 1-1/8 threadless                              <NA>
3 48,560               21 & 21.5          <NA>             <NA>                              <NA>
4 33,790 17.5, 18.5, 19.5 & 21.5          <NA>             <NA>          Tektro alloy linear-pull
5 37,990       17.5, 18.5 & 19.5          <NA>             <NA>            HL 280 mechanical disc
6 43,690       17.5, 18.5 & 19.5          <NA>             <NA> Tektro M290 hydraulic disc brakes

---

使用过的数据：

mydf <- structure(list(V2 = c("Price :  33,990", "Price :  30,830", "Price :  48,560", "Price :  33,790", "Price :  37,990", "Price :  43,690"), 
                       V3 = c("Size : 16, 17 & 18.5", "Size : 13, 16, 18 & 19.5", "Size : 21 & 21.5", "Size : 17.5, 18.5, 19.5 & 21.5", "Size : 17.5, 18.5 & 19.5", "Size : 17.5, 18.5 & 19.5"), 
                       V4 = c("Grips : Bontrager SSR", "Headset : 1-1/8 threadless", "", "Brakeset : Tektro alloy linear-pull", "Brakeset : HL 280 mechanical disc", "Brakeset : Tektro M290 hydraulic disc brakes")), 
                  .Names = c("V2", "V3", "V4"), class = "data.frame", row.names = c(NA, -6L))