r - 将多个记录转换为 R 中的单个记录

Question

所以，我知道你们中的很多人会认为这个问题已被问过很多次，但我认为没有。本质上，我希望完全扁平化我的数据，这意味着我希望每个人都有一个单个 记录。这是我的数据的可重现示例:

   id      BusinessUnit var1 var2 var3
1   1 Risk & Compliance    8    7    7
2   1       Investments    7    8    7
3   1      Credit Cards    8    9    7
4   2       Investments    9   10    8
5   2      Credit Cards    9   10    8
6   3 Risk & Compliance    9   10    9
7   3      Credit Cards   10    9   10
8   3       Call Center    6    9   10
9   4       Investments    7    6   10
10  4      Call Centers    7    5    9
11  5 Risk & Compliance   10    7    9
12  6 Risk & Compliance    6    8    9
13  6      Credit Cards    5   10    6

我想结束的是这样的:

  id     BusinessUnit1 var1_1 var2_1 var3_1 BusinessUnit2 var1_2 var2_2 var3_2
1  1 Risk & Compliance      8      7      7   Investments      7      8      7
2  2       Investments      9     10      8  Credit Cards      9     10      8
  BusinessUnit3 var1_3 var2_3 var3_3
1  Credit Cards      8      9      7
2          <NA>     NA     NA     NA

我已经尝试使用 reshape2 包的 cast() 函数，但它要我聚合我不想做的数据。此外，我不希望每个业务部门都有一个单独的记录，因为这只会让我回到我已经拥有的东西。有没有其他方法可以避免使用 for 循环？

Answer 1

reshape 比 dcast 做得更好。只需先添加一个“时间”变量:

mydf$time <- ave(rep(1, nrow(mydf)), mydf$id, FUN = seq_along)
reshape(mydf, idvar="id", direction = "wide")
#    id    BusinessUnit.1 var1.1 var2.1 var3.1 BusinessUnit.2 var1.2 var2.2 var3.2
# 1   1 Risk & Compliance      8      7      7    Investments      7      8      7
# 4   2       Investments      9     10      8   Credit Cards      9     10      8
# 6   3 Risk & Compliance      9     10      9   Credit Cards     10      9     10
# 9   4       Investments      7      6     10   Call Centers      7      5      9
# 11  5 Risk & Compliance     10      7      9           <NA>     NA     NA     NA
# 12  6 Risk & Compliance      6      8      9   Credit Cards      5     10      6
#    BusinessUnit.3 var1.3 var2.3 var3.3
# 1    Credit Cards      8      9      7
# 4            <NA>     NA     NA     NA
# 6     Call Center      6      9     10
# 9            <NA>     NA     NA     NA
# 11           <NA>     NA     NA     NA
# 12           <NA>     NA     NA     NA