英文:
Mutate DataFrames in Julia
问题
寻找一个类似于 by 但不会合并我的DataFrame的函数。在R中，我会使用 dplyr 的 groupby(b) %>% mutate(x1 = sum(a))。我不想丢失表格中的信息，比如变量 :c。
mydf = DataFrame(a = 1:4, b = repeat(1:2,2), c=4:-1:1)
bypreserve(mydf, :b, x -> sum(x.a))
结果如下：
│ Row │ a │ b │ c │ x1 │
│ │ Int64 │ Int64 │ Int64 │ Int64 │
├─────┼───────┼───────┼───────┼───────┤
│ 1 │ 1 │ 1 │ 4 │ 4 │
│ 2 │ 2 │ 2 │ 3 │ 6 │
│ 3 │ 3 │ 1 │ 2 │ 4 │
│ 4 │ 4 │ 2 │ 1 │ 6 │
英文:
Looking for a function that works like by but doesn't collapse my DataFrame. In R I would use dplyr's groupby(b) %>% mutate(x1 = sum(a)). I don't want to lose information from the table such as that in variable :c.
mydf = DataFrame(a = 1:4, b = repeat(1:2,2), c=4:-1:1)
bypreserve(mydf, :b, x -> sum(x.a))
│ Row │ a │ b │ c │ x1
│ │ Int64 │ Int64 │ Int64 │Int64
├─────┼───────┼───────┼───────┤───────
│ 1 │ 1 │ 1 │ 4 │ 4
│ 2 │ 2 │ 2 │ 3 │ 6
│ 3 │ 3 │ 1 │ 2 │ 4
│ 4 │ 4 │ 2 │ 1 │ 6
答案1
得分: 4
将此功能添加讨论了一下，但我认为需要几个月才能发布（一般的想法是允许select具有groupby关键字参数，并添加transform函数，其工作方式类似于select但保留源数据帧的列）。
目前的解决方法是在by之后使用join：
join(mydf, by(mydf, :b, x1 = :a => sum), on=:b)
英文:
Adding this functionality is discussed, but I would say that it will take several months to be shipped (the general idea is to allow select to have groupby keyword argument + also add transform function that will work like select but preserve columns of the source data frame).
For now the solution is to use join after by:
join(mydf, by(mydf, :b, x1 = :a => sum), on=:b)
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