如何在dplyr突变而不会失去顺序？ [英] how can I mutate in dplyr without losing order?

问题描述

使用 data.table 我可以执行以下操作：

Using data.table I can do the following:

library(data.table)
dt = data.table(a = 1:2, b = c(1,2,NA,NA))
#   a  b
#1: 1  1
#2: 2  2
#3: 1 NA
#4: 2 NA

dt[, b := b[1], by = a]
#   a b
#1: 1 1
#2: 2 2
#3: 1 1
#4: 2 2

尝试在 dplyr 中执行相同的操作，但是数据被加扰/ c $ c> a ：

Attempting the same operation in dplyr however the data gets scrambled/sorted by a:

library(dplyr)
dt = data.table(a = 1:2, b = c(1,2,NA,NA))
dt %.% group_by(a) %.% mutate(b = b[1])
#  a b
#1 1 1
#2 1 1
#3 2 2
#4 2 2

（因为上面还排序了原来的 dt ，这有点让我感到困惑 dplyr 不会修改的哲学 - 我猜这是一个与 dplyr 如何与 data.table接口的错误）

(as an aside the above also sorts the original dt, which is somewhat confusing for me given dplyr's philosophy of not modifying in place - I'm guessing that's a bug with how dplyr interfaces with data.table)

dplyr 是什么方式实现上述？

What's the dplyr way of achieving the above?

推荐答案

b个表：

In the current development version of dplyr (which will eventually become dplyr 0.2) the behaviour differs between data frames and data tables:

library(dplyr)
library(data.table)

df <- data.frame(a = 1:2, b = c(1,2,NA,NA))
dt <- data.table(df)

df %.% group_by(a) %.% mutate(b = b[1])

## Source: local data frame [4 x 2]
## Groups: a
## 
##   a b
## 1 1 1
## 2 2 2
## 3 1 1
## 4 2 2

dt %.% group_by(a) %.% mutate(b = b[1])

## Source: local data table [4 x 2]
## Groups: a
## 
##   a b
## 1 1 1
## 2 1 1
## 3 2 2
## 4 2 2

这是因为 group_by（）应用于 data.table
自动将 setkey（）假设索引将
未来操作速度更快。

This happens because group_by() applied to a data.table automatically does setkey() on the assumption that the index will make future operations faster.

如果有强烈的感觉，这是一个不良的默认值，我很乐意改变它。

If there's a strong feeling that this is a bad default, I'm happy to change it.

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