使用 dplyr 删除重复行 [英] Remove duplicated rows using dplyr

问题描述

我有一个像这样的 data.frame -

I have a data.frame like this -

set.seed(123)
df = data.frame(x=sample(0:1,10,replace=T),y=sample(0:1,10,replace=T),z=1:10)
> df
   x y  z
1  0 1  1
2  1 0  2
3  0 1  3
4  1 1  4
5  1 0  5
6  0 1  6
7  1 0  7
8  1 0  8
9  1 0  9
10 0 1 10

我想根据前两列删除重复的行.预期输出 -

I would like to remove duplicate rows based on first two columns. Expected output -

df[!duplicated(df[,1:2]),]
  x y z
1 0 1 1
2 1 0 2
4 1 1 4

我专门寻找使用 dplyr 包的解决方案.

I am specifically looking for a solution using dplyr package.

推荐答案

注意:dplyr 现在包含用于此目的的 distinct 函数.

Note: dplyr now contains the distinct function for this purpose.

以下原答案:

library(dplyr)
set.seed(123)
df <- data.frame(
  x = sample(0:1, 10, replace = T),
  y = sample(0:1, 10, replace = T),
  z = 1:10
)

一种方法是分组，然后只保留第一行:

One approach would be to group, and then only keep the first row:

df %>% group_by(x, y) %>% filter(row_number(z) == 1)

## Source: local data frame [3 x 3]
## Groups: x, y
## 
##   x y z
## 1 0 1 1
## 2 1 0 2
## 3 1 1 4

(在 dplyr 0.2 中，您不需要虚拟 z 变量，而只是能够写row_number() == 1)

(In dplyr 0.2 you won't need the dummy z variable and will just be able to write row_number() == 1)

我也一直在考虑添加一个 slice() 函数像这样工作:

I've also been thinking about adding a slice() function that would work like:

df %>% group_by(x, y) %>% slice(from = 1, to = 1)

或者可能是 unique() 的一个变体，它可以让你选择哪个要使用的变量:

Or maybe a variation of unique() that would let you select which variables to use:

df %>% unique(x, y)

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