dplyr n_distinct与条件 [英] dplyr n_distinct with condition

问题描述

使用dplyr来总结一个数据集，我想调用n_distinct来计算列中唯一出现的次数。但是，我也想对另一列中的条件满足的列中的所有唯一的出现进行另一个summarize（）。

名为a的示例数据框： / p>

  AB 
 1 Y 
 2 N 
 3 Y 
 1 Y 
  

a％>％summaryize（count = n_distinct（A））

但是，我还想添加一个 n_distinct（A）的计数，其中 B ==Y

结果应该是：

  count 
 3 
  

当您添加条件结果应该是：

  count 
 2 
  

我试图实现的最终结果是两个语句合并成一个调用，给我一个结果，如

  count_all count_BisY 
 3 2 
  

什么是适当的方式用dplyr来解决这个问题？

另一种方法是使用 dplyr 内的 data.table 中的 uniqueN 函数：

 库（dplyr）
库（data.table）
a％>％summarize（count_all = n_distinct（A），count_BisY = uniqueN（A [B =='Y']））
  

它给出：

  count_all count_BisY 
 1 3 2 
  

---

您还可以使用 data.table ：

  library（data.table）
 setDT（a）[，。（count_all = uniqueN（A），count_BisY = uniqueN（A [B = ='Y']）]] 
  

其结果相同。

Using dplyr to summarise a dataset, I want to call n_distinct to count the number of unique occurrences in a column. However, I also want to do another summarise() for all unique occurrences in a column where a condition in another column is satisfied.

Example dataframe named "a":

A B
1 Y
2 N
3 Y
1 Y

a %>% summarise(count = n_distinct(A))

However I also want to add a count of n_distinct(A) where B == "Y"

The result should be:

count
    3

when you add the condition the result should be:

count
    2

The end result I am trying to achieve is both statements merged into one call that gives me a result like

count_all  count_BisY
        3           2

What is the appropriate way to go about this with dplyr?

解决方案

An alternative is to use the uniqueN function from data.table inside dplyr:

library(dplyr)
library(data.table)
a %>% summarise(count_all = n_distinct(A), count_BisY = uniqueN(A[B == 'Y']))

which gives:

  count_all count_BisY
1         3          2

---

You can also do everything with data.table:

library(data.table)
setDT(a)[, .(count_all = uniqueN(A), count_BisY = uniqueN(A[B == 'Y']))]

which gives the same result.

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