如何使用 dplyr 对列进行范围分组? [英] How to do range grouping on a column using dplyr?

问题描述

我想根据列的范围值对 data.table 进行分组，我该如何使用 dplyr 库执行此操作?

I want to group a data.table based on a column's range value, how can I do this with the dplyr library?

例如，我的数据表如下:

For example, my data table is like below:

library(data.table)
library(dplyr)
DT <- data.table(A=1:100, B=runif(100), Amount=runif(100, 0, 100))

现在我想将 DT 以 B 列的 0.05 间隔分成 20 组，并计算每组中有多少行.例如，任何 B 列值在 [0, 0.05) 范围内的行将形成一个组；B 列值在 [0.05, 0.1) 范围内的任何行将形成另一个组，依此类推.有没有有效的方法来完成这个小组功能?

Now I want to group DT into 20 groups at 0.05 interval of column B, and count how many rows are in each group. e.g., any rows with a column B value in the range of [0, 0.05) will form a group; any rows with the column B value in the range of [0.05, 0.1) will form another group, and so on. Is there an efficient way of doing this group function?

非常感谢.

-----------------------------关于阿克伦回答的更多问题.感谢阿克伦的回答.我有一个关于剪切"功能的新问题.如果我的 DT 如下所示:

-----------------------------More question on akrun's answer. Thanks akrun for your answer. I got a new question about the "cut" function. If my DT is like below:

DT <- data.table(A=1:10, B=c(0.01, 0.04, 0.06, 0.09, 0.1, 0.13, 0.14, 0.15, 0.17, 0.71)) 

通过使用以下代码:

DT %>% 
  group_by(gr=cut(B, breaks= seq(0, 1, by = 0.05), right=F) ) %>% 
  summarise(n= n()) %>%
  arrange(as.numeric(gr))

我希望看到这样的结果:

I expect to see results like this:

          gr n
1   [0,0.05) 2
2 [0.05,0.1) 2
3 [0.1,0.15) 3
4 [0.15,0.2) 2
5 [0.7,0.75) 1

但我得到的结果是这样的:

but the result I got is like this:

          gr n
1   [0,0.05) 2
2 [0.05,0.1) 2
3 [0.1,0.15) 4
4 [0.15,0.2) 1
5 [0.7,0.75) 1 

看起来值 0.15 没有正确分配.对此有什么想法吗?

Looks like the value 0.15 is not correctly allocated. Any thoughts on this?

推荐答案

我们可以使用cut来进行分组.我们在 group_by 中创建 'gr' 列，使用 summarise 来创建每个组中的元素数 (n())，然后根据gr"对输出进行排序(arrange).

We can use cut to do the grouping. We create the 'gr' column within the group_by, use summarise to create the number of elements in each group (n()), and order the output (arrange) based on 'gr'.

library(dplyr)
 DT %>% 
     group_by(gr=cut(B, breaks= seq(0, 1, by = 0.05)) ) %>% 
     summarise(n= n()) %>%
     arrange(as.numeric(gr))

<小时>

由于初始对象是 data.table，这可以使用 data.table 方法来完成(包括@Frank 建议使用 keyby)

---

As the initial object is data.table, this can be done using data.table methods (included @Frank's suggestion to use keyby)

library(data.table)
DT[,.N , keyby = .(gr=cut(B, breaks=seq(0, 1, by=0.05)))]

根据 OP 帖子中的更新，我们可以从 seq

Based on the update in the OP's post, we could substract a small number to the seq

lvls <- levels(cut(DT$B, seq(0, 1, by =0.05)))
DT %>%
   group_by(gr=cut(B, breaks= seq(0, 1, by = 0.05) -
                 .Machine$double.eps, right=FALSE, labels=lvls)) %>% 
   summarise(n=n()) %>% 
   arrange(as.numeric(gr))
#          gr n
#1   (0,0.05] 2
#2 (0.05,0.1] 2
#3 (0.1,0.15] 3
#4 (0.15,0.2] 2
#5 (0.7,0.75] 1

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