dplyr sample_n 通过一个变量通过另一个变量

Question

我有一个数据框，其中包含一个 "grouping" 变量 season 和另一个变量 year，每个月重复一次。

df <- data.frame(month = as.character(sapply(month.name,function(x)rep(x,4))),
                  season = c(rep("winter",8),rep("spring",12),rep("summer",12),rep("autumn",12),rep("winter",4)), 
                  year = rep(2021:2024,12))

我想使用 dplyr::sample_n 或类似的东西在每个季节的数据框中选择 2 个月，并在所有年份保持相同的月份，例如：

     month        season     year
1     January     winter     2021
2     January     winter     2022
3     January     winter     2023
4     January     winter     2024
5     February    winter     2021
6     February    winter     2022
7     February    winter     2023
8     February    winter     2024
9     March       spring     2021
10    March       spring     2022
11    March       spring     2023
12    March       spring     2024
13    May         spring     2021
14    May         spring     2022
15    May         spring     2023
16    May         spring     2024
17    June        summer     2021
18    June        summer     2022
19    June        summer     2023
20    June        summer     2024
21    July        summer     2021
22    July        summer     2022
23    July        summer     2023
24    July        summer     2024
25    October     autumn     2021
26    October     autumn     2022
27    October     autumn     2023
28    October     autumn     2024
29    November    autumn     2021
30    November    autumn     2022
31    November    autumn     2023
32    November    autumn     2024

我不能df %>% group_by(season,year) %>% sample_n(2)因为它每年选择不同的月份

谢谢！

Answer 1

我们可以从 month 和 filter 中按组随机 sample 2 个值。

library(dplyr)

df %>%
  group_by(season) %>%
  filter(month %in% sample(unique(month),2))


#   month    season  year
#   <chr>    <chr>  <int>
# 1 January  winter  2021
# 2 January  winter  2022
# 3 January  winter  2023
# 4 January  winter  2024
# 5 February winter  2021
# 6 February winter  2022
# 7 February winter  2023
# 8 February winter  2024
# 9 March    spring  2021
#10 March    spring  2022
# … with 22 more rows

如果某些组的值少于 2 个 unique，我们可以 select min最小值介于 2 和组中的唯一值之间 sample。

df %>%
  group_by(season) %>%
  filter(month %in% sample(unique(month),min(2, n_distinct(month))))

使用与基数 R 相同的逻辑，我们可以使用 ave

df[as.logical(with(df, ave(month, season, 
             FUN = function(x) x %in% sample(unique(x),2)))), ]

Answer 2

一个选项使用slice

library(dplyr)
df %>% 
   group_by(season) %>% 
    slice(which(!is.na(match(month, sample(unique(month), 2)))))
# A tibble: 32 x 3
# Groups:   season [4]
#   month    season  year
#   <fct>    <fct>  <int>
# 1 October  autumn  2021
# 2 October  autumn  2022
# 3 October  autumn  2023
# 4 October  autumn  2024
# 5 November autumn  2021
# 6 November autumn  2022
# 7 November autumn  2023
# 8 November autumn  2024
# 9 April    spring  2021
#10 April    spring  2022
# … with 22 more rows

或使用base R

by(df, df$season, FUN = function(x) subset(x, month %in% sample(unique(month), 2 )))

dplyr sample_n 通过一个变量通过另一个变量

dplyr sample_n by one variable through another one

r

sample

dplyr