dplyr sample_n通过一个变量通过另一个变量

Question

我有一个带有“分组”可变season和另一个可变year的数据框，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或类似的方法在每个季节的数据框中选择2个月，并在所有年份中保留相同的月份，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

我无法使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随机sample 2个值，然后按组filter它们。

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

如果对于某些组，少于2个unique值，我们可以在要sample的组中的2个和唯一值之间选择min 。

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

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对基数R使用相同的逻辑，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

使用slice的选项

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

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或使用base R

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