r - 如何为每个分组元素选择随机的非连续日期？

Question

我目前正在尝试为每个分组列选择非连续的日期。

换句话说，我有以下数据框:

我想基本上 group_by(Site) 然后为每个分组的网站只保留 3 个随机的非连续日期。例如，如果 HP37P1B 的日期对应于 3 月 12 日、3 月 13 日、3 月 14 日和 3 月 7 日 - 我需要一个数据框(例如)只有:

HP37P1B 3 月 12 日

HP37P1B 3 月 14 日

HP37P1B 3 月 7 日

到目前为止，我已经尝试了一些使用 diff()、ave() 和 lubridate 包的 stackoverflow 帖子，但是我没有取得任何成功。

编辑

根据下面的评论，我正在努力使这个问题可重现

dput(uniqueSiteDate)

structure(list(Site = c("HP37P1B", "HP37P2B", "HP37P4B", "HP4008U", 
"INME03R", "INME03U", "INOA03R", "IPTO04R", "IPTO04U", "IPTO06R", 
"IPTO06U", "OLCAP2B", "OLCAP3B", "OLCAP5B", "PANMP1B", "PANMP2B", 
"PANMP3B", "STIN02R", "STIN02U", "UPMAP1B", "UPMAP3B", "UPMAP4B", 
"UPMAP5B", "UPMAP6B", "VAR210R", "VAR310R", "VAR310U", "VAR410R", 
"VAR410U", "HP36P1B", "HP36P3B", "HP36P4B", "HP4008R", "INBS04R", 
"INBS04U", "SEL107R", "SEL107U", "SEL207R", "SEL207U", "OLV110R", 
"OLV110U", "OLV208R", "OLV208U", "THEN10U", "HP37P1B", "HP37P2B", 
"HP37P4B", "HP4008U", "INME03R", "INME03U", "INOA03R", "IPTO04R", 
"IPTO04U", "IPTO06R", "IPTO06U", "OLCAP2B", "OLCAP3B", "OLCAP5B", 
"PANMP1B", "PANMP2B", "PANMP3B", "STIN02R", "STIN02U", "UPMAP1B", 
"UPMAP3B", "UPMAP4B", "UPMAP5B", "UPMAP6B", "VAR210R", "VAR310R", 
"VAR310U", "VAR410R", "VAR410U", "OLV110R", "OLV110U", "OLV208R", 
"OLV208U", "THEN10U", "HP37P1B", "HP37P2B", "HP37P4B", "HP4008U", 
"INME03R", "INME03U", "INOA03R", "IPTO04R", "IPTO04U", "IPTO06R", 
"IPTO06U", "OLCAP2B", "OLCAP3B", "OLCAP5B", "PANMP1B", "PANMP2B", 
"PANMP3B", "STIN02R", "STIN02U", "UPMAP1B", "UPMAP3B", "UPMAP4B", 
"UPMAP5B", "UPMAP6B", "VAR210R", "VAR310R", "VAR310U", "VAR410R", 
"VAR410U", "OLV110R", "OLV110U", "OLV208R", "OLV208U", "THEN10U", 
"HP37P1B", "HP37P2B", "HP37P4B", "HP4008U", "INME03R", "INME03U", 
"INOA03R", "IPTO04R", "IPTO04U", "IPTO06R", "IPTO06U", "OLCAP2B", 
"OLCAP3B"), Date = structure(c(18333, 18333, 18333, 18333, 18335, 
18335, 18335, 18338, 18335, 18338, 18335, 18333, 18333, 18333, 
18334, 18334, 18334, 18331, 18331, 18331, 18330, 18330, 18330, 
18330, 18332, 18332, 18332, 18332, 18332, 18325, 18325, 18325, 
18325, 18327, 18327, 18327, 18327, 18327, 18328, 18340, 18340, 
18340, 18340, 18340, 18334, 18334, 18334, 18334, 18336, 18336, 
18336, 18339, 18336, 18340, 18336, 18335, 18334, 18334, 18335, 
18335, 18335, 18332, 18332, 18332, 18331, 18331, 18331, 18331, 
18333, 18333, 18333, 18333, 18333, 18341, 18341, 18341, 18341,
18341, 18335, 18335, 18335, 18335, 18383, 18383, 18383, 18384, 
18384, 18384, 18384, 18385, 18385, 18335, 18342, 18342, 18341, 
18383, 18383, 18345, 18349, 18349, 18349, 18349, 18340, 18339, 
18340, 18341, 18339, 18386, 18386, 18348, 18346, 18347, 18328, 
18328, 18328, 18328, 18390, 18389, 18391, 18392, 18392, 18392, 
18392, 18392, 18392), class = "Date")), row.names = c(NA, -125L
), groups = structure(list(Site = c("HP36P1B", "HP36P3B", "HP36P4B", 
"HP37P1B", "HP37P2B", "HP37P4B", "HP4008R", "HP4008U", "INBS04R", 
"INBS04U", "INME03R", "INME03U", "INOA03R", "IPTO04R", "IPTO04U", 
"IPTO06R", "IPTO06U", "OLCAP2B", "OLCAP3B", "OLCAP5B", "OLV110R", 
"OLV110U", "OLV208R", "OLV208U", "PANMP1B", "PANMP2B", "PANMP3B", 
"SEL107R", "SEL107U", "SEL207R", "SEL207U", "STIN02R", "STIN02U", 
"THEN10U", "UPMAP1B", "UPMAP3B", "UPMAP4B", "UPMAP5B", "UPMAP6B", 
"VAR210R", "VAR310R", "VAR310U", "VAR410R", "VAR410U"), .rows = structure(list(
    30L, 31L, 32L, c(1L, 45L, 79L, 113L), c(2L, 46L, 80L, 114L
    ), c(3L, 47L, 81L, 115L), 33L, c(4L, 48L, 82L, 116L), 34L, 
    35L, c(5L, 49L, 83L, 117L), c(6L, 50L, 84L, 118L), c(7L, 
    51L, 85L, 119L), c(8L, 52L, 86L, 120L), c(9L, 53L, 87L, 121L
    ), c(10L, 54L, 88L, 122L), c(11L, 55L, 89L, 123L), c(12L, 
    56L, 90L, 124L), c(13L, 57L, 91L, 125L), c(14L, 58L, 92L), 
    c(40L, 74L, 108L), c(41L, 75L, 109L), c(42L, 76L, 110L), 
    c(43L, 77L, 111L), c(15L, 59L, 93L), c(16L, 60L, 94L), c(17L, 
    61L, 95L), 36L, 37L, 38L, 39L, c(18L, 62L, 96L), c(19L, 63L, 
    97L), c(44L, 78L, 112L), c(20L, 64L, 98L), c(21L, 65L, 99L
    ), c(22L, 66L, 100L), c(23L, 67L, 101L), c(24L, 68L, 102L
    ), c(25L, 69L, 103L), c(26L, 70L, 104L), c(27L, 71L, 105L
    ), c(28L, 72L, 106L), c(29L, 73L, 107L)), ptype = integer(0), class = c("vctrs_list_of", 
"vctrs_vctr", "list"))), row.names = c(NA, -44L), class = c("tbl_df", 
"tbl", "data.frame"), .drop = TRUE), class = c("grouped_df", 
"tbl_df", "tbl", "data.frame"))

要回答其他问题，有时每个站点有 3 个以上的日期，但有时每个站点只有 1 个日期。但想法是选择 n 个给定站点的非连续日期。换句话说，如果特定站点有 4 个日期，我需要 3 个不连续的日期。如果特定站点只有 1 个日期，我们就将其保留。

Answer 1

请检查是否达到目的？实际上，根据提供标准选择最大可能日期是很困难的(至少对我而言)。我们可以通过以下策略识别连续和非连续组中的日期。但是请考虑一组连续 3 个日期中的两种情况。如果随机样本包含 2 个单元，则这些单元也可以是连续的或非连续的。假设如果我们进一步选择奇数 (2) 或偶数 (1) 行，那么在我看来样本将是判断性的而不是随机的。这是采用的策略-

• 将数据分组
• 通过purrr::map_df分别对每组进行操作，最终行绑定(bind)数据
• 将数据(现在分组)分成连续和非连续日期(每个连续日期将在其自己的组中)。从每个组中选择唯一的行。
• 最后从每一行中选择三个(或更少，根据小组结果)。

library(tidyverse)

df %>% 
  ungroup() %>% 
  group_split(Site) %>% 
  map_df(., ~ .x %>% ungroup() %>%
        arrange(Date) %>%
        mutate(n = 1) %>%
        complete(Date = seq.Date(first(Date), last(Date), by = 'days')) %>%
        group_by(n = cumsum(is.na(n))) %>%
        filter(!is.na(Site)) %>%
        sample_n(1) %>%
        ungroup() %>%
        sample_n(min(n(), 3))) %>%
  select(-n)

# A tibble: 86 x 2
   Date       Site   
   <date>     <chr>  
 1 2020-03-04 HP36P1B
 2 2020-03-04 HP36P3B
 3 2020-03-04 HP36P4B
 4 2020-03-07 HP37P1B
 5 2020-03-12 HP37P1B
 6 2020-03-07 HP37P2B
 7 2020-03-12 HP37P2B
 8 2020-03-07 HP37P4B
 9 2020-03-12 HP37P4B
10 2020-03-04 HP4008R
# ... with 76 more rows

注意:你的输入是分组的，所以我不得不在代码的第二行添加 ungroup()，你可以删除它