计算 x 天内每个组的唯一 ID

Question

这是我已在此处发布的问题的后续问题：Count occurrence of IDs within the last x days in R

我正在尝试进行另一个滚动计数。我有以下数据：

date = c("2014-04-01", "2014-04-12", "2014-04-07", "2014-05-03", "2014-04-14", "2014-05-04", "2014-03-31", "2014-04-18", "2014-04-23", "2014-04-01")
group = c("G","G","F","G","E","E","H","H","H","A")
ID = c(2, 3, 4, 2, 3, 1, 2, 4, 2, 1)

    group       date ID
 1:     G 2014-04-01  2
 2:     G 2014-04-12  3
 3:     F 2014-04-07  4
 4:     G 2014-05-03  2
 5:     E 2014-04-14  3
 6:     E 2014-05-04  1
 7:     H 2014-03-31  2
 8:     H 2014-04-18  4
 9:     H 2014-04-23  2
10:     A 2014-04-01  1

对于每个group，我想计算当前date 在过去30 天内唯一ID 的数量。所需的计数列如下所示：

    group       date ID  count
 1:     G 2014-04-01  2      1
 2:     G 2014-04-12  3      2
 3:     F 2014-04-07  4      1
 4:     G 2014-05-03  1      2
 5:     E 2014-04-14  3      1
 6:     E 2014-05-04  1      2
 7:     H 2014-03-31  2      1
 8:     H 2014-04-18  4      2
 9:     H 2014-04-23  2      3
10:     A 2014-04-01  1      1

在我之前的帖子中，@ThomasIsCoding 提供了一个解决方案。我尝试通过执行以下操作来修改他的代码以执行我现在尝试执行的任务：

dt[date <= first(date) + 30, date := as.Date(date)][, count := uniqueN(ID), group]
    group       date ID count
 1:     G 2014-04-01  2     2
 2:     G 2014-04-12  3     2
 3:     F 2014-04-07  4     1
 4:     G 2014-05-03  2     2
 5:     E 2014-04-14  3     2
 6:     E 2014-05-04  1     2
 7:     H 2014-03-31  2     2
 8:     H 2014-04-18  4     2
 9:     H 2014-04-23  2     2
10:     A 2014-04-01  1     1

但它似乎没有考虑时间范围条件。非常感谢任何建议！

Answer 1

使用非等连接的选项：

DT[, onemthago := date - 30L]    
DT[, count := 
    DT[.SD, on=.(group, date>=onemthago, date<=date),
        by=.EACHI, length(unique(ID))]$V1
]

输出：

    group       date ID  onemthago count
 1:     G 2014-04-01  2 2014-03-02     1
 2:     G 2014-04-12  3 2014-03-13     2
 3:     F 2014-04-07  4 2014-03-08     1
 4:     G 2014-05-03  2 2014-04-03     2
 5:     E 2014-04-14  3 2014-03-15     1
 6:     E 2014-05-04  1 2014-04-04     2
 7:     H 2014-03-31  2 2014-03-01     1
 8:     H 2014-04-18  4 2014-03-19     2
 9:     H 2014-04-23  2 2014-03-24     2
10:     A 2014-04-01  1 2014-03-02     1

数据：

date = as.Date(c("2014-04-01", "2014-04-12", "2014-04-07", "2014-05-03", "2014-04-14", "2014-05-04", "2014-03-31", "2014-04-18", "2014-04-23", "2014-04-01"))
group = c("G","G","F","G","E","E","H","H","H","A")
ID = c(2, 3, 4, 2, 3, 1, 2, 4, 2, 1)
library(data.table)
DT <- data.table(group, date, ID)

---

编辑以解决对多个回溯期的评论。您可以尝试以下方法：

for (x in c(30L, 90L)) {
    DT[, daysago := date - x]
    
    DT[, paste0("count", x) := 
        .SD[.SD, on=.(group, date>=daysago, date<=date),
            by=.EACHI, length(unique(ID))]$V1
    ][]
}
DT

Answer 2

如果我正确理解了您的问题，tidyverse 中的另一种方法是：

library(tidyverse)

tb <- dplyr::tibble(date = c("2014-04-01", "2014-04-12", "2014-04-07", "2014-05-03", "2014-04-14", "2014-05-04", "2014-03-31", "2014-04-18", "2014-04-23", "2014-04-01"),
                    group = c("G","G","F","G","E","E","H","H","H","A"),
                    ID = c(2, 3, 4, 2, 3, 1, 2, 4, 2, 1))

tb %>% 
  dplyr::group_by(group) %>% 
  dplyr::mutate(as.numeric(difftime(Sys.Date(), date)) < 31) %>% 
  dplyr::distinct(ID) %>% 
  dplyr::count(group) %>% 
  dplyr::right_join(tb) %>% 
  dplyr::select(group, date, ID, Count = n)

   group date          ID Count
    <chr> <chr>      <dbl> <int>
 1 A     2014-04-01     1     1
 2 E     2014-04-14     3     2
 3 E     2014-05-04     1     2
 4 F     2014-04-07     4     1
 5 G     2014-04-01     2     2
 6 G     2014-04-12     3     2
 7 G     2014-05-03     2     2
 8 H     2014-03-31     2     2
 9 H     2014-04-18     4     2
10 H     2014-04-23     2     2

对于滚动窗口类型的函数，这应该是一个解决方案：

tb %>% 
  dplyr::full_join(tb, by = "group") %>% 
  dplyr::filter(as.numeric(difftime(as.Date(date.x), as.Date(date.y), units = "days")) >= 0 & as.numeric(difftime(date.x, date.y, units = "days")) < 31) %>% 
  dplyr::distinct(group, date.x, ID.y) %>% 
  dplyr::count(group, date.x) %>% 
  # you might want to cut the pipe here and look at the result (do not forget to delete the %>% in the line above when removing the part below
  dplyr::right_join(tb, by = c("group", "date.x" = "date")) %>% 
  dplyr::select(group, date = date.x, ID, count = n)

   group date          ID count
   <chr> <chr>      <dbl> <int>
 1 A     2014-04-01     1     1
 2 E     2014-04-14     3     1
 3 E     2014-05-04     1     2
 4 F     2014-04-07     4     1
 5 G     2014-04-01     2     1
 6 G     2014-04-12     3     2
 7 G     2014-05-03     2     2
 8 H     2014-03-31     2     1
 9 H     2014-04-18     4     2
10 H     2014-04-23     2     2

Answer 3

看看这个建议的解决方案对您的情况有多好。

date = c("2014-04-01", "2014-04-12", "2014-04-07", "2014-05-03", "2014-04-14", "2014-05-04", "2014-03-31", "2014-04-18", "2014-04-23", "2014-04-01")
group = c("G","G","F","G","E","E","H","H","H","A")
ID = c(2, 3, 4, 2, 3, 1, 2, 4, 2, 1)

dt <- data.table( date=as.Date(date), group, ID )
setkey( dt, group, date )
calc.id <- function(ID,date) {
    ## there is always going to be at least 1
    id.count <- 1
    if( length(ID) > 1 ) {
        v <- sapply( 2:length(ID), function(i) {
            j <- date[ 1:i ] >= date[i] - 30
            uniqueN( ID[j] )
        })
        id.count <- c( id.count, v )
    }
    return( id.count )
}
dt[ , count := calc.id(ID,date), by=group ]

dt

除了使用 sapply 进行滚动计算之外，我没有找到其他方法。我怀疑它会胜过上面的解决方案。

它产生这个：

          date group ID count
 1: 2014-04-01     A  1     1
 2: 2014-04-14     E  3     1
 3: 2014-05-04     E  1     2
 4: 2014-04-07     F  4     1
 5: 2014-04-01     G  2     1
 6: 2014-04-12     G  3     2
 7: 2014-05-03     G  2     2
 8: 2014-03-31     H  2     1
 9: 2014-04-18     H  4     2
10: 2014-04-23     H  2     2