在R中使用dplyr根据类型和滚动日期进行计数和标记

我的问题类似于dplyr：使用滚动时间窗口对数据进行分组和汇总/变异 https://stackoverflow.com/questions/36187931/dplyr-grouping-and-summarizing-mutating-data-with-rolling-time-windows我已经使用它作为参考，但还没有成功地操纵它来完成我需要做的事情。

我的数据看起来像这样：

a <- data.table("TYPE" = c("A", "A", "B", "B",
                       "C", "C", "C", "C",
                       "D", "D", "D", "D"), 
            "DATE" = c("4/20/2018 11:47",
                       "4/25/2018 7:21",
                       "4/15/2018 6:11",
                       "4/19/2018 4:22",
                       "4/15/2018 17:46",
                       "4/16/2018 11:59",
                       "4/20/2018 7:50",
                       "4/26/2018 2:55",
                       "4/27/2018 11:46",
                       "4/27/2018 13:03",
                       "4/20/2018 7:31",
                       "4/22/2018 9:45"),
            "CLASS" = c(1, 2, 3, 4,
                        1, 2, 3, 4,
                        1, 2, 3, 4))

由此我首先通过以下方式订购了数据TYPE然后通过DATE并创建一个仅包含日期并忽略时间的列DATE column:

a <- a[order(TYPE, DATE), ]
a[, YMD := date(a$DATE)]

现在我正在尝试使用TYPE列和YMD列以产生新列。这是我试图满足的标准：
1）维护原始数据集中的所有列
2）创建一个名为 say 的新列EVENTS
3）对于每个TYPE如果它发生超过n30天内多次然后放Y in the EVENTS每个列TYPE and YMD这使得该小组获得资格并且N否则。 （注意这是为了n独特的日期，所以它必须有n30 天内的唯一日期才有资格）。

这将是预期的输出，如果n = 4:

这是我拥有的最接近的示例，但它没有考虑唯一的日期，也没有保留表中的所有列：

a %>% mutate(DATE = as.POSIXct(DATE, format = "%m/%d/%Y %H:%M")) %>%
  inner_join(.,., by="TYPE") %>%
  group_by(TYPE, DATE.x) %>%
  summarise(FLAG = as.integer(sum(abs((DATE.x-DATE.y)/(24*60*60))<=30)>=4))

任何建议表示赞赏。

Update

下面的两个答案都适用于我的原始示例数据，但是，如果我们再添加一些实例D然后他们都标记所有D as 1而不是标记前 4 个实例0以及最后 4 个实例1这就是“滚动窗口”发挥作用的地方。

更新数据集：

a <- data.table("TYPE" = c("A", "A", "B", "B",
                       "C", "C", "C", "C",
                       "D", "D", "D", "D",
                       "D", "D", "D", "D"), 
            "DATE" = c("4/20/2018 11:47",
                       "4/25/2018 7:21",
                       "4/15/2018 6:11",
                       "4/19/2018 4:22",
                       "4/15/2018 17:46",
                       "4/16/2018 11:59",
                       "4/20/2018 7:50",
                       "4/26/2018 2:55",
                       "4/27/2018 11:46",
                       "4/27/2018 13:03",
                       "4/20/2018 7:31",
                       "4/22/2018 9:45",
                       "6/01/2018 9:07",
                       "6/03/2018 12:34",
                       "6/07/2018 1:57",
                       "6/10/2018 2:22"),
            "CLASS" = c(1, 2, 3, 4,
                        1, 2, 3, 4,
                        1, 2, 3, 4,
                        1, 2, 3, 4))

新更新的预期输出将是：

这是 dplyr 的解决方案：

根据OP编辑进行更新

library(dplyr)
library(lubridate)
a <- data.frame("TYPE" = c("A", "A", "B", "B",
                           "C", "C", "C", "C",
                           "D", "D", "D", "D",
                           "D", "D", "D", "D"), 
                "DATE" = c("4/20/2018 11:47",
                           "4/25/2018 7:21",
                           "4/15/2018 6:11",
                           "4/19/2018 4:22",
                           "4/15/2018 17:46",
                           "4/16/2018 11:59",
                           "4/20/2018 7:50",
                           "4/26/2018 2:55",
                           "4/27/2018 11:46",
                           "4/27/2018 13:03",
                           "4/20/2018 7:31",
                           "4/22/2018 9:45",
                           "6/01/2018 9:07",
                           "6/03/2018 12:34",
                           "6/07/2018 1:57",
                           "6/10/2018 2:22"),
                "CLASS" = c(1, 2, 3, 4,
                            1, 2, 3, 4,
                            1, 2, 3, 4,
                            1, 2, 3, 4))

# a function to flag rows that are 4th or more within window w
count_window <- function(df, date, w, type){
  min_date <- date - w
  df2 <- df %>% filter(TYPE == type, YMD >= min_date, YMD <= date)
  out <- n_distinct(df2$YMD)
  res <- ifelse(out >= 4, 1, 0)
  return(res)
}

v_count_window <- Vectorize(count_window, vectorize.args = c("date","type"))

res <- a %>% mutate(DATE = as.POSIXct(DATE, format = "%m/%d/%Y %H:%M")) %>%
  mutate(YMD = date(DATE)) %>% 
  arrange(TYPE, YMD) %>% 
  #group_by(TYPE) %>% 
  mutate(min_date = YMD - 30,
         count = v_count_window(., YMD, 30, TYPE)) %>% 
  group_by(TYPE) %>% 
  mutate(FLAG = case_when(
    any(count == 1) & YMD >= min_date[match(1,count)] ~ 1,
    TRUE ~ 0
  ))%>% 
  select(nms,FLAG)

我不知道如何在自定义函数中使用该组，因此我将按类型过滤硬编码到函数中。