按多个条件对时间序列中的事件类型进行计数 [英] Count event types over time series by multiple conditions

问题描述

我希望在一个时间序列中计算组内每个子组的计数/总和。

I'm hoping to calculate count/sum of each subgroup within groups over a time series.

我的问题与这个问题非常相似
< a href = https://stackoverflow.com/questions/40369820/rolling-count-of-events-over-time-series?answertab=votes#tab-top>随时间序列滚动事件数 。

My question is very similar to this question Rolling Count of Events Over Time Series.

抱歉，我一直在寻找方法来计算时间范围内（第1组的当前日期和前一个N（例如4） ） 天）。我想对组2中的每个子类型重复此过程，即组2是一个较大的组，可能/可能不包含组1中的所有类别。

Apologies for cross-posting, I have been looking for ways to count events for each category in group 1 within a time range (Present date and the previous N (say 4) days). I want to repeat this process for every subtype in group 2, i.e. Group 2 is a larger group that may/may not contains all the categories within Group 1.

，如果我们有一个如下所示的数据框

For example, if we have a data frame that looks like the following

dates = as.Date(c("2011-10-09",
    "2011-10-15",
    "2011-10-16", 
    "2011-10-18", 
    "2011-10-21", 
    "2011-10-22", 
    "2011-10-24")) 
group1=c("A",
     "A",
     "A", 
     "A", 
     "L", 
     "L", 
     "A")
group2=c("I",
     "I",
     "I", 
     "I", 
     "I", 
     "I", 
     "II")

df1 <- data.frame(dates, group1, group2) 

寻找与此类似的输出。 （编辑）最终，我想散布数据集，以便将组1中的类别放在单独的列中，并根据日期和组2排列行。如何确保组1类别的计数结转到新行（并满足上述时间范围）？

And I'm looking for output similar to this. (Edited) Eventually, I want to spread my dataset so that I will have categories in Group 1 in separate columns, and arrange rows according to dates and Group 2. How can I make sure the count of Group 1 categories is carried forward to the new row (and satisfy the timeframe stated above)?

            dates  group1 group2  count (A)   count (L)
     1 2011-10-09      A      I        1         0
     2 2011-10-15      A      I        1         0
     3 2011-10-16      A      I        2         0
     4 2011-10-18      A      I        3         0
     5 2011-10-21      L      I        0         1
     6 2011-10-22      L      I        0         2
     7 2011-10-24      A      II       1         0

谢谢！

推荐答案

如果要显示组1和组2中所有事件的发生次数在活动开始前的4天里，您可以只 group_by 两个活动组，然后计数（使用 sapply 分别访问每个日期）。

If you are trying to show the count of all occurrences of events from group1 and group2 in the 4 days leading up to an event, you can just group_by the two event groups and then count (using sapply to access each date separately).

df1 %>%
  group_by(group1, group2) %>%
  mutate(count = sapply(dates
                        , function(x){
                          sum(dates <= x & dates > (x-4))
                          }))

返回值：

       dates group1 group2 count
      <date> <fctr> <fctr> <int>
1 2011-10-09      A      I     1
2 2011-10-15      A      I     1
3 2011-10-16      A      I     2
4 2011-10-18      A      I     3
5 2011-10-21      L      I     1
6 2011-10-22      L      I     2
7 2011-10-24      A     II     1

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