Question

我想要特定日期之间的计数（行）的sum（）。我在堆栈上找到了一些解决方案，但要点是我的第二个数据帧比第一个数据帧大得多。

数据集一

dim（foo1）＃600/2

Start                      End
2017-10-24 22:33:59   2017-10-24 22:43:59
2017-11-13 06:34:59   2017-11-13 06:44:59
2017-11-13 06:52:00   2017-11-13 07:02:00
2017-11-13 07:16:59   2017-11-13 07:26:59
2017-11-13 07:35:59   2017-11-13 07:45:59

数据集二

dim（foo2）＃60.000 / 2

Count           Time
1              2017-10-01 13:45:02
1              2017-10-01 12:53:23
1              2017-10-01 12:20:56
1              2017-10-01 12:31:12

我想要foo2中所有行（计数）的总和出现在foo1中的开始日期和结束日期之间）。结果应为Foo1 + new_column（包含计数）

这是我最初无法解决的“解决方案”：

for(i in 1:nrow(foo1)){
  foo1$new_column[i] <-sum(foo2$Count[which( 
    foo2$Time >= foo2$Start[i] &
      foo2$Time <= foo2$End[i])]) 
}

Answer 1

您的样本数据似乎存在问题，因为Time中的foo2（全部在2017年10月1日）不在foo1的时间间隔内（范围始于2017-10-24）。

为此，我创建了自己的示例数据。

library(data.table)

foo1 <- data.table( Start = c("2017-10-24 22:33:59", "2017-11-13 06:34:59", "2017-11-13 06:52:00", "2017-11-13 07:16:59", "2017-11-13 07:35:59"),
                    End = c("2017-10-24 22:43:59", "2017-11-13 06:44:59", "2017-11-13 07:02:00", "2017-11-13 07:26:59", "2017-11-13 07:45:59"),
                    stringsAsFactors = FALSE)

#                  Start                 End
# 1: 2017-10-24 22:33:59 2017-10-24 22:43:59
# 2: 2017-11-13 06:34:59 2017-11-13 06:44:59
# 3: 2017-11-13 06:52:00 2017-11-13 07:02:00
# 4: 2017-11-13 07:16:59 2017-11-13 07:26:59
# 5: 2017-11-13 07:35:59 2017-11-13 07:45:59

foo2 <- data.table( Count = c(1,1,1,1),
                    Time = c("2017-10-24 22:37:02", "2017-10-24 22:38:23", "2017-11-13 07:20:56", "2017-10-01 12:31:12"),
                    stringsAsFactors = FALSE)

#    Count                Time
# 1:     1 2017-10-24 22:37:02
# 2:     1 2017-10-24 22:38:23
# 3:     1 2017-11-13 07:20:56
# 4:     1 2017-10-01 12:31:12

#set times as POSIXct
foo1[, Start := as.POSIXct(Start, format = "%Y-%m-%d %H:%M:%S")]
foo1[, End := as.POSIXct(End, format = "%Y-%m-%d %H:%M:%S")]
foo2[, Time :=  as.POSIXct(Time, format = "%Y-%m-%d %H:%M:%S")]

#add a dummy-column to create a time-range (of 1 second)
foo2[, dummy := Time]

#set data.table keys
setkey(foo1, Start, End)
setkey(foo2, Time, dummy)

#overlap-join, lose the dummy-column
foo3 <- foverlaps(foo2, foo1, type = "within", mult = "first", nomatch = 0L)[, dummy := NULL]

#                  Start                 End Count                Time
# 1: 2017-10-24 22:33:59 2017-10-24 22:43:59     1 2017-10-24 22:37:02
# 2: 2017-10-24 22:33:59 2017-10-24 22:43:59     1 2017-10-24 22:38:23
# 3: 2017-11-13 07:16:59 2017-11-13 07:26:59     1 2017-11-13 07:20:56

foo3[, sum(Count), by = "Start"]
#                  Start V1
# 1: 2017-10-24 22:33:59  2
# 2: 2017-11-13 07:16:59  1

Answer 2

由于您的原始数据集似乎没有任何重叠，因此在示例中添加了另一行。我使用dplyr mutate添加了一个列，其中包含对每个between和Start的逐行End比较到foo2$Time的整个列表，然后将foo2$Count作为结果集。

library(dplyr)
foo2 <- foo2 %>% add_row(Count = 3, Time = as.Date("2017-10-24 22:35:00", tz = "UTC"))
foo1 %>% rowwise() %>%  mutate(Count = sum(foo2$Count[between(as.Date(foo2$Time), as.Date(Start), as.Date(End))]))

#     Source: local data frame [500 x 3]
# Groups: <by row>
# 
# A tibble: 500 x 3
#    Start               End                 Count
#    <dttm>              <dttm>              <dbl>
#  1 2017-10-24 22:33:59 2017-10-24 22:43:59  3.00
#  2 2017-11-13 06:34:59 2017-11-13 06:44:59  0   
#  3 2017-11-13 06:52:00 2017-11-13 07:02:00  0   
#  4 2017-11-13 07:16:59 2017-11-13 07:26:59  0   
#  5 2017-11-13 07:35:59 2017-11-13 07:45:59  0   
#  6 2017-11-13 09:46:00 2017-11-13 09:56:00  0   
#  7 2017-11-13 10:46:00 2017-11-13 10:56:00  0   
#  8 2017-11-13 11:11:00 2017-11-13 11:21:00  0   
#  9 2017-11-13 13:33:00 2017-11-13 13:43:00  0   
# 10 2017-11-13 13:50:59 2017-11-13 14:00:59  0   
# # ... with 490 more rows

在两个数据框中特定匹配两个日期

2 个答案: