[英]how to count a “batch” every 15 minutes
1 2021-01-01 12:59:38
2 2021-01-01 14:08:59
3 2021-01-01 14:09:08
4 2021-01-01 14:11:30
5 2021-01-01 14:22:19
6 2021-01-01 14:41:07
I want to be able to count the number of entries every 15 minutes but on a rolling basis.我希望能够每 15 分钟计算一次条目数,但要滚动计算。 Eg 12:59 would be 1 within 15 mins, 14:08 => 14:22 would all be within 15 minutes so this would return 4 in this batch and finally 14:41 would be by itself in another 15 minute batch.例如 12:59 将在 15 分钟内为 1, 14:08 => 14:22 将在 15 分钟内,因此这将在这批中返回 4,最后 14:41 将在另一个 15 分钟内单独出现。
I hope this makes sense and thanks in advance.我希望这是有道理的,并提前感谢。
Apologies for not including this抱歉没有包括这个
> dput(df)
structure(list(ClickedDate = structure(c(1609460198.707, 1609462979.593,
1609465088.437, 1609476270.88, 1609478479.177, 1609479667.373,
1609493081.887, 1609499187.29, 1609507506.37, 1609510989.533,
1609511522.023, 1609511894.067, 1609512194.773, 1609512377.227,
1609514474.153), tzone = "UTC", class = c("POSIXct", "POSIXt"
)), batch_no = c(1L, 2L, 3L, 4L, 5L, 6L, 7L, 8L, 9L, 10L, 11L,
12L, 12L, 12L, 13L), batch_size = c(1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 3L, 3L, 3L, 1L)), row.names = c(NA, -15L), class = c("tbl_df",
"tbl", "data.frame"))
NEW EDIT - Thank you for working on this.新编辑 - 感谢您为此工作。 I am getting an error我收到一个错误
Error in UseMethod("mutate") :
no applicable method for 'mutate' applied to an object of class "c('integer', 'numeric')"
This seems odd, my variable is in class这看起来很奇怪,我的变量在 class
> class(df$ClickedDate)
[1] "POSIXct" "POSIXt"
Does this work with mutate, or do I need to convert this?这是否适用于变异,还是我需要转换它?
> dput(df)
structure(list(ClickedDate = structure(c(1609460198.707, 1609462979.593,
1609465088.437, 1609476270.88, 1609478479.177, 1609479667.373,
1609493081.887, 1609499187.29, 1609507506.37, 1609510989.533,
1609511522.023, 1609511894.067, 1609512194.773, 1609512377.227,
1609514474.153), tzone = "UTC", class = c("POSIXct", "POSIXt"
)), batch_no = c(1L, 2L, 3L, 4L, 5L, 6L, 7L, 8L, 9L, 10L, 11L,
12L, 12L, 12L, 13L), batch_size = c(1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 3L, 3L, 3L, 1L)), row.names = c(NA, -15L), class = c("tbl_df",
"tbl", "data.frame"))
Thanks in advance提前致谢
Usage of runner
package will help in this scenario.在这种情况下,使用runner
package 会有所帮助。 Use the following strategy使用以下策略
library(tidyverse)
library(runner)
df %>% mutate(b_len = runner::runner(x = ClickedDate,
idx = ClickedDate,
k = "15 mins",
lag = "-14 mins",
f = length),
b_no = purrr::accumulate(seq_len(length(b_len)-1), .init = b_len[1], ~ifelse(.x > .y, .x, .x + b_len[.x +1])),
b_no = as.integer(as.factor(b_no))) %>%
group_by(b_no) %>%
mutate(b_len = n())
# A tibble: 15 x 3
# Groups: b_no [12]
ClickedDate b_len b_no
<dttm> <int> <int>
1 2021-01-01 00:16:38 1 1
2 2021-01-01 01:02:59 1 2
3 2021-01-01 01:38:08 1 3
4 2021-01-01 04:44:30 1 4
5 2021-01-01 05:21:19 1 5
6 2021-01-01 05:41:07 1 6
7 2021-01-01 09:24:41 1 7
8 2021-01-01 11:06:27 1 8
9 2021-01-01 13:25:06 1 9
10 2021-01-01 14:23:09 2 10
11 2021-01-01 14:32:02 2 10
12 2021-01-01 14:38:14 3 11
13 2021-01-01 14:43:14 3 11
14 2021-01-01 14:46:17 3 11
15 2021-01-01 15:21:14 1 12
Notes -备注 -
lag
argument in runner
function allows a backward time window (rolling) so I am using negative lag to use forward time window. runner
function 中的lag
参数允许向后时间 window(滚动)所以我使用负滞后来使用向前时间 window。k
argument in runner
is for given length of rolling window runner
中的k
参数是给定的滚动长度 windowb_no
column initially identifies the sliding/rolling window upto the earliest window is exhausted and thereafter takes new window. b_no
列最初标识滑动/滚动 window 直到最早的 window 用完,然后取新的 window。dense_rank
can also be used (see alternative below)也可以使用dense_rank
(参见下面的替代方案)Alternatively或者
df %>% mutate(b_len = runner::runner(x = ClickedDate,
idx = ClickedDate,
k = "15 mins",
lag = "-14 mins",
f = length),
b_no = purrr::accumulate(seq_len(length(b_len)-1), .init = b_len[1], ~ifelse(.x > .y, .x, .x + b_len[.x +1])),
b_no = dense_rank(b_no)) %>%
group_by(b_no) %>%
mutate(b_len = n()) %>%
ungroup()
# A tibble: 15 x 3
ClickedDate b_len b_no
<dttm> <int> <int>
1 2021-01-01 00:16:38 1 1
2 2021-01-01 01:02:59 1 2
3 2021-01-01 01:38:08 1 3
4 2021-01-01 04:44:30 1 4
5 2021-01-01 05:21:19 1 5
6 2021-01-01 05:41:07 1 6
7 2021-01-01 09:24:41 1 7
8 2021-01-01 11:06:27 1 8
9 2021-01-01 13:25:06 1 9
10 2021-01-01 14:23:09 2 10
11 2021-01-01 14:32:02 2 10
12 2021-01-01 14:38:14 3 11
13 2021-01-01 14:43:14 3 11
14 2021-01-01 14:46:17 3 11
15 2021-01-01 15:21:14 1 12
data used使用的数据
df
> df
# A tibble: 15 x 1
ClickedDate
<dttm>
1 2021-01-01 00:16:38
2 2021-01-01 01:02:59
3 2021-01-01 01:38:08
4 2021-01-01 04:44:30
5 2021-01-01 05:21:19
6 2021-01-01 05:41:07
7 2021-01-01 09:24:41
8 2021-01-01 11:06:27
9 2021-01-01 13:25:06
10 2021-01-01 14:23:09
11 2021-01-01 14:32:02
12 2021-01-01 14:38:14
13 2021-01-01 14:43:14
14 2021-01-01 14:46:17
15 2021-01-01 15:21:14
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