使用R计算每个周期内的时间间隔。

huangapple
go评论98阅读模式
英文:

Calculate the time intervals within each cycle using R

问题

I have translated the content you provided into Chinese as you requested:

我有这样的数据：

  1. data<-data.frame(time=c(20230404001040, 20230404001050,20230404001100, 20230404001110, 20230404001120,20230404001130,
  2.                         20230404001140,20230404001150,20230404001200),
  3.                  on=c("FALSE", "FALSE", "FALSE", "TRUE","TRUE","TRUE","FALSE","FALSE","FALSE"))

'time' 以ymd_hms格式表示。我认为可以使用 data[,1] <- ymd_hms(data[,1])。

如果 on 为 FALSE，表示开关关闭。
如果 on 为 TRUE，表示开关打开。

我想要计算每次开关事件的持续时间。每行的 time 间隔为10秒。因此，我可以计算每个开关事件内有多少行，然后乘以10。所以我的期望输出应该如下所示：

  1. data<-data.frame(time=c(20230404001040, 20230404001050,20230404001100, 20230404001110, 20230404001120,20230404001130,
  2.                         20230404001140,20230404001150,20230404001200),
  3.                  on=c("FALSE", "FALSE", "FALSE", "TRUE","TRUE","TRUE","FALSE","FALSE","FALSE"),
  4.                  time_after_switch=c(0,10,20,0,10,20,0,10,20))

对于我的 data，前3行是关闭事件，接下来的3行是打开事件，最后的3行是关闭事件。所以我可以将它视为3个循环。在每个循环内，持续时间分别为0,10,20,0,10,20,0,10,20。我想要编写R代码来计算 time_after_switch 的值。

英文:

I have a data like this:

  1. data&lt;-data.frame(time=c(20230404001040, 20230404001050,20230404001100, 20230404001110, 20230404001120,20230404001130,
  2.                         20230404001140,20230404001150,20230404001200),
  3.                  on=c(&quot;FALSE&quot;, &quot;FALSE&quot;, &quot;FALSE&quot;, &quot;TRUE&quot;,&quot;TRUE&quot;,&quot;TRUE&quot;,&quot;FALSE&quot;,&quot;FALSE&quot;,&quot;FALSE&quot;))

'time' is written as ymd_hms representation. I think I can use data[,1] &lt;- ymd_hms(data[,1]).
If on is FALSE, it means that the switch is off.
If on is TRUE, it means that the switch is on.

I want to calculate the duration time of each on/off event. Each row of time is 10-second interval. So I can count how many rows within each on/off event and multiply to 10. So my desired output should look like this:

  1. data&lt;-data.frame(time=c(20230404001040, 20230404001050,20230404001100, 20230404001110, 20230404001120,20230404001130,
  2.                         20230404001140,20230404001150,20230404001200),
  3.                  on=c(&quot;FALSE&quot;, &quot;FALSE&quot;, &quot;FALSE&quot;, &quot;TRUE&quot;,&quot;TRUE&quot;,&quot;TRUE&quot;,&quot;FALSE&quot;,&quot;FALSE&quot;,&quot;FALSE&quot;),
  4.                  time_after_switch=c(0,10,20,0,10,20,0,10,20))

For my data first 3 rows are switch off event, next 3 rows are switch on event, finally last 3 rows are switch off event. So I can think of it as 3 cycles. Within each cycle, the duration times are 0,10,20,0,10,20,0,10,20. I want to make r code calculating the values of time_after_switch.

答案1

得分: 1

以下是代码部分的中文翻译：

  1. ## 用于为连续状态块添加唯一标签的辅助函数
  2. ## 以便稍后进行分组持续时间求和：
  4. get_block_labels <- function(xs){
  5.   rls <- rle(xs)$lengths
  6.   rep(1:length(rls), times = rls)
  7. }
  9. library(dplyr)
  11. data %>%
  12.   arrange(time) %>%
  13.   mutate(time = time |> as.character() |> ymd_hms(),
  14.          dt = (time - lag(time, default = time[1])) |> as.integer(),
  15.          block = get_block_labels(on)
  16.          ) %>%
  17.   group_by(block) %>%
  18.   mutate(dur = cumsum(dt))

输出结果:

  1. + # 一个 tibble: 9 x 5
  2. # 组:   block [3]
  3.   time                on       dt block   dur
  4.   <dttm>              <chr> <int> <int> <int>
  5. 1 2023-04-04 00:10:40 FALSE     0     1     0
  6. 2 2023-04-04 00:10:50 FALSE    10     1    10
  7. 3 2023-04-04 00:11:00 FALSE    10     1    20
  8. 4 2023-04-04 00:11:10 TRUE     10     2    10
  9. 5 2023-04-04 00:11:20 TRUE     10     2    20
  10. 6 2023-04-04 00:11:30 TRUE     10     2    30
  11. 7 2023-04-04 00:11:40 FALSE    10     3    10
  12. 8 2023-04-04 00:11:50 FALSE    10     3    20
  13. 9 2023-04-04 00:12:00 FALSE    10     3    30

请注意，代码部分已经翻译，输出结果保持原文。如果您需要更多帮助，请告诉我。

英文:

one approach (using the actual time spans between log entries):

  1. ## helper function to uniquely label blocks
  2. ## of continuous state for later groupwise
  3. ## duration summing:
  5. get_block_labels &lt;- function(xs){
  6.   rls &lt;- rle(xs)$lengths
  7.   rep(1:length(rls), times = rls)
  8. }
  10. library(dplyr)
  12. data |&gt;
  13.   arrange(time) |&gt;
  14.   mutate(time = time |&gt; as.character() |&gt;  ymd_hms(),
  15.          dt = (time - lag(time, default = time[1])) |&gt; as.integer(),
  16.          block = get_block_labels(on)
  17.          ) |&gt;
  18.   group_by(block) |&gt;
  19.   mutate(dur = cumsum(dt))

output:

  1. + # A tibble: 9 x 5
  2. # Groups:   block [3]
  3.   time                on       dt block   dur
  4.   &lt;dttm&gt;              &lt;chr&gt; &lt;int&gt; &lt;int&gt; &lt;int&gt;
  5. 1 2023-04-04 00:10:40 FALSE     0     1     0
  6. 2 2023-04-04 00:10:50 FALSE    10     1    10
  7. 3 2023-04-04 00:11:00 FALSE    10     1    20
  8. 4 2023-04-04 00:11:10 TRUE     10     2    10
  9. 5 2023-04-04 00:11:20 TRUE     10     2    20
  10. 6 2023-04-04 00:11:30 TRUE     10     2    30
  11. 7 2023-04-04 00:11:40 FALSE    10     3    10
  12. 8 2023-04-04 00:11:50 FALSE    10     3    20
  13. 9 2023-04-04 00:12:00 FALSE    10     3    30

huangapple
  • 本文由 发表于 2023年5月6日 22:59:27
  • 转载请务必保留本文链接：https://go.coder-hub.com/76189564.html
  • dataframe
  • dplyr
  • mutate
  • r
  • time
匿名

发表评论

匿名网友

:?: :razz: :sad: :evil: :!: :smile: :oops: :grin: :eek: :shock: :???: :cool: :lol: :mad: :twisted: :roll: :wink: :idea: :arrow: :neutral: :cry: :mrgreen:

确定