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：
data<-data.frame(time=c(20230404001040, 20230404001050,20230404001100, 20230404001110, 20230404001120,20230404001130,
20230404001140,20230404001150,20230404001200),
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：
data<-data.frame(time=c(20230404001040, 20230404001050,20230404001100, 20230404001110, 20230404001120,20230404001130,
20230404001140,20230404001150,20230404001200),
on=c("FALSE", "FALSE", "FALSE", "TRUE","TRUE","TRUE","FALSE","FALSE","FALSE"),
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:
data<-data.frame(time=c(20230404001040, 20230404001050,20230404001100, 20230404001110, 20230404001120,20230404001130,
20230404001140,20230404001150,20230404001200),
on=c("FALSE", "FALSE", "FALSE", "TRUE","TRUE","TRUE","FALSE","FALSE","FALSE"))
'time' is written as ymd_hms representation. I think I can use data[,1] <- 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:
data<-data.frame(time=c(20230404001040, 20230404001050,20230404001100, 20230404001110, 20230404001120,20230404001130,
20230404001140,20230404001150,20230404001200),
on=c("FALSE", "FALSE", "FALSE", "TRUE","TRUE","TRUE","FALSE","FALSE","FALSE"),
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：
## 用于为连续状态块添加唯一标签的辅助函数
## 以便稍后进行分组持续时间求和：
get_block_labels <- function(xs){
rls <- rle(xs)$lengths
rep(1:length(rls), times = rls)
}
library(dplyr)
data %>%
arrange(time) %>%
mutate(time = time |> as.character() |> ymd_hms(),
dt = (time - lag(time, default = time[1])) |> as.integer(),
block = get_block_labels(on)
) %>%
group_by(block) %>%
mutate(dur = cumsum(dt))
输出结果:
+ # 一个 tibble: 9 x 5
# 组: block [3]
time on dt block dur
<dttm> <chr> <int> <int> <int>
1 2023-04-04 00:10:40 FALSE 0 1 0
2 2023-04-04 00:10:50 FALSE 10 1 10
3 2023-04-04 00:11:00 FALSE 10 1 20
4 2023-04-04 00:11:10 TRUE 10 2 10
5 2023-04-04 00:11:20 TRUE 10 2 20
6 2023-04-04 00:11:30 TRUE 10 2 30
7 2023-04-04 00:11:40 FALSE 10 3 10
8 2023-04-04 00:11:50 FALSE 10 3 20
9 2023-04-04 00:12:00 FALSE 10 3 30
请注意，代码部分已经翻译，输出结果保持原文。如果您需要更多帮助，请告诉我。
英文:
one approach (using the actual time spans between log entries):
## helper function to uniquely label blocks
## of continuous state for later groupwise
## duration summing:
get_block_labels <- function(xs){
rls <- rle(xs)$lengths
rep(1:length(rls), times = rls)
}
library(dplyr)
data |>
arrange(time) |>
mutate(time = time |> as.character() |> ymd_hms(),
dt = (time - lag(time, default = time[1])) |> as.integer(),
block = get_block_labels(on)
) |>
group_by(block) |>
mutate(dur = cumsum(dt))
output:
+ # A tibble: 9 x 5
# Groups: block [3]
time on dt block dur
<dttm> <chr> <int> <int> <int>
1 2023-04-04 00:10:40 FALSE 0 1 0
2 2023-04-04 00:10:50 FALSE 10 1 10
3 2023-04-04 00:11:00 FALSE 10 1 20
4 2023-04-04 00:11:10 TRUE 10 2 10
5 2023-04-04 00:11:20 TRUE 10 2 20
6 2023-04-04 00:11:30 TRUE 10 2 30
7 2023-04-04 00:11:40 FALSE 10 3 10
8 2023-04-04 00:11:50 FALSE 10 3 20
9 2023-04-04 00:12:00 FALSE 10 3 30
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