“Unnest”重叠时间间隔

Question

我正在尝试为一组以超前/滞后方式运行的过滤器创建图。

关于领先/滞后的简短描述：

当一个新的过滤器上线时，它被置于滞后位置，这意味着水在通过初级（又名铅）过滤器后通过它。当前置过滤器堵塞时，当前的滞后过滤器移动到前置位置。总而言之，过滤器从滞后位置开始，然后进入领先位置。

在视觉上，你可以想象成这样：

我需要做的是“unnest”（因为没有更好的词）重叠的时间段。换句话说，我希望每个过滤器都有连续运行的时间戳，无论它处于领先/落后位置。

数据结构如下：

data <- structure(list(record_timestamp = structure(c(1608192000, 1608192060, 1608192120, 1608192180, 1608192240, 1608192300, 1608192360, 1608192420, 1608192480, 1608192540, 1608192600, 1608192660, 1608192720, 1608192780, 1608192840, 1608192900, 1608192960, 1608193020, 1608193080, 1608193140, 1608193200, 1608193260, 1608193320, 1608193380, 1608193440, 1608193500, 1608193560, 1608193620, 1608193680, 1608193740, 1608193800), class = c("POSIXct", "POSIXt"), tzone = "UTC"), flow = c(20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10), lag_start = structure(c(1608192000, 1608192000, 1608192000, 1608192000, 1608192000, 1608192000, 1608192000, 1608192000, 1608192000, 1608192000, 1608192000, 1608192660, 1608192660, 1608192660, 1608192660, 1608192660, 1608192660, 1608192660, 1608192660, 1608192660, 1608192660, 1608193260, 1608193260, 1608193260, 1608193260, 1608193260, 1608193260, 1608193260, 1608193260, 1608193260, 1608193260), class = c("POSIXct", "POSIXt"), tzone = "UTC"), lead_start = structure(c(NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, 1608192660, 1608192660, 1608192660, 1608192660, 1608192660, 1608192660, 1608192660, 1608192660, 1608192660, 1608192660, 1608193260, 1608193260, 1608193260, 1608193260, 1608193260, 1608193260, 1608193260, 1608193260, 1608193260, 1608193260), class = c("POSIXct", "POSIXt"), tzone = "UTC"), changeout_interval = new("Interval",     .Data = c(0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 660, 0, 0, 0, 0,     0, 0, 0, 0, 0, 600, 0, 0, 0, 0, 0, 0, 0, 0, 0, NA), start = structure(c(1608192000,     1608192000, 1608192000, 1608192000, 1608192000, 1608192000,     1608192000, 1608192000, 1608192000, 1608192000, 1608192000,     1608192660, 1608192660, 1608192660, 1608192660, 1608192660,     1608192660, 1608192660, 1608192660, 1608192660, 1608192660,     1608193260, 1608193260, 1608193260, 1608193260, 1608193260,     1608193260, 1608193260, 1608193260, 1608193260, 1608193260    ), tzone = "UTC", class = c("POSIXct", "POSIXt")), tzone = "UTC")), class = c("spec_tbl_df", "tbl_df", "tbl", "data.frame"), row.names = c(NA, -31L), spec = structure(list(    cols = list(record_timestamp = structure(list(), class = c("collector_character",     "collector")), flow = structure(list(), class = c("collector_double",     "collector")), polish_start = structure(list(), class = c("collector_character",     "collector")), lead_start = structure(list(), class = c("collector_character",     "collector"))), default = structure(list(), class = c("collector_guess",     "collector")), skip = 1), class = "col_spec"))

我对数据最终结果的设想：

end_data <- structure(list(record_timestamp = structure(c(1608192000, 1608192060,1608192120, 1608192180, 1608192240, 1608192300, 1608192360, 1608192420,1608192480, 1608192540, 1608192600, 1608192660, 1608192720, 1608192780,1608192840, 1608192900, 1608192960, 1608193020, 1608193080, 1608193140,1608193200, 1608192660, 1608192720, 1608192780, 1608192840, 1608192900,1608192960, 1608193020, 1608193080, 1608193140, 1608193200, 1608193260,1608193320, 1608193380, 1608193440, 1608193500, 1608193560,1608193620,1608193680, 1608193740, 1608193800), class = c("POSIXct", "POSIXt"), tzone = "UTC"), flow = c(20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA), lag_start = structure(c(1608192000, 1608192000, 1608192000,1608192000, 1608192000, 1608192000, 1608192000, 1608192000,1608192000,1608192000, 1608192000, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, 1608192660, 1608192660, 1608192660, 1608192660, 1608192660, 1608192660,1608192660, 1608192660, 1608192660, 1608192660, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA), class = c("POSIXct", "POSIXt"), tzone = "UTC"), lead_start = structure(c(NA, NA, NA, NA, NA, NA, NA, NA,NA, NA, NA, 1608192660, 1608192660, 1608192660, 1608192660,1608192660, 1608192660, 1608192660, 1608192660, 1608192660,1608192660, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, 1608193260,1608193260, 1608193260, 1608193260, 1608193260, 1608193260,1608193260, 1608193260, 1608193260, 1608193260), class = c("POSIXct","POSIXt"), tzone = "UTC"), filter_id = c(1, 1, 1, 1, 1, 1,1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2)), class = c("spec_tbl_df",                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                       "tbl_df", "tbl", "data.frame"), row.names = c(NA, -41L), spec = structure(list(cols = list(record_timestamp = structure(list(), class = c("collector_character","collector")), flow = structure(list(), class = c("collector_double","collector")), polish_start = structure(list(), class = c("collector_character","collector")), lead_start = structure(list(), class = c("collector_character", "collector")), filter_id = structure(list(), class = c("collector_double","collector"))), default = structure(list(), class = c("collector_guess","collector")), skip = 1), class = "col_spec"))

这会使时间戳加倍，但可以更轻松地绘制，因为我可以在 filter_id 列上group_by。

到目前为止，我所拥有的是每个过滤器的一组时间间隔，从开始到结束，通过滞后。这是代码：

intervals <-  data %>% 
  distinct(lag_start, .keep_all = TRUE) %>% 
  mutate(changeout_interval = interval(lag_start, lead(lag_start, 2))) %>%
  select(record_timestamp, changeout_interval)

从那里，我如何过滤每个时间间隔内的所有时间戳？几乎就像有条件的pivot_longer。

最终目标是能够仅用几行 ggplot2 绘制过滤器的整个生命周期，包括超前和滞后。这是我对情节的设想：

grouped_data <- data %>%
  group_by(lag_start) %>%
  mutate(elapsed_time = difftime(record_timestamp,
                                  record_timestamp[1],
                                  units = "mins"),
         total_flow = cumsum(flow))

ggplot(grouped_data, aes(x = elapsed_time, y = total_flow)) +
  geom_line(aes(color = as.factor(lag_start)))

但此图不包括每个过滤器更改为前导位置时的流量。

Answer 1

使用dense_rank 将过滤器按lag_start 分组，然后为每个过滤器创建一条记录。由于interval 和end_data 具有不同的数据结构，因此这会留下宽格式的信息。

library(dplyr)
library(lubridate)

data %>%
  select(-changeout_interval) %>% # example only as interval appeared to calculate this
  mutate(filter_id = dense_rank(lag_start)) %>%
  group_by(filter_id) %>%
  slice(1) %>%
  ungroup() %>%
  mutate(lead_start = lead(lead_start), lead_end = lead(lead_start), changeout_interval = interval(lag_start, lead_end))

# A tibble: 3 x 7
  record_timestamp     flow lag_start           lead_start          filter_id lead_end           
  <dttm>              <dbl> <dttm>              <dttm>                  <int> <dttm>             
1 2020-12-17 08:00:00    20 2020-12-17 08:00:00 2020-12-17 08:11:00         1 2020-12-17 08:21:00
2 2020-12-17 08:11:00    15 2020-12-17 08:11:00 2020-12-17 08:21:00         2 NA                 
3 2020-12-17 08:21:00    10 2020-12-17 08:21:00 NA                          3 NA  

更新以回应澄清问题的补充。使用与dense_rank 相同的方法，然后通过pivot_longer 切换到长格式以使cumsum 要求更易于绘制。

library(dplyr)
library(tidyr)
library(ggplot2)

plot_data <- data %>%
  select(-changeout_interval) %>% # example only as interval appeared to calculate this
  mutate(filter_lag = dense_rank(lag_start),
         filter_lead = filter_lag - 1) %>%
  select(-lag_start, -lead_start) %>%
  pivot_longer(cols = starts_with("filter_"),
               names_to = "position",
               names_prefix = "filter_",
               values_to = "filter") %>%
  filter(filter > 0) %>% # drops the starting filter as data shows no lead filter?
  group_by(filter) %>%
  mutate(elapsed_time = difftime(record_timestamp, record_timestamp[1], units = "mins"),
         rolling_flow = cumsum(flow))

绘制elapsed_time 和rolling_flow

ggplot(plot_data, aes(x = as.numeric(elapsed_time),
                      y = rolling_flow,
                      color = factor(filter))) +
  geom_line()