R - 将事件日志(异步日志)转换为时间序列(同步日志)

Question

想知道你们是否有更有效或更优雅的方法将事件日志转换为时间序列。

并不是以 tidyverse 为中心，但很好奇您是否有一个很好的 tidyverse 方法？我试图利用 dplyr::mutate 的滞后函数在值为 NA 时进行观察，但我似乎无法重复滞后。

这是一个简单的例子

library(dplyr)
set.seed(1)
events <- tibble(
  t = runif(10, 0, 100) %>% sort(),
  value = runif(10, 0, 1)
)

events
# A tibble: 10 x 2
           t     value
       <dbl>     <dbl>
 1  6.178627 0.2059746
 2 20.168193 0.1765568
 3 26.550866 0.6870228
 4 37.212390 0.3841037
 5 57.285336 0.7698414
 6 62.911404 0.4976992
 7 66.079779 0.7176185
 8 89.838968 0.9919061
 9 90.820779 0.3800352
10 94.467527 0.7774452

这是执行此操作的一种 super hacky 方法。事件到时间序列:

accordian <- function(events_data, freq = 1){
  t_seq = seq(
    from = min(events_data$t)-freq %>% round(0), 
    to = max(events_data$t) + freq, 
    by = freq)
  timeseries = tibble(
    t = t_seq,
    value = NA
  )
  timeseries = bind_rows(
    events_data,
    timeseries
  ) %>%
    arrange(t) 
  for (i in 2:length(timeseries$value)){
    if (is.na(timeseries$value[i])){ timeseries$value[i] = timeseries$value[i-1] }
  }
  timeseries = timeseries %>%
    filter(t %in% t_seq)

  return(timeseries)
}

accordian(events)
# A tibble: 102 x 3
           t     value       type
       <dbl>     <dbl>      <chr>
 1  6.178627 0.2059746 events log
 2 20.168193 0.1765568 events log
 3 26.550866 0.6870228 events log
 4 37.212390 0.3841037 events log
 5 57.285336 0.7698414 events log
 6 62.911404 0.4976992 events log
 7 66.079779 0.7176185 events log
 8 89.838968 0.9919061 events log
 9 90.820779 0.3800352 events log
10 94.467527 0.7774452 events log
# ... with 92 more rows

并根据事件日志明确区分事件日志和时间序列:

library(ggplot2)
bind_rows(
  events %>%
    mutate(type = "events log"),
  accordian(events) %>%
    mutate(type = "time series"),
) %>%
ggplot(
  aes(x = t, y = value, color = type)
) +
  geom_line()

我很想听听您的建议!

Answer 1

这不是您问题的答案，但根据您的评论，我想向您展示我的 base-R 方法。也许我们可以将其转化为 tidyverse 解决方案:

async_to_sync <- function(async_df) {
  # Creates a synchronous (multivariate) time series from an asynchronous event log.
  # It is assumed the asynchronous log contains no missing values.

  # creates simple time sequence, could be modified to have a different freuency
  time_sequence <- seq(min(async_df$time), max(async_df$time), by = 1)

  # constructing a new empty dataframe:
  sync_df <- data.frame(matrix(ncol = ncol(async_df), nrow = length(time_sequence)))
  colnames(sync_df) <- colnames(async_df)
  sync_df$time <- time_sequence

  # Fill in already known values in the synchronous time series:
  for(i in 1:nrow(async_df)) {
    time_value <- async_df$time[i]
    sync_df[which(sync_df$time == time_value, arr.ind = TRUE), ] <- async_df[i, ] 
  }

  # Filling in the blanks:
  for(i in 1:nrow(sync_df)) {
    if(sync_df[i, ] %>% is.na %>% any) {
      sync_df[i, ] <- sync_df[i - 1, ]
    }
  }

  return(sync_df)
}

在示例上运行:

> set.seed(1234)
> async_df <- data.frame(time = c(2, 6, 7, 14), x1 = LETTERS[1:4], x2 = 1:4, stringsAsFactors = FALSE)
> async_df
  time x1 x2
1    2  A  1
2    6  B  2
3    7  C  3
4   14  D  4
> async_to_sync(async_df = async_df)
   time x1 x2
1     2  A  1
2     2  A  1
3     2  A  1
4     2  A  1
5     6  B  2
6     7  C  3
7     7  C  3
8     7  C  3
9     7  C  3
10    7  C  3
11    7  C  3
12    7  C  3
13   14  D  4