是否有方法隔离数据集中的多个索引点，并在R中的索引点周围隔离时间窗口？

Question

我有一个被观察72小时的病人的数据集。病人的氧气水平每4秒测量一次，但由于数据的准确性问题，一些观察不得不移除。因此，病人有不同数量的观察。

在观察病人的同时，他们接受了各种干预。我的分析的目的是评估干预是否影响患者的氧气水平。因此，我正在对干预前、干预期间和干预后的氧水平进行比较.

虽然实际分析并不太困难，但我很难对数据进行细分。例如，我只想在干预开始前300秒和干预后300秒进行观察。我必须考虑到这样一个事实:一个人在时间窗的过程中可能有多种干预措施，而且有多个主题。

下面我提供了一些示例代码来生成数据集，但是如果我遗漏了什么，请告诉我。

id <- rep(c(1,2,3), each = 1000)
intervention <- c(rep(0,200), rep(1,10), rep(0,153), rep(0,5), rep(0,284), rep(0,20), rep(0,159), rep(0,23), rep(0,146),
                  rep(0,123), rep(1,23), rep(0,356), rep(1,8), rep(0,234), rep(1,23), rep(0,233),
                  rep(0,345), rep(1,12), rep(0,48), rep(1,15), rep(0,74), rep(1,4), rep(0,233), rep(1,82), rep(0,187))
final <- data.frame(id, intervention)
final <- final %>%
  group_by(id) %>%
  mutate(time = row_number() * 4) 

到目前为止，我已经尝试过这种方法，但我只能在干预前5分钟隔离单个观察，而不是在这些时间窗口之间进行所有观察(即干预开始前5分钟的单次观察和干预后5分钟的单次观察，而不是这三点之间的所有观察)。

data <- final4 %>%
  filter(intervention == 1) %>%
  mutate(five_mins_after = time + 300, #5 mins after intervention
         five_mins_before = time - 300) %>% #5 mins before intervention %>%
  filter(id == "1")

data2 <- final4 %>%
  filter(intervention == 0,
        id == "1")

data_after <- data %>%
  dplyr::select(five_mins_after)

data_before <- data %>%
  dplyr::select(five_mins_before)

data3 <- merge(data2, data_after, by.x = "time", by.y = "five_mins_after")
data4 <- merge(data2, data_before, by.x = "time", by.y = "five_mins_before")

final <- final %>%
  dplyr::bind_rows(data3) %>%
  dplyr::bind_rows(data4)

如果您需要更多的信息，请告诉我，谢谢您的时间！

PS:如果我错过了什么，很抱歉，我第一次在这里寻求帮助

Answer 1

这是答案。虽然时间很长，但在乞讨干预前300秒钟和乞讨后300秒的集合时间，效果都很好。

如果你想进一步解释，或者我是否误解了什么，请告诉我。

library(magrittr)
library(tidyverse)

### Sample code
id <- rep(c(1,2,3), each = 1000)
intervention <- c(rep(0,200), rep(1,10), rep(0,153), rep(0,5), rep(0,284), rep(0,20), rep(0,159), rep(0,23), rep(0,146),
                  rep(0,123), rep(1,23), rep(0,356), rep(1,8), rep(0,234), rep(1,23), rep(0,233),
                  rep(0,345), rep(1,12), rep(0,48), rep(1,15), rep(0,74), rep(1,4), rep(0,233), rep(1,82), rep(0,187))
final <- data.frame(id, intervention)
final <- final %>%
  group_by(id) %>%
  mutate(time = row_number() * 4) 

### Start of data processing to get wanted observations

# Order it by id and time
final %<>% arrange(id, time)

# Loop over the unique ids
obs_to_keep <- list()
for(i in unique(final$id)) {
  
  # Get starts of treatment
  time_zero_intervention <- final %>% 
    filter(id == i & intervention == 0) %>% 
    select(time)
  
  # Obtain all times after zero interventions, that could be intervention == 1
  time_plus_4 <- time_zero_intervention$time + 4
  
  # Where in the times after 0 intervention there is a 1 intervention
  starts_of_interventions <- final %>% 
    filter(id == i & time %in% time_plus_4) %>% 
    filter(intervention == 1)
  
  # Loop over each one of the times where intervention starts
  all_times <- list()
  for(n in 1:length(starts_of_interventions$time)) {
    # Gather 300 secs prior and post
    time_300_before <- starts_of_interventions$time[n] - 300
    time_300_after <- starts_of_interventions$time[n] + 300
    
    # Filter for observations in this interval
    all_times[[n]] <- final %>%
      filter(id == i) %>%
      filter(time >= time_300_before & time <= time_300_after)
  }
  if(length(all_times) == 1){
    obs_to_keep[[i]] <- as.data.frame(all_times)
  }
  else {
    obs_to_keep[[i]] <- do.call(rbind, all_times)
  }
}

# Make a data frame from the list
df <- do.call(rbind, obs_to_keep)

# Order it by id and time
df %<>% arrange(id, time)