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首页 > 解决方案 > 分组，然后在日期时间超过某个时间时创建一个“中断”，在原始分组列（R，dplyr）中创建一个新值

r - 分组，然后在日期时间超过某个时间时创建一个“中断”，在原始分组列（R，dplyr）中创建一个新值

问题描述

我有数据集，df，

  Subject      Folder     Message    Date
  A            Out                   9/9/2019 5:46:38 PM
  A            Out                   9/9/2019 5:46:40 PM
  A            Out                   9/9/2019 5:46:42 PM
  A            Out                   9/9/2019 5:46:43 PM
  A            Out                   9/9/2019 9:30:00 PM
  A            Out                   9/9/2019 9:30:01 PM
  B            Out                   9/9/2019 9:35:00 PM
  B            Out                   9/9/2019 9:35:01 PM

我正在尝试按主题对其进行分组，找到持续时间，然后创建一个新的持续时间列。如果日期时间超过一定时间，我还希望创建一个阈值。我的困境是，在A组内，时间从第4排的5点46分到第5排的9点30分。这在 A 组中给出了不准确的持续时间。当时间超过 10 分钟时，我希望“打破”那个时间并找到新的持续时间，同时在主题中创建一个新值 (A1)。我不确定是否应该为此使用循环？

 Subject   Duration   Group
 A         5 sec      outdata1
 A1        1 sec      outdata2
 B         1 sec      outdata3

这是我的输入：

structure(list(Subject = structure(c(1L, 1L, 1L, 1L, 1L, 1L, 
2L, 2L), .Label = c("A", "B"), class = "factor"), Folder = structure(c(1L, 
1L, 1L, 1L, 1L, 1L, 1L, 1L), .Label = "Out", class = "factor"), 
Message = c("", "", "", "", "", "", "", ""), Date = structure(1:8, .Label = c("9/9/2019 5:46:38 PM", 
"9/9/2019 5:46:40 PM", "9/9/2019 5:46:42 PM", "9/9/2019 5:46:43 PM", 
"9/9/2019 9:30:00 PM", "9/9/2019 9:30:01 PM", "9/9/2019 9:35:00 PM", 
"9/9/2019 9:35:01 PM"), class = "factor")), row.names = c(NA, 
-8L), class = "data.frame")

这是我尝试过的：

thresh <- duration(10, units = "minutes")

df %>%  
mutate(Date = mdy_hms(Date)) %>% 
transmute(Subject, Duration = diff = difftime(as.POSIXct(Date, format = 
"%m/%d/%Y %I:%M:%S %p"),as.POSIXct(Date, 
format = "%m/%d/%Y %I:%M:%S %p" ), units = "secs")) %>% 
ungroup %>% 
distinct %>% 
mutate(grp = str_c("Outdata", row_number()))

 mutate(delta = if_else(grp < thresh1, grp, NA_real_))

标签： rloopsdplyr

解决方案

我们可以计算连续Date值之间的持续时间以创建新组，然后计算每个组之间min的时间差。max

library(dplyr)
thresh <- 10

df %>%  
  mutate(Date = as.POSIXct(Date, format = "%m/%d/%Y %I:%M:%S %p")) %>%
  group_by(Subject, Group = cumsum(difftime(Date, 
            lag(Date, default = first(Date)), units = "mins") > thresh)) %>%
  summarise(Duration = difftime(max(Date), min(Date), units = "secs")) %>%
  ungroup %>%
  mutate(Group = paste0('outdata', row_number()))

# A tibble: 3 x 3
#  Subject Group    Duration
#  <fct>   <chr>    <drtn>  
#1 A       outdata1 5 secs  
#2 A       outdata2 1 secs  
#3 B       outdata3 1 secs

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