将 dplyr 的超前和滞后用于向量中的微分值

Using lead and lag from dplyr for differential values within a vector

我有一个数据框

structure(list(Time = structure(c(1531056854, 1531057121, 1517382101, 
1517386850, 1517386951, 1517399987, 1517400523, 1517400523), class = c("POSIXct", 
"POSIXt")), Data = c("Start", "Exit", "Start", "Start", "Exit", 
"Start", "Exit", "Exit"), same = c(0, 0, 1, 0, 0, 0, 1, NA)), class = "data.frame", .Names = c("Time", 
"Data", "same"), row.names = c(NA, -8L))

第 2 列的理想情况是 Start 后跟 Exit

但是,在某些情况下,我可以使用 Start``StartExitStart 后跟 Exit``Exit。我试图通过这段代码来识别后续的启动和退出:

library(dplyr)
df <- df %>% mutate(same = ifelse(Data == lead(Data), 1, 0))

这为我提供了以下输出:

                  Time  Data same
1 2018-07-08 19:04:14 Start    0
2 2018-07-08 19:08:41  Exit    0
3 2018-01-31 12:31:41 Start    1
4 2018-01-31 13:50:50 Start    0
5 2018-01-31 13:52:31  Exit    0
6 2018-01-31 17:29:47 Start    0
7 2018-01-31 17:38:43  Exit    1
8 2018-01-31 17:38:43  Exit   NA

我想弄清楚如何识别 第二个 Start 如果序列中有两个 Startfirst Exit 如果一个序列中有两个Exit,标记为1。需要的输出如下:

                  Time  Data same
1 2018-07-08 19:04:14 Start    0
2 2018-07-08 19:08:41  Exit    0
3 2018-01-31 12:31:41 Start    0
4 2018-01-31 13:50:50 Start    1 #this should be one
5 2018-01-31 13:52:31  Exit    0
6 2018-01-31 17:29:47 Start    0
7 2018-01-31 17:38:43  Exit    1 #this should be one
8 2018-01-31 17:38:43  Exit    0

我尝试在 ifelse 中使用 if 条件,但它变得一团糟。

library(tidyverse)
df %>% 
  mutate( same2 = ifelse( Data == "Start" & lag( Data ) == Data, 1, 0 )) %>%
  mutate( same2 = ifelse( Data == "Exit" & lead( Data ) == Data, 1, same2 ) )

#                  Time  Data same same2
# 1 2018-07-08 15:34:14 Start    0    NA
# 2 2018-07-08 15:38:41  Exit    0     0
# 3 2018-01-31 08:01:41 Start    1     0
# 4 2018-01-31 09:20:50 Start    0     1
# 5 2018-01-31 09:22:31  Exit    0     0
# 6 2018-01-31 12:59:47 Start    0     0
# 7 2018-01-31 13:08:43  Exit    1     1
# 8 2018-01-31 13:08:43  Exit   NA    NA

我们可以使用 as.integer

将逻辑强制转换为二进制
df %>% 
    mutate(same2 = as.integer((Data == 'Start' & lag(Data) == Data)|
                              (Data == 'Exit' &  lead(Data) == Data)))