在R中的数据表中查找字符串的第一次迭代

Finding first iteration of a string in a datatable in R

我是 R 的新手,所以我想弄清楚如何才能做得更好。我有一个数据 table,它包含两列(Day 和 Sleepstatus)。我如何根据列 day 找到睡眠和清醒的第一次迭代,并改变另一列以指示人何时开始睡眠(第一行睡眠)和停止睡眠(第一行清醒)。剩余的睡眠时长,该列应显示 N.A.

Day SleepStatus
1 Sleeping
1 Sleeping
1 Sleeping
1 Awake
2 Sleeping
2 Sleeping
2 Sleeping
2 Awake

期望的输出

Day SleepStatus Final Status
1 Sleeping Start Sleep
1 Sleeping NA
1 Sleeping Stop Sleep
1 Awake NA
2 Sleeping Start Sleep
2 Sleeping NA
2 Sleeping Stop Sleep
2 Awake NA

这是一种可能的解决方案:

library(data.table)

dt <- data.table::data.table(
          Day = c(1L, 1L, 1L, 1L, 2L, 2L, 2L, 2L),
  SleepStatus = c("Sleeping","Sleeping","Sleeping",
                  "Awake","Sleeping","Sleeping","Sleeping","Awake")
)

dt[, `Final Status` := {ifelse(
  cumsum(SleepStatus != "Sleeping") != shift(cumsum(SleepStatus != "Sleeping"), fill = 0, type = "lag"),
  "Stop Sleep", "Start Sleep")}]
dt[, `Final Status` := {ifelse(
  `Final Status` == shift(`Final Status`, fill = "NA", type = "lag"),
  NA, `Final Status`)}]
dt
#>    Day SleepStatus Final Status
#> 1:   1    Sleeping  Start Sleep
#> 2:   1    Sleeping         <NA>
#> 3:   1    Sleeping         <NA>
#> 4:   1       Awake   Stop Sleep
#> 5:   2    Sleeping  Start Sleep
#> 6:   2    Sleeping         <NA>
#> 7:   2    Sleeping         <NA>
#> 8:   2       Awake   Stop Sleep

如果将代码分解成更小的块,代码会更有意义。我已经使用下面的 tidyverse 函数完成了此操作,因为我觉得它更容易理解,但如果您愿意,我可以将其更改为 data.table 语法。

library(data.table)

dt <- data.table::data.table(
          Day = c(1L, 1L, 1L, 1L, 2L, 2L, 2L, 2L),
  SleepStatus = c("Sleeping","Sleeping","Sleeping",
                  "Awake","Sleeping","Sleeping","Sleeping","Awake")
)

library(tidyverse)
df <- as.data.frame(dt)

# When the Sleepstatus is not "Sleeping", increment the variable by one
df2 <- df %>%
  mutate(Sleeping = cumsum(SleepStatus != "Sleeping"))
df2
#>   Day SleepStatus Sleeping
#> 1   1    Sleeping        0
#> 2   1    Sleeping        0
#> 3   1    Sleeping        0
#> 4   1       Awake        1
#> 5   2    Sleeping        1
#> 6   2    Sleeping        1
#> 7   2    Sleeping        1
#> 8   2       Awake        2

# If the previous value in "Sleeping" is different to the current value,
# add the "stop sleeping" flag (i.e. show when "Sleeping" changes)
df3 <- df2 %>%
  mutate(Sleep_label = ifelse(Sleeping != lag(Sleeping, default = 0), "Stop sleeping", "Start sleeping"))
df3
#>   Day SleepStatus Sleeping    Sleep_label
#> 1   1    Sleeping        0 Start sleeping
#> 2   1    Sleeping        0 Start sleeping
#> 3   1    Sleeping        0 Start sleeping
#> 4   1       Awake        1  Stop sleeping
#> 5   2    Sleeping        1 Start sleeping
#> 6   2    Sleeping        1 Start sleeping
#> 7   2    Sleeping        1 Start sleeping
#> 8   2       Awake        2  Stop sleeping

# Then, if the value in Sleep_label is equal to the previous label,
# change it to NA
df4 <- df3 %>%
  mutate(Final_status = ifelse(Sleep_label == lag(Sleep_label, default = "NA"), NA, Sleep_label))
df4
#>   Day SleepStatus Sleeping    Sleep_label   Final_status
#> 1   1    Sleeping        0 Start sleeping Start sleeping
#> 2   1    Sleeping        0 Start sleeping           <NA>
#> 3   1    Sleeping        0 Start sleeping           <NA>
#> 4   1       Awake        1  Stop sleeping  Stop sleeping
#> 5   2    Sleeping        1 Start sleeping Start sleeping
#> 6   2    Sleeping        1 Start sleeping           <NA>
#> 7   2    Sleeping        1 Start sleeping           <NA>
#> 8   2       Awake        2  Stop sleeping  Stop sleeping

reprex package (v2.0.1)

于 2022-05-20 创建

这有意义吗?还是我只是让事情变得更混乱了?

在 Base R 中,您可以执行以下操作:

x <- dt$SleepStatus
is.na(x) <- -cumsum(c(1,head(rle(x)$lengths,-1)))
dt$final_status <- c(Sleeping = 'Start Sleep', Awake = 'Stop Sleep')[x]
dt

  Day SleepStatus final_status
1   1    Sleeping  Start Sleep
2   1    Sleeping         <NA>
3   1    Sleeping         <NA>
4   1       Awake   Stop Sleep
5   2    Sleeping  Start Sleep
6   2    Sleeping         <NA>
7   2    Sleeping         <NA>
8   2       Awake   Stop Sleep