在dplyr中如何通过列值索引外部列表并生成时间格式列

我有一个存储分析机器设置的对象列表data.times，其中每个对象是包含时、分、秒的时间列表。另有一个directory.times数据框，包含data.times中对象的名称。需要为directory.times添加一列，当abif_type为"time"时，插入hh:mm:ss格式的时间。

示例数据 #

data.times <- list(RUNT.1 = list(hour = 17L, minute = 48L, second = 56L, hsecond = 0L), 
    RUNT.2 = list(hour = 19L, minute = 0L, second = 47L, hsecond = 0L), 
    RUNT.3 = list(hour = 18L, minute = 19L, second = 8L, hsecond = 0L), 
    RUNT.4 = list(hour = 19L, minute = 0L, second = 48L, hsecond = 0L))

directory.times数据框示例 #

directory.times <- structure(list(data_label = c("RUNT.1", "RUNT.2", "RUNT.3", "RUNT.4"
), abif_type = c("time", "time", "time", "time"), description = c("Run start time", 
"Run stop time", "Data Collection start time", "Data Collection stop time"
), value = list(RUNT.1 = list(hour = 17L, minute = 48L, second = 56L, 
    hsecond = 0L), RUNT.2 = list(hour = 19L, minute = 0L, second = 47L, 
    hsecond = 0L), RUNT.3 = list(hour = 18L, minute = 19L, second = 8L, 
    hsecond = 0L), RUNT.4 = list(hour = 19L, minute = 0L, second = 48L, 
    hsecond = 0L))), row.names = c(NA, -4L), class = "data.frame")

单个时间提取正常运行代码 #

library(lubridate)
hms(paste(data.times[["RUNT.1"]][["hour"]],
          data.times[["RUNT.1"]][["minute"]],
          data.times[["RUNT.1"]][["second"]],
          sep=":"))

报错代码及信息 #

尝试用dplyr实现时报错no such index at level 2，代码如下：

time.entry <- directory.times %>%
    mutate(time = case_when(abif_type == "time" ~
                              hms(paste(data.times[[paste0(data_label)]]["hour"],
                                        data.times[[paste0(data_label)]]["minute"],
                                        data.times[[paste0(data_label)]]["second"],
                                        sep="-"))))

报错原因是data_label是向量，直接用[[索引列表时无法逐行匹配，需要用逐行处理或向量化映射的方式。

方法1：使用rowwise()逐行处理 #

library(dplyr)
library(lubridate)

time.entry <- directory.times %>%
  rowwise() %>%
  mutate(
    time = if_else(
      abif_type == "time",
      hms(paste(data.times[[data_label]]$hour,
                data.times[[data_label]]$minute,
                data.times[[data_label]]$second,
                sep = ":")),
      NA_character_
    )
  ) %>%
  ungroup()

方法2：使用purrr::map2()向量化映射 #

library(dplyr)
library(lubridate)
library(purrr)

time.entry <- directory.times %>%
  mutate(
    time = map2_chr(data_label, abif_type, function(label, type) {
      if (type != "time") return(NA_character_)
      hms_str <- paste(data.times[[label]]$hour,
                       data.times[[label]]$minute,
                       data.times[[label]]$second,
                       sep = ":")
      as.character(hms(hms_str))
    })
  )

方法3：直接利用directory.times自带的value列 #

注意到directory.times的value列已经包含时间数据，无需额外从data.times提取，效率更高：

library(dplyr)
library(lubridate)

time.entry <- directory.times %>%
  rowwise() %>%
  mutate(
    time = if_else(
      abif_type == "time",
      hms(paste(value[[1]]$hour, value[[1]]$minute, value[[1]]$second, sep = ":")),
      NA_character_
    )
  ) %>%
  ungroup()

内容的提问来源于stack exchange，提问作者Mike