如何将小时作为浮子转换为r的12h时间戳

发布于 2025-02-10 11:49:02 字数 400 浏览 2 评论 0原文

我有一个数据框，其中包含8.6，9.32，9.79等值之类的值。这些值代表小时的小时和分数。例如，8.6小时等于08:36 AM，而9.79小时相当于09:47 AM。我正在寻找一种简单的方法，将这些浮子转换为时间戳。一种方法可能是解析浮子的分数部分，然后乘以60，以获取分钟数，然后转换为时间戳。但是，我想知道是否有人使用lubridate将Float转换为时间戳。

df <- structure(list(start_time = c(8.6, 9.32, 9.79, 10.09, 10.63, 
8.64)), row.names = c(NA, 6L), class = "data.frame")

原文

I have a dataframe that contains values such as 8.6, 9.32, 9.79, etc. These values represent hours and fractions of hours. For instance, 8.6 hours equates to 08:36am, and 9.79 hours equates to 09:47am. I'm looking for an easy way to convert these floats into a timestamp. One approach could be to parse the fractional portion of the float and multiply by 60 to get the number of minutes, then convert to a timestamp. However, I'm wondering if anyone has a better solution using something like lubridate to convert the float to a timestamp.

df <- structure(list(start_time = c(8.6, 9.32, 9.79, 10.09, 10.63, 
8.64)), row.names = c(NA, 6L), class = "data.frame")

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在风中等你 2025-02-17 11:49:02

df$start_time_hms = hms::as_hms(df$start_time*60*60)

  start_time  start_time_hms
1       8.60 08:36:00.000000
2       9.32 09:19:12.000000
3       9.79 09:47:24.000000
4      10.09 10:05:24.000000
5      10.63 10:37:48.000000
6       8.64 08:38:24.000000

或者如果您想要Posixct DateTime：

df$start_time_hms = lubridate::ymd_hms("2022-06-24 00:00:00") + df$start_time*60*60


  start_time      start_time_hms
1       8.60 2022-06-24 08:36:00
2       9.32 2022-06-24 09:19:12
3       9.79 2022-06-24 09:47:24
4      10.09 2022-06-24 10:05:24
5      10.63 2022-06-24 10:37:48
6       8.64 2022-06-24 08:38:24

df$start_time_hms = hms::as_hms(df$start_time*60*60)

  start_time  start_time_hms
1       8.60 08:36:00.000000
2       9.32 09:19:12.000000
3       9.79 09:47:24.000000
4      10.09 10:05:24.000000
5      10.63 10:37:48.000000
6       8.64 08:38:24.000000

Or if you want a POSIXct datetime:

df$start_time_hms = lubridate::ymd_hms("2022-06-24 00:00:00") + df$start_time*60*60


  start_time      start_time_hms
1       8.60 2022-06-24 08:36:00
2       9.32 2022-06-24 09:19:12
3       9.79 2022-06-24 09:47:24
4      10.09 2022-06-24 10:05:24
5      10.63 2022-06-24 10:37:48
6       8.64 2022-06-24 08:38:24

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