使用Lubridate进行第二轮操作

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英文:

Round Second with Lubridate

问题

我正在使用R，需要将时间从09:14:59四舍五入到09:15:00，我有多条记录如下。

我尝试使用lubridate包中的round_date函数：

  1. temperature = worksheet_temperature %>%
  2.   mutate(measurement_date = round_date(measurement_date, unit=second(as_datetime(as_date(measurement_date, origin = "1899-12-30")))))

但是我收到以下错误：

  1. Error: `mutate()`列`measurement_date`出现问题。
  2. i `measurement_date = round_date(...)`。
  3. x 无效的周期名称：4
  4. 运行 `rlang::last_error()` 以查看错误发生的位置。
  5. 另外：警告信息：
  6. `mutate()`列`measurement_date`出现问题。
  7. i `measurement_date = round_date(...)`。
  8. i 单位参数长度大于1。取第一个元素

我该如何解决这个问题？

英文:

使用Lubridate进行第二轮操作

I'm using R, I need to round the seconds referring to the hours 09:14:59 to 09:15:00 I have several records like this.

I tried to use the round_date from the lubridate package

  1. temperature = worksheet_temperature %>%
  2.   mutate(measurement_date = round_date(measurement_date, unit=second(as_datetime(as_date(measurement_date, origin = "1899-12-30")))))

but I get the following error:

  1. Error: Problem with `mutate()` column `measurement_date`.
  2. i `measurement_date = round_date(...)`.
  3. x Invalid period name: 4
  4. Run `rlang::last_error()` to see where the error occurred.
  5. In addition: Warning message:
  6. Problem with `mutate()` column `measurement_date`.
  7. i `measurement_date = round_date(...)`.
  8. i Unit argument longer than 1. Taking first element

how can i solve this?

答案1

得分: 2

根据您对输出应该是什么样子的解释，您希望将时间舍入到最近的分钟。在round_date()函数的unit参数中，只需指定"minute"：

  1. lubridate::round_date(
  2.   lubridate::ymd_hms("2022-02-18 09:14:59"),
  3.   unit = "minute"
  4. )

这将返回："2022-02-18 09:15:00 UTC"。

英文:

From your explanation of what the output should look like, you want to round to the nearest minute. In the unit argument of round_date() simply specify "minute":

  1. lubridate::round_date(
  2.   lubridate::ymd_hms("2022-02-18 09:14:59"),
  3.   unit = "minute"
  4. )

this returns: "2022-02-18 09:15:00 UTC"

答案2

得分: 1

根据您的描述，“将秒数四舍五入到... 09:14:59 到 09:15:00”，我假设您想要将时间四舍五入到最接近的分钟。以下是如何做到这一点的代码示例：

  1. df <- structure(list(measurement_date = c("2022-02-18 09:14:59", "2022-02-19 08:29:59", 
  2. "2022-02-20 16:40:00", "2022-02-21 09:00:00", "2022-02-22 09:04:59", 
  3. "2022-02-23 09:59:59", "2022-02-24 10:15:00", "2022-02-25 08:35:59", 
  4. "2022-02-26 09:30:00", "2022-02-27 15:00:00", "2022-02-28 08:32:59", 
  5. "2022-03-01 09:35:00", "2022-01-19 08:26:59")), class = "data.frame", row.names = c(NA, 
  6. -13L))
  8. library(tidyverse)
  10. mutate(df, measurement_date = round_date(as_datetime(measurement_date), unit = "minute"))
  12. # 输出:
  13.       measurement_date
  14. 1  2022-02-18 09:15:00
  15. 2  2022-02-19 08:30:00
  16. 3  2022-02-20 16:40:00
  17. 4  2022-02-21 09:00:00
  18. 5  2022-02-22 09:05:00
  19. 6  2022-02-23 10:00:00
  20. 7  2022-02-24 10:15:00
  21. 8  2022-02-25 08:36:00
  22. 9  2022-02-26 09:30:00
  23. 10 2022-02-27 15:00:00
  24. 11 2022-02-28 08:33:00
  25. 12 2022-03-01 09:35:00
  26. 13 2022-01-19 08:27:00

还有许多其他四舍五入日期时间的方法。请查看此处的round_date文档。

英文:

Based on your description "round the seconds... 09:14:59 to 09:15:00" I assume you are wanting to round to the nearest minute. This is how you can do this:

  1. df &lt;- structure(list(measurement_date = c(&quot;2022-02-18 09:14:59&quot;, &quot;2022-02-19 08:29:59&quot;, 
  2. &quot;2022-02-20 16:40:00&quot;, &quot;2022-02-21 09:00:00&quot;, &quot;2022-02-22 09:04:59&quot;, 
  3. &quot;2022-02-23 09:59:59&quot;, &quot;2022-02-24 10:15:00&quot;, &quot;2022-02-25 08:35:59&quot;, 
  4. &quot;2022-02-26 09:30:00&quot;, &quot;2022-02-27 15:00:00&quot;, &quot;2022-02-28 08:32:59&quot;, 
  5. &quot;2022-03-01 09:35:00&quot;, &quot;2022-01-19 08:26:59&quot;)), class = &quot;data.frame&quot;, row.names = c(NA, 
  6. -13L))
  8. library(tidyverse)
  10. mutate(df, measurement_date = round_date(as_datetime(measurement_date), unit = &quot;minute&quot;))
  12. # Output:
  13.       measurement_date
  14. 1  2022-02-18 09:15:00
  15. 2  2022-02-19 08:30:00
  16. 3  2022-02-20 16:40:00
  17. 4  2022-02-21 09:00:00
  18. 5  2022-02-22 09:05:00
  19. 6  2022-02-23 10:00:00
  20. 7  2022-02-24 10:15:00
  21. 8  2022-02-25 08:36:00
  22. 9  2022-02-26 09:30:00
  23. 10 2022-02-27 15:00:00
  24. 11 2022-02-28 08:33:00
  25. 12 2022-03-01 09:35:00
  26. 13 2022-01-19 08:27:00

There are a variety of other ways of rounding datetimes though. See the documentation for round_date here.

答案3

得分: 0

如果您想将所有日期都舍入到最接近的边界，您可以使用 ceiling_date()。请参阅 r4ds。

  1. df <- structure(list(measurement_date = c(
  2.   "2022-02-18 09:14:59", "2022-02-19 08:29:59",
  3.   "2022-02-20 16:40:00", "2022-02-21 09:00:00", "2022-02-22 09:04:59",
  4.   "2022-02-23 09:59:59", "2022-02-24 10:15:00", "2022-02-25 08:35:59",
  5.   "2022-02-26 09:30:00", "2022-02-27 15:00:00", "2022-02-28 08:32:59",
  6.   "2022-03-01 09:35:00", "2022-01-19 08:26:59"
  7. )), class = "data.frame", row.names = c(
  8.   NA,
  9.   -13L
  10. ))
  12. library(tidyverse)
  14. df %>%
  15.   mutate(measurement_date_rounded = ceiling_date(
  16.     x = ymd_hms(measurement_date),
  17.     unit = "minute"
  18.   ))
  19. #>       measurement_date measurement_date_rounded
  20. #> 1  2022-02-18 09:14:59      2022-02-18 09:15:00
  21. #> 2  2022-02-19 08:29:59      2022-02-19 08:30:00
  22. #> 3  2022-02-20 16:40:00      2022-02-20 16:40:00
  23. #> 4  2022-02-21 09:00:00      2022-02-21 09:00:00
  24. #> 5  2022-02-22 09:04:59      2022-02-22 09:05:00
  25. #> 6  2022-02-23 09:59:59      2022-02-23 10:00:00
  26. #> 7  2022-02-24 10:15:00      2022-02-24 10:15:00
  27. #> 8  2022-02-25 08:35:59      2022-02-25 08:36:00
  28. #> 9  2022-02-26 09:30:00      2022-02-26 09:30:00
  29. #> 10 2022-02-27 15:00:00      2022-02-27 15:00:00
  30. #> 11 2022-02-28 08:32:59      2022-02-28 08:33:00
  31. #> 12 2022-03-01 09:35:00      2022-03-01 09:35:00
  32. #> 13 2022-01-19 08:26:59      2022-01-19 08:27:00
英文:

If you want to have all the dates rounded up to the nearest boundary, you can use ceiling_date(). See r4ds.

  1. df &lt;- structure(list(measurement_date = c(
  2. &quot;2022-02-18 09:14:59&quot;, &quot;2022-02-19 08:29:59&quot;,
  3. &quot;2022-02-20 16:40:00&quot;, &quot;2022-02-21 09:00:00&quot;, &quot;2022-02-22 09:04:59&quot;,
  4. &quot;2022-02-23 09:59:59&quot;, &quot;2022-02-24 10:15:00&quot;, &quot;2022-02-25 08:35:59&quot;,
  5. &quot;2022-02-26 09:30:00&quot;, &quot;2022-02-27 15:00:00&quot;, &quot;2022-02-28 08:32:59&quot;,
  6. &quot;2022-03-01 09:35:00&quot;, &quot;2022-01-19 08:26:59&quot;
  7. )), class = &quot;data.frame&quot;, row.names = c(
  8. NA,
  9. -13L
  10. ))
  11. library(tidyverse)
  12. df |&gt;
  13. mutate(measurement_date_rounded = ceiling_date(
  14. x = ymd_hms(measurement_date),
  15. unit = &quot;minute&quot;
  16. ))
  17. #&gt;       measurement_date measurement_date_rounded
  18. #&gt; 1  2022-02-18 09:14:59      2022-02-18 09:15:00
  19. #&gt; 2  2022-02-19 08:29:59      2022-02-19 08:30:00
  20. #&gt; 3  2022-02-20 16:40:00      2022-02-20 16:40:00
  21. #&gt; 4  2022-02-21 09:00:00      2022-02-21 09:00:00
  22. #&gt; 5  2022-02-22 09:04:59      2022-02-22 09:05:00
  23. #&gt; 6  2022-02-23 09:59:59      2022-02-23 10:00:00
  24. #&gt; 7  2022-02-24 10:15:00      2022-02-24 10:15:00
  25. #&gt; 8  2022-02-25 08:35:59      2022-02-25 08:36:00
  26. #&gt; 9  2022-02-26 09:30:00      2022-02-26 09:30:00
  27. #&gt; 10 2022-02-27 15:00:00      2022-02-27 15:00:00
  28. #&gt; 11 2022-02-28 08:32:59      2022-02-28 08:33:00
  29. #&gt; 12 2022-03-01 09:35:00      2022-03-01 09:35:00
  30. #&gt; 13 2022-01-19 08:26:59      2022-01-19 08:27:00

huangapple
  • 本文由 发表于 2023年7月23日 23:44:10
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  • dplyr
  • lubridate
  • r
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