将POSIXct对象问题嵌入到R函数中。

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英文:

POSIXct objects problem into a R function

问题

抱歉，您遇到了一个时间格式的问题。要解决这个问题，您可以在函数内部确保将时间列（Datetime）的数据类型转换为 POSIXct 类型。请在函数的以下部分添加这一行代码，将 Datetime 列转换为 POSIXct 类型：

  1. df <- df %>%
  2.   mutate(Datetime = as.POSIXct(Datetime))

这样，您的时间列就会被正确地转换为 POSIXct 类型，然后您的函数应该能够正常运行，不再出现时间格式的错误。希望这可以帮助您解决问题。如果您还有其他问题，请随时提出。

英文:

I have the following part of a large dataset:

  1. structure(list(year = c(2020, 2020, 2020, 2020, 2020, 2020, 2020, 
  2. 2020, 2020, 2020, 2020, 2020, 2020, 2020, 2020, 2020, 2020, 2020, 
  3. 2020, 2020, 2020, 2020, 2020, 2020, 2020, 2020, 2020, 2020, 2020, 
  4. 2020, 2020, 2020, 2020, 2020, 2020, 2020, 2020, 2020, 2020, 2020, 
  5. 2020, 2020, 2020, 2020, 2020, 2020, 2020, 2020, 2020, 2020, 2020, 
  6. 2020, 2020), month = c(11, 11, 11, 11, 11, 11, 11, 11, 11, 11, 
  7. 11, 11, 11, 11, 11, 11, 11, 11, 11, 11, 11, 11, 11, 11, 11, 11, 
  8. 11, 11, 11, 11, 11, 11, 11, 11, 11, 11, 11, 11, 11, 11, 11, 11, 
  9. 11, 11, 11, 11, 11, 11, 11, 11, 11, 11, 11), day = c(4, 4, 4, 
  10. 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 
  11. 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 
  12. 4, 4, 4, 4, 4, 4, 4, 4), hour = c(11, 11, 11, 11, 11, 11, 11, 
  13. 11, 11, 11, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 19, 19, 
  14. 19, 19, 19, 19, 19, 19, 19, 19, 19, 20, 20, 20, 20, 20, 20, 20, 
  15. 20, 20, 20, 23, 23, 23, 23, 23, 23, 23, 23, 23, 23, 23), min = c(3, 
  16. 3, 4, 4, 4, 4, 4, 4, 4, 4, 34, 34, 34, 34, 34, 34, 34, 34, 34, 
  17. 34, 34, 39, 39, 39, 39, 39, 39, 39, 39, 39, 39, 39, 8, 8, 8, 
  18. 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 9, 9, 9, 9, 38, 38, 38, 38), seg = c(58.91, 
  19. 59.91, 0.91, 1.91, 2.91, 3.91, 4.91, 5.91, 6.91, 7.91, 28.85, 
  20. 29.85, 30.85, 31.85, 32.85, 33.85, 34.85, 35.85, 36.85, 37.85, 
  21. 38.85, 0.21, 1.21, 2.21, 3.21, 4.21, 5.21, 6.21, 7.21, 8.21, 
  22. 9.21, 10.21, 6.52, 7.52, 8.52, 9.52, 10.52, 11.52, 12.52, 13.52, 
  23. 14.52, 15.52, 57.28, 58.28, 59.28, 0.28, 1.28, 2.28, 3.28, 41.21, 
  24. 42.21, 43.21, 44.21), Conductivity = c(56.257, 56.258, 56.256, 
  25. 56.256, 56.267, 56.265, 56.265, 56.265, 56.262, 56.259, 56.291, 
  26. 56.29, 56.291, 56.291, 56.292, 56.291, 56.289, 56.291, 56.29, 
  27. 56.291, 56.291, 56.147, 56.146, 56.148, 56.148, 56.149, 56.149, 
  28. 56.148, 56.148, 56.147, 56.149, 56.149, 56.146, 56.143, 56.143, 
  29. 56.144, 56.144, 56.144, 56.145, 56.144, 56.147, 56.146, 55.956, 
  30. 55.956, 55.955, 55.954, 55.952, 55.956, 55.957, 56.087, 56.086, 
  31. 56.087, 56.085), Temperature = c(26.251, 26.252, 26.243, 26.249, 
  32. 26.259, 26.259, 26.258, 26.26, 26.261, 26.251, 26.329, 26.329, 
  33. 26.329, 26.33, 26.33, 26.33, 26.33, 26.33, 26.329, 26.329, 26.329, 
  34. 26.226, 26.225, 26.226, 26.226, 26.226, 26.226, 26.226, 26.226, 
  35. 26.226, 26.227, 26.226, 26.228, 26.227, 26.227, 26.227, 26.227, 
  36. 26.227, 26.227, 26.227, 26.226, 26.228, 26.109, 26.109, 26.109, 
  37. 26.109, 26.106, 26.109, 26.11, 26.311, 26.311, 26.31, 26.309), 
  38.     Pression = c(0.78, 0.82, 0.82, 0.82, 0.8, 0.8, 0.76, 0.78, 
  39.     0.76, 0.77, 0.78, 0.78, 0.78, 0.8, 0.81, 0.8, 0.79, 0.79, 
  40.     0.77, 0.78, 0.8, 0.77, 0.78, 0.79, 0.8, 0.8, 0.78, 0.78, 
  41.     0.81, 0.78, 0.77, 0.79, 0.8, 0.8, 0.79, 0.8, 0.79, 0.79, 
  42.     0.79, 0.76, 0.75, 0.76, 0.77, 0.77, 0.77, 0.74, 0.75, 0.76, 
  43.     0.74, 0.78, 0.77, 0.79, 0.77), Chlorophyll = c(0.08, 0.08, 
  44.     0.08, 0.08, 0.08, 0.07, 0.08, 0.08, 0.08, 0.09, 0.12, 0.14, 
  45.     0.13, 0.14, 0.13, 0.13, 0.14, 0.13, 0.12, 0.14, 0.12, 0.11, 
  46.     0.11, 0.11, 0.1, 0.12, 0.11, 0.12, 0.11, 0.12, 0.13, 0.11, 
  47.     0.11, 0.1, 0.1, 0.09, 0.1, 0.1, 0.11, 0.1, 0.1, 0.1, 0.11, 
  48.     0.1, 0.09, 0.09, 0.09, 0.1, 0.1, 0.12, 0.12, 0.1, 0.1), Turbidity = c(1.63, 
  49.     1.64, 1.64, 1.64, 1.64, 1.64, 1.64, 1.64, 1.64, 1.64, 1.71, 
  50.     1.71, 1.71, 1.71, 1.71, 1.71, 1.71, 1.72, 1.72, 1.72, 1.72, 
  51.     1.51, 1.48, 1.45, 1.43, 1.39, 1.4, 1.4, 1.4, 1.38, 1.36, 
  52.     1.34, 2.72, 2.61, 2.5, 2.4, 2.34, 2.26, 2.18, 2.09, 2.02, 
  53.     2.02, 0.99, 1, 1, 1, 1, 1, 1, 0.79, 0.8, 0.8, 0.8), OD = c(229.4, 
  54.     229.4, 229.4, 229.4, 229.4, 229.4, 229.4, 229.3, 229.3, 229.3, 
  55.     224.2, 224.2, 224.2, 224.2, 224.2, 224.2, 224.2, 224.2, 224.2, 
  56.     224.2, 224.2, 229.1, 229.1, 229.1, 229.1, 229.1, 229.2, 229.2, 
  57.     229.2, 229.2, 229.2, 229.2, 228.5, 228.6, 228.6, 228.6, 228.5, 
  58.     228.5, 228.5, 228.5, 228.5, 228.5, 217.9, 217.7, 217.7, 217.7, 
  59.     217.7, 217.7, 217.7, 233.6, 233.5, 233.5, 233.5), Battery = c(7.95, 
  60.     7.95, 7.97, 7.95, 7.97, 7.95, 7.95, 7.95, 7.95, 7.95, 7.95, 
  61.     7.97, 7.95, 7.95, 7.95, 7.95, 7.95, 7.97, 7.95, 7.95, 7.95, 
  62.     7.95, 7.95, 7.95, 7.97, 7.95, 7.97, 7.95, 7.95, 7.95, 7.97, 
  63.     7.95, 7.95, 7.95, 7.95, 7.95, 7.95, 7.95, 7.95, 7.95, 7.97, 
  64.     7.95, 7.95, 7.97, 7.95, 7.95, 7.95, 7.95, 7.95, 7.95, 7.95, 
  65.     7.95, 7.95), Salinity = c(36.36765, 36.36758, 36.37325, 36.36849, 
  66.     36.3686, 36.36714, 36.36795, 36.36636, 36.36338, 36.36911, 
  67.     36.33071, 36.32998, 36.33071, 36.32991, 36.33064, 36.32991, 
  68.     36.32846, 36.32991, 36.32998, 36.33071, 36.3307, 36.30722, 
  69.     36.30728, 36.30795, 36.30794, 36.30867, 36.30868, 36.30795, 
  70.     36.30794, 36.30722, 36.30789, 36.30868, 36.3049, 36.30351, 
  71.     36.30351, 36.30424, 36.30424, 36.30424, 36.30497, 36.30425, 
  72.     36.30723, 36.30492, 36.26034, 36.26034, 36.25961, 36.25889, 
  73.     36.2598, 36.26035, 36.26029, 36.19642, 36.19569, 36.1972, 
  74.     36.19654)), row.names = c(NA, -53L), class = c(&quot;tbl_df&quot;, 
  75. &quot;tbl&quot;, &quot;data.frame&quot;))

And I build the following function:

  1. plot_amb2 &lt;- function(data, amb_var, time_scale, 
  2.                       xlab, ylab, ybreak, xbreak, 
  3.                       cor, leglab, quant_range) {
  4.   
  5.   df &lt;- tab_ambi_laps %&gt;% 
  6.     mutate(date_time = make_datetime(year, month, day, hour, min, seg)) %&gt;% 
  7.     mutate(cycle_rounded = round_date(date_time, &quot;30 min&quot;)) %&gt;% 
  8.     filter(!!ensym(amb_var) &gt;= quantile(!!ensym(amb_var), 
  9.                                         quant_range[1], na.rm = T) &amp; 
  10.              !!ensym(amb_var) &lt;= quantile(!!ensym(amb_var), 
  11.                                           quant_range[2], na.rm = T))
  12.   
  13.   cycles &lt;- tibble(cycle_rounded = seq(ymd_hm(&quot;2020-11-01 0:00&quot;),
  14.                                        ymd_hm(&quot;2022-06-10 23:59&quot;),
  15.                                        by = &quot;30 min&quot;))
  16.   
  17.   df &lt;- full_join(df, cycles)
  18.   
  19.   df &lt;- df %&gt;%
  20.     mutate(cycle_rounded = as.POSIXct(cycle_rounded)) %&gt;%
  21.     group_by(Datetime = round_date(cycle_rounded, time_scale)) %&gt;% 
  22.     summarise_at(vars(!!ensym(amb_var)), mean, na.rm = TRUE) %&gt;%
  23.     mutate(date_ = if_else(is.na(Datetime), NA, format(Datetime, &quot;%Y-%m-%d&quot;) %&gt;% ymd()),
  24.            time_ = if_else(is.na(Datetime), NA, format(Datetime, &quot;%H:%M:%S&quot;) %&gt;% as_hms),
  25.            amb_var := if_else(is.nan(!!ensym(amb_var)), NA, !!ensym(amb_var))) %&gt;% 
  26.     drop_na()
  27.   
  28.   lims2 &lt;- as.Date(c(&quot;2020-11-01&quot;,&quot;2022-06-10&quot;))
  29.   
  30.   ggplot(data = df) + 
  31.     geom_raster(aes(x = date_, y = time_, fill = !!ensym(amb_var))) + 
  32.     scale_fill_gradientn(colours = cor, na.value = &quot;white&quot;) +
  33.     scale_y_datetime(date_breaks = ybreak, date_labels = &quot;%H:%M&quot;) +
  34.     scale_x_datetime(date_labels = &quot;%m-%Y&quot;, breaks = xbreak, 
  35.                      limits = as.POSIXct(lims2)) +
  36.     labs(y = ylab, x = xlab, fill = leglab) +
  37.     theme_test() +
  38.     theme(axis.text.x = element_text(size = 14, angle = 45, 
  39.                                      vjust = 1, hjust = 1,
  40.                                      color = &quot;black&quot;, face = &quot;bold&quot;),
  41.           axis.text.y = element_text(size = 14,
  42.                                      color = &quot;black&quot;, face = &quot;bold&quot;), 
  43.           axis.title.x = element_text(size = 16,
  44.                                       color = &quot;black&quot;, face = &quot;bold&quot;), 
  45.           axis.title.y = element_text(size = 16,
  46.                                       color = &quot;black&quot;, face = &quot;bold&quot;),
  47.           legend.text = element_text(size = 14, face = &quot;bold&quot;),
  48.           legend.title = element_text(size = 16, face = &quot;bold&quot;))
  49. }

However, when I run this function on the following way:

  1. plot_amb2(data = tab_ambi_laps, amb_var = Chlorophyll, 
  2.           time_scale = &quot;1 hour&quot;, quant_range = c(0.15, 0.95),
  3.           xlab = &quot;Data&quot;, ylab = &quot;Hora&quot;, leglab = &quot;Clorofila\n(\u00B5g/L)&quot;,
  4.           ybreak = &quot;3 hours&quot;, xbreak = &quot;2 month&quot;,
  5.           cor = c(&quot;lightblue&quot;, &quot;yellow&quot;, &quot;darkred&quot;))

I have this error message:

> Error: Invalid input: time_trans works with objects of class POSIXct
> only

Apparently, I have a problem with the time format of my function.
When I run these parts of codes separately from my function its works well. But I need to incorporate everything into a function.

How can I solve this?

Thanks of any help

答案1

得分: 1

只返回翻译好的部分：

如果我将time_定义为一个datetime，那么这段代码就可以正常工作：

  1. time_ = if_else(is.na(Datetime), NA, 
  2.                make_datetime(hour = hour(Datetime), 
  3.                              min = minute(Datetime), 
  4.                              sec = second(Datetime))),

并且让x轴使用Date数据：

  1. scale_y_datetime(date_breaks = ybreak, date_labels = "%H:%M") +
  2. scale_x_date(date_labels = "%m-%Y", breaks = xbreak, limits = lims2)

可以推测，如果使用完整的数据，这看起来会更有趣。这个示例只反映了跨多年的一个日期的一小部分。

英文:

Works for me if I define time_ as a datetime:

  1. time_ = if_else(is.na(Datetime), NA, 
  2.                            make_datetime(hour = hour(Datetime), 
  3.                                          min = minute(Datetime), 
  4.                                          sec = second(Datetime))),

and make the x axis use Date data:

  1. scale_y_datetime(date_breaks = ybreak, date_labels = &quot;%H:%M&quot;) +
  2. scale_x_date(date_labels = &quot;%m-%Y&quot;, breaks = xbreak,
  3.                  limits = lims2) +

Presumably this looks more interesting with your full data. The sample only reflects a small slice of one date across multiple years.

将POSIXct对象问题嵌入到R函数中。

huangapple
  • 本文由 发表于 2023年6月22日 02:10:07
  • 转载请务必保留本文链接：https://go.coder-hub.com/76526055.html
  • ggplot2
  • lubridate
  • posixct
  • r
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