问题

你的代码中有一些问题，首先，在你的代码中，似乎有一些HTML实体编码（如<和>）和HTML实体引用（如"），需要替换成正常的R代码。另外，你提到输出中的日期和时间列出现了NA值，这可能需要进一步检查你的数据或代码。

以下是你提供的代码的翻译（已经替换了HTML实体编码和引用），以及你所期望的输出格式：

# 从`Date/Time`列中分离日期和时间
x <- df %>%
  separate(`Date/Time`, into = c("Date", "Time"), sep = "T")

# 按每15行分组并计算每组的均值
mn <- x %>%
  group_by(group = as.integer(gl(n(), 15, n()))) %>%
  summarise_all(funs(mean))

# 将结果写入CSV文件
write.csv(mn, 'C:/Users/Alexia/Desktop/Test/15row.csv')

至于你期望的输出格式，你需要将日期和时间合并为一个列，并确保时间格式为"hh:mm:ss+00"，列名为"time_sp"。你可以使用R的代码来实现这一点，以下是一个示例：

# 合并日期和时间列
mn$Time_sp <- paste(mn$Date, "00:00:00+00", sep=" ")

# 删除原来的Date列
mn <- mn %>% select(-Date)

# 重新排列列的顺序，如果需要
mn <- mn %>% select(Time_sp, everything())

这将产生你期望的输出格式，其中日期和时间合并为一列，格式为"hh:mm:ss+00"，列名为"time_sp"。

英文:

I have calculated every 15rows mean of my data (.txt file) using the code given below in R.

x &lt;- df %&gt;% separate(`Date/Time`, into = c(&quot;Date&quot;, &quot;Time&quot;), sep = &quot;T&quot;)
mn &lt;- x %&gt;%
 group_by(group = as.integer(gl(n(), 15, n()))) %&gt;%
 summarise_all(funs(mean))
write.csv(min, &#39;C:/Users/Alexia/Desktop/Test/15row.csv&#39;)

I am getting the output successfully but in the Date and Time columns output, I am receiving NA. However, the desired output should be as follows: (Date and Time should be in one column with time written as hh:mm:ss+00 and name of column needs to be time_sp)

Time_sp   Col1   Col2   Col3....
2021-01-01 00:00:00+00  12  36  56
2021-01-01 00:15:00+00  34  54  43
2021-01-01 00:30:00+00  24  23  21
2021-01-01 00:45:00+00  12  36  56
2021-01-01 01:00:00+00  34  54  43
2021-01-01 01:15:00+00  24  23  21
2021-01-01 01:30:00+00  12  36  43
2021-01-01 01:45:00+00  12  36  34
2021-01-01 02:00:00+00  12  36  34
.
.
.

My input data (.txt) is of per minute and has Date and Time in following manner:

Date/Time   Col1   Col2   Col3....
2021-01-01T00:00:00  20  12  34...
2021-01-01T00:01:00  .....
2021-01-01T00:02:00  .....
2021-01-01T00:03:00  .....
2021-01-01T01:04:00  .....
2021-01-01T01:05:00  .....
2021-01-01T01:05:00  .....
2021-01-01T01:07:00  .....
2021-01-01T02:08:00  .....

The output of dput(df) is as follows:

structure(list(`Date/Time` = c(&quot;2021-03-01T00:01:00&quot;, &quot;2021-03- 
01T00:02:00&quot;, &quot;2021-03-01T00:03:00&quot;, &quot;2021-03-01T00:04:00&quot;, &quot;2021-03- 
01T00:05:00&quot;, &quot;2021-03-01T00:06:00&quot;, &quot;2021-03-01T00:07:00&quot;, &quot;2021-03- 
01T00:08:00&quot;, &quot;2021-03-01T00:09:00&quot;, &quot;2021-03-01T00:10:00&quot;, &quot;2021-03- 
01T00:11:00&quot;, &quot;2021-03-01T00:12:00&quot;, &quot;2021-03-01T00:13:00&quot;, &quot;2021-03- 
01T00:14:00&quot;, &quot;2021-03-01T00:15:00&quot;, &quot;2021-03-01T00:16:00&quot;, &quot;2021-03- 
01T00:17:00&quot;, &quot;2021-03-01T00:18:00&quot;, &quot;2021-03-01T00:19:00&quot;, &quot;2021-03- 
01T00:20:00&quot;, &quot;2021-03-01T00:21:00&quot;, &quot;2021-03-01T00:22:00&quot;, &quot;2021-03- 
01T00:23:00&quot;, &quot;2021-03-01T00:24:00&quot;, &quot;2021-03-01T00:25:00&quot;, &quot;2021-03- 
01T00:26:00&quot;, &quot;2021-03-01T00:27:00&quot;, &quot;2021-03-01T00:28:00&quot;, &quot;2021-03- 
01T00:29:00&quot;, &quot;2021-03-01T00:30:00&quot;), `XY [XY]` = c(0.990641, 0.990641, 
0.990641, 0.990641, 0.990641, 0.990641, 0.990641, 0.990641, 0.990641, 
0.990641, 0.990641, 0.990641, 0.990641, 0.990641, 0.990641, 0.990641, 
0.990641, 0.990641, 0.990641, 0.990641, 0.990641, 0.990641, 0.990641, 
0.990641, 0.990641, 0.990641, 0.990641, 0.990641, 0.990641, 0.990641), 
`C1 [CC]` = c(257L, 257L, 257L, 257L, 257L, 257L, 257L, 
257L, 257L, 257L, 257L, 257L, 257L, 257L, 257L, 257L, 257L, 
257L, 257L, 257L, 257L, 257L, 257L, 257L, 257L, 257L, 257L, 
257L, 257L, 257L), Cc = c(0L, 0L, 0L, 0L, 0L, 0L, 0L, 
0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 
0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L), `C2 [C2]` = c(285L, 
285L, 285L, 285L, 285L, 285L, 285L, 285L, 285L, 285L, 285L, 
285L, 285L, 285L, 285L, 285L, 285L, 285L, 285L, 285L, 285L, 
285L, 285L, 285L, 285L, 285L, 285L, 285L, 285L, 285L), Dc = c(0L, 
0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 
0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L), 
`C3 [C2]` = c(255L, 255L, 255L, 255L, 255L, 255L, 255L, 
255L, 255L, 255L, 255L, 255L, 255L, 255L, 255L, 255L, 255L, 
255L, 255L, 255L, 255L, 255L, 255L, 255L, 255L, 255L, 255L, 
255L, 255L, 255L), Ac = c(0L, 0L, 0L, 0L, 0L, 0L, 0L, 
0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 
0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L), C4 = c(0.463735, 0.463735, 
0.463735, 0.463735, 0.463735, 0.463735, 0.463735, 0.463735, 
0.463735, 0.463735, 0.463735, 0.463735, 0.463735, 0.463735, 
0.463735, 0.463735, 0.463735, 0.463735, 0.463735, 0.463735, 
0.463735, 0.463735, 0.463735, 0.463735, 0.463735, 0.463735, 
0.463735, 0.463735, 0.463735, 0.463735), `C5 [h]` = c(1013L, 
1013L, 1013L, 1013L, 1013L, 1013L, 1013L, 1013L, 1013L, 1013L, 
1013L, 1013L, 1013L, 1013L, 1013L, 1013L, 1013L, 1013L, 1013L, 
1013L, 1013L, 1013L, 1013L, 1013L, 1013L, 1013L, 1013L, 1013L, 
1013L, 1013L), `C6 [%]` = c(43L, 43L, 43L, 43L, 43L, 43L, 
43L, 43L, 43L, 43L, 43L, 43L, 43L, 43L, 43L, 43L, 43L, 43L, 
43L, 43L, 43L, 43L, 43L, 43L, 43L, 43L, 43L, 43L, 43L, 43L
), `C7 [E2]` = c(390L, 390L, 390L, 390L, 390L, 390L, 
390L, 390L, 390L, 390L, 390L, 390L, 390L, 390L, 390L, 390L, 
390L, 390L, 390L, 390L, 390L, 390L, 390L, 390L, 390L, 390L, 
390L, 390L, 390L, 390L), Jc = c(0L, 0L, 0L, 0L, 0L, 0L, 
0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 
0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L), `D [S]` = c(62.3716, 
62.3716, 62.3716, 62.3716, 62.3716, 62.3716, 62.3716, 62.3716, 
62.3716, 62.3716, 62.3716, 62.3716, 62.3716, 62.3716, 62.3716, 
62.3716, 62.3716, 62.3716, 62.3716, 62.3716, 62.3716, 62.3716, 
62.3716, 62.3716, 62.3716, 62.3716, 62.3716, 62.3716, 62.3716, 
62.3716), `Sw [S2]` = c(1392.95, 1392.95, 1392.95, 1392.95, 
1392.95, 1392.95, 1392.95, 1392.95, 1392.95, 1392.95, 1392.95, 
1392.95, 1392.95, 1392.95, 1392.95, 1392.95, 1392.95, 1392.95, 
1392.95, 1392.95, 1392.95, 1392.95, 1392.95, 1392.95, 1392.95, 
1392.95, 1392.95, 1392.95, 1392.95, 1392.95), `SW [Q2]` = 
c(389.164, 389.164, 389.164, 389.164, 389.164, 389.164, 389.164, 
389.164, 389.164, 389.164, 389.164, 389.164, 389.164, 389.164, 389.164, 
389.164, 389.164, 389.164, 389.164, 389.164, 389.164, 389.164, 
389.164, 389.164, 389.164, 389.164, 389.164, 389.164, 389.164, 
389.164), `OA [H2]` = c(646.61, 646.61, 646.61, 646.61, 
646.61, 646.61, 646.61, 646.61, 646.61, 646.61, 646.61, 646.61, 
646.61, 646.61, 646.61, 646.61, 646.61, 646.61, 646.61, 646.61, 
646.61, 646.61, 646.61, 646.61, 646.61, 646.61, 646.61, 646.61, 
646.61, 646.61), `T2 [C]` = c(3.7, 3.7, 3.7, 3.7, 3.7, 
3.7, 3.7, 3.7, 3.7, 3.7, 3.7, 3.7, 3.7, 3.7, 3.7, 3.7, 3.7, 
3.7, 3.7, 3.7, 3.7, 3.7, 3.7, 3.7, 3.7, 3.7, 3.7, 3.7, 3.7, 
3.7), Lc = c(0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 
0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 
0L, 0L, 0L, 0L)), class = &quot;data.frame&quot;, row.names = c(NA, 
-30L))

答案1

得分: 1

以下是您要翻译的内容：

问题出在您的数据类型上 - 您需要告诉 R 您正在使用日期和时间，否则它会假定您正在使用字符向量。如果您对字符向量取平均值，它会产生 NA。

尝试一下：

library(lubridate)
x <- df %>% separate(`Date/Time`, into = c("Date", "Time"), sep = "T")

min <- x %>%
  as_tibble() %>%
  group_by(group = as.integer(gl(n(), 15, n()))) %>%
  mutate(
    # 将日期列转换为日期数据类型
    Date = lubridate::ymd(Date), 
    # 将时间列转换为周期数据类型（HMS）。然后，
    # 将其转换为秒数
    Time = period_to_seconds(hms(Time))
  ) %>%
  summarise(across(everything(), mean)) %>%
  # 将时间列从秒数转换回周期数据类型（HMS）。如果您希望平均值以秒为单位，请省略此行
  mutate(Time = seconds_to_period(Time))

min
#> # A tibble: 2 × 21
#>   group Date       Time     `XY [XY]` `C1 [CC]`    Cc `C2 [C2]`
#>   <int> <date>     <Period>     <dbl>     <dbl> <dbl>     <dbl>
#> 1     1 2021-03-01 8M 0S        0.991       257     0       285
#> 2     2 2021-03-01 23M 0S       0.991       257     0       285
#> # ℹ 14 more variables: Dc <dbl>, `C3 [C2]` <dbl>, Ac <dbl>,
#> #   C4 <dbl>, `C5 [h]` <dbl>, `C6 [%]` <dbl>, `C7 [E2]` <dbl>,
#> #   Jc <dbl>, `D [S]` <dbl>, `Sw [S2]` <dbl>, `SW [Q2]` <dbl>,
#> #   `OA [H2]` <dbl>, `T2 [C]` <dbl>, Lc <dbl>

write.csv(min, 'C:/Users/Alexia/Desktop/Test/15row.csv')

英文:

The issue is with your data types - you need to tell R that you are using dates and times, or it will assume you are using character vectors. If you take the mean of a character vector, it produces NA.

Try:

library(lubridate)
x &lt;- df %&gt;% separate(`Date/Time`, into = c(&quot;Date&quot;, &quot;Time&quot;), sep = &quot;T&quot;)

min &lt;- x %&gt;% 
  as_tibble() %&gt;%
  group_by(group = as.integer(gl(n(), 15, n()))) %&gt;%
  mutate(
    # Convert Date column into the Date datatype
    Date = lubridate::ymd(Date), 
    # Convert Time column into the Period datatype (HMS). Then, 
    # change this to number of seconds
    Time = period_to_seconds(hms(Time))
  ) %&gt;%
  summarise(across(everything(), mean)) %&gt;% 
  # Convert Time column from number of seconds 
  # back into the Period datatype (HMS). Omit this line
  # if you&#39;d prefer to have the average in seconds
  mutate(Time = seconds_to_period(Time))

min
#&gt; # A tibble: 2 &#215; 21
#&gt;   group Date       Time     `XY [XY]` `C1 [CC]`    Cc `C2 [C2]`
#&gt;   &lt;int&gt; &lt;date&gt;     &lt;Period&gt;     &lt;dbl&gt;     &lt;dbl&gt; &lt;dbl&gt;     &lt;dbl&gt;
#&gt; 1     1 2021-03-01 8M 0S        0.991       257     0       285
#&gt; 2     2 2021-03-01 23M 0S       0.991       257     0       285
#&gt; # ℹ 14 more variables: Dc &lt;dbl&gt;, `C3 [C2]` &lt;dbl&gt;, Ac &lt;dbl&gt;,
#&gt; #   C4 &lt;dbl&gt;, `C5 [h]` &lt;dbl&gt;, `C6 [%]` &lt;dbl&gt;, `C7 [E2]` &lt;dbl&gt;,
#&gt; #   Jc &lt;dbl&gt;, `D [S]` &lt;dbl&gt;, `Sw [S2]` &lt;dbl&gt;, `SW [Q2]` &lt;dbl&gt;,
#&gt; #   `OA [H2]` &lt;dbl&gt;, `T2 [C]` &lt;dbl&gt;, Lc &lt;dbl&gt;

write.csv(min, &#39;C:/Users/Alexia/Desktop/Test/15row.csv&#39;)

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