在 R 中拆分多个日期和时间变量并计算时间平均值

【问题标题】：Splitting multiple date and time variables & computing time average in R在 R 中拆分多个日期和时间变量并计算时间平均值
【发布时间】：2020-06-23 15:59:18
【问题描述】：

我有以下数据集，其中，我有此人的 ID、他们居住的地区和街道，以及他们将数据上传到服务器的最后日期/时间。变量“last_down_”包含一个人上传数据的最后日期/时间，并以这样的方式命名，以显示我下载数据的日期。例如，“last_upload_2020-06-12”表示我在 6 月 12 日从服务器下载了数据。

对于下面的数据集，我想在每个变量中（一次全部）溢出日期和时间，以便创建的新分隔变量名称为“last_date_（我的下载日期）” & "last_time_(我的下载日期)"

 district block id  last_upload_2020-06-12 last_upload_2020-06-13 last_upload_2020-06-14 last_upload_2020-06-15
    A   X   11  2020-02-06 11:53:19.0   2020-02-06 11:53:19.0   2020-02-06 11:53:19.0   2020-02-06 11:53:19.0
    A   X   12  2020-06-11 12:40:26.0   2020-06-11 12:40:26.0   2020-06-14 11:40:26.0   2020-06-15 18:50:26.0
    A   X                                                       2020-06-14 11:08:12.0   2020-06-14 11:08:12.0
    A   X   14  2020-06-12 11:31:07.0   2020-06-13 11:31:07.0   2020-06-14 17:37:07.0   2020-06-14 17:37:07.0
    A   Y   15  2020-06-10 12:45:48.0   2020-06-10 12:45:48.0   2020-06-10 12:45:48.0   2020-06-10 12:45:48.0
    A   Y   16  2020-04-04 02:26:57.0   2020-04-04 02:26:57.0   2020-04-04 02:26:57.0   2020-04-04 02:26:57.0
    A   Y   17  2020-03-31 08:10:03.0   2020-03-31 08:10:03.0   2020-03-31 08:10:03.0   2020-03-31 08:10:03.0
    A   Y   18  2020-05-30 12:08:15.0   2020-05-30 12:08:15.0   2020-05-30 12:08:15.0   2020-05-30 12:08:15.0
    A   Z   19  2020-04-09 15:21:52.0   2020-04-09 15:21:52.0   2020-04-09 15:21:52.0   2020-04-09 15:21:52.0
    A   Z   20  2020-05-30 17:42:33.0   2020-05-30 17:42:33.0   2020-05-30 17:42:33.0   2020-05-30 17:42:33.0
    A   Z   21  2020-04-12 14:23:29.0   2020-04-12 14:23:29.0   2020-04-12 14:23:29.0   2020-04-12 14:23:29.0
    A   Z   22  2020-05-13 23:18:19.0   2020-05-13 23:18:19.0   2020-05-13 23:18:19.0   2020-05-13 23:18:19.0
    A   X   23  2020-04-30 09:53:31.0   2020-04-30 09:53:31.0   2020-04-30 09:53:31.0   2020-04-30 09:53:31.0
    A   X   24  2020-06-10 10:28:59.0   2020-06-10 10:28:59.0   2020-06-10 10:28:59.0   2020-06-15 11:31:33.0
    A   Y   25              
    A   Y   26  2020-05-30 12:14:09.0   2020-05-30 12:14:09.0   2020-05-30 12:14:09.0   2020-05-30 12:14:09.0
    B   E   31              
    B   C   32  2020-06-12 16:43:23.0   2020-06-12 16:43:23.0   2020-06-12 16:43:23.0   2020-06-12 16:43:23.0
    B   C   33  2019-10-24 22:30:35.0   2019-10-24 22:30:35.0   2019-10-24 22:30:35.0   2019-10-24 22:30:35.0
    B   C   34  2020-06-09 15:38:18.0   2020-06-09 15:38:18.0   2020-06-09 15:38:18.0   2020-06-15 14:35:41.0
    B   C   35  2020-06-11 14:39:51.0   2020-06-11 14:39:51.0   2020-06-11 14:39:51.0   2020-06-11 14:39:51.0
    B   D   36  2020-06-12 11:53:15.0   2020-06-12 11:53:15.0   2020-06-12 11:53:15.0   2020-06-15 13:02:39.0
    B   D   37  2020-04-21 15:43:43.0   2020-04-21 15:43:43.0   2020-04-21 15:43:43.0   2020-04-21 15:43:43.0
    B   D   38  2020-05-13 04:07:17.0   2020-05-13 04:07:17.0   2020-05-13 04:07:17.0   2020-05-13 04:07:17.0
    B   E   39  2020-04-30 13:51:20.0   2020-04-30 13:51:20.0   2020-04-30 13:51:20.0   2020-04-30 13:51:20.0
    B   E   40  2020-05-12 16:51:01.0   2020-05-12 16:51:01.0   2020-05-12 16:51:01.0   2020-05-12 16:51:01.0
    B   E   41  2020-04-16 12:14:24.0   2020-04-16 12:14:24.0   2020-04-16 12:14:24.0   2020-04-16 12:14:24.0
    B   C   42  2018-06-07 15:12:18.0   2018-06-07 15:12:18.0   2018-06-07 15:12:18.0   2018-06-07 15:12:18.0
    B   D   43  2019-09-28 10:08:51.0   2019-09-28 10:08:51.0   2019-09-28 10:08:51.0   2019-09-28 10:08:51.0

注意：我的日期/时间变量是数字。

一旦我得到数据，我还想做以下事情：

  1. 在单独的列中获取“last_upload_2020-06-12”下所有观察的年份和月份。

  2. 同样，我的数据集中的最后一个日期是“last_upload_2020-06-15”。我可以自动化 R 选择最后一个日期，比如 Sys.Date()-1 吗？我将始终拥有比当前日期少一个日期的数据。

  3. 计算每个 ID 的平均上传时间，即一个人通常在什么时间左右将数据上传到服务器？平均值应基于唯一的时间值。

如果有人可以帮助解决这个问题将非常有帮助！

谢谢， 拉奇塔

【问题讨论】：

    标签： r datetime split lubridate posixct


    【解决方案1】：

    您可以在原始数据集中尝试以下代码。这可能会帮助您回答问题的介绍性、第一部分、第三部分以及最后的第二部分。

    library(lubridate)
library(tidyverse)
district <- c("A","A","B","B","C","C")
block <- c("X","Y","Z","X","Y","Z")
id <- c(11,11,12,12,13,13)
upload_dt <- ymd_hms(c("2020-06-13 11:31:07", 
                       "2020-04-12 14:23:29",
                       "2020-04-30 13:51:20",
                       "2020-06-12 11:53:15",
                       "2019-09-28 02:08:51",
                       "2020-04-12 16:23:29"))
df <- data.frame(district, block, id, upload_dt)
df <- df %>% 
    separate(upload_dt, into = c("date","time"),
             sep = " ", remove = F)
df$upload_date <- paste("last_upload_date_is", df$date)
df$upload_time <- paste("last_upload_time_is", df$time)
df <- df %>% 
    mutate(date_added = ymd(df$date),
           year_upload = year(date),
           month_upload = month(date)) 
df

    介绍性和第一部分问题的输出如下：-

    district block id           upload_dt       date     time                    upload_date
1        A     X 11 2020-06-13 11:31:07 2020-06-13 11:31:07 last_upload_date_is 2020-06-13
2        A     Y 11 2020-04-12 14:23:29 2020-04-12 14:23:29 last_upload_date_is 2020-04-12
3        B     Z 12 2020-04-30 13:51:20 2020-04-30 13:51:20 last_upload_date_is 2020-04-30
4        B     X 12 2020-06-12 11:53:15 2020-06-12 11:53:15 last_upload_date_is 2020-06-12
5        C     Y 13 2019-09-28 02:08:51 2019-09-28 02:08:51 last_upload_date_is 2019-09-28
6        C     Z 13 2020-04-12 16:23:29 2020-04-12 16:23:29 last_upload_date_is 2020-04-12
                   upload_time date_added year_upload month_upload
1 last_upload_time_is 11:31:07 2020-06-13        2020            6
2 last_upload_time_is 14:23:29 2020-04-12        2020            4
3 last_upload_time_is 13:51:20 2020-04-30        2020            4
4 last_upload_time_is 11:53:15 2020-06-12        2020            6
5 last_upload_time_is 02:08:51 2019-09-28        2019            9
6 last_upload_time_is 16:23:29 2020-04-12        2020            4

    问题第三部分的代码和输出如下：-

    df %>% group_by(id) %>% 
    summarise(avg_time_per_id = format(mean(strptime(time, "%H:%M:%S")), "%H:%M:%S")) %>% 
    ungroup()

# A tibble: 3 x 2
     id avg_time_per_id
  <dbl> <chr>          
1    11 12:57:18       
2    12 12:52:17       
3    13 09:16:10

    问题第二部分的代码和输出如下：-

    （注意我已经创建了一个新的数据框。）您可以将此解决方案应用于现有数据集。

    df <- data.frame(
    id = c(1:5),
    district = c("X","Y","X","Y","X"),
    block = c("A","A","B","B","C"),
    upload_date_a = paste0(rep("2020-06-13"), " ", rep("11:31:07")),
    upload_date_b = paste0(rep("2010-08-15"), " ", rep("02:45:27")),
    upload_date_c = paste0(rep("2000-10-30"), " ", rep("16:45:51")),
    stringsAsFactors = F
)
col_ind <- grep(x = names(df), pattern = "upload_date", value = T, ignore.case = T)
cols_list <- lapply(seq_along(col_ind), function(x){
    q1 <- do.call(rbind, strsplit(df[[col_ind[[x]]]], split = " "))
    q2 <- data.frame(q1, stringsAsFactors = F)
    i <- ncol(q2)
    colnames(q2) <- paste0(col_ind[[x]], c(1:i))
    return(q2)
    
}
)
df_new <- cbind(df[1:3], do.call(cbind, cols_list))
df_new


id district block upload_date_a1 upload_date_a2 upload_date_b1
1  1        X     A     2020-06-13       11:31:07     2010-08-15
2  2        Y     A     2020-06-13       11:31:07     2010-08-15
3  3        X     B     2020-06-13       11:31:07     2010-08-15
4  4        Y     B     2020-06-13       11:31:07     2010-08-15
5  5        X     C     2020-06-13       11:31:07     2010-08-15
  upload_date_b2 upload_date_c1 upload_date_c2
1       02:45:27     2000-10-30       16:45:51
2       02:45:27     2000-10-30       16:45:51
3       02:45:27     2000-10-30       16:45:51
4       02:45:27     2000-10-30       16:45:51
5       02:45:27     2000-10-30       16:45:51

    【讨论】：

    • 谢谢，斯里·斯雷什坦！但是，您能帮我自动拆分变量吗？在当前的虚拟数据集中，我有 4 个日期/时间列，但实际上有很多。我怎样才能一次选择它们并通过分离功能传递它们？
    • @Rachita 。我已经编辑了上面的答案。希望对您有所帮助。
    【解决方案2】：

    Df 看起来很复杂，我认为复制它可能会更好。 然后，我使用了一个函数来获取您想要的每一列，并根据需要将其分成 last_date 和 last_time。在函数内部，临时 DF 是 cbind 到在循环之外构建的 DF。该 DF 由未在循环中处理的列组成。 此循环的结果是所需的 DF。 [colnames 有点长]

    第二个任务的关键是将last_time转为小时，然后进行分组和汇总。

    我希望这是你想要的。

    我认为以此为基础，您可以处理 no2。

    有一些警告与 NA 相关。

    下面的reprex中有更多解释。

    library(tidyverse)


df <- read.table(text = '
district block id  last_upload_2020_06_12 last_upload_2020_06_13 last_upload_2020_06_14 last_upload_2020_06_15
"A"   "X"   11  "2020-02-06 11:53:19.0"   "2020-02-06 11:53:19.0"   "2020-02-06 11:53:19.0"   "2020-02-06 11:53:19.0"
"A"   "X"   12  "2020-06-11 12:40:26.0"   "2020-06-11 12:40:26.0"   "2020-06-14 11:40:26.0"   "2020-06-15 18:50:26.0"
"A"   "X"   NA  "NA"                      "NA"                      "2020-06-14 11:0812.0"    "2020-06-14 11:0812.0"
"A"   "X"   14  "2020-06-12 11:31:07.0"   "2020-06-13 11:31:07.0"   "2020-06-14 17:37:07.0"   "2020-06-14 17:37:07.0"
"A"   "Y"   15  "2020-06-10 12:45:48.0"   "2020-06-10 12:45:48.0"   "2020-06-10 12:45:48.0"   "2020-06-10 12:45:48.0"
"A"   "Y"   16  "2020-04-04 02:26:57.0"   "2020-04-04 02:26:57.0"   "2020-04-04 02:26:57.0"   "2020-04-04 02:26:57.0"
"A"   "Y"   17  "2020-03-31 08:10:03.0"   "2020-03-31 08:10:03.0"   "2020-03-31 08:10:03.0"   "2020-03-31 08:10:03.0"
"A"   "Y"   18  "2020-05-30 12:08:15.0"   "2020-05-30 12:08:15.0"   "2020-05-30 12:08:15.0"   "2020-05-30 12:08:15.0"
"A"   "Z"   19  "2020-04-09 15:21:52.0"   "2020-04-09 15:21:52.0"   "2020-04-09 15:21:52.0"   "2020-04-09 15:21:52.0"
"A"   "Z"   20  "2020-05-30 17:42:33.0"   "2020-05-30 17:42:33.0"   "2020-05-30 17:42:33.0"   "2020-05-30 17:42:33.0"
"A"   "Z"   21  "2020-04-12 14:23:29.0"   "2020-04-12 14:23:29.0"   "2020-04-12 14:23:29.0"   "2020-04-12 14:23:29.0"
"A"   "Z"   22  "2020-05-13 23:18:19.0"   "2020-05-13 23:18:19.0"   "2020-05-13 23:18:19.0"   "2020-05-13 23:18:19.0"
"A"   "X"   23  "2020-04-30 09:53:31.0"   "2020-04-30 09:53:31.0"   "2020-04-30 09:53:31.0"   "2020-04-30 09:53:31.0"
"A"   "X"   24  "2020-06-10 10:28:59.0"   "2020-06-10 10:28:59.0"   "2020-06-10 10:28:59.0"   "2020-06-15 11:31:33.0"
"A"   "Y"   25  " "                        ""                     ""                         ""
"A"   "Y"   26  "2020-05-3012:14:09.0"   "2020-05-30 12:14:09.0"   "2020-05-30 12:14:09.0"   "2020-05-30 12:14:09.0"
"B"   "E"   31  ""            ""                      ""                        ""          ""
"B"   "C"   32  "2020-06-1 16:43:23.0"   "2020-06-12 16:43:23.0"   "2020-06-12 16:43:23.0"   "2020-06-12 16:43:23.0"
"B"   "C"   33  "2019-10-24 22:30:35.0"   "2019-10-24 22:30:35.0"   "2019-10-24 22:30:35.0"   "2019-10-24 22:30:35.0"
"B"   "C"   34  "2020-06-09 15:38:18.0"   "2020-06-09 15:38:18.0"   "2020-06-09 15:38:18.0"   "2020-06-15 14:35:41.0"
"B"   "C"   35  "2020-06-11 14:39:51.0"   "2020-06-11 14:39:51.0"   "2020-06-11 14:39:51.0"   "2020-06-11 14:39:51.0"
"B"   "D"   36  "2020-06-12 11:53:15.0"   "2020-06-12 11:53:15.0"   "2020-06-12 11:53:15.0"   "2020-06-15 13:02:39.0"
"B"   "D"   37  "2020-04-21 15:43:43.0"   "2020-04-21 15:43:43.0"   "2020-04-21 15:43:43.0"   "2020-04-21 15:43:43.0"
"B"   "D"   38  "2020-05-13 04:07:17.0"   "2020-05-13 04:07:17.0"   "2020-05-13 04:07:17.0"   "2020-05-13 04:07:17.0"
"B"   "E"   39  "2020-04-30 13:51:20.0"   "2020-04-30 13:51:20.0"   "2020-04-30 13:51:20.0"   "2020-04-30 13:51:20.0"
"B"   "E"   40  "2020-05-12 16:51:01.0"   "2020-05-12 16:51:01.0"   "2020-05-12 16:51:01.0"   "2020-05-12 16:51:01.0"
"B"   "E"   41  "2020-04-16 12:14:24.0"   "2020-04-16 12:14:24.0"   "2020-04-16 12:14:24.0"   "2020-04-16 12:14:24.0"
"B"   "C"   42  "2018-06-07 15:12:18.0"   "2018-06-07 15:12:18.0"   "2018-06-07 15:12:18.0"   "2018-06-07 15:12:18.0"
"B"   "D"   43  "2019-09-28 10:08:51.0"   "2019-09-28 10:08:51.0"   "2019-09-28 10:08:51.0"   "2019-09-28 10:08:51.0"
           ', header =T)


# TASK: create for each column which contains 'last_upload' new columns 
# with date and time

# get the colnames of the cols to be split or separated
ccl <- colnames(df %>% select(last_upload_2020_06_12:last_upload_2020_06_15))

# create new DF with first 3 columns, to which other columns are bound in
# the following function
dff <- df %>% select(district:id) 

# function to separate each col in ccl to _date and _time
for (cl in ccl) {
  tmp <- separate(df,
    col = cl, sep = " ",
    into = c(paste0(cl, "_date"), paste0(cl, "_time"))
  ) %>%
    select(contains("_date") | contains("_time"))
  dff <- cbind(dff, tmp)
}


dff %>% head()
#>   district block id last_upload_2020_06_12_date last_upload_2020_06_12_time
#> 1        A     X 11                  2020-02-06                  11:53:19.0
#> 2        A     X 12                  2020-06-11                  12:40:26.0
#> 3        A     X NA                        <NA>                        <NA>
#> 4        A     X 14                  2020-06-12                  11:31:07.0
#> 5        A     Y 15                  2020-06-10                  12:45:48.0
#> 6        A     Y 16                  2020-04-04                  02:26:57.0
#>   last_upload_2020_06_13_date last_upload_2020_06_13_time
#> 1                  2020-02-06                  11:53:19.0
#> 2                  2020-06-11                  12:40:26.0
#> 3                        <NA>                        <NA>
#> 4                  2020-06-13                  11:31:07.0
#> 5                  2020-06-10                  12:45:48.0
#> 6                  2020-04-04                  02:26:57.0
#>   last_upload_2020_06_14_date last_upload_2020_06_14_time
#> 1                  2020-02-06                  11:53:19.0
#> 2                  2020-06-14                  11:40:26.0
#> 3                  2020-06-14                   11:0812.0
#> 4                  2020-06-14                  17:37:07.0
#> 5                  2020-06-10                  12:45:48.0
#> 6                  2020-04-04                  02:26:57.0
#>   last_upload_2020_06_15_date last_upload_2020_06_15_time
#> 1                  2020-02-06                  11:53:19.0
#> 2                  2020-06-15                  18:50:26.0
#> 3                  2020-06-14                   11:0812.0
#> 4                  2020-06-14                  17:37:07.0
#> 5                  2020-06-10                  12:45:48.0
#> 6                  2020-04-04                  02:26:57.0

# TASK: Calculate the average time of a day each id does a download
# new DF from original brought into long format
# split the date/time into last_date and last_time
ddf <- df %>%
  pivot_longer(cols = last_upload_2020_06_12:last_upload_2020_06_15) %>%
  separate(col = value, sep = ' ', into = c('last_date', 'last_time')) %>%
  mutate(last_date = lubridate::ymd(last_date), last_time= lubridate::hms(last_time))


# calculating the mean hour of the day at which each id does a 
# download, by calculating last_time to hours (of the day) and
# after grouping build mean hour
ddf %>% 
  mutate(hours = as.numeric(lubridate::hms(last_time), unit = 'hour')) %>%
  group_by(id) %>% summarise(meanHourOfTheDay = mean(hours, na.rm = T))

#> # A tibble: 29 x 2
#>       id meanHourOfTheDay
#>    <int>            <dbl>
#>  1    11            11.9 
#>  2    12            14.0 
#>  3    14            14.6 
#>  4    15            12.8 
#>  5    16             2.45
#>  6    17             8.17
#>  7    18            12.1 
#>  8    19            15.4 
#>  9    20            17.7 
#> 10    21            14.4 
#> # … with 19 more rows

    【讨论】：

    • 感谢简化的解决方案。使用 for 循环将数据拆分为多列要简单得多。
    • 非常感谢，MarBlo！你正确理解了我的问题。
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