我有一个名为DF的数据框,列有时间和日期 . 我想根据这些列中的值对DF进行子集化 . 对于日期,我有DATES中的日期列表,并且在DATES中存在DF $ Date的DF行的子集 . 当时,我想从00:04:00到00:06:00进行分组 . 我不知道怎么做后者 .
理想情况下,我希望通过指定范围来进行子集,如00:04:00到00:06:00,以及指定前瞻的起点和分钟,如00:04:00和3分钟(两种不同的方法) . 我想这一切都归结为制作一段时间,并将这样的序列放在一个单独的向量中,然后我可以用它进行匹配 .
请注意,这只是一个简短的可重现的例子 . 我正在寻找一种通用的方法来实现这一点,因为在实践中我想要分配大量的时间 . 另请注意,即使在此示例中只有一个匹配日期,实际上会有多个匹配日期跨越多年 . 这就是为什么我认为不可能使用 POSIXlt
来制作时间序列 . 非常感谢你 .
#DF looks like this:
# DateTime XXX Time Date
#1371 2016-04-25 00:08:00 14 00:08:00 2016-04-25
#1372 2016-04-25 00:07:00 13 00:07:00 2016-04-25
#1373 2016-04-25 00:06:00 14 00:06:00 2016-04-25
#1374 2016-04-25 00:05:00 3 00:05:00 2016-04-25
#1375 2016-04-25 00:04:00 2 00:04:00 2016-04-25
#1376 2016-04-25 00:03:00 4 00:03:00 2016-04-25
#1377 2016-04-25 00:02:00 6 00:02:00 2016-04-25
#1387 2016-04-24 23:52:00 41 23:52:00 2016-04-24
#1388 2016-04-24 23:51:00 93 23:51:00 2016-04-24
#1389 2016-04-24 23:50:00 53 23:50:00 2016-04-24
#Code for DF, DATES, and to subset DF based on DATES
DF <- structure(list(DateTime = structure(list(sec = c(0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0), min = c(8L, 7L, 6L, 5L, 4L, 3L, 2L, 1L, 0L, 59L, 58L, 57L, 56L, 55L, 54L, 53L, 52L, 51L, 50L), hour = c(0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 23L, 23L, 23L, 23L, 23L, 23L, 23L, 23L, 23L, 23L), mday = c(25L, 25L, 25L, 25L, 25L, 25L, 25L, 25L, 25L, 24L, 24L, 24L, 24L, 24L, 24L, 24L, 24L, 24L, 24L), mon = c(3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L), year = c(116L, 116L, 116L, 116L, 116L, 116L, 116L, 116L, 116L, 116L, 116L, 116L, 116L, 116L, 116L, 116L, 116L, 116L, 116L), wday = c(1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L), yday = c(115L, 115L, 115L, 115L, 115L, 115L, 115L, 115L, 115L, 114L, 114L, 114L, 114L, 114L, 114L, 114L, 114L, 114L, 114L), isdst = c(1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L), zone = c("EDT", "EDT", "EDT", "EDT", "EDT", "EDT", "EDT", "EDT", "EDT", "EDT", "EDT", "EDT", "EDT", "EDT", "EDT", "EDT", "EDT", "EDT", "EDT"), gmtoff = c(NA_integer_, NA_integer_, NA_integer_, NA_integer_, NA_integer_, NA_integer_, NA_integer_, NA_integer_, NA_integer_, NA_integer_, NA_integer_, NA_integer_, NA_integer_, NA_integer_, NA_integer_, NA_integer_, NA_integer_, NA_integer_, NA_integer_)), .Names = c("sec", "min", "hour", "mday", "mon", "year", "wday", "yday", "isdst", "zone", "gmtoff"), class = c("POSIXlt", "POSIXt")), Open = c(14, 13, 14, 3, 2, 4, 6, 4, 15, 15, 23, 24, 33, 14, 65, 54, 41, 93, 53), Time = c("00:08:00", "00:07:00", "00:06:00", "00:05:00", "00:04:00", "00:03:00", "00:02:00", "00:01:00", "00:00:00", "23:59:00", "23:58:00", "23:57:00", "23:56:00", "23:55:00", "23:54:00", "23:53:00", "23:52:00", "23:51:00", "23:50:00"), Date = structure(c(16916, 16916, 16916, 16916, 16916, 16916, 16916, 16916, 16916, 16915, 16915, 16915, 16915, 16915, 16915, 16915, 16915, 16915, 16915), class = "Date")), .Names = c("DateTime", "XXX", "Time", "Date"), row.names = c("1371", "1372", "1373", "1374", "1375", "1376", "1377", "1378", "1379", "1380", "1381", "1382", "1383", "1384", "1385", "1386", "1387", "1388", "1389"), class = "data.frame")
DATES <- structure(c(12431, 12432, 10445, 10480, 11487, 12494, 12501, 12508, 13115, 13522, 14529, 15536, 16916, 16935), class = "Date")
SELEC <- DF[DF$Date %in% DATES,]
#Result of subsetting by Date:
# DateTime XXX Time Date
# 1371 2016-04-25 00:08:00 14 00:08:00 2016-04-25
# 1372 2016-04-25 00:07:00 13 00:07:00 2016-04-25
# 1373 2016-04-25 00:06:00 14 00:06:00 2016-04-25
# 1374 2016-04-25 00:05:00 3 00:05:00 2016-04-25
# 1375 2016-04-25 00:04:00 2 00:04:00 2016-04-25
# 1376 2016-04-25 00:03:00 4 00:03:00 2016-04-25
# 1377 2016-04-25 00:02:00 6 00:02:00 2016-04-25
# 1378 2016-04-25 00:01:00 4 00:01:00 2016-04-25
# 1379 2016-04-25 00:00:00 15 00:00:00 2016-04-25
#How the final product would look like if using a larger data base spanning many years:
# DateTime XXX Time Date
#2016-04-25 00:06:00 13 00:06:00 2016-04-25
#2016-04-25 00:05:00 14 00:05:00 2016-04-25
#2016-04-25 00:04:00 3 00:04:00 2016-04-25
#2014-03-11 00:06:00 94 00:06:00 2014-03-11
#2014-03-11 00:05:00 6 00:05:00 2014-03-11
#2014-03-11 00:04:00 14 00:04:00 2014-03-11
#2011-08-06 00:06:00 13 00:06:00 2011-08-06
#2011-08-06 00:05:00 19 00:05:00 2011-08-06
#2011-08-06 00:04:00 41 00:04:00 2011-08-06
2 回答
Possibility 1: lexicographic comparison
如果所有时间值都存储为具有相同分隔符的零填充24小时字符串,例如
%H:%M:%S
,则可以使用词典比较来应用过滤器 .当然,词典解决方案并不理想,因为它们不适合基于时间的数学运算,例如加,减,乘,除等 .
更好的解决方案涉及将时间值转换为数字类型,该数字类型将持续时间编码为显式或未指定基准时间的偏移量 . 这是流行的日期/时间库编码类型的方式,例如boost date_time表示C,Joda-Time表示Java,POSIXct表示difftime,而Rubridate表示R.
Possibility 2: manual numerics
可以自己解析字符串以构造表示持续时间的数字,并使用数字比较来应用过滤器 .
Possibility 3: POSIXt
我们可以生成POSIXt(即POSIXct或POSIXlt)值的向量,并对这些向量使用矢量化比较 .
Possibility 4: difftime
R中唯一的内置持续时间数据类型是difftime类型,使用它可能有点挑剔 . 但对于这个问题,它相当简单 .
Possibility 5: lubridate
lubridate包被广泛认为是R中日期/时间处理的最佳包 . 它提供表示常规持续时间的持续时间类型,以及允许表示各种不规则时间单位计数的句点类型 . 从历史上看,日期/时间库有时会失败,因为他们缺乏对不规则时段和常规时段之间区别的认识 .
在以下解决方案中,
hms()
调用返回句点类型的实例,因此我们实际上是在比较不同的时间单位 . 顺便提一下,关于单独时间单位的实际存储,lubridate的设计是将秒值存储为双向量的实际有效载荷,其余单位(分钟,小时,天,月和年)作为属性存储物体 .这个怎么样?
这使: