假设我有:
R> str(data)
'data.frame': 4 obs. of 2 variables:
$ datetime: Factor w/ 4 levels "2011-01-05 09:30:00.001",..: 1 2 3 4
$ price : num 18.3 18.3 18.3 18.3
R> data
datetime price
1 2011-01-05 09:30:00.001 18.31
2 2011-01-05 09:30:00.321 18.33
3 2011-01-05 09:30:01.511 18.33
4 2011-01-05 09:30:02.192 18.34
当我尝试将其加载到xts
对象中时,时间戳会被巧妙地改变:
R> x <- xts(data[-1], as.POSIXct(strptime(data$datetime, '%Y-%m-%d %H:%M:%OS')))
R> str(x)
An ‘xts’ object from 2011-01-05 09:30:00.000 to 2011-01-05 09:30:02.191 containing:
Data: num [1:4, 1] 18.3 18.3 18.3 18.3
- attr(*, "dimnames")=List of 2
..$ : NULL
..$ : chr "price"
Indexed by objects of class: [POSIXct,POSIXt] TZ:
xts Attributes:
NULL
R> x
price
2011-01-05 09:30:00.000 18.31
2011-01-05 09:30:00.321 18.33
2011-01-05 09:30:01.510 18.33
2011-01-05 09:30:02.191 18.34
您会注意到时间戳已被更改。第一个条目现在位于09:30:00.000
,而不是原始数据所说的09:30:00.001
。第三和第四行也不正确。
造成这种情况的原因是什么?我做了一些根本错误的事吗?我尝试了各种咒语将数据转换为xts
对象,它们似乎都表现出这种行为。
编辑:添加sessionInfo()
R> sessionInfo()
R version 2.13.1 (2011-07-08)
Platform: x86_64-unknown-linux-gnu (64-bit)
locale:
[1] LC_CTYPE=en_US.UTF-8 LC_NUMERIC=C LC_TIME=en_US.UTF-8
[4] LC_COLLATE=C LC_MONETARY=C LC_MESSAGES=en_US.UTF-8
[7] LC_PAPER=en_US.UTF-8 LC_NAME=C LC_ADDRESS=C
[10] LC_TELEPHONE=C LC_MEASUREMENT=en_US.UTF-8 LC_IDENTIFICATION=C
attached base packages:
[1] stats graphics grDevices utils datasets methods base
other attached packages:
[1] xts_0.8-2 zoo_1.7-4
loaded via a namespace (and not attached):
[1] grid_2.13.1 lattice_0.19-30 tools_2.13.1
编辑2:如果我将源数据修改为微秒精度,如下所示:
datetime,price
2011-01-05 09:30:00.001000,18.31
2011-01-05 09:30:00.321000,18.33
2011-01-05 09:30:01.511000,18.33
2011-01-05 09:30:02.192000,18.34
然后加载它,所以我有:
R> test
datetime price
1 2011-01-05 09:30:00.001000 18.31
2 2011-01-05 09:30:00.321000 18.33
3 2011-01-05 09:30:01.511000 18.33
4 2011-01-05 09:30:02.192000 18.34
然后,最后,将其转换为xts
对象并设置索引格式:
R> x <- xts(test[,-1], as.POSIXct(strptime(test$datetime, '%Y-%m-%d %H:%M:%OS')))
R> indexFormat(x) <- '%Y-%m-%d %H:%M:%OS6'
R> x
[,1]
2011-01-05 09:30:00.000999 18.31
2011-01-05 09:30:00.321000 18.33
2011-01-05 09:30:01.510999 18.33
2011-01-05 09:30:02.191999 18.34
您也可以看到效果。我希望增加额外的精确度会有所帮助,但不幸的是它没有。
编辑3:请参阅@DWin's answer,了解重现此行为的端到端测试用例。
编辑4:此行为似乎不是以毫秒为导向的。以下显示了微秒分辨率时间戳的相同更改结果。如果我将输入数据更改为:
R> data
datetime price
1 2011-01-05 09:30:00.001001 18.31
2 2011-01-05 09:30:00.321001 18.33
3 2011-01-05 09:30:01.511001 18.33
4 2011-01-05 09:30:02.192005 18.34
然后创建一个xts
对象:
R> x <- xts(data[-1],
as.POSIXct(strptime(as.character(data$datetime), '%Y-%m-%d %H:%M:%OS')))
R> indexFormat(x) <- '%Y-%m-%d %H:%M:%OS6'
R> x
price
2011-01-05 09:30:00.001000 18.31
2011-01-05 09:30:00.321001 18.33
2011-01-05 09:30:01.511001 18.33
2011-01-05 09:30:02.192004 18.34
编辑5:这似乎是一个浮点精度问题。观察:
R> t <- as.POSIXct("2011-01-05 09:30:00.001001")
R> t
[1] "2011-01-05 09:30:00.001 CST"
R> as.numeric(t)
[1] 1294241400.0010008812
这表现出错误行为,并且与编辑4中的示例一致。但是,使用未显示错误的示例:
R> t <- as.POSIXct("2011-01-05 09:30:01.511001")
R> t
[1] "2011-01-05 09:30:01.511001 CST"
R> as.numeric(t)
[1] 1294241401.5110011101
似乎xts
或某些基础组件正在向下舍入而不是最近?
答案 0 :(得分:3)
你有时间在一个因素:
R> str(data)
'data.frame': 4 obs. of 2 variables:
$ datetime: Factor w/ 4 levels "2011-01-05 09:30:00.001",..: 1 2 3 4
[...]
这不是最佳起点。你需要转换为角色。因此,而不是
x <- xts(data[-1], as.POSIXct(strptime(data$datetime, '%Y-%m-%d %H:%M:%OS')))
我建议
x <- xts(data[-1],
order.by=as.POSIXct(strptime(as.character(data$datetime),
'%Y-%m-%d %H:%M:%OS')))
根据我的经验,围绕一个因素的as.character()
是至关重要的。因素对于建模非常有用,但是当你不小心阅读数据时它们会有点麻烦。使用stringsAsFactor=FALSE
对您有利,并在数据导入时避免使用它们。
编辑:所以这似乎指向strptime / strftime实现。为了使事情变得更有趣,R从操作系统中获取其中的一部分并在src/main/datetime.c
中重新实现一些。
另外,请注意您可以添加到时间变量的最小 epsilon ,并且仍然将R视为相等。在我的64位Linux系统上,这发生了10 ^ -7:
R> sapply(seq(1, 8), FUN=function(x) identical(now, now+1/10^x))
[1] FALSE FALSE FALSE FALSE FALSE FALSE TRUE TRUE
R>
答案 1 :(得分:2)
我发布这个只是为了让那些想要探索它的人可以有一个可重复的例子,它表明它不仅仅发生在OP的系统上。 as.character
因素不会阻止它发生。
dat <- read.table(textConnection(" datetime\tprice
1\t2011-01-05 09:30:00.001\t18.31
2\t2011-01-05 09:30:00.321\t18.33
3\t2011-01-05 09:30:01.511\t18.33
4\t2011-01-05 09:30:02.192\t18.34"), header =TRUE, sep="\t")
as.character(dat$datetime)
#[1] "2011-01-05 09:30:00.001" "2011-01-05 09:30:00.321" "2011-01-05 09:30:01.511"
#[4] "2011-01-05 09:30:02.192"
strptime(as.character(dat$datetime), '%Y-%m-%d %H:%M:%OS')
#[1] "2011-01-05 09:30:00" "2011-01-05 09:30:00" "2011-01-05 09:30:01"
#[4] "2011-01-05 09:30:02"
as.POSIXct(strptime(as.character(dat$datetime),
'%Y-%m-%d %H:%M:%OS'))
#[1] "2011-01-05 09:30:00 EST" "2011-01-05 09:30:00 EST" "2011-01-05 09:30:01 EST"
#[4] "2011-01-05 09:30:02 EST"
x <- xts(dat[-1],
order.by=as.POSIXct(strptime(as.character(dat$datetime),
'%Y-%m-%d %H:%M:%OS')))
x
#### price
2011-01-05 09:30:00 18.31
2011-01-05 09:30:00 18.33
2011-01-05 09:30:01 18.33
2011-01-05 09:30:02 18.34
indexFormat(x) <- '%Y-%m-%d %H:%M:%OS6'
x
price
2011-01-05 09:30:00.000999 18.31
2011-01-05 09:30:00.321000 18.33
2011-01-05 09:30:01.510999 18.33
2011-01-05 09:30:02.191999 18.34
sessionInfo()
R version 2.13.1 RC (2011-07-03 r56263)
Platform: x86_64-apple-darwin9.8.0/x86_64 (64-bit)
locale:
[1] en_US.UTF-8/en_US.UTF-8/C/C/en_US.UTF-8/en_US.UTF-8
attached base packages:
[1] grid splines stats graphics grDevices utils datasets methods
[9] base
other attached packages:
[1] xts_0.8-2 zoo_1.7-4 sculpt3d_0.2-2 RGtk2_2.20.12
[5] rgl_0.92.798 survey_3.24 hexbin_1.26.0 spam_0.23-0
[9] xtable_1.5-6 polspline_1.1.5 Ryacas_0.2-10 XML_3.4-0
[13] rms_3.3-1 Hmisc_3.8-3 survival_2.36-9 sos_1.3-0
[17] brew_1.0-6 lattice_0.19-30
loaded via a namespace (and not attached):
[1] cluster_1.14.0 tools_2.13.1
答案 2 :(得分:2)
似乎问题只出在打印上。使用OP的原始data
:
ind <- as.POSIXct(strptime(data$datetime, '%Y-%m-%d %H:%M:%OS'))
as.numeric(ind)*1e6 # as expected
# [1] 1294241400001000 1294241400321000 1294241401511000 1294241402192000
ind # wrong
# [1] "2011-01-05 09:30:00.000 CST" "2011-01-05 09:30:00.321 CST"
# [3] "2011-01-05 09:30:01.510 CST" "2011-01-05 09:30:02.191 CST"
x <- xts(data[-1], ind)
x # wrong
# price
# 2011-01-05 09:30:00.000 18.31
# 2011-01-05 09:30:00.321 18.33
# 2011-01-05 09:30:01.510 18.33
# 2011-01-05 09:30:02.191 18.34
as.numeric(index(x))*1e6 # but the underlying index values are as expected
# [1] 1294241400001000 1294241400321000 1294241401511000 1294241402192000