我正在尝试使用已经处理成唯一时间戳向量的xts个时间戳向量来对xts对象进行子集化。这是继this上一个仅部分回答的问题之后。
一些示例数据：
dput(sample.data.merge, control="all")
structure(c(11.65, 11.13, 11.13, 11.5, 11.8, 11.45, 11.45, 11.08,
11.08, 11.25, 9.8, 10.45, 10.9, 10.9, 10.9, 10.9, 10.9, 10.9,
10.45, 10.5, 10.5, 10.08, 10.08, 10.65, 10.08, 10.65, 10.6, 10.65,
10.65, 10.085, 10.145, 11.9, 11.085, 9.35, 9.15, 9.15, 9.9, 9.0875,
9.3, 9.3, 9.3, 9.35, 9.35, 9.35, 9.25, 9.5, 9.45, 9.3, 11.15,
11.15, 11.15, 11.15, 11.8, 8, 10.05, 10.05, 10.25, 10.4, 10.15,
10.15, 10.3, 10.15, 10.1, 11.08, 11.08, 11.08, 11.65, 11.85,
11.9, 11.9, 11.9, 12.65, 13.35, 13.35, 15.95, 15.9, 15.4, 15.4,
15.4, 15.4, 15.13, 12.13, 12.35, 11.082, 11.082, 11.08, 12.1,
12.3, 12.3, 12.4, 12.6, 12.6, 12.13, 12.45, 12.9, 12.9, 12.9,
14, 12.6, 12.6, 12.45, 15.25, 12.085, 12.95, 12.95, 12.35, 12.13,
12.8, 14, 14, 12.45, 12.45, 12.45, 12.45, 12.25, 12.6, 12.085,
15.1, 15.15, 15.35, 15.3, 12.5, 12.5, 12.15, 12.2, 11.085, 11.35,
11.45, 11.13, 11.13, 11.35, 11.2, 12.5, 12.6, 12.95, 12.95, 12.5,
12.45, 12.3, 12.3, 12.3, 12.45, 12.45, 12.45, 12.5, 12.45, 12.45,
12.13, 12.13, 12.65, 190, 190, 190, 190, 130, 190, 190, 190,
190, 190, 130, 190, 130, 130, 445, 445, 445, 445, 130, 445, 190,
445, 445, 190, 190, 190, 190, 130, 190, 190, 190, 190, 190, 190,
190, 190, 190, 190, 190, 190, 190, 190, 190, 190, 190, 190, 190,
190, 275, 190, 190, 190, 190, 190, 190, 190, 190, 190, 130, 130,
190, 190, 190, 130, 130, 130, 190, 130, 190, 190, 190, 130, 190,
190, 190, 190, 190, 190, 190, 190, 190, 190, 190, 190, 190, 190,
1190, 190, 190, 130, 130, 130, 190, 1130, 190, 190, 130, 190,
190, 190, 190, 190, 190, 130, 130, 190, 190, 375, 190, 190, 190,
130, 190, 130, 190, 190, 190, 190, 130, 190, 190, 190, 190, 190,
190, 190, 190, 190, 190, 190, 190, 190, 190, 190, 190, 190, 190,
130, 130, 130, 190, 130, 190, 190, 190, 130, 130, 445, 445, 130,
NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,
NA, 0, 0, NA, NA, NA, NA, NA, 0.21, 0.21, 0.26, 0.0250000000000004,
0, 0.0250000000000004, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,
NA, NA, NA, 0.0249999999999995, 0.0250000000000004, NA, NA, NA,
NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, 0.0250000000000004,
0.100000000000001, 0.39, NA, NA, NA, NA, NA, 0.0250000000000004,
NA, NA, NA, NA, NA, 0.524999999999999, 0.25, 0, 0, NA, NA, NA,
NA, NA, NA, NA, NA, NA, NA, 0.149999999999999, 0.135000000000001,
0.149999999999999, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,
NA, 0.409999999999999, 0.375, 0.3, 0.635, 0.385, 0.335, 0.175000000000001,
0, NA, NA, NA, NA, NA, 1.4, 0.2, NA, NA, NA, NA, NA, NA, NA,
NA, NA, NA, 0.109999999999999, NA, NA, NA, NA, NA, NA, NA, NA,
NA, 0.0749999999999993, 0.0749999999999993, 0.0749999999999993,
0.0250000000000004, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,
NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, 0, 0, NA, NA, NA,
NA, NA, 127.5, 0, 0, 0, 0, 0, NA, NA, NA, NA, NA, NA, NA, NA,
NA, NA, NA, NA, NA, 0, 0, NA, NA, NA, NA, NA, NA, NA, NA, NA,
NA, NA, NA, NA, NA, NA, NA, NA, NA, 30, 30, 30, NA, NA, NA, NA,
NA, 0, NA, NA, NA, NA, NA, 0, 0, 0, 0, NA, NA, NA, NA, NA, NA,
NA, NA, NA, NA, 30, 30, 30, NA, NA, NA, NA, NA, NA, NA, NA, NA,
NA, NA, NA, 0, 30, 30, 0, 0, 0, 0, 0, NA, NA, NA, NA, NA, 0,
0, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, 0, NA, NA, NA, NA,
NA, NA, NA, NA, NA, 0, 0, 30, 0, NA, NA, NA, NA, NA, NA, NA,
NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, 10.9,
10.9, NA, NA, NA, NA, NA, 10.29, 10.29, 10.34, 10.625, 10.65,
10.625, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, 9.325,
9.325, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,
NA, NA, NA, NA, 10.15, 10.225, 10.69, NA, NA, NA, NA, NA, 11.9,
NA, NA, NA, NA, NA, 15.4, 15.4, 15.4, 15.4, NA, NA, NA, NA, NA,
NA, NA, NA, NA, NA, 12.35, 12.35, 12.425, NA, NA, NA, NA, NA,
NA, NA, NA, NA, NA, NA, NA, 12.65, 12.575, 12.875, 12.875, 12.625,
12.625, 12.625, 12.45, NA, NA, NA, NA, NA, 13.85, 15.125, NA,
NA, NA, NA, NA, NA, NA, NA, NA, NA, 11.275, NA, NA, NA, NA, NA,
NA, NA, NA, NA, 12.375, 12.375, 12.375, 12.45, NA, NA, NA, NA,
NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,
NA, NA, 445, 445, NA, NA, NA, NA, NA, 317.5, 190, 190, 190, 190,
190, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, 190,
190, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,
NA, NA, NA, NA, 160, 160, 160, NA, NA, NA, NA, NA, 190, NA, NA,
NA, NA, NA, 190, 190, 190, 190, NA, NA, NA, NA, NA, NA, NA, NA,
NA, NA, 160, 160, 160, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,
NA, NA, 190, 190, 190, 190, 190, 190, 190, 190, NA, NA, NA, NA,
NA, 190, 190, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, 190, NA,
NA, NA, NA, NA, NA, NA, NA, NA, 130, 130, 160, 190, NA, NA, NA,
NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,
NA, NA, NA, NaN, Inf, NA, NA, NA, NA, NA, 0.999999999999996,
1.71428571428572, 1, 1, NaN, 21.5999999999997, NA, NA, NA, NA,
NA, NA, NA, NA, NA, NA, NA, NA, NA, 1.00000000000004, 2.99999999999993,
NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,
NA, NA, 37.1999999999995, 8.54999999999987, 0.999999999999998,
NA, NA, NA, NA, NA, 29.9999999999996, NA, NA, NA, NA, NA, 0,
0, NaN, Inf, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, 1.66666666666666,
1.62962962962963, 0.166666666666658, NA, NA, NA, NA, NA, NA,
NA, NA, NA, NA, NA, NA, 1.26829268292683, 0.600000000000004,
3.75, 1.77165354330709, 0.454545454545457, 0.522388059701495,
1, NaN, NA, NA, NA, NA, NA, 1.07142857142857, 0.875000000000003,
NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, 0.681818181818179, NA,
NA, NA, NA, NA, NA, NA, NA, NA, 1, 1, 1, 2, NA, NA, NA, NA, NA,
NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,
NA, NaN, Inf, NA, NA, NA, NA, NA, 1, NaN, NaN, Inf, NaN, NaN,
NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NaN, NaN,
NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,
NA, NA, 1, 1, 1, NA, NA, NA, NA, NA, Inf, NA, NA, NA, NA, NA,
NaN, NaN, NaN, NaN, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, 1,
1, 32.3333333333333, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,
NA, NA, NaN, 6.16666666666667, 0, NaN, NaN, Inf, NaN, Inf, NA,
NA, NA, NA, NA, NaN, NaN, NA, NA, NA, NA, NA, NA, NA, NA, NA,
NA, NaN, NA, NA, NA, NA, NA, NA, NA, NA, NA, NaN, Inf, 1, NaN,
NA, NA, NA, NA, NA), .Dim = c(150L, 8L), .Dimnames = list(NULL,
c("price", "volume", "madprice", "madvolume", "medianprice",
"medianvolume", "absdevmadprice", "absdevmadvolume")), index = structure(c(1325584080,
1325594940, 1325594940, 1325604600, 1325759100, 1325762520, 1325762520,
1325769300, 1325769300, 1325848080, 1325864880, 1326128220, 1326196500,
1326196500, 1326196500, 1326196500, 1326196500, 1326196500, 1326209700,
1326279480, 1326283620, 1326288300, 1326288300, 1326289680, 1326289680,
1326289680, 1326292320, 1326294060, 1326294600, 1326297600, 1326387000,
1326456720, 1326467160, 1326711600, 1326723000, 1326724260, 1326809940,
1326814860, 1326885960, 1326885960, 1326889980, 1326894000, 1326895200,
1326895200, 1326898080, 1326986700, 1326987240, 1326992100, 1327072140,
1327328040, 1327328040, 1327328040, 1327417920, 1327423140, 1327424820,
1327425240, 1327483200, 1327496520, 1327570320, 1327570320, 1327575420,
1327588680, 1327588980, 1327595880, 1327595880, 1327595880, 1327664820,
1327674720, 1327680660, 1327680780, 1327680780, 1327683960, 1327914300,
1327914300, 1327915260, 1327918140, 1327924860, 1327924920, 1327924980,
1327924980, 1327927680, 1328013360, 1328014200, 1328025000, 1328025000,
1328026740, 1328089440, 1328091360, 1328091360, 1328110620, 1328111340,
1328111340, 1328112420, 1328113800, 1328193540, 1328194080, 1328194140,
1328196720, 1328274360, 1328274420, 1328278320, 1328519280, 1328520120,
1328520600, 1328520600, 1328524140, 1328527980, 1328531580, 1328540880,
1328540880, 1328547600, 1328547660, 1328547720, 1328547780, 1328607060,
1328608080, 1328618760, 1328623380, 1328623380, 1328625720, 1328631480,
1328717760, 1328717880, 1328793000, 1328797980, 1329132840, 1329210480,
1329215400, 1329215820, 1329215820, 1329219480, 1329223140, 1329300900,
1329301620, 1329315240, 1329315240, 1329388740, 1329389700, 1329390000,
1329390000, 1329390180, 1329391860, 1329391860, 1329391860, 1329402120,
1329467700, 1329467700, 1329469080, 1329469080, 1329471300), tzone = "", tclass = c("POSIXlt",
"POSIXt")), .indexCLASS = c("POSIXlt", "POSIXt"), .indexTZ = "", tclass = c("POSIXlt",
"POSIXt"), tzone = "", class = c("xts", "zoo"))
代码：
sample.data.mergesub <- sample.data.merge['T10:30/T17:30']
sample.data.mergeout <- sample.data.mergesub[ which((sample.data.mergesub$absdevmadprice >=5 & sample.data.mergesub$absdevmadprice < Inf) | (sample.data.mergesub$absdevmadvol>=10 & sample.data.mergesub$absdevmadvol<Inf)),]
sample.data.unique <- unique(.indexday(sample.data.mergeout))
因此sample.data.unique是索引天数的向量。 问题：我想使用此功能从原始数据集sample.data中提取全天数据，以便稍后绘制交易的整天，而不是数据子集。例如，如果2012年1月3日10:53:00满足absdevmadprice＆gt; = 5且小于无限的条件，那么我想将当天（2012年1月3日）返回到矢量并将其用于子集原始数据集。这将选择当天的所有观察（因此在整个交易期间），然后我可以在今天绘制图表。
我已经尝试过这段代码（基于Joshua的回答here），但它不起作用：
> sample.data.uniquePOS<-sample.data.merge[paste(as.Date(as.POSIXct(sample.data.unique, origin = "1970-01-01 00:00.00 UTC", tz="GMT")))]
它只返回列名：
> sample.data.uniquePOS
price volume madprice madvolume medianprice medianvolume absdevmadprice
absdevmadvolume
有关信息，变量的结构：
> str(sample.data.merge)
An ‘xts’ object on 2012-01-03 09:48:00/2012-02-17 09:35:00 containing:
Data: num [1:150, 1:8] 11.6 11.1 11.1 11.5 11.8 ...
- attr(*, "dimnames")=List of 2
..$ : NULL
..$ : chr [1:8] "price" "volume" "madprice" "madvolume" ...
Indexed by objects of class: [POSIXlt,POSIXt] TZ:
xts Attributes:
NULL
> str(sample.data.uniquePOS)
An 'xts' object of zero-width
> str(sample.data.unique)
num 15371
感谢您的帮助（如果有人能解释为什么代码不起作用！）。
答案 0 :(得分：1)
使用这些帖子（Ananda对this的回答，Joshua对this的回答，以及我发现的关于here的as.Date.numeric函数，我能够解决自己的问题问题。这行代码似乎是这样做的：
sample.data.uniquePOS <- sample.data.merge[paste(as.Date.numeric(sample.data.unique, origin= "1970-01-01 00:00.00 UTC", tz="GMT")),]
与下面的内容相比，无法对其工作原理给出一个很好的解释，但as.POSIXct可能不能采用与as.Date.numeric相同的格式？
sample.data.uniquePOS <- sample.data.merge[paste(as.Date(as.POSIXct(sample.data.unique, origin = "1970-01-01 00:00.00 UTC", tz="GMT")))]