我想要使用已被處理成唯一時間戳向量的向量xts時間戳將xts對象子集。這從之前的this以前的問題得到部分回答。使用唯一索引天向量的子集xts對象

某些樣本數據：

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"))

的代碼：

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))

這sample.data.unique因此是指數天的載體。 問題：我想用它從原始數據集sample.data中提取整天的數據，以便稍後繪製整個交易日，而不是數據的子集。例如，如果Jan 07 2012 10:53:00符合absdevmadprice> = 5且小於無限的條件，那麼我想將該日（2012年1月3日）返回到向量中，並將其用於子集原始數據集。這將選擇當天的所有觀察結果（所以在整個交易期間），然後我可以在這一天圖表。

我試過這個代碼（基於約書亞的答案here），但它不工作：

> sample.data.uniquePOS<-sample.data.merge[paste(as.Date(as.POSIXct(sample.data.unique, origin = "1970-01-01 00:00.00 UTC", tz="GMT")))]

它只是返回的列名：

> sample.data.uniquePOS 
price volume madprice madvolume medianprice medianvolume absdevmadprice 
    absdevmadvolume

對於信息的結構的變量：

> 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

感謝您的幫助（如果有人可以解釋爲什麼th e代碼不起作用！）。

來源

2013-03-25 Rothsom

無需重複您的問題。我建議你刪除以前的那個。 – agstudy 2013-03-25 17:24:22

我設法用這段代碼解決它。當SO允許我時，將它作爲答案發布。 sample.data.uniquePOS < - sample.data.merge [paste（as.Date.numeric（sample.data.unique，origin =「1970-01-01 00：00.00 UTC」，tz =「GMT」）），] – Rothsom 2013-03-25 20:06:28

你可以添加更多有意義的樣本數據嗎？您添加的樣本數據對於'sample.data.mergesub'，對於'absdevmadprice'和'absdevmadvol'有NA？ – 2013-03-26 03:25:26

回答自己的問題：

使用這些帖子（阿難的回答this，約書亞的答案this和as.Date.numeric功能我發現了here）我能解決我自己的問題。這行代碼似乎做到這一點：

，爲什麼它的作品相比，低於

sample.data.uniquePOS <- sample.data.merge[paste(as.Date.numeric(sample.data.unique, origin= "1970-01-01 00:00.00 UTC", tz="GMT")),]

不能給出很好的解釋，但或許as.POSIXct不能採取相同的格式as.Date.numeric可以嗎？

sample.data.uniquePOS <- sample.data.merge[paste(as.Date(as.POSIXct(sample.data.unique, origin = "1970-01-01 00:00.00 UTC", tz="GMT")))]

來源

2013-03-26 14:35:58 Rothsom

你不需要'as.Date.numeric'。 'as.Date'工作得很好。 'as.POSIXct'不起作用，因爲'sample.data。unique'是自起源（'Date'）以來的數字向量，而不是從原點（'POSIXt'）開始的秒。 – 2013-03-26 14:46:33

謝謝Joshua，非常有幫助！ – Rothsom 2013-03-27 16:43:07

使用唯一索引天向量的子集xts對象

回答

回答自己的問題：

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