可扩展的时间序列xts

转载自：http://blog.fens.me/r-xts/

前言

本文是继R语言zoo时间序列基础库的扩展实现。看上去简单的时间序列，内藏复杂的规律。zoo作为时间序列的基础库，是面向通用的设计，可以用来定义股票数据，也可以分析天气数据。但由于业务行为的不同，我们需要更多的辅助函数，来帮助我们更高效的完成任务。

xts扩展了zoo，提供更多的数据处理和数据变换的函数。

目录

xts介绍

xts安装

xts数据结构

xts的API介绍

xts使用

1. xts介绍

xts是对时间序列数据(zoo)的一种扩展实现，目标是为了统一时间序列的操作接口。实际上，xts类型继承了zoo类型，丰富了时间序列数据处理的函数，API定义更贴近使用者，更实用，更简单！

xts项目地址：http://r-forge.r-project.org/projects/xts/

2. xts安装

系统环境

Win7 64bit

R: 3.0.1 x86_64-w64-mingw32/x64 b4bit

xts安装

> install.packages("xts")
also installing the dependency ‘zoo’

trying URL 'http://mirror.bjtu.edu.cn/cran/bin/windows/contrib/3.0/zoo_1.7-10.zip'
Content type 'application/zip' length 875046 bytes (854 Kb)
opened URL
downloaded 854 Kb

trying URL 'http://mirror.bjtu.edu.cn/cran/bin/windows/contrib/3.0/xts_0.9-7.zip'
Content type 'application/zip' length 661664 bytes (646 Kb)
opened URL
downloaded 646 Kb

package ‘zoo’ successfully unpacked and MD5 sums checked
package ‘xts’ successfully unpacked and MD5 sums checked

3. xts数据结构

xts扩展zoo的基础结构，由3部分组合。

索引部分：时间类型向量

数据部分：以矩阵为基础类型，支持可以与矩阵相互转换的任何类型

属性部分：附件信息，包括时区，索引时间类型的格式等

4. xts的API介绍

xts基础

xts: 定义xts数据类型，继承zoo类型

coredata.xts: 对xts部分数据赋值

xtsAttributes: xts对象属性赋值

[.xts: 用[]语法，取数据子集

dimnames.xts: xts维度名赋值

sample_matrix: 测试数据集，包括180条xts对象的记录，matrix类型

xtsAPI: C语言API接口

类型转换

as.xts: 转换对象到xts(zoo)类型

as.xts.methods: 转换对象到xts函数

plot.xts: 为plot函数，提供xts的接口作图

.parseISO8601: 把字符串(ISO8601格式)输出为，POSIXct类型的，包括开始时间和结束时间的list对象

firstof: 创建一个开始时间，POSIXct类型

lastof: 创建一个结束时间，POSIXct类型

indexClass: 取索引类型

.indexDate: 取索引的

.indexday: 索引的日值

.indexyday: 索引的年(日)值

.indexmday: 索引的月(日)值

.indexwday: 索引的周(日)值

.indexweek: 索引的周值

.indexmon: 索引的月值

.indexyear: 索引的年值

.indexhour: 索引的时值

.indexmin: 索引的分值

.indexsec: 索引的秒值

数据处理

align.time: 以下一个时间对齐数据，秒，分钟，小时

endpoints: 按时间单元提取索引数据

merge.xts: 合并多个xts对象，重写zoo::merge.zoo函数

rbind.xts: 数据按行合并，为rbind函数，提供xts的接口

split.xts: 数据分隔，为split函数，提供xts的接口

na.locf.xts: 替换NA值，重写zoo:na.locf函数

数据统计

apply.daily: 按日分割数据，执行函数

apply.weekly: 按周分割数据，执行函数

apply.monthly: 按月分割数据，执行函数

apply.quarterly: 按季分割数据，执行函数

apply.yearly: 按年分割数据，执行函数

to.period: 按期间分割数据

period.apply: 按期间执行自定义函数

period.max: 按期间计算最大值

period.min: 按期间计算最小值

period.prod: 按期间计算指数

period.sum: 按期间求和

nseconds: 计算数据集，包括多少秒

nminutes: 计算数据集，包括多少分

nhours: 计算数据集，包括多少时

ndays: 计算数据集，包括多少日

nweeks: 计算数据集，包括多少周

nmonths: 计算数据集，包括多少月

nquarters: 计算数据集，包括多少季

nyears: 计算数据集，包括多少年

periodicity: 查看时间序列的期间

辅助工具

first: 从开始到结束，设置条件取子集

last: 从结束到开始，设置条件取子集

timeBased: 判断是否是时间类型

timeBasedSeq: 创建时间的序列

diff.xts: 计算步长和差分

isOrdered: 检查向量是否是顺序的

make.index.unique: 强制时间唯一，增加毫秒随机数

axTicksByTime: 计算X轴刻度标记位置按时间描述

indexTZ: 查询xts对象的时区

5. xts使用

1). xts类型基本操作

2). xts的作图

3). xts类型转换

4). xts数据处理

5). xts数据统计计算

6). xts时间序列工具使用

1). xts类型基本操作

测试数据集sample_matrix

> library(xts)
> data(sample_matrix)
> head(sample_matrix)
Open     High      Low    Close
2007-01-02 50.03978 50.11778 49.95041 50.11778
2007-01-03 50.23050 50.42188 50.23050 50.39767
2007-01-04 50.42096 50.42096 50.26414 50.33236
2007-01-05 50.37347 50.37347 50.22103 50.33459
2007-01-06 50.24433 50.24433 50.11121 50.18112
2007-01-07 50.13211 50.21561 49.99185 49.99185

定义xts类型对象

> sample.xts <- as.xts(sample_matrix, descr='my new xts object')
> class(sample.xts)
[1] "xts" "zoo"

> str(sample.xts)
An ‘xts’ object on 2007-01-02/2007-06-30 containing:
Data: num [1:180, 1:4] 50 50.2 50.4 50.4 50.2 ...
- attr(*, "dimnames")=List of 2
..$ : NULL
..$ : chr [1:4] "Open" "High" "Low" "Close"
Indexed by objects of class: [POSIXct,POSIXt] TZ:
xts Attributes:
List of 1
$ descr: chr "my new xts object"

> head(sample.xts)
Open     High      Low    Close
2007-01-02 50.03978 50.11778 49.95041 50.11778
2007-01-03 50.23050 50.42188 50.23050 50.39767
2007-01-04 50.42096 50.42096 50.26414 50.33236
2007-01-05 50.37347 50.37347 50.22103 50.33459
2007-01-06 50.24433 50.24433 50.11121 50.18112
2007-01-07 50.13211 50.21561 49.99185 49.99185

> attr(sample.xts,'descr')
[1] "my new xts object"

xts数据查询

> head(sample.xts['2007'])
Open     High      Low    Close
2007-01-02 50.03978 50.11778 49.95041 50.11778
2007-01-03 50.23050 50.42188 50.23050 50.39767
2007-01-04 50.42096 50.42096 50.26414 50.33236
2007-01-05 50.37347 50.37347 50.22103 50.33459
2007-01-06 50.24433 50.24433 50.11121 50.18112
2007-01-07 50.13211 50.21561 49.99185 49.99185

> head(sample.xts['2007-03/'])
Open     High      Low    Close
2007-03-01 50.81620 50.81620 50.56451 50.57075
2007-03-02 50.60980 50.72061 50.50808 50.61559
2007-03-03 50.73241 50.73241 50.40929 50.41033
2007-03-04 50.39273 50.40881 50.24922 50.32636
2007-03-05 50.26501 50.34050 50.26501 50.29567
2007-03-06 50.27464 50.32019 50.16380 50.16380

> head(sample.xts['2007-03-06/2007'])
Open     High      Low    Close
2007-03-06 50.27464 50.32019 50.16380 50.16380
2007-03-07 50.14458 50.20278 49.91381 49.91381
2007-03-08 49.93149 50.00364 49.84893 49.91839
2007-03-09 49.92377 49.92377 49.74242 49.80712
2007-03-10 49.79370 49.88984 49.70385 49.88698
2007-03-11 49.83062 49.88295 49.76031 49.78806

> sample.xts['2007-01-03']
Open     High     Low    Close
2007-01-03 50.2305 50.42188 50.2305 50.39767

2). 操作xts的作图

曲线图

> data(sample_matrix)
> plot(sample_matrix)

> plot(as.xts(sample_matrix))
Warning message:
In plot.xts(as.xts(sample_matrix)) :
only the univariate series will be plotted

K线图

> plot(as.xts(sample_matrix), type='candles')

3). xts类型转换

分别创建首尾时间：firstof, lastof

> firstof(2000)
[1] "2000-01-01 CST"

> firstof(2005,01,01)
[1] "2005-01-01 CST"

> lastof(2007)
[1] "2007-12-31 23:59:59.99998 CST"

> lastof(2007,10)
[1] "2007-10-31 23:59:59.99998 CST"

创建首尾时间

> .parseISO8601('2000')
$first.time
[1] "2000-01-01 CST"

$last.time
[1] "2000-12-31 23:59:59.99998 CST"

> .parseISO8601('2000-05/2001-02')
$first.time
[1] "2000-05-01 CST"

$last.time
[1] "2001-02-28 23:59:59.99998 CST"

> .parseISO8601('2000-01/02')
$first.time
[1] "2000-01-01 CST"

$last.time
[1] "2000-02-29 23:59:59.99998 CST"

> .parseISO8601('T08:30/T15:00')
$first.time
[1] "1970-01-01 08:30:00 CST"

$last.time
[1] "1970-12-31 15:00:59.99999 CST"

取索引类型

> x <- timeBasedSeq('2010-01-01/2010-01-02 12:00')
> x <- xts(1:length(x), x)

> head(x)
[,1]
2010-01-01 00:00:00    1
2010-01-01 00:01:00    2
2010-01-01 00:02:00    3
2010-01-01 00:03:00    4
2010-01-01 00:04:00    5
2010-01-01 00:05:00    6

> indexClass(x)
[1] "POSIXt"  "POSIXct"

索引时间格式化

> indexFormat(x) <- "%Y-%b-%d %H:%M:%OS3"
> head(x)
[,1]
2010-一月-01 00:00:00.000    1
2010-一月-01 00:01:00.000    2
2010-一月-01 00:02:00.000    3
2010-一月-01 00:03:00.000    4
2010-一月-01 00:04:00.000    5
2010-一月-01 00:05:00.000    6

取索引时间

> .indexhour(head(x))
[1] 0 0 0 0 0 0

> .indexmin(head(x))
[1] 0 1 2 3 4 5

4). xts数据处理

数据对齐

> x <- Sys.time() + 1:30

#整10秒对齐
> align.time(x, 10)
[1] "2013-11-18 15:42:30 CST" "2013-11-18 15:42:30 CST"
[3] "2013-11-18 15:42:30 CST" "2013-11-18 15:42:40 CST"
[5] "2013-11-18 15:42:40 CST" "2013-11-18 15:42:40 CST"
[7] "2013-11-18 15:42:40 CST" "2013-11-18 15:42:40 CST"
[9] "2013-11-18 15:42:40 CST" "2013-11-18 15:42:40 CST"
[11] "2013-11-18 15:42:40 CST" "2013-11-18 15:42:40 CST"
[13] "2013-11-18 15:42:40 CST" "2013-11-18 15:42:50 CST"
[15] "2013-11-18 15:42:50 CST" "2013-11-18 15:42:50 CST"
[17] "2013-11-18 15:42:50 CST" "2013-11-18 15:42:50 CST"
[19] "2013-11-18 15:42:50 CST" "2013-11-18 15:42:50 CST"
[21] "2013-11-18 15:42:50 CST" "2013-11-18 15:42:50 CST"
[23] "2013-11-18 15:42:50 CST" "2013-11-18 15:43:00 CST"
[25] "2013-11-18 15:43:00 CST" "2013-11-18 15:43:00 CST"
[27] "2013-11-18 15:43:00 CST" "2013-11-18 15:43:00 CST"
[29] "2013-11-18 15:43:00 CST" "2013-11-18 15:43:00 CST"

#整60秒对齐
> align.time(x, 60)
[1] "2013-11-18 15:43:00 CST" "2013-11-18 15:43:00 CST"
[3] "2013-11-18 15:43:00 CST" "2013-11-18 15:43:00 CST"
[5] "2013-11-18 15:43:00 CST" "2013-11-18 15:43:00 CST"
[7] "2013-11-18 15:43:00 CST" "2013-11-18 15:43:00 CST"
[9] "2013-11-18 15:43:00 CST" "2013-11-18 15:43:00 CST"
[11] "2013-11-18 15:43:00 CST" "2013-11-18 15:43:00 CST"
[13] "2013-11-18 15:43:00 CST" "2013-11-18 15:43:00 CST"
[15] "2013-11-18 15:43:00 CST" "2013-11-18 15:43:00 CST"
[17] "2013-11-18 15:43:00 CST" "2013-11-18 15:43:00 CST"
[19] "2013-11-18 15:43:00 CST" "2013-11-18 15:43:00 CST"
[21] "2013-11-18 15:43:00 CST" "2013-11-18 15:43:00 CST"
[23] "2013-11-18 15:43:00 CST" "2013-11-18 15:43:00 CST"
[25] "2013-11-18 15:43:00 CST" "2013-11-18 15:43:00 CST"
[27] "2013-11-18 15:43:00 CST" "2013-11-18 15:43:00 CST"
[29] "2013-11-18 15:43:00 CST" "2013-11-18 15:43:00 CST"

按时间分割数据，并计算

> xts.ts <- xts(rnorm(231),as.Date(13514:13744,origin="1970-01-01"))
> apply.monthly(xts.ts,mean)
[,1]
2007-01-31  0.17699984
2007-02-28  0.30734220
2007-03-31 -0.08757189
2007-04-30  0.18734688
2007-05-31  0.04496954
2007-06-30  0.06884836
2007-07-31  0.25081814
2007-08-19 -0.28845938

> apply.monthly(xts.ts,function(x) var(x))
[,1]
2007-01-31 0.9533217
2007-02-28 0.9158947
2007-03-31 1.2821450
2007-04-30 1.2805976
2007-05-31 0.9725438
2007-06-30 1.5228904
2007-07-31 0.8737030
2007-08-19 0.8490521

> apply.quarterly(xts.ts,mean)
[,1]
2007-03-31 0.12642053
2007-06-30 0.09977926
2007-08-19 0.04589268

> apply.yearly(xts.ts,mean)
[,1]
2007-08-19 0.09849522

按期间分隔：to.period

> data(sample_matrix)
> to.period(sample_matrix)
sample_matrix.Open sample_matrix.High sample_matrix.Low sample_matrix.Close
2007-01-31           50.03978           50.77336          49.76308            50.22578
2007-02-28           50.22448           51.32342          50.19101            50.77091
2007-03-31           50.81620           50.81620          48.23648            48.97490
2007-04-30           48.94407           50.33781          48.80962            49.33974
2007-05-31           49.34572           49.69097          47.51796            47.73780
2007-06-30           47.74432           47.94127          47.09144            47.76719
> class(to.period(sample_matrix))
[1] "matrix"

> samplexts <- as.xts(sample_matrix)
> to.period(samplexts)
samplexts.Open samplexts.High samplexts.Low samplexts.Close
2007-01-31       50.03978       50.77336      49.76308        50.22578
2007-02-28       50.22448       51.32342      50.19101        50.77091
2007-03-31       50.81620       50.81620      48.23648        48.97490
2007-04-30       48.94407       50.33781      48.80962        49.33974
2007-05-31       49.34572       49.69097      47.51796        47.73780
2007-06-30       47.74432       47.94127      47.09144        47.76719
> class(to.period(samplexts))
[1] "xts" "zoo"

按期间分割索引数据

> data(sample_matrix)

> endpoints(sample_matrix)
[1]   0  30  58  89 119 150 180

> endpoints(sample_matrix, 'days',k=7)
[1]   0   6  13  20  27  34  41  48  55  62  69  76  83  90  97 104 111 118 125
[20] 132 139 146 153 160 167 174 180

> endpoints(sample_matrix, 'weeks')
[1]   0   7  14  21  28  35  42  49  56  63  70  77  84  91  98 105 112 119 126
[20] 133 140 147 154 161 168 175 180

> endpoints(sample_matrix, 'months')
[1]   0  30  58  89 119 150 180

数据合并：按列合并

> (x <- xts(4:10, Sys.Date()+4:10))
[,1]
2013-11-22    4
2013-11-23    5
2013-11-24    6
2013-11-25    7
2013-11-26    8
2013-11-27    9
2013-11-28   10

> (y <- xts(1:6, Sys.Date()+1:6))
[,1]
2013-11-19    1
2013-11-20    2
2013-11-21    3
2013-11-22    4
2013-11-23    5
2013-11-24    6

> merge(x,y)
x  y
2013-11-19 NA  1
2013-11-20 NA  2
2013-11-21 NA  3
2013-11-22  4  4
2013-11-23  5  5
2013-11-24  6  6
2013-11-25  7 NA
2013-11-26  8 NA
2013-11-27  9 NA
2013-11-28 10 NA

#取索引将领合并
> merge(x,y, join='inner')
x y
2013-11-22 4 4
2013-11-23 5 5
2013-11-24 6 6

#以左侧为基础合并
> merge(x,y, join='left')
x  y
2013-11-22  4  4
2013-11-23  5  5
2013-11-24  6  6
2013-11-25  7 NA
2013-11-26  8 NA
2013-11-27  9 NA
2013-11-28 10 NA

数据合并：按行合并

> x <- xts(1:3, Sys.Date()+1:3)

> rbind(x,x)
[,1]
2013-11-19    1
2013-11-19    1
2013-11-20    2
2013-11-20    2
2013-11-21    3
2013-11-21    3

数据切片：按行切片

> data(sample_matrix)
> x <- as.xts(sample_matrix)

按月切片
> split(x)[[1]]
Open     High      Low    Close
2007-01-02 50.03978 50.11778 49.95041 50.11778
2007-01-03 50.23050 50.42188 50.23050 50.39767
2007-01-04 50.42096 50.42096 50.26414 50.33236
2007-01-05 50.37347 50.37347 50.22103 50.33459
2007-01-06 50.24433 50.24433 50.11121 50.18112
2007-01-07 50.13211 50.21561 49.99185 49.99185
2007-01-08 50.03555 50.10363 49.96971 49.98806
2007-01-09 49.99489 49.99489 49.80454 49.91333
2007-01-10 49.91228 50.13053 49.91228 49.97246
2007-01-11 49.88529 50.23910 49.88529 50.23910
2007-01-12 50.21258 50.35980 50.17176 50.28519
2007-01-13 50.32385 50.48000 50.32385 50.41286
2007-01-14 50.46359 50.62395 50.46359 50.60145
2007-01-15 50.61724 50.68583 50.47359 50.48912
2007-01-16 50.62024 50.73731 50.56627 50.67835
2007-01-17 50.74150 50.77336 50.44932 50.48644
2007-01-18 50.48051 50.60712 50.40269 50.57632
2007-01-19 50.41381 50.55627 50.41278 50.41278
2007-01-20 50.35323 50.35323 50.02142 50.02142
2007-01-21 50.16188 50.42090 50.16044 50.42090
2007-01-22 50.36008 50.43875 50.21129 50.21129
2007-01-23 50.03966 50.16961 50.03670 50.16961
2007-01-24 50.10953 50.26942 50.06387 50.23145
2007-01-25 50.20738 50.28268 50.12913 50.24334
2007-01-26 50.16008 50.16008 49.94052 50.07024
2007-01-27 50.06041 50.09777 49.97267 50.01091
2007-01-28 49.96586 50.00217 49.87468 49.88096
2007-01-29 49.85624 49.93038 49.76308 49.91875
2007-01-30 49.85477 50.02180 49.77242 50.02180
2007-01-31 50.07049 50.22578 50.07049 50.22578

按周切片
> split(x, f="weeks")[[1]]
Open     High      Low    Close
2007-01-02 50.03978 50.11778 49.95041 50.11778
2007-01-03 50.23050 50.42188 50.23050 50.39767
2007-01-04 50.42096 50.42096 50.26414 50.33236
2007-01-05 50.37347 50.37347 50.22103 50.33459
2007-01-06 50.24433 50.24433 50.11121 50.18112
2007-01-07 50.13211 50.21561 49.99185 49.99185
2007-01-08 50.03555 50.10363 49.96971 49.98806
> split(x, f="weeks")[[2]]
Open     High      Low    Close
2007-01-09 49.99489 49.99489 49.80454 49.91333
2007-01-10 49.91228 50.13053 49.91228 49.97246
2007-01-11 49.88529 50.23910 49.88529 50.23910
2007-01-12 50.21258 50.35980 50.17176 50.28519
2007-01-13 50.32385 50.48000 50.32385 50.41286
2007-01-14 50.46359 50.62395 50.46359 50.60145
2007-01-15 50.61724 50.68583 50.47359 50.48912

NA值处理

> x <- xts(1:10, Sys.Date()+1:10)
> x[c(1,2,5,9,10)] <- NA
> x
[,1]
2013-11-19   NA
2013-11-20   NA
2013-11-21    3
2013-11-22    4
2013-11-23   NA
2013-11-24    6
2013-11-25    7
2013-11-26    8
2013-11-27   NA
2013-11-28   NA

#取前一个
> na.locf(x)
[,1]
2013-11-19   NA
2013-11-20   NA
2013-11-21    3
2013-11-22    4
2013-11-23    4
2013-11-24    6
2013-11-25    7
2013-11-26    8
2013-11-27    8
2013-11-28    8

#取后一个
> na.locf(x, fromLast=TRUE)
[,1]
2013-11-19    3
2013-11-20    3
2013-11-21    3
2013-11-22    4
2013-11-23    6
2013-11-24    6
2013-11-25    7
2013-11-26    8
2013-11-27   NA
2013-11-28   NA

5). xts数据统计计算
取开始时间，结束时间

> xts.ts <- xts(rnorm(231),as.Date(13514:13744,origin="1970-01-01"))

> start(xts.ts)
[1] "2007-01-01"
> end(xts.ts)
[1] "2007-08-19"

> periodicity(xts.ts)
Daily periodicity from 2007-01-01 to 2007-08-19

计算时间区间

> data(sample_matrix)
> ndays(sample_matrix)
[1] 180
> nweeks(sample_matrix)
[1] 26
> nmonths(sample_matrix)
[1] 6
> nquarters(sample_matrix)
[1] 2
> nyears(sample_matrix)
[1] 1

按期间计算统计指标

> zoo.data <- zoo(rnorm(31)+10,as.Date(13514:13744,origin="1970-01-01"))

#按周获得期间
> ep <- endpoints(zoo.data,'weeks')
> ep
[1]   0   7  14  21  28  35  42  49  56  63  70  77  84  91  98 105 112 119
[19] 126 133 140 147 154 161 168 175 182 189 196 203 210 217 224 231

#计算周的均值
> period.apply(zoo.data, INDEX=ep, FUN=function(x) mean(x))
2007-01-07 2007-01-14 2007-01-21 2007-01-28 2007-02-04 2007-02-11 2007-02-18
10.200488   9.649387  10.304151   9.864847  10.382943   9.660175   9.857894
2007-02-25 2007-03-04 2007-03-11 2007-03-18 2007-03-25 2007-04-01 2007-04-08
10.495037   9.569531  10.292899   9.651616  10.089103   9.961048  10.304860
2007-04-15 2007-04-22 2007-04-29 2007-05-06 2007-05-13 2007-05-20 2007-05-27
9.658432   9.887531  10.608082   9.747787  10.052955   9.625730  10.430030
2007-06-03 2007-06-10 2007-06-17 2007-06-24 2007-07-01 2007-07-08 2007-07-15
9.814703  10.224869   9.509881  10.187905  10.229310  10.261725   9.855776
2007-07-22 2007-07-29 2007-08-05 2007-08-12 2007-08-19
9.445072  10.482020   9.844531  10.200488   9.649387

#计算周的最大值
> head(period.max(zoo.data, INDEX=ep))
[,1]
2007-01-07 12.05912
2007-01-14 10.79286
2007-01-21 11.60658
2007-01-28 11.63455
2007-02-04 12.05912
2007-02-11 10.67887

#计算周的最小值
> head(period.min(zoo.data, INDEX=ep))
[,1]
2007-01-07 8.874509
2007-01-14 8.534655
2007-01-21 9.069773
2007-01-28 8.461555
2007-02-04 9.421085
2007-02-11 8.534655

#计算周的一个指数值
> head(period.prod(zoo.data, INDEX=ep))
[,1]
2007-01-07 11140398
2007-01-14  7582350
2007-01-21 11930334
2007-01-28  8658933
2007-02-04 12702505
2007-02-11  7702767

6). xts时间序列工具使用
检查时间类型

> timeBased(Sys.time())
[1] TRUE
> timeBased(Sys.Date())
[1] TRUE
> timeBased(200701)
[1] FALSE

创建时间序列

#按年
> timeBasedSeq('1999/2008')
[1] "1999-01-01" "2000-01-01" "2001-01-01" "2002-01-01" "2003-01-01"
[6] "2004-01-01" "2005-01-01" "2006-01-01" "2007-01-01" "2008-01-01"

#按月
> head(timeBasedSeq('199901/2008'))
[1] "十二月 1998" "一月 1999"   "二月 1999"   "三月 1999"   "四月 1999"
[6] "五月 1999"

#按日
> head(timeBasedSeq('199901/2008/d'),40)
[1] "十二月 1998" "一月 1999"   "一月 1999"   "一月 1999"   "一月 1999"
[6] "一月 1999"   "一月 1999"   "一月 1999"   "一月 1999"   "一月 1999"
[11] "一月 1999"   "一月 1999"   "一月 1999"   "一月 1999"   "一月 1999"
[16] "一月 1999"   "一月 1999"   "一月 1999"   "一月 1999"   "一月 1999"
[21] "一月 1999"   "一月 1999"   "一月 1999"   "一月 1999"   "一月 1999"
[26] "一月 1999"   "一月 1999"   "一月 1999"   "一月 1999"   "一月 1999"
[31] "一月 1999"   "一月 1999"   "二月 1999"   "二月 1999"   "二月 1999"
[36] "二月 1999"   "二月 1999"   "二月 1999"   "二月 1999"   "二月 1999"

#按数量创建，100分钟的数据集
> timeBasedSeq('20080101 0830',length=100)
$from
[1] "2008-01-01 08:30:00 CST"
$to
[1] NA
$by
[1] "mins"
$length.out
[1] 100

按索引取数据first, last

> x <- xts(1:100, Sys.Date()+1:100)

> head(x)
[,1]
2013-11-19    1
2013-11-20    2
2013-11-21    3
2013-11-22    4
2013-11-23    5
2013-11-24    6

> first(x, 10)
[,1]
2013-11-19    1
2013-11-20    2
2013-11-21    3
2013-11-22    4
2013-11-23    5
2013-11-24    6
2013-11-25    7
2013-11-26    8
2013-11-27    9
2013-11-28   10

> first(x, '1 day')
[,1]
2013-11-19    1

> last(x, '1 weeks')
[,1]
2014-02-24   98
2014-02-25   99
2014-02-26  100

计算步长和差分

> x <- xts(1:5, Sys.Date()+1:5)
#正向
> lag(x)
[,1]
2013-11-19   NA
2013-11-20    1
2013-11-21    2
2013-11-22    3
2013-11-23    4

#反向
> lag(x, k=-1, na.pad=FALSE)
[,1]
2013-11-19    2
2013-11-20    3
2013-11-21    4
2013-11-22    5

#1阶差分
> diff(x)
[,1]
2013-11-19   NA
2013-11-20    1
2013-11-21    1
2013-11-22    1
2013-11-23    1

#2阶差分
> diff(x, lag=2)
[,1]
2013-11-19   NA
2013-11-20   NA
2013-11-21    2
2013-11-22    2
2013-11-23    2

检查向量是否排序好的

> isOrdered(1:10, increasing=TRUE)
[1] TRUE

> isOrdered(1:10, increasing=FALSE)
[1] FALSE

> isOrdered(c(1,1:10), increasing=TRUE)
[1] FALSE

> isOrdered(c(1,1:10), increasing=TRUE, strictly=FALSE)
[1] TRUE

强制唯一索引

> x <- xts(1:5, as.POSIXct("2011-01-21") + c(1,1,1,2,3)/1e3)
> x
[,1]
2011-01-21 00:00:00.000    1
2011-01-21 00:00:00.000    2
2011-01-21 00:00:00.000    3
2011-01-21 00:00:00.002    4
2011-01-21 00:00:00.003    5

> make.index.unique(x)
[,1]
2011-01-21 00:00:00.000999    1
2011-01-21 00:00:00.001000    2
2011-01-21 00:00:00.001001    3
2011-01-21 00:00:00.002000    4
2011-01-21 00:00:00.003000    5

查询xts对象时区

> x <- xts(1:10, Sys.Date()+1:10)

> indexTZ(x)
[1] "UTC"
> tzone(x)
[1] "UTC"

> str(x)
An ‘xts’ object on 2013-11-19/2013-11-28 containing:
Data: int [1:10, 1] 1 2 3 4 5 6 7 8 9 10
Indexed by objects of class: [Date] TZ: UTC
xts Attributes:
NULL

xts给了zoo类型时间序列更多的API支持，这样我们就有了更方便的工具，可以做各种的时间序列的转换和变形了。