R语言入门：时间序列分析实战指南

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"《用R语言处理时间序列：英文版 A Little Book of R for Time Series》是一本面向初学者的简明指南，作者Avril Coghlan，来自英国剑桥的Wellcome Trust Sanger Institute的Parasite Genomics Group。该书专注于介绍如何利用R语言进行时间序列分析，适合那些希望掌握数据挖掘和时间序列分析技巧的读者。第1章主要介绍了R语言的基础安装和配置。章节从R的概述开始，包括其简介、如何下载和安装R软件，以及R的基本运行环境和交互方式。此外，还提供了链接和进一步阅读的资源，以便于读者深入了解和扩展学习。在第2章，作者详细讲解了如何在R中进行时间序列分析。这部分涵盖了关键步骤，如读取时间序列数据（如CSV或Excel文件）、基本的数据可视化（如绘制时间序列图），以及数据分解（如季节性和趋势的识别）。随后，作者介绍了指数平滑法用于时间序列预测，并深入探讨了ARIMA模型（自回归整合移动平均模型）的应用，这是时间序列分析中的经典方法。每节末尾都提供了丰富的链接和进一步阅读资料，鼓励读者探索更深入的主题和相关研究。第2章的最后部分再次强调了联系信息和版权许可。第3章和第4章则是对前两章内容的总结和致谢，而第5章则包含了全书的版权许可信息，确保读者了解并尊重作者的知识产权。《A Little Book of R for Time Series》以其简洁易懂的方式，为R语言新手提供了一个实用的时间序列分析工具箱，无论是学术研究还是商业应用，都能找到相应的学习资源。通过阅读本书，读者不仅能掌握R语言的实践技能，还能对时间序列分析有更深入的理解。"

A Little Book of R For Time Series, Release 0.2

Alternatively, you can use the name of the fourth element in the table (“John”) to ﬁnd the value of that table

element:

> mytable[["John"]]

[1] 2

Functions in R usually require arguments, which are input variables (ie. objects) that are passed to them, which

they then carry out some operation on. For example, the log10() function is passed a number, and it then calculates

the log to the base 10 of that number:

> log10(100)

In R, you can get help about a particular function by using the help() function. For example, if you want help

about the log10() function, you can type:

> help("log10")

When you use the help() function, a box or webpage will pop up with information about the function that you

asked for help with.

If you are not sure of the name of a function, but think you know part of its name, you can search for the function

name using the help.search() and RSiteSearch() functions. The help.search() function searches to see if you

already have a function installed (from one of the R packages that you have installed) that may be related to some

topic you’re interested in. The RSiteSearch() function searches all R functions (including those in packages that

you haven’t yet installed) for functions related to the topic you are interested in.

For example, if you want to know if there is a function to calculate the standard deviation of a set of numbers, you

can search for the names of all installed functions containing the word “deviation” in their description by typing:

> help.search("deviation")

Help files with alias or concept or title matching

’deviation’ using fuzzy matching:

genefilter::rowSds

Row variance and standard deviation of

a numeric array

nlme::pooledSD Extract Pooled Standard Deviation

stats::mad Median Absolute Deviation

stats::sd Standard Deviation

vsn::meanSdPlot Plot row standard deviations versus row

Among the functions that were found, is the function sd() in the “stats” package (an R package that comes with

the standard R installation), which is used for calculating the standard deviation.

In the example above, the help.search() function found a relevant function (sd() here). However, if you did not

ﬁnd what you were looking for with help.search(), you could then use the RSiteSearch() function to see if a search

of all functions described on the R website may ﬁnd something relevant to the topic that you’re interested in:

> RSiteSearch("deviation")

The results of the RSiteSearch() function will be hits to descriptions of R functions, as well as to R mailing list

discussions of those functions.

We can perform computations with R using objects such as scalars and vectors. For example, to calculate the

average of the values in the vector myvector (ie. the average of 8, 6, 9, 10 and 5), we can use the mean() function:

> mean(myvector)

[1] 7.6

We have been using built-in R functions such as mean(), length(), print(), plot(), etc. We can also create our own

functions in R to do calculations that you want to carry out very often on different input data sets. For example,

we can create a function to calculate the value of 20 plus square of some input number:

1.5. A brief introduction to R 9

CHAPTER 2

Using R for Time Series Analysis

2.1 Time Series Analysis

This booklet itells you how to use the R statistical software to carry out some simple analyses that are common in

analysing time series data.

This booklet assumes that the reader has some basic knowledge of time series analysis, and the principal focus of

the booklet is not to explain time series analysis, but rather to explain how to carry out these analyses using R.

If you are new to time series analysis, and want to learn more about any of the concepts presented here, I would

highly recommend the Open University book “Time series” (product code M249/02), available from from the

Open University Shop.

In this booklet, I will be using time series data sets that have been kindly made available by Rob Hyndman in his

Time Series Data Library at http://robjhyndman.com/TSDL/.

There is a pdf version of this booklet available at https://media.readthedocs.org/pdf/a-little-book-of-r-for-time-

series/latest/a-little-book-of-r-for-time-series.pdf.

If you like this booklet, you may also like to check out my booklet on using R for biomedical statistics, http://a-

little-book-of-r-for-biomedical-statistics.readthedocs.org/, and my booklet on using R for multivariate analysis,

http://little-book-of-r-for-multivariate-analysis.readthedocs.org/.

2.2 Reading Time Series Data

The ﬁrst thing that you will want to do to analyse your time series data will be to read it into R, and to plot the

time series. You can read data into R using the scan() function, which assumes that your data for successive time

points is in a simple text ﬁle with one column.

For example, the ﬁle http://robjhyndman.com/tsdldata/misc/kings.dat contains data on the age of death of succes-

sive kings of England, starting with William the Conqueror (original source: Hipel and Mcleod, 1994).

The data set looks like this:

Age of Death of Successive Kings of England

#starting with William the Conqueror

#Source: McNeill, "Interactive Data Analysis"

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