In [11]:

    

# Load the packages you intend to work with every time you start a new session
library(tseries)
library(TSA)
library(forecast)


    

    




Loading required package: leaps
Loading required package: locfit
locfit 1.5-9.1 	 2013-03-22
Loading required package: mgcv
Loading required package: nlme
This is mgcv 1.8-12. For overview type 'help("mgcv-package")'.

Attaching package: ‘TSA’

The following objects are masked from ‘package:stats’:

    acf, arima

The following object is masked from ‘package:utils’:

    tar

Loading required package: zoo

Attaching package: ‘zoo’

The following objects are masked from ‘package:base’:

    as.Date, as.Date.numeric

Loading required package: timeDate

Attaching package: ‘timeDate’

The following objects are masked from ‘package:TSA’:

    kurtosis, skewness

This is forecast 7.1 


Attaching package: ‘forecast’

The following objects are masked from ‘package:TSA’:

    fitted.Arima, plot.Arima

The following object is masked from ‘package:nlme’:

    getResponse

In [4]:

    

ndvi <- read.csv("http://www.escet.urjc.es/biodiversos/R/ndvi.csv", header=T, sep="\t")


    

In [5]:

    

head(ndvi)


    

    






year
juldays
X1
X2
X3
X4
X5

	
1
   2000.0000
2000049.0000
      0.5732
      0.6348
      0.5495
      0.5583
      0.6459
	
2
   2000.0000
2000065.0000
      0.5867
      0.6238
      0.5495
      0.5588
      0.5482
	
3
   2000.0000
2000081.0000
      0.5703
      0.5854
      0.5289
      0.5172
      0.5309
	
4
   2000.0000
2000097.0000
      0.5720
      0.5990
      0.5374
      0.5047
      0.5877
	
5
   2000.0000
2000113.0000
      0.5737
      0.5815
      0.5813
      0.5637
      0.5370
	
6
   2000.0000
2000129.0000
      0.6365
      0.5786
      0.5130
      0.5257
      0.5275

In [6]:

    

str(ndvi)


    

    




'data.frame':	201 obs. of  7 variables:
 $ year   : int  2000 2000 2000 2000 2000 2000 2000 2000 2000 2000 ...
 $ juldays: int  2000049 2000065 2000081 2000097 2000113 2000129 2000145 2000161 2000177 2000193 ...
 $ X1     : num  0.573 0.587 0.57 0.572 0.574 ...
 $ X2     : num  0.635 0.624 0.585 0.599 0.582 ...
 $ X3     : num  0.549 0.549 0.529 0.537 0.581 ...
 $ X4     : num  0.558 0.559 0.517 0.505 0.564 ...
 $ X5     : num  0.646 0.548 0.531 0.588 0.537 ...

In [7]:

    

class(ndvi)


    

    





'data.frame'

In [27]:

    

head(ndvi[,-1]) # all columns except for the first


    

    






juldays
X1
X2
X3
X4
X5

	
1
2000049.0000
      0.5732
      0.6348
      0.5495
      0.5583
      0.6459
	
2
2000065.0000
      0.5867
      0.6238
      0.5495
      0.5588
      0.5482
	
3
2000081.0000
      0.5703
      0.5854
      0.5289
      0.5172
      0.5309
	
4
2000097.0000
      0.5720
      0.5990
      0.5374
      0.5047
      0.5877
	
5
2000113.0000
      0.5737
      0.5815
      0.5813
      0.5637
      0.5370
	
6
2000129.0000
      0.6365
      0.5786
      0.5130
      0.5257
      0.5275

In [41]:

    

table(ndvi$year) # 3 missing vals at beginning and end


    

    






2000 2001 2002 2003 2004 2005 2006 2007 2008 
  20   23   23   23   23   23   23   23   20 

In [42]:

    

# 23 observations per year (except for the first and last year)
ndvi.ts <- ts(ndvi[,3:7], start=c(2000, 4), frequency=23)


    

In [36]:

    

dim(ndvi)


    

    






	
201
	
7

In [43]:

    

dim(ndvi.ts)


    

    






	
201
	
5

In [47]:

    

class(ndvi.ts) # it's a multivariate time series (mts)


    

    






	
'mts'
	
'ts'
	
'matrix'

In [50]:

    

plot(ndvi.ts)


    

In [53]:

    

# all time series in one plot w/ different colors
plot(ndvi.ts, plot.type = "single", col=1:5)
# add a legend w/ solid line type
legend("bottomleft", lty=rep(1,5), col=1:5, legend=paste("X", 1:5, sep=""))


    

In [44]:

    

# strptime(ndvi$year, format="%Y")