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• How to remove some of the TimeSeries titles in a AChartEngine Time Series View

  - by user1831310

  As a workaround of not being able to change colors of selected points in a series on an AChartEngine Time Chart, I was using an additional series for each point whose color has to be changed. I need to disable series titles for those additional series. Using empty string as the argument to the Time Series construtor: TimeSeries ts = TimeSeries(""); still results in the line-and-point symbol being placed with empty series title string under the X-axis labels for each such series. It would be a desirable feature for AChartEngine to remove both the line-and-point symbol and the series title string for a series created with a null argument to the TimeSeries construtor call: TimeSeries ts = TimeSeries(null); But this currently resulted in nullPointerException instead. Would the AChartEngine developers consider the above suggestion and until then, is there a way to remove some of the TimeSeries titles from a AChartEngine Time Series View? Best regards.

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• failure on creating a Scikits.TimeSeries object

  - by user311906

  Hi All I am trying to create a scikit.timeseries object starting from 2 datetime objects. If I understood correctly it should be possible to create a scikits.timeseries starting from datetime objects. I try the following code but it says that Insufficient parameters. The 2 datetime differs for few microseconds. In this case what should be the value for freq parameter? Is what I am trying allowed? In theory, since timeseries can be based on datetime objects it should be possible to hanlde up to microsecond , is this correct? I think that this is not really clear to me. Regards Eo import datetime import sckilits.timeseries as ts tm1 = datetime.datetime( 2010,1,1, 10,10,2, 123456 ) tm2 = datetime.datetime( 2010,1,1, 10,10,2, 345678 ) d = [ tm1, tm2 ] tseries = ts.time_series( dates=d ) tseries = ts.time_series( d )

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• R Question: calculating deltas in a timeseries

  - by Jörg Beyer

  I have a timeseries of samples in R: > str(d) 'data.frame': 5 obs. of 3 variables: $ date: POSIXct, format: "2010-03-04 20:47:00" "2010-03-04 21:47:00" ... $ x : num 0 10 11 15.2 20 $ y : num 0 5 7.5 8.4 12.5 > d date x y 1 2010-03-04 20:47:00 0.0 0.0 2 2010-03-04 21:47:00 10.0 5.0 3 2010-03-04 22:47:00 11.0 7.5 4 2010-03-04 23:47:00 15.2 8.4 5 2010-03-05 00:47:00 20.0 12.5 In this example samples for x and y are taken every hour (but the time delta is not fix). The x and y values are always growing (like a milage counter in a car). I need the deltas, how much was the growth in between, something like this: 1 2010-03-04 20:47:00 0.0 0.0 2 2010-03-04 21:47:00 10.0 5.0 3 2010-03-04 22:47:00 1.0 2.5 4 2010-03-04 23:47:00 4.2 0.9 5 2010-03-05 00:47:00 4.8 4.1 And I also need the detlas per time (x and y delta, divided by the time - delta per hour) How would I do this in R?

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• Recommendations for TimeSeries db

  - by Kumar

  preferably open source for storing tick info.

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• What is the best way to store anciliary data with a 2D timeseries object in R?

  - by Mike52

  I currently try to move from matlab to R. I have 2D measurements, consisting of irradiance in time and wavelength together with quality flags and uncertainty and error estimates. In Matlab I extended the timeseries object to store both the wavelength array and the auxiliary data. What is the best way in R to store this data? Ideally I would like this data to be stored together such that e.g. window(...) keeps all data synchronized.

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• How do I add values in an array when there is a null entry?

  - by Angela

  I want to create a real time-series array. Currently, I am using the statistics gem to pull out values for each 'day': define_statistic :sent_count, :count => :all, :group => 'DATE(date_sent)', :filter_on => {:email_id => 'email_id > = ?'}, :order => 'DATE(date_sent) ASC' What this does is create an array where there are values for a date, for example [["12-20-2010",1], ["12-24-2010",3]] But I need it to fill in the null values, so it looks more like: [["12-20-2010",1], ["12-21-2010",0], ["12-22-2010",0], ["12-23-2010",0], ["12-24-2010",3]] Notice how the second example has "0" values for the days that were missing from the first array.

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• how to set default value as zero to <valueExpression> in TimeSeries chart?

  - by Aru

  Hi, I am using TimeSerirs chart to display cpuusage per time. Code is- valueExpression ![CDATA[$F{cpuUsage}]] /valueExpression Problem is when it displays chart it always start with 5 or non-zero number. It takes non-zero number as its first point then displays it. How to set zero as its first point?

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• Cannot implicitly convert type ...

  - by Newbie

  I have the following function public Dictionary<DateTime, object> GetAttributeList( EnumFactorType attributeType ,Thomson.Financial.Vestek.Util.DateRange dateRange) { DateTime startDate = dateRange.StartDate; DateTime endDate = dateRange.EndDate; return (( //Step 1: Iterate over the attribute list and filter the records by // the supplied attribute type from assetAttribute in AttributeCollection where assetAttribute.AttributeType.Equals(attributeType) //Step2:Assign the TimeSeriesData collection into a temporary variable let timeSeriesList = assetAttribute.TimeSeriesData //Step 3: Iterate over the TimeSeriesData list and filter the records by // the supplied date from timeSeries in timeSeriesList.ToList() where timeSeries.Key >= startDate && timeSeries.Key <= endDate //Finally build the needed collection select new AssetAttribute() { TimeSeriesData = PopulateTimeSeriesData(timeSeries.Key, timeSeries.Value) }).ToList<AssetAttribute>().Select(i => i.TimeSeriesData)); } private Dictionary<DateTime, object> PopulateTimeSeriesData(DateTime dateTime, object value) { Dictionary<DateTime, object> timeSeriesData = new Dictionary<DateTime, object>(); timeSeriesData.Add(dateTime, value); return timeSeriesData; } Error:Cannot implicitly convert type 'System.Collections.Generic.IEnumerable' to 'System.Collections.Generic.Dictionary'. An explicit conversion exists (are you missing a cast?) Using C#3.0 Please help

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• Real-time graphing in Java

  - by thodinc

  I have an application which updates a variable about between 5 to 50 times a second and I am looking for some way of drawing a continuous XY plot of this change in real-time. Though JFreeChart is not recommended for such a high update rate, many users still say that it works for them. I've tried using this demo and modified it to display a random variable, but it seems to use up 100% CPU usage all the time. Even if I ignore that, I do not want to be restricted to JFreeChart's ui class for constructing forms (though I'm not sure what its capabilities are exactly). Would it be possible to integrate it with Java's "forms" and drop-down menus? (as are available in VB) Otherwise, are there any alternatives I could look into? EDIT: I'm new to Swing, so I've put together a code just to test the functionality of JFreeChart with it (while avoiding the use of the ApplicationFrame class of JFree since I'm not sure how that will work with Swing's combo boxes and buttons). Right now, the graph is being updated immediately and CPU usage is high. Would it be possible to buffer the value with new Millisecond() and update it maybe twice a second? Also, can I add other components to the rest of the JFrame without disrupting JFreeChart? How would I do that? frame.getContentPane().add(new Button("Click")) seems to overwrite the graph. package graphtest; import java.util.Random; import javax.swing.JFrame; import org.jfree.chart.ChartFactory; import org.jfree.chart.ChartPanel; import org.jfree.chart.JFreeChart; import org.jfree.chart.axis.ValueAxis; import org.jfree.chart.plot.XYPlot; import org.jfree.data.time.Millisecond; import org.jfree.data.time.TimeSeries; import org.jfree.data.time.TimeSeriesCollection; public class Main { static TimeSeries ts = new TimeSeries("data", Millisecond.class); public static void main(String[] args) throws InterruptedException { gen myGen = new gen(); new Thread(myGen).start(); TimeSeriesCollection dataset = new TimeSeriesCollection(ts); JFreeChart chart = ChartFactory.createTimeSeriesChart( "GraphTest", "Time", "Value", dataset, true, true, false ); final XYPlot plot = chart.getXYPlot(); ValueAxis axis = plot.getDomainAxis(); axis.setAutoRange(true); axis.setFixedAutoRange(60000.0); JFrame frame = new JFrame("GraphTest"); frame.setDefaultCloseOperation(JFrame.EXIT_ON_CLOSE); ChartPanel label = new ChartPanel(chart); frame.getContentPane().add(label); //Suppose I add combo boxes and buttons here later frame.pack(); frame.setVisible(true); } static class gen implements Runnable { private Random randGen = new Random(); public void run() { while(true) { int num = randGen.nextInt(1000); System.out.println(num); ts.addOrUpdate(new Millisecond(), num); try { Thread.sleep(20); } catch (InterruptedException ex) { System.out.println(ex); } } } } }

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• R Random Data Sets within loops

  - by jugossery

  Here is what I want to do: I have a time series data frame with let us say 100 time-series of length 600 - each in one column of the data frame. I want to pick up 4 of the time-series randomly and then assign them random weights that sum up to one (ie 0.1, 0.5, 0.3, 0.1). Using those I want to compute the mean of the sum of the 4 weighted time series variables (e.g. convex combination). I want to do this let us say 100k times and store each result in the form ts1.name, ts2.name, ts3.name, ts4.name, weight1, weight2, weight3, weight4, mean so that I get a 9*100k df. I tried some things already but R is very bad with loops and I know vector oriented solutions are better because of R design. Thanks Here is what I did and I know it is horrible The df is in the form v1,v2,v2.....v100 1,5,6,.......9 2,4,6,.......10 3,5,8,.......6 2,2,8,.......2 etc e=NULL for (x in 1:100000) { s=sample(1:100,4)#pick 4 variables randomly a=sample(seq(0,1,0.01),1) b=sample(seq(0,1-a,0.01),1) c=sample(seq(0,(1-a-b),0.01),1) d=1-a-b-c e=c(a,b,c,d)#4 random weights average=mean(timeseries.df[,s]%*%t(e)) e=rbind(e,s,average)#in the end i get the 9*100k df } The procedure runs way to slow. EDIT: Thanks for the help i had,i am not used to think R and i am not very used to translate every problem into a matrix algebra equation which is what you need in R. Then the problem becomes a little bit complex if i want to calculate the standard deviation. i need the covariance matrix and i am not sure i can if/how i can pick random elements for each sample from the original timeseries.df covariance matrix then compute the sample variance (t(sampleweights)%*%sample_cov.mat%*%sampleweights) to get in the end the ts.weighted_standard_dev matrix Last question what is the best way to proceed if i want to bootstrap the original df x times and then apply the same computations to test the robustness of my datas thanks

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• How to use OO for data analysis? [closed]

  - by Konsta

  In which ways could object-orientation (OO) make my data analysis more efficient and let me reuse more of my code? The data analysis can be broken up into get data (from db or csv or similar) transform data (filter, group/pivot, ...) display/plot (graph timeseries, create tables, etc.) I mostly use Python and its Pandas and Matplotlib packages for this besides some DB connectivity (SQL). Almost all of my code is a functional/procedural mix. While I have started to create a data object for a certain collection of time series, I wonder if there are OO design patterns/approaches for other parts of the process that might increase efficiency?

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• Plot numpy datetime64 with matplotlib

  - by enedene

  I have two numpy arrays 1D, one is time of measurement in datetime64 format, for example: array([2011-11-15 01:08:11, 2011-11-16 02:08:04, ..., 2012-07-07 11:08:00], dtype=datetime64[us]) and other array of same length and dimension with integer data. I'd like to make a plot in matplotlib time vs data. If I put the data directly, this is what I get: plot(timeSeries, data) Is there a way to get time in more natural units? For example in this case months/year would be fine.

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• time series in python up to microseconds

  - by Abruzzo Forte e Gentile

  Hi All I would like to handle time series in python. I have been suggested to use scikit.timeseries but I need to handle up to microseconds and this last, as far as I know, handles up to milliseconds. Do you know any other library able to do that? At some point I need to merge 2 time series sampled at different time, and I would like to avoid rewriting such kind of features or any new classes from scratch whenever it is possible. I thank you all AFG

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• Save/Load jFreechart TimeSeriesCollection chart from XML

  - by IMAnis_tn

  I'm working with this exemple wich put rondom dynamic data into a TimeSeriesCollection chart. My problem is that i can't find how to : 1- Make a track of the old data (of the last hour) when they pass the left boundary (because the data point move from the right to the left ) of the view area just by implementing a horizontal scroll bar. 2- Is XML a good choice to save my data into when i want to have all the history of the data? public class DynamicDataDemo extends ApplicationFrame { /** The time series data. */ private TimeSeries series; /** The most recent value added. */ private double lastValue = 100.0; public DynamicDataDemo(final String title) { super(title); this.series = new TimeSeries("Random Data", Millisecond.class); final TimeSeriesCollection dataset = new TimeSeriesCollection(this.series); final JFreeChart chart = createChart(dataset); final ChartPanel chartPanel = new ChartPanel(chart); final JPanel content = new JPanel(new BorderLayout()); content.add(chartPanel); chartPanel.setPreferredSize(new java.awt.Dimension(500, 270)); setContentPane(content); } private JFreeChart createChart(final XYDataset dataset) { final JFreeChart result = ChartFactory.createTimeSeriesChart( "Dynamic Data Demo", "Time", "Value", dataset, true, true, false ); final XYPlot plot = result.getXYPlot(); ValueAxis axis = plot.getDomainAxis(); axis.setAutoRange(true); axis.setFixedAutoRange(60000.0); // 60 seconds axis = plot.getRangeAxis(); axis.setRange(0.0, 200.0); return result; } public void go() { final double factor = 0.90 + 0.2 * Math.random(); this.lastValue = this.lastValue * factor; final Millisecond now = new Millisecond(); System.out.println("Now = " + now.toString()); this.series.add(new Millisecond(), this.lastValue); } public static void main(final String[] args) throws InterruptedException { final DynamicDataDemo demo = new DynamicDataDemo("Dynamic Data Demo"); demo.pack(); RefineryUtilities.centerFrameOnScreen(demo); demo.setVisible(true); while(true){ demo.go(); Thread.currentThread().sleep(1000); } } }

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• How to extract a Date from an SQLDateTime object in Mathematica

  - by andrews

  I am trying to do a plot of a time series with DateListPlot. I want to feed it a time series I obtain from an SQL database. When I retrieve the time series the list is composed of SQLDateTime entries that DateListPlot doesn't understand. In[24]:= t=SQLExecute[conn, "select timestamp,value from timeseries order by timestamp asc"] Out[24]={{SQLDateTime[{2010,1,1}],12.3},{SQLDateTime[{2010,1,2}],12.51}} Doesn't work: In[25]:= DateListPlot[t] DateListPlot requires a Date tuple and doesn't understand SQLDateTime. What can I do?

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• JFreeChart Legend Display

  - by Richard B

  In my JFreeChart timeseries plots I find the legends lines to thin to see the colour accurately. Another post [ jfreechart - change sample of colors in legend ] suggested overriding a renderer method as follows: renderer = new XYLineAndShapeRenderer() { private static final long serialVersionUID = 1L; public Shape lookupLegendShape(int series) { return new Rectangle(15, 15); } }; this approach works fine until you do what I did renderer.setSeriesShapesVisible(i, false); Once I did that the legend reverts back to a line. Is there any way round this?

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• Which NoSQL db to use with C?

  - by systemsfault

  Hello all, I'm working on an application that I'm going to write with C and i am considering to use a nosql db for storing timeseries data with at most 8 or 9 fields. But in every 5 minutes there will huge write operations such as 2-10 million rows and then there will be reads(but performance is not as crucial in read as in the write operation). I'm considering to use a NoSQL db here in order to store the data but couldn't decide on which one to use. Couchdb seems to have a stable driver called pillowtalk for C; but Mongo's driver doesn't look as promising as pillowtalk. I'm also open to other suggestions. What is your recommendation?

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• jasper Chart can't be displayed using tomcat

  - by Aru

  Hi, I am using jasperreports-3.5.0 to generate timeSeries chart. When I run it through eclipse it is working fine. But if I create .war file of project and run through tomcat by deploying the project into tomcat / webapps folder then chart can not be displayed. What could be the problem? code: PrintWriter out = response.getWriter(); response.setContentType("text/html"); JRDataSource dataSource = createReportDataSource(perfArrayListSample.toArray()); InputStream input = getServletConfig().getServletContext().getResourceAsStream("/CpuUsage.jrxml"); JasperDesign design = JRXmlLoader.load(input); JasperReport report = JasperCompileManager.compileReport(design); JasperPrint print = JasperFillManager.fillReport(report, new HashMap(), dataSource); JRHtmlExporter exporter = new JRHtmlExporter(); request.getSession().setAttribute(ImageServlet.DEFAULT_JASPER_PRINT_SESSION_ATTRIBUTE, print); exporter.setParameter(JRExporterParameter.JASPER_PRINT, print); exporter.setParameter(JRExporterParameter.OUTPUT_WRITER, out); exporter.setParameter(JRHtmlExporterParameter.IMAGES_URI, "image?ver="+new Date().getTime() +"&image="); exporter.exportReport(); perfArrayListSample.clear();

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• Why do I get rows of zeros in my 2D fft?

  - by Nicholas Pringle

  I am trying to replicate the results from a paper. "Two-dimensional Fourier Transform (2D-FT) in space and time along sections of constant latitude (east-west) and longitude (north-south) were used to characterize the spectrum of the simulated flux variability south of 40degS." - Lenton et al(2006) The figures published show "the log of the variance of the 2D-FT". I have tried to create an array consisting of the seasonal cycle of similar data as well as the noise. I have defined the noise as the original array minus the signal array. Here is the code that I used to plot the 2D-FT of the signal array averaged in latitude: import numpy as np from numpy import ma from matplotlib import pyplot as plt from Scientific.IO.NetCDF import NetCDFFile ### input directory indir = '/home/nicholas/data/' ### get the flux data which is in ### [time(5day ave for 10 years),latitude,longitude] nc = NetCDFFile(indir + 'CFLX_2000_2009.nc','r') cflux_southern_ocean = nc.variables['Cflx'][:,10:50,:] cflux_southern_ocean = ma.masked_values(cflux_southern_ocean,1e+20) # mask land nc.close() cflux = cflux_southern_ocean*1e08 # change units of data from mmol/m^2/s ### create an array that consists of the seasonal signal fro each pixel year_stack = np.split(cflux, 10, axis=0) year_stack = np.array(year_stack) signal_array = np.tile(np.mean(year_stack, axis=0), (10, 1, 1)) signal_array = ma.masked_where(signal_array > 1e20, signal_array) # need to mask ### average the array over latitude(or longitude) signal_time_lon = ma.mean(signal_array, axis=1) ### do a 2D Fourier Transform of the time/space image ft = np.fft.fft2(signal_time_lon) mgft = np.abs(ft) ps = mgft**2 log_ps = np.log(mgft) log_mgft= np.log(mgft) Every second row of the ft consists completely of zeros. Why is this? Would it be acceptable to add a randomly small number to the signal to avoid this. signal_time_lon = signal_time_lon + np.random.randint(0,9,size=(730, 182))*1e-05 EDIT: Adding images and clarify meaning The output of rfft2 still appears to be a complex array. Using fftshift shifts the edges of the image to the centre; I still have a power spectrum regardless. I expect that the reason that I get rows of zeros is that I have re-created the timeseries for each pixel. The ft[0, 0] pixel contains the mean of the signal. So the ft[1, 0] corresponds to a sinusoid with one cycle over the entire signal in the rows of the starting image. Here are is the starting image using following code: plt.pcolormesh(signal_time_lon); plt.colorbar(); plt.axis('tight') Here is result using following code: ft = np.fft.rfft2(signal_time_lon) mgft = np.abs(ft) ps = mgft**2 log_ps = np.log1p(mgft) plt.pcolormesh(log_ps); plt.colorbar(); plt.axis('tight') It may not be clear in the image but it is only every second row that contains completely zeros. Every tenth pixel (log_ps[10, 0]) is a high value. The other pixels (log_ps[2, 0], log_ps[4, 0] etc) have very low values.

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• R Package Installation with Oracle R Enterprise

  - by Sherry LaMonica-Oracle

  Normal 0 false false false EN-US X-NONE X-NONE Programming languages give developers the opportunity to write reusable functions and to bundle those functions into logical deployable entities. In R, these are called packages. R has thousands of such packages provided by an almost equally large group of third-party contributors. To allow others to benefit from these packages, users can share packages on the CRAN system for use by the vast R development community worldwide. R's package system along with the CRAN framework provides a process for authoring, documenting and distributing packages to millions of users. In this post, we'll illustrate the various ways in which such R packages can be installed for use with R and together with Oracle R Enterprise. In the following, the same instructions apply when using either open source R or Oracle R Distribution. In this post, we cover the following package installation scenarios for: R command line Linux shell command line Use with Oracle R Enterprise Installation on Exadata or RAC Installing all packages in a CRAN Task View Troubleshooting common errors 1. R Package Installation BasicsR package installation basics are outlined in Chapter 6 of the R Installation and Administration Guide. There are two ways to install packages from the command line: from the R command line and from the shell command line. For this first example on Oracle Linux using Oracle R Distribution, we’ll install the arules package as root so that packages will be installed in the default R system-wide location where all users can access it, /usr/lib64/R/library.Within R, using the install.packages function always attempts to install the latest version of the requested package available on CRAN:R> install.packages("arules")If the arules package depends upon other packages that are not already installed locally, the R installer automatically downloads and installs those required packages. This is a huge benefit that frees users from the task of identifying and resolving those dependencies.You can also install R from the shell command line. This is useful for some packages when an internet connection is not available or for installing packages not uploaded to CRAN. To install packages this way, first locate the package on CRAN and then download the package source to your local machine. For example:$ wget http://cran.r-project.org/src/contrib/arules_1.1-2.tar.gz Then, install the package using the command R CMD INSTALL:$ R CMD INSTALL arules_1.1-2.tar.gzA major difference between installing R packages using the R package installer at the R command line and shell command line is that package dependencies must be resolved manually at the shell command line. Package dependencies are listed in the Depends section of the package’s CRAN site. If dependencies are not identified and installed prior to the package’s installation, you will see an error similar to:ERROR: dependency ‘xxx’ is not available for package ‘yyy’As a best practice and to save time, always refer to the package’s CRAN site to understand the package dependencies prior to attempting an installation. If you don’t run R as root, you won’t have permission to write packages into the default system-wide location and you will be prompted to create a personal library accessible by your userid. You can accept the personal library path chosen by R, or specify the library location by passing parameters to the install.packages function. For example, to create an R package repository in your home directory: R> install.packages("arules", lib="/home/username/Rpackages")or$ R CMD INSTALL arules_1.1-2.tar.gz --library=/home/username/RpackagesRefer to the install.packages help file in R or execute R CMD INSTALL --help at the shell command line for a full list of command line options.To set the library location and avoid having to specify this at every package install, simply create the R startup environment file .Renviron in your home area if it does not already exist, and add the following piece of code to it:R_LIBS_USER = "/home/username/Rpackages" 2. Setting the RepositoryEach time you install an R package from the R command line, you are asked which CRAN mirror, or server, R should use. To set the repository and avoid having to specify this during every package installation, create the R startup command file .Rprofile in your home directory and add the following R code to it:cat("Setting Seattle repository")r = getOption("repos") r["CRAN"] = "http://cran.fhcrc.org/"options(repos = r)rm(r) This code snippet sets the R package repository to the Seattle CRAN mirror at the start of each R session. 3. Installing R Packages for use with Oracle R EnterpriseEmbedded R execution with Oracle R Enterprise allows the use of CRAN or other third-party R packages in user-defined R functions executed on the Oracle Database server. The steps for installing and configuring packages for use with Oracle R Enterprise are the same as for open source R. The database-side R engine just needs to know where to find the R packages.The Oracle R Enterprise installation is performed by user oracle, which typically does not have write permission to the default site-wide library, /usr/lib64/R/library. On Linux and UNIX platforms, the Oracle R Enterprise Server installation provides the ORE script, which is executed from the operating system shell to install R packages and to start R. The ORE script is a wrapper for the default R script, a shell wrapper for the R executable. It can be used to start R, run batch scripts, and build or install R packages. Unlike the default R script, the ORE script installs packages to a location writable by user oracle and accessible by all ORE users - $ORACLE_HOME/R/library.To install a package on the database server so that it can be used by any R user and for use in embedded R execution, an Oracle DBA would typically download the package source from CRAN using wget. If the package depends on any packages that are not in the R distribution in use, download the sources for those packages, also.  For a single Oracle Database instance, replace the R script with ORE to install the packages in the same location as the Oracle R Enterprise packages. $ wget http://cran.r-project.org/src/contrib/arules_1.1-2.tar.gz$ ORE CMD INSTALL arules_1.1-2.tar.gzBehind the scenes, the ORE script performs the equivalent of setting R_LIBS_USER to the value of $ORACLE_HOME/R/library, and all R packages installed with the ORE script are installed to this location. For installing a package on multiple database servers, such as those in an Oracle Real Application Clusters (Oracle RAC) or a multinode Oracle Exadata Database Machine environment, use the ORE script in conjunction with the Exadata Distributed Command Line Interface (DCLI) utility.$ dcli -g nodes -l oracle ORE CMD INSTALL arules_1.1-1.tar.gz The DCLI -g flag designates a file containing a list of nodes to install on, and the -l flag specifies the user id to use when executing the commands. For more information on using DCLI with Oracle R Enterprise, see Chapter 5 in the Oracle R Enterprise Installation Guide.If you are using an Oracle R Enterprise client, install the package the same as any R package, bearing in mind that you must install the same version of the package on both the client and server machines to avoid incompatibilities. 4. CRAN Task ViewsCRAN also maintains a set of Task Views that identify packages associated with a particular task or methodology. Task Views are helpful in guiding users through the huge set of available R packages. They are actively maintained by volunteers who include detailed annotations for routines and packages. If you find one of the task views is a perfect match, you can install every package in that view using the ctv package - an R package for automating package installation. To use the ctv package to install a task view, first, install and load the ctv package.R> install.packages("ctv")R> library(ctv)Then query the names of the available task views and install the view you choose.R> available.views() R> install.views("TimeSeries") 5. Using and Managing R packages To use a package, start up R and load packages one at a time with the library command.Load the arules package in your R session. R> library(arules)Verify the version of arules installed.R> packageVersion("arules")[1] '1.1.2'Verify the version of arules installed on the database server using embedded R execution.R> ore.doEval(function() packageVersion("arules"))View the help file for the apropos function in the arules packageR> ?aproposOver time, your package repository will contain more and more packages, especially if you are using the system-wide repository where others are adding additional packages. It’s good to know the entire set of R packages accessible in your environment. To list all available packages in your local R session, use the installed.packages command:R> myLocalPackages <- row.names(installed.packages())R> myLocalPackagesTo access the list of available packages on the ORE database server from the ORE client, use the following embedded R syntax: R> myServerPackages <- ore.doEval(function() row.names(installed.packages()) R> myServerPackages 6. Troubleshooting Common ProblemsInstalling Older Versions of R packagesIf you immediately upgrade to the latest version of R, you will have no problem installing the most recent versions of R packages. However, if your version of R is older, some of the more recent package releases will not work and install.packages will generate a message such as: Warning message: In install.packages("arules") : package ‘arules’ is not availableThis is when you have to go to the Old sources link on the CRAN page for the arules package and determine which version is compatible with your version of R.Begin by determining what version of R you are using:$ R --versionOracle Distribution of R version 3.0.1 (--) -- "Good Sport" Copyright (C) The R Foundation for Statistical Computing Platform: x86_64-unknown-linux-gnu (64-bit)Given that R-3.0.1 was released May 16, 2013, any version of the arules package released after this date may work. Scanning the arules archive, we might try installing version 0.1.1-1, released in January of 2014:$ wget http://cran.r-project.org/src/contrib/Archive/arules/arules_1.1-1.tar.gz$ R CMD INSTALL arules_1.1-1.tar.gzFor use with ORE:$ ORE CMD INSTALL arules_1.1-1.tar.gzThe "package not available" error can also be thrown if the package you’re trying to install lives elsewhere, either another R package site, or it’s been removed from CRAN. A quick Google search usually leads to more information on the package’s location and status.Oracle R Enterprise is not in the R library pathOn Linux hosts, after installing the ORE server components, starting R, and attempting to load the ORE packages, you may receive the error:R> library(ORE)Error in library(ORE) : there is no package called ‘ORE’If you know the ORE packages have been installed and you receive this error, this is the result of not starting R with the ORE script. To resolve this problem, exit R and restart using the ORE script. After restarting R and ">running the command to load the ORE packages, you should not receive any errors.$ ORER> library(ORE)On Windows servers, the solution is to make the location of the ORE packages visible to R by adding them to the R library paths. To accomplish this, exit R, then add the following lines to the .Rprofile file. On Windows, the .Rprofile file is located in R\etc directory C:\Program Files\R\R-<version>\etc. Add the following lines:.libPaths("<path to $ORACLE_HOME>/R/library")The above line will tell R to include the R directory in the Oracle home as part of its search path. When you start R, the path above will be included, and future R package installations will also be saved to $ORACLE_HOME/R/library. This path should be writable by the user oracle, or the userid for the DBA tasked with installing R packages.Binary package compiled with different version of RBy default, R will install pre-compiled versions of packages if they are found. If the version of R under which the package was compiled does not match your installed version of R you will get an error message:Warning message: package ‘xxx’ was built under R version 3.0.0The solution is to download the package source and build it for your version of R.$ wget http://cran.r-project.org/src/contrib/Archive/arules/arules_1.1-1.tar.gz$ R CMD INSTALL arules_1.1-1.tar.gzFor use with ORE:$ ORE CMD INSTALL arules_1.1-1.tar.gzUnable to execute files in /tmp directoryBy default, R uses the /tmp directory to install packages. On security conscious machines, the /tmp directory is often marked as "noexec" in the /etc/fstab file. This means that no file under /tmp can ever be executed, and users who attempt to install R package will receive an error:ERROR: 'configure' exists but is not executable -- see the 'R Installation and Administration Manual’The solution is to set the TMP and TMPDIR environment variables to a location which R will use as the compilation directory. For example:$ mkdir <some path>/tmp$ export TMPDIR= <some path>/tmp$ export TMP= <some path>/tmpThis error typically appears on Linux client machines and not database servers, as Oracle Database writes to the value of the TMP environment variable for several tasks, including holding temporary files during database installation. 7. Creating your own R packageCreating your own package and submitting to CRAN is for advanced users, but it is not difficult. The procedure to follow, along with details of R's package system, is detailed in the Writing R Extensions manual.

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