Tag: Library

Viva Libre Office

WordPerfect 5.1 for DOS.
Image via Wikipedia

The Document Foundation is happy to announce the release candidate of
LibreOffice 3.3.1. This release candidate is the first in a series of
frequent bugfix releases on top of our LibreOffice 3.3 product. Please
be aware that LibreOffice 3.3.1 RC1 is not yet ready for production
use, you should continue to use LibreOffice for that.

http://listarchives.documentfoundation.org/www/announce/msg00028.html

Following is the list of changes against LibreOffice 3.3:

Key changes at a glance:

* Numerous translation updates
* new mimetype icons for LibreOffice – explained here:
http://luxate.blogspot.com/2011/01/not-even-included-but-already-improved.html
* quite a few crasher fixes

Detailed change log:

* translation updates
* Removed old/unmaintained icon themes
* Fix for https://bugzilla.novell.com/show_bug.cgi?id=664516: Don’t
use a reference or the default formula string will be changed
* Install bash completion for oo* wrappers when enabled
(https://bugzilla.novell.com/show_bug.cgi?id=665402)
* Build fix: get the stlport compat workaround working for gcc 4.6.0
* Build fix: no ddraw.h or ddraw.lib in the June 2010 DirectX SDK,
removed usage
* Windows installer: padded nologobanner.bmp, new size is 102×58
* removed gd – Gaelic, ky – Kirghiz, pap – Papiamento, ti – Tigrinya,
ms – Malay, ps – Pashto, ur – Urdu. UI localization does not exist
in these languages. So it makes no sense to ship packages.
* Build fix: pass thru PYTHON, found by configure. Will be used by
filter/source/config/fragments/makefile.mk.
* Upgraded libwpd (WordPerfect filter) to 0.9.1
* Fixed BrOffice Windows start menu branding
* Removed language code ‘kid’. kid is not Koshin, but key id pseudo
language which is good for debugging UI but should no be included
in the product
* Added ca_XV and ast language/local name and description
* Fixed incorrect page number in page preview mode
(https://bugs.freedesktop.org/show_bug.cgi?id=33155). When the
window is large enough to show several ‘Page X’ strings,
the page number was not properly incremented.
* Fixed incorrect import of cell attributes from Excel
documents. When a cell with non-default formatting attribute starts
with non-first row in a column, the filter would incorrectly apply
the same format to all the cells above it if they didn’t have any
formats.
* Ubuntu: fix for lp#696527 – enable human icon theme in LibreOffice
* Fix for https://bugzilla.redhat.com/show_bug.cgi?id=673819 crash on
changing position of drawing object in header.
* Changed OpenOffice.org to LibreOffice in nsplugin
* Added Occitan dictionary
* Added Ukrainian dictionaries
* Fix window focus for langpack installation on Mac –
https://bugs.freedesktop.org/show_bug.cgi?id=33056
* Added/modified NLPsolver translations from Pootle
* Fix for https://bugzilla.novell.com/show_bug.cgi?id=655763
* Fix for RTF export crasher
(https://bugzilla.novell.com/show_bug.cgi?id=656503)
* Use LibreOffice as product name for EPS Creator header
* Parse svg ‘color’ property (fixes
https://bugs.freedesktop.org/show_bug.cgi?id=33551)
* Use double instead of float in writerfilter import
* Build fix: use PYTHON as passed through by set_soenv.in.
* Fix for https://bugs.freedesktop.org/show_bug.cgi?id=33237 remove
debug line
* Fix for https://bugs.freedesktop.org/show_bug.cgi?id=33237 – fixes
ole object import for writer (docx)
* Fix for https://bugs.freedesktop.org/show_bug.cgi?id=33249 –
rename OOo -> LibO on Getting Support Page
* Fix ooxml import: handle css::table::BorderLine in addition to
css::table::BorderLine2 That means that table cell properties are
correctly set on import again.
* Fix for https://bugs.freedesktop.org/show_bug.cgi?id=33258 –
wikihelp: Improve the check for existence of the localized help.
* Fix for https://bugs.freedesktop.org/show_bug.cgi?id=33994 – fixes
several crashes around config UNO API
* Fix for https://bugs.freedesktop.org/show_bug.cgi?id=30879
* Fix for https://bugs.freedesktop.org/show_bug.cgi?id=32872 –
Implementation names weren’t matching with xcu.
* Fix: don’t pushback and process a corrupt extension
* Fix: wikihelp – do not check for existence of the localized
help. In case we do not have the help installed, it is up to the
online service to decide the fallback in case a language version is
not available.
* Fix README: change su urpmi to sudo urpmi for Mandriva section
* Fix README formatting –
https://bugs.freedesktop.org/show_bug.cgi?id=32741 – using CRLF
instead of LF on WIN platform
* Fix README: word wrap at column 75 for better readability
* Build fix: KDE3 library search order
(https://bugs.freedesktop.org/show_bug.cgi?id=32797). Use LINKFLAGS
instead of STDLIBS.
* Start using technical.dic instead of oracle.dic
(https://bugs.freedesktop.org/show_bug.cgi?id=31798)
* Build fix: add explicit QRegion* for clipRegion to fix compile of
kde backend
* Cleanup: removed obsolete m_bSingleAltPress
* Remove the menu when Left Alt Key was pressed for GTK
* Fix for https://bugs.freedesktop.org/show_bug.cgi?id=33459: use
year of era in long format for zh_TW by default
* Fix wrong collation for Catalan language
* Fix for https://bugs.freedesktop.org/show_bug.cgi?id=31271 wrong
line break with “(”
* Fix for https://bugs.freedesktop.org/show_bug.cgi?id=32561 – crash
when iterating over the database types.
* Default currency for Estonia should be Euro – fixes
https://bugs.freedesktop.org/show_bug.cgi?id=33160
* Avoid a pointless GetHelpText() call in the toolbox. Fixes
https://bugs.freedesktop.org/show_bug.cgi?id=33315. GetHelpText()
can be quite heavy, see
https://bugs.freedesktop.org/show_bug.cgi?id=33088.
* Paint toolbar handle positioned properly
(https://bugs.freedesktop.org/show_bug.cgi?id=32558)
* Build fix: move cxxabi.h after stl headers to workaround gcc 4.6.0
and stlport
* Fix for https://bugs.freedesktop.org/show_bug.cgi?id=33355 –
manipulate also the C runtime’s environment
* Fix for CTL/Other Default Font #i25247#, #i25561#, #i48064#,
#i92341#
* RTF export crasher
(https://bugzilla.novell.com/show_bug.cgi?id=656503)
* Fixed an infinite loop in RTF exporter
* UI: translations need more space on word count dialog, made space
for it.
* Fix for https://bugzilla.novell.com/show_bug.cgi?id=660816 improve
formfield checkbox binary export (and import)

Again a BIG Thank You!

Again whats Libre Office

What does LibreOffice give you?

Writer is the word processor inside LibreOffice. Use it for everything, from dashing off a quick letter to producing an entire book with tables of contents, embedded illustrations, bibliographies and diagrams. The while-you-type auto-completion, auto-formatting and automatic spelling checking make difficult tasks easy (but are easy to disable if you prefer). Writer is powerful enough to tackle desktop publishing tasks such as creating multi-column newsletters and brochures. The only limit is your imagination.

Calc tames your numbers and helps with difficult decisions when you’re weighing the alternatives. Analyze your data with Calc and then use it to present your final output. Charts and analysis tools help bring transparency to your conclusions. A fully-integrated help system makes easier work of entering complex formulas. Add data from external databases such as SQL or Oracle, then sort and filter them to produce statistical analyses. Use the graphing functions to display large number of 2D and 3D graphics from 13 categories, including line, area, bar, pie, X-Y, and net – with the dozens of variations available, you’re sure to find one that suits your project.

Impress is the fastest and easiest way to create effective multimedia presentations. Stunning animation and sensational special effects help you convince your audience. Create presentations that look even more professional than the standard presentations you commonly see at work. Get your collegues’ and bosses’ attention by creating something a little bit different.

Draw lets you build diagrams and sketches from scratch. A picture is worth a thousand words, so why not try something simple with box and line diagrams? Or else go further and easily build dynamic 3D illustrations and special effects. It’s as simple or as powerful as you want it to be.

Base is the database front-end of the LibreOffice suite. With Base, you can seamlessly integrate your existing database structures into the other components of LibreOffice, or create an interface to use and administer your data as a stand-alone application. You can use imported and linked tables and queries from MySQL, PostgreSQL or Microsoft Access and many other data sources, or design your own with Base, to build powerful front-ends with sophisticated forms, reports and views. Support is built-in or easily addable for a very wide range of database products, notably the standardly-provided HSQL, MySQL, Adabas D, Microsoft Access and PostgreSQL.

Math is a simple equation editor that lets you lay-out and display your mathematical, chemical, electrical or scientific equations quickly in standard written notation. Even the most-complex calculations can be understandable when displayed correctly. E=mc2.

LibreOffice also comes configured with a PDF file creator, meaning you can distribute documents that you’re sure can be opened and read by users of almost any computing device or operating system.

Download LibreOffice now and try it out today.

http://www.libreoffice.org/features/

 

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R for Analytics is now live

Okay, through the weekend I created a website for a few of my favourite things.

It’s on at https://rforanalytics.wordpress.com/

Graphical User Interfaces for R

 

Jerry Rubin said: “Don’t trust anyone over thirty

I dont trust anyone not using atleast one R GUI. Here’s a list of the top 10.

 

Code Enhancers for R

Here is a list of top 5 code enhancers,editors in R

R Commercial Software

A list of companies and software making (and) selling R software (and) services. Hint- it is almost 5 (unless I missed someone)

R Graphs Resources

R’s famous graphing capabilities and equally famous learning curve can be made a bit more humane- using some of these resources.

Internet Browsing

Because that’s what I do (all I do as per my cat) , and I am pretty good at it.

Using R from other Software

R can be used successfully from a lot of analytical software including some surprising ones praising the great 3000 packages library.

(to be continued- as I find more stuff I will keep it there, some ideas- database access from R, prominent R consultants, prominent R packages, famous R interviewees 😉 )

ps- The quote from Jerry Rubin seems funny for a while. I turn 34 this year.

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GrapheR

GNU General Public License
Image via Wikipedia

GrapherR

GrapheR is a Graphical User Interface created for simple graphs.

Depends: R (>= 2.10.0), tcltk, mgcv
Description: GrapheR is a multiplatform user interface for drawing highly customizable graphs in R. It aims to be a valuable help to quickly draw publishable graphs without any knowledge of R commands. Six kinds of graphs are available: histogram, box-and-whisker plot, bar plot, pie chart, curve and scatter plot.
License: GPL-2
LazyLoad: yes
Packaged: 2011-01-24 17:47:17 UTC; Maxime
Repository: CRAN
Date/Publication: 2011-01-24 18:41:47

More information about GrapheR at CRAN
Path: /cran/new | permanent link

Advantages of using GrapheR

  • It is bi-lingual (English and French) and can import in text and csv files
  • The intention is for even non users of R, to make the simple types of Graphs.
  • The user interface is quite cleanly designed. It is thus aimed as a data visualization GUI, but for a more basic level than Deducer.
  • Easy to rename axis ,graph titles as well use sliders for changing line thickness and color

Disadvantages of using GrapheR

  • Lack of documentation or help. Especially tips on mouseover of some options should be done.
  • Some of the terms like absicca or ordinate axis may not be easily understood by a business user.
  • Default values of color are quite plain (black font on white background).
  • Can flood terminal with lots of repetitive warnings (although use of warnings() function limits it to top 50)
  • Some of axis names can be auto suggested based on which variable s being chosen for that axis.
  • Package name GrapheR refers to a graphical calculator in Mac OS – this can hinder search engine results

Using GrapheR

  • Data Input -Data Input can be customized for CSV and Text files.
  • GrapheR gives information on loaded variables (numeric versus Factors)
  • It asks you to choose the type of Graph 
  • It then asks for usual Graph Inputs (see below). Note colors can be customized (partial window). Also number of graphs per Window can be easily customized 
  • Graph is ready for publication



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Google Books Ngram Viewer

Here is a terrific data visualization from Google based on their digitized books collection. How does it work, basically you can test the frequency of various words across time periods from 1700s to 2010.

Like the frequency and intensity of kung fu vs yoga, or pizza versus hot dog. The basic datasets scans millions /billions of words.

Here is my yoga vs kung fu vs judo graph.

http://ngrams.googlelabs.com/info

What’s all this do?

When you enter phrases into the Google Books Ngram Viewer, it displays a graph showing how those phrases have occurred in a corpus of books (e.g., “British English”, “English Fiction”, “French”) over the selected years. Let’s look at a sample graph:

This shows trends in three ngrams from 1950 to 2000: “nursery school” (a 2-gram or bigram), “kindergarten” (a 1-gram or unigram), and “child care” (another bigram). What the y-axis shows is this: of all the bigrams contained in our sample of books written in English and published in the United States, what percentage of them are “nursery school” or “child care”? Of all the unigrams, what percentage of them are “kindergarten”? Here, you can see that use of the phrase “child care” started to rise in the late 1960s, overtaking “nursery school” around 1970 and then “kindergarten” around 1973. It peaked shortly after 1990 and has been falling steadily since.

(Interestingly, the results are noticeably different when the corpus is switched to British English.)

Corpora

Below are descriptions of the corpora that can be searched with the Google Books Ngram Viewer. All of these corpora were generated in July 2009; we will update these corpora as our book scanning continues, and the updated versions will have distinct persistent identifiers.

Informal corpus name Persistent identifier Description
American English googlebooks-eng-us-all-20090715 Same filtering as the English corpus but further restricted to books published in the United States.
British English googlebooks-eng-gb-all-20090715 Same filtering as the English corpus but further restricted to books published in Great Britain.
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Choosing R for business – What to consider?

A composite of the GNU logo and the OSI logo, ...
Image via Wikipedia

Additional features in R over other analytical packages-

1) Source Code is given to ensure complete custom solution and embedding for a particular application. Open source code has an advantage that is extensively peer- reviewed in Journals and Scientific Literature.  This means bugs will found, shared and corrected transparently.

2) Wide literature of training material in the form of books is available for the R analytical platform.

3) Extensively the best data visualization tools in analytical software (apart from Tableau Software ‘s latest version). The extensive data visualization available in R is of the form a variety of customizable graphs, as well as animation. The principal reason third-party software initially started creating interfaces to R is because the graphical library of packages in R is more advanced as well as rapidly getting more features by the day.

4) Free in upfront license cost for academics and thus budget friendly for small and large analytical teams.

5) Flexible programming for your data environment. This includes having packages that ensure compatibility with Java, Python and C++.

 

6) Easy migration from other analytical platforms to R Platform. It is relatively easy for a non R platform user to migrate to R platform and there is no danger of vendor lock-in due to the GPL nature of source code and open community.

Statistics are numbers that tell (descriptive), advise ( prescriptive) or forecast (predictive). Analytics is a decision-making help tool. Analytics on which no decision is to be made or is being considered can be classified as purely statistical and non analytical. Thus ease of making a correct decision separates a good analytical platform from a not so good analytical platform. The distinction is likely to be disputed by people of either background- and business analysis requires more emphasis on how practical or actionable the results are and less emphasis on the statistical metrics in a particular data analysis task. I believe one clear reason between business analytics is different from statistical analysis is the cost of perfect information (data costs in real world) and the opportunity cost of delayed and distorted decision-making.

Specific to the following domains R has the following costs and benefits

  • Business Analytics
    • R is free per license and for download
    • It is one of the few analytical platforms that work on Mac OS
    • It’s results are credibly established in both journals like Journal of Statistical Software and in the work at LinkedIn, Google and Facebook’s analytical teams.
    • It has open source code for customization as per GPL
    • It also has a flexible option for commercial vendors like Revolution Analytics (who support 64 bit windows) as well as bigger datasets
    • It has interfaces from almost all other analytical software including SAS,SPSS, JMP, Oracle Data Mining, Rapid Miner. Existing license holders can thus invoke and use R from within these software
    • Huge library of packages for regression, time series, finance and modeling
    • High quality data visualization packages
    • Data Mining
      • R as a computing platform is better suited to the needs of data mining as it has a vast array of packages covering standard regression, decision trees, association rules, cluster analysis, machine learning, neural networks as well as exotic specialized algorithms like those based on chaos models.
      • Flexibility in tweaking a standard algorithm by seeing the source code
      • The RATTLE GUI remains the standard GUI for Data Miners using R. It was created and developed in Australia.
      • Business Dashboards and Reporting
      • Business Dashboards and Reporting are an essential piece of Business Intelligence and Decision making systems in organizations. R offers data visualization through GGPLOT, and GUI like Deducer and Red-R can help even non R users create a metrics dashboard
        • For online Dashboards- R has packages like RWeb, RServe and R Apache- which in combination with data visualization packages offer powerful dashboard capabilities.
        • R can be combined with MS Excel using the R Excel package – to enable R capabilities to be imported within Excel. Thus a MS Excel user with no knowledge of R can use the GUI within the R Excel plug-in to use powerful graphical and statistical capabilities.

Additional factors to consider in your R installation-

There are some more choices awaiting you now-
1) Licensing Choices-Academic Version or Free Version or Enterprise Version of R

2) Operating System Choices-Which Operating System to choose from? Unix, Windows or Mac OS.

3) Operating system sub choice- 32- bit or 64 bit.

4) Hardware choices-Cost -benefit trade-offs for additional hardware for R. Choices between local ,cluster and cloud computing.

5) Interface choices-Command Line versus GUI? Which GUI to choose as the default start-up option?

6) Software component choice- Which packages to install? There are almost 3000 packages, some of them are complimentary, some are dependent on each other, and almost all are free.

7) Additional Software choices- Which additional software do you need to achieve maximum accuracy, robustness and speed of computing- and how to use existing legacy software and hardware for best complementary results with R.

1) Licensing Choices-
You can choose between two kinds of R installations – one is free and open source from http://r-project.org The other R installation is commercial and is offered by many vendors including Revolution Analytics. However there are other commercial vendors too.

Commercial Vendors of R Language Products-
1) Revolution Analytics http://www.revolutionanalytics.com/
2) XL Solutions- http://www.experience-rplus.com/
3) Information Builder – Webfocus RStat -Rattle GUI http://www.informationbuilders.com/products/webfocus/PredictiveModeling.html
4) Blue Reference- Inference for R http://inferenceforr.com/default.aspx

  1. Choosing Operating System
      1. Windows

 

Windows remains the most widely used operating system on this planet. If you are experienced in Windows based computing and are active on analytical projects- it would not make sense for you to move to other operating systems. This is also based on the fact that compatibility problems are minimum for Microsoft Windows and the help is extensively documented. However there may be some R packages that would not function well under Windows- if that happens a multiple operating system is your next option.

        1. Enterprise R from Revolution Analytics- Enterprise R from Revolution Analytics has a complete R Development environment for Windows including the use of code snippets to make programming faster. Revolution is also expected to make a GUI available by 2011. Revolution Analytics claims several enhancements for it’s version of R including the use of optimized libraries for faster performance.
      2. MacOS

 

Reasons for choosing MacOS remains its considerable appeal in aesthetically designed software- but MacOS is not a standard Operating system for enterprise systems as well as statistical computing. However open source R claims to be quite optimized and it can be used for existing Mac users. However there seem to be no commercially available versions of R available as of now for this operating system.

      1. Linux

 

        1. Ubuntu
        2. Red Hat Enterprise Linux
        3. Other versions of Linux

 

Linux is considered a preferred operating system by R users due to it having the same open source credentials-much better fit for all R packages and it’s customizability for big data analytics.

Ubuntu Linux is recommended for people making the transition to Linux for the first time. Ubuntu Linux had an marketing agreement with revolution Analytics for an earlier version of Ubuntu- and many R packages can  installed in a straightforward way as Ubuntu/Debian packages are available. Red Hat Enterprise Linux is officially supported by Revolution Analytics for it’s enterprise module. Other versions of Linux popular are Open SUSE.

      1. Multiple operating systems-
        1. Virtualization vs Dual Boot-

 

You can also choose between having a VMware VM Player for a virtual partition on your computers that is dedicated to R based computing or having operating system choice at the startup or booting of your computer. A software program called wubi helps with the dual installation of Linux and Windows.

  1. 64 bit vs 32 bit – Given a choice between 32 bit versus 64 bit versions of the same operating system like Linux Ubuntu, the 64 bit version would speed up processing by an approximate factor of 2. However you need to check whether your current hardware can support 64 bit operating systems and if so- you may want to ask your Information Technology manager to upgrade atleast some operating systems in your analytics work environment to 64 bit operating systems.

 

  1. Hardware choices- At the time of writing this book, the dominant computing paradigm is workstation computing followed by server-client computing. However with the introduction of cloud computing, netbooks, tablet PCs, hardware choices are much more flexible in 2011 than just a couple of years back.

Hardware costs are a significant cost to an analytics environment and are also  remarkably depreciated over a short period of time. You may thus examine your legacy hardware, and your future analytical computing needs- and accordingly decide between the various hardware options available for R.
Unlike other analytical software which can charge by number of processors, or server pricing being higher than workstation pricing and grid computing pricing extremely high if available- R is well suited for all kinds of hardware environment with flexible costs. Given the fact that R is memory intensive (it limits the size of data analyzed to the RAM size of the machine unless special formats and /or chunking is used)- it depends on size of datasets used and number of concurrent users analyzing the dataset. Thus the defining issue is not R but size of the data being analyzed.

    1. Local Computing- This is meant to denote when the software is installed locally. For big data the data to be analyzed would be stored in the form of databases.
      1. Server version- Revolution Analytics has differential pricing for server -client versions but for the open source version it is free and the same for Server or Workstation versions.
      2. Workstation
    2. Cloud Computing- Cloud computing is defined as the delivery of data, processing, systems via remote computers. It is similar to server-client computing but the remote server (also called cloud) has flexible computing in terms of number of processors, memory, and data storage. Cloud computing in the form of public cloud enables people to do analytical tasks on massive datasets without investing in permanent hardware or software as most public clouds are priced on pay per usage. The biggest cloud computing provider is Amazon and many other vendors provide services on top of it. Google is also coming for data storage in the form of clouds (Google Storage), as well as using machine learning in the form of API (Google Prediction API)
      1. Amazon
      2. Google
      3. Cluster-Grid Computing/Parallel processing- In order to build a cluster, you would need the RMpi and the SNOW packages, among other packages that help with parallel processing.
    3. How much resources
      1. RAM-Hard Disk-Processors- for workstation computing
      2. Instances or API calls for cloud computing
  2. Interface Choices
    1. Command Line
    2. GUI
    3. Web Interfaces
  3. Software Component Choices
    1. R dependencies
    2. Packages to install
    3. Recommended Packages
  4. Additional software choices
    1. Additional legacy software
    2. Optimizing your R based computing
    3. Code Editors
      1. Code Analyzers
      2. Libraries to speed up R

citation-  R Development Core Team (2010). R: A language and environment for statistical computing. R Foundation for Statistical Computing,Vienna, Austria. ISBN 3-900051-07-0, URL http://www.R-project.org.

(Note- this is a draft in progress)

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Trying out Google Prediction API from R

Ubuntu Login
Image via Wikipedia

So I saw the news at NY R Meetup and decided to have a go at Prediction API Package (which first started off as a blog post at

http://onertipaday.blogspot.com/2010/11/r-wrapper-for-google-prediction-api.html

1)My OS was Ubuntu 10.10 Netbook

Ubuntu has a slight glitch plus workaround for installing the RCurl package on which the Google Prediction API is dependent- you need to first install this Ubuntu package for RCurl to install libcurl4-gnutls-dev

Once you install that using Synaptic,

Simply start R

2) Install Packages rjson and Rcurl using install.packages and choosing CRAN

Since GooglePredictionAPI is not yet on CRAN

,

3) Download that package from

https://code.google.com/p/google-prediction-api-r-client/downloads/detail?name=googlepredictionapi_0.1.tar.gz&can=2&q=

You need to copy this downloaded package to your “first library ” folder

When you start R, simply run

.libPaths()[1]

and thats the folder you copy the GooglePredictionAPI package  you downloaded.

5) Now the following line works

  1. Under R prompt,
    2. > install.packages("googlepredictionapi_0.1.tar.gz", repos=NULL, type="source")

6) Uploading data to Google Storage using the GUI (rather than gs util)

Just go to https://sandbox.google.com/storage/

and thats the Google Storage manager

Notes on Training Data-

Use a csv file

The first column is the score column (like 1,0 or prediction score)

There are no headers- so delete headers from data file and move the dependent variable to the first column  (Note I used data from the kaggle contest for R package recommendation at

http://kaggle.com/R?viewtype=data )

6) The good stuff:

Once you type in the basic syntax, the first time it will ask for your Google Credentials (email and password)

It then starts showing you time elapsed for training.

Now you can disconnect and go off (actually I got disconnected by accident before coming back in a say 5 minutes so this is the part where I think this is what happened is why it happened, dont blame me, test it for yourself) –

and when you come back (hopefully before token expires)  you can see status of your request (see below)

> library(rjson)
> library(RCurl)
Loading required package: bitops
> library(googlepredictionapi)
> my.model <- PredictionApiTrain(data="gs://numtraindata/training_data")
The request for training has sent, now trying to check if training is completed
Training on numtraindata/training_data: time:2.09 seconds
Training on numtraindata/training_data: time:7.00 seconds

7)

Note I changed the format from the URL where my data is located- simply go to your Google Storage Manager and right click on the file name for link address  ( https://sandbox.google.com/storage/numtraindata/training_data.csv)

to gs://numtraindata/training_data  (that kind of helps in any syntax error)

8) From the kind of high level instructions at  https://code.google.com/p/google-prediction-api-r-client/, you could also try this on a local file

Usage

## Load googlepredictionapi and dependent libraries
library(rjson)
library(RCurl)
library(googlepredictionapi)

## Make a training call to the Prediction API against data in the Google Storage.
## Replace MYBUCKET and MYDATA with your data.
my.model <- PredictionApiTrain(data="gs://MYBUCKET/MYDATA")

## Alternatively, make a training call against training data stored locally as a CSV file.
## Replace MYPATH and MYFILE with your data.
my.model <- PredictionApiTrain(data="MYPATH/MYFILE.csv")

At the time of writing my data was still getting trained, so I will keep you posted on what happens.

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How to Analyze Wikileaks Data – R SPARQL

Logo for R
Image via Wikipedia

Drew Conway- one of the very very few Project R voices I used to respect until recently. declared on his blog http://www.drewconway.com/zia/

Why I Will Not Analyze The New WikiLeaks Data

and followed it up with how HE analyzed the post announcing the non-analysis.

“If you have not visited the site in a week or so you will have missed my previous post on analyzing WikiLeaks data, which from the traffic and 35 Comments and 255 Reactions was at least somewhat controversial. Given this rare spotlight I thought it would be fun to use the infochimps API to map out the geo-location of everyone that visited the blog post over the last few days. Unfortunately, after nearly two years with the same web hosting service, only today did I realize that I was not capturing daily log files for my domain”

Anyways – non American users of R Project can analyze the Wikileaks data using the R SPARQL package I would advise American friends not to use this approach or attempt to analyze any data because technically the data is still classified and it’s possession is illegal (which is the reason Federal employees and organizations receiving federal funds have advised not to use this or any WikiLeaks dataset)

https://code.google.com/p/r-sparql/

Overview

R is a programming language designed for statistics.

R Sparql allows you to run SPARQL Queries inside R and store it as a R data frame.

The main objective is to allow the integration of Ontologies with Statistics.

It requires Java and rJava installed.

Example (in R console):

> library(sparql)> data <- query("SPARQL query>","RDF file or remote SPARQL Endpoint")

and the data in a remote SPARQL  http://www.ckan.net/package/cablegate

SPARQL is an easy language to pick  up, but dammit I am not supposed to blog on my vacations.

http://code.google.com/p/r-sparql/wiki/GettingStarted

Getting Started

1. Installation

1.1 Make sure Java is installed and is the default JVM:

$ sudo apt-get install sun-java6-bin sun-java6-jre sun-java6-jdk$ sudo update-java-alternatives -s java-6-sun

1.2 Configure R to use the correct version of Java

$ sudo R CMD javareconf

1.3 Install the rJava library

$ R> install.packages("rJava")> q()

1.4 Download and install the sparql library

Download: http://code.google.com/p/r-sparql/downloads/list

$ R CMD INSTALL sparql-0.1-X.tar.gz

2. Executing a SPARQL query

2.1 Start R

#Load the librarylibrary(sparql)#Run the queryresult <- query("SELECT ... ", "http://...")#Print the resultprint(result)

3. Examples

3.1 The Query can be a string or a local file:

query("SELECT ?date ?number ?season WHERE {  ... }", "local-file.rdf")
query("my-query.rq", "local-file.rdf")

The package will detect if my-query.rq exists and will load it from the file.

3.3 The uri can be a file or an url (for remote queries):

query("SELECT ... ","local-file.db")
query("SELECT ... ","http://dbpedia.org/sparql")

3.4 Get some examples here: http://code.google.com/p/r-sparql/downloads/list

SPARQL Tutorial-

http://openjena.org/ARQ/Tutorial/index.html

Also read-

http://webr3.org/blog/linked-data/virtuoso-6-sparqlgeo-and-linked-data/

and from the favorite blog of Project R- Also known as NY Times

http://bits.blogs.nytimes.com/2010/11/15/sorting-through-the-government-data-explosion/?twt=nytimesbits

In May 2009, the Obama administration started putting raw 
government data on the Web. 
It started with 47 data sets. Today, there are more than
 270,000 government data sets, spanning every imaginable 
category from public health to foreign aid.

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