The Year 2010

Nokia N800 internet tablet, with open source s...
Image via Wikipedia

My annual traffic to this blog was almost 99,000 . Add in additional views on networking sites plus the 400 plus RSS readers- so I can say traffic was 1,20,000 for 2010. Nice. Thanks for reading and hope it was worth your time. (this is a long post and will take almost 440 secs to read but the summary is just given)

My intent is either to inform you, give something useful or atleast something interesting.

see below-

Jan Feb Mar Apr May Jun
2010 6,311 4,701 4,922 5,463 6,493 4,271
Jul Aug Sep Oct Nov Dec Total
5,041 5,403 17,913 16,430 11,723 10,096 98,767

 

 

Sandro Saita from http://www.dataminingblog.com/ just named me for an award on his blog (but my surname is ohRi , Sandro left me without an R- What would I be without R :)) ).

Aw! I am touched. Google for “Data Mining Blog” and Sandro is the best that it is in data mining writing.

DMR People Award 2010
There are a lot of active people in the field of data mining. You can discuss with them on forums. You can read their blogs. You can also meet them in events such as PAW or KDD. Among the people I follow on a regular basis, I have elected:

Ajay Ori

He has been very active in 2010, especially on his blog . Good work Ajay and continue sharing your experience with us!”

What did I write in 2010- stuff.

What did you read on this blog- well thats the top posts list.

2009-12-31 to Today

Title Views
Home page More stats 21,150
Top 10 Graphical User Interfaces in Statistical Software More stats 6,237
Wealth = function (numeracy, memory recall) More stats 2,014
Matlab-Mathematica-R and GPU Computing More stats 1,946
The Top Statistical Softwares (GUI) More stats 1,405
About DecisionStats More stats 1,352
Using Facebook Analytics (Updated) More stats 1,313
Test drive a Chrome notebook. More stats 1,170
Top ten RRReasons R is bad for you ? More stats 1,157
Libre Office More stats 1,151
Interview Hadley Wickham R Project Data Visualization Guru More stats 1,007
Using Red R- R with a Visual Interface More stats 854
SAS Institute files first lawsuit against WPS- Episode 1 More stats 790
Interview Professor John Fox Creator R Commander More stats 764
R Package Creating More stats 754
Windows Azure vs Amazon EC2 (and Google Storage) More stats 726
Norman Nie: R GUI and More More stats 716
Startups for Geeks More stats 682
Google Maps – Jet Ski across Pacific Ocean More stats 670
Not so AWkward after all: R GUI RKWard More stats 579
Red R 1.8- Pretty GUI More stats 570
Parallel Programming using R in Windows More stats 569
R is an epic fail or is it just overhyped More stats 559
Enterprise Linux rises rapidly:New Report More stats 537
Rapid Miner- R Extension More stats 518
Creating a Blog Aggregator for free More stats 504
So which software is the best analytical software? Sigh- It depends More stats 473
Revolution R for Linux More stats 465
John Sall sets JMP 9 free to tango with R More stats 460

So how do people come here –

well I guess I owe Tal G for almost 9000 views ( incidentally I withdrew posting my blog from R- Bloggers and Analyticbridge blogs – due to SEO keyword reasons and some spam I was getting see (below))

http://r-bloggers.com is still the CAT’s whiskers and I read it  a lot.

I still dont know who linked my blog to a free sex movie site with 400 views but I have a few suspects.

2009-12-31 to Today

Referrer Views
r-bloggers.com 9,131
Reddit 3,829
rattle.togaware.com 1,500
Twitter 1,254
Google Reader 1,215
linkedin.com 717
freesexmovie.irwanaf.com 422
analyticbridge.com 341
Google 327
coolavenues.com 322
Facebook 317
kdnuggets.com 298
dataminingblog.com 278
en.wordpress.com 185
google.co.in 151
xianblog.wordpress.com 130
inside-r.org 124
decisionstats.com 119
ifreestores.com 117
bits.blogs.nytimes.com 108

Still reading this post- gosh let me sell you some advertising. It is only $100 a month (yes its a recession)

Advertisers are treated on First in -Last out (FILO)

I have been told I am obsessed with SEO , but I dont care much for search engines apart from Google, and yes SEO is an interesting science (they should really re name it GEO or Google Engine Optimization)

Apparently Hadley Wickham and Donald Farmer are big keywords for me so I should be more respectful I guess.

Search Terms for 365 days ending 2010-12-31 (Summarized)

2009-12-31 to Today

Search Views
libre office 925
facebook analytics 798
test drive a chrome notebook 467
test drive a chrome notebook. 215
r gui 203
data mining 163
wps sas lawsuit 158
wordle.net 133
wps sas 123
google maps jet ski 123
test drive chrome notebook 96
sas wps 89
sas wps lawsuit 85
chrome notebook test drive 83
decision stats 83
best statistics software 74
hadley wickham 72
google maps jetski 72
libreoffice 70
doug savage 65
hive tutorial 58
funny india 56
spss certification 52
donald farmer microsoft 51
best statistical software 49

What about outgoing links? Apparently I need to find a way to ask Google to pay me for the free advertising I gave their chrome notebook launch. But since their search engine and browser is free to me, guess we are even steven.

Clicks for 365 days ending 2010-12-31 (Summarized)

2009-12-31 to Today

URL Clicks
rattle.togaware.com 378
facebook.com/Decisionstats 355
rapid-i.com/content/view/182/196 319
services.google.com/fb/forms/cr48basic 313
red-r.org 228
decisionstats.wordpress.com/2010/05/07/the-top-statistical-softwares-gui 199
teamwpc.co.uk/products/wps 162
r4stats.com/popularity 148
r-statistics.com/2010/04/r-and-the-google-summer-of-code-2010-accepted-students-and-projects 138
socserv.mcmaster.ca/jfox/Misc/Rcmdr 138
spss.com/certification 116
learnr.wordpress.com 114
dudeofdata.com/decisionstats 108
r-project.org 107
documentfoundation.org/faq 104
goo.gl/maps/UISY 100
inside-r.org/download 96
en.wikibooks.org/wiki/R_Programming 92
nytimes.com/external/readwriteweb/2010/12/07/07readwriteweb-report-google-offering-chrome-notebook-test-11919.html 92
sourceforge.net/apps/mediawiki/rkward/index.php?title=Main_Page 92
analyticdroid.togaware.com 88
yeroon.net/ggplot2 87

so in 2010,

SAS remained top daddy in business analytics,

R made revolutionary strides in terms of new packages,

JMP  launched a new version,

SPSS got integrated with Cognos,

Oracle sued Google and did build a great Data Mining GUI,

Libre Office gave you a non Oracle Open office ( or open even more office)

2011 looks like  a fun year. Have safe partying .

Zen and the art of applying T tests to Spam Data

Decisionstats traffic seemed up mmm but Spam is way way up

Whos spamming my dear bloggie

hmm

is it the russians doing a link spam. unlikely they dont bot against Akismet that much (as they fail)

And Captcha can be failed by python (apparently. sigh)

Is there a co relation of certain tags of posts, and count of spam- hoping to distort say blogs’s search engine rankings for SAS WPS Lawsuit in Google or jet ski across  pacific in Google.

Sigh- an old retired outlaw black hat is never kept in peace. Try doing a blog search for R in Google- Revo  is now down to number 7 (which is hmm given Google Instant)

Of course I think too much about SEO, but I dont run CPC ads- I made much more money when traffic is low – say 5-10 small businesses needing to forecast their sales .

and enjoy your Thanksgiving. Remember the Indians bring the Turkeys.

 

The SEO mess on joining blog aggregators

 

Mug shot of Paris Hilton.
Image via Wikipedia

 

If you are an analytics blogger who writes, and is aggregated on an analytical community- read on- Here’s how blog aggregation communities can help you lose 30% of all future traffic long term, while giving you a short term.

The problem is not created by Blogging Communities (like R-Bloggers, or PlanteR, or Smart Data Collective or AnalyticBridge or even BeyeBlogs )

It is created by the way Google Page Rank is structured- you see given exactly the same content on two different we pages- Google Page Rank will place the higher Page Rank results higher. This is counter intutive and quite simple to rectify- The Google Spider can just use the Time Stamp for choosing which article was published where first (Obviously on your blog, AND then later to the aggregator).

How bad is the mess? Well joining ANY blog aggregation will lead to an instant lift of upto 10-50 % of your current traffic as similar bloggers try and read about you. However you can lose the long term 30% proportion which is a benchmark of search engine created traffic for you.

So do you opt out of blog aggregation? No. It’s a SEO mess and it’s unfair to punish your blog aggregator, most of whom are running on ad-supported sponsors or their own funds on dry fumes to publish your content. Most of the fore mentioned communities are created by excellent people I interacted with heavily- and they are genuinely motivated to give readers an easy way to keep up with blogs. Especially Smart Data Collective, Analyticbridge and R-bloggers whose founders I have known personally.

You can do one thing- create manual summaries in the excerpt feature of your blog posts- it’s just below the WordPress page. And switch your RSS feed to summary rather than full. It avoids losing keyword rank to other websites, it prevents the Blog Aggregation from gaining too much influence in key word related searches, and it keeps your whole eco system happy, Best of All it helps readers of Blog Aggregators- since most of them use a summary on the front page anyways.

An additional thought on Google Page Rank- something I have sulked over but not spoken for a long long time.  It ignores the value of reader- If Bill Gates, Steve Jobs, and 500 ceos from Fortune 500 companies read my blog but do not link to it- it will count daily traffic as 500. Probably it will give more weightage to Paris Hilton fans.

A suggestion-humbly- you can use IP Address lookup of visitors to see if traffic is coming from corporate sources or retail sources -Clicky from GetClicky does this. Use it as feedback in Google Analytics as well as Google Trends.

And maybe PageRank needs to add quantity and quality of visitors as additional variables . Do a A/B test guys some Chi Square juice- its not quite Mad Men Adverting but its still good fun.

 

PageRank
Image via Wikipedia

 

and the world is one big community as per xkcd


The auto-suggest link/tags for WP.com blogs

WordPress.com blogs have a great new option for generating tags, and links and thus improving their search engine optimization for posts.

Just go to Users-Personal Settings- and check the options shown. Thats it every time you write a post it suggests links and tags. Links are helpful for your readers (like Wikipedia links to understand dense technical jargon, or associated websites). Tags help to classify your contents so that all visitors to the web site including spiders ,search engines and your readers can search it better.

The bad thing is I need to go back to all 1025 posts on this site and auto generate tags for the archives ! Oh well. Great collaboration between zementa and Automattic for this new feature.

Google Instant could kill Black-Hat SEO

Google Instant is a relatively newer feature in Google Search Engine- it suggests websites at each type of keyword rather than wait for you to type the whole keyword.

The impact on user experience is incredible- rather than search or scroll through the results- you are more likely to click on the almost one of the ten websites you would have seen by the time you finished typing- or just clicking on the relevant ad (which probably changes on the right margin as fast as the websites below)

This spells a death for all those who indulged in black hat SEO– or link building, link exchanging- as these techniques pushed up your rank in search page only incrementally and rarely to the top 2-3 for a keyword.

Remember the size of the screen is such that each Google instant snapshot basically shows you or rather makes you focus on the top ranked search (and then presumably type on to get a newer result- rather than scroll down as the case was before).

It would be interesting to see or research the effect of keywords in the auction pricing, as well as compare those keyword pricing with Bing.com- Maybe there should be a website api tool for advertisers -like Adwords Instant that would show them the price instantly of keywords,comparison with Bing AND the search engine results for the keyword in a visual way.

Anyways- it is a incredible innovation and it is good Google is back to the math after the flings with being “Mad Men” of advertising.

and yes- I heard there is a new movie coming- it is called “The Search Engine” 🙂

An interesting web hack is Google Images Instant athttp://hartlabs.net/instant/images/

Interfaces to R

This is a fairly long post and is a basic collection  of material for a book/paper. It is on interfaces to use R. If you feel I need to add more on a  particular R interface, or if there is an error in this- please feel to contact me on twitter @decisionstats or mail ohri2007 on google mail.

R Interfaces

There are multiple ways to use the R statistical language.

Command Line- The default method is using the command prompt by the installed software on download from http://r-project.org
For windows users there is a simple GUI which has an option for Packages (loading package, installing package, setting CRAN mirror for downloading packages) , Misc (useful for listing all objects loaded in workspace as well as clearing objects to free up memory), and Help Menu.

Using Click and Point- Besides the command prompt, there are many Graphical User Interfaces which enable the analyst to use click and point methods to analyze data without getting into the details of learning complex and at times overwhelming R syntax. R GUIs are very popular both as mode of instruction in academia as well as in actual usage as it cuts down considerably on time taken to adapt to the language. As with all command line and GUI software, for advanced tweaks and techniques, command prompt will come in handy as well.

Advantages and Limitations of using Visual Programming Interfaces to R as compared to Command Line.

 

Advantages Limitations
Faster learning for new programmers Can create junk analysis by clicking menus in GUI
Easier creation of advanced models or graphics Cannot create custom functions unless you use command line
Repeatability of analysis is better Advanced techniques and custom flexibility of data handling R can be done in command line
Syntax is auto-generated Can limit scope and exposure in learning R syntax




A brief list of the notable Graphical User Interfaces is below-

1) R Commander- Basic statistics
2) Rattle- Data Mining
3) Deducer- Graphics (including GGPlot Integration) and also uses JGR (a Jave based  GUI)
4) RKward- Comprehensive R GUI for customizable graphs
5) Red-R – Dataflow programming interface using widgets

1) R Commander- R Commander was primarily created by Professor John Fox of McMaster University to cover the content of a basic statistics course. However it is extensible and many other packages can be added in menu form to it- in the form R Commander Plugins. Quite noticeably it is one of the most widely used R GUI and it also has a script window so you can write R code in combination with the menus.
As you point and click a particular menu item, the corresponding R code is automatically generated in the log window and executed.

It can be found on CRAN at http://cran.r-project.org/web/packages/Rcmdr/index.html



Advantages of Using  R Commander-
1) Useful for beginner in R language to do basic graphs and analysis and building models.
2) Has script window, output window and log window (called messages) in same screen which helps user as code is auto-generated on clicking on menus, and can be customized easily. For example in changing labels and options in Graphs.  Graphical output is shown in seperate window from output window.
3) Extensible for other R packages like qcc (for quality control), Teaching Demos (for training), survival analysis and Design of Experiments (DoE)
4) Easy to understand interface even for first time user.
5) Menu items which are not relevant are automatically greyed out- if there are only two variables, and you try to build a 3D scatterplot graph, that menu would simply not be available and is greyed out.

Comparative Disadvantages of using R Commander-
1) It is basically aimed at a statistical audience( originally students in statistics) and thus the terms as well as menus are accordingly labeled. Hence it is more of a statistical GUI rather than an analytics GUI.
2) Has limited ability to evaluate models from a business analysts perspective (ROC curve is not given as an option) even though it has extensive statistical tests for model evaluation in model sub menu. Indeed creating a Model is treated as a subsection of statistics rather than a separate menu item.
3) It is not suited for projects that do not involve advanced statistical testing and for users not proficient in statistics (particularly hypothesis testing), and for data miners.

Menu items in the R Commander window:
File Menu – For loading script files and saving Script files, Output and Workspace
It is also needed for changing the present working directory and for exiting R.
Edit Menu – For editing scripts and code in the script window.
Data Menu – For creating new dataset, inputting or importing data and manipulating data through variables. Data Import can be from text,comma separated values,clipboard, datasets from SPSS, Stata,Minitab, Excel ,dbase,  Access files or from url.
Data manipulation included deleting rows of data as well as manipulating variables.
Also this menu has the option for merging two datasets by row or columns.
Statistics Menu-This menu has options for descriptive statistics, hypothesis tests, factor analysis and clustering and also for creating models. Note there is a separate menu for evaluating the model so created.
Graphs Menu-It has options for creating various kinds of graphs including box-plot, histogram, line, pie charts and x-y plots.
The first option is color palette- it can be used for customizing the colors. It is recommended you adjust colors based on your need for publication or presentation.
A notable option is 3 D graphs for evaluating 3 variables at a time- this is really good and impressive feature and exposes the user to advanced graphs in R all at few clicks. You may want to dazzle a presentation using this graph.
Also consider scatterplot matrix graphs for graphical display of variables.
Graphical display of R surpasses any other statistical software in appeal as well as ease of creation- using GUI to create graphs can further help the user to get the most of data insights using R at a very minimum effort.
Models Menu-This is somewhat of a labeling peculiarity of R Commander as this menu is only for evaluating models which have been created using the statistics menu-model sub menu.
It includes options for graphical interpretation of model results,residuals,leverage and confidence intervals and adding back residuals to the data set.
Distributions Menu- is for cumulative probabilities, probability density, graphs of distributions, quantiles and features for standard distributions and can be used in lieu of standard statistical tables for the distributions. It has 13 standard statistical continuous distributions and 5 discrete distributions.
Tools Menu- allows you to load other packages and also load R Commander plugins (which are then added to the Interface Menu after the R Commander GUI is restarted). It also contains options sub menu for fine tuning (like opting to send output to R Menu)
Help Menu- Standard documentation and help menu. Essential reading is the short 25 page manual in it called Getting “Started With the R Commander”.

R Commander Plugins- There are twenty extensions to R Commander that greatly enhance it’s appeal -these include basic time series forecasting, survival analysis, qcc and more.

see a complete list at

  1. DoE – http://cran.r-project.org/web/packages/RcmdrPlugin.DoE/RcmdrPlugin.DoE.pdf
  2. doex
  3. EHESampling
  4. epack- http://cran.r-project.org/web/packages/RcmdrPlugin.epack/RcmdrPlugin.epack.pdf
  5. Export- http://cran.r-project.org/web/packages/RcmdrPlugin.Export/RcmdrPlugin.Export.pdf
  6. FactoMineR
  7. HH
  8. IPSUR
  9. MAc- http://cran.r-project.org/web/packages/RcmdrPlugin.MAc/RcmdrPlugin.MAc.pdf
  10. MAd
  11. orloca
  12. PT
  13. qcc- http://cran.r-project.org/web/packages/RcmdrPlugin.qcc/RcmdrPlugin.qcc.pdf and http://cran.r-project.org/web/packages/qcc/qcc.pdf
  14. qual
  15. SensoMineR
  16. SLC
  17. sos
  18. survival-http://cran.r-project.org/web/packages/RcmdrPlugin.survival/RcmdrPlugin.survival.pdf
  19. SurvivalT
  20. Teaching Demos

Note the naming convention for above e plugins is always with a Prefix of “RCmdrPlugin.” followed by the names above
Also on loading a Plugin, it must be already installed locally to be visible in R Commander’s list of load-plugin, and R Commander loads the e-plugin after restarting.Hence it is advisable to load all R Commander plugins in the beginning of the analysis session.

However the notable E Plugins are
1) DoE for Design of Experiments-
Full factorial designs, orthogonal main effects designs, regular and non-regular 2-level fractional
factorial designs, central composite and Box-Behnken designs, latin hypercube samples, and simple D-optimal designs can currently be generated from the GUI. Extensions to cover further latin hypercube designs as well as more advanced D-optimal designs (with blocking) are planned for the future.
2) Survival- This package provides an R Commander plug-in for the survival package, with dialogs for Cox models, parametric survival regression models, estimation of survival curves, and testing for differences in survival curves, along with data-management facilities and a variety of tests, diagnostics and graphs.
3) qcc -GUI for  Shewhart quality control charts for continuous, attribute and count data. Cusum and EWMA charts. Operating characteristic curves. Process capability analysis. Pareto chart and cause-and-effect chart. Multivariate control charts
4) epack- an Rcmdr “plug-in” based on the time series functions. Depends also on packages like , tseries, abind,MASS,xts,forecast. It covers Log-Exceptions garch
and following Models -Arima, garch, HoltWinters
5)Export- The package helps users to graphically export Rcmdr output to LaTeX or HTML code,
via xtable() or Hmisc::latex(). The plug-in was originally intended to facilitate exporting Rcmdr
output to formats other than ASCII text and to provide R novices with an easy-to-use,
easy-to-access reference on exporting R objects to formats suited for printed output. The
package documentation contains several pointers on creating reports, either by using
conventional word processors or LaTeX/LyX.
6) MAc- This is an R-Commander plug-in for the MAc package (Meta-Analysis with
Correlations). This package enables the user to conduct a meta-analysis in a menu-driven,
graphical user interface environment (e.g., SPSS), while having the full statistical capabilities of
R and the MAc package. The MAc package itself contains a variety of useful functions for
conducting a research synthesis with correlational data. One of the unique features of the MAc
package is in its integration of user-friendly functions to complete the majority of statistical steps
involved in a meta-analysis with correlations.
You can read more on R Commander Plugins at http://wp.me/p9q8Y-1Is
—————————————————————————————————————————-
Rattle- R Analytical Tool To Learn Easily (download from http://rattle.togaware.com/)
Rattle is more advanced user Interface than R Commander though not as popular in academia. It has been designed explicitly for data mining and it also has a commercial version for sale by Togaware. Rattle has a Tab and radio button/check box rather than Menu- drop down approach towards the graphical design. Also the Execute button needs to be clicked after checking certain options, just the same as submit button is clicked after writing code. This is different from clicking on a drop down menu.

Advantages of Using Rattle
1) Useful for beginner in R language to do building models,cluster and data mining.
2) Has separate tabs for data entry,summary, visualization,model building,clustering, association and evaluation. The design is intuitive and easy to understand even for non statistical background as the help is conveniently explained as each tab, button is clicked. Also the tabs are placed in a very sequential and logical order.
3) Uses a lot of other R packages to build a complete analytical platform. Very good for correlation graph,clustering as well decision trees.
4) Easy to understand interface even for first time user.
5) Log  for R code is auto generated and time stamp is placed.
6) Complete solution for model building from partitioning datasets randomly for testing,validation to building model, evaluating lift and ROC curve, and exporting PMML output of model for scoring.
7) Has a well documented online help as well as in-software documentation. The help helps explain terms even to non statistical users and is highly useful for business users.

Example Documentation for Hypothesis Testing in Test Tab in Rattle is ”
Distribution of the Data
* Kolomogorov-Smirnov     Non-parametric Are the distributions the same?
* Wilcoxon Signed Rank    Non-parametric Do paired samples have the same distribution?
Location of the Average
* T-test               Parametric     Are the means the same?
* Wilcoxon Rank-Sum    Non-parametric Are the medians the same?
Variation in the Data
* F-test Parametric Are the variances the same?
Correlation
* Correlation    Pearsons Are the values from the paired samples correlated?”

Comparative Disadvantages of using Rattle-
1) It is basically aimed at a data miner.  Hence it is more of a data mining GUI rather than an analytics GUI.
2) Has limited ability to create different types of graphs from a business analysts perspective Numeric variables can be made into Box-Plot, Histogram, Cumulative as well Benford Graphs. While interactivity using GGobi and Lattiticist is involved- the number of graphical options is still lesser than other GUI.
3) It is not suited for projects that involve multiple graphical analysis and which do not have model building or data mining.For example Data Plot is given in clustering tab but not in general Explore tab.
4) Despite the fact that it is meant for data miners, no support to biglm packages, as well as parallel programming is enabled in GUI for bigger datasets, though these can be done by R command line in conjunction with the Rattle GUI. Data m7ining is typically done on bigger datsets.
5) May have some problems installing it as it is dependent on GTK and has a lot of packages as dependencies.

Top Row-
This has the Execute Button (shown as two gears) and which has keyboard shortcut F2. It is used to execute the options in Tabs-and is equivalent of submit code button.
Other buttons include new Projects,Save  and Load projects which are files with extension to .rattle an which store all related information from Rattle.
It also has a button for exporting information in the current Tab as an open office document, and buttons for interrupting current process as well as exiting Rattle.

Data Tab-
It has the following options.
●        Data Type- These are radio buttons between Spreadsheet (and Comma Separated Values), ARFF files (Weka), ODBC (for Database Connections),Library (for Datasets from Packages),R Dataset or R datafile, Corpus (for Text Mining) and Script for generating the data by code.
●        The second row-in Data Tab in Rattle is Detail on Data Type- and its apperance shifts as per the radio button selection of data type in previous step. For Spreadsheet, it will show Path of File, Delimiters, Header Row while for ODBC it will show DSN, Tables, Rows and for Library it will show you a dropdown of all datasets in all R packages installed locally.
●        The third row is a Partition field for splitting dataset in training,testing,validation and it shows ratio. It also specifies a Random seed which can be customized for random partitions which can be replicated. This is very useful as model building requires model to be built and tested on random sub sets of full dataset.
●        The fourth row is used to specify the variable type of inputted data. The variable types are
○        Input: Used for modeling as independent variables
○        Target: Output for modeling or the dependent variable. Target is a categoric variable for classification, numeric for regression and for survival analysis both Time and Status need to be defined
○        Risk: A variable used in the Risk Chart
○        Ident: An identifier for unique observations in the data set like AccountId or Customer Id
○        Ignore: Variables that are to be ignored.
●        In addition the weight calculator can be used to perform mathematical operations on certain variables and identify certain variables as more important than others.

Explore Tab-
Summary Sub-Tab has Summary for brief summary of variables, Describe for detailed summary and Kurtosis and Skewness for comparing them across numeric variables.
Distributions Sub-Tab allows plotting of histograms, box plots, and cumulative plots for numeric variables and for categorical variables Bar Plot and Dot Plot.
It also has Benford Plot for Benford’s Law on probability of distribution of digits.
Correlation Sub-Tab– This displays corelation between variables as a table and also as a very nice plot.
Principal Components Sub-Tab– This is for use with Principal Components Analysis including the SVD (singular value decomposition) and Eigen methods.
Interactive Sub-Tab- Allows interactive data exploration using GGobi and Lattice software. It is a powerful visual tool.

Test Tab-This has options for hypothesis testing of data for two sample tests.
Transform Tab-This has options for rescaling data, missing values treatment, and deleting invalid or missing values.
Cluster Tab-It gives an option to KMeans, Hierarchical and Bi-Cluster clustering methods with automated graphs,plots (including dendogram, discriminant plot and data plot) and cluster results available. It is highly recommended for clustering projects especially for people who are proficient in clustering but not in R.

Associate Tab-It helps in building association rules between categorical variables, which are in the form of “if then”statements. Example. If day is Thursday, and someone buys Milk, there is 80% chance they will buy Diapers. These probabilities are generated from observed frequencies.

Model Tab-The Model tab makes Rattle one of the most advanced data mining tools, as it incorporates decision trees(including boosted models and forest method), linear and logistic regression, SVM,neural net,survival models.
Evaluate Tab-It as functionality for evaluating models including lift,ROC,confusion matrix,cost curve,risk chart,precision, specificity, sensitivity as well as scoring datasets with built model or models. Example – A ROC curve generated by Rattle for Survived Passengers in Titanic (as function of age,class,sex) This shows comparison of various models built.

Log Tab- R Code is automatically generated by Rattle as the respective operation is executed. Also timestamp is done so it helps in reviewing error as well as evaluating speed for code optimization.
—————————————————————————————————————————-
JGR- Deducer- (see http://www.deducer.org/pmwiki/pmwiki.php?n=Main.DeducerManual
JGR is a Java Based GUI. Deducer is recommended for use with JGR.
Deducer has basically been made to implement GGPLOT in a GUI- an advanced graphics package based on Grammer of Graphics and was part of Google Summer of Code project.

It first asks you to either open existing dataset or load a new dataset with just two icons. It has two initial views in Data Viewer- a Data view and Variable view which is quite similar to Base SPSS. The other Deducer options are loaded within the JGR console.

Advantages of Using  Deducer
1.      It has an option for factor as well as reliability analysis which is missing in other graphical user interfaces like R Commander and Rattle.
2.      The plot builder option gives very good graphics -perhaps the best in other GUIs. This includes a color by option which allows you to shade the colors based on variable value. An addition innovation is the form of templates which enables even a user not familiar with data visualization to choose among various graphs and click and drag them to plot builder area.
3.      You can set the Java Gui for R (JGR) menu to automatically load some packages by default using an easy checkbox list.
4.      Even though Deducer is a very young package, it offers a way for building other R GUIs using Java Widgets.
5.      Overall feel is of SPSS (Base GUI) to it’s drop down menu, and selecting variables in the sub menu dialogue by clicking to transfer to other side.SPSS users should be more comfortable at using this.
6.      A surprising thing is it rearranges the help documentation of all R in a very presentable and organized manner
7.      Very convenient to move between two or more datasets using dropdown.
8.      The most convenient GUI for merging two datasets using common variable.

Dis Advantages of Using  Deducer
1.      Not able to save plots as images (only options are .pdf and .eps), you can however copy as image.
2.      Basically a data viualization GUI – it does offer support for regression, descriptive statistics in the menu item Extras- however the menu suggests it is a work in progress.
3.      Website for help is outdated, and help documentation specific to Deducer lacks detail.



Components of Deducer-
Data Menu-Gives options for data manipulation including recoding variables,transform variables (binning, mathematical operation), sort dataset,  transpose dataset ,merge two datasets.
Analysis Menu-Gives options for frequency tables, descriptive statistics,cross tabs, one sample tests (with plots) ,two sample tests (with plots),k sample tests, correlation,linear and logistic models,generalized linear models.
Plot Builder Menu- This allows plots of various kinds to be made in an interactive manner.

Correlation using Deducer.

————————————————————————————————————————–
Red-R – A dataflow user interface for R (see http://red-r.org/

Red R uses dataflow concepts as a user interface rather than menus and tabs. Thus it is more similar to Enterprise Miner or Rapid Miner in design. For repeatable analysis dataflow programming is preferred by some analysts. Red-R is written in Python.


Advantages of using Red-R
1) Dataflow style makes it very convenient to use. It is the only dataflow GUI for R.
2) You can save the data as well as analysis in the same file.
3) User Interface makes it easy to read R code generated, and commit code.
4) For repeatable analysis-like reports or creating models it is very useful as you can replace just one widget and other widget/operations remain the same.
5) Very easy to zoom into data points by double clicking on graphs. Also to change colors and other options in graphs.
6) One minor feature- It asks you to set CRAN location just once and stores it even for next session.
7) Automated bug report submission.

Disadvantages of using Red-R
1) Current version is 1.8 and it needs a lot of improvement for building more modeling types as well as debugging errors.
2) Limited features presently.
———————————————————————————————————————-
RKWard (see http://rkward.sourceforge.net/)

It is primarily a KDE GUI for R, so it can be used on Ubuntu Linux. The windows version is available but has some bugs.

Advantages of using RKWard
1) It is the only R GUI for time series at present.
In addition it seems like the only R GUI explicitly for Item Response Theory (which includes credit response models,logistic models) and plots contains Pareto Charts.
2) It offers a lot of detail in analysis especially in plots(13 types of plots), analysis and  distribution analysis ( 8 Tests of normality,14 continuous and 6 discrete distributions). This detail makes it more suitable for advanced statisticians rather than business analytics users.
3) Output can be easily copied to Office documents.

Disadvantages of using RKWard
1) It does not have stable Windows GUI. Since a graphical user interface is aimed at making interaction easier for users- this is major disadvantage.
2) It has a lot of dependencies so may have some issues in installing.
3) The design categorization of analysis,plots and distributions seems a bit unbalanced considering other tabs are File, Edit, View, Workspace,Run,Settings, Windows,Help.
Some of the other tabs can be collapsed, while the three main tabs of analysis,plots,distributions can be better categorized (especially into modeling and non-modeling analysis).
4) Not many options for data manipulation (like subset or transpose) by the GUI.
5) Lack of detail in documentation as it is still on version 0.5.3 only.

Components-
Analysis, Plots and Distributions are the main components and they are very very extensive, covering perhaps the biggest range of plots,analysis or distribution analysis that can be done.
Thus RKWard is best combined with some other GUI, when doing advanced statistical analysis.

 

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/newpermanent 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



Related Articles

 

Summary of R GUIs


Using R from other software- Please note that interfaces to R exist from other software as well. These include software from SAS Institute, IBM SPSS, Rapid Miner,Knime  and Oracle.

A brief list is shown below-

1) SAS/IML Interface to R- You can read about the SAS Institute’s SAS/ IML Studio interface to R at http://www.sas.com/technologies/analytics/statistics/iml/index.html
2) Rapid  Miner Extension to R-You can view integration with Rapid Miner’s extension to R here at http://www.youtube.com/watch?v=utKJzXc1Cow
3) IBM SPSS plugin for R-SPSS software has R integration in the form of a plugin. This was one of the earliest third party software offering interaction with R and you can read more at http://www.spss.com/software/statistics/developer/
4) Knime- Konstanz Information Miner also has R integration. You can view this on
http://www.knime.org/downloads/extensions
5) Oracle Data Miner- Oracle has a data mining offering to it’s very popular database software which is integrated with the R language. The R Interface to Oracle Data Mining ( R-ODM) allows R users to access the power of Oracle Data Mining’s in-database functions using the familiar R syntax. http://www.oracle.com/technetwork/database/options/odm/odm-r-integration-089013.html
6) JMP- JMP version 9 is the latest to offer interface to R.  You can read example scripts here at http://blogs.sas.com/jmp/index.php?/archives/298-JMP-Into-R!.html

R Excel- Using R from Microsoft Excel

Microsoft Excel is the most widely used spreadsheet program for data manipulation, entry and graphics. Yet as dataset sizes have increased, Excel’s statistical capabilities have lagged though it’s design has moved ahead in various product versions.

R Excel basically works at adding a .xla plugin to
Excel just like other Plugins. It does so by connecting to R through R packages.

Basically it offers the functionality of R
functions and capabilities to the most widely distributed spreadsheet program. All data summaries, reports and analysis end up in a spreadsheet-

R Excel enables R to be very useful for people not
knowing R. In addition it adds (by option) the menus of R Commander as menus in Excel spreadsheet.


Advantages-
Enables R and Excel to communicate thus tieing an advanced statistical tool to the most widely used business analytics tool.

Disadvantages-
No major disadvatage at all to a business user. For a data statistical user, Microsoft Excel is limited to 100,000 rows, so R data needs to be summarized or reduced.

Graphical capabilities of R are very useful, but to a new user, interactive graphics in Excel may be easier than say using Ggplot ot Ggobi.
You can read more on this at http://rcom.univie.ac.at/ or  the complete Springer Book http://www.springer.com/statistics/computanional+statistics/book/978-1-4419-0051-7

The combination of cloud computing and internet offers a new kind of interaction possible for scientists as well analysts.

Here is a way to use R on an Amazon EC2 machine, thus renting by hour hardware and computing resources which are scaleable to massive levels , whereas the software is free.

Here is how you can connect to Amazon EC2 and run R.
Running R for Cloud Computing.
1) Logging onto Amazon Console http://aws.amazon.com/ec2/
Note you need your Amazon Id (even the same id which you use for buying books).Note we are into Amazon EC2 as shown by the upper tab. Click upper tab to get into the Amazon EC2
2) Choosing the right AMI-On the left margin, you can click AMI -Images. Now you can search for the image-I chose Ubuntu images (linux images are cheaper) and latest Ubuntu Lucid  in the search .You can choose whether you want 32 bit or 64 bit image. 64 bit images will lead to  faster processing of data.Click on launch instance in the upper tab ( near the search feature). A pop up comes up, which shows the 5 step process to launch your computing.
3) Choose the right compute instance- – there are various compute instances and they all are at different multiples of prices or compute units. They differ in terms of RAM memory and number of processors.After choosing the compute instance of your choice (extra large is highlighted)- click on continue-
4) Instance Details-Do not  choose cloudburst monitoring if you are on a budget as it has a extra charge. For critical production it would be advisable to choose cloudburst monitoring once you have become comfortable with handling cloud computing..
5) Add Tag Details- If you are running a lot of instances you need to create your own tags to help you manage them. It is advisable if you are going to run many instances.
6) Create a key pair- A key pair is an added layer of encryption. Click on create new pair and name it (note the name will be handy in coming steps)
7) After clicking and downloading the key pair- you come into security groups. Security groups is just a set of instructions to help keep your data transfer secure. You want to enable access to your cloud instance to certain IP addresses (if you are going to connect from fixed IP address and to certain ports in your computer. It is necessary in security group to enable  SSH using Port 22.
Last step- Review Details and Click Launch
8) On the Left margin click on instances ( you were in Images.>AMI earlier)
It will take some 3-5 minutes to launch an instance. You can see status as pending till then.
9) Pending instance as shown by yellow light-
10) Once the instance is running -it is shown by a green light.
Click on the check box, and on upper tab go to instance actions. Click on connect-
You see a popup with instructions like these-
· Open the SSH client of your choice (e.g., PuTTY, terminal).
·  Locate your private key, nameofkeypair.pem
·  Use chmod to make sure your key file isn’t publicly viewable, ssh won’t work otherwise:
chmod 400 decisionstats.pem
·  Connect to your instance using instance’s public DNS [ec2-75-101-182-203.compute-1.amazonaws.com].
Example
Enter the following command line:
ssh -i decisionstats2.pem root@ec2-75-101-182-203.compute-1.amazonaws.com

Note- If you are using Ubuntu Linux on your desktop/laptop you will need to change the above line to ubuntu@… from root@..

ssh -i yourkeypairname.pem -X ubuntu@ec2-75-101-182-203.compute-1.amazonaws.com

(Note X11 package should be installed for Linux users- Windows Users will use Remote Desktop)

12) Install R Commander on the remote machine (which is running Ubuntu Linux) using the command

sudo apt-get install r-cran-rcmdr


Interview Michael J. A. Berry Data Miners, Inc

Here is an interview with noted Data Mining practitioner Michael Berry, author of seminal books in data mining, noted trainer and consultantmjab picture

Ajay- Your famous book “Data Mining Techniques: For Marketing, Sales, and Customer Relationship Management” came out in 2004, and an update is being planned for 2011. What are the various new data mining techniques and their application that you intend to talk about in that book.

Michael- Each time we do a revision, it feels like writing a whole new book. The first edition came out in 1997 and it is hard to believe how much the world has changed since then. I’m currently spending most of my time in the on-line retailing world. The things I worry about today–improving recommendations for cross-sell and up-sell,and search engine optimization–wouldn’t have even made sense to me back then. And the data sizes that are routine today were beyond the capacity of the most powerful super computers of the nineties. But, if possible, Gordon and I have changed even more than the data mining landscape. What has changed us is experience. We learned an awful lot between the first and second editions, and I think we’ve learned even more between the second and third.

One consequence is that we now have to discipline ourselves to avoid making the book too heavy to lift. For the first edition, we could write everything we knew (and arguably, a bit more!); now we have to remind ourselves that our intended audience is still the same–intelligent laymen with a practical interest in getting more information out of data. Not statisticians. Not computer scientists. Not academic researchers. Although we welcome all readers, we are primarily writing for someone who works in a marketing department and has a title with the word “analyst” or “analytics” in it. We have relaxed our “no equations” rule slightly for cases when the equations really do make things easier to explain, but the core explanations are still in words and pictures.

The third edition completes a transition that was already happening in the second edition. We have fully embraced standard statistical modeling techniques as full-fledged components of the data miner’s toolkit. In the first edition, it seemed important to make a distinction between old, dull, statistics, and new, cool, data mining. By the second edition, we realized that didn’t really make sense, but remnants of that attitude persisted. The third edition rectifies this. There is a chapter on statistical modeling techniques that explains linear and logistic regression, naive Bayes models, and more. There is also a brand new chapter on text mining, a curious omission from previous editions.

There is also a lot more material on data preparation. Three whole chapters are devoted to various aspects of data preparation. The first focuses on creating customer signatures. The second is focused on using derived variables to bring information to the surface, and the third deals with data reduction techniques such as principal components. Since this is where we spend the greatest part of our time in our work, it seemed important to spend more time on these subjects in the book as well.

Some of the chapters have been beefed up a bit. The neural network chapter now includes radial basis functions in addition to multi-layer perceptrons. The clustering chapter has been split into two chapters to accommodate new material on soft clustering, self-organizing maps, and more. The survival analysis chapter is much improved and includes material on some of our recent application of survival analysis methods to forecasting. The genetic algorithms chapter now includes a discussion of swarm intelligence.

Ajay- Describe your early career and how you came into Data Mining as a profession. What do you think of various universities now offering MS in Analytics. How do you balance your own teaching experience with your consulting projects at The Data Miners.

Michael- I fell into data mining quite by accident. I guess I always had a latent interest in the topic. As a high school and college student, I was a fan of Martin Gardner‘s mathematical games in in Scientific American. One of my favorite things he wrote about was a game called New Eleusis in which one players, God, makes up a rule to govern how cards can be played (“an even card must be followed by a red card”, say) and the other players have to figure out the rule by watching what plays are allowed by God and which ones are rejected. Just for my own amusement, I wrote a computer program to play the game and presented it at the IJCAI conference in, I think, 1981.

That paper became a chapter in a book on computer game playing–so my first book was about finding patterns in data. Aside from that, my interest in finding patterns in data lay dormant for years. At Thinking Machines, I was in the compiler group. In particular, I was responsible for the run-time system of the first Fortran Compiler for the CM-2 and I represented Thinking Machines at the Fortran 8X (later Fortran-90) standards meetings.

What changed my direction was that Thinking Machines got an export license to sell our first machine overseas. The machine went to a research lab just outside of Paris. The connection machine was so hard to program, that if you bought one, you got an applications engineer to go along with it. None of the applications engineers wanted to go live in Paris for a few months, but I did.

Paris was a lot of fun, and so, I discovered, was actually working on applications. When I came back to the states, I stuck with that applied focus and my next assignment was to spend a couple of years at Epsilon, (then a subsidiary of American Express) working on a database marketing system that stored all the “records of charge” for American Express card members. The purpose of the system was to pick ads to go in the billing envelope. I also worked on some more general purpose data mining software for the CM-5.

When Thinking Machines folded, I had the opportunity to open a Cambridge office for a Virginia-based consulting company called MRJ that had been a major channel for placing Connection Machines in various government agencies. The new group at MRJ was focused on data mining applications in the commercial market. At least, that was the idea. It turned out that they were more interested in data warehousing projects, so after a while we parted company.

That led to the formation of Data Miners. My two partners in Data Miners, Gordon Linoff and Brij Masand, share the Thinking Machines background.

To tell the truth, I really don’t know much about the university programs in data mining that have started to crop up. I’ve visited the one at NC State, but not any of the others.

I myself teach a class in “Marketing Analytics” at the Carroll School of Management at Boston College. It is an elective part of the MBA program there. I also teach short classes for corporations on their sites and at various conferences.

Ajay- At the previous Predictive Analytics World, you took a session on Forecasting and Predicting Subsciber levels (http://www.predictiveanalyticsworld.com/dc/2009/agenda.php#day2-6) .

It seems inability to forecast is a problem many many companies face today. What do you think are the top 5 principles of business forecasting which companies need to follow.

Michael- I don’t think I can come up with five. Our approach to forecasting is essentially simulation. We try to model the underlying processes and then turn the crank to see what happens. If there is a principal behind that, I guess it is to approach a forecast from the bottom up rather than treating aggregate numbers as a time series.

Ajay- You often partner your talks with SAS Institute, and your blog at http://blog.data-miners.com/ sometimes contain SAS code as well. What particular features of the SAS software do you like. Do you use just the Enterprise Miner or other modules as well for Survival Analysis or Forecasting.

Michael- Our first data mining class used SGI’s Mineset for the hands-on examples. Later we developed versions using Clementine, Quadstone, and SAS Enterprise Miner. Then, market forces took hold. We don’t market our classes ourselves, we depend on others to market them and then share in the revenue.

SAS turned out to be much better at marketing our classes than the other companies, so over time we stopped updating the other versions. An odd thing about our relationship with SAS is that it is only with the education group. They let us use Enterprise Miner to develop course materials, but we are explicitly forbidden to use it in our consulting work. As a consequence, we don’t use it much outside of the classroom.

Ajay- Also any other software you use (apart from SQL and J)

Michael- We try to fit in with whatever environment our client has set up. That almost always is SQL-based (Teradata, Oracle, SQL Server, . . .). Often SAS Stat is also available and sometimes Enterprise Miner.

We run into SPSS, Statistica, Angoss, and other tools as well. We tend to work in big data environments so we’ve also had occasion to use Ab Initio and, more recently, Hadoop. I expect to be seeing more of that.

Biography-

Together with his colleague, Gordon Linoff, Michael Berry is author of some of the most widely read and respected books on data mining. These best sellers in the field have been translated into many languages. Michael is an active practitioner of data mining. His books reflect many years of practical, hands-on experience down in the data mines.

Data Mining Techniques cover

Data Mining Techniques for Marketing, Sales and Customer Relationship Management

by Michael J. A. Berry and Gordon S. Linoff
copyright 2004 by John Wiley & Sons
ISB

Mining the Web cover

Mining the Web

by Michael J.A. Berry and Gordon S. Linoff
copyright 2002 by John Wiley & Sons
ISBN 0-471-41609-6

Non-English editions available in Traditional Chinese and Simplified Chinese

This book looks at the new opportunities and challenges for data mining that have been created by the web. The book demonstrates how to apply data mining to specific types of online businesses, such as auction sites, B2B trading exchanges, click-and-mortar retailers, subscription sites, and online retailers of digital content.

Mastering Data Mining

by Michael J.A. Berry and Gordon S. Linoff
copyright 2000 by John Wiley & Sons
ISBN 0-471-33123-6

Non-English editions available in JapaneseItalianTraditional Chinese , and Simplified Chinese

A case study-based guide to applying data mining techniques for solving practical business problems. These “warts and all” case studies are drawn directly from consulting engagements performed by the authors.

A data mining educator as well as a consultant, Michael is in demand as a keynote speaker and seminar leader in the area of data mining generally and the application of data mining to customer relationship management in particular.

Prior to founding Data Miners in December, 1997, Michael spent 8 years at Thinking Machines Corporation. There he specialized in the application of massively parallel supercomputing techniques to business and marketing applications, including one of the largest database marketing systems of the time.