Tag: visualization

Sunburst and Cartograms in R

There are still some graphs that cannot be yet made in R using a straightforward function or package.

One is sunburst (which is  radial kind of treemap-that can be made in R). See diagrams below to see the difference. Note sunburst is visually similar to coxcomb (Nightangle) graphs. Coxcombs can also be manipulated and made- but I am yet to find a straight package to make coxcomb using a single function _histdata package in R comes close in terms on historical datasets.

The Treemap uses a rectangular, space-filling slice-and-dice technique to visualize objects in the different levels of a hierarchy. The area and color of each item corresponds to an attribute of the item as well.

The Sunburst technique is an alternative, space-filling visualization that uses a radial rather than a rectangular layout. An example Sunburst display is shown below. citation- http://www.cc.gatech.edu/gvu/ii/sunburst/

Coxcomb Below-

 

 

Other is cartogram -whose packages are MIA  -RCartogram is very basic package http://www.omegahat.org/Rcartogram/ – It is better to use Toad Scraper software than R for this kind of map.

Cartograms are  used to produce spatial plots where the boundaries of regions can be transformed to be proportional to density/counts/populations. This is illustrated in plots such as

Mark Newman’s plot of People living with HIV/AIDS
Citation: Friendly, Michael (2001), Gallery of Data Visualization, Electronic document, http://www.datavis.ca/gallery/,Accessed: 03/23/2012 18:23:33

Web Analytics using R , Google Analytics and TS Forecasting

This is a continuation of the previous post on using Google Analytics .

Now that we have downloaded and plotted the data- we try and fit time series to the website data to forecast future traffic.

Some observations-

1) Google Analytics has 0 predictive analytics, it is just descriptive analytics and data visualization models (including the recent social analytics). However you can very well add in basic TS function using R to the GA API.

Why do people look at Website Analytics? To know today’s traffic and derive insights for the Future

2) Web Data clearly follows a 7 day peak and trough for weekly effects (weekdays and weekends), this is also true for hourly data …and this can be used for smoothing historic web data for future forecast.

3) On an advanced level, any hugely popular viral posts can be called a level shift (not drift) and accoringly dampened.

Test and Control!

Similarly using ARIMAX, we can factor in quantity and tag of posts as X regressor variables.

and now the code-( dont laugh at the simplicity please, I am just tinkering and playing with data here!)

You need to copy and paste the code at the bottom of   this post  http://www.decisionstats.com/using-google-analytics-with-r/ if you want to download your GA data down first.

Note I am using lubridate ,forecast and timeSeries packages in this section.

#Plotting the Traffic  plot(ga.data$data[,2],type="l") 

library(timeSeries)
library(forecast)

#Using package lubridate to convert character dates into time
library(lubridate)
ga.data$data[,1]=ymd(ga.data$data[,1])
ls()
dataset1=ga.data$data
names(dataset1) <- make.names(names(dataset1))
str(dataset1)
head(dataset1)
dataset2 <- ts(dataset1$ga.visitors,start=0,frequency = frequency(dataset1$ga.visitors), names=dataset1$ga.date)
str(dataset2)
head(dataset2)
ts.test=dataset2[1:200]
ts.control=dataset2[201:275]

 #Note I am splitting the data into test and control here

fitets=ets(ts.test)
plot(fitets)
testets=ets(ts.control,model=fitets)
accuracy(testets)
plot(testets)
spectrum(ts.test,method='ar')
decompose(ts.test)

library("TTR")
bb=SMA(dataset2,n=7)#We are doing a simple moving average for every 7 days. Note this can be 24 hrs for hourly data, or 30 days for daily data for month # 

to month comparison or 12 months for annual
#We notice that Web Analytics needs sommethening for every 7 thday as there is some relation to traffic on weekedays /weekends /same time last week
head(dataset2,40)
head(bb,40)

par(mfrow=c(2,1))
plot(bb,type="l",main="Using Seven Day Moving Average for Web Visitors")
plot(dataset2,main="Original Data")

Created by Pretty R at inside-R.org

Though I still wonder why the R query, gA R code /package could not be on the cloud (why it  needs to be downloaded)– cloud computing Gs?

Also how about adding some MORE predictive analytics to Google Analytics, chaps!

To be continued-

auto.arima() and forecasts!!!

cross validations!!!

and adapting the idiosyncratic periods and cycles  of web analytics to time series !!

Interview Kelci Miclaus, SAS Institute Using #rstats with JMP

Here is an interview with Kelci Miclaus, a researcher working with the JMP division of the SAS Institute, in which she demonstrates examples of how the R programming language is a great hit with JMP customers who like to be flexible.

 

Ajay- How has JMP been using integration with R? What has been the feedback from customers so far? Is there a single case study you can point out where the combination of JMP and R was better than any one of them alone?

Kelci- Feedback from customers has been very positive. Some customers are using JMP to foster collaboration between SAS and R modelers within their organizations. Many are using JMP’s interactive visualization to complement their use of R. Many SAS and JMP users are using JMP’s integration with R to experiment with more bleeding-edge methods not yet available in commercial software. It can be used simply to smooth the transition with regard to sending data between the two tools, or used to build complete custom applications that take advantage of both JMP and R.

One customer has been using JMP and R together for Bayesian analysis. He uses R to create MCMC chains and has found that JMP is a great tool for preparing the data for analysis, as well as displaying the results of the MCMC simulation. For example, the Control Chart platform and the Bubble Plot platform in JMP can be used to quickly verify convergence of the algorithm. The use of both tools together can increase productivity since the results of an analysis can be achieved faster than through scripting and static graphics alone.

I, along with a few other JMP developers, have written applications that use JMP scripting to call out to R packages and perform analyses like multidimensional scaling, bootstrapping, support vector machines, and modern variable selection methods. These really show the benefit of interactive visual analysis of coupled with modern statistical algorithms. We’ve packaged these scripts as JMP add-ins and made them freely available on our JMP User Community file exchange. Customers can download them and now employ these methods as they would a regular JMP platform. We hope that our customers familiar with scripting will also begin to contribute their own add-ins so a wider audience can take advantage of these new tools.

(see http://www.decisionstats.com/jmp-and-r-rstats/)

Ajay- Are there plans to extend JMP integration with other languages like Python?

Kelci- We do have plans to integrate with other languages and are considering integrating with more based on customer requests. Python has certainly come up and we are looking into possibilities there.

 Ajay- How is R a complimentary fit to JMP’s technical capabilities?

Kelci- R has an incredible breadth of capabilities. JMP has extensive interactive, dynamic visualization intrinsic to its largely visual analysis paradigm, in addition to a strong core of statistical platforms. Since our brains are designed to visually process pictures and animated graphs more efficiently than numbers and text, this environment is all about supporting faster discovery. Of course, JMP also has a scripting language (JSL) allowing you to incorporate SAS code, R code, build analytical applications for others to leverage SAS, R and other applications for users who don’t code or who don’t want to code.

JSL is a powerful scripting language on its own. It can be used for dialog creation, automation of JMP statistical platforms, and custom graphic scripting. In other ways, JSL is very similar to the R language. It can also be used for data and matrix manipulation and to create new analysis functions. With the scripting capabilities of JMP, you can create custom applications that provide both a user interface and an interactive visual back-end to R functionality. Alternatively, you could create a dashboard using statistical and/or graphical platforms in JMP to explore the data and with the click of a button, send a portion of the data to R for further analysis.

Another JMP feature that complements R is the add-in architecture, which is similar to how R packages work. If you’ve written a cool script or analysis workflow, you can package it into a JMP add-in file and send it to your colleagues so they can easily use it.

Ajay- What is the official view on R from your organization? Do you think it is a threat, or a complimentary product or another statistical platform that coexists with your offerings?

Kelci- Most definitely, we view R as complimentary. R contributors are providing a tremendous service to practitioners, allowing them to try a wide variety of methods in the pursuit of more insight and better results. The R community as a whole is providing a valued role to the greater analytical community by focusing attention on newer methods that hold the most promise in so many application areas. Data analysts should be encouraged to use the tools available to them in order to drive discovery and JMP can help with that by providing an analytic hub that supports both SAS and R integration.

Ajay-  While you do use R, are there any plans to give back something to the R community in terms of your involvement and participation (say at useR events) or sponsoring contests.

 Kelci- We are certainly open to participating in useR groups. At Predictive Analytics World in NY last October, they didn’t have a local useR group, but they did have a Predictive Analytics Meet-up group comprised of many R users. We were happy to sponsor this. Some of us within the JMP division have joined local R user groups, myself included.  Given that some local R user groups have entertained topics like Excel and R, Python and R, databases and R, we would be happy to participate more fully here. I also hope to attend the useR! annual meeting later this year to gain more insight on how we can continue to provide tools to help both the JMP and R communities with their work.

We are also exploring options to sponsor contests and would invite participants to use their favorite tools, languages, etc. in pursuit of the best model. Statistics is about learning from data and this is how we make the world a better place.

About- Kelci Miclaus

Kelci is a research statistician developer for JMP Life Sciences at SAS Institute. She has a PhD in Statistics from North Carolina State University and has been using SAS products and R for several years. In addition to research interests in statistical genetics, clinical trials analysis, and multivariate analysis/visualization methods, Kelci works extensively with JMP, SAS, and R integration.

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Top 5 XKCD on Data Visualization

By request, an analysis of Top 5  XKCDs on data visualization. Statisticians and Data Scientists to note-

1) DOT PLOT

 

2)  LINE PLOTS

3) FLOW CHARTS

4) PIE CHARTS and 5) BAR GRAPHS

I am not going into the big big graphs of course like the Star Wars Plot data visualization at

http://xkcd.com/657/ or the Money Chart at http://xkcd.com/980/ because I dont believe in data visualization to show off, but to keep it simple simply 🙂

Now I gotta find me a software that can write my blog for me 🙂

Analytics for Cyber Conflict

 

The emerging use of Analytics and Knowledge Discovery in Databases for Cyber Conflict and Trade Negotiations

 

The blog post is the first in series or articles on cyber conflict and the use of analytics for targeting in both offense and defense in conflict situations.

 

It covers knowledge discovery in four kinds of databases (so chosen because of perceived importance , sensitivity, criticality and functioning of the geopolitical economic system)-

  1. Databases on Unique Identity Identifiers- including next generation biometric databases connected to Government Initiatives and Banking, and current generation databases of identifiers like government issued documents made online
  2. Databases on financial details -This includes not only traditional financial service providers but also online databases with payment details collected by retail product selling corporates like Sony’s Playstation Network, Microsoft ‘s XBox and
  3. Databases on contact details – including those by offline businesses collecting marketing databases and contact details
  4. Databases on social behavior- primarily collected by online businesses like Facebook , and other social media platforms.

It examines the role of

  1. voluntary privacy safeguards and government regulations ,

  2. weak cryptographic security of databases,

  3. weakness in balancing marketing ( maximized data ) with privacy (minimized data)

  4. and lastly the role of ownership patterns in database owning corporates

A small distinction between cyber crime and cyber conflict is that while cyber crime focusses on stealing data, intellectual property and information  to primarily maximize economic gains

cyber conflict focuses on stealing information and also disrupt effective working of database backed systems in order to gain notional competitive advantages in economics as well as geo-politics. Cyber terrorism is basically cyber conflict by non-state agents or by designated terrorist states as defined by the regulations of the “target” entity. A cyber attack is an offensive action related to cyber-infrastructure (like the Stuxnet worm that disabled uranium enrichment centrifuges of Iran). Cyber attacks and cyber terrorism are out of scope of this paper, we will concentrate on cyber conflicts involving databases.

Some examples are given here-

Types of Knowledge Discovery in –

1) Databases on Unique Identifiers- including biometric databases.

Unique Identifiers or primary keys for identifying people are critical for any intensive knowledge discovery program. The unique identifier generated must be extremely secure , and not liable to reverse engineering of the cryptographic hash function.

For biometric databases, an interesting possibility could be determining the ethnic identity from biometric information, and also mapping relatives. Current biometric information that is collected is- fingerprint data, eyes iris data, facial data. A further feature could be adding in voice data as a part of biometric databases.

This is subject to obvious privacy safeguards.

For example, Google recently unveiled facial recognition to unlock Android 4.0 mobiles, only to find out that the security feature could easily be bypassed by using a photo of the owner.

 

 

Example of Biometric Databases

In Afghanistan more than 2 million Afghans have contributed iris, fingerprint, facial data to a biometric database. In India, 121 million people have already been enrolled in the largest biometric database in the world. More than half a million customers of the Tokyo Mitsubishi Bank are are already using biometric verification at ATMs.

Examples of Breached Online Databases

In 2011, Playstation Network by Sony (PSN) lost data of 77 million customers including personal information and credit card information. Additionally data of 24 million customers were lost by Sony’s Sony Online Entertainment. The websites of open source platforms like SourceForge, WineHQ and Kernel.org were also broken into 2011. Even retailers like McDonald and Walgreen reported database breaches.

 

The role of cyber conflict arises in the following cases-

  1. Databases are online for accessing and authentication by proper users. Databases can be breached remotely by non-owners ( or “perpetrators”) non with much lesser chance of intruder identification, detection and penalization by regulators, or law enforcers (or “protectors”) than offline modes of intellectual property theft.

  2. Databases are valuable to external agents (or “sponsors”) subsidizing ( with finance, technology, information, motivation) the perpetrators for intellectual property theft. Databases contain information that can be used to disrupt the functioning of a particular economy, corporation (or “ primary targets”) or for further chain or domino effects in accessing other data (or “secondary targets”)

  3. Loss of data is more expensive than enhanced cost of security to database owners

  4. Loss of data is more disruptive to people whose data is contained within the database (or “customers”)

So the role play for different people for these kind of databases consists of-

1) Customers- who are in the database

2) Owners -who own the database. They together form the primary and secondary targets.

3) Protectors- who help customers and owners secure the databases.

and

1) Sponsors- who benefit from the theft or disruption of the database

2) Perpetrators- who execute the actual theft and disruption in the database

The use of topic models and LDA is known for making data reduction on text, and the use of data visualization including tied to GPS based location data is well known for investigative purposes, but the increasing complexity of both data generation and the sophistication of machine learning driven data processing makes this an interesting area to watch.

 

 

The next article in this series will cover-

the kind of algorithms that are currently or being proposed for cyber conflict, the role of non state agents , and what precautions can knowledge discovery in databases practitioners employ to avoid breaches of security, ethics, and regulation.

Citations-

  1. Michael A. Vatis , CYBER ATTACKS DURING THE WAR ON TERRORISM: A PREDICTIVE ANALYSIS Dartmouth College (Institute for Security Technology Studies).
  2. From Data Mining to Knowledge Discovery in Databases Usama Fayyad, Gregory Piatetsky-Shapiro, and Padhraic Smyt

Graphs in Statistical Analysis

One of the seminal papers establishing the importance of data visualization (as it is now called) was the 1973 paper by F J Anscombe in http://www.sjsu.edu/faculty/gerstman/StatPrimer/anscombe1973.pdf

It has probably the most elegant introduction to an advanced statistical analysis paper that I have ever seen-

1. Usefulness of graphs

Most textbooks on statistical methods, and most statistical computer programs, pay too little attention to graphs. Few of us escape being indoctrinated with these notions:

(1) numerical calculations are exact, but graphs are rough;

(2) for any particular kind of statistical data there is just one set of calculations constituting a correct statistical analysis;

(3) performing intricate calculations is virtuous, whereas actually looking at the data is cheating.

A computer should make both calculations and graphs. Both sorts of output should be studied; each will contribute to understanding.

Of course the dataset makes it very very interesting for people who dont like graphical analysis too much.

From http://en.wikipedia.org/wiki/Anscombe%27s_quartet

 The x values are the same for the first three datasets.

Anscombe’s Quartet
I II III IV
x y x y x y x y
10.0 8.04 10.0 9.14 10.0 7.46 8.0 6.58
8.0 6.95 8.0 8.14 8.0 6.77 8.0 5.76
13.0 7.58 13.0 8.74 13.0 12.74 8.0 7.71
9.0 8.81 9.0 8.77 9.0 7.11 8.0 8.84
11.0 8.33 11.0 9.26 11.0 7.81 8.0 8.47
14.0 9.96 14.0 8.10 14.0 8.84 8.0 7.04
6.0 7.24 6.0 6.13 6.0 6.08 8.0 5.25
4.0 4.26 4.0 3.10 4.0 5.39 19.0 12.50
12.0 10.84 12.0 9.13 12.0 8.15 8.0 5.56
7.0 4.82 7.0 7.26 7.0 6.42 8.0 7.91
5.0 5.68 5.0 4.74 5.0 5.73 8.0 6.89

For all four datasets:

Property Value
Mean of x in each case 9 exact
Variance of x in each case 11 exact
Mean of y in each case 7.50 (to 2 decimal places)
Variance of y in each case 4.122 or 4.127 (to 3 d.p.)
Correlation between x and y in each case 0.816 (to 3 d.p.)
Linear regression line in each case y = 3.00 + 0.500x (to 2 d.p. and 3 d.p. resp.)
But see the graphical analysis –
While R has always been great in emphasizing graphical analysis, thanks in part due to work by H Wickham and others, SAS products and  language has also modified its approach at http://www.sas.com/technologies/analytics/statistics/datadiscovery/
 SAS Visual Data Discovery combines top-selling SAS products (Base SAS, SAS/STAT® and SAS/GRAPH®), along with two interfaces (SAS® Enterprise Guide® for guided tasks and batch analysis and JMP® software for discovery and exploratory analysis).
 and  ODS Statistical Graphs at
While ODS Statistical graphs is still not as smooth as say R’s GGPLOT2 http://tinyurl.com/ggplot2-book, it still is a progressive step
Pretty graphs make for better decisions too !

 

 

Webinar: Using R within Oracle #rstats

Webinar: Using R within Oracle — Nov 30, noon EST

==========================================
Oracle now supports the R open source statistical programming language. Come to this webinar to learn more about using R within an Oracle environment.

— URL for TechCast: https://stbeehive.oracle.com/bconf/confDetails?confID=334B:3BF0:owch:38893C00F42F38A1E0404498C8A6612B0004075AECF7&guest=true&confKey=608880
— Web Conference ID: 303397
— Web Conference Key: 608880
— Dialup:             1-866-682-4770      , ID 5548204, passcode 1234

After a steady rise in the past few years, in 2010 the open source data mining software R overtook other tools to become the tool used by more data miners (43%) than any other (http://www.rexeranalytics.com/Data-Miner-Survey-Results-2010.html).

Several analytic tool vendors have added R-integration to their software. However, Oracle is the largest company to throw their weight behind R. On October 3, Oracle unveiled their integration of R: Oracle R Enterprise (http://www.oracle.com/us/corporate/features/features-oracle-r-enterprise-498732.html) as part of their Oracle Big Data Appliance announcement (http://www.oracle.com/us/corporate/press/512001).

Oracle R Enterprise allows users to perform statistical analysis with advanced visualization on data stored in Oracle Database. Oracle R Enterprise enables scalable R solutions, while facilitating production deployment of R scripts and Hadoop based solutions, as well as integration of R results with Oracle BI Publisher and OBIEE dashboards.

This TechCast introduces the various Oracle R Enterprise components and features, along with R script demonstrations that interface with Oracle Database.

TechCast presenter: Mark Hornick, Senior Manager, Oracle Advanced Analytics Development.
This TechCast is part of the ongoing TechCasts series coordinated by Oracle BIWA: The BI, Warehousing and Analytics SIG (http://www.oracleBIWA.org).