Tag: Analytics

Predictive Analytics Conferences-Four PAWs Coming – PAW Boston Super Early Ends Friday

  • Message from PAW Conferences

Friday, July 13th is your final opportunity to take advantage of the super early bird pricing for Predictive Analytics World Boston, Sept 30 – Oct 4.
INFO: www.pawcon.com/boston

AGENDA AT A GLANCE: www.pawcon.com/boston/2012/agenda_overview.php

Register now and realize savings of up to $600 over onsite registration:
www.pawcon.com/boston/register.php

– – – – – – – – – – – – – – –

All ANALYTICS EVENTS:

PAW Government: Sept 17-18, 2012 – www.pawgov.com
PAW Boston: Sept 30-Oct 4, 2012 – http://www.pawcon.com/boston
Text Analytics World Boston: Oct 3-4, 2012 – www.tawcon.com/boston
PAW Düsseldorf: Nov 6-7, 2012 – predictiveanalyticsworld.de
PAW London: Nov 27-28, 2012 – www.pawcon.com/london
PAW Videos: Available on-demand – www.pawcon.com/video

Talking on Big Data Analytics

I am going  being sponsored to a Government of India sponsored talk on Big Data Analytics at Bangalore on Friday the 13 th of July. If you are in Bangalore, India you may drop in for a dekko. Schedule and Abstracts (i am on page 7 out 9) .

Your tax payer money is hard at work- (hassi majak only if you are a desi. hassi to fassi.)

13 July 2012 (9.30 – 11.00 & 11.30 – 1.00)
Big Data Big Analytics
The talk will showcase using open source technologies in statistical computing for big data, namely the R programming language and its use cases in big data analysis. It will review case studies using the Amazon Cloud, custom packages in R for Big Data, tools like Revolution Analytics RevoScaleR package, as well as the newly launched SAP Hana used with R. We will also review Oracle R Enterprise. In addition we will show some case studies using BigML.com (using Clojure) , and approaches using PiCloud. In addition it will showcase some of Google APIs for Big Data Analysis.

Lastly we will talk on social media analysis ,national security use cases (i.e. cyber war) and privacy hazards of big data analytics.

Schedule

View more presentations from Ajay Ohri.
Abstracts

View more documents from Ajay Ohri.

 

Saving Output in R for Presentations

While SAS language has a beautifully designed ODS (Output Delivery System) for saving output from certain analysis in excel files (and html and others), in R one can simply use the object, put it in a write.table and save it a csv file using the file parameter within write.table.

As a business analytics consultant, the output from a Proc Means, Proc Freq (SAS) or a summary/describe/table command (in R) is to be presented as a final report. Copying and pasting is not feasible especially for large amounts of text, or remote computers.

Using the following we can simple save the output  in R

 

> getwd()
[1] “C:/Users/KUs/Desktop/Ajay”
> setwd(“C:\Users\KUs\Desktop”)

#We shifted the directory, so we can save output without putting the entire path again and again for each step.

#I have found the summary command most useful for initial analysis and final display (particularly during the data munging step)

nams=summary(ajay)

# I assigned a new object to the analysis step (summary), it could also be summary,names, describe (HMisc) or table (for frequency analysis),
> write.table(nams,sep=”,”,file=”output.csv”)

Note: This is for basic beginners in R using it for business analytics dealing with large number of variables.

 

pps: Note

If you have a large number of files in a local directory to be read in R, you can avoid typing the entire path again and again by modifying the file parameter in the read.table and changing the working directory to that folder

 

setwd(“C:/Users/KUs/Desktop/”)
ajayt1=read.table(file=”test1.csv”,sep=”,”,header=T)

ajayt2=read.table(file=”test2.csv”,sep=”,”,header=T)

 

and so on…

maybe there is a better approach somewhere on Stack Overflow or R help, but this will work just as well.

you can then merge the objects created ajayt1 and ajayt2… (to be continued)

2012 Web Analytics H1

Decisionstats.com is doing okay it seems as per my web analytics software

Decisionstats.com -2012-06-30
and the poetry traffic is getting lot more love now!

Interview Jason Kuo SAP Analytics #Rstats

Here is an interview with Jason Kuo who works with SAP Analytics as Group Solutions Marketing Manager. Jason answers questions on SAP Analytics and it’s increasing involvement with R statistical language.

Ajay- What made you choose R as the language to tie important parts of your technology platform like HANA and SAP Predictive Analysis. Did you consider other languages like Julia or Python.

Jason- It’s the most popular. Over 50% of the statisticians and data analysts use R. With 3,500+ algorithms its arguably the most comprehensive statistical analysis language. That said,we are not closing the door on others.

Ajay- When did you first start getting interested in R as an analytics platform?

Jason- SAP has been tracking R for 5+ years. With R’s explosive growth over the last year or two, it made sense for us to dramatically increase our investment in R.

Ajay- Can we expect SAP to give back to the R community like Google and Revolution Analytics does- by sponsoring Package development or sponsoring user meets and conferences?

Will we see SAP’s R HANA package in this year’s R conference User 2012 in Nashville

Jason- Yes. We plan to provide a specific driver for HANA tables for input of the data to native R. This planned for end of 2012. We’ll then review our event strategy. SAP has been a sponsor of Predictive Analytics World for several years and was indeed a founding sponsor. We may be attending the year’s R conference in Nashville.

Ajay- What has been some of the initial customer feedback to your analytics expansion and offerings. 

Jason- We have completed two very successful Pilots of the R Integration for HANA with two of SAP’s largest customers.

About-

Jason has over 15 years of BI and Data Warehousing industry experience. Having worked at Oracle, Business Objects, and now SAP, Jason has been involved in numerous technical marketing roles involving performance management dashboards, information management, text analysis, predictive analytics, and now big data. He has a bachelor’s of science in operations research from the University of Michigan.

 

R for Business Analytics- Book by Ajay Ohri

So the cover art is ready, and if you are a reviewer, you can reserve online copies of the book I have been writing for past 2 years. Special thanks to my mentors, detractors, readers and students- I owe you a beer!

You can also go here-

http://www.springer.com/statistics/book/978-1-4614-4342-1

 

R for Business Analytics

R for Business Analytics

Ohri, Ajay

2012, 2012, XVI, 300 p. 208 illus., 162 in color.

Hardcover
Information

ISBN 978-1-4614-4342-1

Due: September 30, 2012

(net)

approx. 44,95 €
  • Covers full spectrum of R packages related to business analytics
  • Step-by-step instruction on the use of R packages, in addition to exercises, references, interviews and useful links
  • Background information and exercises are all applied to practical business analysis topics, such as code examples on web and social media analytics, data mining, clustering and regression models

R for Business Analytics looks at some of the most common tasks performed by business analysts and helps the user navigate the wealth of information in R and its 4000 packages.  With this information the reader can select the packages that can help process the analytical tasks with minimum effort and maximum usefulness. The use of Graphical User Interfaces (GUI) is emphasized in this book to further cut down and bend the famous learning curve in learning R. This book is aimed to help you kick-start with analytics including chapters on data visualization, code examples on web analytics and social media analytics, clustering, regression models, text mining, data mining models and forecasting. The book tries to expose the reader to a breadth of business analytics topics without burying the user in needless depth. The included references and links allow the reader to pursue business analytics topics.

 

This book is aimed at business analysts with basic programming skills for using R for Business Analytics. Note the scope of the book is neither statistical theory nor graduate level research for statistics, but rather it is for business analytics practitioners. Business analytics (BA) refers to the field of exploration and investigation of data generated by businesses. Business Intelligence (BI) is the seamless dissemination of information through the organization, which primarily involves business metrics both past and current for the use of decision support in businesses. Data Mining (DM) is the process of discovering new patterns from large data using algorithms and statistical methods. To differentiate between the three, BI is mostly current reports, BA is models to predict and strategize and DM matches patterns in big data. The R statistical software is the fastest growing analytics platform in the world, and is established in both academia and corporations for robustness, reliability and accuracy.

Content Level » Professional/practitioner

Keywords » Business Analytics – Data Mining – Data Visualization – Forecasting – GUI – Graphical User Interface – R software – Text Mining

Related subjects » Business, Economics & Finance – Computational Statistics – Statistics

TABLE OF CONTENTS

Why R.- R Infrastructure.- R Interfaces.- Manipulating Data.- Exploring Data.- Building Regression Models.- Data Mining using R.- Clustering and Data Segmentation.- Forecasting and Time-Series Models.- Data Export and Output.- Optimizing your R Coding.- Additional Training Literature.- Appendix

Data Quality in R #rstats

Many Data Quality Formats give problems when importing in your statistical software.A statistical software is quite unable to distingush between $1,000, 1000% and 1,000 and 1000 and will treat the former three as character variables while the third as a numeric variable by default. This issue is further compounded by the numerous ways we can represent date-time variables.

The good thing is for specific domains like finance and web analytics, even these weird data input formats are fixed, so we can fix up a list of handy data quality conversion functions in R for reference.

 

After much muddling about with coverting internet formats (or data used in web analytics) (mostly time formats without date like 00:35:23)  into data frame numeric formats, I found that the way to handle Date-Time conversions in R is

Dataset$Var2= strptime(as.character(Dataset$Var1),”%M:%S”)

The problem with this approach is you will get the value as a Date Time format (02/31/2012 04:00:45-  By default R will add today’s date to it.)  while you are interested in only Time Durations (4:00:45 or actually just the equivalent in seconds).

this can be handled using the as.difftime function

dataset$Var2=as.difftime(paste(dataset$Var1))

or to get purely numeric values so we can do numeric analysis (like summary)

dataset$Var2=as.numeric(as.difftime(paste(dataset$Var1)))

(#Maybe there is  a more elegant way here- but I dont know)

The kind of data is usually one we get in web analytics for average time on site , etc.

 

 

 

 

 

and

for factor variables

Dataset$Var2= as.numeric(as.character(Dataset$Var1))

 

or

Dataset$Var2= as.numeric(paste(Dataset$Var1))

 

Slight problem is suppose there is data like 1,504 – it will be converted to NA instead of 1504

The way to solve this is use the nice gsub function ONLy on that variable. Since the comma is also the most commonly used delimiter , you dont want to replace all the commas, just only the one in that variable.

 

dataset$Variable2=as.numeric(paste(gsub(“,”,””,dataset$Variable)))

 

Now lets assume we have data in the form of % like 0.00% , 1.23%, 3.5%

again we use the gsub function to replace the % value in the string with  (nothing).

 

dataset$Variable2=as.numeric(paste(gsub(“%”,””,dataset$Variable)))

 

 

If you simply do the following for a factor variable, it will show you the level not the value. This can create an error when you are reading in CSV data which may be read as character or factor data type.

Dataset$Var2= as.numeric(Dataset$Var1)

An additional way is to use substr (using substr( and concatenate (using paste) for manipulating string /character variables.

 

iris$sp=substr(iris$Species,1,3) –will reduce the famous Iris species into three digits , without losing any analytical value.

The other issue is with missing values, and na.rm=T helps with getting summaries of numeric variables with missing values, we need to further investigate how suitable, na.omit functions are for domains which have large amounts of missing data and need to be treated.