Interviews and Reviews: More R #rstats

I got interviewed on moving on from Excel to R in Human Resources (HR) here at http://www.hrtecheurope.com/blog/?p=5345

“There is a lot of data out there and it’s stored in different formats. Spreadsheets have their uses but they’re limited in what they can do. The spreadsheet is bad when getting over 5000 or 10000 rows – it slows down. It’s just not designed for that. It was designed for much higher levels of interaction.

In the business world we really don’t need to know every row of data, we need to summarise it, we need to visualise it and put it into a powerpoint to show to colleagues or clients.”

And a more recent interview with my fellow IIML mate, and editor at Analytics India Magazine

http://analyticsindiamag.com/interview-ajay-ohri-author-r-for-business-analytics/

AIM: Which R packages do you use the most and which ones are your favorites?

AO: I use R Commander and Rattle a lot, and I use the dependent packages. I use car for regression, and forecast for time series, and many packages for specific graphs. I have not mastered ggplot though but I do use it sometimes. Overall I am waiting for Hadley Wickham to come up with an updated book to his ecosystem of packages as they are very formidable, completely comprehensive and easy to use in my opinion, so much I can get by the occasional copy and paste code.

 

A surprising review at R- Bloggers.com /Intelligent Trading

http://intelligenttradingtech.blogspot.in/2012/10/book-review-r-for-business-analytics.html

The good news is that many of the large companies do not view R as a threat, but as a beneficial tool to assist their own software capabilities.

After assisting and helping R users navigate through the dense forest of various GUI interface choices (in order to get R up and running), Mr. Ohri continues to handhold users through step by step approaches (with detailed screen captures) to run R from various simple to more advanced platforms (e.g. CLOUD, EC2) in order to gather, explore, and process data, with detailed illustrations on how to use R’s powerful graphing capabilities on the back-end.

Do you want to write a review too? You can visit the site here

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

 

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)

Interview Prof Benjamin Alamar , Sports Analytics

Here is an interview with Prof Benjamin Alamar, founding editor of the Journal of Quantitative Analysis in Sport, a professor of sports management at Menlo College and the Director of Basketball Analytics and Research for the Oklahoma City Thunder of the NBA.

Ajay – The movie Moneyball recently sparked out mainstream interest in analytics in sports.Describe the role of analytics in sports management

Benjamin- Analytics is impacting sports organizations on both the sport and business side.
On the Sport side, teams are using analytics, including advanced data management, predictive anlaytics, and information systems to gain a competitive edge. The use of analytics results in more accurate player valuations and projections, as well as determining effective strategies against specific opponents.
On the business side, teams are using the tools of analytics to increase revenue in a variety of ways including dynamic ticket pricing and optimizing of the placement of concession stands.
Ajay-  What are the ways analytics is used in specific sports that you have been part of?

Benjamin- A very typical first step for a team is to utilize the tools of predictive analytics to help inform their draft decisions.

Ajay- What are some of the tools, techniques and software that analytics in sports uses?
Benjamin- The tools of sports analytics do not differ much from the tools of business analytics. Regression analysis is fairly common as are other forms of data mining. In terms of software, R is a popular tool as is Excel and many of the other standard analysis tools.
Ajay- Describe your career journey and how you became involved in sports management. What are some of the tips you want to tell young students who wish to enter this field?

Benjamin- I got involved in sports through a company called Protrade Sports. Protrade initially was a fantasy sports company that was looking to develop a fantasy game based on advanced sports statistics and utilize a stock market concept instead of traditional drafting. I was hired due to my background in economics to develop the market aspect of the game.

There I met Roland Beech (who now works for the Mavericks) and Aaron Schatz (owner of footballoutsiders.com) and learned about the developing field of sports statistics. I then changed my research focus from economics to sports statistics and founded the Journal of Quantitative Analysis in Sports. Through the journal and my published research, I was able to establish a reputation of doing quality, useable work.

For students, I recommend developing very strong data management skills (sql and the like) and thinking carefully about what sort of questions a general manager or coach would care about. Being able to demonstrate analytic skills around actionable research will generally attract the attention of pro teams.

About-

Benjamin Alamar, Professor of Sport Management, Menlo College

Benjamin Alamar

Professor Benjamin Alamar is the founding editor of the Journal of Quantitative Analysis in Sport, a professor of sports management at Menlo College and the Director of Basketball Analytics and Research for the Oklahoma City Thunder of the NBA. He has published academic research in football, basketball and baseball, has presented at numerous conferences on sports analytics. He is also a co-creator of ESPN’s Total Quarterback Rating and a regular contributor to the Wall Street Journal. He has consulted for teams in the NBA and NFL, provided statistical analysis for author Michael Lewis for his recent book The Blind Side, and worked with numerous startup companies in the field of sports analytics. Professor Alamar is also an award winning economist who has worked academically and professionally in intellectual property valuation, public finance and public health. He received his PhD in economics from the University of California at Santa Barbara in 2001.

Prof Alamar is a speaker at Predictive Analytics World, San Fransisco and is doing a workshop there

http://www.predictiveanalyticsworld.com/sanfrancisco/2012/agenda.php#day2-17

2:55-3:15pm

All level tracks Track 1: Sports Analytics
Case Study: NFL, MLB, & NBA
Competing & Winning with Sports Analytics

The field of sports analytics ties together the tools of data management, predictive modeling and information systems to provide sports organization a competitive advantage. The field is rapidly developing based on new and expanded data sources, greater recognition of the value, and past success of a variety of sports organizations. Teams in the NFL, MLB, NBA, as well as other organizations have found a competitive edge with the application of sports analytics. The future of sports analytics can be seen through drawing on these past successes and the developments of new tools.

You can know more about Prof Alamar at his blog http://analyticfootball.blogspot.in/ or journal at http://www.degruyter.com/view/j/jqas. His detailed background can be seen at http://menlo.academia.edu/BenjaminAlamar/CurriculumVitae