Cookies help us display personalized product recommendations and ensure you have great shopping experience.

By using this site, you agree to the Privacy Policy and Terms of Use.
Accept
SmartData CollectiveSmartData Collective
  • Analytics
    AnalyticsShow More
    image fx (60)
    Data Analytics Driving the Modern E-commerce Warehouse
    13 Min Read
    big data analytics in transporation
    Turning Data Into Decisions: How Analytics Improves Transportation Strategy
    3 Min Read
    sales and data analytics
    How Data Analytics Improves Lead Management and Sales Results
    9 Min Read
    data analytics and truck accident claims
    How Data Analytics Reduces Truck Accidents and Speeds Up Claims
    7 Min Read
    predictive analytics for interior designers
    Interior Designers Boost Profits with Predictive Analytics
    8 Min Read
  • Big Data
  • BI
  • Exclusive
  • IT
  • Marketing
  • Software
Search
© 2008-25 SmartData Collective. All Rights Reserved.
Reading: The R-Files: Paul Teetor
Share
Notification
Font ResizerAa
SmartData CollectiveSmartData Collective
Font ResizerAa
Search
  • About
  • Help
  • Privacy
Follow US
© 2008-23 SmartData Collective. All Rights Reserved.
SmartData Collective > Data Management > Best Practices > The R-Files: Paul Teetor
Best PracticesR Programming Language

The R-Files: Paul Teetor

DavidMSmith
DavidMSmith
5 Min Read
SHARE

“The R-Files” is an occasional series from Revolution Analytics, where we profile prominent members of the R Community.

R-Files 

“The R-Files” is an occasional series from Revolution Analytics, where we profile prominent members of the R Community.

More Read

Webinar: A Brief Introduction to R for SAS and SPSS Users
Question Assumptions Before Initiating Big Data Projects
The Ethics of Data, Visualized [INFOGRAPHIC]
Alpha Testing RevoScaleR Running in Hadoop
Building an Analytical Portal to Support Analytical Culture

R-Files 

Paul Teetor

Name: Paul Teetor

Profession: Quantitative developer (freelance)

Nationality: American

Years Using R: 7

Known for: Author of R Cookbook (O’Reilly Media, 2011)

An active member of the R community, Paul Teetor is a quantitative developer and statistical consultant based in the Chicago area. He’s been using R for seven years, during which time his contributions to the community have been significant — particularly in the field of finance. He’s currently a freelance consultant largely focused on time series analysis. Teetor is also the author of the popular R Cookbook, which was published by O’Reilly Media this past March and offers new users over 200 “recipes” for performing more efficient data analysis with R.

He was first drawn to R for the flexibility it offered him in developing trading systems. Citing his own background in software engineering and the need to perform sophisticated statistical analysis in a programmable — and cost-effective — environment, Teetor said that R emerged as the perfect fit for him. Since then, he has performed the majority of his financial analyses in R and has also emerged as a leading evangelist for the community. He gradually collected a catalog of tricks and techniques for R, many of which were compiled into the R Cookbook. He’s been a participant at conferences such as the Joint Statistical Meetings and the R/Finance Conference where he evangelized the role of R in quantitative finance. Some of those talks and papers are available on his website.

“Prior to R, I did most of my statistical analysis in Excel — and occasionally SAS,” said Teetor. “However, performing statistical analyses for financial tables in either was extremely tedious and puts you in a specific box. R is a modern, programmable language, so I can make it do what I need it to do in a timely manner. It’s been a pleasure to be able to take what I’ve learned from R and share it with other community members – and to continue learning new tips and tricks from them as well.”

Teetor uses R for the majority of his finance work because, as he puts it, it does things other languages “simply cannot do.” He cited the example of hedge ratio calculations which benefit from the flexibility of R, a topic on which he gave a lightning talk at R/Finance this past summer. He was also quick to credit fellow R user Jeff Ryan (whom we profiled here earlier this year) as an influential member of the R community, citing his finance packages as particularly useful. “I use nearly every finance package he’s written, they’re incredibly helpful and greatly streamline the process of R-based financial analysis.”

When asked about the relationship between financial analysis and the rise of the data science movement, Teetor noted, “People in data science are experiencing what financial analysts have experienced for years: out of the box data analysis is not realistic. You need to incorporate a heavy amount of custom statistics, something that’s not easy to do with a commercial product where you can’t get to the source code. Data scientists need a way to construct custom analyses and R gives them that opportunity. Nothing else on the horizon that can compete with that, in terms of finance or the wider field of data science.”

Looking ahead, Teetor sees a bright future for the continued evolution of R. Since there is no real alternative on the market, he argues, R’s potential for future growth is nearly unlimited. He did, however, cite R’s capacity (or lack thereof) for software engineering as one possible area of improvement. “When R was originally envisioned, it wasn’t thought of as a vehicle for software engineering. Nobody expected people to keep their scripts as opposed to just throwing them away. As it’s grown though, people are building larger, more complex systems with longer lifetimes.” It’s an area that Teetor cites as one of the main struggles with R today, but also one which he cites as a great opportunity on which to innovate.

Share This Article
Facebook Pinterest LinkedIn
Share

Follow us on Facebook

Latest News

image fx (60)
How Finance & BI Teams Choose Accounting Software
Big Data Business Intelligence Exclusive
Why the AI Race Is Being Decided at the Dataset Level
Why the AI Race Is Being Decided at the Dataset Level
Artificial Intelligence Big Data Exclusive
image fx (60)
Data Analytics Driving the Modern E-commerce Warehouse
Analytics Big Data Exclusive
ai for building crypto banks
Building Your Own Crypto Bank with AI
Blockchain Exclusive

Stay Connected

1.2kFollowersLike
33.7kFollowersFollow
222FollowersPin

You Might also Like

big data tools
AnalyticsBest PracticesBig DataBusiness IntelligenceDecision Management

With Big Data, Smaller Can Be Better: Find the “Gems”

3 Min Read

Big Data Analytics a Key Enabler for Social CRM – Airlines Case Study

3 Min Read
self driving vehicles
Best PracticesBig DataData CollectionExclusive

Your Guide To Different Telematics Solutions And The Data They Collect

6 Min Read

Business Discovery Apps: Data Visualization Plus

3 Min Read

SmartData Collective is one of the largest & trusted community covering technical content about Big Data, BI, Cloud, Analytics, Artificial Intelligence, IoT & more.

data-driven web design
5 Great Tips for Using Data Analytics for Website UX
Big Data
giveaway chatbots
How To Get An Award Winning Giveaway Bot
Big Data Chatbots Exclusive

Quick Link

  • About
  • Contact
  • Privacy
Follow US
© 2008-25 SmartData Collective. All Rights Reserved.
Go to mobile version
Welcome Back!

Sign in to your account

Username or Email Address
Password

Lost your password?