![Agenda
● What is R [slides 5-8]
● What can R be used for [slides 9-10]
● Recap & where to learn more [slides 11-12]](https://image.slidesharecdn.com/executiveintrotorpdf-161222162000/85/Executive-Intro-to-R-4-320.jpg)
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Executive Intro to R
- 1. Executive Intro to R William M. Cohee November 2016 Prepared using Apache OpenOffice 4.1.2
- 2. Presenter Bio ● 15+ years of Wall Street Technology experience ● Expertise in front-office Fixed Income Systems, Analytics, Pricing, Instrument, & Entity Reference Data Management ● BA, Computer Science ● MS, Information Systems Engineering ● Certified Bloomberg Specialist ● Currently in the Chief Data Office @ HSBC ● www.linkedin.com/in/billcohee
- 3. Topic ● Tool of choice for Statisticians, Data Analysts, & Data Scientists ● Popularity and use of R is on the rise ● R Community is vibrant & the talent pool is growing rapidly ● R is evolving from its statistical computing roots into a development platform for robust, reusable software ● A lot of commercial, third-party systems are adding support ● Oracle, Microsoft becoming big players ● R can be used to manage & analyze data in Hadoop ● A growing ecosystem is accelerating industry acceptance/adoption ● R savvy IT leaders can deliver more effective, lower cost solutions
- 4. Agenda ● What is R [slides 5-8] ● What can R be used for [slides 9-10] ● Recap & where to learn more [slides 11-12]
- 5. R – What is it? ● A powerful computing environment for Data Analysis & Statistics ● 'R' proper, is an open-source programming language ● Developed as a dialect of 'S' ● S developed by Bell Labs to 'turn ideas into software, quickly and faithfully' c.1976 ● strong desire at the time for an alternative to writing FORTRAN subroutines for analyzing data ● Ross Ihaka and Robert Gentleman recognized as original creators of R while professors at the University of Auckland in New Zealand c.1995 ● v1.0 came onto the scene in the early 2000s
- 6. R – What is it? ● Traditional user base consists of ● Researchers ● Statisticians ● Academia ● 'New wave' R users ● Wall Street Desk Quants ● Risk Analysts & Financial Modelers ● Data Scientists ● Advent of Big Data and the nascent field of Data Science are serving as catalysts to the sudden rise of this 16+ year old technology
- 7. R – What is it? ● When people speak of R, they are usually referring to the broader ecosystem, not the language ● R for Windows, Microsoft R Open – command line interpreters ● RStudio, R Tools for Visual Studio – IDEs (Interactive Development Environments) ● user-friendly, robust, graphical front-ends for working with R ● CRAN and MRAN ● Comprehensive R Archive Network ● Microsoft R Open Archive Network ● repositories of open-source extensions to R known as 'Packages' ● think of a Package as a pre-built library of functions & data
- 8. R – What is it? ● R was not created with 'coders' in mind ● Creators were focused on how to make Data Analysis easier on the users of data ● Geared toward the power-user who has to work with large amounts of data while avoiding coding as much as practically possible ● Why is it called R ??? ● the co-creators were Ross & Robert! ● it was trendy to give languages letter names (B, C, S, etc) ● As R becomes more mainstream, it may have everyday applications for people in roles requiring them to work with or 'be in the data'
- 9. R – What can it be used for? ● For presenting & solving data-oriented problems ● Exploratory Analysis ● discovering data about the data ● clustering & visualizing data ● quickly building summaries of the data being worked with ● Wrangling/Munging & re-shaping data ● working with structured & unstructured data ● sub-setting, filtering, and merging data ● making data 'tidy' – datasets that facilitate some kind of analysis ● dplyr & tidyr Packages popular
- 10. R – What can it be used for? ● Predictive Analytics & Machine Learning ● modeling, sampling, forecasting, trending, regression ● caret, h2o, quantmod Packages popular ● Data Visualization ● powerful, publication-quality graphing & plotting Packages ● ggplot2, leaflets, and shiny Packages popular ● shiny example: Where are the so-called 'SuperZIPs'? ● US postal codes scored on a scale of 0-100, 100 being highest ● score is a function of median household income and education level ● Top 5% are deemed the 'SuperZIPs' ● click to see the R + shiny powered Interactive data map
- 11. Recap & Resources ● R is an open-source environment that can be used for complex Data 'work' ● essential part of a Data Scientist's Toolbox ● Also a functional programming language ● can be used to create programs to automate routine, repetitive data tasks and for general software development ● Becoming a mainstream tool ● benefiting from increased commercial support ● maturing ecosystem of Packages ● Agility, flexibility, growing talent pool, & low cost of ownership all a part of R's appeal
- 12. Recap & Resources ● Where to learn more... ● The R Homepage: https://www.r-project.org ● RStudio: https://www.rstudio.com/products/RStudio ● CRAN: https://cran.r-project.org ● Oracle and R: http://bit.ly/2dUC24a ● Microsoft and R: http://bit.ly/2e5CT5m ● The R Consortium: https://www.r-consortium.org ● Playlist of R video tutorials: http://bit.ly/1iRcgyn ● Free Courses ● https://www.coursera.org/learn/r-programming ● https://www.datacamp.com/courses/free-introduction-to-r Scan this QR code to view online from a mobile device