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Los Angeles R users group - Nov 17 2010 - Part 2


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Los Angeles R users group - Nov 17 2010 - Part 2

  1. 1. Introduction to the Future of R Avram Aelony November 2010 Wednesday, November 17, 2010
  2. 2. Talk Outline: 1. Strengths II. Criticisms III. Challenges IV. Remedies and Solutions V. The Future Wednesday, November 17, 2010
  3. 3. Quick disclaimer: - I don’t consider myself an R expert - I don’t have a crystal ball informing of the Future - This talk is about polite observations - The future is dynamic YMMD <- your-mileage-may-differ() Wednesday, November 17, 2010 ?
  4. 4. R’s Strengths - a many good things, too many to mention individually ... but let’s try... Wednesday, November 17, 2010
  5. 5. Strengths of R - A high quality statistical platform, yielding reproducible results - Open Source, free and available - Large, active community - Intuitive language structure - Data as rows and columns - Package plugin architecture - there are many packages, top packages in widespread use - Distributed contributions written/offered/controlled by many/multiple individuals - Data processing for most individual needs. - Emerging success and increasing corporate adoption e.g. some corporate needs (often used for prototyping and adhoc analytics) Wednesday, November 17, 2010
  6. 6. Strengths of R More succinctly... based on a paraphrasing of a post by Ted Dunning * 1. Library II. Language III. Community * http://www.stat.columbia.edu/~cook/movabletype/archives/2010/09/ the_future_of_r.html Wednesday, November 17, 2010
  7. 7. Criticisms of R - Small grievances: syntax, elegance, and managing complexity “Most packages are very good, but I regret to say some are pretty inefficient and others downright dangerous.” -Bill Venables, quote from 2007 http://www.mail-archive.com/r-help@r-project.org/msg06853.html “...R functions used to be lean and mean, and now they’re full of exception-handling and calls to other packages. R functions are spaghetti-like messes of connections in which I keep expecting to run into syntax like “GOTO 120...” - comment taken from Gelman blog on the future of R. http://www.stat.columbia.edu/~cook/movabletype/archives/2010/09/the_future_of_r.html - Larger grievances: memory and inefficiency “One of the most vexing issues in R is memory. For anyone who works with large datasets - even if you have 64-bit R running and lots (e.g., 18Gb) of RAM, memory can still confound, frustrate, and stymie even experienced R users.” http://www.matthewckeller.com/html/memory.html Wednesday, November 17, 2010
  8. 8. However, greater challenges for R lie ahead 1. Big Data is coming... II. Isn’t Big Data already here ? How can we imagine an ideal environment to address Big Data? Wednesday, November 17, 2010
  9. 9. - What is Big Data? "Every 2 Days We Create As Much Information As We Did Up To 2003" - Eric Schmidt, Chairman & CEO, Google. http://techcrunch.com/2010/08/04/schmidt-data/ "Data is abundant, Information is useful, Knowledge is precious." http://hadoop-karma.blogspot.com/2010/03/how-much-data-is-generated-on-internet.html - Freshness, this data will self destruct in 5 seconds... !! "How Much Time Do You Have Before Web‐Generated Leads Go Cold?" http://www.matrixintegratedmarketing.com/MIT.pdf Get ready: “Web Scale Big Data - 100’s of Terabytes” -John Sichi, Facebook, on intended usage with Hive. http://www.slideshare.net/jsichi/hive-evolution-apachecon-2010 slide #6. Wednesday, November 17, 2010
  10. 10. What is Big Data? Wikipedia - http://en.wikipedia.org/wiki/Big_data Wednesday, November 17, 2010 ?
  11. 11. Solving the “Big” Data problem ... as I see it, there are 5 competing possible solution “avenues” Wednesday, November 17, 2010
  12. 12. The “Big” Data problem: Solution #1 Use R in Conjunction with other specialized tools. Examples: - R remains a language for small datasets but has “hooks” and “bridges” that enable use with MapReduce style tools (Hadoop, Streaming, Hive, Pig, Cascading, others...) Wednesday, November 17, 2010
  13. 13. The “Big” Data problem: Solution #2 Packages that enable new functionality for reading and processing very large data sets Examples: - Saptarshi Guha’s RHIPE (R and Hadoop Processing Environment) - Kane & Emerson’s bigmemory - Adler et al.‘s ff package - Henrik Bengtsson’s R.huge package (deprecated) - (many new yet-to-be-developed possibilities here ) So.... enhance functions, but no enhancements to the core language Wednesday, November 17, 2010
  14. 14. The “Big” Data problem: Solution #3 Same language but have R “do the right thing” under the hood. Examples: - Out of memory algorithms, think: “I see you’re trying to analyze a sizable amount of data...” - Either seamlessly or after user approval to go ahead... # perhaps, perhaps... d <- read.table(fn=”s3//:mybucket.name”, enormous.data=TRUE) or if possible, enhance core language as well as functionality!!! Wednesday, November 17, 2010
  15. 15. The “Big” Data problem: Solution #4 - Completely start over 2008 http://www.stat.auckland.ac.nz/%7Eihaka/downloads/Compstat-2008.pdf Wednesday, November 17, 2010
  16. 16. The “Big” Data problem: 2010 http://www.stat.auckland.ac.nz/%7Eihaka/downloads/JSM-2010.pdf Wednesday, November 17, 2010
  17. 17. The “Big” Data problem: The Ihaka/Lang “Back to the Future” paper came out in 2008. The Ihaka “Lessons Learned” 2010 paper mentions: - the need of an “effective language for handling large-scale computations” - nostalgia for Lisp Have there been any Lisp-like advances since then? What about Clojure ? Wednesday, November 17, 2010
  18. 18. The “Big” Data problem: Solution #5 - Does Clojure fit the bill ? H0: Clojure already has many of the things Ross Ihaka would ask for H1: Really? -Rich Hickey http://clojure.org/rationale Clojure may be seen as a solution, or as an example path for R to follow, improve upon, or choose to differ... Wednesday, November 17, 2010
  19. 19. Clojure -Rich Hickey http://clojure.org Wednesday, November 17, 2010
  20. 20. The problem with many new languages is that initially there are no libraries... Clojure already has many, and can use any Java library directly as necessary. - Core Clojure - Incanter: "a Clojure-based, R-like platform for statistical computing and graphics" http://incanter.org/ - Infer: "a (Clojure) library for machine learning and statistical inference, designed to be used in real production systems." https://github.com/bradford/infer - Cascalog: “Data processing on Hadoop without the hassle” “a Clojure-based query language for Hadoop” Wednesday, November 17, 2010
  21. 21. What will the Future really hold for R ? Wednesday, November 17, 2010
  22. 22. Thanks for listening... Wednesday, November 17, 2010
  23. 23. Appendix: A few slides on Clojure, and three powerful Clojure libraries: Incanter Infer Cascalog Wednesday, November 17, 2010
  24. 24. Clojure - a quick tour -Rich Hickey http://clojure.org Wednesday, November 17, 2010
  25. 25. David Edgar Liebke’s Incanter Please see http://incanter.org/docs/data-sorcery-new.pdf for an excellent intro to Incanter. Wednesday, November 17, 2010
  26. 26. Below are example snippets from Incanter Wednesday, November 17, 2010
  27. 27. Bradford Cross’ Infer: "a (Clojure) library for machine learning and statistical inference, designed to be used in real production systems." https://github.com/bradford/infer Wednesday, November 17, 2010
  28. 28. Nathan Marz’s Cascalog: http://nathanmarz.com/blog/introducing-cascalog-a-clojure-based-query-language-for-hado.html Wednesday, November 17, 2010