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R and Data Science

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Presented by: Joseph Rickert, Data Scientist Community Manager, Revolution Analytics, Sep 25 2014.

Whenever data scientists are asked about what software they use R always comes up at the top of the list. In one recent survey, only SQL was rated higher than R. In this webinar we will explore what makes R so popular and useful. Starting with the big picture, we describe how R is organized and how to find your way around the R world. Then we will work through some examples highlighting features of R that make it attractive for data science work including:

Acquiring data
Data manipulation
Exploratory data analysis
Model building
Machine learning

Published in: Technology
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R and Data Science

  1. 1. Revolution Confidential Revolution Analytics R and Data Science Joseph B Rickert September 25, 2014
  2. 2. Revolution Confidential What is R?  Most widely used data analysis software  Used by 2M+ data scientists, statisticians and analysts  Most powerful statistical programming language  Flexible, extensible and comprehensive for productivity  Platform for beautiful and unique data visualizations  As seen in New York Times, Twitter and Flowing Data  Thriving open-source community  Leading edge of analytics research www.revolutionanalytics.com/what-r
  3. 3. OPEN SOURCE R
  4. 4. Revolution Confidential 4 R’s popularity is growing rapidly R Usage Growth Rexer Data Miner Survey, 2007-2013 • Rexer Data Miner Survey • IEEE Spectrum, July 2014 #9: R Language Popularity IEEE Spectrum Top Programming Languages
  5. 5. Revolution Confidential Poll Question #1  What are the statistical programming languages/platforms you are most familiar with? (choose all that apply)  A) R  B) SAS  C) SPSS  D) KXEN  E) Statistica 5
  6. 6. Revolution Confidential Tools for Data Science Source: O’Reilly Data Science Survey 6
  7. 7. Revolution Confidential 7 R is among the highest-paid IT skills in the US Dice Tech Salary Survey, January 2014 O’Reilly Strata 2013 Data Science Salary Survey
  8. 8. Revolution Confidential 8 Photo by Ksayer1 on flickr.
  9. 9. Revolution Confidential Why R for Data Science? X <- if (!is.empty.model(mt)) model.matrix(mt, mf, contrasts) else matrix(, NROW(Y), 0L) weights <- as.vector(model.weights(mf)) if (!is.null(weights) && !is.numeric(weights)) stop("'weights' must be a numeric vector") if (!is.null(weights) && any(weights < 0)) stop("negative weights not allowed") offset <- as.vector(model.offset(mf)) if (!is.null(offset)) { if (length(offset) != NROW(Y)) stop(gettextf("number of offsets is %d should equal %d (number of observations)", length(offset), NROW(Y)), domain = NA) } mustart <- model.extract(mf, "mustart") etastart <- model.extract(mf, "etastart") fit <- eval(call(if (is.function(method)) "method" else method, Algorithms x = X, y = Y, weights = weights, start = start, etastart = etastart, mustart = mustart, offset = offset, family = family, control = control, intercept = attr(mt, "intercept") > 0L)) if (length(offset) && attr(mt, "intercept") > 0L) { fit2 <- eval(call(if (is.function(method)) "method" else method, x = X[, "(Intercept)", drop = FALSE], y = Y, weights = weights, offset = offset, family = family, control = control, intercept = TRUE)) if (!fit2$converged) warning("fitting to calculate the null deviance did not converge -- increase 'maxit'?") fit$null.deviance <- fit2$deviance } if (model) fit$model <- mf fit$na.action <- attr(mf, "na.action") if (x) fit$x <- X if (!y) fit$y <- NULL fit <- c(fit, list(call = call, formula = formula, terms = mt, data = data, offset = offset, control = control, method = method, contrasts = attr(X, "contrasts"), xlevels = .getXlevels(mt, mf))) class(fit) <- c(fit$class, c("glm", "lm")) fit 9 Task Views
  10. 10. Revolution Confidential R Growth Put this astonishing growth in perspective:  SAS.V 9.3S contains ~ 1,200 commands that are roughly equivalent to R functions  R packages contain a median of 5 functions  Therefore R has ~ 36,820 functions  During 2013 alone, R added more functions than SAS Institute has written in its entire history! Bob Muenchen 10 5882 packages 9/25/14
  11. 11. Revolution Confidential Why R for Data Science? Visualizations 11
  12. 12. Revolution Confidential Why R for Data Science?  Scripting  Functional programming  Parallel programming  Data structures  Objects  Data Types  Regular expressions  Data connections  Interfaces to other Programming languages 12
  13. 13. Revolution Confidential Why R for Data Science? Data Manipulation 13 “It's often said that 80% of the effort of analysis is spent just getting the data ready to analyse, the process of data cleaning. Data cleaning is not only a vital first step, but it is often repeated multiple times over the course of an analysis as new problems come to light.” Hadley Wickham Tidy Data
  14. 14. Revolution Confidential Why R for Data Science? R Integrates  Web applications  Internet graphics  D3  Potly  Other Languages  C, C++  Java  BI Tools  Data bases  SQL  MongoDB 14
  15. 15. Revolution Confidential Poll Question #2  What are the data platforms that you are connecting to regularly? (choose all that apply)  A) Hadoop  B) Spark  C) Cloud-based (Azure/AWS/Google)  D) Data Warehouses  E) Servers (Grid or Cluster) 15
  16. 16. Revolution Confidential Why R for Data Science Hadoop Servers & Clusters Data Warehouses R Scales
  17. 17. Revolution Confidential Poll Question #3  What are the types of models that you are working with most? (choose all that apply)  A) Linear models / Regression / GLM  B) Decision Trees / Random Forests  C) Survival Models  D) GBM  E) Time Series models 17
  18. 18. Let’s look at some code. www.revolutionanalytics.com 1.855.GET.REVO Twitter: @RevolutionR
  19. 19. Revolution Confidential 19 Why is R Right for Data Science?  R is open source  R is a powerful language  Data Manipulation  Computational Statistics  Machine Learning  R is an innovation engine  R has a rich and expanding ecosystem
  20. 20. Revolution Confidential 20 Q&A / Resources R Code and Markdown Files https://github.com/joseph-rickert/DataScienceRWebinar What is R? revolutionanalytics.com/what-is-r Companies using R revolutionanalytics.com/companies-using-r AcademyR training revolutionanalytics.com/AcademyR AcademyR Certification revolutionanalytics.com/AcademyR-certification Contact Revolution Analytics revolutionanalytics.com/contact-us
  21. 21. Thank you Revolution Analytics is the leading commercial provider of software and support for the popular open source R statistics language. www.revolutionanalytics.com, 1.855.GET.REVO, Twitter: @RevolutionR 21

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