Data analysis with R

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The goal of this workshop is to introduce fundamental capabilities of R as a tool for performing data analysis. Here, we learn about the most comprehensive statistical analysis language R, to get a basic idea how to analyze real-word data, extract patterns from data and find causality.

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  • Data analysis with R

    1. 1. SHARETHIS DATA ANALYSIS with R Hassan Namarvar
    2. 2. 2 WHAT IS R? • R is a free software programming language and software development for statistical computing and graphics. • It is similar to S language developed at AT&T Bell Labs by Rick Becker, John Chambers and Allan Wilks. • R was initially developed by Ross Ihaka and Robert Gentleman (1996), from the University of Auckland, New Zealand. • R source code is written in C, Fortran, and R.
    3. 3. 3 R PARADIGMS Multi paradigms: – Array – Object-oriented – Imperative – Functional – Procedural – Reflective
    4. 4. 4 STATISTICAL FEATURES • Graphical Techniques • Linear and nonlinear modeling • Classical statistical tests • Time-series analysis • Classification • Clustering • Machine learning
    5. 5. 5 PROGRAMMING FEATURES • R is an interpreted language • Access R through a command-line interpreter • Like MATLAB, R supports matrix arithmetic • Data structures: – Vectors – Metrics – Array – Data Frames – Lists
    6. 6. 6 ADVANTAGES OF R • The most comprehensive statistical analysis package available. • Outstanding graphical capabilities • Open source software – reviewed by experts • R is free and licensed under the GNU. • R has over 5,578 packages as of May 31, 2014! • R is cross-platform. GNU/Linux, Mac, Windows. • R plays well with CSV, SAS, SPSS, Excel, Access, Oracle, MySQL, and SQLite.
    7. 7. 7 HOW TO INSTALL R? • Download an install the latest version from: – http://cran.r-project.org • Install packages from R Console: – > install.packages(‘package_name’) • R has its own LaTeX-like documentation: – > help()
    8. 8. 8 STARTING WITH R • In R console: – > x <- 2 – > x – > y <- x^2 – > y – > ls() – > rm(y) • Vectors: – > v <- c(4, 7, 23.5, 76.2, 80) – > Summary(v)
    9. 9. 9 STARTING WITH R • Histogram: – > r <- rnorm(100) – > summary(r) – > plot(r) – > hist(r) • QQ-Plot (Quantile): – > qqplot(r, rnorm(1000))
    10. 10. 10 STARTING WITH R • Factors: – > g <- c(‘f’, ‘m’, ‘m’, ‘m’, ‘f’, ‘m’, ‘f’, ‘m’) – > h <- factor(g) – > table(g) • Matrices: – > r <- rnorm(100) – > dim(r) <- c(50,2) – > r – > Summary(r) – > M <- matrix(c(45, 23, 66, 77, 33, 44), 2, 3, byrow=T)
    11. 11. 11 STARTING WITH R • Data Frames: – > n = c(2, 3, 5) – > s = c("aa", "bb", "cc") – > b = c(TRUE, FALSE, TRUE) – > df = data.frame(n, s, b) • Built-in Data Set: – > state.x77 – > st = as.data.frame(state.x77) – > st$Density = st$Population * 1000 / st$Area – > summary(st) – > cor(st) – > pairs(st)
    12. 12. 12 STARTING WITH R Population 3000 5500 68 71 40 55 0e+00 5e+05 015000 30005500 Income Illiteracy 0.52.0 6871 Life Exp Murder 2814 4055 HS Grad Frost 0100 0e+005e+05 Area 0 15000 0.5 2.0 2 8 14 0 100 0 600 0600 Density
    13. 13. 13 LINEAR REGRESSION MODEL IN R • Linear Regression Model: – > x <- 1:100 – > y <- x^3 – Model y = a + b . x – > lm(y ~ x) – > model <- lm(y ~ x) – > summary(model) – > par(mfrow=c(2,2)) – > plot(model)
    14. 14. 14 LM MODEL – Call: – lm(formula = y ~ x) – Residuals: – Min 1Q Median 3Q Max – -129827 -103680 -29649 85058 292030 – Coefficients: – Estimate Std. Error t value Pr(>|t|) – (Intercept) -207070.2 23299.3 -8.887 3.14e-14 *** – x 9150.4 400.6 22.844 < 2e-16 *** – --- – Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 – Residual standard error: 115600 on 98 degrees of freedom – Multiple R-squared: 0.8419, Adjusted R-squared: 0.8403 – F-statistic: 521.9 on 1 and 98 DF, p-value: < 2.2e-16
    15. 15. 15 LM MODEL 0 20 40 60 80 100 0e+002e+054e+056e+058e+051e+06 y=x^3 x y
    16. 16. 16 DIAGNOSIS PLOT -2e+05 2e+05 4e+05 6e+05 -1e+051e+053e+05 Fitted values Residuals Residuals vs Fitted 100 99 98 -2 -1 0 1 2 -10123 Theoretical Quantiles Standardizedresiduals Normal Q-Q 100 99 98 -2e+05 2e+05 4e+05 6e+05 0.00.51.01.5 Fitted values Standardizedresiduals Scale-Location 100 99 98 0.00 0.01 0.02 0.03 0.04 -10123 Leverage Standardizedresiduals Cook's distance Residuals vs Leverage 100 99 98
    17. 17. 17 LINEAR REGRESSION MODEL IN R • Model Built-in Data: – > colnames(st)[4] = "Life.Exp" – > colnames(st)[6] = "HS.Grad" – model1 = lm(Life.Exp ~ Population + Income + Illiteracy + Murder + HS.Grad + Frost + Area + Density, data=st) – > summary(model1) – > model2 <- step(model1) – > model3 = update(model2, .~.-Population) – > Summary(model3)
    18. 18. 18 LINEAR REGRESSION MODEL IN R • Confidence limits on Estimated Coefficients: – > confint(model3) – > predict(model3, list(Murder=10.5, HS.Grad=48, Frost=100))
    19. 19. 19 OUTLIERS • Boxplot: – > v <- rnorm(100) – > v = c(v,10) – > boxplot(v) – > rug(jitter(v), side=2) -20246810
    20. 20. 20 PROBABILITY DENSITY FUNCTION • PDF: – > r <- rnorm(1000) – > hist(r, prob=T) – > lines(density(r), col="red") Histogram of r r Density -3 -2 -1 0 1 2 3 0.00.10.20.30.4
    21. 21. 21 CASE STUDY: SHARETHIS EXAMPLE • Relationship of clicks with winning price and Impression on ADX: • Data – Analyzed ADX Hourly Impression Logs • Method – Detected outliers – Predicted clicks using a regression tree model
    22. 22. 22 CASE STUDY: SHARETHIS EXAMPLE • Outlier Detection: Clicks Impressions
    23. 23. 23 CASE STUDY: SHARETHIS EXAMPLE • Regression Tree – One of the most powerful classification/regression – > library(rpart) – > fit <- rpart(log(CLK) ~ log(IMP) + AVG_PRICE + SD_PRICE, data=x) – > plot(fit) – > text(fit) – > plot(predict(fit), log(x$CLK))
    24. 24. 24 CASE STUDY: SHARETHIS EXAMPLE • Regression Tree | log(IMP)< 9.33 log(IMP)< 8.349 log(IMP)< 11.28 SD_PRICE< 0.2604 log(IMP)>=10.04 log(IMP)< 10.39 AVG_PRICE>=1.713 AVG_PRICE>=1.247 AVG_PRICE< 0.8555 log(IMP)< 12.49 0.751 1.387 1.541 2.869 1.959 2.729 3.003 3.104 4.331 3.577 4.753
    25. 25. 25 CASE STUDY: SHARETHIS EXAMPLE • Predict Log of Clicks 0 1 2 3 4 5 6 7 1234 log(x$CLK) predict(fit)
    26. 26. 26 CASE STUDY: COLOR DETECTION • Detect color from product image: -1.0 -0.5 0.0 0.5 1.0 -1.0-0.50.00.51.0 -1.0 -0.5 0.0 0.5 1.0 -1.0-0.50.00.51.0 -1.0 -0.5 0.0 0.5 1.0 -1.0-0.50.00.51.0
    27. 27. 27 RESOURCES • Books: – An Introduction to Statistical Learning: with Applications in R by G. James, D. Witten, T. Hatie, R. Tibshirani, 2013 – The Art of R Programming: A Tour of Statistical Software Design, N. Matloff, 2011 – R Cookbook (O'Reilly Cookbooks), P. Teetor, 2011 • R Blog: – http://www.r-bloggers.com

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