Introduction to R. A Statistical Computing Environment. J.C. Wang. Department of Statistics Western Michigan University
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1 Introduction to R A Statistical Computing Environment J.C. Wang Department of Statistics Western Michigan University September 19, 2008 / Statistics Seminar
2 Outline 1 Introduction What is R R Environment 2 Examples Statistics Graphics Modeling
3 Outline 1 Introduction What is R R Environment 2 Examples Statistics Graphics Modeling
4 What is R a language and environment for statistical computing and graphics a dialect of S language (John Chambers, 1998 ACM Software System Award winner) initially written by Robert Gentleman and Ross Ihaka actively developed/improved by R core team and many contributors
5 What is R a language and environment for statistical computing and graphics a dialect of S language (John Chambers, 1998 ACM Software System Award winner) initially written by Robert Gentleman and Ross Ihaka actively developed/improved by R core team and many contributors
6 What is R a language and environment for statistical computing and graphics a dialect of S language (John Chambers, 1998 ACM Software System Award winner) initially written by Robert Gentleman and Ross Ihaka actively developed/improved by R core team and many contributors
7 What is R a language and environment for statistical computing and graphics a dialect of S language (John Chambers, 1998 ACM Software System Award winner) initially written by Robert Gentleman and Ross Ihaka actively developed/improved by R core team and many contributors
8 A Programming Environment a true computer language fully planned and coherent allows users to add additional functionality by defining new functions easy to extend by packages
9 A Programming Environment a true computer language fully planned and coherent allows users to add additional functionality by defining new functions easy to extend by packages
10 A Programming Environment a true computer language fully planned and coherent allows users to add additional functionality by defining new functions easy to extend by packages
11 A Programming Environment a true computer language fully planned and coherent allows users to add additional functionality by defining new functions easy to extend by packages
12 Integrated Suite of Software Facilities including an effective data handling and storage facility a suite of operators for calculations on arrays/matrices a large collection of intermediate tools for data analysis graphical facilities for data analysis and display a well-developed, simple and effective programming language
13 Integrated Suite of Software Facilities including an effective data handling and storage facility a suite of operators for calculations on arrays/matrices a large collection of intermediate tools for data analysis graphical facilities for data analysis and display a well-developed, simple and effective programming language
14 Integrated Suite of Software Facilities including an effective data handling and storage facility a suite of operators for calculations on arrays/matrices a large collection of intermediate tools for data analysis graphical facilities for data analysis and display a well-developed, simple and effective programming language
15 Integrated Suite of Software Facilities including an effective data handling and storage facility a suite of operators for calculations on arrays/matrices a large collection of intermediate tools for data analysis graphical facilities for data analysis and display a well-developed, simple and effective programming language
16 Integrated Suite of Software Facilities including an effective data handling and storage facility a suite of operators for calculations on arrays/matrices a large collection of intermediate tools for data analysis graphical facilities for data analysis and display a well-developed, simple and effective programming language
17 Outline 1 Introduction What is R R Environment 2 Examples Statistics Graphics Modeling
18 Descriptive Statistics mean, median, variance and standard deviation summary (5-number summary and mean) quantile table and flate-table of categorical variables column sum/column means, row sum/row means
19 Descriptive Statistics mean, median, variance and standard deviation summary (5-number summary and mean) quantile table and flate-table of categorical variables column sum/column means, row sum/row means
20 Descriptive Statistics mean, median, variance and standard deviation summary (5-number summary and mean) quantile table and flate-table of categorical variables column sum/column means, row sum/row means
21 Descriptive Statistics mean, median, variance and standard deviation summary (5-number summary and mean) quantile table and flate-table of categorical variables column sum/column means, row sum/row means
22 Descriptive Statistics mean, median, variance and standard deviation summary (5-number summary and mean) quantile table and flate-table of categorical variables column sum/column means, row sum/row means
23 Distributions and Random Variates density: ddistr probability (cdf): pdistr quantile (inverse cdf): qdistr random variates: rdistr distr include: normal, t, f, chisquare, binomial, poisson,...
24 Distributions and Random Variates density: ddistr probability (cdf): pdistr quantile (inverse cdf): qdistr random variates: rdistr distr include: normal, t, f, chisquare, binomial, poisson,...
25 Distributions and Random Variates density: ddistr probability (cdf): pdistr quantile (inverse cdf): qdistr random variates: rdistr distr include: normal, t, f, chisquare, binomial, poisson,...
26 Distributions and Random Variates density: ddistr probability (cdf): pdistr quantile (inverse cdf): qdistr random variates: rdistr distr include: normal, t, f, chisquare, binomial, poisson,...
27 Distributions and Random Variates density: ddistr probability (cdf): pdistr quantile (inverse cdf): qdistr random variates: rdistr distr include: normal, t, f, chisquare, binomial, poisson,...
28 Simple Plots examples scatterplots (plot) boxplots (boxplot) histograms (hist)
29 Simple Plots examples scatterplots (plot) boxplots (boxplot) histograms (hist)
30 Simple Plots examples scatterplots (plot) boxplots (boxplot) histograms (hist)
31 Exploratory Plots examples scatterplot matrix (pairs) condition plot (coplot)
32 Exploratory Plots examples scatterplot matrix (pairs) condition plot (coplot)
33 Publication-quality Plots lattice package univariate: barchart, bwplot, stripplot, dotplot, histogram, qqmath, densityplot bivariate: xyplot, qq trivariate: levelplot, contourplot, cloud, wireframe hypervariate: splom, parallel others: rfs, tmd
34 Publication-quality Plots lattice package univariate: barchart, bwplot, stripplot, dotplot, histogram, qqmath, densityplot bivariate: xyplot, qq trivariate: levelplot, contourplot, cloud, wireframe hypervariate: splom, parallel others: rfs, tmd
35 Publication-quality Plots lattice package univariate: barchart, bwplot, stripplot, dotplot, histogram, qqmath, densityplot bivariate: xyplot, qq trivariate: levelplot, contourplot, cloud, wireframe hypervariate: splom, parallel others: rfs, tmd
36 Publication-quality Plots lattice package univariate: barchart, bwplot, stripplot, dotplot, histogram, qqmath, densityplot bivariate: xyplot, qq trivariate: levelplot, contourplot, cloud, wireframe hypervariate: splom, parallel others: rfs, tmd
37 Publication-quality Plots lattice package univariate: barchart, bwplot, stripplot, dotplot, histogram, qqmath, densityplot bivariate: xyplot, qq trivariate: levelplot, contourplot, cloud, wireframe hypervariate: splom, parallel others: rfs, tmd
38 Modeling Tools ANOVA model (aov, anova) linear model (lm) nonlinear model (nls) time-series model factor model (factanal) linear/nonlinear mixed-effect model (nlme in package nlme)
39 Modeling Tools ANOVA model (aov, anova) linear model (lm) nonlinear model (nls) time-series model factor model (factanal) linear/nonlinear mixed-effect model (nlme in package nlme)
40 Modeling Tools ANOVA model (aov, anova) linear model (lm) nonlinear model (nls) time-series model factor model (factanal) linear/nonlinear mixed-effect model (nlme in package nlme)
41 Modeling Tools ANOVA model (aov, anova) linear model (lm) nonlinear model (nls) time-series model factor model (factanal) linear/nonlinear mixed-effect model (nlme in package nlme)
42 Modeling Tools ANOVA model (aov, anova) linear model (lm) nonlinear model (nls) time-series model factor model (factanal) linear/nonlinear mixed-effect model (nlme in package nlme)
43 Modeling Tools ANOVA model (aov, anova) linear model (lm) nonlinear model (nls) time-series model factor model (factanal) linear/nonlinear mixed-effect model (nlme in package nlme)
44 Projects Data Expo 2009: Airline on time data Aim: to provide a graphical summary of important features of the data set John Chambers Award
45 Projects Data Expo 2009: Airline on time data Aim: to provide a graphical summary of important features of the data set John Chambers Award
46 R Links R Homepage Comprehensive R Archive Network One R Tip a Day R Graph Gallery R Wiki R_note: The Exploration of Statistical Software R Technical Notes on the R Programming Language John Chambers Award Data Expo 2009
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