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Alternative text for elementary statistics

Alternative text for elementary statistics. www.statsoft.com/textbook/stathome.html www.statsoft.com/textbook/stathome.html Elementary Concepts Basic Statistics. Introduction to R. Autumn 2007 Petter Mostad mostad@chalmers.se. What is R? .

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Alternative text for elementary statistics

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  1. Alternative text for elementary statistics • www.statsoft.com/textbook/stathome.html • www.statsoft.com/textbook/stathome.html • Elementary Concepts • Basic Statistics

  2. Introduction to R Autumn 2007 Petter Mostad mostad@chalmers.se

  3. What is R? • A programming language for statistical data analysis called S developed at Bell Labs. Later extended to S+. • R is a free, open source version of S/S+. • Available from www.r-project.org • Under active cooperative development; versions change frequently. • Very popular tool in statistics community: New methods often now implemented there.

  4. Basics of R • Command-driven language • Data (and functions) stored as named objects • Objects can be fairly simple (vectors, matrices) or more comples (assembled from other objects)

  5. Vectors and matrices • Calculations often done on vectors or matrices • Elementwise operations • Subsetting, indexing • Logical indexing

  6. Help and documentation • Use ”An introduction to R” • Some recommended sections: 1.1, 1.7-1.11, 2, 5.1-5.4, 5.8, 6, 7.1, 10.1, 12.1, 12.3 • help(mean) • help.search(”<subject>”) • help.start()

  7. Organizing your computations • R has a ”current directory” • Your objects are contained in your ”current workspace”, which can be saved any time • Keep separate projects in separate workspaces/directories • Keep it tidy!

  8. Graphical visualization • A ”generic” function: plot() • High level commands, like pairs, image, contour... • Lower level commands, adding stuff: points(), lines(), text(), title(), legend()... • plotting characters pch, colors col...

  9. Functions • Most of R consists of functions • The arguments can either be input in the right order, or using argument names • Use help(...) !

  10. Functions related to basic statistics • For every distribution, we may: • Compute the probability density function • Compute the cumulative density function • Simulate random values from the distribution • Compute the quantiles • See for example help(rnorm) • pnorm, pf, pt, pchisq, ….

  11. Doing tests in R • For example t.test(…) can be done in R • Many other tests are readily implemented: prop.test(…), binom.test(…), chisq.test(…), cor.test(…) • help(test) will not work; try help.search(”test”)

  12. Doing ANOVA in R • Considered in R to be a part of linear modelling • Use, for example, anova(lm(Y ~ factor(X1))) anova(lm(Y ~ factor(X1) + factor(X2))) etc.

  13. Writing your own functions • Collecting commands into a function • Arguments to function • Returning result as a list • Assignments within functions • programming: conditional statements, loops (avoid them!) etc... • fix(<myfunction>)

  14. Import and export of data • Useful functions: read.table, write.table • Works with mixed-type data (numbers and text columns) • Works well with tab-separated data (connection to Excel) • scan()

  15. R packages • A package: collection of functions (and data) concerning special application • Contributed from different sources/persons • Can be downloaded from CRAN or BioConductor • must be ”loaded” with library() • search() • help(package = graphics) • Also: Documentation from help.start()

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