1. Introductionto Alejandro Buren & Paul Regular BIOL 7220 September 2012

2. What to expect from this workshop • Intro to R • Data summaries • Create plots • Run General linear models • Easily extensible to glm, gam, glmm, gamm • BUT you have to know what you’re doing

3. What NOT to expect from this workshop • A course in statistics • A showcase of R’s capabilities

4. What is R? • Environment and language for • Statistics • Graphics • Etc. • Open source

5. Why use R? • It’s FREE! • Flexible • One stop shop • Large up-front cost, but BIG payoff • Growing number of users • Expansive!

6. Download & Install • Program • R & Tinn-R installed? • Packages • Required to use statistical and graphical packages not included in the base package • Install once • Load each new session

8. Vectors • R has symbolic vectors which can be assigned values • The traditional way to do this in R is the ’<-’ operator • Possible vector names flexible • Vector names cannot start with a digit • Names are case-sensitive • Some common names are already used by R • c, q, t, C, D, F, I, T,

9. Functions • MANY built in functions • log() • exp() • sin() • sqrt() • mean() • max() • Etc.

10. Data Types • Vectors (Numeric, Character, Logical) • Matrix • All columns in a matrix must have same mode • Array • N-dimensional matrix • Dataframe • Columns in a data frame can have different modes. Similar to SAS and SPSS datasets • Lists • Collection of objects, can be different modes and dimensions • Factor

11. Data Management • Import & Export • Useful functions • subset • na.omit • cbind, rbind • sort • summaryBy • Operate over dataframe • Random numbers

12. Good Practice • Set up a working directory • Comment on each line of code • Avoid attach function • Define parameters in first lines of code • Use standard format • use.dots, OrCapitals, avoid spaces anywhere • Clean up

13. Statistics & Graphics • Working example • Look at data • Graph data • Run one regression and one ANOVA

14. HELP! • Useful websites • Official site (http://www.r-project.org/) • Manuals found on this site (http://cran.r-project.org/manuals.html) • R search site (http://finzi.psych.upenn.edu/search.html) • Quick-R (http://www.statmethods.net/index.html) • R graphics gallery (http://addictedtor.free.fr/graphiques/) • Books • The R Book • Etc. • List serve • ?function

15. Useful Packages...for us • Statistical • VEGAN • lme4 • gam • nnet • Rcmdr • Graphical • lattice • Rcmdr • gplots • Data management • RODBC • doBy • reshape

16. Possibilities... • Customized graphics... • Complex statistics...

17. GENERAL LINEAR MODELS ε ~ Normal R: lm() ANOVA Multiple Linear Regression t-test Simple Linear Regression ANCOVA

18. GENERALIZED LINEAR MODELS Linear combination of parameters R: glm() Multinomial Binomial Poisson GENERAL LINEAR MODELS ε ~ Normal R: lm() ANOVA Multiple Linear Regression t-test Simple Linear Regression ANCOVA Exponential Gamma Negative Binomial Inverse Gaussian

19. GENERALIZED ADDITIVE MODELS R: gam() GENERALIZED LINEAR MODELS Linear combination of parameters R: glm() Non-linear effect of covariates Multinomial Binomial Poisson GENERAL LINEAR MODELS ε ~ Normal R: lm() ANOVA Multiple Linear Regression t-test Simple Linear Regression ANCOVA Exponential Gamma Negative Binomial Inverse Gaussian

20. Fixed effects model Random effects model