1. Why R? Free Powerful (add-on packages) Online help from statistical community Code-based (can build programs) Publication-quality graphics

2. Why not? Time to learn code Very simple statistics may be faster with “point-and-click” software (e.g. Statistica, JMP)

3. Why generalized linear models (GLMs)? Most ecological data FAIL these two assumptions of parametric statistics: Variance is independent of mean (“homoscedasticity”) Data are normally distributed

4. Taylors power law: most ecological data has 1>b>2 Mean Variance Variance = a* Mean b

5. Many types of ecological data are expected to be non-normal Count data are expected to be Poisson Examples: population size, species richness Binary (0,1) data are expected to be binomial Examples: survivorship, species presence

6. Workshop in R & GLMs Session 1: Basic commands + linear models Session 2: Testing parametric assumptions Session 3: How generalized linear models work Session 4: Model simplification and overdispersion

7. Exercise 1. Open R “>” is the command prompt 2. Write: x <- “hello” x 3. What do the arrow keys do? And the “end” key? Ready!

8. Exercise x <- 5 y<- 1 x+y; x*y; x/y ; x^y sqrt(x); log (x); exp (x) Careful! Capitalization matters, Y and y are different. Spaces do not matter, x<-5 is the same as x < - 5. “;” means new command follows

9. Vectors 82598259 X <- c(8,2,5,9) “c” means combine

10. Vectors x <- rep (0,4) x <- 1:4 x <- seq (1,7, by=2) 0,0,0,0 1,2,3,4 1,3,5,7 Create a vector called “test” 0,0,0,0,2,4,6,8,10 using all of the commands c, rep, seq test<- c (rep(0,4), seq(2,10,by=2))

11. Vectors Select an element of your vector (x = 1,3,5,7): x[2]3 1,5 3,5,7 x[c(1,3)] x[2:4] Change an element of your vector (x = 1,3,5,7): x[1] <- 9 ; x 9,3,5,7

12. Matrices Dog <- c(1,4,6,8) Cat<- c(2,3,5,7) Animals<-cbind (Dog, Cat) DogCat 12 4 3 6 5 8 7 vector matrix

13. Logical operators x<- 5; y<- 6 x > y x< y x==y x!=y True is the same as 1, false is the same as 0 false true false true 2 + (x>=y) 2 + (x<=y) 2323

14. Logical operators x<- c(1,2,3,4); y<- c(5,6,7,8) z = 7]; z Useful for quickly making subsets of your data! 3,4 x<- c(1,0.01,3,0.02) In this vector, change all values <1 to 0 x[x<1]<-0

15. Conditional operators x<- 5 ; z<-0 if (x>4) {z<-2}; z Could have a large program running in { } 2

16. Loops y<-0; x<-0 for (y in 1:20) {x<- x+ 0.5; print(x)} Useful for programming randomization procedures. Bootstrap example: y<-0; x<-1:50 output<-rep(0,1000) for (y in 1:1000) {output [y] <- var (sample (x, replace=T))} mean(output) 207.3996

17. Writing programs I encourage you to use the script editor! File > New script Write your code Select the code you want to run (CTRL-A is all code) Run code (CTRL-R) File > Save as R script files are always *.R

18. Entering data 1. In Excel, give your data columns/rows and text data simple one word labels (e.g."treatment") 2. Format cells so < 8 digits per cell. 3. Save as "csv" file. 4. Use the following command to find and load your file: diane<-read.table(file.choose(),sep=“,”,header=TRUE) 5. Check it is there! diane Invent a dataframe name

19. Dataframes Dataframes are analogous to spreadsheets Best if all columns in your dataframe have the same length Missing values are coded as "NA" in R If you coded your missing values with a different label in your spreadsheet (e.g. "none") then: read.table (….., na.strings="none")

20. Dataframes Two ways to identify a column (called "treatment") in your dataframe (called "diane"): diane$treatment OR attach(diane); treatment At end of session, remember to: detach(diane)

21. Summary statistics length (x) mean (x) var (x) cor (x,y) sum (x) summary (x) minimum, maximum, mean, median, quartiles What is the correlation between two variables in your dataset?

22. Factors A factor has several discrete levels (e.g. control, herbicide) If a vector contains text, R automatically assumes it is a factor. To manually convert numeric vector to a factor: x <- as.factor(x) To check if your vector is a factor, and what the levels are: is.factor(x) ; levels(x)

23. 1. Download R on your computer. Either go to http://www.r-project.org/ and follow the download CRAN linkshttp://www.r-project.org/ or directly to http://mirror.cricyt.edu.ar/r/http://mirror.cricyt.edu.ar/r/ 2. Instruction Manuals to R are found at main webpage: http://www.r-project.org/ follow links to Documentation > Manuals I recommend "An Introduction to R" Homework

24. 3. Write a short program that: Allows you to import the data from Lakedata_06.csv (posted on www.zoology.ubc.ca/~srivast/zool502) Make lake area into a factor called AreaFactor: Area 0 to 5 ha: small Area 5.1 to 10: medium Area > 10 ha: large

25. hints You will need to: 1. Tell R how long AreaFactor will be. 2. Assign cells in AreaFactor to each of the 3 levels 3. Make AreaFactor into a factor, then check that it is a factor