Data Analysis in Paleontology Using R. Looping Basics

Transcription

1 Data Analysis in Paleontology Using R Session 4 26 Jan 2006 Gene Hunt Dept. of Paleobiology NMNH, SI Looping Basics Situation: you have a set of objects (sites, species, measurements, etc.) and want to do some computation to each one femur <- c(10, 8, 14, 22, 32) # 5 femora lengths log.femur <- array(dim=5) # want to log transform them for (i in 1:5) log.femur[i] <- log(femur[i]) # save to a new variable Here, looping is not necessary log.femur <- log(femur) 1

2 If / else statemtents Commands can be executed depending on some condition being TRUE, using if() and else x <- 4 if (x==4) print( Oh ) Oh if (x==3) print( Oh ) # Nothing happens if (x==3) print( Oh ) else print( Hey ) Hey Writing Functions There are many functions built-in to R Sometimes, need to do something for which no function exists For example: people who wrote vegan wanted to rarefy and compute diversity metrics If it is a general enough task, it can be useful to write your own function 2

3 A totally unnecessary function The function to make functions is called function() function name times5 <- function(x) { } result <- x*5 return (result) argument Once defined, it can be used just like built-in functions: times5(10) 50 result gets returned as the function output A more useful function: RMA Ordinary Least-squares regression assumes all error is in y-variable Often, x-variable has error too Reduced Major Axis draws a line that allows for error in both x and y RMA LS slope intercept b 1 = ±s y s x b 0 = y " b 1 x 3

4 A new function: rma() rma <- function(x,y) { mx <- mean(x) my <- mean(y) sx <- sd(x) sy <- sd(y) rxy <- cor(x,y) b1 <- sy/sx*sign(rxy) b0 <- my - b1*mx Need 5 pieces of information: mean of x mean of y stand. dev. of x stand. dev. of y correlation btwn x and y b 1 = ±s y s x b 0 = y " b 1 x } result <- c(b0,b1) return(result) Sourcing R scripts We have been entering commands, one at a time, at the interactive R prompt We can also write commands in a text file, and then tell R to do all of them consecutively The text file of commands = script Running commands from a file = sourcing Open Script Source Script Mac File > Open Document File > Source File Win File > Open Script File > Source R Code 4

5 Sourcing R scripts Example script file: sample.r One difference: expressions not printed by default Side note: syntax highlighting is really nice! Win Users: see TINN-R ( Saving your Work 1. Save the Workspace All variables / data that have been created When loaded, have access to same variables again Does not save commands (you get x, but not command that created it) Save/Load via Menus Mac Win Workspace > Save Workspace File File > Save Workspace Workspace > Load Workspace File File > Load Workspace 5

6 Saving your Work 2. Save the History The History is the set of commands entered during an R session The variables are not saved/loaded, but the commands you used are Save/Load via Menus Mac Win Open Command Sidebar > Save History File > Save History Open Command Sidebar > Load History File > Load History Saving your Work 3. Write / copy results to file Functions write.table(), write(), Copy output/results from R Console to Text editor 4. Build / Save analyses as R Scripts Can source these to reconstruct analysis Start with data import, and build up analysis, testing commands using the interactive prompt Also useful for making figures 6

7 Miscellanea: Math Matrix Algebra Matrix Multiplication: X %*% Y Matrix Transpose: t(x) Matrix Inverse: solve(x) Extract / replace diagonal: diag(x) Probability Distributions Many probability distributions built-in to R: normal, t, F, exponential, binomial, Poisson, For each, can generate random variates, access statistical tables 7

8 Miscellanea: Graphing Open new plot window: quartz() windows() # Mac # Win Windows version also has plot history: <Ins> to add plot, <Page Up> <Page Down> to scroll through Multiple graph layout Can place multiple graphs on same device: layout(1:2) # lots of options plot (rnorm(10)) # see also par(mfrow), par(mfcol) plot (rnorm(200)) Exercise 12. Functions and Sourcing 1. Open the script file rma.r, and look at it. Note the use of comments to explain what the function is doing. Source this file to make the function rma() available in your R session. 2. Type data(mtcars) and then attach(mtcars) to make the car data available. Plot horsepower (hp) as a function of engine displacement (disp). Perform a linear regression of hp on disp, saving the result to a variable called w.ls. Use the rma() function to calculate the reduced major axis for these variables, and save the results to a variable called w.rma. Use abline() to add the regression and RMA lines to the scatterplot; use different colors for the two lines. 3. One measure of evenness, is calculated as E = H / log(s), where H is the Shannon-Weiner diversity, and S is species richness. Write a function to calculate E for a community data matrix. Note: you should take advantage of the vegan functions to do hard parts! The body of the function needs no more than 4 lines. Use your new function to calculate E for the BCI data set (remember you ll have to use data(bci) to make this accessible). 8

9 Answers to Exercises [4] Exercise 12. Functions and Sourcing 1. Pretty self-explanatory. 2. plot(disp, hp); w.ls<- lm(hp~disp); w.rma <- rma(disp, hp); abline(w.ls, col= blue ); abline(w.rma, col= red ) 3. Evenness<- function(x) { } H<- diversity(x, index= shannon ) S<- specnumber(x) ee<- H/log(S) return(ee) data(bci) Evenness(BCI) # prints E values for all sites in BCI 9