R for Macroecology

R for Macroecology Functions and plotting

A few words on for • for( i in 1:10 )

A few words on for • for( i in 1:10 ) 1 2 3 4 5 6 7 8 9 10

A few words on for • for( i in 1:10 ) 1 2 3 4 5 6 7 8 9 10 i = 1 Do any number of functions with i print(i) x = sqrt(i)

i as an Index X = c(17,3,-1,10,9)Y = NULL for(i in 1:length(X)) { if(X[i] < 12) { Y[i] = X[i] + 5 } } 17 3 -1 10 9 X =

i as an Index X = c(17,3,-1,10,9)Y = NULL for(i in 1:length(X)) { if(X[i] < 12) { Y[i] = X[i] + 5 } } 17 3 -1 10 9 X = Y =

i as an Index X = c(17,3,-1,10,9)Y = NULL for(i in 1:length(X)) { if(X[i] < 12) { Y[i] = X[i] + 5 } } 17 3 -1 10 9 X = Y = 1 2 3 4 5 i = 1 (so X[i] = 17)

i as an Index X = c(17,3,-1,10,9)Y = NULL for(i in 1:length(X)) { if(X[i] < 12) { Y[i] = X[i] + 5 } } F 17 3 -1 10 9 X = Y = 1 2 3 4 5 i = 1 (so X[i] = 17)

i as an Index X = c(17,3,-1,10,9)Y = NULL for(i in 1:length(X)) { if(X[i] < 12) { Y[i] = X[i] + 5 } } 17 3 -1 10 9 X = Y = 1 2 3 4 5 i = 2 (so X[i] = 3)

i as an Index X = c(17,3,-1,10,9)Y = NULL for(i in 1:length(X)) { if(X[i] < 12) { Y[i] = X[i] + 5 } } T 17 3 -1 10 9 X = Y = 1 2 3 4 5 i = 2 (so X[i] = 3)

i as an Index X = c(17,3,-1,10,9)Y = NULL for(i in 1:length(X)) { if(X[i] < 12) { Y[i] = X[i] + 5 } } 17 3 -1 10 9 NA 8 X = Y = 1 2 3 4 5 i = 2 (so X[i] = 3)

i as an Index X = c(17,3,-1,10,9)Y = NULL for(i in 1:length(X)) { if(X[i] < 12) { Y[i] = X[i] + 5 } } 17 3 -1 10 9 NA 8 4 15 14 X = Y = 1 2 3 4 5

i as an Index X = c(17,3,-1,10,9)Y = NULL for(i in 1:length(X)) { if(X[i] < 12) { Y[i] = X[i] + 5 } } 17 3 -1 10 9 NA 8 4 15 14 X = Y = 1 2 3 4 5 This vector (created by the for) indexes vectors X and Y

2-dimension equivalent X = matrix(1:6,ncol = 2,nrow = 3) Y = matrix(NA,ncol = 2,nrow = 3) for(i in 1:nrow(X)) { for(j in 1:ncol(X)) { Y[i,j] = X[i,j]^2 } } 1 4 NA NA 2 5 NA NA X = Y = 3 6 NA NA

2-dimension equivalent X = matrix(1:6,ncol = 2,nrow = 3) Y = matrix(NA,ncol = 2,nrow = 3) for(i in 1:nrow(X)) { for(j in 1:ncol(X)) { Y[i,j] = X[i,j]^2 } } i j 1 4 NA NA 2 5 NA NA X = Y = 3 6 NA NA

2-dimension equivalent X = matrix(1:6,ncol = 2,nrow = 3) Y = matrix(NA,ncol = 2,nrow = 3) for(i in 1:nrow(X)) { for(j in 1:ncol(X)) { Y[i,j] = X[i,j]^2 } } i j 1 1 1 4 1 NA 2 5 NA NA X = Y = 3 6 NA NA

2-dimension equivalent X = matrix(1:6,ncol = 2,nrow = 3) Y = matrix(NA,ncol = 2,nrow = 3) for(i in 1:nrow(X)) { for(j in 1:ncol(X)) { Y[i,j] = X[i,j]^2 } } i j 1 2 1 1 1 2 1 4 1 16 2 5 4 NA X = Y = 3 6 NA NA

2-dimension equivalent X = matrix(1:6,ncol = 2,nrow = 3) Y = matrix(NA,ncol = 2,nrow = 3) for(i in 1:nrow(X)) { for(j in 1:ncol(X)) { Y[i,j] = X[i,j]^2 } } i j 1 2 1 2 1 2 1 1 2 2 3 3 1 4 1 16 2 5 4 25 X = Y = 3 6 9 36

Onward to today’s topics! • Looking more at functions • Plotting your data

Packages • Sets of functions for a particular purpose • We will explore some of these in detail install.packages() require(package.name) CRAN!

Function help Syntax Arguments Return

Function help

Writing your own functions • Why bother? • We often have blocks of code that we want to execute many times, with small changes • Repetitive code is hard to read and likely to contain errors • Think about what variables the function should work on, and what the function should produce myFunction = function(argument, argument . . .) { stuff more stuff return(anObject) }

Defining a function SayHi = function(input) { print(paste(“Hello”,input)) } SayHi(“Brody”)

Defining a function SayHi = function(input) { print(paste(“Hello”,input)) } SayHi(“Brody”) [1] “Hello Brody”

Defining a function SayHi = function(input) { print(paste(“Hello”,input)) } SayHi(“Brody”) [1] “Hello Brody” SayHi() Error in paste("Hello", input) : argument "input" is missing, with no default

Defining a function SayHi = function(input) { print(paste(“Hello”,input)) } SayHi(“Brody”) [1] “Hello Brody” SayHi() Error in paste("Hello", input) : argument "input" is missing, with no default Functions (usually) only have access to the variables given as arguments! input = “Bob” SayHi() Error in paste("Hello", input) : argument "input" is missing, with no default

Defining a function with defaults SayHi2 = function(input = “Sven”) { print(paste(“Hello”,input)) } SayHi2(“Brody”) [1] “Hello Brody” SayHi2() [1] “Hello Sven”

Things to remember about functions • Use them whenever you have chunks of repeated code • Remember to use return() to have the function return the desired object • Not always necessary, sometimes you might just want a function to plot something, or print something • Local/Global • Functions only have access to variables passed as arguments • Changes to variables to and new variables defined within the function are not available outside the function

A break to try things out Function Vector Number Value

Plotting • For creating a plot • plot() • hist() • For drawing on a plot • points() • segments() • polygons() • For controlling how plots look • par() • Make a new plotting window • x11()

plot() x = 1:10y = 10:1plot(x,y)

plot() x = 1:10y = 10:1plot(x,y,main = “A plot”,xlab = “Temperature”, ylab = “Pirates”)

type = “l” “b” ”h” “o” “s”

Plotting size and characters cex = 2 or cex = 3

Plotting size and characters pch = A, cex = 3 pch = A, cex = x pch = 10, cex = 3

Color • By name • “blue” or “dark grey” . . . • By function • grey() • rainbow() • rgb()

Color x = rep(1:10,10) y = rep(1:10,each=10) plot(x,y)

Color x = rep(1:10,10) y = rep(1:10,each=10) plot(x,y,pch = 15,cex = 2)

Color x = rep(1:10,10) y = rep(1:10,each=10) plot(x,y,pch = 15,cex = 2,col = “dark green”)

Color x = rep(1:10,10) y = rep(1:10,each=10) plot(x,y,pch = 15,cex = 2,col = rgb(0.8,0.1,0.2))

Color x = rep(1:10,10) y = rep(1:10,each=10) plot(x,y,pch = 15,cex = 2,col = rgb(seq(0,1,by = 0.01),0.1,0.2))

Drawing on plots • points(x,y) adds points to existing plots (withverysimilar options to plot()) • segments(x0,y0,x1,y1) draws lines from points to other points • polygons()

The wonderful world of par() • 70 different options to control your plots!

Plotting to a file • pdf(), bmp() • dev.off()

Some examples All created entirely within R!

One last fun thing • Scatterplots of massive data can be hard to read 170265 data points

R for Macroecology