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This workshop focuses on flow control in R programming, emphasizing loops and conditionals to manage the execution of scripts. Participants will learn through practical examples how to use "for," "while," and "if" statements effectively. The session will cover the structure and application of loops, including breaking points and conditional statements. Attendees will also be introduced to strategies to optimize code performance, especially when working with large datasets. Expect hands-on exercises and real-world applications for better understanding.
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R basics workshop J. Sebastián Tello Iván Jiménez Center forConservation and SustainableDevelopment Missouri Botanical Garden
Flow control letsyou define howyour scripts run • There are a number of constructs in R thatallowyouto control theflow of thecode • There are mainly 3 types: • Loops – for, while, repeat • Breaking points – break, next • Conditionals – if, else and ifelse • Wewillfocuson: for, while and if • Forhelp: ?Control
“for” loops • A loop is the repetition of a piece of code “n” times • for is the most common construct to create loops • This is the general structure of a “for” loop: for(i in v) { code… } Tab/space Which means: For each value that i takes from vector v, repeat: { this code }
“for” loops • Easy example 1: v <- 1:10 for(i in v) { print(i) } Which means: The vector v has values from 1 to 10 every 1 For each value that itakes from vectorv, repeat: print the value of i into the screen
“for” loops • Easyexample 2: v<- letters v for(i in v) { print(i) }
“for” loops • Easyexample 3: v<- letters length(v) result <- 0 for(i in v) { print(i) result<- result+ 1 } result
“for” loops • Easyexample 4: v <- c(1,3,5,2,4) result <- 0 for(i in 1:length(v)) { print( c(i, v[i]) ) result <- result + v[i] } result
“for” loops • Easyexample 5: col.v <- rainbow(100) cex.v <- seq(1, 10, length.out=100) plot(0:1, 0:1, type="n") for(i in 1:200) { print(i) points(runif(1), runif(1), pch=16, col=sample(col.v, 1), cex=sample(cex.v, 1)) Sys.sleep(0.1) }
“for” loops • Open the file “BatsEnviroAmerica.txt” BatData <- read.table(file=file.choose(), header=TRUE, sep="\t") Or if the files is in your working directory: BatData <- read.table(file="BatsEnviroAmerica.txt", header=TRUE, sep="\t")
“for” loops • Open the file “BatsEnviroAmerica.txt” class(BatData) names(BatData) rich <- BatData$richness enviro <- BatData[,5:ncol(BatData)] enviro[1:5, ]
“for” loops LM.R2 <- rep(NA, ncol(enviro)) LM.R2 for(i in 1:ncol(enviro)) { LM.i <- lm(rich ~ enviro[,i]) res.LM.i <- summary(LM.i) LM.R2[i] <- res.LM.i$adj.r.squared } LM.R2
“for” loops LM.R2 names(LM.R2) <- names(enviro) barplot(LM.R2)
“while” loops • while is sometimes also very useful • This is the general structure of a “while” loop: while(condition) { code… } Which means: While this condition is TRUE, repeat: { this code }
“while” loops • Easy example 1: v <- 1:10 for(iin v) { print(i) } v <- 1:10 i <- 0 while(i < max(v)) { i <- i+1 print(i) }
“while” loops • Easy example 1: i <- 0 while(i < max(v)) { i <- i+1 print(i) } v <- 1:10 Version 1 i <- 0 while(i < max(v)) { print(i) i <- i+1 } Version 2
“while” loops • Easy example 2: Bp <- 0.1;Dp <- 0.1;Np <- 1-Bp-Dp max.t <- 100; time <- 0; abund<- 10 plot(c(0, max.t), c(0, 100), type="n") while(abund>0 & time<= max.t) { change <- sample(c(-1,0,1), size=abund, prob=c(Dp, Np, Bp), replace=TRUE) abund <- abund + sum(change) time <- time + 1 points(time, abund, pch=16, col="black") }
“if” condition • if controls the flow by allowing code to run only if a condition is met • Easy example 1: v <- 1:10 for(i in v) { print(i) if(i == 5) print("Reached 5") }
“if” condition and “break” • if controls the flow by allowing code to run only if a condition is met • Easy example 1: v <- 1:10 for(i in v) { print(i) if(i == 5) { print("Reached 5") break() } }
“if” condition trait<- 0; max.time <- 100 plot(c(0,max.time), c(-20, 20), type="n", ylab="Trait Value", xlab="Time") points(0, trait, pch=16, col="black") for(i in 1:max.time) { trait.shift <- rnorm(1, 0, 0.5) trait<- trait+ trait.shift if(trait.shift> 0) COL <- "gold" if(trait.shift< 0) COL <- "lightblue" points(i, trait, pch=16, col=COL) Sys.sleep(0.2) }
Avoiding loops • Loops are extremely useful, but slow. When possible, avoid them. • Often, you will be working with large data sets. Lets simulate a matrix of 50 species abundances in 1,000,000 sites M <- matrix(rpois(50000000, 10), ncol=50) M[1:5,] dim(M)
Avoiding loops • How to calculate the number of individuals at each site (sum by rows)? • Option 1 – a ‘for’ lool abund.1 <- numeric() system.time( { for(iin 1:nrow(M)) { abund.1 <- c(abund.1, sum(M[i,])) } })
Avoiding loops • How to calculate the number of individuals at each site (sum by rows)? • Option 2 – a better ‘for’ lool abund.2 <- rep(NA, nrow(M)) system.time( { for(iin 1:nrow(M)) { abund.2[i] <- sum(M[i,]) } })
Avoiding loops • How to calculate the number of individuals at each site (sum by rows)? • Option 3 – use a function of the family ‘apply’ system.time( { abund.3 <- apply(M, 1, sum) }) ?apply
Vectorization • How to calculate the number of individuals at each site (sum by rows)? • Option 4 – Vectorize!Use a built-in function in R that was written in other code (e.g., C, C++, Fortran) system.time( { abund.4 <- colSums(M) })
Exercise 9 Flow Control
