Each section provides a function that supposedly works as expected, but quickly proves to misbehave. The exercise aims at first writing some dedicated testing functions that will identify the problems and then update the function so that it passes the specific tests. This practice is called unit testing and we use the `RUnit` package for this. See the [Unit Testing How-To](/developers/how-to/unitTesting-guidelines/) guide for details on unit testing using `RUnit`. # Subsetting ## The buggy function ## Example isIn <- function(x, y) { sel <- match(x, y) y[sel] } ## Expected x <- sample(LETTERS, 5) isIn(x, LETTERS) ## Bug! isIn(c(x, "a"), LETTERS) ## A unit test and a solution ## Unit test: library("RUnit") test_isIn <- function() { x <- c("A", "B", "Z") checkIdentical(x, isIn(x, LETTERS)) checkIdentical(x, isIn(c(x, "a"), LETTERS)) } test_isIn() ## updated function isIn <- function(x, y) { sel <- x %in% y x[sel] } test_isIn() # Character matching ## The buggy function ## Example isExactIn <- function(x, y) y[grep(x, y)] ## Expected isExactIn("a", letters) ## Bugs isExactIn("a", c("abc", letters)) isExactIn(c("a", "z"), c("abc", letters)) # If conditions with length > 1 ## The buggy function ## Example ifcond <- function(x, y) { if (x > y) { ans <- x*x - y*y } else { ans <- x*x + y*y } ans } ## Expected do(3, 2) do(2, 2) do(1, 2) ## Bug! do(3:1, c(2, 2, 2)) # Know your inputs ## The function ## Example distances <- function(point, pointVec) { x <- point[1] y <- point[2] xVec <- pointVec[,1] yVec <- pointVec[,2] sqrt((xVec - x)^2 + (yVec - y)^2) } ## Expected x <- rnorm(5) y <- rnorm(5) m <- cbind(x, y) p <- m[1, ] distances(p, m) ## Bug! dd <- data.frame(x, y) q <- dd[1, ] distances(q, dd) # Iterate on 0 length ## The buggy function ## Example sqrtabs <- function(x) { v <- abs(x) sapply(1:length(v), function(i) sqrt(v[i])) } ## Expected all(sqrtabs(c(-4, 0, 4)) == c(2, 0, 2)) ## Bug! sqrtabs(numeric())