gs gs110 Control Structures, Functions, Scoping Rules, Loop Functions and Debugging - Control Structures - Quiz No.2
gs gs110 R Language Quiz
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Question 1: What will be the output of the following R code?
> x <- list(a = 1:4, b = rnorm(10), c = rnorm(20, 1), d = rnorm(100, 5)) > lapply(x, mean) $a [1] 2.5 $b [1] 1.248845 $c [1] 1.9935285 Loop Functions 90 $d [1] 5.051388 $a [1] 2.5 $b [1] 0.248845 $c [1] 0.9935285 Loop Functions 90 $d [1] 5.051388 $a [1] 3.5 $b [1] 0.248845 $c [1] 0.9935285 Loop Functions 90 $d [1] 5.051388
Error
Question 2: Point out the wrong statement?
The sapply() function behaves similarly to lapply()
With multiple factors and many levels, creating an interaction can result in many levels that are empty
apply() can be thought of as a combination of split() and sapply() for vectors only
You can use tsplit() to evaluate a function single time each with a same argument
Question 3: What will be the output of the following R code?
> x <- 1:4 > lapply(x, runif) [[1]] [1] 0.02778712 [[2]] [1] 0.5273108 0.8803191 [[3]] [1] 0.37306337 0.04795913 0.13862825 [[4]] [1] 0.3214921 0.1548316 0.1322282 0.2213059 [[1]] [1] 1.02778712 [[2]] [1] 2.5273108 0.8803191 [[3]] [1] 3.37306337 0.04795913 0.13862825 [[4]] [1] 0.3214921 0.1548316 0.1322282 0.2213059 [[1]] [1] 1.02778712 [[2]] [1] 0.5273108 0.8803191 [[3]] [1] 0.37306337 0.04795913 0.13862825 [[4]] [1] 3.3214921 2.1548316 1.1322282 0.2213059
Error
Question 4: Which of the following R code will print the following result?
[[1]] [1] 2.263808 [[2]] [1] 1.314165 9.815635 [[3]] [1] 3.270137 5.069395 6.814425 [[4]] [1] 0.9916910 1.1890256 0.5043966 9.2925392 > x <- 1:4 > lapply(x, runif, min = 0, max = 10) > x <- 1:4 > lapply(x, runif, min = 0, max = 9) > x <- 1:3 > lapply(x, runif, min = 0, max = 10) > x <- 1:4 > lapply(x, runif, min = 0, max = 6)
Question 5: Point out the correct statement?
split() takes elements of the list and passes them as the first argument of the function you are applying
You can use tsplit() to evaluate a function single time each with a same argument
Sequence of operations is sometimes referred to as “map-reduce”
apply() can be thought of as a combination of split() and sapply() for vectors only
Question 6: What will be the output of the following R code?
> x <- list(a = matrix(1:4, 2, 2), b = matrix(1:6, 3, 2)) > lapply(x, function(elt) { elt[,1] }) $a [1] 1 2 $b [1] 1 2 3 $a [1] 1 2 3 $b [1] 1 2 3 $a [1] 1 2 3 $b [1] 1 2
Error
Question 7: The _____ function takes a vector or other objects and splits it into groups determined by a factor or list of factors.
apply()
lsplit()
split()
mapply()
Question 8: Which of the following is valid body of split function?
function (x, f)
function (x, drop = FALSE, …)
function (x, f, drop = FALSE, …)
function (drop = FALSE, …)
Question 9: What will be the output of the following R code?
> f <- function(elt) { + elt[, 1] + } > lapply(x, f) $a [1] 1 2 $b [1] 1 2 3 $a [1] 1 2 3 $b [1] 1 2 3 $a [1] 1 2 3 $b [1] 1 2
Error
Question 10: What will be the output of the following R code?
> x <- list(a = 1:4, b = rnorm(10), c = rnorm(20, 1), d = rnorm(100, 5)) > sapply(x, mean) a b c d 2.500000 -0.251483 1.481246 4.968715 a b c d 2.500000 -3.251483 2.481246 5.968715 a b c d 3.500000 0.251483 1.481246 4.968715
Error
