Welcome to the Scoping Rules and Debugging MCQs Page

Dive deep into the fascinating world of Scoping Rules and Debugging with our comprehensive set of Multiple-Choice Questions (MCQs). This page is dedicated to exploring the fundamental concepts and intricacies of Scoping Rules and Debugging, a crucial aspect of R Programming. In this section, you will encounter a diverse range of MCQs that cover various aspects of Scoping Rules and Debugging, from the basic principles to advanced topics. Each question is thoughtfully crafted to challenge your knowledge and deepen your understanding of this critical subcategory within R Programming.

Check out the MCQs below to embark on an enriching journey through Scoping Rules and Debugging. Test your knowledge, expand your horizons, and solidify your grasp on this vital area of R Programming.

Note: Each MCQ comes with multiple answer choices. Select the most appropriate option and test your understanding of Scoping Rules and Debugging. You can click on an option to test your knowledge before viewing the solution for a MCQ. Happy learning!

Scoping Rules and Debugging MCQs | Page 4 of 6

Explore more Topics under R Programming

01

History and Overview of R Programming Language

02

Basics of R Programming Language

03

Data Types and Dataset Reading

04

Formats,Objects and Operations

05

Control Structures and Functions

06

Profiling,Simulation and Data Analysis

07

Commands and Packages

08

Visualizing Data and Linear Regression

09

Distributions and Graphics in R programming

Q31.

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

Discuss

Answer: (b).$a
[1] 2.5
$b
[1] 0.248845
$c
[1] 0.9935285
Loop Functions 90
$d
[1] 5.051388

Q32.

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

Discuss

Answer: (c).apply() can be thought of as a combination of split() and sapply() for vectors only

Q33.

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

Discuss

Answer: (a).[[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

Q34.

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)

Discuss

Answer: (a).> x <- 1:4
> lapply(x, runif, min = 0, max = 10)

Q35.

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

Discuss

Answer: (c).Sequence of operations is sometimes referred to as “map-reduce”

Q36.

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

Discuss

Answer: (a).$a
[1] 1 2
$b
[1] 1 2 3

Q37.

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()

Discuss

Answer: (c).split()

Q38.

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, …)

Discuss

Answer: (c).function (x, f, drop = FALSE, …)

Q39.

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

Discuss

Answer: (a).$a
[1] 1 2
$b
[1] 1 2 3

Q40.

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

Discuss

Answer: (a).a b c d
2.500000 -0.251483 1.481246 4.968715

Page 4 of 6

Welcome to the Scoping Rules and Debugging MCQs Page

Scoping Rules and Debugging MCQs | Page 4 of 6

Explore more Topics under R Programming

History and Overview of R Programming Language

Basics of R Programming Language

Data Types and Dataset Reading

Formats,Objects and Operations

Control Structures and Functions

Profiling,Simulation and Data Analysis

Commands and Packages

Visualizing Data and Linear Regression

Distributions and Graphics in R programming

Suggested Topics

Operating System

MongoDB

Cloud Computing

Management Information Systems

Digital Communication

Web Technologies

Hadoop

UNIX

Digital Image Processing (DIP)