Welcome to the Data Types and Dataset Reading MCQs Page

Dive deep into the fascinating world of Data Types and Dataset Reading with our comprehensive set of Multiple-Choice Questions (MCQs). This page is dedicated to exploring the fundamental concepts and intricacies of Data Types and Dataset Reading, a crucial aspect of R Programming. In this section, you will encounter a diverse range of MCQs that cover various aspects of Data Types and Dataset Reading, 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 Data Types and Dataset Reading. 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 Data Types and Dataset Reading. You can click on an option to test your knowledge before viewing the solution for a MCQ. Happy learning!

Data Types and Dataset Reading MCQs | Page 1 of 10

Explore more Topics under R Programming

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History and Overview of R Programming Language

02

Basics of R Programming Language

03

Formats,Objects and Operations

04

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Scoping Rules and Debugging

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Profiling,Simulation and Data Analysis

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Q1.

R has how many atomic classes of objects?

Discuss

Answer: (d).5

Q2.

Point out the correct statement?

Empty vectors can be created with the vector() function

A sequence is represented as a vector but can contain objects of different classes

“raw” objects are commonly used directly in data analysis

The value NaN represents undefined value

Discuss

Answer: (a).Empty vectors can be created with the vector() function

Q3.

Numbers in R are generally treated as _______ precision real numbers.

single

double

real

imaginary

Discuss

Answer: (b).double

Q4.

If you explicitly want an integer, you need to specify the _____ suffix.

Discuss

Answer: (c).L

Q5.

Point out the correct statement?

The value NaN represents undefined value

Number Inf represents infinity in R

NaN can also be thought of as a missing value

“raw” objects are commonly used directly in data analysis

Discuss

Answer: (b).Number Inf represents infinity in R

Q6.

Attributes of an object (if any) can be accessed using the ______ function.

objects()

attrib()

attributes()

obj()

Discuss

Answer: (c).attributes()

Q7.

R objects can have attributes, which are like ________ for the object.

metadata

features

expression

dimensions

Discuss

Answer: (a).metadata

Q8.

Which of the following can be considered as object attribute?

dimensions

class

length

all of the mentioned

Discuss

Answer: (d).all of the mentioned

Q9.

What will be the output of the following R code?

> x <- vector("numeric", length = 10)
> x

0 0 0 0 0 0 0 0 0 0

00120

Discuss

Answer: (b).0 0 0 0 0 0 0 0 0 0

Q10.

The ________ function can be used to create vectors of objects by concatenating things together.

cp()

c()

concat()

con()

Discuss

Answer: (b).c()

Page 1 of 10

Welcome to the Data Types and Dataset Reading MCQs Page

Data Types and Dataset Reading MCQs | Page 1 of 10

Explore more Topics under R Programming

History and Overview of R Programming Language

Basics of R Programming Language

Formats,Objects and Operations

Control Structures and Functions

Scoping Rules and Debugging

Profiling,Simulation and Data Analysis

Commands and Packages

Visualizing Data and Linear Regression

Distributions and Graphics in R programming

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