Welcome to the Profiling,Simulation and Data Analysis MCQs Page

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

Profiling,Simulation and Data Analysis MCQs | Page 8 of 11

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

Scoping Rules and Debugging

07

Commands and Packages

08

Visualizing Data and Linear Regression

09

Distributions and Graphics in R programming

Q71.

How many functions exist for tidying the data?

one

two

three

four

Discuss

Answer: (c).three

Q72.

Point out the correct statement?

gather() makes “wide” data longer

Two functions are present for tidy data

separate() makes “wide” data longer

spread() function takes multiple columns

Discuss

Answer: (c).separate() makes “wide” data longer

Q73.

Which of the following function takes multiple columns?

spread()

gather()

separate()

extract()

Discuss

Answer: (b).gather()

Q74.

__________ uses regex groups instead of a splitting pattern or position.

spread()

gather()

separate()

extract()

Discuss

Answer: (c).separate()

Q75.

Which of the following function works similar to separate()?

extract()

gather()

sep()

separate()

Discuss

Answer: (a).extract()

Q76.

Which of the following takes two columns and spreads them into multiple columns?

ggmissplot

printplot

print.ggplot

ggplot

Discuss

Answer: (c).print.ggplot

Q77.

__________ is used when you have variables that form rows instead of columns.

tidy()

spread()

separate()

gather()

Discuss

Answer: (b).spread()

Q78.

Point out the correct statement?

tidyr and dplyr packages do not make use of the pipe operator

tidyr does less than reshape2

tidyr provides ability to string multiple functions together by incorporating %

tidyr does greater than reshape2

Discuss

Answer: (b).tidyr does less than reshape2

Q79.

Which of the following merges two variables into one?

spread()

gather()

separate()

unite()

Discuss

Answer: (b).gather()

Q80.

How many functions exist for wrangling the data with dplyr package?

one

seven

three

five

Discuss

Answer: (b).seven

Page 8 of 11

Welcome to the Profiling,Simulation and Data Analysis MCQs Page

Profiling,Simulation and Data Analysis MCQs | Page 8 of 11

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

Scoping Rules and Debugging

Commands and Packages

Visualizing Data and Linear Regression

Distributions and Graphics in R programming

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