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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.

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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 10 of 10

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Q91.
What will be the Correct R code for the following output? 
foo bar
1 1 TRUE
2 2 TRUE
3 3 FALSE
4 4 FALSE

a.

> x <- data.frame(foo = 1:4, bar = c(F, T, F, F))
> x

b.

> x <- data.frame(foo = 1:6, bar = c(F, T, F, F))
> x

c.

> x <- data.frame(foo = 1:4, bar = c(T, T, F, F))
> x

d.

> x <- data.frame(foo = 14:1, bar = c(F, T, F, F))
> x

Discuss
Answer: (c).> x <- data.frame(foo = 1:4, bar = c(T, T, F, F))
> x
Q92.
Point out the wrong statement?

a.

is.nan() is used to test objects if they are NA

b.

is.nan() is used to test for NaN

c.

NA values have a class

d.

NA values have a class, so there are integer NA, character NA, etc

Discuss
Answer: (a).is.nan() is used to test objects if they are NA
Q93.
Data frames can be converted to a matrix by calling data _______

a.

as.matr()

b.

as.mat()

c.

as.matrix()

d.

as.max()

Discuss
Answer: (c).as.matrix()
Q94.
What will be the output of the following R code? 
> x <- data.frame(foo = 1:4, bar = c(T, T, F, F))
> ncol(x)

a.

2

b.

4

c.

7

d.

9

Discuss
Answer: (a).2
Q95.
Point out the correct statement?

a.

Using factors with labels is better than using integers because factors are self-describing

b.

Factors are used to represent categorical data and can be unordered or ordered

c.

Factors are important in statistical modeling and are treated specially by modelling functions like lm() and glm()

d.

All of the mentioned

Discuss
Answer: (d).All of the mentioned
Q96.
What will be the output of the following R code? 
> x <- 1:3
> names(x)

a.

NULL

b.

1

c.

2

d.

4.5

Discuss
Answer: (a).NULL
Q97.
What will be the output of the following R code? 
> m <- matrix(1:4, nrow = 2, ncol = 2)
> dimnames(m) <- list(c("a", "b"), c("c", "d"))
> m

a.

c d
a 1 3
B 2 4

b.

c d
a 1 2
B 2 3

c.

c d
a 1 3
B 4 2

d.

Error

Discuss
Answer: (a).c d
a 1 3
B 2 4
Q98.
Which of the following statement changes column name to h and f?

a.

colnames(m) <- c(“h”, “f”)

b.

columnnames(m) <- c(“h”, “f”)

c.

rownames(m) <- c(“h”, “f”)

d.

rownames(m) <- c(“f”, “f”)

Discuss
Answer: (a).colnames(m) <- c(“h”, “f”)
Q99.
Which of the following is used for reading tabular data?

a.

read.csv

b.

dget

c.

readLines

d.

writeline

Discuss
Answer: (a).read.csv
Page 10 of 10

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