Question

Question 2

If you read in a csv file using read.csv() function into R, what is the resulting datastructure in which R stores the read-in data?
A. numeric

B. matrix   

C. data.frame   

D. vector

  
Question 3

Suppose you have 4 integers, 4 characters, and 4 logical values. Which datastructure can you use to store all 12 values? Choose one or more options.

A. a vector

B. a matrix

C. a list

D. a data frame

Question 4

Suppose you have a vector c(1:20). You make a matrix with it using the matrix() function, with the argument nrow=5. What will be the 4th column of this matrix?  

A. 16 17 18 19 20

B. 4 9 14 19  

C. 3 8 13 18

D. 11 12 13 14 15

Question 5

Suppose you survey students regarding their sexual orientation, and code the results as
1=Hetrosexual, 2=Homosexual, 3=Bisexual, 4=Asexual. Here is the data:
1 2 4 2 4 1 2 1 3 4 2 2 3 1 1 1 3 2 2 1 3 4 1 2 1 3 4 3 4 1 1 1 2 1 1 2 3 3 2

Assume you read in the data as an integer vector S. However, the numbers need to be treated as values of a categorical variable in further analysis. What should you do with S to facilitate this? Choose the most appropriate answer.
      

  A. Convert S to a data frame:
S = as.data.frame(S)

B. Convert S to a character vector

C. Convert S to a vector of factors   

D. Leave it as is, and do no calculations

Question 6

Suppose you survey students regarding their sexual orientation, and code the results as
1=Hetrosexual, 2=Homosexual, 3=Bisexual, 4=Asexual. Here is the data in a csv file, SexualOrientation.csv:

ID,SexualOrient
1,1
2,2
3,4
4,2
5,4
6,1
7,2
8,1
9,3
10,4
If I want to get a vector, v, of factors corresponding to sexual orientation, what would I do?
      

A. so<-read.csv("SexualOrientation.csv")

v<-c(as.factor(so$SexualOrient))

B. so<-read.csv("SexualOrientation.csv")

v=as.vector(as.factor(so$SexualOrient))   

C. so<-read.csv("SexualOrientation.csv")

v<-as.factor(so$SexualOrient)

D. so<-read.csv("SexualOrientation.csv")

v=as.vector(so$SexualOrient)

Question 7

You want to read in the data file T1.csv (on BlackBoard under “Course Documents→ Data” folder) into your R working directory. Which one of the following will read it in correctly? Missing data must be read in as NA, not the character used to represent the missing data in the file. (There may be multiple correct answers).
      

A. T1<- read.table("T1.csv", header=T, comment.char="#",na.strings="-" )   

B. T1=read.csv("T1.csv", comment.char="#",na.strings="-" )  

C. T1 <-read.table("T1.csv", sep=",", header=T, comment.char="#")

D. T1<-read.table("T1.csv", sep=",", header=T, comment.char="#",na.strings="-" )
  

Question 8

Suppose you have a vector, x=c("1") and another y=rbind(c(1,2,3),c(2,3,4)). If you call cbind with x and y as arguments, what will be the type of the resulting matrix? Think "coercion"   

A. matrix

B. columns vector

C. character

D. integer

  
Question 9

Suppose you have data from a survey. The responses consist of a person's sex("Male"/"Female"/"Other"), age (integer), and height(float). What datastructure will you use to hold the survey data?

A. A matrix   

B. A row of vectors

C. a data frame  

D. A matrix of characters and numeric data types.

Answer 1

Solution

Question 2

C. data frame

Question 3
C. a list

Question 4
B. 4 9 14 19

Question 5
C. Convert S to a vector of factors

Explanation

List is capable of storing n number of elements

so List is the right choice

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all the best