Skilltest on R for Data Science – Solution

R programming, Fusion Analytics World

Introduction

R is the most commonly used tool in analytics industry today. No doubt, python is catching up quickly. Many companies which were heavily reliant on SAS, have now started R in their day to day analysis. Since R is easy to learn, your proficiency in R can be a massive advantage to your candidature.

This test wasn’t designed for freshers. But, for people having some knowledge of R. If you’ve taken this test thoroughly, you might be either disappointed or happy with your performance and keen to know the solutions. As expected, we’ve complied the list of Q&A so that you can learn and improve.

A best way to learn is to solve these questions at your end. You’ll learn multiple ways to perform a task in R. In other words, you’ll be able to add more weapons to your R armory.

Skill Test Questions & Answers

1). Two vectors X and Y are defined as follows – X <- c(3, 2, 4) and Y <- c(1, 2). What will be output of vector Z that is defined as Z <- X*Y

A – 3,4,0

B – 3,4,4

C – error

D – 3,4,8

Solution: B

Vector recycling takes place when 2 vectors of unequal lengths are multiplied.

 

2). If you want to know all the values in c (1, 3, 5, 7, 10) that are not in c (1, 5, 10, 12, 14). Which code in R can be used to do this?

A – setdiff(c(1,3,5,7),c(1,5,10,12,14))

B – diff(c(1,3,5,7),c(1,5,10,12,14))

C – unique(c(1,3,5,7),c(1,5,10,12,14))

D – None of the Above.

Solution: A

setdiff() function finds the values which are different in any given two vectors.

 

3). What is the output of f(2) ?

b <- 4
f <- function (a){
b <- 3
b^3 + g (a)
}

g <- function (a) {
a*b
}

A – 33

B – 35

C – 37

D – 31

Solution: B

g(a) uses b <- 4 because it is globally available. Globally means to every variable in the environment. f(a) uses b <- 3 because it is locally available for the function. Therefore, for a function locally available information takes precedence over global information.

 

4) The data shown below is from a csv file. Which of the following commands can read this csv file as a dataframe into R?

Male 25.5 0
Female 35.6 1
Female 12.03 0
Female 11.30 0
Male 65.46 1

Table1.csv

A – read.csv(“Table1.csv”)

B – read.csv(“Table1.csv”,header=FALSE)

C – read.table(“Table1.csv”)

D – read.csv2(“Table1.csv”,header=FALSE)

Solution: B

Since the table has no headers, it is imperative to specify it in the read.csv command.

 

5). The missing values in the data shown from a csv file have been represented by ‘?’. Which of the below code will read this csv file correctly into R?

A 10 Sam
B ? Peter
C 30 Harry
D 40 ?
E 50 Mark

Table2.csv

A – read.csv(“Table2.csv”)

B – read.csv(“Table2.csv”,header=FALSE,strings.na=”?”)

C – read.csv2(“Table2.csv”,header=FALSE,sep=”,”,na.strings=”?”)

D – read.table(“Table2.csv”)

Solution: C

Since missing values comes in many forms and not just standard NA, it is essential to define by what character the NA values are represented. na.strings will tell read.csv to treat every question mark? as a missing value.

 

6). The table shown below from a csv file has row names as well as column names. This table will be used in the following questions:

Which of the following code can read this csv file properly into R?

Column 1 Column 2 Column 3
Row 1 15.5 14.12 69.5
Row 2 18.6 56.23 52.4
Row 3 21.4 47.02 63.21
Row 4 36.1 56.63 36.12

Table3.csv

A – read.delim(“Train3.csv”,header=T,sep=”,”,row.names=1)

B – read.csv2(“Train3.csv”,header=TRUE,row.names=TRUE)

C – read.table(“Train3.csv”,header=TRUE,sep=”,”)

D – read.csv(“Train3.csv”,row.names=TRUE,header=TRUE,sep=”,”)

Solution: A

Since the first column has row names, it is important to specify it using row.names while loading data.row.names = 1 says that row names are available in the first column of the table.

 

7). Which of the following code will fail to read the first two rows of the csv file?

Column 1 Column 2 Column 3
Row 1 15.5 14.12 69.5
Row 2 18.6 56.23 52.4
Row 3 21.4 47.02 63.21
Row 4 36.1 56.63 36.12

Table3.csv

A – read.csv(“Table3.csv”,header=TRUE,row.names=1,sep=”,”,nrows=2)

B – read.csv(“Table3.csv”,row.names=1,nrows=2)

C – read.delim2(“Table3.csv”,header=T,row.names=1,sep=”,”,nrows=2)

D – read.table(“Table3.csv”,header=TRUE,row.names=1,sep=”,”,skip.last=2)

Solution- D

Except D, rest all the options will successfully read the first 2 lines of this table. nrows parameter helps to determine how many rows from a data set should be read.

 

8). Which of the following code will read only the second and the third column into R?

Column 1 Column 2 Column 3
Row 1 15.5 14.12 69.5
Row 2 18.6 56.23 52.4
Row 3 21.4 47.02 63.21
Row 4 36.1 56.63 36.12

Table3.csv

A – read.table(“Table3.csv”,header=T,row.names=1,sep=”,”,colClasses=c(“NULL”,NA,NA))

B – read.csv(“Table3.csv”,header=TRUE,row.names=1,sep=”,”,colClasses=c(“NULL”,”NA”,”NA”))

C – read.csv(“Table3.csv”,row.names=1,colClasses=c(“Null”,na,na))

D – read.csv(“Table3.csv”,row.names=T,  colClasses=TRUE)

Solution: A

You can skip reading columns using NULL in colclasses parameter while reading data.

 

9). Below is a data frame which has already been read into R and stored in a variable named dataframe1.

Which of the below code will produce a summary (mean, mode, median etc if applicable) of the entire data set in a single line of code?

V1 V2 V3
1 Male 12.5 46
2 Female 56 135
3 Male 45 698
4 Female 63 12
5 Male 12.36 230
6 Male 25.23 456
7 Female 12 457

Dataframe 1

A – summary(dataframe1)

B – stats(dataframe1)

C – summarize(dataframe1)

D – summarise(dataframe1)

Solution:A

 

10) dataframe2 has been read into R properly with missing values labelled as NA. This dataframe2 will be used for the following questions:

Which of the following code will return the total number of missing values in the dataframe?

A 10 Sam
B NA Peter
C 30 Harry
D 40 NA
E 50 Mark

dataframe2

A – table(dataframe2==NA)

B – table(is.na(dataframe2))

C – table(hasNA(dataframe2))

D – which(is.na(dataframe2)

Solution: B

 

11). Which of the following code will not return the number of missing values in each column?

A 10 Sam
B NA Peter
C 30 Harry
D 40 NA
E 50 Mark

dataframe2

A – colSums(is.na(dataframe2))

B – apply(is.na(dataframe2),2,sum)

C – sapply(dataframe2,function(x) sum(is.na(x))

D – table(is.na(dataframe2))

Solution: D

Rest of the options will traverse through every column to calculate and return the number of missing values per variable.

 

12). The data shown below has been loaded into R in a variable named dataframe3. The first row of data represent column names. The powerful data manipulation package ‘dplyr’ has been loaded. This data set will be used in following questions:

Which of the following code can select only the rows for which Gender is Male?

Gender Marital Status Age Dependents
Male Married 50 2
Female Married 45 5
Female Unmarried 25 0
Male Unmarried 21 0
Male Unmarried 26 1
Female Married 30 2
Female Unmarried 18 0

dataframe3

A – subset(dataframe3, Gender=”Male”)

B – subset(dataframe3, Gender==”Male”)

C – filter(dataframe3,Gender==”Male”)

D – option 2 and 3

Solution: D

filter function comes from dplyr package. subset is the base function. Both does the same job.

 

13). Which of the following code can select the data with married females only?

Gender Marital Status Age Dependents
Male Married 50 2
Female Married 45 5
Female Unmarried 25 0
Male Unmarried 21 0
Male Unmarried 26 1
Female Married 30 2
Female Unmarried 18 0

dataframe 3

A – subset(dataframe3,Gender==”Female” & Marital Status==”Married”)

B – filter(dataframe3, Gender==”Female” , Marital Status==”Married”)

C – Only 1

D – Both 1 and 2

Solution: D

 

14). Which of the following code can select all the rows from Age and Dependents?

Gender Marital Status Age Dependents
Male Married 50 2
Female Married 45 5
Female Unmarried 25 0
Male Unmarried 21 0
Male Unmarried 26 1
Female Married 30 2
Female Unmarried 18 0

dataframe3

A – subset(dataframe3, select=c(“Age”,”Dependents”))

B – select(dataframe3, Age,Dependants)

C – dataframe3[,c(“Age”,”Dependants”)]

D – All of the above

Solution: D

If you got this wrong, refer to the basics of sub-setting a data frame.

 

15). Which of the following codes will convert the class of the Dependents variable to a factor class?

Gender Marital Status Age Dependents
Male Married 50 2
Female Married 45 5
Female Unmarried 25 0
Male Unmarried 21 0
Male Unmarried 26 1
Female Married 30 2
Female Unmarried 18 0

Dataframe 3

A – dataframe3$Dependents=as.factor(dataframe3$Dependents)

B – dataframe3[,’Dependents’]=as.factor(dataframe3[,’Dependents’])

C – transform(dataframe3,Dependents=as.factor(Dependents))

D – All of the Above

Solution: D

as.factor() is used to coerce class type to factor.

 

16). Which of the following code can calculate the mean age of Female?

Gender Marital Status Age Dependents
Male Married 50 2
Female Married 45 5
Female Unmarried 25 0
Male Unmarried 21 0
Male Unmarried 26 1
Female Married 30 2
Female Unmarried 18 0

Dataframe3

A – dataframe3%>%filter(Gender==”Female”)%>%summarise(mean(Age))

B – mean(dataframe3$Age[which(dataframe3$Gender==”Female”)])

C – mean(dataframe3$Age,dataframe3$Female)

D – Both 1 and 2

Solution: D

Option A describes the method using dplyr package. Option B uses the base functions to accomplish this task.

 

17). The data shown below has been read into R and stored in a dataframe named dataframe4. It is given that Has_Dependents column is read as a factor variable. We wish to convert this variable to numeric class. Which code will help us achieve this?

Gender Marital Status Age Has_Dependents
Male Married 50 0
Female Married 45 1
Female Unmarried 25 0
Male Unmarried 21 0
Male Unmarried 26 1
Female Married 30 1
Female Unmarried 18 0

dataframe4

A – dataframe4$Has Dependents=as.numeric(dataframe4$Has_Dependents)

B – dataframe4[,”Has Dependents”]=as.numeric(as.character(dataframe4$Has_ Dependents))

C – transform(dataframe4,Has Dependents=as.numeric(Has_Dependents))

D – All of the above

Solution: B

 

18). There are two dataframes stored in two respective variables named Dataframe1 and Dataframe2.

Dataframe1

Feature1 Feature2 Feature3
A 1000 25.5
B 2000 35.5
C 3000 45.5
D 4000 55.5
Dataframe2

Feature1 Feature2 Feature3
E 5000 65.5
F 6000 75.5
G 7000 85.5
H 8000 95.5

Which of the following codes will produce the output as shown below?

Feature1 Feature2 Feature3
A 1000 25.5
B 2000 35.5
C 3000 45.5
D 4000 55.5
E 5000 65.5
F 6000 75.5
G 7000 85.5
H 8000 95.5

A – merge(dataframe1,dataframe2,all=TRUE)

B – merge(dataframe1,dataframe2)

C – merge(dataframe1,dataframe2,by=intersect(names(x),names(y))

D – None of the above

Solution: A

The parameter all=TRUE says to merge both the data sets, and even if there is no match found for a particular observation, return NA.

 

19). Which of the following codes will create a new column named Size(MB) from the existing Size(KB) column? The dataframe is stored in a variable named dataframe5. Given 1MB = 1024KB

Package Name Creator Size(kB)
Swirl Sean Kross 2568
Ggplot Hadley Wickham 5463
Dplyr Hadley Wickham 8961
Lattice Deepayan Sarkar 3785

dataframe5

A –  dataframe5$Size(MB)=dataframe$Size(KB)/1024

B – dataframe5$Size(KB)=dataframe$Size(KB)/1024

C – dataframe5%>%mutate(Size(MB)=Size(KB)/1024)

D – Both 1 and 3

Solution: D

 

20). Following question will use the dataframe shown below:

Gender Marital Status Age Has Dependents
Male Married 50 0
Female Married 45 1
Female Unmarried 25 0
Male Unmarried 21 0
Male Unmarried 26 1
Female Married 30 1
Female Unmarried 18 0

Dataframe6

Certain Algorithms like XGBOOST work only with numerical data. In that case, categorical variables present in dataset are converted to DUMMY variables which represent the presence or absence of a level of a categorical variable in the dataset. From Dataframe6, after creating the dummy variable for Gender, the dataset looks like below.

Gender_Male Gender_Female Marital Status Age Has Dependents
1 0 Married 50 0
0 1 Married 45 1
0 1 Unmarried 25 0
1 0 Unmarried 21 0
1 0 Unmarried 26 1
0 1 Married 30 1
0 1 Unmarried 18 0

Which of the following commands would have helped us to achieve this?

A – dummies:: dummy.data.frame(dataframe6,names=c(“Gender”))

B – dataframe6[,”Gender”] <- split(dataframe6$Gender, ifelse(dataframe6$Gender == “Male”,0,1))

C – contrasts(dataframe6$Gender) <- contr.treatment(2)

D – None of the above

Solution: A

For Option A, install and load dummies package. With its fairly easy code syntax, one hot encoding in R was never easy before.

 

21). We wish to calculate the correlation between column 2 and column 3. Which of the below codes will achieve the purpose?

  Column1 Column2 Column3 Column4 Column5 Column6
Name1 Male 12 24 54 0 Alpha
Name2 Female 16 32 51 1 Beta
Name3 Male 52 104 32 0 Gamma
Name4 Female 36 72 84 1 Delta
Name5 Female 45 90 32 0 Phi
Name6 Male 12 24 12 0 Zeta
Name7 Female 32 64 64 1 Sigma
Name8 Male 42 84 54 0 Mu
Name9 Male 56 112 31 1 Eta

Dataframe 7

A – cor(dataframe7$column2,dataframe7$column3)

B – (cov(dataframe7$column2,dataframe7$column3))/(sd(dataframe7$column4)*sd(dataframe7$column3))

C – (cov(dataframe7$column2,dataframe7$column3))/(var(dataframe7$column4)*var(dataframe7$column3))

D – All of the above

Solution: A

cor is the base function used to calculate correlation between two numerical variables.

 

22). Column 3 has 2 missing values represented as NA in the dataframe below stored in the variable named dataframe8. We wish to impute the missing values using the mean of the column 3. Which code will help us do that?

  Column1 Column2 Column3 Column4 Column5 Column6
Name1 Male 12 24 54 0 Alpha
Name2 Female 16 32 51 1 Beta
Name3 Male 52 104 32 0 Gamma
Name4 Female 36 72 84 1 Delta
Name5 Female 45 NA 32 0 Phi
Name6 Male 12 24 12 0 Zeta
Name7 Female 32 NA 64 1 Sigma
Name8 Male 42 84 54 0 Mu
Name9 Male 56 112 31 1 Eta

Dataframe 8

A – dataframe8$Column3[which(dataframe8$Column3==NA)]=mean(dataframe8$Column3)

B – dataframe8$Column3[which(is.na(dataframe8$Column3))]=mean(dataframe8$Column3)

C – dataframe8$Column3[which(is.na(dataframe8$Column3))]=mean(dataframe8$Column3,na.rm=TRUE)

D – dataframe8$Column3[which(is.na(dataframe8$Column3))]=mean(dataframe8$Column3,rm.na=TRUE)

Solution: C

Option na.rm=TRUE says that impute the missing values by calculating the mean of all available observations.

 

23). Column7 contains some names with the salutations. In such cases, it is always advisable to extract salutations in a new column since they can provide more information to our predictive model.  Your work is to choose the code that cannot extract the salutations out of names in Column7 and store the salutations in Column8.

  Column1 Column2 Column3 Column4 Column5 Column6 Column7
Name1 Male 12 24 54 0 Alpha Mr.Sam
Name2 Female 16 32 51 1 Beta Ms.Lilly
Name3 Male 52 104 32 0 Gamma Mr.Mark
Name4 Female 36 72 84 1 Delta Ms.Shae
Name5 Female 45 NA 32 0 Phi Ms.Ria
Name6 Male 12 24 12 0 Zeta Mr.Patrick
Name7 Female 32 NA 64 1 Sigma Ms.Rose
Name8 Male 42 84 54 0 Mu Mr.Peter
Name9 Male 56 112 31 1 Eta Mr.Roose

Dataframe 9

A – dataframe9$Column8<-sapply(strsplit(as.character(dataframe9$Column7),split = “[.]”),function(x){x[1]})

B – dataframe9$Column8<-sapply(strsplit(as.character(dataframe9$Column7),split = “.”),function(x){x[1]})

C – dataframe9$Column8<-sapply(strsplit(as.character(dataframe9$Column7),split = “.”,fixed=TRUE),function(x){x[1]})

D – dataframe9$Column8<-unlist(strsplit(as.character(dataframe9$Column7),split = “.”,fixed=TRUE))[seq(1,18,2)]

Solution: B

strsplit is used to split a text variable based on some splitting criteria. Try running these codes at your end, you’ll understand the difference.

 

24). Column 3 in the data frame shown below is supposed to contain dates in ddmmyyyy format but as you can see, there is some problem with its format. Which of the following code can convert the values present in Column 3 into date format?

  Column1 Column2 Column3 Column4 Column5 Column6 Column7
Name1 Male 12 24081997 54 0 Alpha Mr.Sam
Name2 Female 16 30062001 51 1 Beta Ms.Lilly
Name3 Male 52 10041998 32 0 Gamma Mr.Mark
Name4 Female 36 17021947 84 1 Delta Ms.Shae
Name5 Female 45 15031965 32 0 Phi Ms.Ria
Name6 Male 12 24111989 12 0 Zeta Mr.Patrick
Name7 Female 32 26052015 64 1 Sigma Ms.Rose
Name8 Male 42 18041999 54 0 Mu Mr.Peter
Name9 Male 56 11021994 31 1 Eta Mr.Roose

Dataframe 10

A – as.Date(as.character(dataframe10$Column3),format=”%d%m%Y”)

B – as.Date(dataframe10$Column3,format=”%d%m%Y”)

C -as.Date(as.character(dataframe10$Column3),format=”%d%m%y”)

D -as.Date(as.character(dataframe10$column3),format=”%d%B%Y”)

Solution: A

 

25). Some algorithms work very well with normalized data. Your task is to convert the Column2 in the dataframe shown below into a normalised one. Which of the following code would not achieve that? The normalised column should be stored in a column named column8.

  Column1 Column2 Column3 Column4 Column5 Column6 Column7
Name1 Male 12 24081997 54 0 Alpha Mr.Sam
Name2 Female 16 30062001 51 1 Beta Ms.Lilly
Name3 Male 52 10041998 32 0 Gamma Mr.Mark
Name4 Female 36 17021947 84 1 Delta Ms.Shae
Name5 Female 45 15031965 32 0 Phi Ms.Ria
Name6 Male 12 24111989 12 0 Zeta Mr.Patrick
Name7 Female 32 26052015 64 1 Sigma Ms.Rose
Name8 Male 42 18041999 54 0 Mu Mr.Peter
Name9 Male 56 11021994 31 1 Eta Mr.Roose

dataframe 11

A – dataframe11$Column8<-(dataframe11$Column2-mean(dataframe11$column2))/sd(dataframe11$Column2)

B – dataframe11$Column8<-scale(dataframe11$Column2)

C – All of the above

Solution: C

Option A describes simply the mathematical formula for standarization i.e x – μ/σ

 

26). dataframe12 is the output of a certain task. We wish to save this dataframe into a csv file named “result.csv”. Which of the following commands would help us accomplish this task?

  Column1 Column2 Column3 Column4 Column5 Column6 Column7
Name1 Male 12 24081997 54 0 Alpha Mr.Sam
Name2 Female 16 30062001 51 1 Beta Ms.Lilly
Name3 Male 52 10041998 32 0 Gamma Mr.Mark
Name4 Female 36 17021947 84 1 Delta Ms.Shae
Name5 Female 45 15031965 32 0 Phi Ms.Ria
Name6 Male 12 24111989 12 0 Zeta Mr.Patrick
Name7 Female 32 26052015 64 1 Sigma Ms.Rose
Name8 Male 42 18041999 54 0 Mu Mr.Peter
Name9 Male 56 11021994 31 1 Eta Mr.Roose

dataframe 12

A – write.csv(“result.csv”, dataframe12)

B – write.csv(dataframe12,”result.csv”, row.names = FALSE)

C – write.csv(file=”result.csv”,x=dataframe12,row.names = FALSE)

D – Both 2 and 3.

Solution: C

 

27) y=seq(1,1000,by=0.5)

What is the length of vector y ?

A – 2000

B – 1000

C – 1999

D – 1998

Solution: C

 

28). The dataset has been stored in a variable named dataframe13. We wish to see the location of all those persons who have “Ms” in their names stored in Column7. Which of the following code will not help us achieve that?

  Column1 Column2 Column3 Column4 Column5 Column6 Column7
Name1 Male 12 24081997 54 0 Alpha Mr.Sam
Name2 Female 16 30062001 51 1 Beta Ms.Lilly
Name3 Male 52 10041998 32 0 Gamma Mr.Mark
Name4 Female 36 17021947 84 1 Delta Ms.Shae
Name5 Female 45 15031965 32 0 Phi Ms.Ria
Name6 Male 12 24111989 12 0 Zeta Mr.Patrick
Name7 Female 32 26052015 64 1 Sigma Ms.Rose
Name8 Male 42 18041999 54 0 Mu Mr.Peter
Name9 Male 56 11021994 31 1 Eta Mr.Roose

dataframe13

A – grep(pattern=”Ms”,x=dataframe13$Column7)

B – grep(pattern=”ms”,x=dataframe13$Column7, ignore.case=T)

C – grep(pattern=”Ms”,x=dataframe13$Column7,fixed=T)

D – grep(pattern=”ms”,x=dataframe13$Column7,ignore.case=T,fixed=T)

Solution- D

In option D, we tell the function to find the match irrespective of lower or upper case i.e. it just matches the spelling the and return the output.

 

29). The data below has been stored in a variable named dataframe14. We wish to find and replace all the instances of Male in Column1 with Man. Which of the following code will not  help us do that?

  Column1 Column2 Column3 Column4 Column5 Column6 Column7
Name1 Male 12 24081997 54 0 Alpha Mr.Sam
Name2 Female 16 30062001 51 1 Beta Ms.Lilly
Name3 Male 52 10041998 32 0 Gamma Mr.Mark
Name4 Female 36 17021947 84 1 Delta Ms.Shae
Name5 Female 45 15031965 32 0 Phi Ms.Ria
Name6 Male 12 24111989 12 0 Zeta Mr.Patrick
Name7 Female 32 26052015 64 1 Sigma Ms.Rose
Name8 Male 42 18041999 54 0 Mu Mr.Peter
Name9 Male 56 11021994 31 1 Eta Mr.Roose

dataframe 14

A – sub(“Male”,”Man”,dataframe14$Column1)

B – gsub(“Male”,”Man”,dataframe14$Column1)

C – dataframe14$Column1[which(dataframe14$Column1==”Male”)]=”Man”

D – None of the above.

Solution: D

Try running these codes at your end. Every option will do this task gracefully.

 

30) Which of the following command will display the classes of each column for the following dataframe ?

  Column1 Column2 Column3 Column4 Column5 Column6 Column7
Name1 Male 12 24081997 54 0 Alpha Mr.Sam
Name2 Female 16 30062001 51 1 Beta Ms.Lilly
Name3 Male 52 10041998 32 0 Gamma Mr.Mark
Name4 Female 36 17021947 84 1 Delta Ms.Shae
Name5 Female 45 15031965 32 0 Phi Ms.Ria
Name6 Male 12 24111989 12 0 Zeta Mr.Patrick
Name7 Female 32 26052015 64 1 Sigma Ms.Rose
Name8 Male 42 18041999 54 0 Mu Mr.Peter
Name9 Male 56 11021994 31 1 Eta Mr.Roose

A – lapply(dataframe,class)

B – sapply(dataframe,class)

C – Both 2 and 3

D – None of the above

Solution: C

The only difference in the answer of lapply and sapply is that lapply will return a list and sapply will return a vector/matrix.

 

31).The questions below deal with the tidyr package which forms an important part of the data cleaning task in R.

Which of the following command will combine Male and Female column into a single column named Sex and create another variable named Count as the count of male or female per Name.

Name Male Female
A 1 6
B 5 9

Initial dataframe

Name Sex Count
A Male 1
B Male 5
A Female 6
B Female 9

Final dataframe

A – collect(dataframe,Male:Female,Sex,Count)

B – gather(dataframe,Sex,Count,-Name)

C – gather(dataframe,Sex,Count)

D – collect(dataframe,Male:Female,Sex,Count,-Name)

Solution: B

 

32). The dataframe below contains one category of messy data where multiple columns are stacked into one column which is highly undesirable.

Sex_Class Count
Male_1 1
Male_2 2
Female_1 3
Female_2 4

Which of the following code will convert the above dataframe to the dataframe below ? The dataframe is stored in a variable named dataframe.

Sex Class Count
Male 1 1
Male 2 2
Female 1 3
Female 2 4

A – separate(dataframe,Sex_Class,c(“Sex”,”Class”))

B – split(dataframe,Sex_Class,c(“Sex”,”Class”))

C – disjoint(dataframe,Sex_Class,c(“Sex”,”Class”))

D – None of the above

Solution: A

 

33). The dataset below suffers from a problem where variables “Term” and “Grade” are stored in separate columns which can be displayed more effectively. We wish to convert the structure of these variables into each separate variable named Mid and Final.

Name Class Term Grade
Alaska 1 Mid A
Alaska 1 Final B
Banta 2 Mid A
Banta 2 Final A

Which of the following code will convert the above dataset into the one showed below? The dataframe is stored in a variable named dataframe.

Name Class Mid Final
Alaska 1 A B
Banta 2 A A

A – melt(dataframe, Term, Mid,Final,Grade)

B – transform(dataframe,unique(Term),Grade)

C – spread(dataframe,Term,Grade)

D – None of the above

Solution: C

 

34). The ________ function takes an arbitrary number of arguments and concatenates them one by one into character strings.

A – copy()

B – paste()

C – bind()

D – None of the above.

Solution: B

 

35). Point out the correct statement :

A – Character strings are entered using either matching double (“) or single (‘) quote.

B – Character vectors may be concatenated into a vector by the c() function.

C – Subsets of the elements of a vector may be selected by appending to the name of the vector an index vector in square brackets.

D – All of the above

Solution:D

 

36) What will be the output of the following code ?

> x <- 1:3
> y <- 10:12
> rbind(x, y)

1-  [,1] [,2] [,3] x      1      2    3
y     10    11  12

2-  [,1] [,2] [,3] x       1     2     3
y      10 11

3-  [,1] [,2] [,3] x       1     2     3
y       4     5     6

4 –  All of the above

Solution: A

 

37). Which of the following method make vector of repeated values?

A – rep()

B – data()

C – view()

D – None of the above

Solution: A

 

38). Which of the following finds the position of quantile in a dataset ?

A – quantile()

B – barplot()

C – barchart()

D – None of the Above

Solution: A

 

39) Which of the following function cross-tabulate tables using formulas ?

A – table()

B – stem()

C – xtabs()

D – All of the above

Solution: D

 

40) What is the output of the following function?

>     f <- function(num = 1) {
                 hello <- "Hello, world!\n"
        for(i in seq_len(num)) {
                  cat(hello)
         }
         chars <- nchar(hello) * num
         chars
 }

> f()

A – Hello, world!

14

B – Hello, world!\n

14

C – Hello, world!

13

D – Hello, world!\n

13

Solution: A

 

41- Which is the missing value from running the quantile function on a numeric vector in comparison to running the summary function on the same vector ?

A – Median

B – Mean

C – Maximum

D – Minimum

Solution: B

 

 

42- Which of the following command will plot a blue boxplot of a numeric vector named vec?

A – boxplot(vec,col=”blue”)

B – boxplot(vec,color=”blue”)

C – boxplot(vec,color=”BLUE”)

D – None of the above

Solution: A

 

43- Which of the following command will create a histogram with 100 buckets of data ?

A – hist(vec,buckets=100)

B – hist(vec,into=100)

C – hist(vec,breaks=100)

D – None of the above

Solution: C

 

44- What does the “main” parameter in barplot command does ?

A – x axis label

B – Title of the graph

C – I can’t tell

D – y axis label

Solution: B

 

45- The below dataframe is stored in a variable named sam:

A B
12 East
15 West
13 East
15 East
14 West

We wish to create a boxplotin a single line of code per B i.e a total of two boxplots (one for East and one for West). Which of the following command will achieve the purpose ?

A – boxplot(A~B,data=sam)

B – boxplot(A,B,data=sam)

C – boxplot(A|B,data=sam)

D – None of the above

Solution: A

 

46- Which of the following command will split the plotting window into 3 X 4 windows and where the plots enter the window row wise.

A – par(split=c(3,4))

B – par(mfcol=c(3,4))

C – par(mfrow=c(3,4))

D – par(col=c(3,4))

Solution – C

 

47- A dataframe named frame contains two numerical columns named A and B. Which of the following commands will draw a scatter plot between the two columns of the dataframe?

A – with(frame,plot(A,B))

B – plot(frame$A,frame$B)

C – ggplot(data = frame, aes(A,B))+geom_point()

D – All of the above

Solution: D

 

48- The dataframe below is stored in a variable named frame.

A B C
15 42 East
11 31 West
56 54 East
45 63 East
12 26 West

Which of the following command will draw a scatter plot between A and B differentiated by different color of C like the one below.

Q.8 Image 1

A – plot(frame$A,frame$B,col=frame$C)

B – with(frame,plot(A,B,col=C)

C- 1 and 2

D- None of the above.

Solution: A

 

49- Which of the following does not apply to R’s base plotting system ?

A – Can easily go back once the plot has started.(eg: to change margins etc)

B – It is convenient and mirrors how we think of building plots and analysing data

C – starts with plot(or similar) function

D – Use annotation functions to add/modify (text, lines etc)

Solution: A

 

The following questions revolve around the ggplot2 package, which is the most widely used plotting package used in the R community and provides great customisation and flexibility over plotting.

50- Which of the following function is used to create plots in ggplot2 ?

A – qplot

B – gplot

C – plot

D – xyplot

Solution: A

 

51- What is true regarding the relation between the number of plots drawn by facet_wrap and facet_grid ?

A – facet_wrap > facet_grid

B – facet_wrap < facet_grid

C – facet_wrap <= facet_grid

D – None of the above

Solution: C

 

52- Which function in ggplot2 allows the coordinates to be flipped? (i.e x bexomes y and vice-versa) ?

A – coordinate_flip

B – coord_flip

C – coordinate_rotate

D – coord_rotate

Solution: B

 

53- The below dataset is stored in a variable called frame.

A B
alpha 100
beta 120
gamma 80
delta 110

Which of the following commands will create a bar plot for the above dataset with the values in column B being the height of the bar?

A – ggplot(frame,aes(A,B))+geom_bar(stat=”identity”)

B – ggplot(frame,aes(A,B))+geom_bar(stat=”bin”)

C – ggplot(frame,aes(A,B))+geom_bar()

D – None of the above

Solution: A

 

54- The following dataframe is stored in a variable named frame and is a subset of a very popular dataset named mtcars.

qa

We wish to create a stacked bar chart for cyl variable with stacking criteria being vs variable .which of the following commands will help us do this ?

A – qplot(factor(cyl),data=frame,geom=”bar”,fill=factor(vs))

B – ggplot(mtcars,aes(factor(cyl),fill=factor(vs)))+geom_bar()

C – All of the above

D – None of the above

Solution: C

 

55 – The question is same as above . The only difference is that you have to create a dodged bar chart instead of a stacked one. Which of the following command will help us do that ?

A – qplot(factor(cyl),data=frame,geom=”bar”,fill=factor(vs),position=”dodge”)

B – ggplot(mtcars,aes(factor(cyl),fill=factor(vs)))+geom_bar(position=”dodge”)

C – All of the above

D – None of the above

Solution: B

This article taken from http://analyticsvidhya.com

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Kalyan Banga203 Posts

I am Kalyan Banga, a Post Graduate in Business Analytics from Indian Institute of Management (IIM) Calcutta, a premier management institute, ranked best B-School in Asia in FT Masters management global rankings. I have spent 6 years in field of Analytics.

1 Comment

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