R Programming MCQs 10

The post is about R Programming MCQs Quiz with Answers. The quiz covers, MCQs about Rstudio, Data Analysis in R, and Some Basics of R Programming Languages. Let us start with the R Programming MCQs Quiz.

Online Multiple Choice Questions about R Programming Language

1. A data analyst is working with spreadsheet data. The analyst imports the data from the spreadsheet into RStudio. Where in RStudio can the analyst find the imported data?

 
 
 
 

2. Many data analysts prefer to use a programming language for which of the following reasons?

 
 
 
 

3. An analyst includes the following calculation in their R programming: midyear_sales <- (quarter_1_sales + quarter_2_sales) - overhead_costsWhich variable will the total from this calculation be assigned to?

 
 
 
 

4. In data analytics, what is CRAN?

 
 
 
 

5. What are the benefits of using a programming language to work with your data?

 
 
 
 

6. A data analyst is searching for a tool that will allow them to communicate instructions that a computer can run. What tool should they use?

 
 
 
 

7. Programming involves _____ a computer to perform an action or set of actions.

 
 
 
 

8. A data analyst wants to write R code that they can access again after they close their current session in RStudio. Where should they write their code?

 
 
 
 

9. When using RStudio, what does the installed.packages()function do?

 
 
 
 

10. What are the benefits of using a programming language for data analysis?

 
 
 
 

11. A data analyst wants to use a programming language that they can modify. What type of programming language should they use?

 
 
 
 

12. Which of the following are the benefits of open-source code? Select all that apply.

 
 
 
 

13. What type of software application is RStudio?

 
 
 
 

14. A data analyst needs to quickly create a series of scatterplots to visualize a very large dataset. What should they use for the analysis?

 
 
 
 

15. What tool gives data analysts the highest level of control over their data analysis?

 
 
 
 

16. Why do analysts use comments In R programming?

 
 
 
 

17. RStudio includes which of the following panes?

 
 
 
 

18. If you write code directly in the R source editor, RStudio can save your code when you close your current session.

 
 

19. The R programming language can be used for which of the following tasks?

 
 
 
 

20. A data analyst is searching for an open-source tool that will allow them to reproduce every step of their analysis, including data cleaning and transformations, calculations, and visualizations. What tool is the best option?

 
 
 
 


Frequently Asked Questions About R Programmign MCQs Quiz

Online R Programming MCQs Quiz

  • Programming involves ___________ a computer to perform an action or set of actions.
  • What are the benefits of using a programming language to work with your data?
  • The R programming language can be used for which of the following tasks?
  • What type of software application is RStudio?
  • RStudio includes which of the following panes?
  • If you write code directly in the R source editor, RStudio can save your code when you close your current session.
  • What tool gives data analysts the highest level of control over their data analysis?
  • A data analyst is searching for a tool that will allow them to communicate instructions that a computer can run. What tool should they use?
  • What are the benefits of using a programming language for data analysis?
  • Many data analysts prefer to use a programming language for which of the following reasons?
  • A data analyst wants to use a programming language that they can modify. What type of programming language should they use?
  • A data analyst is searching for an open-source tool that will allow them to reproduce every step of their analysis, including data cleaning and transformations, calculations, and visualizations. What tool is the best option?
  • When using RStudio, what is the installed.packages()function do?
  • In data analytics, what is CRAN?
  • Why do analysts use comments In R programming?
  • An analyst includes the following calculation in their R programming: midyear_sales <- (quarter_1_sales + quarter_2_sales) – overhead_costsWhich variable will the total from this calculation be assigned to?
  • Which of the following are the benefits of open-source code?
  • A data analyst needs to quickly create a series of scatterplots to visualize a large dataset. What should they use for the analysis?
  • A data analyst wants to write R code that they can access again after they close their current session in RStudio. Where should they write their code?
  • A data analyst is working with spreadsheet data. The analyst imports the data from the spreadsheet into RStudio. Where in RStudio can the analyst find the imported data?

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Binomial Random Numbers Generation in R

We will learn here how to generate Bernoulli or Binomial Random Numbers (Binomial distribution) in R with the example of a flip of a coin. This tutorial is based on how to generate random numbers according to different statistical distributions in R. Our focus is on binomial random number generation in R.

We know that in Bernoulli distribution, either something will happen or not such as a coin flip has two outcomes head or tail (either head will occur or head will not occur i.e. tail will occur). For an unbiased coin, there will be a 50% chance that the head or tail will occur in the long run. To generate a random number that is binomial in R, use the rbinom(n, size, prob) command.

rbinom(n, size, prob) #command has three parameters, namey

where
‘$n$’ is the number of observations
‘$size$’ is the number of trials (it may be zero or more)
‘$prob$’ is the probability of success on each trial for example 1/2

Some Examples

  • One coin is tossed 10 times with a probability of success=0.5
    the coin will be fair (unbiased coin as p=1/2)
    rbinom(n=10, size=1, prob=1/2)
    OUTPUT: 1 1 0 0 1 1 1 1 0 1
  • Two coins are tossed 10 times with a probability of success=0.5
  • rbinom(n=10, size=2, prob=1/2)
    OUTPUT: 2 1 2 1 2 0 1 0 0 1
  • One coin is tossed one hundred thousand times with a probability of success=0.5
    rbinom(n=100,000, size=1, prob=1/2)
  • store simulation results in $x$ vector
    x<- rbinom(n=100000, size=5, prob=1/2)
    count 1’s in x vector
    sum(x)
    find the frequency distribution
    table(x)
    creates a frequency distribution table with frequency
    t = (table(x)/n *100)
    plot frequency distribution table
    plot(table(x),ylab = "Probability",main = "size=5,prob=0.5")
Binomial Random Numbers

View Video tutorial on rbinom command

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Quiz dplyr R Language 9

The post is about a Quiz dplyr R Language. There are some questions from ggplot2 package too. Let us start with Quiz dplyr R Language test.

Please go to Quiz dplyr R Language 9 to view the test

The dplyr package is used for data manipulation and transformation. The package gives a set of functions that make it easy to perform common data manipulation tasks, which include (1) filtering, (2) grouping, (3) summarizing, (4) arranging, and (5) joining data frames.

The package is part of the tidyverse, a collection of R packages designed to work together seamlessly for data analysis and visualization.

Some key functions available in dplyr R Package include:

  • filter(): Used to subset rows based on specified conditions.
  • select(): Used to choose specific columns from a data frame.
  • arrange(): Used to reorder rows based on one or more columns.
  • mutate(): Used to create new columns or modify existing ones.
  • group_by(): Used to group data by one or more variables.
  • summarize(): Used to compute summary statistics for groups of data.
  • join(): Used to merge data frames based on common keys.

The dplyr package provides a powerful and efficient toolkit for data manipulation in R.

Frequently Asked Questions About R Quiz dplyr R Language

Quiz dplyr R Language

  • What is the class of the object defined by the expression x <- c(4, “a”, TRUE)?
  • Suppose, I have a vector x <- c(3, 6, 1, 19, 12, 8)and I want to set all elements of this vector that are less than 6 to be equal to zero. What R code achieves this?
  • We obtain the 10000 random sample of size 6 under SRSWOR using the following population (111, 158, 122, 193, 111, 148, 112, 128, 113, 151, 185, 200, 199, 121, 115, 114) which is the R command for repeating this procedure 150 times?
  • Which is the R command for selecting a sample of size 6 from the population yp <- c(111, 159, 121, 198, 120, 136, 14, 129, 17, 115, 186, 119, 121, 153, 143)
  • For the following population y<- c(1,2,3,4,5), what will be the R command for finding variance?
  • For the following population x<-c(1,2,3,4,5)what will be the R command for finding the mean?
  • Which is the R command for selecting a sample of size 3 from the population y<- c(11, 150, 121, 192, 233, 129, 117, 186, 129, 189, 159)
  • What is the R command for generating bi-variate normal distribution
  • What is the R command for selecting a sample of size $n=10$ by probability proportional to size (PPS) with $N=40$?
  • Which of these dplyr verbs can be used to retrieve columns of a dataset?
  • Who introduced tidyverse to “share an underlying design philosophy, grammar, and data structures, of tidy data?
  • A data analyst is working with a data frame called “salary_data”. They want to create a new column named “total_wages” that adds together data in the “standard_wages” and “overtime_wages” columns. What R code lets the analyst create the “total_wages” column?
  • A data analyst is working with a data frame named “stores”. It has separate columns for city (“city”) and state (“state”). The analyst wants to combine the two columns into a single column named “location”, with the “city” and “state” separated by a comma. What R code lets the analyst create the location column?
  • A data analyst is considering using tibbles instead of basic data frames. What are some of the limitations of tibbles?
  • A data analyst is working with a data frame named cars. The analyst notices that all the column names in the data frame are capitalized. What R code lets the analyst change all the column names to lowercase?
  • In ggplot2, what function do you use to map variables in your data to visual features of your plot?
  • In ggplot2, which of the following concepts refers to the shape, color, and size of data points in a plot?
  • Which of the following functions lets you display smaller groups, or subsets, of your data?
  • A data analyst creates a scatterplot with a lot of data points. It is difficult for the analyst to distinguish the individual points on the plot because they overlap. What function could the analyst use to make the points easier to find?
  • A data analyst creates a plot for visualization. The analyst wants to add a caption to the plot to help communicate important information. What function could the analyst use?

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Important Frequently Asked Questions about R

This post is about some frequently asked Questions about R Language. These questions will help you prepare for examinations and interviews.

Frequently Asked Questions About R

Question: What is a Compiler in R Language?
Answer: A compiler is software that transforms computer code (source code) to another computer language (target language, i.e., object code).

Question: What is a package in R Language?
Answer: The R package is a collection of R functions, compiled code, sample data, and help documentation. The R packages are stored in a directory called “library” in the R environment. The R language also installed a set of packages during installation.

Question: What is JIT?
Answer:
JIT standards for “Just in Time” compiler. It is a method to improve the run-time performance of a computer program.

Question: What is procedural Programming in R Language?
Answer:
Procedural programming is derived from structured programming and it is based on the concept of procedure call. Procedures are also known as routines, subroutines, or functions. It contains a series of computational steps to be carried out. Any procedure may be called (at any point) during a program’s execution.

Question: What is the recycling of elements in a vector?
Answer: When a mathematical operation (such as addition, subtraction, multiplication, division, etc) is performed on two vectors of different lengths (the number of elements in both vectors is different), the element having a shorter length is reused to complete the mathematical operations.

vect1 <- c(4, 1, 4, 5, 6, 9)
vect2 <- c(2, 5)

vect1 * vect2 

###
8, 5, 8, 25, 12, 45

The elements of vect2 are recycled to complete the operation of all elements of vect1.

Question: What is the difference between a data frame and a matrix in R Language?
Answer: In R, the data frame contains heterogeneous data (different columns of the data frame may have different types of variable) while a matrix contains homogeneous data (all the columns of the matrix have the same type of variable). In a matrix, similar data types can be stored while in a data frame, different types of data can be stored.

See Questions about R language Missing Values

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