Important R Programming MCQs 11

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

 
 
 
 

2. Why do analysts use comments In R programming?

 
 
 
 

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

 
 
 
 

4. 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?

 
 
 
 

5. 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?

 
 
 
 

6. What type of software application is RStudio?

 
 
 
 

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

 
 
 
 

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

 
 
 
 

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

 
 
 
 

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

 
 
 
 

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

 
 
 
 

12. 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?

 
 
 
 

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

 
 
 
 

14. 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?

 
 
 
 

15. In data analytics, what is CRAN?

 
 
 
 

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

 
 
 
 

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

 
 

18. RStudio includes which of the following panes?

 
 
 
 

19. 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?

 
 
 
 

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

 
 
 
 

R Programming MCQs

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

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We will learn 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 probability distributions in R. Our focus is on binomial random numbers generation in R.

Binomial Random Numbers 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

Examples of Generation Binomial Random Numbers

  • 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 the Video tutorial on rbinom command

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Sampling in R Language: Important MCQs 10

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The post is about Sampling in R Language. There are 20 multiple-choice questions from the sampling, ggplot2 package, and R language basics too. Let us start with the Quiz sampling in R Language.

Please go to Sampling in R Language: Important MCQs 10 to view the test

The dplyr package is used for data manipulation and transformation. It 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.

R FAQS Logo: Quiz Sampling in R Language

Quiz Sampling in 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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