R Language Quiz

R Language MCQs Test 33

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Test your R programming expertise with this 20-question MCQ quiz! R Language MCQs Test designed for both learners and professionals, this quiz covers essential topics like data wrangling with dplyr (group_by, summarize, pipes), string manipulation, lubridate, tidymodels, and predictive modeling. Perfect for preparing for data scientist job interviews, brushing up on core R concepts, and mastering the tidyverse ecosystem. Let us start with the R Language MCQs Test now.

Online R Language MCQs Test

Online R Language Programming Quiz with Answers

1. You’ve still got this same messy data:
my_strings<-c(
"xyztiger",
" i33tiger",
"898natiger "
)
You want to use a function to take this data and create a column of data that looks like this:

“tiger”
“tiger”
“tiger”
What is the correct function?

 
 
 
 

2. Which of the following can you accomplish using the spread() function?

 
 
 
 

3. When grouping data and calculating the mean of each group as part of your exploratory data analysis, you typically use the group_by() function with which other function?

 
 
 
 

4. What is the main similarity between the summarize() and group_by() functions?

 
 
 
 

5. Which function can you use to read a text file that uses the “%” character as a delimiter?

 
 
 
 

6. How can the factor() function be used to map R onto a relational database management system (RDBMS)?

 
 
 
 

7. When using the predict() function in R, what is the default confidence level?

 
 
 
 

8. What’s the point of using group_by()?

 
 
 
 

9. Assume you have a dataset called “new_dataset”, two predictor variables called X and Y, and a target variable called Z, and you want to fit a multiple linear regression model. Which command should you use?

 
 
 
 

10. Let’s say you want to calculate how many days passed from 14 July, 1789 until 1 December 1941. How can you calculate that?

 
 
 
 

11. What is the result of the following statement?

sub_airline %>% map(~sum(is.na(.)))

 
 
 
 

12. Assume you have a dataset called “new_dataset”, a predictor variable called X, and a target called Y, and you want to fit a simple linear regression model. Which command should you use?

 
 
 
 

13. Which tidymodels function do you use to create the grid for a grid search?

 
 
 
 

14. You’ve got some messy data that looks like this:
my_strings<-c(
"xyztiger",
" i33tiger",
"898natiger "
)
You want to use a function to do a logical test for whether the character string “tiger” is present in any of the items in this vector. What is the correct function?

 
 
 
 

15. You are checking your data using the glimpse() function before beginning your analysis, and determine that the data type of a variable called TimeStamp is in a character format. What should you do next?

 
 
 
 

16. Say you want to split a character vector and split the strings, so you have a matrix with two columns, splitting the string as indicated. Your character vector looks like this:
my_strings<-c(
"paper_store1",
"pens_store1",
"pencils_store1")
You want to split the strings at the underscore. What function do you use?

 
 
 
 

17. You have a variable called “Status” that contains a status code in the format “error_type-severity_level”, for example “10-07”, and you want to reformat the column so that the “error_type” and “severity_level” are in different columns. What is the correct function to do this?

 
 
 
 

18. You have a character vector that looks like this:
my_dates<-c(
“05-28-1984”,
“07-15-1981”,
“9-12-1986”,
“1-15-1982”)
You want to extract the year values from this vector, using the tools in lubridate. Which is correct?

 
 
 
 

19. Which functions do you use together to correct data types in all columns of your dataset?

 
 
 
 

20. What is the purpose of the pipe (%>%) operator?

 
 
 
 

Question 1 of 20

Online R Language MCQs Test with Answers

  • What’s the point of using group_by()?
  • Which tidymodels function do you use to create the grid for a grid search?
  • What is the purpose of the pipe (%>%) operator?
  • You are checking your data using the glimpse() function before beginning your analysis, and determine that the data type of a variable called TimeStamp is in a character format. What should you do next?
  • How can the factor() function be used to map R onto a relational database management system (RDBMS)?
  • Which function can you use to read a text file that uses the “%” character as a delimiter?
  • What is the main similarity between the summarize() and group_by() functions?
  • What is the result of the following statement?
    sub_airline %>% map(~sum(is.na(.)))
  • Which functions do you use together to correct data types in all columns of your dataset?
  • You have a variable called “Status” that contains a status code in the format “error_type-severity_level”, for example “10-07”, and you want to reformat the column so that the “error_type” and “severity_level” are in different columns. What is the correct function to do this?
  • Which of the following can you accomplish using the spread() function?
  • When grouping data and calculating the mean of each group as part of your exploratory data analysis, you typically use the group_by() function with which other function?
  • Assume you have a dataset called “new_dataset”, a predictor variable called X, and a target called Y, and you want to fit a simple linear regression model. Which command should you use?
  • When using the predict() function in R, what is the default confidence level?
  • Assume you have a dataset called “new_dataset”, two predictor variables called X and Y, and a target variable called Z, and you want to fit a multiple linear regression model. Which command should you use?
  • You’ve got some messy data that looks like this:
    my_strings<-c( “xyztiger”, ” i33tiger”, “898natiger ” )
    You want to use a function to do a logical test for whether the character string “tiger” is present in any of the items in this vector. What is the correct function?
  • You’ve still got this same messy data:
    my_strings<-c( “xyztiger”, ” i33tiger”, “898natiger ” )
    You want to use a function to take this data and create a column of data that looks like this:
    “tiger”
    “tiger”
    “tiger”
    What is the correct function?
  • Say you want to split a character vector and split the strings, so you have a matrix with two columns, splitting the string as indicated. Your character vector looks like
    this: my_strings<-c( “paper_store1”, “pens_store1”, “pencils_store1”)
    You want to split the strings at the underscore. What function do you use?
  • You have a character vector that looks like this:
    my_dates<-c( “05-28-1984”, “07-15-1981”, “9-12-1986”, “1-15-1982”)
    You want to extract the year values from this vector, using the tools in lubridate. Which is correct?
  • Let’s say you want to calculate how many days passed from 14 July, 1789 until 1 December 1941. How can you calculate that?

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R Data Visualization Quiz 32

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Test your R data visualization skills with this 20-question R Graphics MCQ quiz! This R Data Visualization Quiz is perfect for R learners, statisticians, and data analysts preparing for exams or job interviews. Covers ggplot2, Plotly, animations, choropleths, SF maps, and best practices in R visualization. Assess your expertise now! Let us start with the R Data Visualization Quiz now.

R Data Visualization Quiz R MCQs
Please go to R Data Visualization Quiz 32 to view the test

R Data Visualization Quiz with Answers

  • Which function do you use to create a pie chart in Base R?
  • What aesthetic do you use to select the variable for painting in a choropleth?
  • What R package do you need to draw Simple Features maps with R in conjunction with ggplot?
  • What geom is used to draw maps using simple features data?
  • Which of these most accurately describes how to fill in the colors for a choropleth made with simple features data?
  • What is the best practice for adding labels to points in a bubbleplot made with simple features data?
  • What is the advantage of the usa_sf() data?
  • What is the closest animated equivalent to making a static figure with facet_wrap and a categorical variable?
  • What is the most straightforward way of saving an animation?
  • What is “easing”?
  • When would you use transition_layers()?
  • How can you control the speed of a transition between frames in transition_states?
  • What is the basic function for adding the plotly interactive interface to a ggplot figure?
  • Which of these is a way to export an interactive plotly figure?
  • What aesthetic do you set in the ggplot() function that allows ggplotly to animate the figure?
  • How do you export an animation created with ggplotly?
  • What is the point of mapping the id’s aesthetic when animating a ggplot figure with ggplotly?
  • What will be the output of this R code? ggplot(data, aes(cty, hwy)) + geom_point() + stat_smooth(method = lm)
  • Is it better to use a .shp file or .geojson file?
  • When you want to use a .geojson or .shp file to draw a simple features map, what should you do with other files that might be associated with those files when you download the data?

Statistics for Data Science & Analytics

R Markdown Quiz 31

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This R Markdown Quiz covers essential and advanced concepts in R Markdown, from basics like file formats and syntax to advanced features like caching, parameterized reports, and debugging. Whether you are a beginner or an experienced user, these questions will challenge your understanding of:

  • Core concepts: What R Markdown is, its file format (.Rmd), and reproducibility.
  • Syntax & formatting: Headers (#), italics (*text*), links, and tables.
  • Code chunk options: Controlling code display (echo, eval, include).
  • Output formats: Exporting to HTML, PDF, Word, and invalid formats.
  • Advanced features: Conditional content, interactive documents (shiny, flexdashboard), caching, and custom output formats.
  • Debugging & optimization: Using knitr::opts_chunk$set() and handling knit failures.

Perfect for R programmers, data scientists, and researchers who use R Markdown for dynamic reporting! Let us start with the R Markdown Quiz now.

Please go to R Markdown Quiz 31 to view the test

Online R Markdown Quiz with Answers

  • What is R Markdown?
  • In R markdown presentations, in the options for code chunks, what command prevents the code from being repeated before results are interpreted in the final interpreted document?
  • In R markdown presentations, in the options for code chunks, what prevents the code from being interpreted?
  • Which of these file formats can you export an R Markdown file in RStudio?
  • What software program is the easiest to use to compile R Markdown files?
  • Are R Markdown reports reproducible?
  • What is the file format for an R Markdown file?
  • What symbol is used in Markdown syntax to denote a header?
  • What kind of formatting would you see if you saw Markdown syntax like this: Example Text
  • Which of these commands would insert a link like the following into a Markdown file? Google
  • Which R function is the best first choice when trying to format a table in Markdown?
  • Which of these chunk setup commands will include R output but not the code that generated the output?
  • What is the process to convert an R Markdown file to an HTML, PDF, or Microsoft Word document?
  • How can you conditionally include/exclude content in an R Markdown document based on a parameter?
  • Which package allows you to create interactive documents with R Markdown?
  • How do you cache computations to avoid re-running heavy code chunks?
  • What is the purpose of knitr::opts_chunk$set()?
  • How do you create a custom output format in R Markdown?
  • How can you debug an R Markdown document that fails to knit?
  • Which of the following is NOT a valid output format in R Markdown?

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Online R markdown Quiz with answers R Language