R Basics Online Quiz 7

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R Basics Online Quiz: The R language is a free and open-source language developed by Ross Ihaka and Robert Gentleman in 1991 at the University of Auckland, New Zealand. The R Language is used for statistical computing and graphics to clean, analyze, and graph your data. Let us start with the R Basics Online Quiz.

This quiz is about R Basics, covering the topics of R sequence operator, R objects, R Environment, and many more.

1. A sequence of integer values can be created using the operator

 
 
 
 

2. Factors in R, are used to represent the

 
 
 
 

3. Which of the following software is used for statistical analysis in R

 
 
 
 

4. R is an _____________ programming language

 
 
 
 

5. Which of the following functions can a data analyst use to get a statistical summary of their dataset?

 
 
 
 

6. What type of plot will the following code create?
ggplot(data = penguins) +
geom_point(mapping = aes(x = flipper_length_mm, y = body_mass_g))

 
 
 
 

7. In ggplot2, you can use the __________ function to specify the data frame to use for your plot.

 
 
 
 

8. A data analyst wants to create the date February 27th, 2027 using the lubridatefunctions. Which of the following are examples of code that would create this value?

 
 
 
 

9. The file “.RData” in the current R session is

 
 
 
 

10. What does CRAN stand for _________ ?

 
 
 
 

11. R was named partly after the first names of _____________ R authors?

 
 
 
 

12. R Language functionality is divided into a number of ________

 
 
 
 

13. In 1991 R Language was created by Ross Ihaka and Robert Gentleman in the Department of Statistics at the University of ____________.

 
 
 
 

14. The ______________ is your current R working environment that includes user-defined objects

 
 
 
 

15. A data analyst inputs the following command:
quartet %>% group_by(set) %>% summarize(mean(x), sd(x), mean(y), sd(y), cor(x, y)).

Which of the functions in this command can help them determine how strongly related their variables are?

 
 
 
 

16. GUI stands for

 
 
 
 

17. The R console is a tool that is used to write (insert) standard

 
 
 
 

18. Which of the following describes R Language best

 
 
 
 

19. ___________ developed R language

 
 
 
 

20. In R, an object name cannot start with

 
 
 
 

Frequently Asked Questions About R R Basics Online Quiz

R Basics Online Quiz with Answers

  • A sequence of integer values can be created using the operator
  • R is an ———— programming language
  • The ———— is your current R working environment that includes user-defined objects
  • Which of the following software is used for statistical analysis in R
  • R was named partly after the first names of ———— R authors.
  • The R console is a tool that is used to write (insert) standard
  • In R, an object name cannot start with
  • The file “.RData” in the current R session is
  • Which of the following describes R Language best
  • R Language functionality is divided into a number of ————
  • GUI stands for
  • What does CRAN stand for ————?
  • In 1991 R Language was created by Ross Ihaka and Robert Gentleman in the Department of Statistics at the University of ————.
  • ———— developed R language
  • Factors in R, are used to represent the
  • Which of the following functions can a data analyst use to get a statistical summary of their dataset?
  • A data analyst inputs the following command: quartet %>% group_by(set) %>% summarize(mean(x), sd(x), mean(y), sd(y), cor(x, y)). Which of the functions in this command can help them determine how strongly related their variables are?
  • In ggplot2, you can use the ———— function to specify the data frame to use for your plot.
  • What type of plot will the following code create? ggplot(data = penguins) + geom_point(mapping = aes(x = flipper_length_mm, y = body_mass_g))
  • A data analyst wants to create the date February 27th, 2027 using the lubridate functions. Which of the following are examples of code that would create this value?

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Important Quiz R Programming Debug 6

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The article is about “Quiz R Programming” which covers different aspects of debugging a function’s execution. Let us start with the Quiz R Programming Debug.

Please go to Important Quiz R Programming Debug 6 to view the test

Debugging is an essential skill in programming, including R programming. In R Programming Language, the debug() function in R allows the user to step through the function’s execution, line by line. At any point, we can print out values of variables or produce a graph of the results within the function.

MCQs Quiz R Programming Debug

Quiz R Programming with Answers

  • Debugging is the process of
  • Which of the following functions initiates an interactive debugging environment that allows you to step through code one expression at a time?
  • Which of the following functions allows you to temporarily insert pieces of code into other functions to modify their behavior?
  • What does the traceback() function do?
  • When should the traceback() function be called?
  • What does calling trace(“f”) for function “f( )” do
  • What is the microbenchmark package useful for?
  • What does the Rprof( ) function do?
  • What does the profvis( ) function do?
  • Which function is better for analyzing fast-running functions: profvis( ) or microbenchmark( )?
  • In R, what includes reusable functions and documentation about how to use the functions?
  • What is the name of the popular package archive dedicated to supporting R users’ authentic, validated code?
  • You want to create a vector with the values 21, 12, 39, in that exact order. After specifying the variable, what R code chunk lets you create the vector?
  • You are compiling an analysis of the average monthly costs for your company. What summary statistic function should you use to calculate the average?
  • You are working with a large data frame. It contains so many columns that they don’t all fit on the screen simultaneously. You want a quick list of all the column names to understand better what is in their data. What function should they use?
  • Where in RStudio can you find the export menu for saving plots?
  • Which of the following files in R have names that follow widely accepted naming convention rules?
  • You want to store a vector in a variable. What type of operator would you use to do this?  
  • You want to create functions, documentation, sample data sets, and code tests they can share and reuse in other projects. What should you create to help them accomplish this?  
  • A data analyst wants a high-level summary of the structure of their data frame, including the column names, the number of rows and variables, and the type of data within a given column. What function should they use?

Statistics and Data Analysis

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Backward Deletion Method Step by Step in R

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Introduction to Backward Deletion Method

With many predictor variables, one can create the most statistically significant model from the data. There are two main choices: forward stepwise regression and backward deletion method.
In Forward Stepwise Regression: Start with the single best variable and add more variables to build your model into a more complex form.

In Backward Deletion (Backward Selection) Regression: put all the variables in the model and reduce the model by removing variables until you are left with only significant terms.

Backward Deletion method (Step by Step Procedure)

Let’s start with a big model and trim it until you get the best (most statistically significant) regression model. This drop1() command can examine a linear model and determine the effect of removing each one from the existing model. Complete the following steps to perform a backward deletion. Note that the model has different R packages for the Backward and Forward Selection of predictors.

Step 1: (Full Model)

Step 1: To start, create a “full” model (all variables at once in the model). It would be tedious to enter all the variables in the model, one can use the shortcut, the dot notation.

mod <- lm(mpg ~., data = mtcars)

Step 2: Formula Function

Step 2: Let’s use the formula() function to see the response and predictor variables used in Step 1.

formula(mod)
Backward Deletion Method

Step 3: Drop1 Function

Step 3: Let’s use the drop1() function to see which term (predictor) should be deleted from the model

drop1(mod)

Step 4: Remove the Term

Step 4: Look to remove the term with the lowest AIC value. Re-form the model without the variable that is non-significant or has the lowest AIC value. The simplest way to do this is to copy the model formula in the clipboard, paste it into a new command, and edit out the term you do not want

mod1 <- lm(mpg ~ ., data = mtcars)

Step 5: Examine the Effect

Step 5: Examine the effect of dropping another term by running the drop1() command once more:

drop1(mod1)

If you see any variable having the lowest AIC value, if found, remove the variable and carry out this process repeatedly until you have a model that you are happy with.

FAQS about Backward Deletion Method in R

  1. Write a step-by-step procedure to perform the Backward Deletion Method in r.
  2. How one can examine the effect of dropping the term from the model?
  3. What is the use of the formula function term in lm() model?
  4. What is the use of drop1() function in r?

Learn more about lm() function

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