R Programming Quiz Questions and Answers 18

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This post is about “R Programming Quiz Questions and Answers”. There are 20 multiple-choice type questions from R programming covering topics related to the output of different R commands, R console, matrices, data frames, factors, vectors, R objects, and different operations on R objects. Let us start with R Programming Quiz Questions and Answers.

Online Multiple Choice Questions about R Programming Language

1. What is the output of 1:4 + 4:1

 
 
 
 

2. Which of the following function can be used to create a vector having repeated values

 
 
 
 

3. What will be the output of the following lines of code?
x <- 31
if (x %% 2 == 0){
print("X is even")
}else {
print("X is odd")
}

 
 
 
 

4. The output of the seq(1, 10, 2) is

 
 
 
 

5. What would be the output of 1:5 + 5:1.

 
 
 
 

6. The following command will find the 60th percentile for the variable $disp$

 
 
 
 

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

 
 
 
 

8. Which command will create a matrix of the first 9 numbers in 3 rows and 3 columns?

 
 
 
 

9. To perform one-way Analysis of variance, one can use function

 
 
 
 

10. In R, object name cannot start with

 
 
 
 

11. What will be the output of the following
x <- vector("numeric", length = 10); print(x)

 
 
 
 

12. What is the data type of vector $a$ if a <- c(1, “m”, FALSE)

 
 
 
 

13. The Vectors, data frames, or matrices containing decimal values can be converted to integers using

 
 
 
 

14. When we compare two vectors element by element the resultant outcome is a

 
 
 
 

15. What will be the output of the following code
X <- factor(c("m", "m", "m", "f", "m", "f", "f")
table(X)

 
 
 
 

16. The output of seq(10, 1, -2) is

 
 
 
 

17. All columns in a matrix must have the

 
 
 
 

18. What would be the output of the following code?
x <- 1:4
y <- 6:9
z <- x + y
print(z)

 
 
 
 

19. What will be the output of the following
X <- factor(c(“yes”, “yes”, “yes”, “no”, “yes”, “no”, “no”)
table(X)

 
 
 
 

20. The multiplication of two matrices $A$ and $B$ can be performed in R using the operator

 
 
 
 

R FAQS R Programming Quiz Questions and Answers

R Programming Quiz Questions and Answers

  • The output of seq(10, 1, -2) is
  • What will be the output of the following lines of code? x <- 31 if (x %% 2 == 0){ print(“X is even”) }else { print(“X is odd”) }
  • What would be the output of 1:5 + 5:1.
  • What will be the output of the following x <- vector(“numeric”, length = 10); print(x)
  • What would be the output of the following code? x <- 1:4 y <- 6:9 z <- x + y print(z)
  • What will be the output of the following X <- factor(c(“yes”, “yes”, “yes”, “no”, “yes”, “no”, “no”) table(X)
  • The output of the seq(1, 10, 2) is
  • What will be the output of the following code X <- factor(c(“m”, “m”, “m”, “f”, “m”, “f”, “f”) table(X)
  • What is the output of 1:4 + 4:1
  • R console is a tool that is used to write (insert) standard
  • The following command will find the 60th percentile for the variable $disp$
  • The multiplication of two matrices $A$ and $B$ can be performed in R using the operator
  • Which command will create a matrix of the first 9 numbers in 3 rows and 3 columns?
  • When we compare two vectors element by element the resultant outcome is a
  • Which of the following function can be used to create a vector having repeated values
  • The Vectors, data frames, or matrices containing decimal values can be converted to integers using
  • What is the data type of vector $a$ if a <- c(1, “m”, FALSE)
  • All columns in a matrix must have the
  • To perform one-way Analysis of variance, one can use function
  • In R, object name cannot start with
R Programming Quiz Questions and Answers

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Data Frames in R Language (2024)

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Data frames in R are one of the most essential data structures. A data frame in R is a list with the class “data.frame“. The data frame structure is used to store tabular data. Data frames in R Language are essentially lists of vectors of equal length, where each vector represents a column and each element of the vector corresponds to a row.

Data frames in R are the workhorse of data analysis, providing a flexible and efficient way to store, manipulate, and analyze data.

Restrictions on Data Frames in R

The following are restrictions on data frames in R:

  1. The components (Columns or features) must be vectors (numeric, character, or logical), numeric matrices, factors, lists, or other data frames.
  2. Lists, Matrices, and data frames provide as many variables to the new data frame as they have columns, elements, or variables.
  3. Numeric vectors, logical vectors, and factors are included as is, by default, character vectors are coerced to be factors, whose levels are the unique values appearing in the vector.
  4. Vecture structures appearing as variables of the data frame must all have the same length, and matrix structures must all have the same row size.

A data frame may for many purposes be regarded as a matrix with columns possibly of differing modes and attributes. It may be displayed in matrix form, and its rows and columns are extracted using matrix indexing conventions.

Key Characteristics of Data Frame

  • Column-Based Operations: R language provides powerful functions and operators for performing operations on entire columns or subsets of columns, making data analysis and manipulation efficient.
  • Heterogeneous Data: Data frames can store data of different data types within the same structure, making them versatile for handling various kinds of data.
  • Named Columns: Each column in a data frame has a unique name, which is used to reference and access specific data within the frame.
  • Row-Based Indexing: Data frames are indexed based on their rows, allowing you to easily extract or manipulate data based on row numbers.

Making/ Creating Data Frames in R

Objects satisfying the restrictions placed on the columns (components) of a data frame may be used to form one using the function data.frame(). For example:

BMI <- data.frame(
  age = c(20, 40, 33, 45),
  weight = c(65, 70, 53, 69),
  height = c(62, 65, 55, 58)
)
Creating Data frames in R manually

Note that a list whose components conform to the restrictions of a data frame may coerced into a data frame using the function as.data.frame().

Other Way of Creating a Data Frame

One can also use read.table(), read.csv(), read_excel(), and read_csv() functions to read an entire data frame from an external file.

Accessing and Manipulating Data

  • Accessing Data: Use column names or row indices to extract specific values or subsets of data.
  • Creating New Columns: Calculate new columns based on existing ones using arithmetic operations, logical expressions, or functions.
  • Grouping and Summarizing: Group data by specific columns and calculate summary statistics (e.g., mean, median, sum).
  • Sorting Data: Arrange rows in ascending or descending order based on column values.
  • Filtering Data: Select rows based on conditions using logical expressions and indexing.
# Create a data frame manually
data <- data.frame(
  Name = c("Ali", "Usman", "Hamza"),
  Age  = c(25, 30, 35),
  City = c("Multan", "Lahore", "Faisalabad")
)

# Accessing data
print(data$Age)      # Displays the "Age" column
print(data[2, ])  # Displays the second row

# Creating a new column
data$Age_Category <- ifelse(data$Age < 30, "Young", "Old")

# Filtering data
young_people <- data[data$Age < 30, ]

# Sort data
sorted_data <- data[order(data$Age), ]
data frame after manipulation

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Generic Functions in R

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The generic functions in R Language are objects that determine how the function will treat it. A generic function performs an action (or task) on its arguments specific to the class of the argument itself. A default action will be performed if an argument lacks any class attribute that is if an argument of the function has a class not catered for specifically by the generic function, a default action will be provided.

The class mechanism in R provides the facility of designing and writing generic functions in R for special purposes. For example, the generic functions in R such as

  • the plot() is used for displaying objects graphically,
  • the summary() is used for summarizing analyses of various types of objects
  • the anova() is used for comparing different statistical models
  • the print() is used to display the results of various types of objects

The Generic Functions in R can handle a large number of classes. For example, the function plot() has a default method and variants for different types of objects such as data.frame, density, factor, and many more. A complete list of Generic Functions in R can be obtained by using

methods(plot)
methods(summary)
Generic Functions in R language

The body of a Generic function in R is concise and short. For example, 

print

## Output
function (x, ...) 
UseMethod("print")
<bytecode: 0x0000029448a0aa40>
<environment: namespace:base>

From the above code, the body of the Generic Function, UseMethod indicates that this is a generic function.

Key Concepts and Characteristics

The following are key concepts and characteristics of generic functions in R.

  • Dispatch: When an object is passed to a generic function, R determines the appropriate method to execute based on the class of the object provided. This process is known as dispatch.
  • Methods: A method is a specific implementation of a generic function for a particular class of the object. It provides instructions on how the function should behave when applied to certain objects of that class.
  • Class Inheritance: R supports class inheritance, allowing methods defined for a parent class to be inherited by its child classes. This enables generic functions to work seamlessly with objects from different classes within a hierarchy.
  • Default Methods: If no method is defined for a specific class, R will look for a default method. The default method is typically defined for the generic function’s base class or a more generic class.

Benefits of Generic Functions in R

The following are some benefits of using and creating generic functions in R

  • Code Reusability: Generic functions can be used with different types of objects, reducing the need for redundant code.
  • Readability: Generic functions can improve code readability by separating the generic interface from the specific implementations.
  • Polymorphism: Generic functions allow the user to write code that can work with objects of different classes, promoting flexibility and adaptability.
  • Extensibility: New methods can be added for custom classes, making it easy to extend the functionality of generic functions.

Best Practices for Creating Generic Functions in R Language

For creating or writing generic functions, the following are the best practices to follow:

  • Give clear and descriptive names to generic functions and their methods.
  • Define methods for commonly used classes to ensure compatibility.
  • Consider using inheritance to avoid redundant code in methods for related classes.
  • Test the generic functions thoroughly to ensure they work as expected with different types of objects.

Example of Creating Generic Functions

To create/write generic functions in R, define a function with the desired name and arguments. One can then define methods for different classes using the UseMethod function within the body of a generic function. Consider the following example

gf <- function(x) {
  UseMethod("gf")
}

gf.numeric <- function(x) {
  # Method for numeric objects
  mean(x)
}

gf.character <- function(x) {
  # Method for character objects
  nchar(x)
}

In the above exemplary code, gf() is defined as a generic function. The UseMethod() function tells R to dispatch the call to the appropriate method based on the class of the argument x. The gf.numeric and gf.character methods provide specific implementations for numeric and character objects, respectively. Let us check the behaviour of the fg() function created as a generic function

x <- 1:5  # Numeric Vector

gf(x)

## Output
[1] 3

gf("statistics")

## Output
[1] 10

Learn about how to get or view the source code of a function or method.

Frequently Asked Questions About R, Generic Functions in R

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