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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Important Python MCQs Test 4

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The post is about the Python MCQS test with Answers. There are 20 multiple-choice questions from Pandas, Data Frame, Python data structures (such as lists, tuples, strings, and dictionaries, etc.), Python Editors, and Functions. Let us start with the Python MCQS Test.

Online Multiple-Choice Questions about Python Programming Language

1. How are the keys obtained as a list from a dictionary word_list?

 
 
 
 

2. Which command will grab the last few rows of a dataframe?

 
 
 
 

3. How do you convert a set into a list?

 
 
 
 

4. In the pandas drop method we have a parameter called inplace, what is it used for?

 
 
 
 

5. Which of the following statements accurately describe Python lists?

 
 
 
 

6. Jupyter server supports only Python.

 
 

7. What is the difference between the union and intersection of two sets in Python?

 
 
 
 

8. Why are functions important?

 
 
 
 

9. What is the first element of “I Love Python”.split()?

 
 
 
 

10. If you want to save data to a file, which of the following libraries should you use?

 
 
 
 

11. Which Python feature enables data professionals to define code once, and then use it many times without having to rewrite it?

 
 
 
 

12. What datatype would the following variable have main_data=read.csv(“path/to/myfile.csv”)?

 
 
 
 

13. A data professional is working with a pandas dataframe. They want to select a subset of rows and columns by index. What method can they use to do so?

 
 
 
 

14. How do you remove a key-value pair from a dictionary in Python?

 
 
 
 

15. Jupyter Notebook is an open-source ————– for creating and sharing documents containing live code, mathematical formulas, visualizations, and text.

 
 
 
 

16. Like Java, a function can be defined anywhere in a Python program.

 
 

17. Lines of code that begin with a ———— serve as comments and don’t get executed.

 
 
 
 

18. How is a definition stored in a dictionary, where ‘word’ is the key?

 
 
 
 

19. What Python data structure allows for the return of multiple items from a function?

 
 
 
 

20. What function(s) would print out the total missing data values for all columns of the dataframe main_data?

 
 
 
 

Python MCQs Test with Answers

Python MCQS Test with Answers
  • Like Java, a function can be defined anywhere in a Python program.
  • In the pandas drop method we have a parameter called inplace, what is it used for?
  • What is the difference between the union and intersection of two sets in Python?
  • How do you convert a set into a list?
  • How do you remove a key-value pair from a dictionary in Python?
  • How is a definition stored in a dictionary, where ‘word’ is the key?
  • How are the keys obtained as a list from a dictionary word_list?
  • What Python data structure allows for the return of multiple items from a function?
  • Jupyter server supports only Python.
  • What is the first element of “I Love Python”.split()?
  • Why are functions important?
  • Jupyter Notebook is an open-source ————– for creating and sharing documents containing live code, mathematical formulas, visualizations, and text.
  • Which Python feature enables data professionals to define code once, and then use it many times without having to rewrite it?
  • Which of the following statements accurately describe Python lists?
  • What datatype would the following variable have main_data=read.csv(“path/to/myfile.csv”)?
  • What function(s) would print out the total missing data values for all columns of the dataframe main_data?
  • Which command will grab the last few rows of a dataframe?
  • A data professional is working with a pandas dataframe. They want to select a subset of rows and columns by index. What method can they use to do so?
  • If you want to save data to a file, which of the following libraries should you use?
  • Lines of code that begin with a ———— serve as comments and don’t get executed.
Python MCQs Test with Answers
Frequently Asked Questions About R

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Frequency Table in R: Factors Variable

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Recall that in R language a factor is a variable that defines a partition into groups. A single factor variable can be used to create a simple frequency table in R, while a pair of factors can be used to define a two-way cross-classification (contingency or frequency distribution). For this purpose, the table() function allows to creation of frequency tables. The frequency table is calculated from equal length factors.

Frequency Table in R of Categorical/ Group/ Factor Variable

We will use the “mtcars” dataset. For the variable $gear$, let us create a frequency table using the table() function. The table() function will count the gear code for each entry in the data vector. For example,

attach(mtcars)

freq <- table(gear)
freq
frequency table using factor

The freq object will give a table of frequencies of each gear code in the sample. It is important to note that, the frequencies are ordered and labeled by the levels attribute of the factor.

Frequency Distribution of a Continuous Variable

One can also create a frequency distribution table for a continuous variable. Suppose from the mtcars data set, we are interested in creating a frequency table of $mpg$ variable. For this purpose, first, we need to define the cut points or bins to define the classes/groups of the frequency table. For example,

cut(mpg, 10+5*(0:5))

## Output
(10,15] (15,20] (20,25] (25,30] (30,35] 
      6      12       8       2       4

The cut() function is used to split the continuous data vector into groups. The groups are defined by creating a sequence of values using 10+5*(0:5), that is

10+5*(0:5)

## Output
10 15 20 25 30 35

The cut() function, cuts and counts the occurrence of each observation of mpg regarding the cut points created using breaks = 10+5*(0:5). The frequency table will be

frequency table of a continuous variable

Creating Graph of Frequency Table

For the frequency table created above, one can easily create different graphical representations, such as pie charts and bar plots of the frequency table. For example,

freq<-table(cut(mpg, 10+5*(0:5)))
pie(freq)
hist(freq)
barplot(freq)
plot(freq)
Bar plot frequency table in R
bar plot in R language
pie chart in frequency table in R language

Note that: for a $k$ factor argument, the result is a $k$-way array of frequencies.

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