R Language Frequently Asked Questions - Statistical Computing Graphics in R

Data Frames in R Language (2024)

October 25, 2024 by Muhammad Imdad Ullah

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:

The components (Columns or features) must be vectors (numeric, character, or logical), numeric matrices, factors, lists, or other data frames.
Lists, Matrices, and data frames provide as many variables to the new data frame as they have columns, elements, or variables.
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.
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)
)

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), ]

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Generic Functions in R

October 18, 2024 by Muhammad Imdad Ullah

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)

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

October 15, 2024October 15, 2024 by Muhammad Imdad Ullah

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.

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.

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Table of Contents

Restrictions on Data Frames in R

Key Characteristics of Data Frame

Making/ Creating Data Frames in R

Other Way of Creating a Data Frame

Accessing and Manipulating Data

Key Concepts and Characteristics

Benefits of Generic Functions in R

Best Practices for Creating Generic Functions in R Language

Example of Creating Generic Functions

Python MCQs Test with Answers