Dynamic Vs Lexical Scoping

Introduction

R is an open-source programming language that is used widely as a data analysis tool and statistical software. It generally comes with a command-line interface. R is used by many platforms like Linux, Windows, and macOS. The value associated with the free variable in the function is determined by the scoping rules of the language.

What is Scoping in R?

The scope is defined as the area or the location where the variable is defined or can be accessed when it is needed. There are two types of variable scopes:

• Global variable scope:
  Variables that are defined outside the function. In R <<- operator is used to create a global variable.
• Local variable scope:
  Variables that are created inside the function. It can be defined by using the <- or = operator.

There are two types of scoping mechanisms:

• Dynamic scoping and
• Lexical scoping

Let's discuss in brief dynamic vs lexical scoping in r in this article.

Dynamic Scoping in R

Dynamic scoping is a mechanism that is used by different programming languages like Bash, Perl, Lisp, etc., but it is not such a popular mechanism. In this mechanism, the scope of the variable is independent of the structure of the program; it depends on the order in which the function is called. According to dynamic scoping, the value of the variable should be changed to the latest assigned value. Let's say we have a variable a and its value as 10; then we update the variable a and assign it to the value 20. Now whenever the variable a is called, it should return the value 20. So whenever there is a reference call for the variable, the scope firstly searches for the recently called function that contains the variable. Until the variable is found or the scope reaches the global scope, this process is repeatedly going on.

Code

Output:

Explanation:

1. x <- 5, variable x is created and assigns the value 5 to it.
2. fun1 function is defined without any arguments.
3. fun1 function prints the value of x.
4. fun2 function is defined without any arguments
5. Inside the fun2 function, fun1 is called.
6. fun3 is defined without any arguments.
7. Inside the fun3, x is declared as a global variable that holds the value 6.
8. Now, the fun2 inside the fun3 function is called.
9. Finally, fun3 is called.

When fun3 is called, it goes to the function and finds the value of x, and fun2 is called. Then it goes inside the fun2 function and finds that fun1 is called inside it. The fun1 function prints the value of x as 6. This is because fun3 is called first, and inside the function variable, x is declared as global using the <<- operator is used, So the value of x changes from 5 to 6. The reason behind declaring the x as a global variable is that, in dynamic scoping, the latest assigned value is taken, and the latest assigned value to x in the above code is 6. Dynamic scoping is not supported by R, so this is done.

Lexical Scoping in R

Lexical Scoping is the mechanism used by different programming languages. It is also used by the R programming language. In R programming language, lexical Scoping is the default scoping method. There is a concept of a free variable in the R language. In the environment where the function was defined, the values for these free variables are searched. The collection of values, symbols, and pairs is called an environment. In every environment, there is a parent class. An environment can have multiple children but an environment without a parent is considered an empty environment. If the value of the variable of the symbol is not found in the current environment where the function is called, then it continues its search in the parent environment. Lots of programming languages use lexical scoping nowadays. Humans can also identify the scope of the variable just by reading the code.

Key Differences Between Dynamic and Lexical Scoping

Below is the table representing dynamic vs lexical scoping in r.

Advantages and Disadvantages of Dynamic Scoping

Pros of Dynamic Scoping

• As access to the variable can be possible from any part of the program makes it much more flexible to use.
• Accessing the variable can be done from anywhere makes writing reusable code easier.
• With the help of dynamic scoping, debugging some types of codes, like codes with complex control flow or recursive functions, can be easier. The reason for this is that the variables can be accessed from anywhere in the program, which makes it easy to keep track of their values and behavior.
• It makes it possible for code to modify more easily in response to changing conditions or demands. This is because, in the program, the variables can be accessed from anywhere, which allows much flexibility in variable scoping freedom.

Cons of Dynamic Scoping

• It is not easy to reason about the scope of variables in a program as the scope of the variable is determined at its runtime.
• The variable's scope is determined at runtime makes execution slower because it requires more searches.
• Variables are accessed from unknown places, so it can lead to more errors.

Choosing the Right Scoping Mechanism for Your Needs

Let's discuss some of the considerations between dynamic vs lexical scoping in r to choose the right mechanism according to your needs.

Considerations for Dynamic Scoping

• The value of a variable does not depend on its position in the program. It is determined by the current scope or environment.
• Tracking and predicting the values of variables is not easy because they can change based on the current scope or environment.
• By changing one part of the variable of the code can affect the behavior of the whole code, so dynamic scoping can lead to unexpected errors.
• It is very important to set up the correct execution context before using code that depends on dynamic scoping.

Considerations for Lexical Scoping

The lexical scoping value of the variable depends on the nearest block. There are some considerations for the lexical scoping in R language are:

• Vaiables of the program can only be accessible within the blocks of the function where they are defined.
• Functions or nested blocks can also access functions from their outer scope.
• There is an inner scope that has the same name as the variable in the outer scope, which confuses, so be aware of this variable shadowing.
• Global scope refers to variables that are accessible from anywhere in the code. On the other hand, module scope allows sharing of variables across different files or modules.
• Lexical scoping supports closures, which are the functions that can remember and access variables from their defining scope even after that scope has completed execution.
• By understanding the rules of the scoping meaning of the variables can also be clear, which makes the code easier to understand and maintain.

Best Practices for Scoping in R

• Limit Global Variables:
  Try not to create too many variables which can be accessed from anywhere in your code. It is better to keep variables within functions or packages to avoid naming conflicts between them and make code easier to understand.
• Use Function Arguments and Return Values:
  Pass data and parameters to functions using the return values and arguments. Each function has its scope and does not depend on global variables, which helps modular and reusable code.
• Be mindful of variable names:
  Try to choose the names of variables that are clear and to avoid confusion. Define the variables with different names in the different scopes to avoid shadowing.
• Understand Lexical Scoping:
  R follows lexical scoping, which means that variables are resolved based on their position in the code. Make sure to define variables within the appropriate scope and take advantage of nested scopes.
• Consider R Packages:
  Prefer creating R packages when working on larger projects or sharing your code, consider creating R packages. Packages provide a structure that organizes functions and data, allowing for explicit scoping and reducing naming conflicts.
• Document Variable Scope:
  The definitions of variables and the purposes for which they are to be used must be clear. This makes it easier for people to understand the range and function of variables, especially when using global variables.
• Embrace Functional Programming:
  Use the concept of functional programming, like mutable variables and avoiding side effects. It makes the code more predictable and reduces scoping-related issues.

Comparison in Practice: Examples and Use Cases

Example: Variable Access in Nested Functions

Code:

Output:

Explanation:

• Lexical Scoping:
  In lexical_inner(), the variable x is accessed from its immediate outer scope, which is the lexical_example() function. The inner x (with a value of 5) shadows the outer x, so the output is 5.
• Dynamic Scoping:
  R does not natively support dynamic scoping, but in other programming languages that do, the output of dynamic_example() would be the value of x in the current execution context (which could be a variable defined outside the function).

Use Cases of Lexical Scoping:

• Lexical scoping is widely used in programming languages like R and Python.
• It helps in organizing code by limiting the visibility of variables to their defined scope.
• Lexical scoping allows for encapsulation, reducing the risk of variable name conflicts and making code easier to read and maintain.
• Functions can access variables from their enclosing function or global scope, promoting modularity and reusable code.

Example: Closures Code:

Output:

Explanation:

In this code example, make_multiplier() creates closures that "remember" the factor parameter from their defining scope. Each closure, multiply_by_2 and multiply_by_3, multiplies its input x with the corresponding factor.

Use Cases of Lexical Scoping:

• Closures are a powerful use case of lexical scoping.
• They allow functions to maintain state and remember values from their defining environment, making them useful for creating factory functions or callbacks.