Interpreter Design Pattern in R

1. Definition

The Interpreter Design Pattern provides a way to evaluate language grammar or expressions for particular languages. It typically involves parsing an input string and producing an output. The pattern involves breaking down a task into its constituent parts and then interpreting each part.

2. Problem Statement

Suppose you're designing a system that needs to evaluate simple mathematical expressions, like "3 + 5 - 2". Writing a unique algorithm for each possible expression would be unwieldy and impractical.

3. Solution

The Interpreter Pattern suggests modeling the domain with a set of classes representing the rules of the language. Then, you can interpret expressions by traversing the class-based model.

4. Real-World Use Cases

1. Compilers and parsers for programming languages.

2. Query language interpreters, such as SQL.

3. Formula evaluators in spreadsheets.

5. Implementation Steps

1. Define an abstract expression class/interface.

2. Implement concrete expression classes for each grammar rule.

3. Use a context class to store and access the global information.

4. Parse the expression into its concrete expressions and evaluate.

6. Implementation in R Programming

# Step 1: Abstract Expression
Expression <- setRefClass("Expression", methods = list(interpret = function(context) {}))
# Step 2: Concrete Expressions
Add <- setRefClass("Add", contains = "Expression",
  fields = list(left = "Expression", right = "Expression"),
  methods = list(
    interpret = function(context) {
      left$interpret(context) + right$interpret(context)
Subtract <- setRefClass("Subtract", contains = "Expression",
  fields = list(left = "Expression", right = "Expression"),
  methods = list(
    interpret = function(context) {
      left$interpret(context) - right$interpret(context)
Number <- setRefClass("Number", contains = "Expression",
  fields = list(value = "numeric"),
  methods = list(
    interpret = function(context) {
# Context (In this simple example, we don't need extra context as our expressions are self-contained)
# In more complex scenarios, you might use this to store variables and their values, for example.
# Client Code
expr1 <- Number$new(value = 3)
expr2 <- Number$new(value = 5)
expr3 <- Number$new(value = 2)
result = Add$new(left = expr1, right = Subtract$new(left = expr2, right = expr3))
cat("Result:", result$interpret(NULL), "\n")


Result: 6


1. We start with an abstract Expression class that has an interpret method.

2. We then define concrete classes for numbers and arithmetic operations like addition and subtraction.

3. Each concrete expression knows how to evaluate itself given a context. For numbers, the interpretation is just the number itself. For arithmetic operations, it involves interpreting the operands and then applying the operation.

4. In the client code, we construct a tree of expressions representing "3 + (5 - 2)" and then interpret the entire expression to get the result.

7. When to use?

Use the Interpreter Design Pattern when:

1. You have a language to interpret, and you can represent statements in the language as abstract syntax trees.

2. The grammar of the language is simple.

3. Efficiency is not critical.

Note: For complex grammars or when efficiency is crucial, tools like parsers and compilers are more suitable than hand-crafted interpreters using this pattern.