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What are the best practices for writing efficient OR code in R?

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Operations research (OR) is the science of applying mathematical models and methods to solve complex decision problems. R is a popular programming language for OR, as it offers a rich set of tools and packages for data analysis, optimization, simulation, and visualization. However, writing efficient OR code in R requires some best practices to ensure speed, accuracy, and readability. Here are some tips to help you improve your OR coding skills in R.

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Ankit Malik

Head of AI | Distinguished AI Engineer

1 Use vectorization and apply functions

One of the main advantages of R is its ability to perform vectorized operations, which means applying a function to multiple elements of a vector or a matrix at once, instead of using loops. Vectorization can significantly reduce the execution time and memory usage of your code, as well as make it more concise and readable. For example, instead of using a for loop to calculate the sum of squares of a vector x, you can use the built-in function sum(x^2). Similarly, you can use the apply family of functions (such as lapply, sapply, vapply, etc.) to apply a function to each element or row or column of a list or a matrix.

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Ankit Malik

Head of AI | Distinguished AI Engineer
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In my leadership role at Walmart, optimizing for efficiency in large-scale applications is paramount. Given the substantial volume of data—often billions of records—each line of code bears weight. To manage cloud costs effectively, my team and I heavily rely on the apply function, urging others to do the same. This approach minimizes resource-intensive loops, enhancing both cost-effectiveness and code clarity. In code reviews, I rigorously enforce the exclusion of inefficient loops to ensure our applications remain performant and fiscally responsible.

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2 Choose the right data structures and packages

Another way to improve your OR code in R is to choose the appropriate data structures and packages for your problem. For example, if you need to store and manipulate data in a tabular format, you can use data frames or tibbles, which are more convenient and efficient than matrices or lists. If you need to perform operations on large or sparse matrices, you can use the Matrix package, which provides specialized classes and methods for such matrices. If you need to solve optimization problems, you can use the optimx package, which offers a unified interface to various optimization algorithms and functions.

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Ankit Malik

Head of AI | Distinguished AI Engineer
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A few years ago, I led a price optimization project during my time with Oracle's data science team. The challenge was to enhance the pricing strategy for a product catalog to maximize revenue. For this project, we chose to employ the optimx package due to its versatility in handling complex optimization problems. The objective was to determine the optimal pricing strategy, accounting for various factors such as demand elasticity, competitor pricing, and seasonality. The optimx package provided a unified interface, allowing us to seamlessly experiment with different methods. Its flexibility enabled us to incorporate intricate constraints, optimizing for revenue gains and customer loyalty.

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3 Write clear and consistent code

A third best practice for writing efficient OR code in R is to write clear and consistent code that follows some style guidelines and conventions. This can help you avoid errors, bugs, and confusion, as well as make your code easier to understand and maintain. For example, you can use meaningful and descriptive names for your variables, functions, and objects, and avoid using reserved words or symbols. You can also use comments and documentation to explain the purpose and logic of your code, and use indentation and spacing to improve its readability. You can also use tools like lintr or styler to check and format your code according to some common standards.

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4 Test and debug your code

A fourth best practice for writing efficient OR code in R is to test and debug your code regularly and systematically. This can help you ensure the correctness and reliability of your code, as well as identify and fix any errors or issues that may arise. For example, you can use the assertthat package to check the validity of your inputs, outputs, and assumptions, and use the testthat package to write and run unit tests for your functions and modules. You can also use the debug or browser functions to step through your code and inspect its behavior and results, and use the traceback or print functions to trace the source of any errors or warnings.

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5 Benchmark and profile your code

A fifth best practice for writing efficient OR code in R is to benchmark and profile your code to measure and improve its performance. This can help you identify and eliminate any bottlenecks or inefficiencies that may slow down or consume too much resources from your code, as well as compare and optimize different solutions or approaches. For example, you can use the microbenchmark package to compare the execution time of different functions or expressions, and use the bench package to measure the memory usage and garbage collection of your code. You can also use the profvis package to visualize the time and memory allocation of your code, and use the Rprof or profr functions to generate detailed profiles of your code.

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6 Here’s what else to consider

This is a space to share examples, stories, or insights that don’t fit into any of the previous sections. What else would you like to add?

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What are the best practices for writing efficient OR code in R?

1

2

3

4

5

6

1 Use vectorization and apply functions

2 Choose the right data structures and packages

3 Write clear and consistent code

4 Test and debug your code

5 Benchmark and profile your code

6 Here’s what else to consider

Operations Research

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What are the best practices for writing efficient OR code in R?

1

2

3

4

5

6

1 Use vectorization and apply functions

2 Choose the right data structures and packages

3 Write clear and consistent code

4 Test and debug your code

5 Benchmark and profile your code

6 Here’s what else to consider

Operations Research

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