Chapter 2 Advanced Topics in Programming
As programs grow in complexity, understanding their performance characteristics becomes essential. Writing correct code is only the first step—ensuring that it runs efficiently and scales well is equally important.
This chapter delves into advanced topics in programming, focusing on three key areas: measuring program performance, analyzing program complexity, and integrating SQL programming with R.
Measuring Program Performance
To optimize code, you need tools to measure execution time and resource use.
The bench package provides benchmarking, while profvis visualizes function calls to find bottlenecks. These tools help diagnose performance issues and guide improvements.
Measuring Program Complexity
Algorithmic efficiency determines how well code scales.
This section introduces Big-O notation, a framework for analyzing time and space complexity.
Understanding complexity helps in choosing or designing efficient algorithms.
Optimizing Program Performance
Good coding practices improve efficiency and maintainability.
This section covers structuring code, vectorization, pre-allocating memory, and other techniques to write optimized R programs.
SQL Programming
SQL is key for handling large datasets.
This section covers querying, filtering, and aggregating data in SQL from R. Combining SQL with R enables efficient data management and analysis.
This will only focus on SQL Query using SELECT. Also, only introduction to syntax will be discussed, with no connection to external database yet.
By the end of this chapter, you will have a deeper understanding of program efficiency, from analyzing execution time and complexity to handling large datasets effectively. These advanced skills will empower you to write faster, more scalable, and more maintainable code.