Shiny applications are great for turning data into interactive dashboards and web apps, making data exploration and visualization more engaging. But even the most visually appealing Shiny app can suffer from performance issues. Lagging visualizations, delayed user interactions, and slow data updates can quickly turn a promising Shiny app into a frustrating user experience.

With increasing amounts of data, your app can become slow. This step-by-step guide will walk you through benchmarking how much memory your app is using.

That’s where profiling comes in. Profiling helps you figure out which parts of your code are slowing things down. By identifying these bottlenecks, you can make the right adjustments to speed up your app and make it more enjoyable to use.

In this guide we will Discover the key tools and techniques for profiling R and Shiny code. You’ll learn how to turn the data from these tools into actionable steps to improve performance. This is just the beginning: stay tuned for more tips and real-world examples to help you optimize your Shiny apps even further.

Why do we actually need profiling?

Even the most dazzling Shiny app can become frustratingly slow if you don’t profile your codeBelow are the main reasons why profiling is an essential step in the development process:

• Exposing hidden bottlenecks: Shiny apps may seem functional on the surface, but sluggish performance can be lurking underneath. Profiling exposes these hidden bottlenecks so you can figure out the root causes of slowdowns.
• Optimization for a smooth user experience: Application performance is a fundamental determinant of a positive user experience (UX). Profiling helps you identify performance issues that hinder responsiveness and create a seamless user experience.
• Prevent premature optimization: Profiling data guides your optimization efforts. You can focus on solving problems that really matter, instead of wasting time on unnecessary code changes that don’t add much value.
• Ensure scalability as your app grows: As your Shiny app attracts more users and processes larger data sets, performance becomes even more important. Profiling helps you build a foundation for a scalable and future-proof application.

Profiling tools and techniques

Rprof

Built-in function for measuring the execution ((wall clock time), CPU usageAnd memory allocation.

professional fish

It is a valuable tool that acts as a magnifying glass, allowing you to peer into the inner workings of your code. By visualizing how different parts of your app are running, profvis helps you identify areas that may be slowing things down. It provides an interactive flame graph which visualizes the app’s call stack over time and highlights the corresponding lines of code when you click on them.

reaction log

Dive deep into reactive expressions And observer events to identify inefficiencies. Reactlog acts as a conversation monitor for your app, showing you how different parts of your code interact and respond to user input. By analyzing these conversations, Reactlog helps you identify areas where your app may be unnecessarily re-evaluating things, potentially slowing down performance. This package also provides a visual representation of the reactive dependency graph. This visualization can be invaluable in understanding the complex interactions and dependencies within your app, further helping to identify and troubleshoot performance issues.

Glossy.tictoc

Sometimes tough questions have simple answers. This package is a simple and effective tool for timing sections of your Shiny code. Just add 1 line of code and you’re done. It gives you a great starting point to begin your profiling journey.

Ryszard Szymański, author of shiny.tictoc, spoke at ShinyConf 2024 about {shiny.tictoc}. Watch the video to learn more about how to measure Shiny performance without headaches.

Client-side tools

Modern browsers come equipped with powerful developer tools that allow you to profile JavaScript execution and DOM manipulation. You’ll find them in the browser’s DevTools, in tabs like Performance, Network, Memory, Lighthouse. These tools can reveal inefficiencies in your Shiny app’s JavaScript code that might not be visible through server-side profiling alone. You can analyze network activity to identify potential issues with data transfer between the server and the client. This can be particularly useful for Shiny apps that process large amounts of data or rely on real-time updates.

How do you build a Shiny dashboard that your target users will embrace? Learn more in this blog post 9 best practices for effective Shiny dashboards.

Interpreting the Profiling Battlefield: Deciphering the Data

The profiling tools we’ve explored have provided you with a war chest of data. To truly overcome performance bottlenecks, however, you need to be able to decipher the data these tools provide. Here’s your guide to navigating the profiling battlefield:

Understanding the statistics:

• Wall Clock Time: This metric reveals the total time elapsed during code execution. Look for functions or blocks of code with disproportionately high clock times to identify potential bottlenecks.
• CPU usage: High CPU usage indicates code that is demanding a lot of processing power from your server. Focus on these areas for optimization, especially if you expect high user concurrency.
• Memory allocation: Spikes in memory allocation can signal memory-intensive operations that can lead to slowdowns or crashes. Be especially vigilant when working with large datasets.
• Evaluation number: This metric, from reactlog, shows how often a reactive expression is re-evaluated. Excessive evaluations can have a significant impact on performance. Try to minimize unnecessary re-evaluations.
• Evaluation time: This metric, from reactlog, reveals the time taken for each reactive expression evaluation. Focus on expressions with high evaluation times to identify areas for optimization.
• Flame graph: This is not just a metric, but a complete interactive visualization of your shiny app, provided by profvis, with a horizontal timeline to evaluate the timing of your app.

Identifying Enemies:

By analyzing the profile data, you can identify the enemies that hinder the performance of your Shiny app:

• Time wasters: These are sections of code that take an excessive amount of time off the clock. They can be computationally intensive functions, inefficient loops, or overly complex calculations.
• Memory hogs: Operations that cause significant spikes in memory allocation are your memory hogs. These can be functions that create large temporary data structures or process huge data sets inefficiently.
• Chatty responses: Reactive expressions that are re-evaluated frequently, especially if the evaluations are unnecessary, are your chatty reactives. They can lead to slow updates and unresponsive UIs.

Correlation of the data:

Do not analyze the data from each tool in isolation. Look for correlations between the metrics to gain a more comprehensive understanding of the bottlenecks. For example, a function with a high clock speed in Rprof may also exhibit high CPU usage, indicating a computationally intensive operation. Similarly, a chatty reactive expression in reactlog may have a correspondingly high evaluation time, further highlighting the need for optimization.

Embrace visualization:

The visual representations of profvis can be invaluable in identifying bottlenecks. Flame graphs allow you to quickly see which functions are taking the most time and how they are nested within each other. This visual perspective can often reveal inefficiencies that you might miss by simply examining the raw data.

By mastering the art of interpreting profiling data, you can turn cryptic metrics into actionable insights. These insights form the basis for the optimization strategies we’ll explore in the next section.

Completing a guide to profiling R and R Shiny Code

Profiling is the key to building fast, efficient Shiny applications. It helps you identify and fix the parts of your code that are slowing things down. Tools like Rprof, profvis, reactlog, and shiny.tictoc can identify these bottlenecks and turn complex data into clear steps for improvement. This ensures that your Shiny apps are not only visually appealing, but also responsive and scalable.

This guide is just the beginning. Stay tuned for more posts where we’ll dive into advanced optimization techniques and provide tips for building top-notch Shiny applications. Keep profiling, keep optimizing, and let’s make your Shiny apps as powerful and smooth as possible.

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The post first appeared on appsilon.com/blog/.

Read more: Revealing Bottlenecks: A Guide to Profiling R and R Shiny Code