File Activity Heatmaps: Nivo Vs. Recharts Comparison

by SLV Team 53 views
File Activity Heatmaps: Nivo vs. Recharts Comparison

Overview

Guys, we need to dive deep into different heatmap chart implementations to visualize file activity patterns, such as files touched and modification frequency, within the Continue codebase. This exploration will help us pinpoint hotspots and understand patterns in our development workflow.

Requirements

Here's what we need to nail down:

  • Create Storybook stories for comparing various heatmap implementations.
  • Support displaying file paths on the Y-axis and time periods (days/weeks) on the X-axis. We must also ensure that the Storybook stories created can handle the different data densities.
  • Color intensity should accurately represent the frequency of changes.
  • The solution must efficiently handle 100+ files.
  • Include interactive tooltips that show exact counts.
  • Ensure a responsive design for usability across devices.

Libraries to Evaluate

Let's consider these libraries:

1. @nivo/heatmap

  • Pros: Built-in animations, rich interactivity, and a professional appearance make @nivo/heatmap a strong contender. This will allow a better user experience, giving our users a more interactive way to visualize the data.
  • Cons: The larger bundle size could be a drawback. Despite the pros offered by @nivo/heatmap, this is still a factor that needs to be considered.
  • Story variants needed: We'll need stories covering basic usage, custom colors, legends, and dense data scenarios (100+ files). Showcasing a variety of cases will give us a clear comparison of the use cases.

2. Recharts

  • Pros: Recharts is lightweight, flexible, and already used for other charts in our project. Leveraging what we already know could save time. In addition, the lightweight nature ensures that Recharts does not weigh down the project.
  • Cons: No native heatmap component means we'll need a custom implementation. Creating a custom implementation for a basic chart could be challenging.
  • Story variants needed: We'll explore a ScatterChart approach and a custom grid component. These stories will help us determine the feasibility of a custom solution. Evaluating the different Story variant implementations will give our team insights on choosing this library.

3. visx/heatmap (optional)

  • Pros: Being D3-based, visx/heatmap offers high customizability.
  • Consider if: the above options don't meet our needs. Customization is key here, as we consider if the current options do not meet our requirements.

Data Structure Examples

// Example data structure for file activity
const fileActivityData = {
 weekly: [
 { file: 'src/index.ts', week1: 12, week2: 8, week3: 15, week4: 23 },
 { file: 'src/components/Button.tsx', week1: 5, week2: 18, week3: 7, week4: 9 }
 ],
 daily: [
 { file: 'src/index.ts', mon: 3, tue: 8, wed: 12, thu: 4, fri: 9 },
 { file: 'src/utils/api.ts', mon: 15, tue: 2, wed: 7, thu: 11, fri: 6 }
 ]
}

Proposed Storybook Structure

stories/
 visualizations/
 FileHeatmap/
 NivoHeatmap.stories.tsx
 RechartsHeatmap.stories.tsx
 FileHeatmap.utils.ts (shared utilities)
 mockData.ts

Acceptance Criteria

Let's make sure we hit these marks:

  • [ ] Create at least 3 story variants per library showing different data densities. This ensures we get a good look at how each library handles varying amounts of information and the different story variants.
  • [ ] Include a performance comparison with 100+ files. We need to know how these libraries perform under load.
  • [ ] Document the pros and cons of each approach in the story descriptions. Full transparency is important for making informed decisions.
  • [ ] Add controls for color schemes, time ranges, and data density. Flexibility is key, so we need to be able to tweak these parameters.
  • [ ] Include an example of real file activity data from Git history. This will give us a realistic view of how the heatmaps will look in practice.
  • [ ] Provide recommendations for production use. Let's give clear guidance on which library is best suited for our needs.

Implementation Tasks

Here's the plan of attack:

  • [ ] Set up a new Storybook category for data visualizations. Organization is crucial!
  • [ ] Install the required dependencies (@nivo/heatmap, etc.). Time to get the tools we need.
  • [ ] Create a mock data generator for different scenarios. This will allow us to test the heatmaps with various data sets.
  • [ ] Implement Nivo heatmap stories. Let's get Nivo up and running.
  • [ ] Implement Recharts heatmap stories. Time to see what Recharts can do.
  • [ ] Add performance monitoring/comparison. Keep an eye on how these libraries perform.
  • [ ] Document findings in story descriptions. Write down what we learn along the way.
  • [ ] Create utils for transforming Git data to heatmap format. We need to massage the data into the right shape.

Questions to Answer

Let's get these questions answered:

  1. Which library performs best with 100+ files? Performance is key, guys.
  2. Which provides the best mobile experience? Gotta make sure it looks good on phones too.
  3. What's the bundle size impact of each option? We don't want to bloat our application.
  4. Which is easiest to integrate with our existing design system? Integration should be as smooth as possible.
  5. What are the accessibility considerations for each? Accessibility is important to consider.

Accessibility Considerations

  • Color Contrast: Ensure sufficient color contrast for users with visual impairments. Use tools to check contrast ratios and adjust color schemes accordingly. Also, aim for a color palette that is colorblind-friendly to accommodate the needs of all users.
  • Keyboard Navigation: Implement keyboard navigation to allow users to navigate the heatmap using the keyboard. Make sure each interactive element is accessible via tab and that focus states are clearly visible.
  • Screen Reader Compatibility: Provide alternative text descriptions for each cell in the heatmap to convey the data to screen reader users. Use ARIA attributes to enhance the accessibility of custom components and ensure they are properly announced by screen readers.
  • Zoom and Responsiveness: Ensure the heatmap is responsive and adapts well to different screen sizes and zoom levels. Test the heatmap at various zoom levels to ensure readability and usability.
  • Interactive Elements: Make sure interactive elements like tooltips and legends are also accessible. Tooltips should be dismissable with the keyboard, and legends should be structured in a way that is easy to understand for screen reader users.
  • Semantic HTML: Use semantic HTML elements whenever possible to provide a clear structure for the heatmap. This helps screen readers and other assistive technologies understand the content and purpose of the heatmap.

References

Notes

This is an exploratory task to evaluate different approaches before implementing a production solution. The goal is to create comprehensive Storybook examples that can inform our decision-making process. Let's make sure we create awesome Storybook examples, everyone!