React Native Chart Kit

Beautiful charts for React Native. Line, area, bar, pie, donut, progress, and contribution heatmaps for dashboards, reports, and data-rich mobile apps.

Data ScienceTypeScriptMIT

Abstract

React Native Chart Kit is an open-source Data Science project. Beautiful charts for React Native. Line, area, bar, pie, donut, progress, and contribution heatmaps for dashboards, reports, and data-rich mobile apps. It is built using TypeScript, React Native, Android. The complete source code is publicly available on GitHub under the MIT License, making it a useful reference for students building a Data Science mini project or final-year project.

1. Introduction

Beautiful charts for React Native. Line, area, bar, pie, donut, progress, and contribution heatmaps for dashboards, reports, and data-rich mobile apps.

The root import stays available for legacy screens. New screens should use react-native-chart-kit/v2.

line, area, bar, pie, donut, progress, and contribution heatmap.

2. Objective

Beautiful charts for React Native. Line, area, bar, pie, donut, progress, and contribution heatmaps for dashboards, reports, and data-rich mobile apps.

This project demonstrates how TypeScript, React Native, Android can be applied to a real-world Data Science problem.

4. Technology Stack

TypeScriptReact NativeAndroid

5. System Requirements

General requirements for this technology stack — check the README for exact versions.

  • Node.js (LTS) and npm / yarn / pnpm
  • VS Code or any code editor
  • Git (to clone the repository)

6. Installation & Setup

git clone https://github.com/chart-kit/react-native-chart-kit.git
cd react-native-chart-kit
npm install react-native-chart-kit react-native-svg
npm install react-native-chart-kit
npx expo install react-native-svg

Full setup instructions are in the project README.

7. Future Enhancements

Suggested extensions you can add to make this your own project.

  • Turn the analysis into an interactive dashboard
  • Automate data refresh with a scheduled job
  • Add a predictive model on top of the analysis

8. Viva / Review Questions

Common questions examiners ask for projects in this domain.

  1. What is the source of the dataset and how was missing data handled?
  2. Which exploratory analysis steps revealed the most useful insight?
  3. Why were these particular charts chosen to present the data?
  4. Which statistical or ML technique supports the conclusions?
  5. How could the analysis be automated or refreshed with new data?

9. Source Code & License

This project is developed by chart-kit and published on GitHub under the MIT License. Please follow the license terms and credit the original author when you use or modify this code.

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