Vue Data UI

Abstract

1. Introduction

A user-empowering data visualization Vue component library for eloquent data storytelling.

2. Objective

An open source user-empowering data visualization Vue 3 components library for eloquent data storytelling

This project demonstrates how Vue, Vue.js can be applied to a real-world Data Science problem.

3. Key Features / Modules

• config.debug (default: false) set to true to show warning messages during development, turn off for production.
• smart label resizing and auto-rotating features
• more charts support responsive mode
• config event callbacks

4. Technology Stack

5. System Requirements

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

• Node.js (LTS) and npm
• Git (to clone the repository)

6. Installation & Setup

git clone https://github.com/graphieros/vue-data-ui.git
cd vue-data-ui

1. VueUiIcon
2. VueUiPattern

npm i vue-data-ui

import { createApp } from 'vue';
import App from './App.vue';
// Include the css;
import 'vue-data-ui/style.css';

// You can declare Vue Data UI components globally
import { VueUiRadar } from 'vue-data-ui';

const app = createApp(App);

app.component('VueUiRadar', VueUiRadar);
app.mount('#app');

<script setup>import {(VueUiRadar, VueUiXy)} from "vue-data-ui";</script>

<script setup>
import { ref } from "vue";
import { VueDataUi } from "vue-data-ui";
// Include the css;
import "vue-data-ui/style.css";

const config = ref({...});
const dataset = ref([...]);

</script>

<template>

  <VueDataUi
    component="VueUiXy"
    :config="config"
    :dataset="dataset"
  />

</template>

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 graphieros 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.