React vs Angular for Data Analysts A Practical Comparison

-- Whatever the front end, the analyst's main job is the data
SELECT
  DATE_TRUNC('month', order_date) AS month,
  category,
  SUM(amount)                     AS revenue
FROM orders
WHERE order_date >= DATE '2026-01-01'
GROUP BY 1, 2
ORDER BY 1, 2;

-- Export this result as JSON or CSV for a chart,
-- or connect it directly to a BI tool.

Do I need React or Angular as an analyst?

[ ] Charts must live inside a web product, not a BI tool
[ ] The design must match a custom brand exactly
[ ] Users need interactions BI tools cannot do
[ ] My team already builds web apps and will maintain it

0-1 ticks: stay with Power BI / Tableau / Excel
2+ ticks: learn React basics + a chart library
Angular only if your organisation already uses it

// npm install recharts
import { LineChart, Line, XAxis, YAxis, Tooltip, CartesianGrid } from "recharts";

const data = [
  { month: "Jul", revenue: 42 },
  { month: "Aug", revenue: 48 },
  { month: "Sep", revenue: 45 },
  { month: "Oct", revenue: 61 },
];

export default function RevenueChart() {
  return (
    <LineChart width={480} height={260} data={data}>
      <CartesianGrid strokeDasharray="3 3" />
      <XAxis dataKey="month" />
      <YAxis label={{ value: "Revenue (lakh)", angle: -90, position: "insideLeft" }} />
      <Tooltip />
      <Line type="monotone" dataKey="revenue" stroke="#0B6E75" />
    </LineChart>
  );
}

// npm install chart.js ng2-charts
// app.config.ts: providers: [provideCharts(withDefaultRegisterables())]
import { Component } from "@angular/core";
import { BaseChartDirective } from "ng2-charts";
import { ChartConfiguration } from "chart.js";

@Component({
  selector: "app-revenue-chart",
  standalone: true,
  imports: [BaseChartDirective],
  template: `<canvas baseChart [data]="chartData" type="line"></canvas>`,
})
export class RevenueChartComponent {
  chartData: ChartConfiguration<"line">["data"] = {
    labels: ["Jul", "Aug", "Sep", "Oct"],
    datasets: [{ data: [42, 48, 45, 61], label: "Revenue (lakh)" }],
  };
}

<!-- Embedding a published report in a web page (illustrative).
     Use your BI tool's official embed method and security settings;
     private data needs authenticated embedding, not public links. -->
<div class="report-box">
  <iframe
    title="Monthly sales report"
    src="https://your-bi-tool.example/embed/report-id"
    width="100%" height="520" frameborder="0"
    allowfullscreen>
  </iframe>
</div>

# pip install fastapi uvicorn pandas
import pandas as pd
from fastapi import FastAPI

app = FastAPI()

@app.get("/api/revenue")
def revenue():
    df = pd.read_csv("monthly_revenue.csv")   # month, revenue
    return df.to_dict(orient="records")

# A React or Angular chart fetches /api/revenue.
# The analyst owns the numbers; developers own the page.

Score 1-5                        | BI tool | Embedded BI | React | Angular
---------------------------------|---------|-------------|-------|--------
Speed to deliver                 |         |             |       |
Fits users' workflow             |         |             |       |
Design control needed            |         |             |       |
Maintenance effort (higher=easy) |         |             |       |
Team skills available            |         |             |       |
TOTAL                            |         |             |       |

If you are an analyst considering front-end skills:

1. Master SQL, Excel and one BI tool first
2. Learn chart design principles
3. Learn basic JavaScript (arrays, objects, fetch)
4. Try React with one chart library
5. Build one custom chart page from your own API

Angular: learn only if your workplace uses it.

Chart specification (share with developers)

Question answered: ______________________
Chart type: line / bar / scatter / table
X axis: ________  Y axis (with units): ________
Filters: ________
Data source / API: ________
Refresh frequency: ________
Who can see it: ________
Definition of each metric: ________