Pie Chart

Introduction to Pie Charts

A pie chart is a circular statistical chart that represents data as sectors of a circle, used to display the proportional relationship of different categories within a total. Each sector’s angle is proportional to the value it represents, and the entire pie represents the sum of the data.

Pie charts are particularly suitable for displaying the proportional relationships of categorical data, intuitively showing the relative importance of each part within the whole. By using different colored sectors to distinguish categories, comparing the proportions of different categories becomes simple and intuitive.

When there are fewer categories (typically no more than 5-7) and emphasis on the relationship between parts and the whole is needed, pie charts are an excellent choice. For cases with many categories, consider merging smaller proportions into an “Others” category.

Other Names: Pie Chart

Components of a Pie Chart

Basic Pie Chart

Chart Type	Basic Pie Chart
Suitable Data	Categorical data: one categorical data field, one continuous data field
Function	Display the proportional relationship of each category within the total
Data-to-Graphics Mapping	Category field maps to sector color and labels Value field maps to sector angle size The entire circle represents the data total
Suitable Scenarios	Proportion display with fewer categories (within 5-7)

Use Cases of Pie Charts

Suitable Use Cases

Example 1: Display market share distribution

The following chart shows the market share proportions of different browsers, clearly displaying the relative position of each browser in the market.

browser	value (market share)
Chrome	61.04
Safari	15.12
Edge	10.52
Firefox	7.19
Samsung Internet	2.98
Opera	3.15

import { Chart } from '@antv/g2';

const chart = new Chart({
  container: 'container',
  theme: 'classic',
});

chart.options({
  type: 'interval',
  autoFit: true,
  data: [
    { browser: 'Chrome', value: 61.04 },
    { browser: 'Safari', value: 15.12 },
    { browser: 'Edge', value: 10.52 },
    { browser: 'Firefox', value: 7.19 },
    { browser: 'Samsung Internet', value: 2.98 },
    { browser: 'Opera', value: 3.15 },
  ],
  encode: {
    y: 'value',
    color: 'browser',
  },
  transform: [{ type: 'stackY' }],
  coordinate: { type: 'theta' },
  legend: {
    color: {
      position: 'right',
      rowPadding: 5,
    },
  },
});

chart.render();

Explanation:

The browser field maps to color, distinguishing different browsers
The value field maps to angle size, representing market share
Use coordinate: { type: 'theta' } to convert bar chart to pie chart
Use transform: [{ type: 'stackY' }] to stack data

Example 2: Display budget allocation

Pie charts are very suitable for displaying budget, expenditure, and other resource allocation situations, allowing people to see the resource proportion of each project at a glance.

import { Chart } from '@antv/g2';

const chart = new Chart({
  container: 'container',
  theme: 'classic',
});

chart.options({
  type: 'interval',
  autoFit: true,
  data: [
    { category: 'R&D', value: 35 },
    { category: 'Marketing', value: 25 },
    { category: 'Sales', value: 20 },
    { category: 'Operations', value: 15 },
    { category: 'Others', value: 5 },
  ],
  encode: {
    y: 'value',
    color: 'category',
  },
  transform: [{ type: 'stackY' }],
  coordinate: { type: 'theta', outerRadius: 0.8 },
  scale: {
    color: {
      palette: 'category10',
    },
  },
  legend: {
    color: {
      position: 'right',
      rowPadding: 5,
    },
  },
  labels: [
    {
      text: (d) => `${d.category}: ${d.value}%`,
      position: 'outside',
      connector: true,
    },
  ],
});

chart.render();

Explanation:

Use external labels to display category names and percentages
connector: true adds label connection lines
outerRadius: 0.8 adjusts pie chart size to leave space for labels

Unsuitable Use Cases

Example 1: Not suitable when there are too many categories

When the number of categories exceeds 7, pie charts become difficult to read and compare. The chart below shows the population proportion of various provinces. Due to the excessive number of categories, it’s difficult to clearly compare the population data proportions of each province.

import { Chart } from '@antv/g2';

const chart = new Chart({
  container: 'container',
  theme: 'classic',
});

chart.options({
  type: 'interval',
  autoFit: true,
  data: [
    { province: 'Beijing', population: 19612368 },
    { province: 'Tianjin', population: 12938693 },
    { province: 'Hebei', population: 71854210 },
    { province: 'Shanxi', population: 27500000 },
    { province: 'Inner Mongolia', population: 24706291 },
    { province: 'Liaoning', population: 43746323 },
    { province: 'Jilin', population: 27452815 },
    { province: 'Heilongjiang', population: 38313991 },
    { province: 'Shanghai', population: 23019196 },
    { province: 'Jiangsu', population: 78660941 },
    { province: 'Zhejiang', population: 54426891 },
    { province: 'Anhui', population: 59500468 },
    { province: 'Fujian', population: 36894217 },
    { province: 'Jiangxi', population: 44567797 },
    { province: 'Shandong', population: 95792719 },
    { province: 'Henan', population: 94029939 },
    { province: 'Hubei', population: 57237727 },
    { province: 'Hunan', population: 65700762 },
    { province: 'Guangdong', population: 104320459 },
    { province: 'Guangxi', population: 46023761 },
    { province: 'Hainan', population: 8671485 },
    { province: 'Chongqing', population: 28846170 },
    { province: 'Sichuan', population: 80417528 },
    { province: 'Guizhou', population: 34748556 },
    { province: 'Yunnan', population: 45966766 },
    { province: 'Tibet', population: 3002165 },
    { province: 'Shaanxi', population: 37327379 },
    { province: 'Gansu', population: 25575263 },
    { province: 'Qinghai', population: 5626723 },
  ],
  encode: {
    y: 'population',
    color: 'province',
  },
  transform: [{ type: 'stackY' }],
  coordinate: { type: 'theta' },
});

chart.render();

Explanation: In such cases, we recommend using bar charts.

Example 2: Not suitable when category proportions are similar

In the chart below, different game genres have similar sales volumes with very small angular differences, making it difficult to visually distinguish size relationships quickly.

import { Chart } from '@antv/g2';

const chart = new Chart({
  container: 'container',
  theme: 'classic',
});

chart.options({
  type: 'interval',
  autoFit: true,
  data: [
    { genre: 'Sports', sold: 15000 },
    { genre: 'Strategy', sold: 14900 },
    { genre: 'Action', sold: 15050 },
    { genre: 'Shooter', sold: 13000 },
    { genre: 'Other', sold: 13900 },
  ],
  encode: {
    y: 'sold',
    color: 'genre',
  },
  transform: [{ type: 'stackY' }],
  coordinate: { type: 'theta', outerRadius: 0.8 },
  legend: {
    color: {
      position: 'bottom',
      layout: { justifyContent: 'center' },
    },
  },
  labels: [
    {
      text: (d, i, data) => {
        const total = data.reduce((acc, curr) => acc + curr.sold, 0);
        const percent = ((d.sold / total) * 100).toFixed(2);
        return `${percent}%`;
      },
      style: {
        fontSize: 10,
      },
    },
  ],
});

chart.render();

Explanation: In the chart above, each category accounts for around 20% with very small differences, making it unsuitable for pie charts. In this case, bar charts would be more appropriate.

Extensions of Pie Charts

Rose Chart (Nightingale Chart)

Rose charts use the radius rather than the angle of sectors to represent values, suitable for displaying categorical data with large value differences.

import { Chart } from '@antv/g2';

const chart = new Chart({
  container: 'container',
  theme: 'classic',
});

chart.options({
  type: 'interval',
  autoFit: true,
  data: [
    { month: 'Jan', value: 120 },
    { month: 'Feb', value: 200 },
    { month: 'Mar', value: 150 },
    { month: 'Apr', value: 80 },
    { month: 'May', value: 70 },
    { month: 'Jun', value: 110 },
    { month: 'Jul', value: 130 },
    { month: 'Aug', value: 80 },
    { month: 'Sep', value: 140 },
    { month: 'Oct', value: 160 },
    { month: 'Nov', value: 180 },
    { month: 'Dec', value: 190 },
  ],
  encode: {
    x: 'month',
    y: 'value',
    color: 'month',
  },
  coordinate: { type: 'polar' },
  scale: {
    x: { padding: 0 },
    color: {
      palette: 'rainbow',
    },
  },
  axis: {
    x: { grid: true },
    y: { tickCount: 5, grid: true },
  },
  legend: false,
});

chart.render();

Explanation:

Use coordinate: { type: 'polar' } to create polar coordinate system
Values map to radius, months map to angle
Suitable for displaying periodic data or cases with large value differences

Radial Jitter Scatter Plot

Radial jitter scatter plots map traditional pie chart data to points in a polar coordinate system, adding radial jitter to avoid overlapping, suitable for displaying the distribution of categorical data.

import { Chart } from '@antv/g2';

const chart = new Chart({ container: 'container' });

chart.options({
  type: 'point',
  autoFit: true,
  data: {
    type: 'fetch',
    value: 'https://gw.alipayobjects.com/os/antvdemo/assets/data/diamond.json',
  },
  encode: { x: 'clarity', color: 'clarity' },
  transform: [{ type: 'jitter' }],
  coordinate: { type: 'polar' },
  legend: false,
});

chart.render();

Explanation:

Data points are distributed by angle within a circular area, simulating pie chart sector distribution
Radial jitter (random offset) is added to avoid data point overlapping
Point size maps to value size, color distinguishes categories
Suitable for displaying spatial distribution and numerical relationships of categorical data

Comparing Pie Charts to Other Charts

Pie Charts and Donut Charts

Pie charts are the most basic form, suitable for simple proportion displays
Donut charts can display additional information in the center, utilizing space more efficiently

Pie Charts and , and Rose Charts

Pie charts display static proportion relationships, suitable for data snapshots at a certain time
Rose charts are suitable for displaying the distribution of categorical data, especially when there are many categories