Building an Emoji Picker Component from Scratch

Building an EmojiEmoji
Palabra japonesa (絵文字) que significa 'carácter imagen' — pequeños símbolos gráficos usados en la comunicación digital para expresar ideas, emociones y objetos. Picker Component from Scratch

Emoji pickers look simple on the surface, but building one that feels fast and accessible involves decisions about data management, virtualization, skin tone state, keyboard navigation, and search. This guide covers the key architectural decisions and implementation patterns.

Data Sourcing

The foundation of any emoji picker is a structured dataset. Good options include:

UnicodeUnicode
Estándar universal de codificación de caracteres que asigna un número único a cada carácter de todos los sistemas de escritura y conjuntos de símbolos, incluidos los emoji. CLDRCLDR (CLDR)
El Common Locale Data Repository (Repositorio de datos de configuración regional común), un proyecto de Unicode que proporciona datos específicos de cada idioma, incluidos nombres y palabras clave de Data

The Common Locale Data Repository includes emoji annotations (short names, keywords) in 60+ languages. The emoji-test.txtemoji-test.txt
El archivo oficial de Unicode que lista todas las secuencias emoji con su estado de calificación, puntos de código y nombres cortos del CLDR. file from the Unicode UCD provides the canonical ordered list of all fully-qualified emoji.

# Download the emoji test file
curl -O https://unicode.org/Public/emoji/latest/emoji-test.txt

emoji-datasource

The emoji-datasource npm package provides pre-processed JSON with categories, names, skin tone variants, and platform image URLs:

npm install emoji-datasource

import data from 'emoji-datasource/emoji.json';

// Structure of each entry:
// {
//   name: "GRINNING FACE",
//   unified: "1F600",
//   short_names: ["grinning"],
//   category: "Smileys & People",
//   sort_order: 1,
//   skin_variations: {
//     "1F3FB": { unified: "1F600-1F3FB", ... },
//     ...
//   }
// }

// Build a usable array
const emojis = data.map(e => ({
  emoji: String.fromCodePoint(...e.unified.split('-').map(h => parseInt(h, 16))),
  name: e.name.toLowerCase(),
  keywords: e.short_names,
  category: e.category,
  skinVariations: e.skin_variations,
}));

Component Architecture

A minimal emoji picker has these layers:

EmojiPicker
├── SearchBar
├── CategoryNav (tabs or sidebar)
├── EmojiGrid (virtualized)
│   └── EmojiButton × N
├── SkinTonePicker
└── Preview (hovered emoji name)

React Implementation

import { useState, useMemo, useCallback, useRef } from 'react';
import { FixedSizeGrid } from 'react-window';
import emojis from './emoji-data';

const COLUMN_COUNT = 8;
const CELL_SIZE = 40;

export function EmojiPicker({ onSelect }) {
  const [query, setQuery] = useState('');
  const [skinTone, setSkinTone] = useState(null); // null = default
  const [category, setCategory] = useState('all');
  const [hovered, setHovered] = useState(null);

  const filtered = useMemo(() => {
    let list = emojis;
    if (category !== 'all') {
      list = list.filter(e => e.category === category);
    }
    if (query.trim()) {
      const q = query.toLowerCase();
      list = list.filter(e =>
        e.name.includes(q) || e.keywords.some(k => k.includes(q))
      );
    }
    return list;
  }, [query, category]);

  const rows = useMemo(() => {
    const result = [];
    for (let i = 0; i < filtered.length; i += COLUMN_COUNT) {
      result.push(filtered.slice(i, i + COLUMN_COUNT));
    }
    return result;
  }, [filtered]);

  const getEmoji = useCallback((item) => {
    if (skinTone && item.skinVariations?.[skinTone]) {
      const unified = item.skinVariations[skinTone].unified;
      return String.fromCodePoint(...unified.split('-').map(h => parseInt(h, 16)));
    }
    return item.emoji;
  }, [skinTone]);

  const Cell = useCallback(({ columnIndex, rowIndex, style }) => {
    const item = rows[rowIndex]?.[columnIndex];
    if (!item) return <div style={style} />;
    const char = getEmoji(item);
    return (
      <div style={style}>
        <button
          className="emoji-btn"
          onClick={() => onSelect(char)}
          onMouseEnter={() => setHovered(item.name)}
          onMouseLeave={() => setHovered(null)}
          aria-label={item.name}
          title={item.name}
        >
          {char}
        </button>
      </div>
    );
  }, [rows, getEmoji, onSelect]);

  return (
    <div className="emoji-picker" role="dialog" aria-label="Emoji picker">
      <SearchBar value={query} onChange={setQuery} />
      <CategoryNav value={category} onChange={setCategory} />
      <FixedSizeGrid
        columnCount={COLUMN_COUNT}
        columnWidth={CELL_SIZE}
        rowCount={rows.length}
        rowHeight={CELL_SIZE}
        height={320}
        width={COLUMN_COUNT * CELL_SIZE}
      >
        {Cell}
      </FixedSizeGrid>
      <div className="picker-footer">
        <SkinTonePicker value={skinTone} onChange={setSkinTone} />
        {hovered && <span className="preview-name">{hovered}</span>}
      </div>
    </div>
  );
}

Virtualization

A full emoji dataset has 3,700+ characters. Rendering all of them as DOM nodes simultaneously causes layout thrashing. Use a virtual list/grid:

• react-window: Lightweight, renders only visible cells
• @tanstack/virtual: Framework-agnostic, more flexible
• CSS content-visibility: auto: A CSS-only approach for simpler cases

/* CSS-only approach for moderate emoji counts */
.emoji-grid {
  display: grid;
  grid-template-columns: repeat(8, 40px);
}

.emoji-btn {
  content-visibility: auto;
  contain-intrinsic-size: 40px 40px;
}

Search Implementation

Fast local search requires pre-processing at startup:

// Pre-build a search index at initialization
const searchIndex = emojis.map(e => ({
  ...e,
  searchString: [e.name, ...e.keywords].join(' ').toLowerCase(),
}));

function searchEmoji(query) {
  if (!query.trim()) return emojis;
  const q = query.toLowerCase().trim();
  // Prioritize name matches, then keyword matches
  const nameMatches = searchIndex.filter(e => e.name.includes(q));
  const kwMatches = searchIndex.filter(
    e => !e.name.includes(q) && e.keywords.some(k => k.includes(q))
  );
  return [...nameMatches, ...kwMatches];
}

For fuzzy search, integrate a library like fuse.js:

import Fuse from 'fuse.js';

const fuse = new Fuse(emojis, {
  keys: ['name', 'keywords'],
  threshold: 0.3,
  includeScore: true,
});

function fuzzySearch(query) {
  return fuse.search(query).map(r => r.item);
}

Skin Tone State

Skin tone preferences should persist across sessions:

const SKIN_TONE_KEY = 'emoji-picker-skin-tone';

// Fitzpatrick modifiers
const SKIN_TONES = {
  default: null,
  light: '1F3FB',      // 🏻
  mediumLight: '1F3FC', // 🏼
  medium: '1F3FD',     // 🏽
  mediumDark: '1F3FE', // 🏾
  dark: '1F3FF',       // 🏿
};

function useSkinTone() {
  const [tone, setTone] = useState(
    () => localStorage.getItem(SKIN_TONE_KEY) || null
  );

  const updateTone = useCallback((newTone) => {
    setTone(newTone);
    if (newTone) {
      localStorage.setItem(SKIN_TONE_KEY, newTone);
    } else {
      localStorage.removeItem(SKIN_TONE_KEY);
    }
  }, []);

  return [tone, updateTone];
}

Keyboard Navigation

Keyboard navigation is essential for accessibility and power users:

function useGridKeyboard({ columns, rowCount, onSelect }) {
  const [focused, setFocused] = useState({ row: 0, col: 0 });

  const handleKeyDown = useCallback((e) => {
    const { row, col } = focused;
    switch (e.key) {
      case 'ArrowRight':
        e.preventDefault();
        setFocused({ row, col: Math.min(col + 1, columns - 1) });
        break;
      case 'ArrowLeft':
        e.preventDefault();
        setFocused({ row, col: Math.max(col - 1, 0) });
        break;
      case 'ArrowDown':
        e.preventDefault();
        setFocused({ row: Math.min(row + 1, rowCount - 1), col });
        break;
      case 'ArrowUp':
        e.preventDefault();
        setFocused({ row: Math.max(row - 1, 0), col });
        break;
      case 'Enter':
      case ' ':
        e.preventDefault();
        onSelect(focused);
        break;
    }
  }, [focused, columns, rowCount, onSelect]);

  return { focused, handleKeyDown };
}

Performance Tips

1. Lazy-load category data: Only parse emojis for the active category
2. Memoize filtered results: Avoid re-filtering on unrelated state changes
3. Debounce search input: 150–200ms debounce prevents excessive filtering
4. Use CSS sprite sheets: Platform-specific emoji image sets (Twitter, Google) ship as sprite sheets for faster loading than individual files

import { useDeferredValue, useMemo } from 'react';

function EmojiPicker({ onSelect }) {
  const [rawQuery, setRawQuery] = useState('');
  // Defer search processing to avoid blocking input
  const query = useDeferredValue(rawQuery);

  const results = useMemo(() => searchEmoji(query), [query]);
  // ...
}

Explore More on EmojiFYI

• Try the native keyboard-based emoji tool: Emoji Keyboard
• Analyze emoji sequences used in your picker: Sequence Analyzer
• Compare how emoji look across platforms: Compare Tool
• Access structured emoji data for your picker: API Reference