研究用開放資料集

// 取得所有 41 家品牌資料
const res = await fetch('https://mlmfact.com/api/brands.json');
const { brands } = await res.json();

// 範例：篩選台灣本土保健類品牌
const taiwanHealthBrands = brands.filter(b =>
  b.origin === 'taiwan' && b.industry === 'health'
);

console.log(`找到 ${taiwanHealthBrands.length} 家台灣本土保健品牌`);
taiwanHealthBrands.forEach(b => console.log(`- ${b.name}`));

import requests
import pandas as pd

# 取得處分案資料
r = requests.get('https://mlmfact.com/api/news.json')
data = r.json()

# 轉成 pandas DataFrame
df = pd.DataFrame(data['rulings'])
print(f"共 {len(df)} 件處分案")

# 分析罰鍰總金額
total_fines = df['fineAmount'].sum()
print(f"罰鍰總金額: NT$ {total_fines:,}")

# 按年份分組
df['year'] = pd.to_datetime(df['rulingDate']).dt.year
by_year = df.groupby('year').agg(
    count=('slug', 'count'),
    total_fine=('fineAmount', 'sum')
)
print(by_year)

# 列出所有罰鍰超過 50 萬的案件
curl -s https://mlmfact.com/api/news.json \
  | jq '.rulings[] | select(.fineAmount >= 500000) | {company: .companyName, fine: .fineAmount, date: .rulingDate}'

# 計算各產業類別之品牌數
curl -s https://mlmfact.com/api/brands.json \
  | jq '.brands | group_by(.industry) | map({industry: .[0].industry, count: length})'

library(jsonlite)
library(dplyr)

# 取得品牌資料
brands <- fromJSON("https://mlmfact.com/api/brands.json")$brands

# 統計各起源國之品牌數
brands %>%
  count(origin, sort = TRUE) %>%
  print()

# 計算各產業之深化進度
brands %>%
  group_by(industry) %>%
  summarise(
    total = n(),
    enriched = sum(hasRevenueData)
  )