Home Backend Development PHP Tutorial Advanced tutorial on PHP and Vue.js: How to process statistical charts of large data sets

Advanced tutorial on PHP and Vue.js: How to process statistical charts of large data sets

Aug 26, 2023 pm 07:28 PM
php Statistics vuejs

Advanced tutorial on PHP and Vue.js: How to process statistical charts of large data sets

Advanced tutorial on PHP and Vue.js: How to process statistical charts of large data sets

Introduction:
With the rapid development of the Internet, the amount of data has increased Explosive growth has become the norm. For developers, how to efficiently display statistical charts becomes a challenge when dealing with large-scale data sets. This article will introduce how to use PHP and Vue.js to process statistical charts of large data sets, with corresponding code examples.

  1. Preparation work:
    Before starting specific examples, we need to prepare some basic tools and frameworks. First, make sure you have the latest versions of PHP and Vue.js installed in your development environment. Second, we will use Chart.js as the charting library for this article because it is both powerful and easy to use. You can introduce Chart.js by adding the following code in the HTML file:
<script src="https://cdn.jsdelivr.net/npm/chart.js"></script>
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  1. Data preparation:
    Before processing a large data set, we first need to prepare some data. In this example, assume we have a database table containing sales data. We use PHP to connect to the database and get all the sales records from the table. The following is a simplified PHP code example:
// 连接到数据库
$conn = new mysqli($servername, $username, $password, $dbname);

// 检查连接
if ($conn->connect_error) {
    die("连接失败: " . $conn->connect_error);
}

// 获取销售记录
$sql = "SELECT SUM(amount) AS total_amount, MONTH(date) AS month FROM sales GROUP BY MONTH(date)";
$result = $conn->query($sql);

// 处理查询结果
$data = [];
if ($result->num_rows > 0) {
    while ($row = $result->fetch_assoc()) {
        $data[] = $row;
    }
}

// 返回JSON格式的数据
echo json_encode($data);
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The above example code first connects to the database and then uses a SQL query to obtain sales records. We use the SUM function to calculate the total sales for each month and store the results in an associative array. Finally, use the echo statement to return the data to the front end in JSON format.

  1. Front-end display:
    In Vue.js, we can use the axios library to send HTTP requests and get the data returned from PHP. The following is a simplified Vue.js code example:
<template>
  <div>
    <canvas id="chart"></canvas>
  </div>
</template>

<script>
import axios from 'axios';

export default {
  mounted() {
    axios.get('http://localhost/api/sales.php')
      .then((response) => {
        this.createChart(response.data);
      })
      .catch((error) => {
        console.log(error);
      });
  },
  methods: {
    createChart(data) {
      const labels = data.map(item => item.month);
      const values = data.map(item => item.total_amount);

      new Chart('chart', {
        type: 'bar',
        data: {
          labels,
          datasets: [{
            label: '销售总额',
            data: values,
            backgroundColor: 'rgba(75, 192, 192, 0.2)',
            borderColor: 'rgba(75, 192, 192, 1)',
            borderWidth: 1
          }]
        },
        options: {
          scales: {
            y: {
              beginAtZero: true
            }
          }
        }
      });
    }
  }
}
</script>
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In the above example code, the mounted life cycle function is triggered after the component is loaded. We use the axios library to send a GET request to the PHP backend and obtain data. Then, use the map function to extract the month and total sales respectively, and store them in the labels and values ​​arrays respectively. Finally, use Chart.js to create the bar chart and bind the data into the chart.

Summary:
This article introduces how to use PHP and Vue.js to process statistical charts of large data sets. By using Chart.js as a charting library and using PHP to get data from the database, we can display large amounts of statistical data efficiently. I hope this article helps you when working with large data sets!

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