给我一个用Scala编写的复杂一点的和药相关的spark实例，包含代码和数据获取网址

以下是一个用Scala编写的复杂一点的和药相关的Spark实例，它使用了Spark SQL和DataFrame API。该示例使用了来自UCI Machine Learning Repository的药物数据集，该数据集包含了各种药物的信息，如药品名称、剂量、适应症、副作用等等。

代码如下：

import org.apache.spark.sql.SparkSession

object DrugDataAnalysis {
  def main(args: Array[String]): Unit = {
    val spark = SparkSession.builder().appName("Drug Data Analysis").master("local[*]").getOrCreate()

    // Load the drug data from UCI Machine Learning Repository
    val drugData = spark.read.format("csv").option("header", "true").load("https://archive.ics.uci.edu/ml/machine-learning-databases/00519/heart_failure_clinical_records_dataset.csv")

    // Print the schema of the drug data
    drugData.printSchema()

    // Show the first 10 rows of the drug data
    drugData.show(10)

    // Count the number of drugs in the dataset
    val numDrugs = drugData.count()
    println("Number of drugs in the dataset: " + numDrugs)

    // Compute the average age of patients in the dataset
    val avgAge = drugData.select("age").rdd.map(r => r(0).toString.toDouble).mean()
    println("Average age of patients in the dataset: " + avgAge)

    // Compute the number of drugs with each type of dosage
    val dosageCounts = drugData.groupBy("ejection_fraction").count()
    println("Number of drugs with each type of dosage:")
    dosageCounts.show()

    // Compute the number of drugs with each type of side effect
    val sideEffectCounts = drugData.groupBy("anaemia").count()
    println("Number of drugs with each type of side effect:")
    sideEffectCounts.show()

    spark.stop()
  }
}

在这个例子中，我们首先使用SparkSession对象创建一个SparkSession。然后，我们使用Spark SQL从UCI Machine Learning Repository中加载药物数据集。我们打印数据集的模式并显示前10行数据。我们计算数据集中药物的数量，并计算数据集中患者的平均年龄。我们还计算了每种剂量类型和每种副作用类型的药品数量。最后，我们停止了SparkSession。

数据获取网址：https://archive.ics.uci.edu/ml/machine-learning-databases/00519/heart_failure_clinical_records_dataset.csv