基于spark关于体检数据的处理与分析代码

好的，这里提供一个基于Spark的体检数据处理与分析的示例代码，供参考：

1. 数据清洗

val rawData = sc.textFile("hdfs://path/to/rawdata")
val cleanedData = rawData.filter(line => line.split(",").length == 10)

2. 数据预处理

import org.apache.spark.ml.feature.{VectorAssembler, StandardScaler}

val assembler = new VectorAssembler()
  .setInputCols(Array("age", "height", "weight", "bp", "bmi"))
  .setOutputCol("features")
val dataWithFeatures = assembler.transform(cleanedData)

val scaler = new StandardScaler()
  .setInputCol("features")
  .setOutputCol("scaledFeatures")
  .setWithStd(true)
  .setWithMean(false)
val scaledData = scaler.fit(dataWithFeatures).transform(dataWithFeatures)

3. 数据分析

import org.apache.spark.sql.functions._

val summary = scaledData.agg(
  min("scaledFeatures"), max("scaledFeatures"), mean("scaledFeatures"), stddev("scaledFeatures")
)

val kmeans = new KMeans()
  .setK(3)
  .setSeed(1L)
  .setFeaturesCol("scaledFeatures")
  .setPredictionCol("cluster")
val model = kmeans.fit(scaledData)
val clusteredData = model.transform(scaledData)

val counts = clusteredData.groupBy("cluster").count()

4. 结果可视化

import com.mongodb.spark._
import com.mongodb.spark.config._

val mongoConfig = ReadConfig(Map("uri" -> "mongodb://localhost:27017/", "database" -> "test", "collection" -> "clustered_data"))

MongoSpark.save(clusteredData, mongoConfig)

counts.show()

以上代码只是简单示例，具体实现还需要根据实际情况进行调整和扩展。