Kotlin for Data Science: Analyzing Data with Kotlin and Apache Spark

If you're looking to get started with data science, you've probably heard of languages like Python, R, and SQL. But did you know that Kotlin, a relatively new programming language, can be used for data science too? It may not be as well-known in this field, but Kotlin is rapidly gaining popularity among data scientists and analysts.

In this article, we'll explore how Kotlin can be used for data science and specifically how it can be used to analyze large datasets using Apache Spark.

Why Kotlin for Data Science?

Kotlin is a modern, statically-typed language that runs on the JVM (Java Virtual Machine). It was created by JetBrains, the same company that developed popular IntelliJ IDEA IDE. Kotlin makes it easy to write concise, expressive, and safe code, which makes it a great fit for data science.

Kotlin's key features, such as data classes, extension functions, null safety, and coroutines, make it easier for programmers to focus on writing the logic of their algorithms without worrying about the plumbing needed to manage data structures, API calls, and error handling.

Kotlin's interoperability with Java also makes it a great fit for data science. Many data science libraries, such as Apache Spark, are written in Java, so Kotlin developers can use them seamlessly.

Analyzing Data with Kotlin and Apache Spark

Apache Spark is an open-source big data processing engine that provides a powerful platform for building data pipelines and performing data analysis at scale. Spark's main programming language is Java, but Spark also supports Python and R. Kotlin can be used with Spark too using Spark Java APIs because it interoperates with Java so easily.

Setting up the Environment

To get started with Kotlin and Apache Spark, you need to install Java and Spark on your machine, and then create a new Kotlin project.

Installing Java

First, download and install Java SE Development Kit (JDK) from the Oracle website.

Installing Spark

Next, download and install Apache Spark. Unzip the downloaded file in your preferred directory and set the SPARK_HOME environment variable to the location of the unzipped directory.

Now that you have Java and Spark installed, you can create your first Kotlin project.

Creating a Kotlin Project

To create a new Kotlin project, use IntelliJ IDEA or another IDE that supports Kotlin. In IntelliJ IDEA, choose File -> New -> Project, and then choose Kotlin/JVM as the project type. IntelliJ IDEA will create a new Kotlin project with a basic project structure.

To use Spark in your Kotlin project, add the following dependencies to your build.gradle.kts file:

implementation("org.apache.spark:spark-core_2.12:3.1.2")
implementation("org.apache.spark:spark-sql_2.12:3.1.2")
implementation("org.apache.spark:spark-mllib_2.12:3.1.2")

These dependencies include Spark's core, SQL, and MLlib libraries, which we'll be using in this project.

Loading and Analyzing Data

Now that you have set up your project with Spark and Kotlin installed, you can start analyzing data. In this example, we'll use Spark to load and analyze a CSV file containing information about bank customers. This file has the following columns:

cust_id, age, gender, balance, num_of_transactions, num_of_complaints

We'll read this data in as a DataFrame and then do some basic analysis.

Reading the Data

To load the CSV file, use Spark's read method, like so:

val spark = SparkSession.builder()
    .appName("Bank Customer Analysis")
    .master("local")
    .getOrCreate()

val df = spark.read()
    .option("header", "true")
    .option("inferSchema", "true")
    .csv("path/to/bankdata.csv")

In this code, we first create a SparkSession, which is the entry point to Spark. We give it a name and tell it to run in local mode (on our local machine).

Next, we read the CSV file into a DataFrame using the .read() method. We set header to true because the first row of the file contains the names of the columns. We set inferSchema to true so that Spark will automatically infer the schema of the file (i.e., the types of each column).

Analyzing the Data

Now that we have loaded the data into a DataFrame, we can start analyzing it. Let's do some basic analysis to get an idea of the customers' balance and age distribution.

df.printSchema()

df.describe("balance", "age").show()

In this code, we first print the schema of the DataFrame to see the types of each column. Then we use the describe method to compute some summary statistics on the balance and age columns. We then show the results on the console.

Transforming Data

After we've analyzed the data, we may need to transform the data to make it more useful. In this example, we'll create a new column called age_group that groups customers into different age categories.

Adding a New Column

val df2 = df.withColumn("age_group", when(col("age").lt(18), "Under 18")
    .when(col("age").lt(30), "18-29")
    .when(col("age").lt(45), "30-44")
    .when(col("age").lt(60), "45-59")
    .otherwise("60+"))

In this code, we use Spark's withColumn method to add a new column to the DataFrame called age_group. We use the when method to conditionally set the value of the new column based on the values in the age column. If the customer is under 18, their age_group is "Under 18". If they're between 18 and 29, their age_group is "18-29", and so on. If they're over 60, their age_group is "60+".

Grouping the Data

Now that we have created a new column, we can group the data by the age_group and compute the average balance for each group.

df2.groupBy("age_group")
    .agg(avg("balance"))
    .show()

In this code, we use groupBy to group the data by the age_group column, and then use agg to compute the average of the balance column for each group. We then print the results on the console.

Visualizing Data

After we've transformed the data, we may want to visualize the results to better understand the trends we've discovered. In this example, we'll use Kotlin and Apache Spark to create a simple bar chart that shows the balance distribution by age group.

Creating a Bar Chart

val balanceByAgeGroup = df2.groupBy("age_group")
    .agg(avg("balance").alias("average_balance"))
    .collectAsList()
    .map {
        Row(it[0], it[1] as Double)
    }
    .let { spark.createDataFrame(it, Row::class.java) }

val dataset = Dataset<Row>(balanceByAgeGroup, spark)
val chart = BarChartBuilder()
    .title("Average Balance by Age Group")
    .xAxisTitle("Age Group")
    .yAxisTitle("Average Balance")
    .build()

chart.series("Average Balance", dataset, "value", "key")
EmbeddedBrowser.display(chart)

In this code, we first group the data by age_group, compute the average balance for each group, and create a new DataFrame with the results. We then use the BarChartBuilder class from Java's JavaFX library to create a bar chart that displays the average balance by age group. We set the title and axis labels, and then use the EmbeddedBrowser class to display the chart in the user's default web browser.

Conclusion

Kotlin is a powerful and versatile programming language that can be used for a wide range of use cases, including data science. In this article, we explored how Kotlin can be used to analyze data with Apache Spark, a popular big data processing engine. We covered setting up the environment, loading and analyzing data, transforming data, and even visualizing data.

If you're a data scientist or analyst looking to try something new, Kotlin is definitely worth a look. With its concise syntax, null safety, and rich set of features, Kotlin can help you to write better and safer code, and analyze large datasets with ease.

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