Compose Multiplatform App Analytics in Jetpack Compose: A Comprehensive Guide

Building cross-platform applications with Jetpack Compose Multiplatform is an exciting prospect. As developers embrace this technology, understanding user behavior through analytics becomes crucial for making data-driven decisions. Integrating analytics into a Compose Multiplatform app can provide valuable insights into user engagement, feature usage, and overall app performance. This guide walks you through how to set up and use analytics in your Compose Multiplatform app.

What is Compose Multiplatform?

Compose Multiplatform is a declarative UI framework developed by JetBrains that allows you to share UI code between Android, iOS, desktop (JVM), and web applications. It extends the Jetpack Compose framework, enabling code reuse across various platforms and simplifying UI development.

Why Use Analytics in Compose Multiplatform Apps?

• Understand User Behavior: Discover how users interact with your app to identify popular features and pain points.
• Improve App Performance: Track performance metrics to optimize speed and stability across different platforms.
• Inform Decision-Making: Base product and development decisions on concrete data.
• Monitor User Engagement: Measure retention rates, session durations, and feature usage to improve engagement.

How to Implement Analytics in Compose Multiplatform

There are several ways to implement analytics in a Compose Multiplatform app. In this guide, we will use a Kotlin Multiplatform library to abstract platform-specific analytics implementations.

Step 1: Set up a Kotlin Multiplatform Project

If you don’t already have a Kotlin Multiplatform project, create one using the Kotlin Multiplatform wizard in IntelliJ IDEA or Android Studio. Ensure the project is configured for the target platforms (Android, iOS, etc.).

Step 2: Add Dependencies

Add the necessary dependencies in your build.gradle.kts file for the commonMain source set. This will include your multiplatform analytics interface. For platform-specific implementations, you will add dependencies to the respective platform source sets (androidMain, iosMain, etc.).

kotlin {
    sourceSets {
        val commonMain by getting {
            dependencies {
                implementation(kotlin("stdlib-common"))
                // Analytics interface - no platform-specific code here
            }
        }
        val androidMain by getting {
            dependencies {
                implementation("androidx.core:core-ktx:1.9.0")
                implementation("androidx.appcompat:appcompat:1.6.1")
                implementation("com.google.android.material:material:1.11.0")
                implementation(kotlin("stdlib"))
                // Firebase Analytics or any other Android-specific analytics library
                implementation("com.google.firebase:firebase-analytics-ktx:22.0.0")
            }
        }
        val iosMain by getting {
            dependencies {
                // Firebase Analytics or any other iOS-specific analytics library
                // Example: implementation(cocoapods("FirebaseAnalytics"))
            }
        }
    }
}

In this example:

• commonMain: Contains code shared across all platforms, including the analytics interface.
• androidMain: Includes Android-specific dependencies like Firebase Analytics.
• iosMain: Includes iOS-specific dependencies (you might use CocoaPods or another dependency manager).

Step 3: Define a Multiplatform Analytics Interface

Create an interface in the commonMain source set to define the analytics functions. This abstraction ensures a consistent API across all platforms.

// commonMain/kotlin/AnalyticsService.kt
interface AnalyticsService {
    fun initialize()
    fun logEvent(eventName: String, properties: Map<String, Any> = emptyMap())
    fun setUserProperty(name: String, value: String)
}

Step 4: Implement the Analytics Interface on Each Platform

Implement the AnalyticsService interface in the platform-specific source sets using the respective platform’s analytics SDK.

Android Implementation

Using Firebase Analytics on Android:

// androidMain/kotlin/AndroidAnalyticsService.kt
import android.content.Context
import com.google.firebase.analytics.FirebaseAnalytics

class AndroidAnalyticsService(private val context: Context) : AnalyticsService {

    private lateinit var firebaseAnalytics: FirebaseAnalytics

    override fun initialize() {
        firebaseAnalytics = FirebaseAnalytics.getInstance(context)
    }

    override fun logEvent(eventName: String, properties: Map<String, Any>) {
        val bundle = android.os.Bundle().apply {
            properties.forEach { (key, value) ->
                when (value) {
                    is String -> putString(key, value)
                    is Int -> putInt(key, value)
                    is Double -> putDouble(key, value)
                    is Long -> putLong(key, value)
                    is Boolean -> putBoolean(key, value)
                    else -> putString(key, value.toString())
                }
            }
        }
        firebaseAnalytics.logEvent(eventName, bundle)
    }

    override fun setUserProperty(name: String, value: String) {
        firebaseAnalytics.setUserProperty(name, value)
    }
}

iOS Implementation

Using Firebase Analytics on iOS (Swift example – create a Kotlin wrapper):

// iosMain/kotlin/IOSAnalyticsService.kt
import platform.Foundation.setValue
import platform.Foundation.create
import platform.Foundation.NSString
import platform.Foundation.NSMutableDictionary
import platform.FirebaseAnalytics.FirebaseAnalytics

class IOSAnalyticsService : AnalyticsService {

    override fun initialize() {
        // Firebase initialization should be done in Swift/Objective-C
    }

    override fun logEvent(eventName: String, properties: Map<String, Any>) {
        val nsDictionary: NSMutableDictionary = NSMutableDictionary.create(capacity = properties.size.toUInt())

        properties.forEach { (key, value) ->
            val nsKey = key as NSString
            val nsValue: NSString = value.toString() as NSString  // Convert value to NSString
            nsDictionary.setValue(nsValue, forKey = nsKey)
        }
        FirebaseAnalytics.logEventWithName(eventName, parameters = nsDictionary)
    }

    override fun setUserProperty(name: String, value: String) {
        FirebaseAnalytics.setUserPropertyString(value, forName = name)
    }
}

You may need to create Kotlin wrappers for Swift/Objective-C code, as shown in the iOS implementation.

Step 5: Inject Analytics Service into Compose Components

Use dependency injection to provide the platform-specific analytics service to your Compose UI components. This allows you to log events from anywhere in your application without tightly coupling UI code to the analytics implementation.

// Example using Koin for dependency injection
import org.koin.core.component.KoinComponent
import org.koin.core.component.inject
import androidx.compose.runtime.Composable
import androidx.compose.material.Button
import androidx.compose.material.Text

class MyKoinComponent: KoinComponent {
    val analyticsService: AnalyticsService by inject()
}

@Composable
fun MyComposable() {
    val koinComponent = MyKoinComponent()
    val analyticsService = koinComponent.analyticsService

    Button(onClick = {
        analyticsService.logEvent("button_clicked", mapOf("button_name" to "MyButton"))
    }) {
        Text("Click Me")
    }
}

Step 6: Initialize Analytics in Platform-Specific Code

Initialize the analytics service in your platform-specific application startup code. For Android, this might be in your Application class. For iOS, you can do this in the AppDelegate.

Android Initialization

// androidMain/kotlin/MyApplication.kt
import android.app.Application
import org.koin.android.ext.koin.androidContext
import org.koin.core.context.startKoin
import org.koin.dsl.module

class MyApplication : Application() {
    override fun onCreate() {
        super.onCreate()

        startKoin {
            androidContext(this@MyApplication)
            modules(appModule)
        }
    }

    val appModule = module {
        single<AnalyticsService> { AndroidAnalyticsService(get()) }
    }
}

iOS Initialization

In your iOS app, initialize Firebase in the AppDelegate.swift or AppDelegate.m:

// AppDelegate.swift
import UIKit
import FirebaseCore

@UIApplicationMain
class AppDelegate: UIResponder, UIApplicationDelegate {

    var window: UIWindow?

    func application(_ application: UIApplication, didFinishLaunchingWithOptions launchOptions: [UIApplication.LaunchOptionsKey: Any]?) -> Bool {
        FirebaseApp.configure()
        return true
    }
}

Remember to set up the necessary Firebase configuration for your iOS app using the GoogleService-Info.plist file.

Step 7: Log Events Throughout Your App

Log events at relevant points in your application to track user behavior and app performance. Make sure to include properties that provide additional context for each event.

analyticsService.logEvent("screen_view", mapOf("screen_name" to "HomeScreen"))

analyticsService.logEvent(
    "item_selected",
    mapOf("item_id" to "123", "item_name" to "Product A")
)

Additional Considerations

• Privacy: Always handle user data responsibly and comply with privacy regulations like GDPR and CCPA.
• Consent: Obtain user consent before collecting analytics data where required by law.
• Testing: Thoroughly test your analytics implementation to ensure events are being logged correctly across all platforms.
• Event Naming: Establish a consistent naming convention for events and properties to ensure data consistency and ease of analysis.

Conclusion

Implementing analytics in your Compose Multiplatform app is essential for understanding user behavior and improving your application. By using a multiplatform library and platform-specific implementations, you can maintain a consistent analytics API while leveraging the unique capabilities of each platform. Always prioritize user privacy and data accuracy to make informed decisions and drive the success of your application.