Mobile App Statistics (Apple iOS app store)

The ever-changing mobile landscape is a challenging space to navigate. . The percentage of mobile over desktop is only increasing. Android holds about 53.2% of the smartphone market, while iOS is 43%. To get more people to download your app, you need to make sure they can easily find your app. Mobile app analytics is a great way to understand the existing strategy to drive growth and retention of future user.

With million of apps around nowadays, the following data set has become very key to getting top trending apps in iOS app store. This data set contains more than 7000 Apple iOS mobile application details. The data was extracted from the iTunes Search API at the Apple Inc website. R and linux web scraping tools were used for this study.

Interactive full Shiny app can be seen here( https://multiscal.shinyapps.io/appStore/)

Data collection date (from API); July 2017

Dimension of the data set; 7197 rows and 16 columns

Content:

appleStore.csv

1. "id" : App ID

2. "track_name": App Name

3. "size_bytes": Size (in Bytes)

4. "currency": Currency Type

5. "price": Price amount

6. "rating_count_tot": User Rating counts (for all version)

7. "rating_count_ver": User Rating counts (for current version)

8. "user_rating" : Average User Rating value (for all version)

9. "user_rating_ver": Average User Rating value (for current version)

10. "ver" : Latest version code

11. "cont_rating": Content Rating

12. "prime_genre": Primary Genre

13. "sup_devices.num": Number of supporting devices

14. "ipadSc_urls.num": Number of screenshots showed for display

15. "lang.num": Number of supported languages

16. "vpp_lic": Vpp Device Based Licensing Enabled

appleStore_description.csv

1. id : App ID
2. track_name: Application name
3. size_bytes: Memory size (in Bytes)
4. app_desc: Application description

Acknowledgements

The data was extracted from the iTunes Search API at the Apple Inc website. R and linux web scraping tools were used for this study.

Inspiration

1. How does the App details contribute the user ratings?
2. Try to compare app statistics for different groups?

Reference: R package From github, with devtools::install_github("ramamet/applestoreR")

Licence

Copyright (c) 2018 Ramanathan Perumal

Global app downloads have plateaued in recent years, especially when comparing between the previous figures provided by data.ai and Sensor Tower. However, global downloads seemed to have recovered in 2025, reaching nearly *** billion unique downloads. Why the difference? Source methodology explains the gap The discrepancy arises from considerable differences in the methodology used by the sources to aggregate and generate the data. Sensor Tower reports only unique downloads per user account, excluding app updates, re-downloads, and installations on multiple devices by the same user. In contrast, data.ai includes these additional activities as well as downloads from third-party Android stores and a broader geographic scope, resulting in substantially higher total counts. As a result, Sensor Tower's numbers better reflect new user acquisition, while data.ai's encompass all market activity and total engagement. Despite stagnating downloads, user spending is growing While the number of downloads is leveling off, consumer spending on in-app purchases and related revenue has grown in 2025 to *** billion U.S. dollars, up from around *** billion U.S. dollars in 2023. While gaming remains the highest-grossing app category overall, other categories drove the growth. The entertainment, photo & video, productivity, and social networking categories each grew by at least *** billion U.S. dollars in revenue in 2025 compared to the previous year.

The mobile app industry has been active for over a decade now, generating billions of dollars in revenue for Apple, Google and thousands of mobile app developers. While originally not perceived as...

Welcome to the Mobile Sales Dataset, a simulated dataset designed to provide insights into global mobile device sales trends. Whether you're analyzing market dynamics, consumer behavior, or building predictive models, this dataset serves as a valuable resource for data science and analytics projects.

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📊 Use this dataset to explore sales trends, develop forecasting models, and enhance your data science skills in retail analytics! 🚀📱

The iOS App Store launched in 2008 with 500 apps. Today, there are over four million apps available across iOS and Android platforms, extending to a wide range of sub-genres and niches. These apps...

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Outside of China, Apple and Google control more than 95 percent of the app store market share through iOS and Android, respectively. Both mobile operating systems originally came with a few...

This dataset provides comprehensive real-time data from Google Play Store. It includes detailed app information, reviews, ratings, download statistics, and more for Android apps and games worldwide. The data covers app attributes like pricing, version history, content rating, size, permissions, and privacy details, as well as user reviews and ratings. Users can leverage this dataset for app market research, competitor analysis, and mobile app intelligence. The API enables real-time access to Play Store's vast app catalog and marketplace data, helping businesses make data-driven decisions about app development, marketing, and positioning. Whether you're conducting market analysis, tracking competitors, or building mobile app tools, this dataset provides current and reliable Play Store data. The dataset is delivered in a JSON format via REST API.

In 2021, WhatsApp's user base in the United Kingdom amounts to approximately 40.23 million users. The number of WhatsApp users in the United Kingdom is projected to reach 38.35 million users by 2025. User figures have been estimated by taking into account company filings or press material, secondary research, app downloads and traffic data. They refer to the average monthly active users over the period.The shown data are an excerpt of Statista's Key Market Indicators (KMI). The KMI are a collection of primary and secondary indicators on the macro-economic, demographic and technological environment in up to 150 countries and regions worldwide. All indicators are sourced from international and national statistical offices, trade associations and the trade press and they are processed to generate comparable data sets (see supplementary notes under details for more information).

This dataset encompasses a wide-ranging collection of Google Play applications, providing a holistic view of the diverse ecosystem within the platform. It includes information on various attributes such as the title, developer, monetization features, images, app descriptions, data safety measures, user ratings, number of reviews, star rating distributions, user feedback, recent updates, related applications by the same developer, content ratings, estimated downloads, and timestamps. By aggregating this data, the dataset offers researchers, developers, and analysts an extensive resource to explore and analyze trends, patterns, and dynamics within the Google Play Store. Researchers can utilize this dataset to conduct comprehensive studies on user behavior, market trends, and the impact of various factors on app success. Developers can leverage the insights derived from this dataset to inform their app development strategies, improve user engagement, and optimize monetization techniques. Analysts can employ the dataset to identify emerging trends, assess the performance of different categories of applications, and gain valuable insights into consumer preferences. Overall, this dataset serves as a valuable tool for understanding the broader landscape of the Google Play Store and unlocking actionable insights for various stakeholders in the mobile app industry.

📊 Google Play Store – Most Downloaded Apps Dataset

This dataset contains detailed information about the most downloaded Android applications available on the Google Play Store.
It is ideal for data analysis, machine learning, market research, and app trend analysis.

The dataset includes key app attributes such as: - App name and category - Number of installs - User ratings and reviews - App size and type (Free / Paid) - Pricing information - Content rating - Supported Android versions

🎯 Use Cases

• Exploratory Data Analysis (EDA)
• App popularity and trend analysis
• Category-wise performance comparison
• Revenue and monetization insights
• Predicting app success based on features
• Data cleaning and preprocessing practice

📌 Who should use this dataset?

• Data Science & Machine Learning students
• Android app market analysts
• Researchers studying mobile app trends
• Python, Pandas, Power BI, and SQL learners

The dataset is clean, well-structured, and suitable for both beginner and advanced-level projects.

The App Data Report offers a thorough analysis of the two key mobile operating systems—Android and iOS. Providing detailed data on consumer spending, app downloads and app store statistics. The...

Context

Mobile apps are everywhere. They are easy to create and can be lucrative. Because of these two factors, more and more apps are being developed. In this notebook, we will do a comprehensive analysis of the Android app market by comparing over ten thousand apps in Google Play across different categories. We'll look for insights in the data to devise strategies to drive growth and retention.

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Install App dataset provides comprehensive, first-party app install intelligence across the APAC region, sourced from AI-driven OS-level keyboard and utility applications. It captures highly granular insights into mobile app installations, updates, and user behavior, enabling precise market analytics, attribution tracking, and growth optimization.

Each record includes hashed device and advertising identifiers, application metadata (package name, app version, category), and timestamped install/update events. The field is_new_install indicates whether the app installation is first-time or an existing reinstall/update, helping distinguish between new user acquisition and returning user activity — a critical signal for campaign performance and user lifecycle analytics.

Alongside app-level insights, the dataset provides detailed device intelligence — including manufacturer, model, OS type/version, language, and user agent — combined with IP-based location data (country, region, city) and daily server timestamps for freshness tracking.

All data is hashed, privacy-compliant, and refreshed daily, making it ideal for organizations seeking high-quality, real-world app install signals across Android and iOS ecosystems.

📊 Key Features • First-party, consented data from OS-level applications • Hashed identifiers (device_id, advertising_id) for privacy-safe integration • Install and update timestamps for temporal and behavioral analysis • is_new_install flag to separate new installs from reinstalls or app updates • Comprehensive app, device, and location attributes • Daily refreshed dataset ensuring data accuracy and timeliness

⚙️ Primary Use Cases • Mobile Attribution & User Acquisition Tracking – Identify new users vs. re-engaged ones via the is_new_install flag • Market Intelligence & Competitive Benchmarking – Analyze install trends across app categories and geographies • Audience Segmentation – Classify users by device type, OS version, and app install behavior • Ad Targeting Optimization – Refine lookalike and re-engagement audiences with verified install data • Product & Growth Analytics – Study retention, uninstall rates, and user churn patterns • App Store Strategy – Evaluate app update frequency and version distribution

📍 Industries Benefiting • Ad-Tech & Mar-Tech Platforms • Mobile App Publishers & Developers • Telecom Operators & Device OEMs • Market Research & Analytics Firms • E-commerce, Fintech & Gaming Companies • Media, Entertainment & OTT Platforms

With millions of verified app installs tracked across Android and iOS, this AI-powered, consent-based dataset delivers actionable insights into app discovery, engagement, and retention, driving smarter decisions in mobile marketing, audience intelligence, and growth analytics.

📊 Play Market 2025 - 1M Reviews, 550+ Titles

This dataset provides cleaned and structured data from the Google Play Market, featuring over 1 million user reviews, detailed metadata for 335 games and 217 applications, and processed fields for advanced analysis.

🧾 Dataset Contents

apps_info.csv

Metadata for mobile applications, including: - app_id: Unique identifier - app_name: Name of the app - description: Cleaned full description - score: Average user rating (0–5) - ratings_count: The number of people who rated the app(int) - downloads: Download count (int) - content_rating: Audience rating (e.g., Everyone, Teen) - section: App section/category - categories: Comma-separated genre tags

apps_reviews.csv

User review data for apps: - app_id: Links to apps_info.csv - review_text: Cleaned review text - review_score: Star rating (1–5) - review_date: Standardized timestamp - helpful_count: Upvote count for review

games_info.csv

Metadata for mobile games, including: - game_id: Unique identifier - game_name: Name of the game - description: Cleaned full description - score: Average user rating (0–5) - ratings_count: The number of people who rated the game (int) - downloads: Download count (int) - content_rating: Audience rating - section: Game section/category - categories: Comma-separated genre tags

games_reviews.csv

User review data for games: - game_id: Links to games_info.csv - review_text: Cleaned review text - review_score: Star rating (1–5) - review_date: Standardized timestamp - helpful_count: Upvote count for review

📦 Dataset Summary

• 🧠 Cleaned and ready-to-use for NLP, sentiment analysis, or recommendation systems
• 🧩 Multi-source: Games and Apps handled separately
• 📅 Temporal component: All reviews include review dates

🔢 Statistics

• 📦 Applications: 217
• 📦 Games: 335
• ✍️ Total Reviews: 1,000,000+

Data was collected from publicly accessible Google Play user reviews for educational and research purposes. All personal information was removed. Not intended for commercial use

Looking for a Google Play apps dataset to analyze mobile app trends? The Google Play Store Apps Dataset delivers ~10,000 app records from the Google Play Store, including key app metadata like app name, category, rating, installs, price, developer details, and more. This dataset is ideal for app market research, mobile analytics, app store optimization studies (ASO), data science projects, and trend analysis.

Collect structured data on apps across genres and niches, so you can build visualizations, train machine-learning models, analyze user engagement, or compare categories like games, productivity, health & fitness, and finance.

Key Features

Rich App Metadata: Includes app_id, app_name, category, rating, review_count, price, installs, content_rating, genres, last_updated, current_version, android_version, developer_name, developer_email, <span style="font-size: 12pt; font-family: 'Roboto Mono',monospace; color: #188038; background-color: transparent; font-weight: 400; font-style: normal; font-variant: normal; text-decoration: none; vertical-align: baseline; white-space:

Bytemine provides access to one of the largest and most accurate US phone number databases available, featuring over 80 million verified mobile numbers. Our data includes both B2C and B2B contacts, enriched with comprehensive personal and professional details that support a wide range of use cases — from sales and marketing outreach to lead enrichment, identity resolution, and platform integration.

Our US Phone Number Data includes:

80 million+ verified US mobile numbers B2C and B2B contacts with name, email, location, and more Work emails and personal emails 57 contact-level data points including job title, company name, seniority, industry, geography, and more

This dataset gives you unmatched access to individuals across the United States, allowing you to connect with professionals and consumers directly through mobile-first campaigns. Whether you're targeting executives, small business owners, or general consumers, Bytemine provides the precision and scale to reach the right audience.

All phone numbers in our database are:

Verified and regularly updated Matched with accurate metadata (name, email, job, etc.) Compliant with data usage policies Sourced through direct licensing from trusted partners including B2B platforms, employment systems, and verified consumer data sources

This data is ideal for:

Cold calling and phone-based outreach SMS marketing and mobile-based campaigns CRM and marketing automation enrichment Identity resolution and contact matching Prospect list building and segmentation B2B and B2C marketing and retargeting App-based user targeting and onboarding Customer data platforms that need verified mobile identifiers

With access to both business and consumer profiles, Bytemine’s US Phone Number Data allows companies to execute highly targeted and personalized campaigns. Each contact is enriched with up to 57 attributes, giving your team deep insight into who the contact is, where they work, and how best to reach them.

Data can be accessed in two flexible ways:

1. Via our web platform — search, filter, and download targeted lists
2. Via API — automate contact discovery, data enrichment, or validation at scale

Our API makes it easy to integrate contact data into your existing tools, workflows, or SaaS platform. Whether you're building a lead generation engine, contact enrichment feature, or an internal prospecting tool, Bytemine delivers the clean, structured data needed to power it.

Bytemine’s phone number dataset is trusted by sales teams, marketing agencies, growth hackers, product teams, and data-driven platforms that rely on accurate contact information to engage the right audience.

If you need verified, mobile-first contact data for B2B or B2C outreach, Bytemine delivers the scale, accuracy, and flexibility required to grow your pipeline, enrich your database, and reach your customers directly.

This dataset contains detailed information about Android applications available on the Google Play Store. It includes key attributes such as app name, category, user ratings, number of reviews, installs, size, price, and content rating. The data provides valuable insights into app performance, popularity, and user engagement across different categories.

The dataset is well-suited for exploratory data analysis, machine learning projects, and visualization tasks. It enables users to analyze trends in app downloads, understand factors affecting app ratings, compare free vs paid applications, and identify patterns across various genres.

This dataset can be used for:

Predicting app ratings based on features

Analyzing user preferences

Market trend analysis

Building recommendation systems

Data cleaning and preprocessing practice

Overall, it serves as a great resource for beginners and professionals to explore real-world app data and derive meaningful business insights.

Project Overview

This project focuses on analyzing a dataset of Google Play Store applications to uncover trends, patterns, and insights about mobile apps and user behavior. The dataset contains detailed information such as app categories, ratings, number of installs, reviews, pricing, and more.

The goal of this project is to explore how different factors influence app popularity and performance. By performing data cleaning, visualization, and statistical analysis, we aim to understand what makes an app successful in the competitive mobile app market.

This project is ideal for practicing data preprocessing, exploratory data analysis (EDA), and building predictive models using real-world data.

Objectives

The main objectives of this project are:

To analyze the distribution of apps across different categories

To examine the relationship between app ratings and installs

To identify the most popular app categories

To compare free and paid apps based on ratings and downloads

To understand how reviews impact app performance

To detect trends based on app size, price, and content rating

To perform data cleaning and handle missing or inconsistent values

To build a predictive model for estimating app ratings

Example Analysis Questions

This dataset can be explored through several interesting analytical questions such as:

Which app categories have the highest number of installs?

What is the average rating of apps across different categories?

Do paid apps generally receive better ratings than free apps?

Is there a relationship between the number of reviews and app rating?

Which genres are most popular among users?

How does app size affect user ratings and installs?

What are the top 10 most reviewed apps?

Which content rating group (Everyone, Teen, etc.) has the most apps?

Are newer apps rated higher than older apps?

Can we predict app ratings based on features like installs, reviews, and price?

Tools and Techniques Used

Data Cleaning and Preprocessing

Exploratory Data Analysis (EDA)

Data Visualization

Statistical Analysis

Machine Learning Models