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 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...

From Harvard Dataverse

Description: We surveyed 10,208 people from more than 15 countries on their mobile app usage behavior. The countries include USA, China, Japan, Germany, France, Brazil, UK, Italy, Russia, India, Canada, Spain, Australia, Mexico, and South Korea. We asked respondents about: (1) their mobile app user behavior in terms of mobile app usage, including the app stores they use, what triggers them to look for apps, why they download apps, why they abandon apps, and the types of apps they download. (2) their demographics including gender, age, marital status, nationality, country of residence, first language, ethnicity, education level, occupation, and household income (3) their personality using the Big-Five personality traits This dataset contains the results of the survey.

Author: Lim, Soo Ling, 2014, "Worldwide Mobile App User Behavior Dataset", https://doi.org/10.7910/DVN/27459, Harvard Dataverse, V1

Author filliation: University College London

This dataset simulates anonymized mobile screen time and app usage data collected from Android/iOS users over a 3-month period (Jan–April 2024). It captures daily usage trends across various app categories including:

Productivity: Google Docs, Notion, Slack

Entertainment: YouTube, Netflix, TikTok

Social Media: Instagram, WhatsApp, Facebook

Utilities: Chrome, Gmail, Maps

For YouTube, additional engagement statistics such as views, likes, and comments are included to analyze video popularity and content consumption behavior.

The dataset enables exploration of:

Productivity vs. entertainment screen time patterns

Daily usage fluctuations

App-specific user engagement

Correlation between time spent and user interactions

YouTube content virality metrics

This is a great resource for:

EDA projects

Behavioral clustering

Dashboard development

Time series and anomaly detection

Building recommendation or focus-assistive apps

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 has been artificially generated to mimic real-world user interactions within a mobile application. It contains 100,000 rows of data, each row of which represents a single event or action performed by a synthetic user. The dataset was designed to capture many of the attributes commonly tracked by app analytics platforms, such as device details, network information, user demographics, session data, and event-level interactions.

Key Features Included

User & Session Metadata

User ID: A unique integer identifier for each synthetic user. Session ID: Randomly generated session identifiers (e.g., S-123456), capturing the concept of user sessions. IP Address: Fake IP addresses generated via Faker to simulate different network origins. Timestamp: Randomized timestamps (within the last 30 days) indicating when each interaction occurred. Session Duration: An approximate measure (in seconds) of how long a user remained active. Device & Technical Details

Device OS & OS Version: Simulated operating systems (Android/iOS) with plausible version numbers. Device Model: Common phone models (e.g., “Samsung Galaxy S22,” “iPhone 14 Pro,” etc.). Screen Resolution: Typical screen resolutions found in smartphones (e.g., “1080x1920”). Network Type: Indicates whether the user was on Wi-Fi, 5G, 4G, or 3G. Location & Locale

Location Country & City: Random global locations generated using Faker. App Language: Represents the user’s app language setting (e.g., “en,” “es,” “fr,” etc.). User Properties

Battery Level: The phone’s battery level as a percentage (0–100). Memory Usage (MB): Approximate memory consumption at the time of the event. Subscription Status: Boolean flag indicating if the user is subscribed to a premium service. User Age: Random integer ranging from teenagers to seniors (13–80). Phone Number: Fake phone numbers generated via Faker. Push Enabled: Boolean flag indicating if the user has push notifications turned on. Event-Level Interactions

Event Type: The action taken by the user (e.g., “click,” “view,” “scroll,” “like,” “share,” etc.). Event Target: The UI element or screen component interacted with (e.g., “home_page_banner,” “search_bar,” “notification_popup”). Event Value: A numeric field indicating additional context for the event (e.g., intensity, count, rating). App Version: Simulated version identifier for the mobile application (e.g., “4.2.8”). Data Quality & “Noise” To better approximate real-world data, 1% of all fields have been intentionally “corrupted” or altered:

Typos and Misspellings: Random single-character edits, e.g., “Andro1d” instead of “Android.” Missing Values: Some cells might be blank (None) to reflect dropped or unrecorded data. Random String Injections: Occasional random alphanumeric strings inserted where they don’t belong. These intentional discrepancies can help data scientists practice data cleaning, outlier detection, and data wrangling techniques.

Usage & Applications

Data Cleaning & Preprocessing: Ideal for practicing how to handle missing values, inconsistent data, and noise in a realistic scenario. Analytics & Visualization: Demonstrate user interaction funnels, session durations, usage by device/OS, etc. Machine Learning & Modeling: Suitable for building classification or clustering models (e.g., user segmentation, event classification). Simulation for Feature Engineering: Experiment with deriving new features (e.g., session frequency, average battery drain, etc.).

Important Notes & Disclaimer

Synthetic Data: All entries (users, device info, IPs, phone numbers, etc.) are artificially generated and do not correspond to real individuals. Privacy & Compliance: Since no real personal data is present, there are no direct privacy concerns. However, always handle synthetic data ethically.

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...

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.

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.

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.

🚀 What’s Inside? ✔️ Comprehensive Sales Data – Covers mobile device sales across different regions. ✔️ Revenue & Performance Trends – Explore units sold, revenue, and market trends. ✔️ Customer Demographics – Modeled data on customer age and gender. ✔️ Retailer & Sales Channels – Insights into where sales take place.

📊 Dataset Overview: This dataset has been synthetically generated and does not contain real-world sales data. It is crafted for educational and analytical purposes, allowing users to explore sales trends and predictive modeling techniques in the mobile industry.

📌 Key Features: 📅 Date of Sale: Modeled sales transaction dates 📱 Device Type: Categories include smartphones, feature phones, and tablets 🌍 Region: Simulated sales data from multiple geographical locations 📊 Units Sold: Number of devices sold per transaction 💰 Sales Revenue: Revenue generated from each sale 👤 Customer Demographics: Modeled customer attributes like age and gender 🏬 Retailer: Simulated retail outlets where sales occurred 🔍 Potential Use Cases: ✅ Market research on mobile sales trends across different regions ✅ Building predictive models to forecast sales and revenue ✅ Analyzing customer demographics and purchase behavior ✅ Evaluating retailer performance and sales distribution

⚠️ Disclaimer: This dataset is 100% synthetic and is not based on real-world sales data. It is designed exclusively for learning, research, and analytical practice.

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

Description:

• The dataset comprises anonymized data on apps available on the Google Play Store, capturing various aspects such as ratings, downloads, and categorization.
• The dataset has 10,841 entries, with some columns containing missing values, particularly in "Rating," "Type," "Content Rating," "Current Ver," and "Android Ver".
• This dataset can be utilized for analyzing trends in mobile app usage, user preferences, and app performance metrics across different categories.
• It can aid in understanding the impact of factors like app size, rating, and category on user downloads and popularity.
• This dataset can be utilized for analyzing app popularity, user preferences, and the relationship between app features (e.g., size, price) and downloads.
• It can help in identifying trends in app categories, assessing user satisfaction through ratings and reviews, and providing insights for app developers and marketers on user engagement and app performance.

Features:

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).

A cite-safe dataset of key mobile app statistics referenced in this guide, including downloads, store split, consumer spending, in-app purchase revenue, and Google Play listings changes with scope notes.

This data set contain 50,000 android apps and 10,000 malware apps collected from different sources and same name apps download from third party app store.

This dataset captures detailed responses from a survey conducted to understand the mobile application usage patterns among various demographics. With 222 respondents, the data spans a range of topics including app usage hours, types of apps used, factors influencing app downloads, social media engagement, and the impact of design on app preference.

This dataset is ideal for analyzing:

Mobile app usage trends across different demographics. Factors influencing app download decisions. The relationship between app features and user satisfaction. Social media platform preferences and usage time. This data can be useful for app developers, marketers, and researchers interested in mobile app usage and trends.

The name and download numbers of government mobile apps.

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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:

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All phone numbers in our database are:

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This data is ideal for:

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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:

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The number of Instagram users in Saudi Arabia was forecast to continuously increase between 2024 and 2028 by in total 1.6 million users (+10.64 percent). According to this forecast, in 2028, the Instagram user base will have increased for the fifth consecutive year to 16.64 million users. User figures, shown here with regards to the platform instagram, 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 and count multiple accounts by persons only once.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).Find more key insights for the number of Instagram users in countries like Bahrain and Oman.