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

This dataset provides comprehensive insights into mobile app usage patterns across different categories, including education, social media, productivity, entertainment, health, news, and shopping applications. It contains screen time data for 500 users with demographic information such as age and gender, making it valuable for analyzing digital behavior patterns and productivity correlations.

🚀 MobileViews: A Large-Scale Mobile GUI Dataset

MobileViews is a large-scale dataset designed to support research on mobile agents and mobile user interface (UI) analysis. The first release, MobileViews-600K, includes over 600,000 mobile UI screenshot-view hierarchy (VH) pairs collected from over 20,000 apps on the Google Play Store. This dataset is based on the DroidBot, which we have optimized for large-scale data collection, capturing more comprehensive interaction details while… See the full description on the dataset page: https://huggingface.co/datasets/mllmTeam/MobileViews.

User Feedback Dataset from the Top 15 Downloaded Mobile Applications

This dataset comprises user feedback data collected from 15 globally acclaimed mobile applications, spanning diverse categories. The included applications are among the most downloaded worldwide, providing a rich and varied source for analysis. The dataset is particularly suitable for Natural Language Processing (NLP) applications, such as text classification and topic modeling.

List of Included Applications:

• TikTok
• Instagram
• Facebook
• WhatsApp
• Telegram
• Zoom
• Snapchat
• Facebook Messenger
• Capcut
• Spotify
• YouTube
• HBO Max
• Cash App
• Subway Surfers
• Roblox

Data Columns and Descriptions:

• review_id: Unique identifiers for each user feedback/application review.
• content: User-generated feedback/review in text format.
• score: Rating or star given by the user.
• TU_count: Number of likes/thumbs up (TU) received for the review.
• app_id: Unique identifier for each application.
• app_name: Name of the application.
• RC_ver: Version of the app when the review was created (RC).

Terms of Use:

This dataset is open access for scientific research and non-commercial purposes. Users are required to acknowledge the authors' work and, in the case of scientific publication, cite the most appropriate reference:

1.Paper

M. H. Asnawi, A. A. Pravitasari, T. Herawan, and T. Hendrawati, "The Combination of Contextualized Topic Model and MPNet for User Feedback Topic Modeling," in IEEE Access, vol. 11, pp. 130272-130286, 2023, doi: https://doi.org/10.1109/ACCESS.2023.3332644

2.Dataset

Asnawi, M. H., Pravitasari, A. A., Herawan, T., & hendrawati, T. (2023). User Feedback Dataset from the Top 15 Downloaded Mobile Applications [Data set]. In The Combination of Contextualized Topic Model and MPNet for User Feedback Topic Modeling (1.0.0, Vol. 11, pp. 130272–130286). Zenodo. https://doi.org/10.5281/zenodo.10204232

Researchers and analysts are encouraged to explore this dataset for insights into user sentiments, preferences, and trends across these top mobile applications. If you have any questions or need further information, feel free to contact the dataset authors.

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.

Multilingual Mobile App Reviews Dataset 2025

Overview

This comprehensive synthetic dataset contains 2,514 authentic mobile app reviews spanning 40+ popular applications across 24 different languages, making it ideal for multilingual NLP, sentiment analysis, and cross-cultural user behavior research.

Dataset Statistics

• Total Records: 2,514 reviews
• Columns: 15 features
• Languages Covered: 24 international languages
• Apps Included: 40+ popular mobile applications
• Time Range: 2023-2025 (2-year span)
• File Format: CSV
• Data Quality: Intentionally includes missing values and mixed data types for data cleaning practice

Column Specifications

Note: Data types marked with asterisk require cleaning/conversion

Language Distribution

The dataset includes reviews in 24 languages: - European: English (en), Spanish (es), French (fr), German (de), Italian (it), Russian (ru), Polish (pl), Dutch (nl), Swedish (sv), Danish (da), Norwegian (no), Finnish (fi) - Asian: Chinese (zh), Hindi (hi), Japanese (ja), Korean (ko), Thai (th), Vietnamese (vi), Indonesian (id), Malay (ms) - Other: Arabic (ar), Turkish (tr), Filipino (tl)

Application Categories

Reviews cover 18 distinct categories: - Social Networking - Entertainment
- Productivity - Travel & Local - Music & Audio - Video Players & Editors - Shopping - Navigation - Finance - Communication - Education - Photography - Dating - Business - Utilities - Health & Fitness - Games - News & Magazines

Popular Apps Included

40+ applications including: - Social: WhatsApp, Instagram, Facebook, Snapchat, TikTok, LinkedIn, Twitter, Reddit, Pinterest - Entertainment: YouTube, Netflix, Spotify - Productivity: Microsoft Office, Google Drive, Dropbox, OneDrive, Zoom, Discord - Travel: Uber, Lyft, Airbnb, Booking.com, Google Maps, Waze - Finance: PayPal, Venmo - Education: Duolingo, Khan Academy, Coursera, Udemy - Tools: Grammarly, Canva, Adobe Photoshop, VLC, MX Player

Geographic Distribution

Reviews from 24 countries across all continents: - Asia: China, India, Japan, South Korea, Thailand, Vietnam, Indonesia, Malaysia, Philippines, Pakistan, Bangladesh - Europe: Germany, United Kingdom, France, Italy, Spain, Russia, Turkey, Poland - Americas: United States, Canada, Brazil, Mexico - Oceania: Australia - Africa: Nigeria

Data Quality Features

Intentional data challenges for learning: - Missing Values: Strategic nulls in review_text (63), rating (38), user_country (50), user_gender (88), app_version (25) - Data Type Issues: - user_id stored as strings (should be integers) - user_age as floats (should be integers)
- Some ratings as strings (should be floats) - Some helpful_votes as strings (should be integers) - Mixed Version Formats: "1.4", "v8.9", "2.8.37.5926", "14.1.60.318-beta"

Use Cases

This dataset is perfect for: - Multilingual NLP projects and sentiment analysis - Cross-cultural user behavior analysis - App store analytics and rating prediction - Data cleaning and preprocessing practice - Text classification across multiple languages - Time series analysis of app reviews - Geographic sentiment analysis - Data engineering pipeline development

Data Cleaning Opportunities

• Convert string IDs to integers
• Standardize rating values to float
• Han...

Explore Crawlora's mobile app dataset: 5,063,929 apps across both stores — 2,113,447 on Apple's App Store and 2,950,482 on Google Play. Categories, ratings, install scale, pricing and global availability — with REST API access.

MobileWorld: Benchmarking Autonomous Mobile Agents in Agent-User Interactive and MCP-Augmented Environments

Mobile World is a substantially more challenging mobile-use benchmark designed to better reflect real-world mobile usage. It comprises 201 tasks across 20 applications, featuring long-horizon, cross-app tasks, and novel task categories including agent-user interaction and MCP-augmented tasks. The difficulty of Mobile World is twofold:

Long-horizon, cross-application tasks.… See the full description on the dataset page: https://huggingface.co/datasets/Tongyi-MAI/MobileWorld.

Aggregate metrics over 5,063,929 apps across the Apple App Store (2,113,447) and Google Play (2,950,482): category mix, ratings, pricing, attention concentration, and global storefront availability — queryable via REST API.

These data have been collected and supplied by Huq Ltd. and comprise of records for the period July 2016 to October 2020. The data contain aggregated geolocated activity counts derived from mobile phone app use across Great Britain.

Mobile phone applications seek user’s consent for recording and storing the mobile device’s location when the app is in use. Activity counts are derived from these locations as the sum of distinct devices per grid cell per day. These data can be used as proxy for estimating activity levels and footfall across the UK.

These aggregate data were created from record level data which comprised individual phone IDs, and multiple entries for each mobile device if it is used multiple times for one app or the user accesses multiple apps. Thus, the following data cleaning and aggregation process has been used to create the activity counts:

1. Cleaning: Daily records comprise unique device ID, time-stamp and location of each entry collected by any app. The time-stamp is reformatted as a single daily date attribute.

2. Spatial linkage to OSGB grid: After turning the daily data-frames into spatial objects, the files are joined to the 1km x 1km OSGB grid, and each impression is attributed a grid cell ID corresponding to its latitude and longitude.

3. Creation of activity counts: Activity counts are created following the previous steps by counting the number of unique device IDs per grid cell per date. This removes multiple appearances of the same device (one device may collect multiple impressions through different apps or due to frequent usage). The final activity count corresponds to the number of unique devices within a 1km square for that day.

4. Output: The output comprises cleaned aggregation counts for each grid cell and day

N.B. More detail on how the data was collected and coverage is available if requesting for this detail in your initial application purpose, or if contacting us by email once you have made your initial application and received the form. Applicants would need to sign a non-disclosure agreement before accessing this detail, and such as request will significantly increase the time for data delivery. You can, of course, make a full application for the data without first receiving this collection/ coverage metadata.

Content

These data are provided at 1km x 1km OSGB Grid cells.

Activity counts of 1-10 devices are masked and replaced by “*” in the database, as low counts present potentially identifiable information.

For detailed description of the columns contained within the data, see the Variable Dictionary; and for an overview of the characteristics of the data, see the Data Summary. These files can be downloaded from the bottom of this page.

Quality, Representation and Bias

Excellent quality and coverage for major towns and cities. The data may be less complete for smaller settlements or more rural areas. Data are subject to suppression of potentially disclosive low counts as detailed above. Huq collects data from a varying mix of apps, the identities of which are commercially sensitive. Apps may be added to or deleted from the secure and summary data products over time. This, along with increasing national coverage and mobile phone uptake, results in general increases in apparent activity over the period covered by the data.

The dataset would benefit from comparison with population estimates (e.g. census data) to investigate coverage issues. 2016 data have the highest percentage of suppressed counts, and data suppression generally decreases over time, particularly in metropolitan (Met) areas. Data suppression levels in metropolitan areas generally fall below 50% by 2020.

As of May 2023, the mobile app version of popular ********************************* used ** of the data points they collected to track their iOS users, as well as collecting ** data points connected to the user's identity. Facebook, which was identified as the most data-hungry app among all the mobile social media, used ***** of its ** collected data points to track users. Dating app ****** collected ** data points collected to the users' identity, as well as **** data points to track users activity.

Mobile Apps for the state of Hawaii

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.

As of July 2026, Microsoft Clarity led among Android apps that used mobile data intelligence software development kits (SDKs) with an integration reach of over 35 percent. StartApp - TrueNet Network and Speed Info was the second most integrated SDK, present in 16 percent of such apps, while Comscore Analytics ranked fifth with integration in over nine percent of apps using data intelligence SDKs.

As of February 2025, video apps accounted for around 76 percent of global mobile data usage every month. Second-ranked social networking accounted for eight percent of global mobile data volume. The two categories, though, can easily overlap, as users can watch videos via video applications, as well as on social networking applications. Most popular social media platforms with video content Facebook, YouTube, and Instagram were among the most popular social networks in the world, as of October 2021. Each of these platforms allow to post, share, and watch video content on a mobile device. One of the fastest growing global brands, Tiktok, is also a social media platform where users can share video content. In September 2021, the platform reached 1 billion monthly active users. Leading types of mobile video content in the U.S. The United States was the third country in the world based on the number of smartphone users as of May 2021, with around 270 million users. Therefore, mobile content usage in the country was one of the highest in the world, and a big part of it was video content. As of the third quarter of 2021, more than 80 percent of survey respondents in the United States reported watching YouTube on their mobile devices. Social media videos were the second most popular type of content for mobile audiences, with almost six in 10 respondents watching videos on social media platforms like TikTok and Twitter.

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.

Explore 531 active Shopify Mobile Apps stores. Review examples, countries and categories, and apps and technology in this BootLeads subcategory report.

The Automated Insights Dataset (AID) brings metadata from the 200 most downloaded free apps from each of the 32 categories on the Google Play Store, totaling 6400 apps, with information that goes beyond that presented by app stores, also bringing metadata from AppBrain. The User Interface Depth Dataset (UID) brings a high-quality sampling of the AID, and delves into the identification of 7540 components of 50 component types and the capture of 1948 screenshots of the interface of 400 apps. The component set was based on components of Google Material Design and Android Studio.

• The datasets can be viewed in the spreadsheets named "Automated Insights Dataset (AID).xlsx" and "User Interface Depth Dataset (UID).xlsx".
• The "UID - Screenshots.zip" file contains screenshots of the apps present in the UID, organized in folders by app IDs.
• The "Source code of the developed tools.zip" file contains Python codes and complementary files used to collect the datasets.
• The "Discarded apps.zip" file contains the apps discarded in the analysis, it presents screenshots of some apps, collected elements and the reasons that led to these apps being discarded.
• The "Data explanation.zip" file contains graphical representations of the UID components and textual representations of each data present in the UID and AID, allowing a better understanding of the criteria used.

As of ********, Etsy collected around ** unique data points from global iOS users, ranking as the least data-hungry app within the shopping and food delivery category. Finance and crypto app Binance collected a total of **** unique data points from its global iOS users, while Khan Academy, an app used by children and students for homework and classes, collected a total of ***** unique data points.

As of May 2023, the mobile app of shopping and marketplace platform Etsy used approximately half of its collected data points to track users. In comparison, health app Noom used only *** of its collected user data point for tracking purposes.