Apple App Store Key StatisticsApps & Games in the Apple App StoreApps in the Apple App StoreGames in the Apple App StoreMost Popular Apple App Store CategoriesPaid vs Free Apps in Apple App...

Get access to information about all apps in the Google Playstore to understand your competitors, market to app developers etc. This dataset includes all the fields available in the play store such as:

• Name, description, rating information etc.
• Technical information such as size, app version etc.
• Permissions.
• Developer information.
• Contact information.
• Parsed app-ads.txt information for publisher domains.
• Reviews (more than 100 million reviews available)

This dataset offers a focused and invaluable window into user perceptions and experiences with applications listed on the Apple App Store. It is a vital resource for app developers, product managers, market analysts, and anyone seeking to understand the direct voice of the customer in the dynamic mobile app ecosystem.

Dataset Specifications:

• Investment: $45.0
• Status: Published and immediately available.
• Category: Ratings and Reviews Data
• Format: Compressed ZIP archive containing JSON files, ensuring easy integration into your analytical tools and platforms.
• Volume: Comprises 10,000 unique app reviews, providing a robust sample for qualitative and quantitative analysis of user feedback.
• Timeliness: Last crawled: (This field is blank in your provided info, which means its recency is currently unknown. If this were a real product, specifying this would be critical for its value proposition.)

Richness of Detail (11 Comprehensive Fields):

Each record in this dataset provides a detailed breakdown of a single App Store review, enabling multi-dimensional analysis:

1. Review Content:

   • review: The full text of the user's written feedback, crucial for Natural Language Processing (NLP) to extract themes, sentiment, and common keywords.
   • title: The title given to the review by the user, often summarizing their main point.
   • isEdited: A boolean flag indicating whether the review has been edited by the user since its initial submission. This can be important for tracking evolving sentiment or understanding user behavior.

2. Reviewer & Rating Information:

   • username: The public username of the reviewer, allowing for analysis of engagement patterns from specific users (though not personally identifiable).
   • rating: The star rating (typically 1-5) given by the user, providing a quantifiable measure of satisfaction.

3. App & Origin Context:

   • app_name: The name of the application being reviewed.
   • app_id: A unique identifier for the application within the App Store, enabling direct linking to app details or other datasets.
   • country: The country of the App Store storefront where the review was left, allowing for geographic segmentation of feedback.

4. Metadata & Timestamps:

   • _id: A unique identifier for the specific review record in the dataset.
   • crawled_at: The timestamp indicating when this particular review record was collected by the data provider (Crawl Feeds).
   • date: The original date the review was posted by the user on the App Store.

Expanded Use Cases & Analytical Applications:

This dataset is a goldmine for understanding what users truly think and feel about mobile applications. Here's how it can be leveraged:

• Product Development & Improvement:

  • Bug Detection & Prioritization: Analyze negative review text to identify recurring technical issues, crashes, or bugs, allowing developers to prioritize fixes based on user impact.
  • Feature Requests & Roadmap Prioritization: Extract feature suggestions from positive and neutral review text to inform future product roadmap decisions and develop features users actively desire.
  • User Experience (UX) Enhancement: Understand pain points related to app design, navigation, and overall usability by analyzing common complaints in the review field.
  • Version Impact Analysis: If integrated with app version data, track changes in rating and sentiment after new app updates to assess the effectiveness of bug fixes or new features.

• Market Research & Competitive Intelligence:

  • Competitor Benchmarking: Analyze reviews of competitor apps (if included or combined with similar datasets) to identify their strengths, weaknesses, and user expectations within a specific app category.
  • Market Gap Identification: Discover unmet user needs or features that users desire but are not adequately provided by existing apps.
  • Niche Opportunities: Identify specific use cases or user segments that are underserved based on recurring feedback.

• Marketing & App Store Optimization (ASO):

  • Sentiment Analysis: Perform sentiment analysis on the review and title fields to gauge overall user satisfaction, pinpoint specific positive and negative aspects, and track sentiment shifts over time.
  • Keyword Optimization: Identify frequently used keywords and phrases in reviews to optimize app store listings, improving discoverability and search ranking.
  • Messaging Refinement: Understand how users describe and use the app in their own words, which can inform marketing copy and advertising campaigns.
  • Reputation Management: Monitor rating trends and identify critical reviews quickly to facilitate timely responses and proactive customer engagement.

• Academic & Data Science Research:

  • Natural Language Processing (NLP): The review and title fields are excellent for training and testing NLP models for sentiment analysis, topic modeling, named entity recognition, and text summarization.
  • User Behavior Analysis: Study patterns in rating distribution, isEdited status, and date to understand user engagement and feedback cycles.
  • Cross-Country Comparisons: Analyze country-specific reviews to understand regional differences in app perception, feature preferences, or cultural nuances in feedback.

This App Store Reviews dataset provides a direct, unfiltered conduit to understanding user needs and ultimately driving better app performance and greater user satisfaction. Its structured format and granular detail make it an indispensable asset for data-driven decision-making in the mobile app industry.

Apple App Store dataset to explore detailed information on app popularity, user feedback, and monetization features. Popular use cases include market trend analysis, app performance evaluation, and consumer behavior insights in the mobile app ecosystem.

Use our Apple App Store dataset to gain comprehensive insights into the mobile app ecosystem, including app popularity, user ratings, monetization features, and user feedback. This dataset covers various aspects of apps, such as descriptions, categories, and download metrics, offering a full picture of app performance and trends.

Tailored for marketers, developers, and industry analysts, this dataset allows you to track market trends, identify emerging apps, and refine promotional strategies. Whether you're optimizing app development, analyzing competitive landscapes, or forecasting market opportunities, the Apple App Store dataset is an essential tool for making data-driven decisions in the ever-evolving mobile app industry.

Dataset Features

• url: The URL linking to the app’s page on the Apple App Store.
• title: The name of the app.
• sub_title: A brief subtitle or tagline for the app.
• developer: The name of the entity or individual that developed the app.
• top_charts: Indicates if the app appears in top charts.
• monetization_features: Information on monetization aspects (such as in-app purchases or advertisements).
• image: A reference to the main app image.
• screenshots: Contains screenshot images of the app.
• description: Detailed app description outlining main features.
• what_new: Details on the latest updates or new features.
• rating: The overall rating based on user reviews.
• number_of_raters: The total number of users who have rated the app.
• reviews_by_stars: Breakdown of the number of reviews by star rating.
• reviews: An aggregation of user reviews.
• events: Any associated events or promotions.
• data_linked_to_you: Indicates if any data is linked to the user.
• seller: The entity responsible for selling or distributing the app.
• category: The category or genre of the app.
• languages: Languages supported by the app.
• copyright: Copyright information provided by the developer.
• size: The file size of the app.
• compatibility: Device or OS compatibility details.
• age_rating: The recommended age rating for the app.
• price: The price of the app.
• In_app_purchases: Details on in-app purchase options.
• support: Information related to app support.
• more_by_this_developer: Suggestions for other apps by the same developer.
• you_might_also_like: Recommendations for similar apps.
• app_support: Additional support details.
• privacy_policy: Link or reference to the app’s privacy policy.
• developer_website: The website of the app developer.
• featured_in: Information on any features or showcases the app has being part of.
• country: The country from which the app’s data was sourced.
• timestamp: A timestamp indicating when the data record was last updated.
• latest_app_version: The most recent version of the app available.
• app_id: A unique identifier for the app.

Distribution

• Data Volume: 36 Columns and 68M Rows
• Format: CSV

Usage

This dataset is versatile and can be used for various applications: - Market Analysis: Analyze app pricing strategies, monetization features, and category distribution to understand market trends and opportunities in the App Store. This can help developers and businesses make informed decisions about their app development and pricing strategies. - User Experience Research: Study the relationship between app ratings, number of reviews, and app features to understand what drives user satisfaction. The detailed review data and ratings can provide insights into user preferences and pain points. - Competitive Intelligence: Track and analyze apps within specific categories, comparing features, pricing, and user engagement metrics to identify successful patterns and market gaps. Particularly useful for developers planning new apps or improving existing ones. - Performance Prediction: Build predictive models using features like app size, category, pricing, and language support to forecast potential app success metrics. This can help in making data-driven decisions during app development. - Localization Strategy: Analyze the languages supported and regional performance to inform decisions about app localization and international market expansion.

Coverage

• Geographic Coverage: Global

License

CUSTOM Please review the respective licenses below: 1. Data Provider's License - Bright Data Master Service Agreement

Who Can Use It

• Data Scientists: Can leverage this dataset for training machine learning algorithms and building predictive models concerning app tr

Context

Saw there was no cumulative dataset of all apps on the app store. Wanted to make something as close to that as possible using web-scraping.

Content

Used Selenium in Python to scrape the app names from the site below. Clicks into each category of app, then for each page, scrapes all the app names.

Source

From this site: https://apps.apple.com/us/genre/ios/id36

[ADVISORY] IMPORTANT

Instructions for citation:

If you use this dataset anywhere in your work, kindly cite as the below: L. Gupta, "Google Play Store Apps," Feb 2019. [Online]. Available: https://www.kaggle.com/lava18/google-play-store-apps

Context

While many public datasets (on Kaggle and the like) provide Apple App Store data, there are not many counterpart datasets available for Google Play Store apps anywhere on the web. On digging deeper, I found out that iTunes App Store page deploys a nicely indexed appendix-like structure to allow for simple and easy web scraping. On the other hand, Google Play Store uses sophisticated modern-day techniques (like dynamic page load) using JQuery making scraping more challenging.

Content

Each app (row) has values for catergory, rating, size, and more.

Acknowledgements

This information is scraped from the Google Play Store. This app information would not be available without it.

Inspiration

The Play Store apps data has enormous potential to drive app-making businesses to success. Actionable insights can be drawn for developers to work on and capture the Android market!

About the Dataset

Context While there are numerous public datasets available, particularly for the Apple App Store (on platforms like Kaggle), there is a noticeable lack of similar datasets for Google Play Store apps. After investigating further, I discovered that the iTunes App Store utilizes a well-organized, index-like structure for easy web scraping. However, Google Play Store relies on more complex modern techniques such as dynamic page loading using JQuery, making it more difficult to scrape the data.

Content Each entry (representing an app) contains attributes like category, rating, size, and other relevant details.

Acknowledgements This dataset was sourced from web scraping the Google Play Store. Without this, the app data would not have been accessible.

Inspiration The data from the Google Play Store offers great potential for driving success in the app development industry. Developers can extract valuable insights to enhance their offerings and effectively tap into the Android market!

A vast collection of data which includes the Top 100 Free Applications in the iOS App Store for each day since February 2024.

Features:

• Date of chart
• Rank
• App name
• App identifier
• Chart collection

Usage:

Market trend analysis, business strategy development.

Coverage:

This will cover the top free app chart in the UK iOS App store.

License:

CCO

Who can use it:

Product Owners or Project Managers can use this data set.

How to use it:

The data set could be used to track specific applications and their position within the App store chart over time.

Dataset for the paper A Longitudinal Study of Removed Apps in iOS App Store (WWW 2021)

Apple is one of the most influential and recognisable brands in the world, responsible for the rise of the smartphone with the iPhone. Valued at over $2 trillion in 2021, it is also the most valuable...

There has been an increased emphasis on plant-based foods and diets. Although mobile technology has the potential to be a convenient and innovative tool to help consumers adhere to dietary guidelines, little is known about the content and quality of free, popular mobile health (mHealth) plant-based diet apps. The objective of the study was to assess the content and quality of free, popular mHealth apps supporting plant-based diets for Canadians. Free mHealth apps with high user ratings, a high number of user ratings, available on both Apple App and GooglePlay stores, and primarily marketed to help users follow plant-based diet were included. Using pre-defined search terms, Apple App and GooglePlay App stores were searched on December 22, 2020; the top 100 returns for each search term were screened for eligibility. Included apps were downloaded and assessed for quality by three dietitians/nutrition research assistants using the Mobile App Rating Scale (MARS) and the App Quality Evaluation (AQEL) scale. Of the 998 apps screened, 16 apps (mean user ratings±SEM: 4.6±0.1) met the eligibility criteria, comprising 10 recipe managers and meal planners, 2 food scanners, 2 community builders, 1 restaurant identifier, and 1 sustainability assessor. All included apps targeted the general population and focused on changing behaviors using education (15 apps), skills training (9 apps), and/or goal setting (4 apps). Although MARS (scale: 1–5) revealed overall adequate app quality scores (3.8±0.1), domain-specific assessments revealed high functionality (4.0±0.1) and aesthetic (4.0±0.2), but low credibility scores (2.4±0.1). The AQEL (scale: 0–10) revealed overall low score in support of knowledge acquisition (4.5±0.4) and adequate scores in other nutrition-focused domains (6.1–7.6). Despite a variety of free plant-based apps available with different focuses to help Canadians follow plant-based diets, our findings suggest a need for increased credibility and additional resources to complement the low support of knowledge acquisition among currently available plant-based apps. This research received no specific grant from any funding agency.

A vast collection of data including the 100 New Free Applications in the App Store for each day since February 2024.

Features:

• Date of chart
• Rank
• App name
• App identifier
• Chart collection

Usage:

Market trend analysis, business strategy development.

Coverage:

This will cover the new free app chart data from the UK iOS App store.

License:

CCO

Who can use it:

Product Owners or Project Managers can use this data set.

How to use it:

The data set could be used to track specific applications and their position within the App store chart over time.

In February 2024, we scraped all iOS App Store listings and extracted descriptions using a custom parser. We release a subset from the approximately 150,000 apps available in the US App Store.

uber-request-a-ride-us- 73787 rows waze-navigation-live-traffic-us- 26260 rows facebook-us- 24200 rows spotify-music-and-podcasts-us- 15580 rows netflix-us- 11760 rows pinterest-us- 10860 rows X-us- 8160 rows tiktok-us- 2542 rows tinder-dating-chat-friends-us- 1060 rows instagram-us- 300 rows These reviews are from Apple App Store

Usage This dataset should paint a good picture on what is the public's perception of the apps over the years. Using this dataset, we can do the following

Extract sentiments and trends Identify which version of an app had the most positive feedback, the worst. Use topic modelling to identify the pain points of the application. (AND MANY MORE!)

Note Images generated using Bing Image Generator

License

CC0

Original Data Source: 🏆Uber, FB, Waze, etc US Apple App Store Reviews

We crawled 90,000 app reviews from both Google Play Store and Apple App Store, including reviews from both free and paid apps. These reviews were filtered for explainability needs, and after this process, 4,495 reviews remained. Among them, 2,185 reviews indicated an explanation need, while 2,310 did not. This resulting gold standard dataset was used to train and evaluate several machine learning models and rule-based approaches for detecting explanation needs in app reviews.

The dataset includes both balanced and unbalanced evaluation sets, as well as the original crawled data from October 2023. In addition to machine learning approaches, rule-based methods optimized for F1 score, precision, and recall are also included.

We provide several pre-trained machine learning models (including BERT, SetFit, AdaBoost, K-Nearest Neighbor, Logistic Regression, Naive Bayes, Random Forest, and SVM) along with training scripts and evaluation notebooks. These models can be applied directly or retrained using the included datasets.

For further details on the structure and usage of the dataset, please refer to the README.md file within the provided ZIP archive.

Here are a few use cases for this project:

1. Agriculture Monitoring: The "Apple" model could be used in precision agriculture for detecting apple varieties in orchards, aiding in the estimation of yields, health assessment, or maturity of the apples.

2. Grocery Store Aid: Retail grocery stores could implement the model to assist customers in identifying various apple varieties, enhancing the shopping experience and promoting more effective inventory management.

3. Educational Tool: This model could serve as an interactive learning tool in educational settings, helping students understand differences between apple varieties via visual recognition.

4. App Development: Developers could use the model to create apps for apple farmers or horticulturalists that can quickly identify apple types, or notify if a different fruit is mistaken as an apple.

5. Food Processing Industry: In apple processing units or factories, the "Apple" model could facilitate Quality Control by assessing the variety and quality of apples used in production process.

The dataset available through the Sightings Map of Invasive Plants in Portugal results from the Citizen Science platform INVASORAS.PT, which records sightings of invasive plants in Portugal (mainland and Archipelagos of Madeira and Azores). This platform was originally created in 2013, in the context of the project “Plantas Invasoras: uma ameaça vinda de fora” (Media Ciência nº 16905), developed by researchers from Centre for Functional Ecology of University of Coimbra and of Coimbra College of Agriculture of the Polytechnic Institute of Coimbra. Currently this project is over, but the platform is maintained by the same team. Sightings are reported by users who register at the platform and submit them, either directly on the website (https://invasoras.pt/pt/mapeamento) or using an app for Android (https://play.google.com/store/apps/details?id=pt.uc.invasoras2) and iOS (https://apps.apple.com/pt/app/plantas-invasoras-em-portugal/id1501776731) devices. Only validated sightings are available on the dataset. Validation is made based on photographs submitted along with the sightings by experts from the platform INVASORAS.PT team. As with all citizen science projects there is some risk of erroneous records and duplication of sightings.

Overview

We have captured and annotated photos of the popular board game, Boggle. Images are predominantly from 4x4 Boggle with about 30 images from Big Boggle (5x5). - 357 images - 7110 annotated letter cubes

These images are released for you to use in training your machine learning models.

Use Cases

We used this dataset to create BoardBoss, an augmented reality board game helper app. You can download BoardBoss in the App Store for free to see the end result! :fa-spacer: https://ph-files.imgix.net/468fa673-26c4-4458-8957-369cb72addcd?auto=format&auto=compress&codec=mozjpeg&cs=strip" alt="BoardBoss"> :fa-spacer: The model trained from this dataset was paired with some heuristics to recreate the board state and overlay it with an AR representation. We then used a traditional recursive backtracking algorithm to find and show the best words on the board.

Using this Dataset

We're releasing the data as public domain. Feel free to use it for any purpose. It's not required to provide attribution, but it'd be nice! :fal-smile-wink:

About Roboflow

Roboflow makes managing, preprocessing, augmenting, and versioning datasets for computer vision seamless.

Developers reduce 50% of their boilerplate code when using Roboflow's workflow, save training time, and increase model reproducibility. :fa-spacer:

Analysis of energy apps from Apple App store, assessing inclusion of gamification and behavioral constructs.

The EVIDENT serious game explores consumer behaviour in response to a malfunctioning home appliance. Specifically, it examines how consumers approach decisions to repair or replace a broken home appliance and the impact of behavioural biases on these decisions. There are two key aims addressed within the EVIDENT serious game. 1) Determine the impact of socio-demographic factors, environmental literacy, and financial literacy on consumer willingness to pay for the repair of home appliances. 2) Determine the impact of information and education mediated through a serious game on consumer in-game and real-world repair/replace decision-making.

The serious game itself is a life-simulation game in which users are tasked with maintaining their virtual home while ensuring their avatar remains comfortable (i.e. basic needs such as hunger, warmth and hygiene are met) while monitoring their financial and energy consumption. Within this game, users learn that an appliance has malfunctioned, and a repairperson is called. Users must then determine how best to proceed by entering a negotiation with the repairperson.

The experiment consists of the following sections: 1) demographic information; 2) financial literacy; 3) environmental literacy; 4) serious game. The game receives as input the replies of the participant on the demographics information section to provide a personalized gameplay experience. Replies regarding participant's age ("What is your age?"), role ("Which of the following apply to you?"), income ("What is your household's annual income?"), gender ("Which character would you like to play with?") and family status ("How many people live in your home (including you) - Children") will be used to adjust players' avatar, starting amount of money, size of the house, age of the player and the negotiation process with the repair person.

The negotiation process differs based on the participants' role ("Which of the following apply to you?"). In this question, the participant can choose one of the following replies: 1) I am a homeowner, 2) I am a tenant (i.e. I pay someone to rent my accommodation), 3) I am a landlord (i.e. I receive payment for accommodation from someone else). Participants who rent (2) or are landlords (3) will be assigned to an additional in-game scenario to explore the unique context in which their energy decisions are made. Random allocation to a role will be applied for participants who select multiple options (i.e., homeowners who are also landlords).

More information on the EVIDENT Serious Game Experiment can be found on the public deliverables of the EVIDENT project https://evident-h2020.eu/deliverables/. More specifically, the serious game implementation design is described in deliverable D2.3 Serious game implementation design, the design of the experiment is reported in D2.2 Optimised Protocols Design, and the experiment preparatory actions are described in D3.1 Specifications of preparatory actions for RCT, surveys and serious game and D3.2 Implementation of preparatory actions for RCT, surveys and serious game.

Finally, the EVIDENT serious game can be found in the following locations:

EVIDENT Website: https://evident-h2020.eu/seriousgame

Google Play: https://play.google.com/store/apps/details?id=com.CERTH.EvidentSeriousGame

App Store: https://apps.apple.com/gr/app/evident-serious-game/id6447255106

EVIDENT Platform (participation in the experiment): https://platform.evident-h2020.eu/sessions/participate_session/1560d6e6-732a-470c-807a-c70472d51c53