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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 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.

Context

Google Play stores top 500 app data based on their rankings on January 2022 for all the available categories. Link to scraping code: https://github.com/Shakthi-Dhar/AppPin Link to backup datafiles: github data files

Content

The dataset contains the top 500 android apps available on the google play store for the following categories: All Categories, Art & Design, Auto & Vehicles, Beauty, Books & Reference, Business, Comics, Communication, Education, Entertainment, Events, Finance, Food & Drink, Health & Fitness, House & Home, Libraries & Demo, Lifestyle, Maps & Navigation, Medical, Music & Audio, News & Magazines, Parenting, Personalization, Photography, Productivity, Shopping, Social, Sports, Tools, Travel & Local, and Video Players & Editors.

The app rankings are based on google play store app rankings for January 2022.

Abbreviations

In Review and Downloads, the alphabet T, L, Cr represents Thousands, Lakhs, Crores as per the google play store naming convention. They are similar to M, B which represent millions, billions. 1L (1 Lakh) = 100T (100 Thousand) 10L (10 Lakhs) = 1M (1 Million) 1Cr( 1 Crore) = 10M (10 Million)

Acknowledgements

This data is not provided directly by Google, so I used Appium an automation tool with python to scrape the data from the google play store app.

Inspiration

Inspired by Fortune500. Fortune500 provides data on top companies in the world, so why not have a data source for top apps in the world.

This dataset is representative of the teamfight tactics set 2 champions, which is currently in beta. As a result, not all the champion data is included, but there is sufficient information to justify its usefulness as a dataset. Teamfight Tactics was created by Riot Games and was inspired by the AutoChess genre. It pits 8 players against each other in an all out brawl of wits and intuition, where decision making reigns as king. Please do consider checking it out. Teamfight Tactics is available through the League of Legends Client, which is free to download and free to play.

Inside this data set is all the currently available data and statistics for Teamfight Tactics set 2. As this set is currently in beta, not all the information is provided. For using this data set, it would be useful to know the background of the game Teamfight Tactics, as well as the items and abilities of each champion, which are not listed in this dataset, as it is hard to assign numerical quantities to them. If the application of this data set requires the items and champion abilities, I will be making another description based data set for items and champion abilities. As it would be description based, it may be more difficult to work with, so don’t feel like it is a requirement by any means.

The data for this dataset was collected with the help of tftactics.gg. This site is a very useful resource for teamfight tactics players that are looking to climb the competitive ladder. It includes helpful information on all champions, tier lists for origins, classes, champions, and items, as well as a team builder and overlay app. Please do consider paying them a visit to take in all that they have to offer. In addition, it is all free of charge.

Possible questions going into this dataset pertain to what is the most efficient and effective strategy for winning in teamfight tactics. To answer this question, the user would have to take into account items, abilities, rolling appearance rates, and RNG, to develop a model for the average flow of gameplay, and possibly a deep learning algorithm to predict the builds and styles that the population will go for. Alternatively, this data set could also be used to find the most powerful champion with the aid of champion abilities.

No license required for use or reproduction.

Good luck and happy coding :)

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