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Ramanathan (2018). Mobile App Store ( 7200 apps) [Dataset]. https://www.kaggle.com/datasets/ramamet4/app-store-apple-data-set-10k-apps
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Mobile App Store ( 7200 apps)

Analytics for Mobile Apps

Explore at:
CroissantCroissant is a format for machine-learning datasets. Learn more about this at mlcommons.org/croissant.
Dataset updated
Jun 10, 2018
Dataset provided by
Kagglehttp://kaggle.com/
Authors
Ramanathan
License

http://www.gnu.org/licenses/old-licenses/gpl-2.0.en.htmlhttp://www.gnu.org/licenses/old-licenses/gpl-2.0.en.html

Description

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

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