Netflix like Content Explorer & Activity Tracker

August 31, 2025 · View on GitHub

An interactive R Shiny application to explore the Netflix content library, receive personalized recommendations, and analyze user viewing activity.
The project demonstrates modular Shiny development, reusable services, and interactive visualizations.

Features

1. The Hub

  • Search bar to quickly find titles.
  • Horizontal content rails:
    • Recommended for You
    • Trending in USA
    • Recently Added
    • Top on IMDB Ratings
  • Open search modal to suggest content by keyword (genre, country, language).

2. Explore Contents

  • Sidebar filters: Type, Language, Title.
  • Choropleth world map:
    • All Titles: density by country of origin.
    • Single Title: highlight origin country.
  • Dynamic movie/show card with poster, cast, runtime, plot, and IMDB rating.

3. Track My Activity

  • Analyze viewing history by day or month.
  • Radar chart for average viewing by hour of day.
  • Wordcloud of most-watched shows.
  • Venn diagram to compare two users’ viewing overlaps.

Data Sources

  • Netflix Movies & TV Shows Dataset (June 2021)

    • Public Kaggle dataset, scraped from IMDb.
    • ~7000 titles with details like title, type, genres, cast, runtime, plot, ratings, country, language.

  • Netflix Watch Log Dataset

    • Public Kaggle dataset with viewing history of multiple users.
    • Used for activity tracking and analysis.

Note: Large raw datasets are excluded from this repo. Place sample data in data/.

Screenshots

The Hub

Hub Screenshot

Movie Card

Movie Card

Global Analysis

Global Analysis

Activity Analysis

Activity Analysis

Setup Instructions

1. Clone the repository

git clone https://github.com/<your-username>/netflix-explorer.git
cd netflix-explorer

2. Initialize R environment

Open R or RStudio and run:

install.packages("renv")
renv::restore()  # installs all dependencies listed in renv.lock

3. Run the app

shiny::runApp(".")

Development Notes

  • Modularized: Each tab (Hub, Explore, Activity) has its own mod_*.R file.
  • Data Service: svc_data.R ensures a single source of truth for datasets.
  • Visualization Helpers: Radar charts, choropleths, Venn diagrams built via dedicated helpers.
  • Styling: Netflix-inspired theme in www/styles.css.

Contributing

  1. Create a new branch from main:

  2. git checkout -b feature/my-improvement

  3. Make your changes.

  4. Commit and push:

git add -A
git commit -m "feat: describe your change"
git push origin feature/my-improvement
  1. Open a Pull Request (PR) on GitHub.