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Movies 1910-2024

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Mmzujewski@wpi.edu
Last edited Sep 12, 2024
Created on Sep 5, 2024

This example displays the raw JSON encoding of the first movie record from the 9k_Movies.csv dataset, showing fields such as title, release date, and user rating. The visualization serves as a minimal data preview, converting the parsed CSV data into a readable object for inspection. Using React and WebGL rendering, the code imports the CSV file, converts rating and vote count to numeric values, and uses `container.innerHTML` to render the JSON stringified with two-space indentation at a font size of 36 pixels, initializing with the dataset’s first entry.

AI-generated description

Dataset Overview: The 16k Movies Dataset contains information on 9,000 movies, covering a range of attributes related to the movie's release, ratings, and key production personnel. This dataset is ideal for exploring trends in movie ratings, the influence of directors and writers on film reception, and the relationship between runtime and viewer engagement.

Tasks for Visualizations:

  1. Compare Movie Ratings by Genre
    Purpose: Understand how movie ratings vary across different genres.
    Visualization: A box plot comparing the distribution of ratings for each genre, allowing for easy comparison of the central tendency and variability across genres.
  2. Compare Movie Ratings by Year
    Purpose: Explore how average movie ratings have changed over time.
    Visualization: A line chart or scatter plot showing the average rating per year, with points or a trendline indicating how ratings fluctuate over time.
  3. Compare Genre Popularity by Year
    Purpose: Analyze which genres were most popular (or produced the most) during specific periods.
    Visualization: A stacked area chart showing the proportion of movies from each genre by year to visualize trends in genre production over time.
  4. Director and Ratings
    Purpose: Identify which directors consistently produce highly-rated movies.
    Visualization: A bar chart showing directors on the x-axis and their average movie ratings on the y-axis. Filter the data to focus on directors with a minimum number of movies or votes.
  5. Correlation Between Movie Rating and Duration
    Purpose: Examine if there is a relationship between how long a movie is and its rating.
    Visualization: A scatter plot with movie duration on one axis and movie rating on the other, using color or size to indicate another dimension (such as genre).

Dataset Breakdown:

Title: The title of the movie. Release Date: The date when the movie was released. Description: A brief summary of the movie. Rating: The movie’s average user rating. No of Persons Voted: Number of people who voted to determine the movie’s rating. Directed by: The director(s) of the movie. Written by: The writer(s) of the movie. Duration: The movie’s runtime in hours and minutes. Genres: The genres associated with the movie, such as Drama, Mystery, or Romance. Link to the original source:https://www.kaggle.com/datasets/kashifsahil/16000-movies-1910-2024-metacritic

MIT Licensed

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