2b:null 2c:["$","title",null,{"children":"From Fairytales to Blockbusters: Disney’s Expanding Genres and Ratings Captivated Generations | Northeastern University John A. Guerra Gomez classes | Observable"}] 31:T9c0,# From Fairytales to Blockbusters: Disney’s Expanding Genres and Ratings Captivated Generations This is a data visuzalization project made by [Shufan Chai](https://observablehq.com/user/@shufan-chai) from Northeastern University for the course CS7295: Information Visualization Fall 2024 with Professor John Alexis Guerra Gomez. Link to the course: https://johnguerra.co/classes/data_visualization_fall_2024/ ## Description It all started with a mouse. From the groundbreaking musical that marked its beginning to today’s diverse blockbusters, Disney’s century-old film empire has created a spiritual home for generations of audiences. Disney has become a symbol of an imaginative utopia in people’s hearts. As of November 2024, Disney has produced and released around 800 films, covering nearly every major movie genre. This project is for Marketers and producers in the Film and Entertainment Industry, as well as Disney movie lovers. It analyzed the Disney Movies Dataset using the Vega-Lite API to visualize data on Disney’s movies produced and released from 1937 to 2016 (including genres, MPAA ratings, and Inflation-Adjusted Gross Revenue), to explore the growth and transformation of this timeless fantasy empire. ## Table of Contents * [Part 1: Real Insights](https://observablehq.com/d/9c522380c8256fda#cell-46) * [Part 2: Data Abstraction](https://observablehq.com/d/9c522380c8256fda#cell-119) * [2.1 Data source](https://observablehq.com/d/9c522380c8256fda#cell-262) * [2.2 Dataset discription](https://observablehq.com/d/9c522380c8256fda#cell-267) * [2.3 Attributes used](https://observablehq.com/d/9c522380c8256fda#cell-276) * [2.4 Data availability](https://observablehq.com/d/9c522380c8256fda#cell-302) * [Part 3: Analysis Process](https://observablehq.com/d/9c522380c8256fda#cell-16) * [3.1 Example made-up insights and considerations](https://observablehq.com/d/9c522380c8256fda#cell-316) * [3.2 Target audience](https://observablehq.com/d/9c522380c8256fda#cell-340) * [3.3 Task abstraction](https://observablehq.com/d/9c522380c8256fda#cell-342) * [3.4 How abstraction](https://observablehq.com/d/9c522380c8256fda#cell-347) * [3.5 Data processing](https://observablehq.com/d/9c522380c8256fda#cell-19) * [The D3 examples and API used in this project](https://observablehq.com/d/9c522380c8256fda#cell-6) * [tweet](https://x.com/cherryflaneur/status/1852468155312116043) & [video](https://youtu.be/EA-M4MfzXLs) explaining the project32:T5ff,### 2.3 Attributes Used * Date Released(Ordinal attribute): The specific release date of each movie. In this project, I used the new Date() function to convert the data type of this attribute. * Genre(Categorical attribute): The categorization of each movie into genres such as Musical, Adventure, and Drama. * MPAA Rating(Categorical attribute): The movie's rating (e.g., G) as assigned by the Motion Picture Association of America. * G (General Audiences): Suitable for all ages; contains no content that would offend parents if viewed by children. * PG (Parental Guidance): Some material may not be suitable for children; parents are urged to provide guidance. * PG-13 (Parents Strongly Cautioned): Some material may be inappropriate for children under 13; parents are strongly urged to supervise. * R (Restricted): Restricted to viewers 17 and older unless accompanied by an adult; may contain adult material not suitable for children. * Inflation Adjusted Gross(Quantitative attribute): The total box office revenue adjusted for inflation, allowing for comparison between movies released in different time periods. The values span a wide range of values, causing most data points to cluster at the bottom. In idiom 2, idiom 3 and idiom 5, I changed the Y-axis to a sqrt scale to better distribute the data. * Amount of movies(Derived attribute, Quantitative attribute): Count the amount of Disney produced movies by MPAA Rating. 33:T4b6,### 3.1 Example made-up insights and considerations * (selected)Example Made-up Insight 1: Disney's strongest movie genre is Animation over the years. * Considerations:
For me to test this, I need enough data from Disney movies of different genres (categorical), gross revenue (quantitative), and release dates (ordinal).

In terms of visualization, I would need to display the trends of the gross revenue for each genre over the years and compare among different genres. A line chart, bar chart, and scatter plot would be good choices for this.

* (selected)Example Made-up Insight 2: Most of the movies that Disney produced are rated G or PG. * Considerations:
For me to test this, I need to have enough data from Disney's movie of their MPAA ratings (categorical), the movies' amount of each MPAA rating(quantitative), gross revenue (quantitative), and release dates (ordinal).

In terms of the idiom (visualization), I would need to display the gross revenue for each MPAA rating and the movie amount of each MPAA rating. I need to show the distributation of movie with different MPAA ratings. A line chart, stacked bar chart would be good choices for this.34:T41e,### 3.3 Task abstraction * T1: Summarize the features of Disney movies’ Inflation-Adjusted Gross Revenue over the years（1937-2016) by Genre. (Discover/Explore/Summarize, Features) * T1.1: Compare the trends of Disney movies’ Inflation-Adjusted Gross Revenue over the years (1937-2016) by Genre. (Discover/Explore/Compare, Trends) * T1.2: Identify the trends and distribution of Disney Animation movies' Inflation-Adjusted Gross Revenue over the years（1937-2016) by Genre. (Discover/Locate/Identify, Trends/Distributon) * T2: Summarize the trends of Disney movies’ Inflation-Adjusted Gross Revenue and the distributation of the movies over the years (1937-2016) by MPAA Rating. (Discover/Explore/Summarize, Trends/Distributation) * T2.1: Compare the trends of Disney movies’ Inflation-Adjusted Gross Revenue over the years (1937-2016) by MPAA Rating. (Discover/Explore/Compare, Features) * T2.2: Identify the distribution of Disney movies' amount rated G or PG over the years (1937-2016). (Discover/Locate/Identify, Distribution) 35:T1211,### 3.4 How abstraction [Idiom 1: Disney movies’ Inflation-Adjusted Gross Revenue by Genre Over Time (1937-2016)](https://observablehq.com/d/9c522380c8256fda#cell-170)
Mark: Line
Channels: * Position on Common Scale (X-axis and Y-axis): The x-axis represents inflation-adjusted gross revenue(quant), allowing for ordered, quantitative comparison. The position along this axis enables users to assess the gross revenue for each movie genre. The y-axis represents the release year(ordinal), enabling a temporal comparison. * Expressiveness: This channel's use on the x-axis and y-axis to represent gross revenue(quant) and release year(ordinal) aligns with the nature of the data, making it expressive in terms of accurately representing revenue values and years. * Effectiveness: Using position along a common scale (the x-axis) allows for accurate comparisons between movies’ gross revenue, aligning well with the goal of comparing magnitude. * Color Hue: Different colors are used to represent various genres (e.g., Action, Adventure, Comedy), acting as an identity channel for categorical differentiation. Each genre has a distinct color, making it easier to differentiate between them. * Expressiveness: The use of color hue effectively expresses the categorical nature of genres, ensuring each genre is distinctly represented. It matches the categorical attribute characteristic of movie genres well. * Effectiveness: This channel is effective for distinguishing between movie genres, as it quickly helps viewers identify categories without confusing them. [Idiom 2: Disney movies’ Inflation-Adjusted Gross Revenue (Sqrt Scale) by Genre Over Time (1937-2016)"](https://observablehq.com/d/9c522380c8256fda#cell-51)
Mark: Line
Channels: * Position on Common Scale (X-axis): The x-axis represents the release year(ordinal), ordered in uniform intervals, allowing for temporal comparisons across years. * Expressiveness:The expressiveness of using a uniform scale for release year is appropriate, as it accurately conveys the temporal order and regularity of movie releases. * Effectiveness: The common scale on the x-axis for release year remains effective for tracking changes over time, as it enables ordered and consistent temporal comparisons. * Position on Unaligned Scale (Y-axis, sqrt scale): The y-axis represents the inflation-adjusted gross revenue(quant) but uses a square root (sqrt) scale rather than a linear scale. The intervals do not represent a consistent change in revenue. The sqrt scale is often used to compress high values, making it easier to visualize differences without skewing the view toward the outliers. * Expressiveness: The square root scale is expressive in the sense that it appropriately represents data with a wide range of values, providing a more nuanced view of trends across genres without overwhelming the chart with outliers. * Effectiveness: It allows for more meaningful comparisons between genres with significantly different revenue ranges, making it easier to observe trends across all genres rather than focusing solely on the highest-grossing ones. * Color Hue: Different colors are used to represent various genres (e.g., Action, Adventure, Comedy), acting as an identity channel for categorical differentiation. Each genre has a distinct color, making it easier to differentiate between them. * Expressiveness: The use of color hue effectively expresses the categorical nature of genres, ensuring each genre is distinctly represented. It matches the categorical attribute characteristic of movie genres well. * Effectiveness: This channel is effective for distinguishing between movie genres, as it quickly helps viewers identify categories without confusing them. [Idiom 3: Disney movies’ Inflation-Adjusted Gross Revenue (Sqrt Scale) by Genre Over Time (1937-2016)](https://observablehq.com/d/9c522380c8256fda#cell-131)
Mark: Point
Channels: The same as in idiom 2 [Idiom 4: Disney movies' amount by MPAA Rating Over Time (1937-2016)](https://observablehq.com/d/9c522380c8256fda#cell-413)
Mark: Line
Channels: The same as in idiom 1. But in this idiom, x-axis represents the amount of movies by MPAA Rating and each color represents a MPAA Rating category. [Idiom 5: Disney movies’ Inflation-Adjusted Gross Revenue (Sqrt Scale) by MPAA Rating Over Time (1937-2016)](https://observablehq.com/d/9c522380c8256fda#cell-490)
Mark: Line
Channels: The same as in idiom 2. But in this idiom, the color hue is for MPAA Rating. 36:[{"id":"8938ee3d76068f4875303ef71c6a692d62fba909c6e2b33e4a7dacd29a32d3a9e1d401d42bae9a914f52f7d7a58fd6b60c5ccbc2a2e557c019720b939c6bf443","href":"https://static.observableusercontent.com/files/8938ee3d76068f4875303ef71c6a692d62fba909c6e2b33e4a7dacd29a32d3a9e1d401d42bae9a914f52f7d7a58fd6b60c5ccbc2a2e557c019720b939c6bf443","name":"disney_movie total_gross.csv","type":"text/csv","lastModified":1730336154400.121,"size":34483}] 2f:{"id":"9c522380c8256fda","slug":null,"title":"From Fairytales to Blockbusters: Disney’s Expanding Genres and Ratings Captivated Generations","thumbnail":"960f3a61d2867d59ca5033eb8636da2a2d2cf28e25c6df2789ea31777ab6c3fc","access_level":"public","create_time":"$D2024-10-30T02:58:30.774Z","update_time":"$D2024-11-22T23:23:32.049Z","view_time":null,"first_public_time":"$D2024-10-31T21:57:51.114Z","has_unpublished_edits":null,"fork":false,"stars":0,"starred":false,"owner":{"type":"team","id":"afbaa6d30585fd36","login":"nujguerra","name":"Northeastern University John A. Guerra Gomez classes","avatar_url":"https://avatars-next.observableusercontent.com/04e5ef72b3edcda1296894b5d1ecaa9c92ef5d52d69e65e4c115de5534928fe9","tier":"education_2026","bio":""},"version":623,"publish_version":null,"role":null,"authored":false,"authors":[{"type":"individual","id":"a66d9c6f7c527559","login":"shufan-chai","name":"Shufan Chai","avatar_url":"https://avatars-next.observableusercontent.com/c9b39c358d361b518562d961d655caa1a16cee2580758c0c44efaf089197ceda","tier":null,"bio":"Thou art more lovely and more temperate."}],"members":[{"type":"individual","id":"a66d9c6f7c527559","login":"shufan-chai","name":"Shufan Chai","avatar_url":"https://avatars-next.observableusercontent.com/c9b39c358d361b518562d961d655caa1a16cee2580758c0c44efaf089197ceda","tier":null,"role":"editor"},{"type":"team","id":"afbaa6d30585fd36","login":"nujguerra","name":"Northeastern University John A. Guerra Gomez classes","avatar_url":"https://avatars-next.observableusercontent.com/04e5ef72b3edcda1296894b5d1ecaa9c92ef5d52d69e65e4c115de5534928fe9","tier":"education_2026","role":"viewer"}],"comments":[],"custom_thumbnail":false,"trashed":false,"lapsed":null,"subscribed":null,"forks":0,"collection":null,"type":"notebook","body":{"titling":"auto","title":"From Fairytales to Blockbusters: Disney’s Expanding Genres and Ratings Captivated Generations","cells":[{"id":0,"value":"$31","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"","database":"$undefined"},{"id":46,"value":"## Part 1: Real Insights\n1. Disney movies' strongest genres are Adventure and Animation, which have consistently achieved high revenue since the beginning.

\n2. Over the years, Disney has expanded its range of movie genres and diversified its audiences. Before 1950, Disney only produced movies in two genres: Adventure and Musical. Additionally, all movies produced before 1980 were rated G. However, as time progressed, Disney gradually developed different types of movies and introduced a broader range of movie ratings to attract a wider audience. In recent years, Disney has primarily produced PG or PG-13 movies, indicating an attempt to create content suitable for more mature audiences, expand its viewer base, and adapt to diverse market demands. As a result, these two ratings have earned Disney a decent gross revenue.\n","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"","database":"$undefined"},{"id":170,"value":"vl.markBar({tooltip:{data:true}})\n .width(800)\n .height(400)\n .encode(\n vl\n .x()\n .sum(\"Inflation_Adjusted_Gross\") \n .axis({ title: \"Inflation Adjusted Gross \" }),\n vl.y().fieldT(\"date\").timeUnit(\"year\").axis({ title: \"Release Year\"}),\n vl.color().fieldN(\"genre\").scale({\n scheme: \"category10\",\n range: [\n \"#1f77b4\", \"#ff7f0e\", \"#2ca02c\", \"#9467bd\", \n \"#8c564b\", \"#e377c2\", \"#7f7f7f\", \"#bcbd22\", \n \"#17becf\", \"#aec7e8\", \"#98df8a\", \"#ffbb78\"\n ]\n }).legend({ title: \"Movie Genre\" }),\n )\n.data(transformedData)\n.title(\"Idiom 1: Disney movies’ Inflation-Adjusted Gross Revenue by Genre Over Time (1937-2016)\")\n.render();","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":51,"value":" vl.markLine({tooltip:{data:true}})\n .width(800) \n .encode(\n vl.x().fieldT(\"date\").axis({ title: \"Release Year\" }),\n vl.y().fieldQ(\"Inflation_Adjusted_Gross\").scale({ zero: false, type: \"sqrt\" }).axis({ title: \"Inflation Adjusted Gross (Sqrt Scale)\" }), \n vl.color().fieldN(\"genre\").scale({\n scheme: \"category10\",\n range: [\n \"#1f77b4\", \"#ff7f0e\", \"#2ca02c\", \"#9467bd\", \n \"#8c564b\", \"#e377c2\", \"#7f7f7f\", \"#bcbd22\", \n \"#17becf\", \"#aec7e8\", \"#98df8a\", \"#ffbb78\"\n ]\n }).legend({ title: \"Movie Genre\" })\n \n )\n .data(transformedData)\n.title(\"Idiom 2: Disney movies’ Inflation-Adjusted Gross Revenue (Sqrt Scale) by Genre Over Time (1937-2016)\")\n.render()","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":131,"value":"vl.markCircle() \n .width(800)\n .height(400)\n .encode(\n vl.x().fieldT(\"date\").axis({ title: \"Release Year\", grid: true }), \n vl.y().fieldQ(\"Inflation_Adjusted_Gross\").scale({ zero: false, type: \"sqrt\" }).axis({ title: \"Inflation Adjusted Gross (Sqrt Scale)\" }), \n vl.color().fieldN(\"genre\").scale({\n scheme: \"category10\",\n range: [\n \"#1f77b4\", \"#ff7f0e\", \"#2ca02c\", \"#9467bd\", \n \"#8c564b\", \"#e377c2\", \"#7f7f7f\", \"#bcbd22\", \n \"#17becf\", \"#aec7e8\", \"#98df8a\", \"#ffbb78\"\n ]\n }).legend({ title: \"Movie Genre\" }),\n vl.size().value(50),\n vl.opacity().value(0.6), \n vl.tooltip([\"movie_title\", \"genre\", \"Inflation_Adjusted_Gross\", \"Total_Gross\", \"date\"]) \n )\n .data(transformedData) \n .title(\"Idiom 3: Disney movies’ Inflation-Adjusted Gross Revenue (Sqrt Scale) by Genre Over Time (1937-2016)\") \n .render();\n","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":413,"value":"vl\n .markBar({tooltip:{data:true}})\n .width(800)\n .height(400)\n .encode(\n vl\n .x()\n .count(\"MPAA_rating\") \n .axis({ title: \"Amount of movies \" }),\n vl.y().fieldT(\"date\").timeUnit(\"year\").axis({ title: \"Release Year\" }),\n vl.color().fieldN(\"MPAA_rating\").legend({ title: \"MPAA Rating\" })\n )\n .data(transformedData1)\n .title(\n \"Idiom 4: Disney movies' amount by MPAA Rating Over Time (1937-2016)\"\n )\n .render()","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":490,"value":" vl.markLine({tooltip:{data:true}})\n .width(800) \n .encode(\n vl.x().fieldT(\"date\").axis({ title: \"Release Year\" }),\n vl.y().fieldQ(\"Inflation_Adjusted_Gross\").scale({ zero: false, type: \"sqrt\" }).axis({ title: \"Inflation Adjusted Gross (Sqrt Scale)\" }), \n vl.color().fieldN(\"MPAA_rating\").legend({ title: \"Movie Genre\" })\n \n )\n .data(transformedData1)\n.title(\"Idiom 5: Disney movies’ Inflation-Adjusted Gross Revenue (Sqrt Scale) by MPAA Rating Over Time (1937-2016)\")\n.render()","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":119,"value":"## Part 2: Data Abstraction","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"","database":"$undefined"},{"id":262,"value":"### 2.1 Data source\nThis dataset, called the Disney Movies Dataset, is from Kaggle. It includes data on Disney movies from 1937 to 2016. \n\nLink to the dataset and its detailed description: https://www.kaggle.com/datasets/suvroo/disney-movies-dataset","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"","database":"$undefined"},{"id":267,"value":"### 2.2 Dataset discription\nThis dataset is a table that originally contained 579 rows and 6 columns. See the table below.","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"","database":"$undefined"},{"id":187,"value":"data","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"","database":"$undefined"},{"id":276,"value":"$32","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"","database":"$undefined"},{"id":302,"value":"### 2.4 Data availability\nStatic","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"","database":"$undefined"},{"id":16,"value":"## Part 3: Analysis Process","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"","database":"$undefined"},{"id":316,"value":"$33","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"","database":"$undefined"},{"id":340,"value":"### 3.2 Target audience\n* Marketers and producers in the Film and Entertainment Industry. They can use this project to analyze trends in Disney movie revenue, genres, and ratings, aiding in business and market decisions.\n* Disney movie lovers and those interested in Disney film history. They can use this project to explore the evolutionary trends of Disney movies.","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"","database":"$undefined"},{"id":342,"value":"$34","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"","database":"$undefined"},{"id":347,"value":"$35","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"","database":"$undefined"},{"id":19,"value":"### 3.5 Data processing","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"","database":"$undefined"},{"id":22,"value":"#### Import data","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"","database":"$undefined"},{"id":24,"value":"data = FileAttachment(\"disney_movie total_gross.csv\").csv({typed: true})","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":29,"value":"#### Filter data by Genre\nIn this section, I dropped the rows where the genre is defined as ‘Unknown.’","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"","database":"$undefined"},{"id":31,"value":"dataFiltered = data\n.filter(d => d[\"Genre\"] !== \"Unknown\") \n.map(d => ({\n movie_title: d[\"Movie Title\"],\n date: d[\"Date Released\"],\n genre: d[\"Genre\"], \n Total_Gross: d[\"Total Gross\"],\n Inflation_Adjusted_Gross: d[\"Inflation Adjusted Gross\"]\n}))","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":57,"value":"#### Transform the data format and data type \nIn this section, I changed the format and type of the ‘Release Year’ attribute based on the filtered data by Genre.","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"","database":"$undefined"},{"id":61,"value":"transformedData = dataFiltered.map(item => {\n\n const [day, month, year] = item.date.split('/');\n const newDate =`${year}-${month}-${day}`;\n\n return {\n movie_title: item.movie_title,\n date: new Date(newDate),\n genre: item.genre,\n Total_Gross: Number(item.Total_Gross),\n Inflation_Adjusted_Gross: Number(item.Inflation_Adjusted_Gross)\n };\n});","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":190,"value":"#### Filter data by MPAA Rating\nIn this section, I dropped the rows where the MPAA rating is defined as 'Unknown' or 'Not Rated'.","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"","database":"$undefined"},{"id":192,"value":"dataFilteredByMPAA = data\n.filter(d => d[\"MPAA Rating\"] !== \"Unknown\" & d[\"MPAA Rating\"] !== \"Not Rated\") \n.map(d => ({\n movie_title: d[\"Movie Title\"],\n date: d[\"Date Released\"],\n MPAA_rating: d[\"MPAA Rating\"], \n Total_Gross: d[\"Total Gross\"],\n Inflation_Adjusted_Gross: d[\"Inflation Adjusted Gross\"]\n}))","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":195,"value":"#### Transform the data format and data type \nIn this section, I changed the format and type of the ‘Release Year’ attribute based on the filtered data by MPAA rating.","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"","database":"$undefined"},{"id":197,"value":"transformedData1 = dataFilteredByMPAA.map(item => {\n\n const [day, month, year] = item.date.split('/');\n const newDate =`${year}-${month}-${day}`;\n\n return {\n movie_title: item.movie_title,\n date: new Date(newDate),\n MPAA_rating: item.MPAA_rating,\n Total_Gross: Number(item.Total_Gross),\n Inflation_Adjusted_Gross: Number(item.Inflation_Adjusted_Gross)\n };\n});","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":34,"value":"#### Overview of data using Navio\nNavio is a data exploration widget created by [Professor John Alexis Guerra Gomez](https://observablehq.com/@john-guerra/navio), which you can use to explore the dataset.","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"","database":"$undefined"},{"id":40,"value":"navio(data, { attribWidth: 27 })","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":6,"value":"## The D3 examples and API used in this project","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"","database":"$undefined"},{"id":36,"value":"import {navio} from \"@john-guerra/navio\"","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":420,"value":"import {vl} from \"@vega/vega-lite-api-v5\"","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"}],"files":"$W36","stdlib":"1"}} 30:"$2f:body"