2b:null 2c:["$","title",null,{"children":"Drama: the genre that sweeps all moviegoers by a landslide | Aparna Krishnan | Observable"}] 31:T7c5,## Example Insights: Deriving example insights will help us understand our tasks, users and the idioms we can use to visualize our data best. Given the nature of the attributes and the network structure of the dataset, we can start with the following example insights: 1. Certain actor-director combinations are surefire IMDb ratings toppers. **Considerations**: In order to test this insight, we need to decide which star to consider. There are four movie stars for each movie - we do not know which of these stars, when working with the director of the movie, made it a ratings success. Therefore, we might have to perform some preliminary correlation analysis even before visualizing the data. 3. Either Action or Drama is the genre with most appeal among moviegoers and makers. (**CHOSEN INSIGHT**) - [Visualization of this insight](https://observablehq.com/d/f1a70ac42699a22d#GraphDir) **Considerations:** We need to assess the database (using the data table above) to see which genres are represented most. On first look, it seems like all genres are more or less equally represented, so we may have to use our best judgement and popular opinion to pick out Action or Drama as the top performing genre. Otherwise, we have enough data to test this insight, as we have director, genre and main star attributes in the dataset. 5. A movie's IMDb user rating directly influences the gross revenue it generates. **Considerations:** This insight should be fairly easy to test, as we can adequately visualize the correlation between *IMDb_Rating* and *Gross*. However, we need to ensure that all movies have an accurate gross revenue value attached to them. On preliminary assessment of the dataset, we can see that there are a considerable amount of missing values for the *Gross* attribute. Also, if we decide to include the *Metascore* attribute to account for professional film critics' ratings, we will have to deal with its missing values as well.32:T61c,## Selecting an Example Insight: To gather unique and purely data-backed insights, we need reliable data. The third insight deals with not one, but two attributes that have missing values in the dataset. This might lead to erroneous results and inaccurate insight derivations. For these reasons, we will eliminate the third insight. When it comes to the other two example insights, they are purely data-backed insights which can be visualized using network-based idioms. However, the ambiguity that arises from having four stars for each top-rated movie, and not knowing which star (along with the director) made the movie a ratings success makes the first insight a little inconclusive. The second insight has a clear Actor/Director relation to the genre, and the data available is more than adequate to test it. Therefore, this is the insight we will choose: **Either Action or Drama is the genre with most appeal among moviegoers and makers.** ### Idiom selection: This insight will be best represented by network-based visualization idioms. So, our choice of idioms will be a **force-directed graph** and a **matrix diagram**. A force-directed graph is a network visualization idiom that assigns forces to the edges and nodes of a graph. The edges acts as 'springs' holding the nodes together. A matrix diagram is a tabular or grid-like way or representing intersections between two or more groups (actors/directors and genres here). These idioms offer a clear and concise way to visualize the relationship between the nodes and links of a network.33:T400,## Marks and Channels abstraction - The How: For our tasks and chosen example insights, these are the attributes that best serve our needs: 1. Director 2. Star1 3. Genre Since our idioms are force-directed graphs and matrix diagrams, and all three attributes are categorical in nature, they will all have the same marks and channels: **Director, Star1, Genre:** **Marks** - For a force-directed graph, the marks for this attribute will be **circles** representing nodes in the graph. The other marks will be **lines** representing edges between values of this attribute and genres. For a matrix diagram, the marks will be **squares or rectangles** that represent the intersection between each actor and genre. **Channels** - For a force-directed graph, the channel will be **color** to represent the different groups of nodes (actors/directors, genres). For a matrix diagram, the channel will be the **spatial position** of each cell representing the intersection between actor/director and genre.34:T465,## The real insight: Thus, the real insight we gather from these visualizations is: **Drama is the genre that sweeps all moviegoers by a landslide.** This finding confirms our example hypothesis: **Either Action or Drama is the genre with most appeal among moviegoers and makers**, although the Action genre didn't nearly have as much of an impact as we expected. This insight is fascinating, as Action movies generate the most buzz before release, and also often have an extremely high budget. In other words, Action is a genre that is very easy to love. Drama movies are popular as well, but their pre-release hype, the response of audiences to pivotal scenes in the movie and nuanced stories (as compared to other genres) suggests that their audience reach is smaller than that of other popular genres. However, this insight has proven it wrong, and it is backed up by robust data. Directors and actors can use this insight to inform their creative decisions. They can focus on creating compelling, nuanced characters and storylines instead of sticking to commercialized, single-dimensional character development.35:[{"id":"484e7ab8f7f29fc93ecbfd092226ac3c4ad238a759226d2e01d0ac97c41a4d6cf83fffac61dd574f7de9e593c6df85892125dcc68952c370380b2dada4c6cfd5","href":"https://static.observableusercontent.com/files/484e7ab8f7f29fc93ecbfd092226ac3c4ad238a759226d2e01d0ac97c41a4d6cf83fffac61dd574f7de9e593c6df85892125dcc68952c370380b2dada4c6cfd5","name":"imdb_top_1000.csv","type":"text/csv","lastModified":1679411145063.008,"size":438096}] 2f:{"id":"f1a70ac42699a22d","slug":null,"title":"Drama: the genre that sweeps all moviegoers by a landslide","thumbnail":"5bca9e02966770b2e8ee13eb4653f6782c3146f5c71fa2cc7570242faef4d5d2","access_level":"public","create_time":"$D2023-03-20T16:17:35.580Z","update_time":"$D2023-03-29T07:48:04.834Z","view_time":null,"first_public_time":"$D2023-03-20T16:17:35.580Z","has_unpublished_edits":null,"fork":false,"stars":0,"starred":false,"owner":{"type":"team","id":"0b92b1438e140e9d","login":"aparnak799","name":"Aparna Krishnan","avatar_url":"https://avatars-next.observableusercontent.com/6753a0acf921239551456a133160847934758fe4e7661d6525748489ce829068","tier":"starter_2024"},"version":328,"publish_version":null,"role":null,"authored":false,"authors":[{"type":"individual","id":"c1b55e41c39ab2cc","login":"aparnak799","name":"Aparna Krishnan","avatar_url":"https://avatars-next.observableusercontent.com/6753a0acf921239551456a133160847934758fe4e7661d6525748489ce829068","tier":null}],"members":[{"type":"individual","id":"c1b55e41c39ab2cc","login":"aparnak799","name":"Aparna Krishnan","avatar_url":"https://avatars-next.observableusercontent.com/6753a0acf921239551456a133160847934758fe4e7661d6525748489ce829068","tier":null,"role":"editor"}],"comments":[],"custom_thumbnail":true,"trashed":false,"lapsed":null,"subscribed":null,"forks":0,"collection":null,"type":"notebook","body":{"title":"Drama: the genre that sweeps all moviegoers by a landslide","cells":[{"id":0,"value":"# Drama: the genre that sweeps all moviegoers by a landslide \nCreated by [Aparna Krishnan](https://observablehq.com/@aparnak799)
\nClass: [Data Visualization / Information Visualization](https://johnguerra.co/classes/data_visualization_spring_2023/)
\nVideo: https://youtu.be/AS6j5j5mEm4
\nTweet: https://twitter.com/aparandom99/status/1638529454153822209?s=20>
","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","output":"","database":"$undefined"},{"id":63,"value":"ForceGraph(DirectorToGenreNetwork, {\n nodeId: (d) => d.name,\n nodeGroup: d => d.type,\n renderer: \"canvas\",\n nodeRadius: (d) => 5,\n width,\n height: 500,\n extent: [\n [0, 0],\n [width, 500]\n ],\n _this: this,\n invalidation // a promise to stop the simulation when the cell is re-run\n})","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":30,"value":"Movies are an extremely subjective artform. This means that there is no one \"formula\" or \"template\" that succeeds amongst **all** audiences - the commercially successful movies may not appeal to the highly discerning palettes of film buffs, and the artsy, heartstring-tugging movies might not interest your average moviegoer who just wants to turn off their brain for the next couple hours. \n\nGiven this fact, one might assume that the top-rated movies on **IMDb** - an online database containing information about the world's movies, curated by movie watchers from all walks of life - would have a healthy, almost equal distribution of movie genres. However, that might not always be the case. \n**Is there a bias for a certain genre of movie? Not by critics and award academies, but by you, the everyday movie-enjoyer who contributes to databases like IMDb?**","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","output":"","database":"$undefined"},{"id":44,"value":"## The Database","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","output":"","database":"$undefined"},{"id":51,"value":"This dataset was sourced from **Kaggle**, an online platform that provides access to data science and machine learning competitions, datasets, and code. This is the link to this dataset: https://www.kaggle.com/datasets/harshitshankhdhar/imdb-dataset-of-top-1000-movies-and-tv-shows","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","output":"","database":"$undefined"},{"id":54,"value":"tempMovies = FileAttachment(\"imdb_top_1000.csv\").csv()","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":57,"value":"tempMovies","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"","database":"$undefined"},{"id":183,"value":"navio(tempMovies)","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":58,"value":"This dataset presents information of the top user-rated 1000 movies, and for every movie, it includes attribtues such as name, rating, genre, director, actors and so on.","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","output":"","database":"$undefined"},{"id":61,"value":"## Data Abstraction - The What: \n\n### Dataset Type:\nNetwork - Generated from a tabular dataset\n\n### Attributes:\nWe want to focus on the network structure of the dataset for insight gathering. Therefore, we won't be using too many attributes for this visualization.\n1. Director - Director of the movie - **Categorical**\n2. Star1 - Main lead of the movie - **Categorical**\n3. Genre - Genre/Type of the movie - **Categorical**\n\nGiven that this is a movie ratings database, we can expect most of the attributes (except ratings, votes and gross revenue) to be categorical.\n\n### Dataset Availability:\nThis dataset from this specific source is **static**. But, given the nature of IMDb, it should be a dynamic dataset as ratings can change as new movies are released.\n\n### Derived Attributes:\nThere are no derived attributes required for this visualization.","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","output":"","database":"$undefined"},{"id":78,"value":"$31","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","output":"","database":"$undefined"},{"id":97,"value":"$32","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","output":"","database":"$undefined"},{"id":107,"value":"## Task Abstraction - The Why:\n### Main Task (T1):\n**Derive** the **node-link** relationship between directors/actors and movie genres. **(Derive, Topology)**\n### Secondary Task (T2):\n**Compare** the number of **connections** between directors/actors and each movie genre. **(Compare, Paths)**\n### Secondary Task (T3):\n**Identify** the genre of the **majority** of top-rated movies. **(Identify, Extremes)**","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","output":"","database":"$undefined"},{"id":111,"value":"## Target Audience:\n**Directors**, **actors** and **producers** are the main target audience of this visualization project. **Moviegoers** of all preferences are also the preferred users of this viz. \n\nDirectors and actors can understand what genres are chartbusters in terms of user and critic ratings. This can guide their preference in directing films of popular genres or exploring a less-preferred, underdog genre. \nProducers often have to make financial decisions based on the trade-off between movie quality and historical box office performance. This viz can help them make those decisions, as it will till them which is the most popular genre and what metrics guide this choice. \n\nMoviegoers can understand their own biases for certain genres. This isn't a bad thing, it will just inform them of which genre makes the most impact on them, and which genres they can foray into next.","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","output":"","database":"$undefined"},{"id":120,"value":"$33","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","output":"","database":"$undefined"},{"id":143,"value":"## Data Pre-processing: \nBefore proceeding with any pre-processing, we must consider the fact that this dataset has 1000 records. This would be the right amount of data for other idioms and tasks, but this might be too voluminous for a network-based idiom. Therefore, instead of considering the top 1000 movies, we will consider the **top 250** movies. ","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","output":"","database":"$undefined"},{"id":151,"value":"filteredMovies = tempMovies.slice(0, 250).map(function(element) {\n return element;\n});","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":154,"value":"This is our database after removing the records:","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","output":"","database":"$undefined"},{"id":156,"value":"filteredMovies","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"","database":"$undefined"},{"id":147,"value":"Since our initial task is to derive a network relationship between actors/directors and genres from the tabular dataset, let us perform a series of extractions from this dataset to create a node-link relationship between directors and genres, and also actors and genres. \nFirst, we will want to map the directors to the genres of their movies. However, we can see that there are often more than two genres per movie. So, let's split them up:","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","output":"","database":"$undefined"},{"id":186,"value":"viewof genreSplit = aq\n .from(filteredMovies)\n .derive({\n Genres: aq.escape((d) => d.Genre.split(\",\").map((d) => d?.trim()))\n })\n .unroll(\"Genres\")\n .view()","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":190,"value":"Now, we can map each individual genre to the director, along with the how many movies the director has made in that particular genre:","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","output":"","database":"$undefined"},{"id":192,"value":"genreSplit\n .groupby([\"Genres\", \"Director\"])\n .count()\n .orderby([\"Director\", aq.desc(\"count\")])\n .view()","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":197,"value":"Now, let's do the same but grouped by the count of movies. We will store it in a table to use as the links of our network.","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","output":"","database":"$undefined"},{"id":202,"value":"viewof DirectorToGenreLinks = genreSplit\n .groupby([\"Director\", \"Genres\"])\n .count()\n .orderby(aq.desc(\"count\"))\n .view()","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":206,"value":"Since we will assess genre popularity according to the main stars as well, we will create another link table, this time linking Star1 to the genres they've acted in.","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","output":"","database":"$undefined"},{"id":208,"value":"viewof Star1ToGenreLinks = genreSplit\n .groupby([\"Star1\", \"Genres\"])\n .count()\n .orderby(aq.desc(\"count\"))\n .view()","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":211,"value":"Now, we will convert our directors, main actors and genres into nodes along with their counts and store them in tables to use in our network.","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","output":"","database":"$undefined"},{"id":213,"value":"viewof DirectorToGenreNodes = aq\n .from(filteredMovies)\n .groupby([\"Director\"])\n .count()\n .orderby(aq.desc(\"count\"))\n .view()","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":218,"value":"viewof Star1ToGenreNodes = aq\n .from(filteredMovies)\n .groupby([\"Star1\"])\n .count()\n .orderby(aq.desc(\"count\"))\n .view()","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":221,"value":"Now, let's get just the genres and their counts, without the director/actor relationship:","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","output":"","database":"$undefined"},{"id":223,"value":"viewof genreTable = genreSplit\n .groupby([\"Genres\"])\n .count()\n .orderby(aq.desc(\"count\"))\n .view()","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":226,"value":"We are now ready to build a node-link network for both director-genre and actor-genre relationships.","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","output":"","database":"$undefined"},{"id":228,"value":"DirectorToGenreNetwork = ({\n nodes: DirectorToGenreNodes\n .objects()\n .map((d) => ({ name: d.Director, value: d.count, type: \"Director\" }))\n .concat(\n genreTable\n .objects()\n .map((d) => ({ name: d.Genres, value: d.count, type: \"Genres\" }))\n ),\n links: DirectorToGenreLinks\n .objects()\n .map((d) => ({ source: d.Director, target: d.Genres, value: d.count }))\n})","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":230,"value":"Star1ToGenreNetwork = ({\n nodes: Star1ToGenreNodes\n .objects()\n .map((d) => ({ name: d.Star1, value: d.count, type: \"Star\" }))\n .concat(\n genreTable\n .objects()\n .map((d) => ({ name: d.Genres, value: d.count, type: \"Genres\" }))\n ),\n links: Star1ToGenreLinks\n .objects()\n .map((d) => ({ source: d.Star1, target: d.Genres, value: d.count }))\n})","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":239,"value":"We are ready to visualize now!","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","output":"","database":"$undefined"},{"id":241,"value":"## The Visualizations: \nLet us first construct the force-directed graph to visualize the relationship between directors and genres:","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","output":"","database":"$undefined"},{"id":259,"value":"***

Director to Genre network

***","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","output":"","database":"$undefined"},{"id":256,"value":"Swatches(d3.scaleOrdinal([\"Directors\", \"Genres\"], d3.schemeCategory10))","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":244,"value":"GraphDir = ForceGraph(DirectorToGenreNetwork, {\n nodeId: (d) => d.name,\n nodeGroup: d => d.type,\n renderer: \"canvas\",\n nodeRadius: (d) => 5,\n width,\n height: 500,\n extent: [\n [0, 0],\n [width, 500]\n ],\n _this: this,\n invalidation // a promise to stop the simulation when the cell is re-run\n})","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":266,"value":"Quite an intricate graph. **We can immediately see that Drama is the genre that has the most connections to directors**. Since a force-directed graph assigns weights to both nodes and links, it will align the most weighted node to the centre of the network, which is what we see here. \nLet us confirm this hypothesis with the Main Star to Genre network:","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","output":"","database":"$undefined"},{"id":270,"value":"***

Main Star to Genre network

***","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","output":"","database":"$undefined"},{"id":272,"value":"Swatches(d3.scaleOrdinal([\"Genres\", \"Main Star\"], d3.schemeCategory10))","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":275,"value":"GraphStar= ForceGraph(Star1ToGenreNetwork, {\n nodeId: (d) => d.name,\n nodeGroup: d => d.type,\n renderer: \"canvas\",\n nodeRadius: (d) => 5,\n width,\n height: 500,\n extent: [\n [0, 0],\n [width, 500]\n ],\n _this: this,\n invalidation // a promise to stop the simulation when the cell is re-run\n})","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":279,"value":"As expected, we see that Drama is once again the \"star\" of the stars. \nIn both networks, Action has quite a few connections to directors and stars as well. However, Drama far outweighs Action in popularity. \n\nThese graphs prove our insight, but they are very complicated to interpret properly. To do due diligence, let us construct a static matrix diagram for both networks:","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","output":"","database":"$undefined"},{"id":284,"value":"***

Director to Genre matrix

***","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","output":"","database":"$undefined"},{"id":290,"value":"MatDir = {\n const base = vl.markRect({ tooltip: true }).encode(\n vl.x().fieldO('target').title(\"Genre\"),\n vl.y().fieldO('source').title(\"Director\"),\n );\n\n const reverse = base.encode(\n vl.x().fieldO('target'),\n vl.y().fieldO('source')\n );\n\n return vl\n .layer(base, reverse)\n .data(DirectorToGenreNetwork.links)\n .width(1000)\n .height(2000)\n .render();\n}","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":293,"value":"This diagram has assured us beyond belief that Drama is the winner by a large margin. The directors who haven't directed in this genre can be counted by hand! \nAgain, let's confirm this using the Main Star to Genre network:","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","output":"","database":"$undefined"},{"id":296,"value":"MatStar = {\n const base = vl.markRect({ tooltip: true }).encode(\n vl.x().fieldO('target').title(\"Genre\"),\n vl.y().fieldO('source').title(\"Main Star\"),\n );\n\n const reverse = base.encode(\n vl.x().fieldO('target'),\n vl.y().fieldO('source')\n );\n\n return vl\n .layer(base, reverse)\n .data(Star1ToGenreNetwork.links)\n .width(1000)\n .height(2000)\n .render();\n}","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":299,"value":"We now know beyond doubt that **Drama is an irresistible genre that captivates moviegoers, directors and actors alike.** The Drama column in both matrix diagrams is almost a solid bar instead of separate cells.","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","output":"","database":"$undefined"},{"id":307,"value":"$34","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","output":"","database":"$undefined"},{"id":321,"value":"## Conclusion: \nNo one genre can satisfy every single moviegoer in the world. What people want from their silver screen entertainment varies over time as society evolves, family structures change and younger demographics change popular culture. However, movies that resonate with the viewer, movies that are human and real with storylines that the viewer can relate with will always stand the test of time. This doesn't have to be a tearjerker - it could also be a happy, wholesome story that millions of people can understand. Therefore, Drama is a genre that is very human, and hence will always remain a fan favorite across the board.","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","output":"","database":"$undefined"},{"id":162,"value":"import {vl} from \"@vega/vega-lite-api-v5\"","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":167,"value":"import {navio} from \"@john-guerra/navio\"","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":169,"value":"import {aq, op} from \"@uwdata/arquero\"","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":174,"value":"import { ForceGraph } from \"@john-guerra/force-directed-graph\"","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":253,"value":"import {Legend, Swatches} from \"@d3/color-legend\"\n","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"}],"files":"$W35","stdlib":"1","titling":"auto"}} 30:"$2f:body"