2b:null 2c:["$","title",null,{"children":"Assignment 2: Data manipulation | Sooa Mo | Observable"}] 31:T5dd,

Data Dictionary

id: Movie Id, non-continuous data
original_title: First title
title: the title of the official English translation
original_language: original language
overview: Description of movie
tagline: Tagline of the movie
release_date: release date
poster_path: Poster image URL
popularity: Popularity of movie
vote_count: Votes for movie
vote_average: Vote average
budget: Budget for movie
revenue: revenue for movie
runtime: Movie runtime
status: Status of movie, Released or not
adult: Adult movie or not, True/False
backdrop_path: Backdrop movie path
genre_names: All movies are horror, but some movie has more tags, such as Thriller, Mystery
collection:
collection_name: Collection name

There is N/A in overview, tagline. also, some movie does not have data on the budget, revenue, runtime, collection, and collection name. Data written as zero, such as budget and runtime, should be excluded. I guess the reason for zero is that the data could not be collected. Therefore, if I try to find a minimum run-time movie, find a minimum run-time excluding zero.

32:T48e,This runtime data is in minutes. And 60 minutes is an hour. I wanted to know the dataset's average run-time rate for horror movies. This runtime Mean is 62.14 min. But I wanted to see in more detail the distribution of the movie runtime. In the **first histogram, step 60**, You can see that many movies are between an hour and two hours, and then there are more than two hours of movies, but very few. **Step 30** has been used in the **second histogram**, and new information can be found through the histogram. Many horror movies are equivalent to 0-30 minutes, 60-90 minutes, and 90-120 minutes. This graph shows fewer movies between 30 and 60 minutes or more than 120 minutes. I found out that the dataset is **Bimodal distribution**. There are two groups. To check the detail, I created **a third histogram**, which has **step 10**. The histogram of the runtime data shows that the dataset exists together with short films of 60 minutes or less and long films of 60 minutes or more. When analyzing using the data set, whether it is based only on short films, long films, or all horror films should be determined according to the content I want to analyze.33:[{"id":"1facde0da1bd87708bba6d453bd8195fb3676eef7caac85ec8c9d801fd20b059ea95a52b7eb0d5d0dced5ab898797d29c336422c98a4ba589310585168445419","href":"https://static.observableusercontent.com/files/1facde0da1bd87708bba6d453bd8195fb3676eef7caac85ec8c9d801fd20b059ea95a52b7eb0d5d0dced5ab898797d29c336422c98a4ba589310585168445419","name":"horror_movies.csv","type":"text/csv","lastModified":1675640848711.735,"size":14424377}] 2f:{"id":"aa510a9d92b94f47","slug":null,"title":"Assignment 2: Data manipulation","thumbnail":"87a956db2ec2540e630a04a3f68c4fd027492c5a4cfd22c70e4a5cc7f524c7e8","access_level":"public","create_time":"$D2023-02-05T23:38:05.122Z","update_time":"$D2023-02-06T03:25:03.616Z","view_time":null,"first_public_time":"$D2023-02-05T23:38:05.122Z","fork":true,"stars":0,"starred":false,"owner":{"type":"team","id":"6e3266892950d6ff","login":"sooamo","name":"Sooa Mo","avatar_url":"https://avatars-next.observableusercontent.com/132a26eabd5134a62a64ff73823721e2bca31fa293c744f76ef1892b0c2e78f1","tier":"starter_2024"},"version":321,"publish_version":null,"role":null,"authored":false,"authors":[{"type":"individual","id":"ca9e56dd7622b70d","login":"sooamo","name":"Sooa Mo","avatar_url":"https://avatars-next.observableusercontent.com/132a26eabd5134a62a64ff73823721e2bca31fa293c744f76ef1892b0c2e78f1","tier":null}],"members":[{"type":"individual","id":"ca9e56dd7622b70d","login":"sooamo","name":"Sooa Mo","avatar_url":"https://avatars-next.observableusercontent.com/132a26eabd5134a62a64ff73823721e2bca31fa293c744f76ef1892b0c2e78f1","tier":null,"role":"editor"}],"comments":[],"custom_thumbnail":false,"trashed":false,"lapsed":null,"subscribed":null,"forks":0,"collection":null,"type":"notebook","body":{"title":"Assignment 2: Data manipulation","cells":[{"id":0,"value":"# Assignment 2: Data manipulation ","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","output":"","database":"$undefined"},{"id":107,"value":"## Sooa Mo (301448491)","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","output":"","database":"$undefined"},{"id":5,"value":"## Part1\n### Horror Movies\n\n- This dataset reference: (https://www.kaggle.com/datasets/evangower/horror-movies?resource=download&select=horror_movies.csv)\n- Through this dataset, you can find the **popular** horror movies by year or decade. Also can figure out what kind of horror topic was popular at that time.\n- Column: id, original_title, title, original_language, overview, tagline, release_date, poster_path, popularity, vote_count, vote_average, budget, revenue, runtime, status, adult, backdrop_path, genre_names, collection, collection_name.","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":148,"value":"$31","pinned":false,"hidden":false,"mode":"html","author":"$undefined","summary":"$undefined","output":"","database":"$undefined"},{"id":128,"value":"horror_movies = await FileAttachment(\"horror_movies.csv\").csv({typed: \"auto\"})","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"horror_movies","database":"$undefined"},{"id":14,"value":"---","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":8,"value":"### My Gist (https://github.com/SooaMo/data/blob/main/horror_movies.csv)\n","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":142,"value":"Structure, Study from (https://observablehq.com/@iat355/02s-data)","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","output":"","database":"$undefined"},{"id":166,"value":"d3 = require(\"d3@7\")","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":171,"value":"import {vl} from '@vega/vega-lite-api'","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":175,"value":"movie = d3.csv(\"https://raw.githubusercontent.com/SooaMo/data/main/horror_movies.csv\")","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":21,"value":"### Part 2\n0. Iterate through and change text fields to appropriate formats\n","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":182,"value":"movieParsed = { \n for (let i=0;i maxRuntime){\n maxRuntime = movie[i].runtime;\n }\n }\n return maxRuntime;\n}","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":194,"value":"runtimeMin = {\n let i = 1;\n let minRuntime = movie[0].runtime;\n\n for (i=1;i0){ \n if (movie[i].runtime < minRuntime){\n minRuntime = movie[i].runtime;\n }\n }\n }\n return minRuntime; \n}","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":203,"value":"Max runtime is 683 min and Min runtime is 1 min.\nI found it difficult to figure out whether 0 **represents null instead of N/A** or **a movie in less than a minute**. If the movie was less than one minute, the smallest minimum could be specified if the decimal point was used to express it as **\"0.5\"**.\n\n** 2) sum values by a specified dimension (total of n in dimension d, where n is quantitative)**","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","output":"","database":"$undefined"},{"id":213,"value":"sumRuntime = {\n let totalSum = d3.sum(movie, function(d){return +d.runtime})\n return totalSum.toFixed(2) \n}","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":216,"value":"Used d3.sum() function from the lecture tutorial(https://observablehq.com/@iat355/02s-data).\n\n**3) average a set of values. Note: choose the right centrality measure! See (6) below.**","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","output":"","database":"$undefined"},{"id":231,"value":"//average a set of runtime\navgRuntime = {\n return d3.mean(sumRuntime).toFixed(2);\n}","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":236,"value":"//average a set of runtime without some movie which has 0 value.\naverageRuntime = {\n let NotZeroRuntime = movie.filter(function(d){ return d.Runtime != 0});\n let NotZeroRuntimeMovie = NotZeroRuntime.map(function(d){return +d.runtime});\n return d3.mean(NotZeroRuntimeMovie).toFixed(2);\n}","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":242,"value":"The first Average Runtime results from including all movies with zero runtime. First, Horror movies' average runtime is 1.11 minutes because many movies have zero runtime figures. Therefore, I assumed that the result was caused by many movies calculated as zero, and the average was obtained after **filtering** the non-run-time zero group.\n\n**4)count how many records match a particular dimension criterion.**\n\nHow many horror movies had a runtime between 60 min to 120 min?","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","output":"","database":"$undefined"},{"id":255,"value":"countRuntime= movie.filter(d=>(d.runtime>60 && d.runtime<120)).length","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":267,"value":"**5)print out the results from the above operations, as well as the information about the data source(s) (where they are from).**\n\nAs I wrote above, the dataset is (https://www.kaggle.com/datasets/evangower/horror-movies?datasetId=2860684).\nFor the study and structure of the function, I used the class tutorial (https://observablehq.com/@iat355/02s-data).\n\n**6) provide a histogram for each dimension (note: comment on how you selected your bin size)\nwhat does the distribution tell you in each case?**","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","output":"","database":"$undefined"},{"id":288,"value":"vl.markBar()\n .data(movie)\n .encode(\n vl.x().fieldQ('runtime').bin({step:60}),\n vl.y().count()\n )\n .render()","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":313,"value":"vl.markBar()\n .data(movie)\n .encode(\n vl.x().fieldQ('runtime').bin({step:30}),\n vl.y().count()\n )\n .render()","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":316,"value":"vl.markBar()\n .data(movie)\n .encode(\n vl.x().fieldQ('runtime').bin({step:10}),\n vl.y().count()\n )\n .render()","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":297,"value":"$32","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","output":"","database":"$undefined"}],"files":"$W33","stdlib":"1","titling":"auto"}} 30:"$2f:body"