30:[] 2e:{"title":"Assignment 4","cells":[{"id":0,"value":"md`# Assignment 4`","pinned":false,"hidden":false,"mode":"ojs"},{"id":204,"value":"md`Ishpreet Sekhon's Analysis.`","pinned":false,"hidden":false,"mode":"ojs"},{"id":238,"value":"md`Brandyn Chew's Notebook: https://observablehq.com/@picklesnek/assignment-4?fbclid=IwAR0xfWPFZyZixfgop56T4EpZqM8a8Zrdkqv1bR9KI--9tbDaWMSyDPWCOS0`","pinned":false,"hidden":false,"mode":"ojs"},{"id":60,"value":"df = dfjs.DataFrame.fromCSV('https://gist.githubusercontent.com/issekhon/1a2a9c77efaf1e8b59ca5f5232fb37ba/raw/546d3ff7ef0d33340c8c80aa2064f7d7260bd26e/googleplaystore.csv')","pinned":false,"hidden":false,"mode":"ojs"},{"id":7,"value":"playData = df.toCollection()","pinned":false,"hidden":false,"mode":"ojs"},{"id":80,"value":"md`## Dataset Preview`","pinned":false,"hidden":false,"mode":"ojs"},{"id":55,"value":"df.show()","pinned":false,"hidden":false,"mode":"ojs"},{"id":19,"value":"md` This dataset consists of app data scrubbed from the google play store. It is narrowed down to only include data from game apps.`","pinned":false,"hidden":false,"mode":"ojs"},{"id":25,"value":"md`## Vis 1: Paid vs Free Ratings`","pinned":false,"hidden":false,"mode":"ojs"},{"id":50,"value":"VegaLite({\n data: {values: playData},\n hconcat:[{\n mark: \"bar\",\n encoding: {\n x: {field: \"Type\", type: \"nominal\"},\n y: {aggregate: \"mean\", field: \"Rating\", type: \"quantitative\"},\n },\n },\n {\n mark: \"bar\",\n encoding: {\n x: {field: \"Type\", type: \"nominal\"},\n y: {aggregate: \"count\", type: \"quantitative\"}\n },\n },\n {\n mark: \"bar\",\n encoding: {\n x: {field: \"Type\", type: \"nominal\"},\n y: {aggregate: \"mean\", field: \"Reviews\", type: \"quantitative\"}\n },\n },\n {\n mark: \"bar\",\n encoding: {\n x: {bin: {maxbins: 20}, field: \"Price\", type: \"quantitative\"},\n y: {aggregate: \"mean\", field: \"Rating\", type: \"quantitative\"}\n }\n }\n ]\n})","pinned":false,"hidden":false,"mode":"ojs"},{"id":36,"value":"md`### Analytic Scope:`","pinned":false,"hidden":false,"mode":"ojs"},{"id":42,"value":"md`\n

Should the game i'm working on be released for free or paid?
Do paid games do as well as free ones?
How much more popular are free games than paid?
Are there a lot of bad free games?

`","pinned":false,"hidden":false,"mode":"ojs"},{"id":196,"value":"md`Data dimensions include average rating, number of records and average number of reviews as they relate to the monetization type. Also includes average rating based on price`","pinned":false,"hidden":false,"mode":"ojs"},{"id":198,"value":"md`Visualized using a series of bar graphs. This format allows for a more immediately understandable difference in sizes, and large differences (For example, average number of reviews per monetization type) are easily visible.`","pinned":false,"hidden":false,"mode":"ojs"},{"id":207,"value":"md`Through this visualization we can see just how different the market for free and paid games is. The first graph shows that while paid games are on average a bit better than free games, free games get a much larger playerbase which is important to sustainability. Also, paid games are generally rated higher than free games, with some outliers towards the high end of the prices`","pinned":false,"hidden":false,"mode":"ojs"},{"id":222,"value":"md`This visualization is intended for a game developer primarily. It can help them decide how they should be monetizing their game and if there is market saturation. If a game relies on keeping players coming back, like the recently popular battle royale games, then a free game will do much better in attracting an audience.` ","pinned":false,"hidden":false,"mode":"ojs"},{"id":33,"value":"md`## Vis 2: Ratings vs Installs`","pinned":false,"hidden":false,"mode":"ojs"},{"id":178,"value":"VegaLite({\n data: {values: playData},\n hconcat:[{\n mark: \"bar\",\n encoding: {\n x: {field: \"Rating\", type: \"Quantitative\"},\n y: {aggregate: \"mean\", field: \"Installs\", type: \"quantitative\"},\n },\n },\n {\n mark: \"bar\",\n encoding: {\n x: {field: \"Content Rating\", type: \"nominal\"},\n y: {aggregate: \"count\", type: \"quantitative\"}\n },\n },\n {\n mark: \"bar\",\n encoding: {\n x: {field: \"Content Rating\", type: \"nominal\"},\n y: {aggregate: \"mean\", field: \"Installs\", type: \"quantitative\"}\n },\n },\n {\n mark: \"bar\",\n encoding: {\n x: {field: \"Content Rating\", type: \"nominal\"},\n y: {aggregate: \"mean\", field: \"Rating\", type: \"quantitative\"}\n },\n }\n ]\n})","pinned":false,"hidden":false,"mode":"ojs"},{"id":108,"value":"md`### Analytic Scope:`","pinned":false,"hidden":false,"mode":"ojs"},{"id":110,"value":"md`\n

How much traffic do types of games get?
Does a better rating guarantee more traffic?
Are games aimed at everyone more successful?

`","pinned":false,"hidden":false,"mode":"ojs"},{"id":227,"value":"md`Data dimensions include average number of installs as it relates to the rating of the game. Then different facets of content rating are explored, such as number of games in that rating, number of players and average rating.`","pinned":false,"hidden":false,"mode":"ojs"},{"id":230,"value":"md`This visualization starts with a histogram because it allows for greater granularity. Being able to see how many installs a game gets based on its rating is clearly visible. Then, content rating is explored as it ties to rating, average installs and number of games using bar graphs. Differences between each type of rating are clearly communicated, as well as similarities.`","pinned":false,"hidden":false,"mode":"ojs"},{"id":233,"value":"md`Using this visualization we can see just how a games rating can affect sales. In particular, a key oddity in the average installs based on ratings can be noticed in the high end of ratings. Despite the ratings being high, the average number of players drops after a 4.5 rating. Also, while the average rating based on content rating is generally consistent, there is a spike in number of games being made for Everyone but generally more installs for games rated 10+. Despite the lower barreir of entry, it's interesting to see more isntalls for 10+ indicating aiming your game at everyone may not be what the users want.`","pinned":false,"hidden":false,"mode":"ojs"},{"id":235,"value":"md`The audience for this visualization is game designers. Determing what audience you are designing for is a decision made fairly early on in the process, so understanding the types of audiences in the desired medium can help inform those early decisions.`","pinned":false,"hidden":false,"mode":"ojs"},{"id":64,"value":"md`## Libraries`","pinned":false,"hidden":false,"mode":"ojs"},{"id":2,"value":"VegaLite = require('@observablehq/vega-lite')","pinned":true,"hidden":false,"mode":"ojs"},{"id":5,"value":"d3 = require(\"d3@5\")","pinned":true,"hidden":false,"mode":"ojs"},{"id":66,"value":"dfjs = require('https://bundle.run/dataframe-js@1.3.2')","pinned":true,"hidden":false,"mode":"ojs"},{"id":10,"value":"md`Dataset Source: https://www.kaggle.com/lava18/google-play-store-apps#googleplaystore.csv`","pinned":false,"hidden":false,"mode":"ojs"}],"files":"$W30","stdlib":"1","titling":"auto"}