2e:null 2f:["$","title",null,{"children":"Create more expressive & articulate data visualizations with Plot 0.5 | Observable | Observable"}] 34:T42b,**Observable Plot** Since May 2021, our amazing community has been using [Observable Plot](https://observablehq.com/plot) to create beautiful and insightful charts. [This collection](https://observablehq.com/collection/@observablehq/plot-examples) highlights Observable Plot best practices, as well as real use cases. Observable Plot is a simple, expressive charting tool that allows you to quickly pull together analytics dashboards that entire teams can multiplayer edit in real time. Whether you are a data analyst/scientist, developer or decision maker, simpler code means more stakeholders are involved in crucial, exploratory data analysis used to uncover insights and make better business decisions. Like a handful of words forming unlimited sentences, Observable Plot embraces a “grammar of graphics” that gives you a rich vocabulary for visualization. You can combine various marks and layers to form entirely new chart types in minutes and since it’s all JavaScript, it works great with lodash, simple-statistics, Arquero and many other libraries.35:T533,**New features to explore in Observable Plot 0.5** - Observable Plot now supports **mark initializers** via the initializer option. Initializers can transform data, channels, and indexes. Unlike data transforms which operate in abstract data space, initializers can operate in screen space such as pixel coordinates and colors. - The new **hexbin transform** aggregates 2D points into hexagonal bins—you can then apply reducers (sum, count, mean, mode) to set visual properties like size and color. - The new **dodge transform** takes a position channel and computes the opposite position channel, which allows for densely packed dots without overlap. (In other words, fast and accurate beeswarms!) - Like other Observable Plot transforms, the hexbin and dodge **transforms work with several mark types**, so you can do creative things like dodge text. - The dodge transform works with Observable Plot’s sort transform to **control the order in which dots are placed** and the resulting layout. - When you use a diverging color scheme such as PiYG, Observable Plot now defaults to a **diverging (vs. linear) color scale**, ensuring that color is visually symmetric around the pivot. - By default, **dots now sort by descending radius** to reduce occlusion; this means that small dots are less likely to be covered by big ones. 36:[{"id":"0be3ccb823afa800433ff42ac9a47e59bb595cdf25fbcf9d223420a0522bdd289787ac622fb3eb5e03294bc75f5ba516a350754c0e00fe1b41e3470b002b8455","href":"https://static.observableusercontent.com/files/0be3ccb823afa800433ff42ac9a47e59bb595cdf25fbcf9d223420a0522bdd289787ac622fb3eb5e03294bc75f5ba516a350754c0e00fe1b41e3470b002b8455","name":"Observable Plot Cheatsheets.png","type":"image/png","lastModified":1655493751734.786,"size":1043442},{"id":"59153baa16cc4db2768e71f56620eabf7155e37c2065022da1adff839019c47b7254f495d5c0e5ca6f6202ec74975d3cbad58fe7b1fa5f9cabf887a694f22a54","href":"https://static.observableusercontent.com/files/59153baa16cc4db2768e71f56620eabf7155e37c2065022da1adff839019c47b7254f495d5c0e5ca6f6202ec74975d3cbad58fe7b1fa5f9cabf887a694f22a54","name":"dodge.png","type":"image/png","lastModified":1655493751734.786,"size":182596},{"id":"22f21588c3e7db4350b2fb36fc00e2bd756f35436d43f8b35bf8631f58a2014f1b4a7ce817c9b82e6970e1a71fbfa3fe5d256bcaea6e4c9aa4ff9dfb462306a7","href":"https://static.observableusercontent.com/files/22f21588c3e7db4350b2fb36fc00e2bd756f35436d43f8b35bf8631f58a2014f1b4a7ce817c9b82e6970e1a71fbfa3fe5d256bcaea6e4c9aa4ff9dfb462306a7","name":"hexbin@1.png","type":"image/png","lastModified":1655493751734.786,"size":52544}] 32:{"id":"ed032a0143a546b1","slug":"introducing-plot-05","title":"Create more expressive & articulate data visualizations with Plot 0.5","thumbnail":"d3bb77a3b01ee7c5cc77c4881031d329f67155709bec3d8897e0319a335f84b2","access_level":"public","create_time":"$D2022-06-17T19:22:31.734Z","update_time":"$D2022-11-22T22:29:52.882Z","view_time":null,"first_public_time":"$D2022-06-17T19:25:46.772Z","has_unpublished_edits":null,"fork":true,"stars":6,"starred":false,"owner":{"type":"team","id":"f35c755083683fe5","login":"observablehq","name":"Observable","avatar_url":"https://avatars-next.observableusercontent.com/424c6c1f7794371c9915a160519bdfb34cf34e50069fcd6def38a1a04d4e9702","tier":"enterprise_2024"},"version":667,"publish_version":null,"role":null,"authored":false,"authors":[{"type":"individual","id":"47a208cae9f42ebb","login":"clintbagley","name":"Clint Bagley","avatar_url":"https://avatars-next.observableusercontent.com/cd1a423b11cc9e12999abcb8886a6a6a6ecba00ea5a2f224575aabd84fdb85e2","tier":null},{"type":"individual","id":"248b532528a464b6","login":"mariaciampa","name":"Maria Ciampa","avatar_url":"https://avatars-next.observableusercontent.com/b0eea018d4e298e4db3effa434448e141a2de3a78d8bf9e1e2ff2e6b41aaaa64","tier":null}],"members":[{"type":"team","id":"f35c755083683fe5","login":"observablehq","name":"Observable","avatar_url":"https://avatars-next.observableusercontent.com/424c6c1f7794371c9915a160519bdfb34cf34e50069fcd6def38a1a04d4e9702","tier":"enterprise_2024","role":"editor"}],"comments":[{"id":"f66e4a4799c14035","reply_to_id":null,"content":"Why not include the actuall chart instead of a png or at least it's source, so we can see how it's done?","create_time":"$D2022-06-23T07:42:43.877Z","update_time":null,"resolved":false,"user":{"type":"individual","id":"ae2fb568d64d2eb1","login":"resmartizh","name":"Res Marti","avatar_url":"https://avatars-next.observableusercontent.com/69661c8e64ee6b7b99aba46691979edfa94971eb1e410861d738d2401ebe034e","tier":null}},{"id":"6582cb3add214165","reply_to_id":"f66e4a4799c14035","content":"Res, great question - thanks. I'll revise asap. This was pulled from our github plot/changelog here: https://github.com/observablehq/plot/blob/main/CHANGELOG.md#050","create_time":"$D2022-06-23T12:02:06.105Z","update_time":null,"resolved":false,"user":{"type":"individual","id":"248b532528a464b6","login":"mariaciampa","name":"Maria Ciampa","avatar_url":"https://avatars-next.observableusercontent.com/b0eea018d4e298e4db3effa434448e141a2de3a78d8bf9e1e2ff2e6b41aaaa64","tier":null}}],"custom_thumbnail":true,"trashed":false,"lapsed":null,"subscribed":null,"forks":2,"collection":{"id":"01e1972879c83ae3","slug":"observable-blog","title":"Observable Blog","owner":{"id":"f35c755083683fe5","login":"observablehq","name":"Observable"}},"type":"notebook","body":{"title":"Create more expressive & articulate data visualizations with Plot 0.5","cells":[{"id":223,"value":"# Create more expressive & articulate data visualizations with Plot 0.5\n## Introducing Observable Plot 0.5","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","output":"","database":"$undefined"},{"id":543,"value":"

Observable Plot 0.5’s new hexbin transform functions similarly to the bin transform, except it aggregates both x and y into hexagonal bins before reducing.

\n","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","output":"","database":"$undefined"},{"id":325,"value":"We are delighted to introduce **Observable Plot 0.5**, which now supports more chart types, including hexbins, beeswarms, and more. These new features allow you to be more expressive and articulate in your data work. See the [release notes ](https://github.com/observablehq/plot/blob/main/CHANGELOG.md#050)for more details, and the new documentation notebooks for the [dodge](/@observablehq/plot-dodge) and the [hexbin](/@observablehq/plot-hexbin) transforms.\n\nAmong other changes, Plot 0.5 now sorts dots by descending radius to reduce occlusion—no more custom sorting necessary to bring up the smaller dots to the front and make them more visible. Also, when you specify a diverging color scheme, Plot now defaults to a diverging scale around a central pivot, saving you time in the most common situations.\n\nThe result: more expressive charts with even less code!","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","output":"","database":"$undefined"},{"id":538,"value":"$34","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","output":"","database":"$undefined"},{"id":555,"value":"

Observable Plot 0.5's new \"dodge transform\" can be used to produce beeswarm plots.

\n","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","output":"","database":"$undefined"},{"id":467,"value":"$35","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","output":"","database":"$undefined"},{"id":596,"value":"** Plot Cheat Sheets, Plot Happy Hours, and More**","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","output":"","database":"$undefined"},{"id":481,"value":"observablePlotCheatsheets = FileAttachment(\"Observable Plot Cheatsheets.png\").image()","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":473,"value":"If you are just getting started with Observable Plot, be sure to check out these helpful [Observable Plot Cheatsheets](https://observablehq.com/@observablehq/plot-cheatsheets?collection=%40observablehq%2Fobservable-blog) that our team created. \n\nOur most recent [Observable Plot Happy Hour](https://www.youtube.com/watch?v=BPciBpaj_h8) focused on using Observable Plot to analyze each of the games from the [2022 NBA Finals](https://observablehq.com/@observablehq/watch-the-nba-finals-players-data-jam-on-observable?collection=%40observablehq%2Fobservable-blog). Learn about our next Plot Happy Hour by joining our [Meetup](https://www.meetup.com/observablehq) group.\n\nLastly, if you’d like to hear about our latest product and community updates, subscribe [here](https://observablehq.com/@observablehq/blog-subscribe). ","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","output":"","database":"$undefined"},{"id":450,"value":"________________________\n\n","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","output":"","database":"$undefined"},{"id":509,"value":"banner(\n \"Interested in learning more about collaborating with data?\",\n \"Sign up for free\",\n \"https://observablehq.com/product\"\n)","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":506,"value":"import {banner} from \"@observablehq/brand-assets\"","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"}],"files":"$W36","stdlib":"1","titling":"auto"}} 33:"$32:body"