-
framework
A static site generator for data apps, dashboards, reports, and more. Observable Framework combines JavaScript on the front-end for interactive graphics with any language on the back-end for data analysis. (by observablehq)
From the Observable Framework point of view, you’re very welcome to use Apache ECharts or any other library instead of Observable Plot, since you can import whatever you like and it’s all just JavaScript.
Since there was a lot of interest in this thread, Mike added a page to the docs with an ECharts example: https://observablehq.com/framework/lib/echarts
There are two pieces of that example code specific to Framework: the html`` tagged template literal creates a DOM element (see https://github.com/observablehq/htl, also usable outside Framework), and the display function inserts it into the document above the code block (see https://observablehq.com/framework/javascript/display). Note that, whereas Observable Plot takes an options object and returns a DOM element, ECharts instead takes a DOM element and mutates it — but in general they should be equally easy to use in Framework.
Like Plot (and Vega-Lite, another great option), ECharts is also now one of Framework’s built-in “recommended libraries” (see https://observablehq.com/framework/javascript/imports#implic...), meaning that if you reference `echarts` Framework will lazy-load it for you. Adding that was a two-line diff: https://github.com/observablehq/framework/pull/811/files#dif.... But I wanna emphasize that Framework doesn’t have to explicitly “support” a given library for you to use it. “Supporting” in this case just means the convenience of saving you a one-line import statement. But don’t wait for our blessing!! Use whatever.
-
SurveyJS
JavaScript Form Builder with No-Code UI & Built-In JSON Schema Editor. Keep full control over the data you collect and tailor the form builder’s entire look and feel to your users’ needs. SurveyJS works with React, Angular, Vue 3, and is compatible with any backend or auth system. Learn more.
-
We’re working on zooming and panning for Observable Plot (https://github.com/observablehq/plot/pull/1738) and other interactions such as brushing (https://github.com/observablehq/plot/pull/721) — all of this is already possible, we just haven’t packaged it up in a convenient way yet (https://github.com/observablehq/plot/pull/1871).
We’ve been focused primarily on the static display of visualizations because that’s what viewers see first, and often that’s often the only thing they see. Relying too heavily on interaction places an onus on the user to find the insights; a good display of data should be opinionated about what it shows and guide the user to what is interesting.
We’re not trying to convince you to switch to JavaScript here — a main value prop of Observable Framework is that you can write data loaders in any language (Python, R, Go, Julia, etc.). So do all your data preparation and analysis in whatever language you like, and then do your front-end in JavaScript to leverage the graphics and interactive compute capabilities of modern browsers. It’s pipes and child_process.spawn under the hood. And you still get instant reactivity when you save changes to your data loaders (when you edit Python) because Framework watches files and pushes new data to the client with reactive hot data & module replacement.
And you can compress (aggregate or filter) the data as much as you like, so it’s up to you how much data you send to the client. For example your data loader could be a minimal CSV file that’s just the numbers you need for a bar chart. Or it could be a Parquet file and you use DuckDB (https://observablehq.com/framework/lib/duckdb) on the client to generate dynamic visualizations.
-
evidence
Business intelligence as code: build fast, interactive data visualizations in SQL and markdown
The new direction seems very similar to what evidence has been doing for a while
https://evidence.dev
-
Me too, and that lead to developing the « datasette-dashboards » plugin[0]. I use this for my company where all the data is gathered by connectors scheduled in CI, storing data in Git, and triggering a SQLite db build and Datasette deployment. « BI as Code » if you will
[0] https://github.com/rclement/datasette-dashboards
-
Yep, Evidence is doing good work. We were most directly inspired by VitePress; we spent months rewriting both D3’s docs (https://d3js.org) and Observable Plot’s docs (https://observablehq.com/plot) in VitePress, and absolutely loved the experience. But we wanted a tool focused on data apps, dashboards, reports — observability and business intelligence use cases rather than documentation. Compared to Evidence, I’d say we’re trying to target data app developers more than data analysts; we offer a lot of power and expressiveness, and emphasize custom visualizations and interaction (leaning on Observable Plot or D3), as well as polyglot programming with data loaders written in any language (Python, R, not just SQL).
-
We’re working on the interactivity, but we’re not going to do a Python wrapper as the goal of Observable Framework (and Plot) is to leverage web technologies with JavaScript in the front-end for interactive graphics — while doing whatever language you like, including Python, on the back-end for data analysis. There is a R wrapper for Observable Plot (https://github.com/juba/obsplot) and so I imagine someone could write one for Python, but ultimately we don’t think you’ll need it with Framework’s polyglot data loaders since you can seamlessly move between languages (front-end and back-end).
-
FYI: Same dev building obsplot is also building a Python version at https://github.com/juba/pyobsplot
-
InfluxDB
InfluxDB – Built for High-Performance Time Series Workloads. InfluxDB 3 OSS is now GA. Transform, enrich, and act on time series data directly in the database. Automate critical tasks and eliminate the need to move data externally. Download now.
-
I've setup a codespace starter template that pulls in the required dependencies to start building dashboards with Observable Framework, right inside your browser. You can access the starter template here:
[0]: https://github.com/dleeftink/observable-codespace
-
I took the query only what you need approach with PulseUI: https://www.timestored.com/pulse/ It emphasises not sending all data to the client. For real-time and very large data sets this is the only option.
-
I think the idea of Framework is really good, but static data limits the applications, excluding monitoring and other cases in which the data is constantly changing, but the dashboard can stay as it is. For example, I'd love to see a revamped Framework version of the LHC beam monitor and related pages (see https://op-webtools.web.cern.ch/vistar/, but check again in 2 months or so, when the accelerator will be running).
In high-energy physics, ROOT is /the/ toolkit for data analysis, and I guess jsROOT (https://root.cern.ch/js/) could also be used to load data to be shown in Framework dashboards. I thought the idea of Framework as a blogging engine with powerful data visualization built-in could be very interesting. Think, for example, about physicists pulling open data (https://opendata.cern.ch) and writing about their analysis or someone pulling data from https://ourworldindata.org/ in their own visualizations to support their case while writing about a particular subject, etc.
-
I think the idea of Framework is really good, but static data limits the applications, excluding monitoring and other cases in which the data is constantly changing, but the dashboard can stay as it is. For example, I'd love to see a revamped Framework version of the LHC beam monitor and related pages (see https://op-webtools.web.cern.ch/vistar/, but check again in 2 months or so, when the accelerator will be running).
In high-energy physics, ROOT is /the/ toolkit for data analysis, and I guess jsROOT (https://root.cern.ch/js/) could also be used to load data to be shown in Framework dashboards. I thought the idea of Framework as a blogging engine with powerful data visualization built-in could be very interesting. Think, for example, about physicists pulling open data (https://opendata.cern.ch) and writing about their analysis or someone pulling data from https://ourworldindata.org/ in their own visualizations to support their case while writing about a particular subject, etc.
-
htl
A tagged template literal that allows safe interpolation of values into HTML, following the HTML5 spec
From the Observable Framework point of view, you’re very welcome to use Apache ECharts or any other library instead of Observable Plot, since you can import whatever you like and it’s all just JavaScript.
Since there was a lot of interest in this thread, Mike added a page to the docs with an ECharts example: https://observablehq.com/framework/lib/echarts
There are two pieces of that example code specific to Framework: the html`` tagged template literal creates a DOM element (see https://github.com/observablehq/htl, also usable outside Framework), and the display function inserts it into the document above the code block (see https://observablehq.com/framework/javascript/display). Note that, whereas Observable Plot takes an options object and returns a DOM element, ECharts instead takes a DOM element and mutates it — but in general they should be equally easy to use in Framework.
Like Plot (and Vega-Lite, another great option), ECharts is also now one of Framework’s built-in “recommended libraries” (see https://observablehq.com/framework/javascript/imports#implic...), meaning that if you reference `echarts` Framework will lazy-load it for you. Adding that was a two-line diff: https://github.com/observablehq/framework/pull/811/files#dif.... But I wanna emphasize that Framework doesn’t have to explicitly “support” a given library for you to use it. “Supporting” in this case just means the convenience of saving you a one-line import statement. But don’t wait for our blessing!! Use whatever.
