If you'd like to use Vega-Lite or Vega from Clojure or ClojureScript, you may wish to check out a small but flexible wrapper library I wrote called Oz: There's also now support for most of the modern scientific notebook environments (Jupyter, Nextjournal, Gorilla REPL), if that's your thing. MXNet just recently got Clojure support). On a final note, I'll second the sentiment of mikera and Ravinder Ram's that Clojure is a great language for data science, and is getting better all the time with new machine learning libraries (e.g. Usually starting out with Vega-Lite, and switching to Vega only as needed is sufficient. It compiles to Vega, which is a somewhat lower level and more powerful, but less automated version of Vega-Lite. Vega-Lite is more or less the higher lever, day-to-day data science tool, focusing on providing high leverage and automation based on very spartan specifications. Thus, any language that can reasonably target JSON can target Vega-Lite & Vega. Moreover, it ups the ante on the declarative nature of the GG in that Vega-Lite and Vega specifications are described as pure data (JSON). Vega-Lite & Vega however take things one step further in providing a grammar of interaction, which allows for the construction of interactive data visualizations and sophisticated explorer views.

The core idea is that data visualizations should be built according to declarative descriptions of how properties of the data map to aesthetics of the dataviz. Vega-Lite & Vega are designed based on the ideas of the Grammar of Graphics, which inspired R's popular ggplot2 library. I highly recommend using Vega-Lite & Vega for data visualizations, from whatever language you chose.

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