# Plotting with Vega-Lite in Nextjournal Nextjournal now fully supports displaying [Vega-Lite](https://vega.github.io/vega-lite/) plots in your notebooks. Any valid Vega-Lite grammar that’s written as JSON to `/results` can be displayed using Nextjournal’s results viewer. ![vega-lite.png][nextjournal#file#92e05e32-1c6a-43f3-bb8a-b2e1786c8b93] If your result has a `.vl.json` file extension, Nextjournal will automatically select the `vega-lite` viewer to display the plot. Here is a simple Bash code cell rendering an example from the [Vega-Lite docs](https://vega.github.io/vega-lite/docs/#spec): ```bash id=4e2b4f41-d579-4cc0-9082-7a58f2d44920 echo '{ "width": 500, "height": 300, "data": {"url": "https://vega.github.io/vega-datasets/data/unemployment-across-industries.json"}, "mark": "area", "encoding": { "x": { "timeUnit": "yearmonth", "field": "date", "type": "temporal", "axis": {"domain": false, "format": "%Y", "tickSize": 0} }, "y": { "aggregate": "sum", "field": "count","type": "quantitative", "axis": null, "stack": "center" }, "color": { "field":"series", "type":"nominal", "scale":{"scheme": "category20b"} } } }' > /results/plot.vl.json ``` [plot.vl.json][nextjournal#output#4e2b4f41-d579-4cc0-9082-7a58f2d44920#plot.vl.json] When not using the `.vl.json` extension, the `vega-lite` viewer can also be selected from the viewers selector on the left-hand side for any JSON result: ![select-vega-viewer.gif][nextjournal#file#1b314022-d7fa-47f8-9fb3-35216c676684] # Using Altair and Vega-Lite in Python While you can always select the viewer (or set the extension) manually, there’s also full support for the [Altair](https://altair-viz.github.io) package in Python. Altair is a wrapper around Vega-Lite that makes working with statistical visualization in Python very simple. Just install and `import` the `altair` package and return a plot to display it. Here is an example using `altair` to plot temperature predication data in various scenarios for the Chicago area based on a subset of the [OpenNEX DCP30 dataset](http://opennex.planetos.com/dcp30): [OpenNEX-chicago-climate.csv][nextjournal#file#daed2500-9a72-4cdf-823d-bde6a8029954] ```bash id=37f3d040-7565-49de-b69d-85b9b16571d8 pip install altair ``` ```python id=75051f8e-3eca-431e-b319-4d085abf4a1f import pandas as pd import altair as alt # Read in the CSV data = pd.read_csv([reference][nextjournal#reference#39247119-9b8e-4003-8746-eb7dbec4fe33]) # Specify categorical data for col in ['Model', 'Scenario', 'Variable']: data[col] = data[col].astype('category') # Coax date strings to beginning of year dates data['Year'] = data['Date'] \ .astype('datetime64') \ .map(lambda d: "%d-01-01" % d.year) \ .astype('datetime64') # Convert temperatures from Kelvin to Celsius data['Temperature'] = data['Value'] - 273.15 # Plot maximum temperature by year model = data.loc[1, 'Model'] title = 'Maximum mean temperature for warmest month using model %s' % model # Allow plotting large datasets alt.data_transformers.disable_max_rows() # Return the plot alt.Chart(data, title=title).mark_line().encode( x='Year:T', y=alt.Y('max_temp:Q', title='Temperature [Celsius]', scale=alt.Scale( domain=(25, 40), clamp=True ) ), color='Scenario' ).transform_aggregate( max_temp='max(Temperature)', groupby=['Year', 'Scenario'] ) ``` [result][nextjournal#output#75051f8e-3eca-431e-b319-4d085abf4a1f#result] # Displaying Vega-Lite Plots in Clojure In Clojure, you can return your Vega-Lite grammar directly as [EDN](https://github.com/edn-format/edn) and set the `^{:nextjournal/viewer "vega-lite"}` [metadata](https://clojure.org/reference/metadata). This tells Nextjournal to interpret the grammar as JSON and show it using the vega-lite viewer: ```clojure id=92cf8845-841d-4679-9c62-f448ac59da05 ^{:nextjournal/viewer :vega-lite} {:width 650 :height 400 :data {:url "https://vega.github.io/vega-datasets/data/us-10m.json" :format {:type "topojson" :feature "counties"}} :transform [{:lookup "id" :from {:data {:url "https://vega.github.io/vega-datasets/data/unemployment.tsv"} :key "id" :fields ["rate"]}}] :projection {:type "albersUsa"} :mark "geoshape" :encoding {:color {:field "rate" :type "quantitative"}}} ``` [result][nextjournal#output#92cf8845-841d-4679-9c62-f448ac59da05#result] # Plotting with VegaLite in Julia Plotting with VegaLite is easy enough with the [VegaLite.jl](https://github.com/queryverse/VegaLite.jl) package and Nextjournal's VegaLite image, that has everything installed: ```julia id=29fffc3b-e468-4be8-890d-41c51e2931e0 using VegaLite, VegaDatasets p = dataset("cars") |> @vlplot( :point, x = :Horsepower, y = :Miles_per_Gallon, color = :Origin, width = 650, height = 400 ) |> VegaLite.interactive() ``` [result][nextjournal#output#29fffc3b-e468-4be8-890d-41c51e2931e0#result] You can find more examples in the [image article ](https://nextjournal.com/julia-environments/vegalite-environment)or in the [VegaLite.jl documentation](https://www.queryverse.org/VegaLite.jl/stable/). [nextjournal#file#92e05e32-1c6a-43f3-bb8a-b2e1786c8b93]: [nextjournal#output#4e2b4f41-d579-4cc0-9082-7a58f2d44920#plot.vl.json]: [nextjournal#file#1b314022-d7fa-47f8-9fb3-35216c676684]: [nextjournal#file#daed2500-9a72-4cdf-823d-bde6a8029954]: [nextjournal#reference#39247119-9b8e-4003-8746-eb7dbec4fe33]: <#nextjournal#reference#39247119-9b8e-4003-8746-eb7dbec4fe33> [nextjournal#output#75051f8e-3eca-431e-b319-4d085abf4a1f#result]: [nextjournal#output#92cf8845-841d-4679-9c62-f448ac59da05#result]: [nextjournal#output#29fffc3b-e468-4be8-890d-41c51e2931e0#result]:
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