Julia Data Kartta -

Because Julia passes by reference, you can update all linked plots simultaneously from a slider or live data feed. Let’s settle the debate. In Python, plotting 10M points with matplotlib is suicide (memory >8GB, render time >2min). In R, ggplot2 will choke on the backend grid engine. In Julia:

Makie is not a wrapper around C/C++ plotting libraries. It’s written entirely in Julia, uses GPU-accelerated rendering (via GLMakie or CairoMakie for publication), and supports interactive 3D scenes. using GLMakie, GeoJSON, ArchGDAL Load a GeoJSON of European regions geojson = GeoJSON.read("europe_regions.geojson") Assume df has columns: :region_name, :gdp_per_capita poly_coords = [feature.geometry for feature in geojson] julia data kartta

fig, ax, plt = poly(poly_coords, color = df.gdp_per_capita, colormap = :viridis, axis = (; aspect = DataAspect())) Because Julia passes by reference, you can update

The best map is the one you build yourself. So fire up the REPL, ]add Makie GeoJSON CSV Proj4 , and start tracing the true shape of your data. Have you built a Julia geospatial workflow? Share your maps or gotchas in the comments. In R, ggplot2 will choke on the backend grid engine