half completed geom_contour

Project.toml

@@ -9,6 +9,7 @@ CategoricalArrays = "324d7699-5711-5eae-9e2f-1d82baa6b597"
 Colors = "5ae59095-9a9b-59fe-a467-6f913c188581"
 DataFrames = "a93c6f00-e57d-5684-b7b6-d8193f3e46c0"
 GLM = "38e38edf-8417-5370-95a0-9cbb8c7f171a"
+KernelDensity = "5ab0869b-81aa-558d-bb23-cbf5423bbe9b"
 Loess = "4345ca2d-374a-55d4-8d30-97f9976e7612"
 Makie = "ee78f7c6-11fb-53f2-987a-cfe4a2b5a57a"
 Reexport = "189a3867-3050-52da-a836-e630ba90ab69"

src/TidierPlots.jl

@@ -21,6 +21,7 @@ using TidierData
 # for geom_smooth fits
 using GLM
 using Loess
+using KernelDensity
 
 include("structs.jl")
 

src/geoms/geom_contour.jl

@@ -1,2 +1,19 @@
 geom_tile = geom_template("geom_tile", ["x", "y", "z"], :Heatmap)
-geom_contour = geom_template("geom_contour", ["x", "y", "z"], :Contour)
+geom_contour = geom_template("geom_contour", ["x", "y"], :Contour; aes_function = stat_density_2d)
+
+function stat_density_2d(aes_dict::Dict{String, Symbol}, 
+    args_dict::Dict{String, Any}, required_aes::Vector{String}, plot_data::DataFrame)
+
+    x = plot_data[!, aes_dict["x"]]
+    y = plot_data[!, aes_dict["y"]]
+
+    k = kde((penguins.bill_length_mm, penguins.bill_depth_mm))
+
+    required_aes = ["z"]
+
+    return_data = DataFrame(
+        "z" => k.density
+    )
+
+    return (aes_dict, args_dict, required_aes, return_data)
+end