both methods for geom_smooth have tests and work

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"
+Loess = "4345ca2d-374a-55d4-8d30-97f9976e7612"
 Makie = "ee78f7c6-11fb-53f2-987a-cfe4a2b5a57a"
 Reexport = "189a3867-3050-52da-a836-e630ba90ab69"
 TidierData = "fe2206b3-d496-4ee9-a338-6a095c4ece80"

src/TidierPlots.jl

@@ -18,8 +18,9 @@ using CategoricalArrays
 # Data manipulation, expression parsing
 using TidierData
 
-# for ... GLMS
+# for geom_smooth fits
 using GLM
+using Loess
 
 include("structs.jl")
 

src/geoms/geom_smooth.jl

@@ -33,35 +33,45 @@ end
 function geom_smooth(args...; kwargs...)
     aes_dict, args_dict = extract_aes(args, kwargs)
     args_dict["geom_name"] = "geom_smooth"
-
-    analysis = stat_loess
 
     if haskey(args_dict, "method")
         if args_dict["method"] == "lm"
-            analysis = stat_linear
+            return [build_geom(aes_dict, 
+                        args_dict, 
+                        ["x", "y"],
+                        :Lines, 
+                        stat_linear),
+                    build_geom(aes_dict,
+                        args_dict, 
+                        ["x", "lower", "upper"],
+                        :Band, 
+                        stat_linear)]
         end
     end
-
-    # geom_smooth returns TWO makie plots: 
-    # :Lines - the center line
-    # :Band - the uncertainty interval
 
-    return [build_geom(aes_dict, 
+    return build_geom(aes_dict, 
                       args_dict, 
                       ["x", "y"],
                       :Lines, 
-                      analysis),
-            build_geom(aes_dict,
-                      args_dict, 
-                      ["x", "lower", "upper"],
-                      :Band, 
-                      analysis)]
+                      stat_loess)
 end
 
 function stat_loess(aes_dict::Dict{String, Symbol}, 
     args_dict::Dict{Any, Any}, required_aes::Vector{String}, plot_data::DataFrame)
 
-    return (aes_dict, args_dict, required_aes, plot_data)
+    x = plot_data[!, aes_dict["x"]]
+    y = plot_data[!, aes_dict["y"]]
+
+    model = Loess.loess(x, y; span = .75, degree = 2)
+    x̂ = range(extrema(x)..., length=200)
+    ŷ = Loess.predict(model, x̂)
+
+    return_data = DataFrame(
+        String(aes_dict["x"]) => x̂,
+        String(aes_dict["y"]) => ŷ 
+    )
+
+    return (aes_dict, args_dict, required_aes, return_data)
 end
 
 function stat_linear(aes_dict::Dict{String, Symbol}, 

test/Manifest.toml

@@ -2,7 +2,7 @@
 
 julia_version = "1.10.2"
 manifest_format = "2.0"
-project_hash = "d3db1469a6d665450c389024fc6c41ad54db31ae"
+project_hash = "5ca544e0ddaec04c2c0551102da3cb1f1f227a35"
 
 [[deps.AbstractFFTs]]
 deps = ["LinearAlgebra"]
@@ -593,6 +593,12 @@ version = "1.0.10+0"
 deps = ["Random"]
 uuid = "9fa8497b-333b-5362-9e8d-4d0656e87820"
 
+[[deps.GLM]]
+deps = ["Distributions", "LinearAlgebra", "Printf", "Reexport", "SparseArrays", "SpecialFunctions", "Statistics", "StatsAPI", "StatsBase", "StatsFuns", "StatsModels"]
+git-tree-sha1 = "273bd1cd30768a2fddfa3fd63bbc746ed7249e5f"
+uuid = "38e38edf-8417-5370-95a0-9cbb8c7f171a"
+version = "1.9.0"
+
 [[deps.GeoInterface]]
 deps = ["Extents"]
 git-tree-sha1 = "d4f85701f569584f2cff7ba67a137d03f0cfb7d0"
@@ -1076,6 +1082,12 @@ git-tree-sha1 = "110897e7db2d6836be22c18bffd9422218ee6284"
 uuid = "d3a379c0-f9a3-5b72-a4c0-6bf4d2e8af0f"
 version = "2.12.0+0"
 
+[[deps.Loess]]
+deps = ["Distances", "LinearAlgebra", "Statistics", "StatsAPI"]
+git-tree-sha1 = "a113a8be4c6d0c64e217b472fb6e61c760eb4022"
+uuid = "4345ca2d-374a-55d4-8d30-97f9976e7612"
+version = "0.6.3"
+
 [[deps.LogExpFunctions]]
 deps = ["DocStringExtensions", "IrrationalConstants", "LinearAlgebra"]
 git-tree-sha1 = "18144f3e9cbe9b15b070288eef858f71b291ce37"
@@ -1776,6 +1788,12 @@ version = "1.3.1"
     ChainRulesCore = "d360d2e6-b24c-11e9-a2a3-2a2ae2dbcce4"
     InverseFunctions = "3587e190-3f89-42d0-90ee-14403ec27112"
 
+[[deps.StatsModels]]
+deps = ["DataAPI", "DataStructures", "LinearAlgebra", "Printf", "REPL", "ShiftedArrays", "SparseArrays", "StatsAPI", "StatsBase", "StatsFuns", "Tables"]
+git-tree-sha1 = "5cf6c4583533ee38639f73b880f35fc85f2941e0"
+uuid = "3eaba693-59b7-5ba5-a881-562e759f1c8d"
+version = "0.7.3"
+
 [[deps.StringManipulation]]
 deps = ["PrecompileTools"]
 git-tree-sha1 = "a04cabe79c5f01f4d723cc6704070ada0b9d46d5"

test/Project.toml

@@ -1,8 +1,10 @@
 [deps]
 CategoricalArrays = "324d7699-5711-5eae-9e2f-1d82baa6b597"
 DataFrames = "a93c6f00-e57d-5684-b7b6-d8193f3e46c0"
+GLM = "38e38edf-8417-5370-95a0-9cbb8c7f171a"
 Images = "916415d5-f1e6-5110-898d-aaa5f9f070e0"
 JDF = "babc3d20-cd49-4f60-a736-a8f9c08892d3"
+Loess = "4345ca2d-374a-55d4-8d30-97f9976e7612"
 Makie = "ee78f7c6-11fb-53f2-987a-cfe4a2b5a57a"
 Test = "8dfed614-e22c-5e08-85e1-65c5234f0b40"
 TidierData = "fe2206b3-d496-4ee9-a338-6a095c4ece80"

test/runtests.jl

@@ -6,6 +6,8 @@ using TidierData
 using Images
 using JDF
 using CategoricalArrays
+using GLM
+using Loess
 
 # functions to compare two images using a difference hash
 # essentially copied from ImageHashes.jl, but package is out of date

test/test_geoms.jl

@@ -217,6 +217,75 @@
         @test plot_images_equal(t, m)
 
     end
-
+
+    @testset "geom_smooth" begin
+        t = ggplot(penguins, aes(x = "bill_length_mm", y = "bill_depth_mm")) + 
+            geom_smooth() + geom_point()
+
+        model = Loess.loess(penguins.bill_length_mm, penguins.bill_depth_mm; span = .75, degree = 2)
+        x̂ = range(extrema(penguins.bill_length_mm)..., length=200)
+        ŷ = Loess.predict(model, x̂)
+
+        m = Makie.plot(
+            Makie.SpecApi.GridLayout(
+                Makie.SpecApi.Axis(
+                    plots = [
+                        Makie.PlotSpec(
+                            :Lines, 
+                            x̂,
+                            ŷ),
+                        Makie.PlotSpec(
+                            :Scatter,
+                            penguins.bill_length_mm,
+                            penguins.bill_depth_mm
+                        )
+                    ]
+                )
+            )
+        )
+
+        @test plot_images_equal(t, m)
+
+        t = ggplot(penguins, aes(x = "bill_length_mm", y = "bill_depth_mm")) + 
+            geom_smooth(method = "lm") + geom_point()
+
+
+        function add_intercept_column(x::AbstractVector{T}) where {T}
+            mat = similar(x, float(T), (length(x), 2))
+            fill!(view(mat, :, 1), 1)
+            copyto!(view(mat, :, 2), x)
+            return mat
+        end
+
+        lin_model = GLM.lm(add_intercept_column(penguins.bill_length_mm), penguins.bill_depth_mm)
+        x̂ = range(extrema(penguins.bill_length_mm)..., length=100)
+        pred = DataFrame(GLM.predict(lin_model, add_intercept_column(x̂); interval = :confidence))
+
+        m = Makie.plot(
+            Makie.SpecApi.GridLayout(
+                Makie.SpecApi.Axis(
+                    plots = [
+                        Makie.PlotSpec(
+                            :Lines, 
+                            x̂,
+                            pred.prediction),
+                        Makie.PlotSpec(
+                            :Scatter,
+                            penguins.bill_length_mm,
+                            penguins.bill_depth_mm
+                        ), 
+                        Makie.PlotSpec(
+                            :Band,
+                            x̂,
+                            pred.lower,
+                            pred.upper
+                        )
+                    ]
+                )
+            )
+        )
+
+        @test plot_images_equal(t, m)
+
     end
-    
+end