remove palmerpenguins package

rdboyes · rdboyes · commit acd0132b462e · 2026-01-05T11:28:01.000-05:00
diff --git a/Project.toml b/Project.toml
@@ -19,7 +19,6 @@ KernelDensity = "5ab0869b-81aa-558d-bb23-cbf5423bbe9b"
 Loess = "4345ca2d-374a-55d4-8d30-97f9976e7612"
 Makie = "ee78f7c6-11fb-53f2-987a-cfe4a2b5a57a"
 Markdown = "d6f4376e-aef5-505a-96c1-9c027394607a"
-PalmerPenguins = "8b842266-38fa-440a-9b57-31493939ab85"
 Parquet2 = "98572fba-bba0-415d-956f-fa77e587d26d"
 PooledArrays = "2dfb63ee-cc39-5dd5-95bd-886bf059d720"
 RDatasets = "ce6b1742-4840-55fa-b093-852dadbb1d8b"
@@ -47,7 +46,6 @@ KernelDensity = "0.6"
 Loess = "0.6"
 Makie = "0.24"
 Markdown = "1.10"
-PalmerPenguins = "0.1.4"
 Parquet2 = "0.2"
 PooledArrays = "1.4"
 RDatasets = "0.7.7"
diff --git a/docs/Project.toml b/docs/Project.toml
@@ -5,7 +5,6 @@ Documenter = "e30172f5-a6a5-5a46-863b-614d45cd2de4"
 DocumenterMarkdown = "997ab1e6-3595-5248-9280-8efb232c3433"
 Literate = "98b081ad-f1c9-55d3-8b20-4c87d4299306"
 Makie = "ee78f7c6-11fb-53f2-987a-cfe4a2b5a57a"
-PalmerPenguins = "8b842266-38fa-440a-9b57-31493939ab85"
 RDatasets = "ce6b1742-4840-55fa-b093-852dadbb1d8b"
 TidierData = "fe2206b3-d496-4ee9-a338-6a095c4ece80"
 TidierPlots = "337ecbd1-5042-4e2a-ae6f-ca776f97570a"
diff --git a/docs/examples/UserGuide/gettingstarted.jl b/docs/examples/UserGuide/gettingstarted.jl
@@ -1,8 +1,8 @@
 using TidierPlots
 using DataFrames
-using PalmerPenguins
+using Parquet2
 
-penguins = dropmissing(DataFrame(PalmerPenguins.load()));
+penguins = DataFrame(Parquet2.readfile(joinpath("..", "test", "penguins.parq")))
 
 # ## `ggplot()`
 # `ggplot()` is the starting point of any plot. It sets up the initial plot with default settings that can be later customized with geoms, scales, theme settings and other specifications. `ggplot` usually used with a data source as an argument, and optionally, a set of aesthetics specified by @aes(). The data source is typically a DataFrame.
diff --git a/docs/examples/geoms/geom_bar.jl b/docs/examples/geoms/geom_bar.jl
@@ -38,9 +38,9 @@
 
 using TidierPlots
 using DataFrames
-using PalmerPenguins
+using Parquet2
 
-penguins = dropmissing(DataFrame(PalmerPenguins.load()));
+penguins = DataFrame(Parquet2.readfile(joinpath("..", "test", "penguins.parq")))
 
 # For basic counting, provide only an x aesthetic. The height of each bar will represent the count of observations in each category.
 
diff --git a/docs/examples/geoms/geom_boxplot.jl b/docs/examples/geoms/geom_boxplot.jl
@@ -48,9 +48,9 @@
 
 using TidierPlots
 using DataFrames
-using PalmerPenguins
+using Parquet2
 
-penguins = dropmissing(DataFrame(PalmerPenguins.load()));
+penguins = DataFrame(Parquet2.readfile(joinpath("..", "test", "penguins.parq")))
 
 # Create a basic boxplot showing the distribution of bill length for each species:
 
diff --git a/docs/examples/geoms/geom_col.jl b/docs/examples/geoms/geom_col.jl
@@ -45,9 +45,9 @@
 using TidierPlots
 using TidierData
 using DataFrames
-using PalmerPenguins
+using Parquet2
 
-penguins = dropmissing(DataFrame(PalmerPenguins.load()));
+penguins = DataFrame(Parquet2.readfile(joinpath("..", "test", "penguins.parq")))
 
 # First, we create a summarized dataset with mean bill length by species and sex:
 
diff --git a/docs/examples/geoms/geom_density.jl b/docs/examples/geoms/geom_density.jl
@@ -41,9 +41,9 @@
 
 using TidierPlots
 using DataFrames
-using PalmerPenguins
+using Parquet2
 
-penguins = dropmissing(DataFrame(PalmerPenguins.load()));
+penguins = DataFrame(Parquet2.readfile(joinpath("..", "test", "penguins.parq")))
 
 # Create a basic density plot showing the distribution of bill length:
 
diff --git a/docs/examples/geoms/geom_errorbars.jl b/docs/examples/geoms/geom_errorbars.jl
@@ -40,10 +40,10 @@
 using TidierPlots
 using TidierData
 using DataFrames
-using PalmerPenguins
 using Statistics
+using Parquet2
 
-penguins = dropmissing(DataFrame(PalmerPenguins.load()));
+penguins = DataFrame(Parquet2.readfile(joinpath("..", "test", "penguins.parq")))
 
 # First, create a summary dataset with means and standard deviations:
 
diff --git a/docs/examples/geoms/geom_hist.jl b/docs/examples/geoms/geom_hist.jl
@@ -47,9 +47,9 @@
 
 using TidierPlots
 using DataFrames
-using PalmerPenguins
+using Parquet2
 
-penguins = dropmissing(DataFrame(PalmerPenguins.load()));
+penguins = DataFrame(Parquet2.readfile(joinpath("..", "test", "penguins.parq")))
 
 # Create a basic histogram of bill length:
 
diff --git a/docs/examples/geoms/geom_hvlines.jl b/docs/examples/geoms/geom_hvlines.jl
@@ -40,9 +40,9 @@
 using TidierPlots
 using TidierData
 using DataFrames
-using PalmerPenguins
+using Parquet2
 
-penguins = dropmissing(DataFrame(PalmerPenguins.load()));
+penguins = DataFrame(Parquet2.readfile(joinpath("..", "test", "penguins.parq")))
 
 # ## Horizontal Lines
 
diff --git a/docs/examples/geoms/geom_lines.jl b/docs/examples/geoms/geom_lines.jl
@@ -88,8 +88,9 @@ ggplot(df, @aes(x = x, y = y)) +
 
 # `geom_path()` connects points in the order they appear in the data. This is useful for trajectories or paths through 2D space:
 
-using PalmerPenguins
-penguins = dropmissing(DataFrame(PalmerPenguins.load()));
+using Parquet2
+
+penguins = DataFrame(Parquet2.readfile(joinpath("..", "test", "penguins.parq")))
 
 ggplot(penguins, @aes(x = bill_length_mm, y = bill_depth_mm)) +
     geom_path(alpha = 0.3)
diff --git a/docs/examples/geoms/geom_point.jl b/docs/examples/geoms/geom_point.jl
@@ -41,12 +41,12 @@
 
 using TidierPlots
 using DataFrames
-using PalmerPenguins
 using RDatasets
 using CategoricalArrays
 using Makie
+using Parquet2
 
-penguins = dropmissing(DataFrame(PalmerPenguins.load()));
+penguins = DataFrame(Parquet2.readfile(joinpath("..", "test", "penguins.parq")))
 diamonds = dataset("ggplot2", "diamonds");
 mtcars = dataset("datasets", "mtcars");
 
diff --git a/docs/examples/geoms/geom_rainclouds.jl b/docs/examples/geoms/geom_rainclouds.jl
@@ -43,9 +43,9 @@
 
 using TidierPlots
 using DataFrames
-using PalmerPenguins
+using Parquet2
 
-penguins = dropmissing(DataFrame(PalmerPenguins.load()));
+penguins = DataFrame(Parquet2.readfile(joinpath("..", "test", "penguins.parq")))
 
 ggplot(penguins) +
 geom_rainclouds(@aes(x = species, y = bill_depth_mm))
diff --git a/docs/examples/geoms/geom_smooth.jl b/docs/examples/geoms/geom_smooth.jl
@@ -36,10 +36,9 @@
 
 using TidierPlots
 using DataFrames
-using PalmerPenguins
-
-penguins = dropmissing(DataFrame(PalmerPenguins.load()));
+using Parquet2
 
+penguins = DataFrame(Parquet2.readfile(joinpath("..", "test", "penguins.parq")))
 # ## Basic LOESS Smoothing
 
 # The default method is LOESS (locally estimated scatterplot smoothing), which creates a flexible smooth line that follows the data:
diff --git a/docs/examples/geoms/geom_text.jl b/docs/examples/geoms/geom_text.jl
@@ -47,9 +47,9 @@
 using TidierPlots
 using TidierData
 using DataFrames
-using PalmerPenguins
+using Parquet2
 
-penguins = dropmissing(DataFrame(PalmerPenguins.load()));
+penguins = DataFrame(Parquet2.readfile(joinpath("..", "test", "penguins.parq")))
 
 # ## Basic Text Labels
 
diff --git a/docs/examples/geoms/geom_tile.jl b/docs/examples/geoms/geom_tile.jl
@@ -84,10 +84,11 @@ ggplot(df_mandelbrot, @aes(x = x, y = y, z = z)) +
 
 # Heatmaps are commonly used to visualize correlation matrices:
 
-using PalmerPenguins
+
 using Statistics
+using Parquet2
 
-penguins = dropmissing(DataFrame(PalmerPenguins.load()));
+penguins = DataFrame(Parquet2.readfile(joinpath("..", "test", "penguins.parq")))
 
 # Select numeric columns and compute correlation
 numeric_cols = [:bill_length_mm, :bill_depth_mm, :flipper_length_mm, :body_mass_g]
diff --git a/docs/examples/geoms/geom_violin.jl b/docs/examples/geoms/geom_violin.jl
@@ -44,10 +44,9 @@
 
 using TidierPlots
 using DataFrames
-using PalmerPenguins
-
-penguins = dropmissing(DataFrame(PalmerPenguins.load()));
+using Parquet2
 
+penguins = DataFrame(Parquet2.readfile(joinpath("..", "test", "penguins.parq")))
 # ## Basic Violin Plot
 
 # Create a basic violin plot showing the distribution of bill length for each species:
diff --git a/test/test_docs_will_render.jl b/test/test_docs_will_render.jl
@@ -1,13 +1,12 @@
 @testitem "docs will render" begin
     using Markdown
-    using PalmerPenguins
     
     for (path, dirs, files) in walkdir("../docs/examples")  
         for file in files
             if endswith(file, ".jl")
                 include(joinpath(path, file))
+                @test true
             end
         end
     end
-    @test true
 end
