examples fixed

Author: rdboyes

Project.toml

@@ -18,6 +18,7 @@ Images = "916415d5-f1e6-5110-898d-aaa5f9f070e0"
 KernelDensity = "5ab0869b-81aa-558d-bb23-cbf5423bbe9b"
 Loess = "4345ca2d-374a-55d4-8d30-97f9976e7612"
 Makie = "ee78f7c6-11fb-53f2-987a-cfe4a2b5a57a"
+PalmerPenguins = "8b842266-38fa-440a-9b57-31493939ab85"
 Parquet2 = "98572fba-bba0-415d-956f-fa77e587d26d"
 PooledArrays = "2dfb63ee-cc39-5dd5-95bd-886bf059d720"
 RDatasets = "ce6b1742-4840-55fa-b093-852dadbb1d8b"
@@ -44,6 +45,7 @@ Images = "0.26.2"
 KernelDensity = "0.6"
 Loess = "0.6"
 Makie = "0.24"
+PalmerPenguins = "0.1.4"
 Parquet2 = "0.2"
 PooledArrays = "1.4"
 RDatasets = "0.7.7"

docs/examples/geoms/geom_errorbars.jl

@@ -45,13 +45,17 @@ using Statistics
 
 penguins = dropmissing(DataFrame(PalmerPenguins.load()));
 
-# First, create a summary dataset with means and standard errors:
+# First, create a summary dataset with means and standard deviations:
 
 df = @chain penguins begin
     @group_by(species)
     @summarize(
         mean_bill = mean(bill_length_mm),
-        se_bill = std(bill_length_mm) / sqrt(n())
+        sd_bill = std(bill_length_mm),
+        n = n()
+    )
+    @mutate(
+        se_bill = sd_bill / sqrt(n)
     )
     @mutate(
         lower = mean_bill - 1.96 * se_bill,
@@ -87,7 +91,11 @@ df_h = @chain penguins begin
     @group_by(species)
     @summarize(
         mean_bill = mean(bill_length_mm),
-        se_bill = std(bill_length_mm) / sqrt(n())
+        sd_bill = std(bill_length_mm),
+        n = n()
+    )
+    @mutate(
+        se_bill = sd_bill / sqrt(n)
     )
     @mutate(
         lower = mean_bill - 1.96 * se_bill,
@@ -101,10 +109,14 @@ ggplot(df_h, @aes(y = species, xmin = lower, xmax = upper)) +
 # Colored error bars by group:
 
 df_grouped = @chain penguins begin
-    @group_by(species, sex)
+    @group_by(species)
     @summarize(
         mean_bill = mean(bill_length_mm),
-        se_bill = std(bill_length_mm) / sqrt(n())
+        sd_bill = std(bill_length_mm),
+        n = n()
+    )
+    @mutate(
+        se_bill = sd_bill / sqrt(n)
     )
     @mutate(
         lower = mean_bill - 1.96 * se_bill,

docs/examples/geoms/geom_lines.jl

@@ -65,7 +65,7 @@ ggplot(economics, @aes(x = Date, y = Unemploy)) +
 
 # Use the `color` aesthetic to draw multiple lines for different groups:
 
-economics_long = @pivot_longer(economics, POP:Unemploy)
+economics_long = @pivot_longer(economics, Pop:Unemploy)
 
 ggplot(economics_long, @aes(x = Date, y = value, color = variable)) +
     geom_line()

docs/examples/geoms/geom_violin.jl

@@ -79,19 +79,19 @@ ggplot(penguins, @aes(x = species, y = bill_length_mm, fill = sex, dodge = sex))
 # Add stroke color and adjust transparency:
 
 ggplot(penguins, @aes(x = species, y = bill_length_mm)) +
-    geom_violin(color = :steelblue, strokecolor = :black, strokewidth = 2, alpha = 0.7)
+    geom_violin(color = :steelblue, strokecolor = :black, strokewidth = 2)
 
 # Different fill colors:
 
 ggplot(penguins, @aes(x = species, y = bill_length_mm, fill = species)) +
-    geom_violin(alpha = 0.8, strokecolor = :black, strokewidth = 1)
+    geom_violin(strokecolor = :black, strokewidth = 1)
 
 # ## Combining with Other Geoms
 
 # Combine violin with boxplot for more information:
 
 ggplot(penguins, @aes(x = species, y = bill_length_mm)) +
-    geom_violin(alpha = 0.5) +
+    geom_violin() +
     geom_boxplot(width = 0.2, alpha = 0.8)
 
 # Add individual data points:
@@ -103,39 +103,39 @@ ggplot(penguins, @aes(x = species, y = bill_length_mm)) +
 # Use jittered points for better visibility:
 
 ggplot(penguins, @aes(x = species, y = bill_length_mm)) +
-    geom_violin(alpha = 0.5, strokecolor = :black) +
+    geom_violin(strokecolor = :black) +
     geom_jitter(alpha = 0.3, width = 0.1)
 
 # ## Comparing Multiple Variables
 
 # Compare distributions across different measurements:
 
 ggplot(penguins, @aes(x = species, y = flipper_length_mm)) +
-    geom_violin(alpha = 0.7) +
+    geom_violin() +
     labs(title = "Flipper Length by Species")
 
 #-
 
 ggplot(penguins, @aes(x = species, y = body_mass_g)) +
-    geom_violin(alpha = 0.7) +
+    geom_violin() +
     labs(title = "Body Mass by Species")
 
 # ## Using with Facets
 
 # Use faceting to compare distributions across multiple categorical variables:
 
 ggplot(penguins, @aes(x = species, y = bill_length_mm)) +
-    geom_violin(alpha = 0.7) +
+    geom_violin() +
     facet_wrap(:sex)
 
 # ## Violin vs Density Comparison
 
 # Violin plots are essentially mirrored density plots. Compare:
 
 ggplot(penguins, @aes(x = bill_length_mm, fill = species)) +
-    geom_density(alpha = 0.5)
+    geom_density()
 
 # The violin plot shows the same information but in a compact, comparative format:
 
 ggplot(penguins, @aes(x = species, y = bill_length_mm, fill = species)) +
-    geom_violin(alpha = 0.7)
+    geom_violin()