chore: design multi-panel schema

examples/multi-panel/Python/facet_matplotlib.py

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+import matplotlib.pyplot as plt
+import seaborn as sns
+
+# Load the penguins dataset using seaborn
+penguins = sns.load_dataset("penguins")
+
+# Get unique species and sexes
+species = penguins["species"].dropna().unique()
+sexes = penguins["sex"].dropna().unique()
+
+# Create a figure and a grid of subplots
+fig, axes = plt.subplots(
+    nrows=len(species), ncols=len(sexes), figsize=(12, 8), sharex=True, sharey=True
+)
+
+# Set a global title for the figure
+fig.suptitle("Penguin Bill Dimensions by Species and Sex", fontsize=16)
+
+# Loop through species and sexes to create each subplot
+for i, sp in enumerate(species):
+    for j, sex in enumerate(sexes):
+        ax = axes[i, j]
+        subset = penguins[(penguins["species"] == sp) & (penguins["sex"] == sex)]
+        ax.scatter(subset["bill_length_mm"], subset["bill_depth_mm"])
+
+        # Set panel-specific titles
+        ax.set_title(f"{sp} - {sex}", fontsize=12)
+
+        # Set x and y labels for the leftmost and bottom plots only
+        if i == len(species) - 1:
+            ax.set_xlabel("Bill Length (mm)")
+        if j == 0:
+            ax.set_ylabel("Bill Depth (mm)")
+
+# Adjust layout to prevent overlap
+plt.tight_layout(rect=[0, 0, 1, 0.95])  # Adjust rect to make space for the global title
+
+# Show the plot
+plt.show()

examples/multi-panel/Python/facet_seaborn.py

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+import matplotlib.pyplot as plt
+import seaborn as sns
+
+penguins = sns.load_dataset("penguins")
+
+g = sns.FacetGrid(penguins, row="species", col="sex")
+g.map(sns.scatterplot, "bill_length_mm", "bill_depth_mm")
+g.fig.suptitle("Penguin Bill Dimensions by Species and Sex", fontsize=16)
+g.fig.subplots_adjust(top=0.9)  # Adjust subplots to fit the title
+g.set_axis_labels("Bill Length (mm)", "Bill Depth (mm)")
+g.set_titles(row_template="{row_name}", col_template="{col_name}")
+plt.show()

examples/multi-panel/R/facet_ggplot.r

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+# Load the required libraries
+library(ggplot2)
+library(palmerpenguins)
+library(svglite)
+
+# Load the penguins dataset
+data("penguins")
+penguins <- na.omit(penguins)
+
+# Create a ggplot
+p <- ggplot(penguins, aes(x = bill_length_mm, y = bill_depth_mm)) +
+  geom_point() +
+  facet_grid(species ~ sex) +
+  labs(
+    title = "Penguin Bill Dimensions by Species and Sex",
+    x = "Bill Length (mm)",
+    y = "Bill Depth (mm)"
+  ) +
+  theme(
+    plot.title = element_text(size = 16, hjust = 0.5),
+    strip.text = element_text(size = 12),
+    axis.title = element_text(size = 12)
+  )
+
+# Save the plot as an SVG file using ggsave and svglite
+ggsave("multi_panel.svg", plot = p, device = svglite, width = 10, height = 8)
+
+# Extract raw data used for plotting per panel
+plot_data <- ggplot_build(p)$data[[1]]
+# Split the data by panel
+plot_data_split <- split(plot_data, plot_data$PANEL)
+
+# Save each panel data as a separate data frame object
+panel_data_list <- lapply(names(plot_data_split), function(panel) {
+  panel_data <- plot_data_split[[panel]]
+  assign(paste0("panel_data_", panel), panel_data, envir = .GlobalEnv)
+  return(panel_data)
+})
+
+# Assign names to the list elements for easier reference
+names(panel_data_list) <- names(plot_data_split)
+
+# Load the required library for JSON
+library(jsonlite)
+
+# Save each panel data as a JSON file containing only x and y variables
+lapply(names(panel_data_list), function(panel) {
+  panel_data <- panel_data_list[[panel]][, c("x", "y")]
+  json_file <- paste0("panel_data_", panel, ".json")
+  write_json(panel_data, json_file)
+})
+
+
+
+# Extract the layout information to get the panel titles
+layout_info <- ggplot_build(p)$layout$layout
+
+# Create a data frame that maps panel numbers to species and sex
+panel_mapping <- data.frame(
+  PANEL = layout_info$PANEL,
+  species = layout_info$species,
+  sex = layout_info$sex
+)
+
+print(panel_mapping)