Multi-Layered, Multi-Panel and Facet Examples

These are the composite plots: several layers on one set of axes, several independent panels in one figure, or one chart repeated per subgroup. maidr keeps each layer and each panel separate, so a reader switches layer or panel with the keyboard and hears each one sonified on its own axes rather than as one merged series. Faceted plots, multi-panel layouts and multi-layered plots are stable plot types (see “Supported plot types” in the README). The examples hub lists every other plot family.

Multi-Layered Plots

Multi-layered plots combine multiple visualization types in a single chart. For example, a histogram overlaid with a density curve, or a bar chart with a line overlay.

Histogram with Density Overlay

ggplot2

p <- ggplot(mtcars, aes(x = mpg)) +
  geom_histogram(
    aes(y = after_stat(density)),
    bins = 15, fill = "lightblue", color = "white"
  ) +
  geom_density(color = "red", linewidth = 1.2) +
  labs(title = "MPG: Histogram with Density Curve") +
  theme_minimal()

p

Base R

hist(mtcars$mpg,
  breaks = 15, freq = FALSE,
  col = "lightblue", border = "white",
  main = "MPG: Histogram with Density Curve",
  xlab = "Miles per Gallon",
  ylab = "Density"
)
lines(density(mtcars$mpg), col = "red", lwd = 2)

Bar Chart with Line Overlay

ggplot2

combo_data <- data.frame(
  month = factor(month.abb[1:6], levels = month.abb[1:6]),
  sales = c(100, 120, 90, 150, 130, 160),
  target = c(110, 110, 110, 140, 140, 140)
)

p <- ggplot(combo_data, aes(x = month)) +
  geom_bar(aes(y = sales), stat = "identity", fill = "steelblue", alpha = 0.7) +
  geom_line(aes(y = target, group = 1), color = "red", linewidth = 1.5) +
  labs(title = "Monthly Sales vs Target", y = "Value") +
  theme_minimal()

p

---

Multi-Panel Plots (Multiple Subplots)

Multi-panel layouts arrange several independent plots in a grid. This is useful for dashboards or comparing different views of the same dataset.

ggplot2 (patchwork)

library(patchwork)

# Line plot with currency formatting
line_df <- data.frame(
  Month = 1:8,
  Revenue = c(2500, 4200, 3100, 5500, 4300, 6700, 5600, 7800)
)
pw_line <- ggplot(line_df, aes(Month, Revenue)) +
  geom_line(color = "steelblue", linewidth = 1) +
  labs(title = "Monthly Revenue", x = "Month", y = "Revenue") +
  theme_minimal()

# Bar plot with rates
bar_df1 <- data.frame(
  Category = c("A", "B", "C", "D", "E"),
  Rate = c(0.15, 0.22, 0.18, 0.28, 0.17)
)
pw_bar1 <- ggplot(bar_df1, aes(Category, Rate)) +
  geom_bar(stat = "identity", fill = "forestgreen", alpha = 0.7) +
  labs(title = "Conversion Rates", x = "Category", y = "Rate") +
  theme_minimal()

# Bar plot with large numbers
bar_df2 <- data.frame(
  Category = c("A", "B", "C", "D", "E"),
  Count = c(125000, 98000, 145000, 112000, 88000)
)
pw_bar2 <- ggplot(bar_df2, aes(Category, Count)) +
  geom_bar(stat = "identity", fill = "royalblue", alpha = 0.7) +
  labs(title = "User Counts", x = "Category", y = "Count") +
  theme_minimal()

# Line plot with exponential growth
line_df2 <- data.frame(
  x = 1:8,
  y = 10^(seq(3, 6.5, length.out = 8))
)
pw_line2 <- ggplot(line_df2, aes(x, y)) +
  geom_line(color = "tomato", linewidth = 1) +
  labs(title = "Exponential Growth", x = "Time", y = "Value") +
  theme_minimal()

combined <- (pw_line + pw_bar1 + pw_bar2 + pw_line2) +
  plot_layout(ncol = 2)
combined

Base R (par)

par(mfrow = c(2, 2))

barplot(table(mtcars$cyl),
  col = "steelblue",
  main = "Cars by Cylinder Count",
  xlab = "Cylinders"
)

hist(mtcars$mpg,
  breaks = 12, col = "coral", border = "white",
  main = "MPG Distribution", xlab = "MPG"
)

plot(mtcars$wt, mtcars$mpg,
  pch = 19, col = "forestgreen",
  main = "MPG vs Weight",
  xlab = "Weight (1000 lbs)", ylab = "MPG"
)

boxplot(hp ~ gear,
  data = mtcars, col = "plum",
  main = "Horsepower by Gear Count",
  xlab = "Gears", ylab = "Horsepower"
)

invisible(par(mfrow = c(1, 1)))

---

Facet Plots

Faceted plots split data into a grid of subplots by one or more grouping variables. Each panel shows the same type of chart for a different subset of the data. This is a ggplot2-only feature using facet_wrap() or facet_grid().

facet_wrap

facet_data <- data.frame(
  x = rep(c("A", "B", "C", "D"), 4),
  y = c(
    30, 25, 35, 20,
    45, 30, 25, 40,
    20, 35, 30, 45,
    35, 40, 20, 30
  ),
  panel = rep(c("Group 1", "Group 2", "Group 3", "Group 4"), each = 4)
)

p <- ggplot(facet_data, aes(x = x, y = y)) +
  geom_bar(stat = "identity", fill = "steelblue") +
  facet_wrap(~panel, ncol = 2) +
  labs(title = "Sales by Category Across Groups", x = "Category", y = "Sales") +
  theme_minimal()

p

facet_grid

p <- ggplot(mtcars, aes(x = wt, y = mpg)) +
  geom_point(size = 2, color = "steelblue") +
  facet_grid(vs ~ am,
    labeller = labeller(
      vs = c("0" = "V-engine", "1" = "Straight"),
      am = c("0" = "Automatic", "1" = "Manual")
    )
  ) +
  labs(
    title = "MPG vs Weight by Engine Type and Transmission",
    x = "Weight (1000 lbs)",
    y = "Miles per Gallon"
  ) +
  theme_minimal()

p