Advanced features

This vignette covers the advanced features of marimekko beyond the basics shown in vignette("getting-started").

Basic marimekko plot

ggplot(titanic) +
  geom_marimekko(aes(fill = Survived, weight = Freq), formula = ~ Class | Survived)

Pearson residuals

Pearson residuals measure how much each cell deviates from the independence assumption. Positive residuals indicate more observations than expected; negative residuals indicate fewer.

Residuals are automatically computed and exposed as the .residuals computed variable, which you can map to an aesthetic via after_stat():

ggplot(titanic) +
  geom_marimekko(
    aes(
      fill = Survived, weight = Freq,
      alpha = after_stat(abs(.residuals))
    ),
    formula = ~ Class | Survived
  ) +
  scale_alpha_continuous(range = c(0.3, 1), guide = "none") +
  labs(title = "Residual shading: stronger opacity = larger deviation")

You can also map residuals to colour instead of relying on fill:

ggplot(titanic) +
  geom_marimekko(aes(fill = Survived, weight = Freq),
    formula = ~ Class | Survived
  ) +
  geom_marimekko_text(aes(
    label = after_stat(round(.residuals, 1))
  ), colour = "white", size = 3) +
  labs(title = "Pearson residuals as labels")

Three-variable nested mosaic

geom_marimekko() supports multi-variable formulas. A three-variable formula (~ X | Y | Z) partitions the plot in alternating directions (horizontal, vertical, horizontal):

First split: horizontal by X (column widths proportional to X)
Second split: vertical by Y within each column
Third split: horizontal by Z within each cell

ggplot(titanic) +
  geom_marimekko(aes(fill = Survived, weight = Freq),
    formula = ~ Class | Survived | Sex
  ) +
  labs(title = "Nested mosaic: Class > Sex > Survived")

This produces a richer view than faceting because all three variables share a single coordinate space, making relative proportions directly comparable.

Y-axis labels

By default geom_marimekko() automatically labels both axes with category names. The y-axis shows proportions from 0 to 1, while the x-axis displays category labels at each column’s midpoint.

Data extraction with fortify

fortify_marimekko() returns computed tile positions as a plain data frame without creating a plot. It accepts the same formula syntax as geom_marimekko():

tiles <- fortify_marimekko(titanic,
  formula = ~ Class | Survived, weight = Freq
)
head(tiles)
#>              xmin      xmax      ymin      ymax weight fill colour .proportion
#> 1st.No  0.0000000 0.1432303 0.0000000 0.3716308    122   No     No   0.3753846
#> 1st.Yes 0.0000000 0.1432303 0.3816308 1.0000000    203  Yes    Yes   0.6246154
#> 2nd.No  0.1532303 0.2788323 0.0000000 0.5801053    167   No     No   0.5859649
#> 2nd.Yes 0.1532303 0.2788323 0.5901053 1.0000000    118  Yes    Yes   0.4140351
#> 3rd.No  0.2888323 0.5999727 0.0000000 0.7403966    528   No     No   0.7478754
#> 3rd.Yes 0.2888323 0.5999727 0.7503966 1.0000000    178  Yes    Yes   0.2521246
#>          .marginal Class Survived          x         y .residuals
#> 1st.No  0.05542935   1st       No 0.07161517 0.1858154  -6.607873
#> 1st.Yes 0.09223080   1st      Yes 0.07161517 0.6908154   9.565772
#> 2nd.No  0.07587460   2nd       No 0.21603135 0.2900526  -1.867159
#> 2nd.Yes 0.05361199   2nd      Yes 0.21603135 0.7950526   2.702959
#> 3rd.No  0.23989096   3rd       No 0.44440254 0.3701983   2.289965
#> 3rd.Yes 0.08087233   3rd      Yes 0.44440254 0.8751983  -3.315027

Multi-variable formulas work too:

tiles_3 <- fortify_marimekko(titanic,
  formula = ~ Class | Survived | Sex, weight = Freq
)
head(tiles_3)
#>                      xmin       xmax      ymin      ymax weight   fill colour
#> 1st.No.Male    0.00000000 0.12886214 0.0000000 0.3716308    118   Male   Male
#> 1st.No.Female  0.13886214 0.14323035 0.0000000 0.3716308      4 Female Female
#> 1st.Yes.Male   0.00000000 0.04069104 0.3816308 1.0000000     62   Male   Male
#> 1st.Yes.Female 0.05069104 0.14323035 0.3816308 1.0000000    141 Female Female
#> 2nd.No.Male    0.15323035 0.25983339 0.0000000 0.5801053    154   Male   Male
#> 2nd.No.Female  0.26983339 0.27883235 0.0000000 0.5801053     13 Female Female
#>                .proportion   .marginal Class Survived    Sex          x
#> 1st.No.Male     0.96721311 0.053611995   1st       No   Male 0.06443107
#> 1st.No.Female   0.03278689 0.001817356   1st       No Female 0.14104625
#> 1st.Yes.Male    0.30541872 0.028169014   1st      Yes   Male 0.02034552
#> 1st.Yes.Female  0.69458128 0.064061790   1st      Yes Female 0.09696070
#> 2nd.No.Male     0.92215569 0.069968196   2nd       No   Male 0.20653187
#> 2nd.No.Female   0.07784431 0.005906406   2nd       No Female 0.27433287
#>                        y .residuals
#> 1st.No.Male    0.1858154 -0.3491072
#> 1st.No.Female  0.1858154 -9.5038375
#> 1st.Yes.Male   0.6908154  0.5053790
#> 1st.Yes.Female 0.6908154 13.7580643
#> 2nd.No.Male    0.2900526  2.9817561
#> 2nd.No.Female  0.2900526 -6.9363838

The returned columns are:

Column	Description
Formula variables	One column per formula variable (e.g. `Class`, `Survived`)
`fill`	The fill variable value
`xmin`, `xmax`	Horizontal extent of the tile
`ymin`, `ymax`	Vertical extent of the tile
`x`, `y`	Tile center coordinates
`weight`	Aggregated count
`.proportion`	Conditional proportion within the parent tile
`.marginal`	Proportion of the grand total
`.residuals`	Pearson residual

Extending with custom ggplot2 layers

The companion layers geom_marimekko_text(), geom_marimekko_label() automatically read tile positions from a preceding geom_marimekko() layer. You only need to specify the label aesthetic:

ggplot(titanic) +
  geom_marimekko(aes(fill = Survived, weight = Freq),
    formula = ~ Class | Survived
  ) +
  geom_marimekko_text(aes(
    label = after_stat(paste(Class, Survived, weight, sep = "\n"))
  ), colour = "white", size = 2.5)

For more control, use fortify_marimekko() to pre-compute tiles and pass them as data to any standard ggplot2 geom. This lets you summarize, filter, or transform the tile data before plotting:

tiles <- fortify_marimekko(titanic,
  formula = ~ Class | Survived, weight = Freq
)

# Highlight cells with significant residuals
tiles$significant <- abs(tiles$.residuals) > 2

ggplot(titanic) +
  geom_marimekko(aes(fill = Survived, weight = Freq),
    formula = ~ Class | Survived
  ) +
  geom_label(
    data = tiles[tiles$significant, ],
    aes(x = x, y = y, label = paste0("r=", round(.residuals, 1))),
    fill = "yellow", size = 3, fontface = "bold"
  ) +
  labs(title = "Significant deviations from independence (|r| > 2)")

Because fortify_marimekko() returns a plain data frame, you can use any ggplot2 geom – geom_segment(), geom_curve(), geom_tile(), ggrepel::geom_label_repel(), etc.

Extending with `StatMarimekkoTiles`

The exported StatMarimekkoTiles ggproto object lets you pair marimekko tile positions with any geom. While the convenience wrappers geom_marimekko_text() and geom_marimekko_label() cover the most common case (text overlays), StatMarimekkoTiles gives you full control by plugging directly into ggplot2::layer().

How it works

StatMarimekkoTiles does not compute tile positions itself — it reads them from a preceding geom_marimekko() layer via an internal shared environment. This means:

A geom_marimekko() layer must appear before any layer that uses StatMarimekkoTiles.
The stat returns one row per tile with columns xmin, xmax, ymin, ymax, x, y (centre), weight, fill, .proportion, .residuals, and .tooltip.
You can reference any of these columns in aes() via after_stat().

Example: bubble overlay

Map point size to weight to show tile counts as bubbles:

ggplot(titanic) +
  geom_marimekko(
    aes(fill = Survived, weight = Freq),
    formula = ~ Class | Survived, alpha = 0.4
  ) +
  layer(
    stat = StatMarimekkoTiles,
    geom = GeomPoint,
    mapping = aes(size = after_stat(weight)),
    data = titanic,
    position = "identity",
    show.legend = FALSE,
    inherit.aes = FALSE,
    params = list(colour = "white", alpha = 0.7)
  ) +
  scale_size_area(max_size = 12) +
  labs(title = "Bubble overlay via StatMarimekkoTiles")

Example: residual markers

Colour and size encode deviation from independence:

ggplot(titanic) +
  geom_marimekko(
    aes(fill = Survived, weight = Freq),
    formula = ~ Class | Survived
  ) +
  layer(
    stat = StatMarimekkoTiles,
    geom = GeomPoint,
    mapping = aes(
      size = after_stat(abs(.residuals)),
      colour = after_stat(ifelse(.residuals > 0, "over", "under"))
    ),
    data = titanic,
    position = "identity",
    show.legend = TRUE,
    inherit.aes = FALSE,
    params = list(alpha = 0.8)
  ) +
  scale_colour_manual(
    values = c(over = "tomato", under = "steelblue"),
    name = "Deviation"
  ) +
  scale_size_continuous(range = c(1, 8), name = "|Residual|") +
  labs(title = "Residual markers via StatMarimekkoTiles")

Example: rectangle outlines

Use GeomRect to draw highlighted borders around specific tiles (e.g. tiles with large residuals):

ggplot(titanic) +
  geom_marimekko(
    aes(fill = Survived, weight = Freq),
    formula = ~ Class | Survived
  ) +
  layer(
    stat = StatMarimekkoTiles,
    geom = GeomRect,
    mapping = aes(
      linewidth = after_stat(ifelse(abs(.residuals) > 2, 1.5, 0))
    ),
    data = titanic,
    position = "identity",
    show.legend = FALSE,
    inherit.aes = FALSE,
    params = list(colour = "red", fill = NA)
  ) +
  labs(title = "Highlight tiles with |residual| > 2")

`StatMarimekkoTiles` vs `fortify_marimekko()`

Both give access to the same computed tile data, but they serve different purposes:

	`StatMarimekkoTiles`	`fortify_marimekko()`
When	At render time (reactive)	Before plotting (static)
Input	Reads from a `geom_marimekko()` layer	Standalone function call
Use case	Adding companion layers on the same plot	Pre-processing, filtering, or using tile data outside ggplot2
Faceting	Automatically panel-aware	Manual panel handling

Use StatMarimekkoTiles when you want to add layers that stay in sync with geom_marimekko() parameters. Use fortify_marimekko() when you need to transform or subset the tile data before passing it to a geom.

Combining layers

Because marimekko produces standard ggplot2 layers, you can freely combine multiple features:

ggplot(titanic) +
  geom_marimekko(
    aes(
      fill = Survived, weight = Freq,
      alpha = after_stat(abs(.residuals))
    ),
    formula = ~ Class | Survived,
    show_percentages = TRUE
  ) +
  geom_marimekko_text(aes(label = after_stat(weight)),
    colour = "white", size = 3.5
  ) +
  scale_alpha_continuous(range = c(0.4, 1), guide = "none") +
  theme_marimekko() +
  labs(
    title = "Full-featured mosaic plot",
    subtitle = "Residual shading + counts + marginal %"
  )

Independent x/y gaps

By default, gap controls both horizontal (between columns) and vertical (between segments) spacing. Use gap_x and gap_y to set them independently:

ggplot(titanic) +
  geom_marimekko(aes(fill = Survived, weight = Freq),
    formula = ~ Class | Survived, gap_x = 0.04, gap_y = 0
  ) +
  labs(title = "Wide column gaps, no vertical gaps")

ggplot(titanic) +
  geom_marimekko(aes(fill = Survived, weight = Freq),
    formula = ~ Class | Survived, gap_x = 0, gap_y = 0.03
  ) +
  labs(title = "No column gaps, visible vertical gaps")

Colour palette

marimekko ships with an Marimekko inspired color pallette. Use theme_marimekko() oe use scale_fill_manual(palette = marimekko_pal):

ggplot(titanic) +
  geom_marimekko(aes(fill = Survived, weight = Freq),
    formula = ~ Class | Survived
  ) +
  theme_marimekko() +
  labs(title = "Earthy Nordic palette")

By default, tile borders match the fill colour (borders blend in). Set colour explicitly to restore visible borders:

ggplot(titanic) +
  geom_marimekko(aes(fill = Survived, weight = Freq),
    formula = ~ Class | Survived, colour = "white"
  ) +
  theme_marimekko() +
  labs(title = "White borders with marimekko palette")

Plotly interactivity

marimekko plots work with plotly::ggplotly() out of the box:

library(plotly)

p <- ggplot(titanic) +
  geom_marimekko(aes(fill = Survived, weight = Freq),
    formula = ~ Class | Survived
  )
ggplotly(p)

In-aesthetic expressions

Unlike some mosaic packages, marimekko supports arbitrary R expressions — both in formulas and inside aes():

# Expressions work in formulas
ggplot(mtcars) +
  geom_marimekko(formula = ~ factor(cyl) | factor(gear)) +
  labs(
    y = "Gears", fill = "Gears",
    title = "factor() inside formula works"
  )

Namespace-qualified usage

marimekko works correctly when called with :: notation (e.g., marimekko::geom_marimekko()) without requiring library(marimekko). This makes it safe to use inside other packages via Imports rather than Depends.

Summary of parameters

Parameter	Used in	Description
`formula`	`geom_marimekko()`, `fortify_marimekko()`	Formula specifying variable hierarchy (`~ a \\| b \\| c`)
`gap`	`geom_marimekko()`, `fortify_marimekko()`	Spacing between tiles (fraction of plot area)
`gap_x`	`geom_marimekko()`, `fortify_marimekko()`	Horizontal gap (overrides `gap` for x)
`gap_y`	`geom_marimekko()`, `fortify_marimekko()`	Vertical gap (overrides `gap` for y)
`standardize`	`fortify_marimekko()`	Equal-width columns (spine plot)
`colour`	`geom_marimekko()`	Tile border colour. Default `NULL` (matches fill)
`show_percentages`	`geom_marimekko()`	Append marginal % to x-axis labels

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