Migrating from ggmosaic to marimekko

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Why migrate?

ggmosaic was archived from CRAN on 2025-11-10 due to uncorrected issues. Key problems included reliance on internal ggplot2 APIs that broke on updates and a dependency on deprecated tidyr functions.

marimekko is a from-scratch replacement that uses only public ggplot2 APIs, depends only on ggplot2 and rlang, and follows standard ggplot2 aesthetic conventions.

The examples below are adapted from Jeppson & Hofmann (2023), showing each ggmosaic pattern and its marimekko equivalent.

Function mapping

ggmosaic	marimekko	Notes
`geom_mosaic()`	`geom_marimekko()`	Standard `aes()`, no `product()`
`geom_mosaic_text()`	`geom_marimekko_text()`
`theme_mosaic()`	`theme_marimekko()`
`scale_x_productlist()`	not needed	Axis labels are automatic; use `show_percentages = TRUE` in `geom_marimekko()`
`scale_y_productlist()`	not needed	Axis labels are automatic
`product()`	not needed	Use `formula = ~ a \\| b`
—	`geom_marimekko_label()`	New: text with background box
—	`fortify_marimekko()`	New: extract tile data as data frame

Side-by-side examples

The ggmosaic paper (Jeppson & Hofmann, 2023, The R Journal 14(4)) uses the fly dataset from ggmosaic and the built-in Titanic dataset. Since ggmosaic is no longer on CRAN, we recreate the fly-based examples using Titanic and HairEyeColor — both built-in R datasets that need no extra packages.

One dimensional — spine plot and bar chart

The paper starts with single-variable plots showing different divider types (Figure 1 in Jeppson & Hofmann, 2023):

# ggmosaic — spine plot (one variable, default divider)
ggplot(data = titanic) +
  geom_mosaic(aes(x = product(Class), fill = Class, weight = Freq))

# marimekko — one variable
ggplot(titanic) +
  geom_marimekko(aes(fill = Class, weight = Freq), formula = ~Class) +
  theme_marimekko() +
  labs(title = "One variable: f(Class)")

# ggmosaic — bar chart (one variable, hbar divider)
ggplot(data = titanic) +
  geom_mosaic(
    aes(x = product(Class), fill = Class, weight = Freq),
    divider = "hbar"
  )

# ggplot — barchart
# todo: needs percentage y scale
ggplot(titanic) +
  geom_bar(aes(Class, weight = Freq, fill = Class)) +
  theme_marimekko() +
  labs(title = "One variable: f(Class)")

The main difference: no product() wrapper — use formula = ~ Var instead. marimekko does not have a divider argument; it always produces vertical columns with horizontal stacking.

Two dimensional — mosaic plot and stacked bar chart

The paper shows two-dimensional mosaic plots where variable order in product() controls the partitioning hierarchy (Figure 2):

# ggmosaic — two variables
ggplot(data = titanic) +
  geom_mosaic(aes(
    x = product(Class),
    fill = Survived,
    weight = Freq
  ))

# marimekko — two variables via formula
ggplot(titanic) +
  geom_marimekko(aes(fill = Survived, weight = Freq),
    formula = ~ Class | Survived
  ) +
  theme_marimekko() +
  labs(title = "Two variables: f(Survived | Class) f(Class)")

Three dimensional

The paper shows two-dimensional mosaic plots where variable order in product() controls the partitioning hierarchy (Figure 3):

# ggmosaic — double-decker variables
ggplot(data = titanic) +
  geom_mosaic(aes(
    x = product(Survived, Class, Sex),
    fill = Survived,
    weight = Freq,
  ), divider = ddecker())

# marimekko — three variables via formula
ggplot(titanic) +
  geom_marimekko(aes(fill = Survived, weight = Freq),
    formula = ~ Sex + Class | Survived
  ) +
  theme_marimekko() +
  labs(title = "Three variables: f(Survived | Class, Sex)")

Three variables — nested mosaic

The paper demonstrates three-variable mosaics where a third variable adds another level of partitioning (Figure 4):

# ggmosaic — three variables via product()
ggplot(data = titanic) +
  geom_mosaic(aes(
    x = product(Sex, Survived, Class),
    fill = Survived,
    weight = Freq
  ))

# marimekko — three variables via formula
ggplot(titanic) +
  geom_marimekko(aes(fill = Survived, weight = Freq),
    formula = ~ Class | Survived | Sex
  ) +
  theme_marimekko() +
  labs(title = "Three variables: Class / Sex / Survived")

Conditioning with `conds`

The paper shows how conds creates conditional distributions (Section 3.2):

# ggmosaic — conditioning
ggplot(data = titanic) +
  geom_mosaic(aes(
    x = product(Class),
    fill = Survived,
    weight = Freq,
    conds = product(Sex)
  ))

marimekko does not have a conds aesthetic. Use facet_wrap() instead:

# marimekko — conditioning via faceting
ggplot(titanic) +
  geom_marimekko(aes(fill = Survived, weight = Freq),
    formula = ~ Class | Survived
  ) +
  facet_wrap(~Sex) +
  theme_marimekko() +
  labs(title = "f(Survived | Class) conditioned on Sex")

Faceting

The paper demonstrates facet_grid() as an alternative to conditioning (Section 3.3):

# ggmosaic — faceting
ggplot(data = titanic) +
  geom_mosaic(aes(
    x = product(Class), fill = Survived, weight = Freq
  )) +
  facet_grid(~Sex)

# marimekko — faceting works the same way
ggplot(titanic) +
  geom_marimekko(aes(fill = Survived, weight = Freq),
    formula = ~ Class | Survived
  ) +
  facet_wrap(~Sex) +
  theme_marimekko() +
  labs(title = "Mosaic faceted by Sex")

Divider types and `standardize`

The paper describes four fundamental partition types: hspine, vspine, hbar, and vbar (Section 4, Figure 5). These are combined to produce spine plots, stacked bar charts, mosaic plots, and double decker plots.

marimekko simplifies this — geom_marimekko() always starts with horizontal splits and alternates direction with each | in the formula. Use coord_flip() if you need vertical-first orientation.

For equal-width columns (spine-plot style), standardize = TRUE is available in geom_marimekko_text() and fortify_marimekko().

# ggmosaic — spine plot via divider
ggplot(titanic) +
  geom_mosaic(aes(x = product(Class), fill = Survived, weight = Freq),
    divider = c("vspine", "hspine")
  )

# marimekko — proportional-width columns (default)
ggplot(titanic) +
  geom_marimekko(aes(fill = Survived, weight = Freq),
    formula = ~ Class | Survived
  ) +
  theme_marimekko() +
  labs(title = "Mosaic plot (proportional-width columns)")

Offset / spacing

The paper shows how offset controls gaps between tiles (Section 5):

# ggmosaic — single offset parameter
ggplot(data = titanic) +
  geom_mosaic(aes(
    x = product(Class), fill = Survived, weight = Freq
  ), offset = 0.02)

marimekko provides independent gap_x and gap_y parameters:

# marimekko — no gaps
ggplot(titanic) +
  geom_marimekko(aes(fill = Survived, weight = Freq),
    formula = ~ Class | Survived, gap_x = 0.02, gap_y = 0.02
  ) +
  theme_marimekko() +
  labs(title = "No gaps")

Text labels

The paper shows geom_mosaic_text() for adding labels (Section 7):

# ggmosaic — automatic text labels
ggplot(titanic) +
  geom_mosaic(aes(x = product(Class), fill = Survived, weight = Freq)) +
  geom_mosaic_text(aes(x = product(Class), fill = Survived, weight = Freq))

# marimekko — automatic tile positions, only label needed
ggplot(titanic) +
  geom_marimekko(aes(fill = Survived, weight = Freq),
    formula = ~ Class | Survived
  ) +
  geom_marimekko_text(aes(
    label = after_stat(paste(Class, Survived, sep = "\n"))
  )) +
  theme_marimekko()

geom_marimekko_text() reads tile positions from the preceding geom_marimekko() layer — only the label aesthetic is needed. Use after_stat() for computed variables like weight (count), .proportion, or the original variable columns.

Axis labels and percentages

The paper uses scale_x_productlist() for axis labels:

# ggmosaic
ggplot(titanic) +
  geom_mosaic(aes(x = product(Class), fill = Survived, weight = Freq)) +
  scale_x_productlist()

# marimekko — with optional marginal percentages
ggplot(titanic) +
  geom_marimekko(aes(fill = Survived, weight = Freq),
    formula = ~ Class | Survived,
    show_percentages = TRUE
  ) +
  theme_marimekko()

HairEyeColor — a larger example

The paper uses multi-variable examples to show how mosaic plots reveal associations. Here we use the built-in HairEyeColor dataset:

ggplot(hair) +
  geom_marimekko(aes(fill = Eye, weight = Freq),
    formula = ~ Hair | Eye,
    show_percentages = TRUE
  ) +
  theme_marimekko() +
  labs(title = "Hair color vs Eye color")

Three-variable version with Sex:

ggplot(hair) +
  geom_marimekko(aes(fill = Eye, weight = Freq),
    formula = ~ Hair | Sex | Eye
  ) +
  theme_marimekko() +
  labs(title = "Hair / Sex / Eye")

Key differences to be aware of

1. No `product()` wrapper

ggmosaic required wrapping variables in product(). marimekko uses a formula-based API:

formula = ~ a | b — specifies variable hierarchy (| alternates direction, + groups)
fill — tile colour (defaults to last formula variable)
weight — observation weights / counts

2. Automatic axis labels

ggmosaic handled axis labels internally. marimekko also adds axis labels automatically via geom_marimekko(). Use show_percentages = TRUE in geom_marimekko() to append marginal percentages to the x-axis labels.

3. No `divider` argument

ggmosaic used divider = c("vspine", "hspine", ...) to control partitioning direction. marimekko encodes direction in the formula — | alternates between horizontal and vertical, always starting horizontal. Use coord_flip() for vertical-first orientation.

4. `inherit.aes` defaults to `TRUE`

ggmosaic set inherit.aes = FALSE by default, requiring you to repeat aesthetics in every layer. marimekko follows ggplot2 convention — inherit.aes = TRUE — so aesthetics set in ggplot(aes(...)) are inherited.

5. Text labels need explicit `label` aesthetic

geom_marimekko_text() requires label = after_stat(weight) or similar. ggmosaic’s geom_mosaic_text() auto-generated labels.

plotly support

marimekko plots work with plotly::ggplotly() out of the box — simply pass your plot object and get an interactive version:

library(plotly)

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

ggplotly(p)

This also works with geom_marimekko_text(), geom_marimekko_label()

Search-and-replace checklist

For a quick mechanical migration, apply these replacements:

geom_mosaic(          →  geom_marimekko(
geom_mosaic_text(     →  geom_marimekko_text(
theme_mosaic(         →  theme_marimekko(
library(ggmosaic)     →  library(marimekko)

Then manually:

Remove all product() wrappers – add formula = ~ Var1 | Var2 instead
Remove conds aesthetic – use facet_wrap() or a multi-variable formula
Remove scale_x_productlist() / scale_y_productlist() – axis labels are now automatic
Remove divider = ... – direction is encoded in the formula
Replace offset = ... with gap / gap_x / gap_y
Add explicit label = after_stat(weight) to text layers
Set inherit.aes = FALSE only where deliberately needed
Move show_percentages = TRUE from scale to geom_marimekko(..., show_percentages = TRUE)

References

Jeppson, H. and Hofmann, H. (2023). Generalized Mosaic Plots in the ggplot2 Framework. The R Journal, 14(4), 50–73. doi:10.32614/RJ-2023-013

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Migrating from ggmosaic to marimekko

Why migrate?

Function mapping

Side-by-side examples

One dimensional — spine plot and bar chart

Two dimensional — mosaic plot and stacked bar chart

Three dimensional

Three variables — nested mosaic

Conditioning with conds

Faceting

Divider types and standardize

Offset / spacing

Text labels

Axis labels and percentages

HairEyeColor — a larger example

Key differences to be aware of

1. No product() wrapper

2. Automatic axis labels

3. No divider argument

4. inherit.aes defaults to TRUE

5. Text labels need explicit label aesthetic

plotly support

Search-and-replace checklist

References

Conditioning with `conds`

Divider types and `standardize`

1. No `product()` wrapper

3. No `divider` argument

4. `inherit.aes` defaults to `TRUE`

5. Text labels need explicit `label` aesthetic