
# ggpmisc <img src="man/figures/logo.png" align="right" width="150" />

## Miscellaneous Extensions to ‘ggplot2’

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## Purpose

Package ‘**ggpmisc**’ (Miscellaneous Extensions to ‘ggplot2’) is a set
of extensions to R package ‘ggplot2’ (\>= 3.0.0) with emphasis on
annotations and plotting related to fitted models. Estimates from model
fit objects can be displayed in ggplots as text, model equations, ANOVA
and summary table. Predicted values, residuals, deviations and weights
can be plotted for various model fit functions. Linear models,
polynomial regression, quantile regression, major axis regression,
non-linear regression and different approaches to robust and resistant
regression, as well as user-defined wrapper functions based on them are
supported. In addition, all model fit functions returning objects for
which accessors are available or supported by package ‘broom’ and its
extensions are also supported but not as automatically. Labelling based
on multiple comparisons supports various *P* adjustment methods and
contrast schemes. Annotation of peaks and valleys in time series, and
scales for volcano and quadrant plots as used for gene expression data
are also provided. Package ‘**ggpmisc**’ continues to give access to
extensions moved as of version 0.4.0 to package
[‘**ggpp**’](https://docs.r4photobiology.info/ggpp/).

## Philosophy

Package ‘**ggpmisc**’ is consistent with the grammar of graphics, and
opens new possibilities retaining the flexibility inherent to this
grammar. Its aim is not to automate plotting or annotations in a way
suitable for fast data exploration by use of a “fits-all-sizes”
predefined design. Package ‘**ggpmisc**’ together with package
‘**ggpp**’, provide new layer functions, position functions and scales.
In fact, these packages follow the tenets of the grammar even more
strictly than ‘**ggplot2**’ in the distinction between geometries and
statistics. The new statistics in ‘**ggpmisc**’ focus mainly on model
fitting, including multiple comparisons among groups. The default
annotations are those most broadly valid and of easiest interpretation.
We follow R’s approach of expecting that users know what they need or
want, and will usually want to adjust how results from model fits are
presented both graphically and textually. The approach and mechanics of
plot construction and rendering remain unchanged from those implemented
in package [‘**ggplot2**’](https://ggplot2.tidyverse.org/).

## Statistics

Statistics that help with reporting the results of model fits are:

| Statistic | Returned values<br> (*default geometry*) | Methods |
|:---|:---|:---|
| **Model equation** | *parameter estimates* |  |
| `stat_poly_eq()` | equation, *R*<sup>2</sup>, *P*, etc. (`text_npc`) | lm, rlm, lts, gls, ma, sma, nls, onls, etc. (1, 2, 7) |
| `stat_ma_eq()` | equation, *R*<sup>2</sup>, *P*, etc. (`text_npc`) | lmodel2 (6, 7) |
| `stat_quant_eq()` | equation, *P*, etc. (`text_npc`) | rq (1, 3, 4, 7) |
| `stat_distrmix_eq()` | equation(s) (`text_npc`) | normalmixEM (2, 7) |
| `stat_correlation()` | correlation, *P*-value, CI (`text_npc`) | Pearson (*t*), Kendall (*z*), Spearman (*S*) |
| `stat_fit_glance()` | equation, *R*<sup>2</sup>, *P*, etc. (`text_npc`) | those supported by ‘broom’ |
| **Model line** | *predicted and fitted values* |  |
| `stat_poly_line()` | line + conf. (`smooth`) | lm, rlm, lts, gls, ma, sma, nls, onls, etc. (1, 2, 7) |
| `stat_ma_line()` | line + slope conf. (`smooth`) | lmodel2 (6, 7) |
| `stat_quant_line()` | line + conf. (`smooth`) | rq, rqss (1, 3, 4, 7) |
| `stat_quant_band()` | line + band, 2 or 3 quantiles (`smooth`) | rq, rqss (1, 4, 5, 7) |
| `stat_distrmix_line()` | lines(s) (`line`) | normalmixEM (2, 7) |
| `stat_fit_augment()` | predicted and other values (`smooth`) | those supported by ‘broom’ |
| `stat_fit_fitted()` | fitted values (`point`) | lm, rlm, lts, rq, gls, ma, sma, nls, onls, etc. (1, 2, 4, 7, 9) |
| `stat_fit_deviations()` | deviations from observations (`segment`) | lm, rlm, lts, rq, gls, ma, sma, nls, onls, etc. (1, 2, 4, 7, 9) |
| **Model table** | *parameter estimates and significance* |  |
| `stat_fit_tb()` | ANOVA and summary tables (`table_npc`) | those supported by ‘broom’ |
| `stat_fit_tidy()` | fit results, e.g., for equation (`text_npc`) | those supported by ‘broom’ |
| **Contrasts** | *Tukey, Dunnet and arbitrary pairwise* |  |
| `stat_multcomp()` | Multiple comparisons (`label_pairwise` or `text`) | those supported by `glht` (1, 2, 7) |
| **Residuals** | *model fit residuals* |  |
| `stat_fit_residuals()` | residuals (`point`) | lm, rlm, lts, rq, gls, ma, sma, nls, onls, etc. (1, 2, 4, 7, 9) |

Notes: (1) *weight* aesthetic supported; (2) user defined model fit
functions including wrappers of supported methods are accepted even if
they modify the model *formula* (additional model fitting methods are
likely to work, but have not been tested); (3) unlimited quantiles
supported; (4) user defined fit functions that return an object of a
class derived from `rq` or `rqs` are supported even if they override the
statistic’s *formula* and/or *quantiles* argument; (5) two and three
quantiles supported; (6) user defined fit functions that return an
object of a class derived from `lmodel2` are supported; (7) `method`
arguments support colon based notation; (8) model fit functions if
method `residuals()` defined for returned value; (9) model fit functions
if method `fitted()` is defined for the returned value.

Statistics `stat_peaks()` and `stat_valleys()` can be used to highlight
and/or label global and/or local maxima and minima in a plot.

## Aesthetics and scales

Scales `scale_x_logFC()`, `scale_y_logFC()`, `scale_colour_logFC()` and
`scale_fill_logFC()` easy the plotting of log fold change data. Scales
`scale_x_Pvalue()`, `scale_y_Pvalue()`, `scale_x_FDR()` and
`scale_y_FDR()` are suitable for plotting *p*-values and adjusted
*p*-values or false discovery rate (FDR). Default arguments are suitable
for volcano and quadrant plots as used for transcriptomics, metabolomics
and similar data.

Scales `scale_colour_outcome()`, `scale_fill_outcome()` and
`scale_shape_outcome()` and functions `outome2factor()`,
`threshold2factor()`, `xy_outcomes2factor()` and
`xy_thresholds2factor()` used together make it easy to map ternary
numeric outputs and logical binary outcomes to color, fill and shape
aesthetics. Default arguments are suitable for volcano, quadrant and
other plots as used for genomics, metabolomics and similar data.

## Migrated

Several geoms and other extensions formerly included in package
‘ggpmisc’ until version 0.3.9 were migrated to package ‘ggpp’. They are
still available when ‘ggpmisc’ is loaded, but the documentation now
resides in the new package
[‘**ggpp**’](https://docs.r4photobiology.info/ggpp/). [![cran
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Functions for the manipulation of layers in ggplot objects, together
with statistics and geometries useful for debugging extensions to
package ‘ggplot2’, included in package ‘ggpmisc’ until version 0.2.17
are now in package
[‘**gginnards**’](https://docs.r4photobiology.info/gginnards/). [![cran
version](https://www.r-pkg.org/badges/version/gginnards)](https://cran.r-project.org/package=gginnards)
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## Examples

``` r
library(ggpmisc)
library(ggrepel)
library(broom)
```

In the first two examples we plot data such that we map a factor to the
*x* aesthetic and label it with the adjusted *P*-values for multitle
comparision using “Tukey” contrasts.

``` r
ggplot(mpg, aes(factor(cyl), cty)) +
  geom_boxplot(width = 0.33) +
  stat_multcomp(label.type = "letters") +
  expand_limits(y = 0)
```

<figure>
<img src="man/figures/README-readme-03a-1.png"
alt="Figure 1. Boxplots with outcome from paiwise contrasts using Tukey’s HSD (honestly significant difference) as criterion with \alpha = 0.05." />
<figcaption aria-hidden="true"><strong>Figure 1.</strong> Boxplots with
outcome from paiwise contrasts using Tukey’s HSD (honestly significant
difference) as criterion with <span
class="math inline"><em>α</em> = 0.05</span>.</figcaption>
</figure>

Using “Dunnet” contrasts and “bars” to annotate individual contrasts
with the adjusted *P*-value, here using Holm’s method.

``` r
ggplot(mpg, aes(factor(cyl), cty)) +
  geom_boxplot(width = 0.33) +
  stat_multcomp(contrasts = "Dunnet",
                p.adjust.method = "holm",
                size = 2.75) +
  expand_limits(y = 0)
```

<figure>
<img src="man/figures/README-readme-03b-1.png"
alt="Figure 2. Boxplots with outcome from paiwise Dunnet contrasts using Holms’ P-adjustment. The adjusted P-values are shown for each pairwise contrast. An ANOVA is first fitted and subsequently its output is used for pairwise contrasts within the stat. Labels are formatted automatically." />
<figcaption aria-hidden="true"><strong>Figure 2.</strong> Boxplots with
outcome from paiwise Dunnet contrasts using Holms’
<em>P</em>-adjustment. The adjusted <em>P</em>-values are shown for each
pairwise contrast. An ANOVA is first fitted and subsequently its output
is used for pairwise contrasts within the <em>stat</em>. Labels are
formatted automatically.</figcaption>
</figure>

In the third example we add the equation for a linear regression, the
adjusted coefficient of determination and *P*-value to a plot showing
the observations plus the fitted curve, deviations and confidence band.
We use `stat_poly_eq()` together with `use_label()` to assemble and map
the desired annotations.

``` r
formula <- y ~ x + I(x^2)
ggplot(cars, aes(speed, dist)) +
  geom_point() +
  stat_fit_deviations(formula = formula, colour = "red") +
  stat_poly_line(formula = formula) +
  stat_poly_eq(use_label(c("eq", "adj.R2", "P")), formula = formula)
```

<figure>
<img src="man/figures/README-readme-04-1.png"
alt="Figure 3. Scatter plot of obsevations with a second degree polynomial fitted by OLS. Fitted model line with 95% confidence band is shown. Deviations for each observation are highlighted as red segments. Fitted model equation, R^2 and P-value are shown as annotations. Model fitted within stats with labels generated automatically." />
<figcaption aria-hidden="true"><strong>Figure 3.</strong> Scatter plot
of obsevations with a second degree polynomial <strong>fitted by
OLS</strong>. Fitted model line with 95% confidence band is shown.
Deviations for each observation are highlighted as red segments. Fitted
model equation, <span class="math inline"><em>R</em><sup>2</sup></span>
and <em>P</em>-value are shown as annotations. Model fitted within
<em>stats</em> with labels generated automatically.</figcaption>
</figure>

The same figure as in the third example but this time annotated with the
ANOVA table for the model fit. We use `stat_fit_tb()` which can be used
to add ANOVA or summary tables.

``` r
formula <- y ~ x + I(x^2)
ggplot(cars, aes(speed, dist)) +
  geom_point() +
  stat_poly_line(method = "lm", formula = formula) +
  stat_fit_tb(method = "lm",
              method.args = list(formula = formula),
              tb.type = "fit.anova",
              tb.vars = c(Effect = "term",
                          "df",
                          "M.S." = "meansq",
                          "italic(F)" = "statistic",
                          "italic(P)" = "p.value"),
              tb.params = c(x = 1, "x^2" = 2),
              label.y = "top", label.x = "left",
              size = 3.5,
              parse = TRUE)
#> Dropping params/terms (rows) from table!
```

<figure>
<img src="man/figures/README-readme-05-1.png"
alt="Figure 4. The same base plot as in Figure 3, but annotated with an inset ANOVA table for the fitted model. Model fitted within stats with table generated automatically. Some column and row headings replaced by user-provided character strings, some of them parsed as plotmath expressions." />
<figcaption aria-hidden="true"><strong>Figure 4.</strong> The same base
plot as in Figure 3, but annotated with an inset ANOVA table for the
fitted model. Model fitted within <em>stats</em> with table generated
automatically. Some column and row headings replaced by user-provided
character strings, some of them parsed as plotmath
expressions.</figcaption>
</figure>

The same figure as in the third example but this time using quantile
regression, median in this example.

``` r
formula <- y ~ x + I(x^2)
ggplot(cars, aes(speed, dist)) +
  geom_point() +
  stat_quant_line(formula = formula, quantiles = 0.5) +
  stat_quant_eq(use_label("eq", "rho", "n"),
                formula = formula, quantiles = 0.5)
```

<figure>
<img src="man/figures/README-readme-04b-1.png"
alt="Figure 5. Same scatter plot of obsevations as in Figures 3 and 4, but with a the second degree polynomial fitted by quantile regression. Fitted model line with 95% confidence band shown. Fitted model equation shown, \rho and the number of observations shown as annotations. Model fitted within stats with labels generated automatically." />
<figcaption aria-hidden="true"><strong>Figure 5.</strong> Same scatter
plot of obsevations as in Figures 3 and 4, but with a the second degree
polynomial <strong>fitted by quantile regression</strong>. Fitted model
line with 95% confidence band shown. Fitted model equation shown, <span
class="math inline"><em>ρ</em></span> and the number of observations
shown as annotations. Model fitted within <em>stats</em> with labels
generated automatically.</figcaption>
</figure>

Band highlighting the region between both quartile regressions and a
line for the median regression.

``` r
formula <- y ~ x + I(x^2)
ggplot(cars, aes(speed, dist)) +
  geom_point() +
  stat_quant_band(formula = formula) +
  stat_quant_eq(formula = formula, quantiles = c(0.25, 0.5, 0.75))
```

<figure>
<img src="man/figures/README-readme-04c-1.png"
alt="Figure 6. The same scatter plot of obsevations as in Figures 3 to 5 but with three second degree polynomials fitted by quantile regression, for the median and the quartiles. The line shows the model fit for the median line, and the band delimits the quartiles. The three fitted model equations are shown as annotations. Model fitted within stats with labels generated automatically." />
<figcaption aria-hidden="true"><strong>Figure 6.</strong> The same
scatter plot of obsevations as in Figures 3 to 5 but with three second
degree polynomials <strong>fitted by quantile regression</strong>, for
the median and the quartiles. The line shows the model fit for the
median line, and the band delimits the quartiles. The three fitted model
equations are shown as annotations. Model fitted within <em>stats</em>
with labels generated automatically.</figcaption>
</figure>

A quadrant plot with counts and labels, using `geom_text_repel()` from
package ‘ggrepel’.

``` r
ggplot(quadrant_example.df, aes(logFC.x, logFC.y)) +
  geom_point(alpha = 0.3) +
  geom_quadrant_lines() +
  stat_quadrant_counts() +
  stat_dens2d_filter(color = "red", 
                     keep.fraction = 0.02, h = 3) +
  stat_dens2d_labels(aes(label = gene), 
                     keep.fraction = 0.02, h = 3,
                     geom = "text_repel", 
                     size = 2, 
                     colour = "red") +
  scale_x_logFC(name = "Transcript abundance after A%unit") +
  scale_y_logFC(name = "Transcript abundance after B%unit",
                expand = expansion(mult = 0.2))
```

<figure>
<img src="man/figures/README-unnamed-chunk-1-1.png"
alt="Figure 7. A quadrant plot as used to compare gene expression under two conditions or in two genotypes. A fraction, 2% in this case, of all genes are labelled, with those in the least dense regions of the scatter plot selected automatically. The manually set bandwidth, h = 3 in this case, has a large effect on which observations are highlighted and labelled." />
<figcaption aria-hidden="true"><strong>Figure 7.</strong> A quadrant
plot as used to compare gene expression under two conditions or in two
genotypes. A fraction, 2% in this case, of all genes are labelled, with
those in the least dense regions of the scatter plot selected
automatically. The manually set bandwidth, <code>h = 3</code> in this
case, has a large effect on which observations are highlighted and
labelled.</figcaption>
</figure>

A time series using the specialized version of `ggplot()` that converts
the time series into a tibble and maps the `x` and `y` aesthetics
automatically. We also highlight and label the peaks using
`stat_peaks()`.

``` r
ggplot(lynx, as.numeric = FALSE) + geom_line() +
  stat_peaks(colour = "red") +
  stat_peaks(geom = "text", colour = "red", angle = 66,
             hjust = -0.1, x.label.fmt = "%Y") +
  stat_peaks(geom = "rug", colour = "red", sides = "b") +
  expand_limits(y = 8000)
```

<figure>
<img src="man/figures/README-readme-03-1.png"
alt="Figure 8. Plot of the lynx time series. The time series was converted on-the-fly into a data frame and x and y mappings set automatically. Automation relies on ggplot() being a generic function exported by package ‘ggplot2’ and the definition of method specializations in ‘ggpp’. Peaks are highlited and annotated with the year extracted and formatted by the stat." />
<figcaption aria-hidden="true"><strong>Figure 8.</strong> Plot of the
<code>lynx</code> time series. The time series was converted on-the-fly
into a data frame and <em>x</em> and <em>y</em> mappings set
automatically. Automation relies on <code>ggplot()</code> being a
generic function exported by package ‘ggplot2’ and the definition of
method specializations in ‘ggpp’. Peaks are highlited and annotated with
the year extracted and formatted by the <em>stat</em>.</figcaption>
</figure>

## Installation

Installation of the most recent stable version from CRAN (sources, Mac
and Win binaries):

``` r
install.packages("ggpmisc")
```

Installation of the current unstable version from R-Universe CRAN-like
repository (binaries for Mac, Win, Webassembly, and Linux, as well as
sources available):

``` r
install.packages("ggpmisc",
                 repos = c("https://aphalo.r-universe.dev",
                           "https://cloud.r-project.org"))
```

Installation of the current unstable version from GitHub (from sources):

``` r
# install.packages("remotes") # nolint: commented_code_linter.
remotes::install_github("aphalo/ggpmisc")
```

## Documentation

HTML documentation for the package, including help pages and the *User
Guide*, is available at <https://docs.r4photobiology.info/ggpmisc/>.

News about updates are regularly posted at
<https://www.r4photobiology.info/>.

Chapter 7 in Aphalo (2020) and Chapter 9 in Aphalo (2024) explain basic
concepts of the grammar of graphics as implemented in ‘ggplot2’ as well
as extensions to this grammar including several of those made available
by packages ‘ggpp’ and ‘ggpmisc’. Information related to the book is
available at <https://www.learnr-book.info/>.

## Contributing

Please report bugs and request new features at
<https://github.com/aphalo/ggpmisc/issues>. Pull requests are welcome at
<https://github.com/aphalo/ggpmisc>.

Testing by `R-CMD-check.yaml` at GitHub is partly ‘ggplot2’-version
dependent and run only under the latest R release because of very small
differences in plots and corresponding graphical “snaps” used as
reference. Visual difference tests are never run by CRAN because they
are “fragile” and prone to unexpectedly and spuriously fail.

## Citation

If you use this package to produce scientific or commercial
publications, please cite according to:

``` r
citation("ggpmisc")
#> To cite package 'ggpmisc' in publications use:
#> 
#>   Aphalo P (2026). _ggpmisc: Miscellaneous Extensions to 'ggplot2'_. R
#>   package version 1.0.0, <https://docs.r4photobiology.info/ggpmisc/>.
#> 
#> A BibTeX entry for LaTeX users is
#> 
#>   @Manual{,
#>     title = {ggpmisc: Miscellaneous Extensions to 'ggplot2'},
#>     author = {Pedro J. Aphalo},
#>     year = {2026},
#>     note = {R package version 1.0.0},
#>     url = {https://docs.r4photobiology.info/ggpmisc/},
#>   }
```

## Acknowledgement

Being an extension to package ‘ggplot2’, some of the code in package
‘ggpmisc’ has been created by using as a template that from layer
functions and scales in ‘ggplot2’. The user interface of ‘ggpmisc’ aims
at being as consistent as possible with ‘ggplot2’ and the layered
grammar of graphics (Wickham 2010). New features added in ‘ggplot2’ are
added when relevant to ‘ggpmisc’, such as support for `orientation` for
flipping of layers. This package does consequently indirectly include
significant contributions from several of the authors and maintainers of
‘ggplot2’, listed at (<https://ggplot2.tidyverse.org/>).

## References

Aphalo, Pedro J. (2024) *Learn R: As a Language.* 2ed. The R Series.
Boca Raton and London: Chapman and Hall/CRC Press. ISBN: 9781032516998.
466 pp.

Aphalo, Pedro J. (2020) *Learn R: As a Language.* 1ed. The R Series.
Boca Raton and London: Chapman and Hall/CRC Press. ISBN: 9780367182533.
350 pp.

Wickham, Hadley. 2010. “A Layered Grammar of Graphics.” Journal of
Computational and Graphical Statistics 19 (1): 3–28.
<https://doi.org/10.1198/jcgs.2009.07098>.

## License

© 2016-2026 Pedro J. Aphalo (<pedro.aphalo@helsinki.fi>). Released under
the GPL, version 2 or greater. This software carries no warranty of any
kind.
