cran version


Package ‘ggpmisc’ (Miscellaneous Extensions to ‘ggplot2’) is a set of extensions to R package ‘ggplot2’ (>= 3.0.0) with emphasis on annotations and highlighting related to fitted models and data summaries. Data summaries shown as text, tables or equations are implemented. New geoms support insets in ggplots. The location of fit summaries and graphical insets within the plotting area needs usually to be set independently of the x and y scales. The “natural” coordinates to use in such cases are expressed in ‘grid’ “npc” units in the range 0..1 for which new aesthetics and their scales are made available.

ggplot methods

Being ggplot() defined as a generic method in ‘ggplot2’ makes it possible to define specializations, and we provide two for time series stored in objects of classes ts and xts which automatically convert these objects into tibbles and set the as default the aesthetic mappings for x and y. A companion function try_tibble() is also exported.


Geometries geom_table(), geom_plot() and geom_grob() make it possible to add inset tables, inset plots, and arbitrary ‘grid’ graphical objects as layers to a ggplot using native coordinates for x and y.

Geometries geom_text_npc(), geom_label_npc(), geom_table_npc(), geom_plot_npc() and geom_grob_npc(), geom_text_npc() and geom_label_npc() are versions of geometries that interpret positions on x and y axes using aesthetics npcx and npcy values expressed in “npc” units.

Geometries geom_x_margin_arrow(), geom_y_margin_arrow(), geom_x_margin_grob(), geom_y_margin_grob(), geom_x_margin_point() and geom_y_margin_point() make it possible to add marks along the x and y axes. geom_vhlines() and geom_quadrant_lines() draw vertical and horizontal reference lines within a single layer.


Statistic stat_fmt_tb() helps with the formatting of tables to be plotted with geom_table().

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

Statistics that help with reporting the results of model fits are stat_poly_eq(), stat_fit_residuals(), stat_fit_deviations(), stat_fit_glance(), stat_fit_augment(), stat_fit_tidy() and stat_fit_tb().

Two statistics, stat_dens2d_filter() and stat_dens2d_label(), implement tagging or selective labeling of observations based on the local 2D density of observations. These two stats are designed to work well together with geom_text_repel() and geom_label_repel() from package ‘ggrepel’.

A summary statistic using special grouping for quadrants stat_quadrant_counts() can be used to automate labeling with the number of observations.

The statistics stat_apply_panel() and stat_apply_group() can be useful for applying arbitrary functions returning numeric vectors. They are specially useful with functions lime cumsum(), cummax() and diff().

Aesthetics and scales

Scales scale_npcx_continuous() and scale_npcy_continuous() and the corresponding new aesthetics npcx and npcy make it possible to add graphic elements and text to plots using coordinates expressed in npc units for the location within the plotting area, improving support for annotations, most notably when using facets.

Scales scale_x_logFC() and scale_y_logFC() are suitable for 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.


Functions for the manipulation of layers in ggplot objects and statistics and geometries that echo their data input to the R console, earlier included in this package are now in package ‘gginnards’. cran version



In the first example we plot 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.

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)

In the second example we add the equation for a fitted polynomial plus the adjusted coefficient of determination to a plot showing the observations plus the fitted curve, deviations and confidence band. We use stat_poly_eq().

formula <- y ~ x + I(x^2)
ggplot(cars, aes(speed, dist)) +
  geom_point() +
  stat_fit_deviations(method = "lm", formula = formula, colour = "red") +
  geom_smooth(method = "lm", formula = formula) +
  stat_poly_eq(aes(label =  paste(stat(eq.label), stat(adj.rr.label), sep = "*\", \"*")),
               formula = formula, parse = TRUE)

The same figure as in the second 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.

formula <- y ~ x + I(x^2)
ggplot(cars, aes(speed, dist)) +
  geom_point() +
  geom_smooth(method = "lm", formula = formula) +
  stat_fit_tb(method = "lm",
              method.args = list(formula = formula),
              tb.type = "fit.anova",
              tb.vars = c(Effect = "term", 
                          "M.S." = "meansq", 
                          "italic(F)" = "statistic", 
                          "italic(P)" = "p.value"),
              tb.params = c(x = 1, "x^2" = 2),
              label.y.npc = "top", label.x.npc = "left",
              size = 2.5,
              parse = TRUE)
#> Dropping column(s) from table.
#> Warning in f(...): Dropping row(s) from table.

A plot with an inset plot.

p <- ggplot(mtcars, aes(factor(cyl), mpg, colour = factor(cyl))) +
  stat_boxplot() +
  labs(y = NULL) +
  theme_bw(9) + theme(legend.position = "none")

ggplot(mtcars, aes(wt, mpg, colour = factor(cyl))) +
  geom_point() +
  annotate("plot_npc", npcx = "left", npcy = "bottom", label = p) +
  expand_limits(y = 0, x = 0)

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

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 = 0.03) +
  stat_dens2d_labels(aes(label = gene), keep.fraction = 0.03, 
                     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")


Installation of the most recent stable version from CRAN:


Installation of the current unstable version from Bitbucket:

# install.packages("devtools")


HTML documentation is available at (, including a User Guide.

News about updates are regularly posted at (


Please report bugs and request new features at ( Pull requests are welcome at (


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

#> To cite package 'ggpmisc' in publications use:
#>   Pedro J. Aphalo (2020). ggpmisc: Miscellaneous Extensions to
#>   'ggplot2'.,
#> A BibTeX entry for LaTeX users is
#>   @Manual{,
#>     title = {ggpmisc: Miscellaneous Extensions to 'ggplot2'},
#>     author = {Pedro J. Aphalo},
#>     year = {2020},
#>     note = {,
#>   }


© 2016-2020 Pedro J. Aphalo ( Released under the GPL, version 2 or greater. This software carries no warranty of any kind.