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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. Package ‘ggpmisc’ continues to give access to extensions moved to package ‘ggpp’. New geoms support insets in ggplots. The grammar of graphics is extended to support native plot coordinates (npc) so that annotations can be easily positioned using special geometries and scales. New position functions facilitate the labeling of observations by nudging data labels away or towards curves or a focal virtual center.

Extended Grammar of graphics

Please, see also the documentation of package ‘ggpp’.

Aesthetics and scales

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.


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().


Several extensions formerly included in package ‘ggpmisc’ before version 0.4.0 were migrated to package ‘ggpp’. They are still available when ‘ggpmisc’ is loaded, but the documentation now resides in the new package ‘ggpp’. cran version

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’ before version 0.3.0 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 params/terms (rows) from table!

The same figure as in the second example but this time using quantile regression.

formula <- y ~ x + I(x^2)
ggplot(cars, aes(speed, dist)) +
  geom_point() +
  geom_quantile(formula = formula, quantiles = 0.5) +
  stat_quant_eq(aes(label = paste(stat(grp.label), stat(eq.label), sep = "*\": \"*")),
               formula = formula, quantiles = 0.5, parse = TRUE)

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 = 0.02) +
  stat_dens2d_labels(aes(label = gene), keep.fraction = 0.02, 
                     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 GitHub:

# 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 (2021). ggpmisc: Miscellaneous Extensions to
#>   'ggplot2'.,
#> A BibTeX entry for LaTeX users is
#>   @Manual{,
#>     title = {ggpmisc: Miscellaneous Extensions to 'ggplot2'},
#>     author = {Pedro J. Aphalo},
#>     year = {2021},
#>     note = {,
#>   }


© 2016-2021 Pedro J. Aphalo ( Released under the GPL, version 2 or greater. This software carries no warranty of any kind.