Skip to contents

ggpmisc: Miscellaneous Extensions to 'ggplot2'

Source: R/ggpmisc.R
ggpmisc-package.Rd

Extensions to 'ggplot2' respecting the grammar of graphics paradigm. Statistics: locate and tag peaks and valleys; label plot with the equation of a fitted polynomial or other types of models including major axis, quantile and robust and resistant regression. Labels for P-value, R^2 or adjusted R^2 or information criteria for fitted models; parametric and non-parametric correlation; label with ANOVA table for fitted models; label with summary table for fitted models; annotations for multiple comparisons with adjusted P-values. Model fit classes for which suitable methods are provided by package 'broom' and 'broom.mixed' are supported as well as user-defined wrappers on model fit functions. Scales and stats to build volcano and quadrant plots based on outcomes, fold changes, p-values and false discovery rates.

Details

Package 'ggpmisc' is over 10 years-old but its development has tracked the changes in 'ggplot2' making use of new features. Although fully compatible with 'ggplot2' (>= 4.0.0) its minimum requirement is 'ggplot2' (>= 3.5.0). Currently 'ggpmisc' focus is on statistical annotations including fitted model equations and curves. Several special geometries used are from package 'ggpp', which is imported and re-exported in whole. These geometries and functions are documented in ggpp-package.

Extensions provided:

  • Stats for annotaions for parametric and non-parametric correlations.

  • Statistics for generating labels for fitted models, including formatted equations. By default labels are R's plotmath expressions but LaTeX, markdown and plain text formatted labels are optionally assembled.

  • Matching statistics for plotting curves and confidence bands bands for the same fitted models.

  • Statistics for adding ANOVA tables and fitted model summaries as inset tables in plots.

  • Statistic for adding annotations based on pairwise multiple comparisons based on arbitrary contrasts and a choice of P adjustment methods.

  • Statistics for locating and tagging "peaks" and "valleys" (local or global maxima and minima).

  • Functions and objects exported by ggpp-package.

The stats for peaks and valleys are coded so as to work correctly both with numeric and POSIXct variables mapped to the x aesthetic.

Note

The signatures of stat_peaks() and stat_valleys() from 'ggpmisc' are identical to those of stat_peaks() and stat_valleys() from package 'ggspectra'. While those from 'ggpmisc' are designed for numeric or time objects mapped to the x aesthetic, those from 'ggspectra' are for light spectra and expect a numeric variable describing wavelength mapped to the x aesthetic.

References

Expanded on-line documentation for 'ggpmisc' at https://docs.r4photobiology.info/ggpmisc/ and source code at https://github.com/aphalo/ggpmisc
Expanded on-line documentation for 'ggpp' at https://docs.r4photobiology.info/ggpp/ and source code at https://github.com/aphalo/ggpp
Package 'ggplot2' documentation at https://ggplot2.tidyverse.org/

See also

Useful links:

Author

Maintainer: Pedro J. Aphalo pedro.aphalo@helsinki.fi (ORCID)

Other contributors:

Examples


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") +
  ylim(NA, 8000)


formula <- y ~ poly(x, 2, raw = TRUE)
ggplot(cars, aes(speed, dist)) +
  geom_point() +
  stat_poly_line(formula = formula) +
  stat_poly_eq(use_label("eq", "R2", "P"),
               formula = formula,
               parse = TRUE) +
  labs(x = expression("Speed, "*x~("mph")),
       y = expression("Stopping distance, "*y~("ft")))


formula <- y ~ x
ggplot(PlantGrowth, aes(group, weight)) +
  stat_summary(fun.data = "mean_se") +
  stat_fit_tb(method = "lm",
              method.args = list(formula = formula),
              tb.type = "fit.anova",
              tb.vars = c(Term = "term", "df", "M.S." = "meansq",
                          "italic(F)" = "statistic",
                          "italic(p)" = "p.value"),
              tb.params = c("Group" = 1, "Error" = 2),
              table.theme = ttheme_gtbw(parse = TRUE)) +
  labs(x = "Group", y = "Dry weight of plants") +
  theme_classic()