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.

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 labelling 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 labelling 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()`

.

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 colour, 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’.

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)
#> Registered S3 method overwritten by 'xts':
#> method from
#> as.zoo.xts zoo
```

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(..eq.label.., ..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",
"df",
"M.S." = "meansq",
"italic(F)" = "statistic",
"italic(P)" = "p.value"),
label.y.npc = "top", label.x.npc = "left",
size = 2.5,
parse = TRUE)
```

A plot with an inset plot.

```
library(tibble)
p <- ggplot(mtcars, aes(factor(cyl), mpg, colour = factor(cyl))) +
stat_boxplot() +
labs(y = NULL) +
theme_bw(9) + theme(legend.position = "none")
df <- tibble(x = 0.01, y = 0.015, plot = list(p))
ggplot(mtcars, aes(wt, mpg, colour = factor(cyl))) +
geom_point() +
geom_plot_npc(data = df, mapping = aes(npcx = x, npcy = y, label = plot),
vjust = 0, hjust = 0) +
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:

HTML documentation is available at (https://docs.r4photobiology.info/ggpmisc/), including a *User Guide*.

News about updates are regularly posted at (https://www.r4photobiology.info/).

Please report bugs and request new features at (https://bitbucket.org/aphalo/ggpmisc/issues). Pull requests are welcome at (https://bitbucket.org/aphalo/ggpmisc).

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

```
citation("ggpmisc")
#>
#> To cite package 'ggpmisc' in publications use:
#>
#> Pedro J. Aphalo (2019). ggpmisc: Miscellaneous Extensions to
#> 'ggplot2'. https://www.r4photobiology.info,
#> https://bitbucket.org/aphalo/ggpmisc.
#>
#> A BibTeX entry for LaTeX users is
#>
#> @Manual{,
#> title = {ggpmisc: Miscellaneous Extensions to 'ggplot2'},
#> author = {Pedro J. Aphalo},
#> year = {2019},
#> note = {https://www.r4photobiology.info, https://bitbucket.org/aphalo/ggpmisc},
#> }
```

© 2016-2019 Pedro J. Aphalo (pedro.aphalo@helsinki.fi). Released under the GPL, version 2 or greater. This software carries no warranty of any kind.