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. To complement these, the widely useful `geom_table()`

and `stat_fmt_tb()`

are defined as well as `ggplot`

constructors for time series objects. The provided `ggplot.ts()`

and `ggplot.xts()`

use `try_tibble()`

which is also exported and accepts objects of additional classes as input.

Statistics useful for highlighting and/or annotating individual data points in regions of plot panels with high/low densities of observations. These stats are designed to work well together with `geom_text_repel()`

and `geom_label_repel()`

from package ‘ggrepel’.

**Note:** 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") +
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 and confidence band. We use `stat_poly_eq()`

.

```
formula <- y ~ x + I(x^2)
ggplot(cars, aes(speed, dist)) +
geom_point() +
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",
parse = TRUE)
```

Installation of the most recent stable version from CRAN:

Installation of the current unstable version from Bitbucket:

```
# install.packages("devtools")
devtools::install_bitbucket("aphalo/ggspectra")
```

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

News on updates to the different packages of the ‘r4photobiology’ suite 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).

Please note that this project is released with a Contributor Code of Conduct. By participating in this project you agree to abide by its terms.

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

```
citation("ggpmisc")
#>
#> To cite ggpmisc in publications, please use:
#>
#> Pedro J. Aphalo. (2016) Learn R ...as you learnt your mother
#> tongue. Leanpub, Helsinki.
#>
#> A BibTeX entry for LaTeX users is
#>
#> @Book{,
#> author = {Pedro J. Aphalo},
#> title = {Learn R ...as you learnt your mother tongue},
#> publisher = {Leanpub},
#> year = {2016},
#> url = {https://leanpub.com/learnr},
#> }
```

© 2016-2018 Pedro J. Aphalo (pedro.aphalo@helsinki.fi). Released under the GPL, version 2 or greater. This software carries no warranty of any kind.