My book on R was published on 28 July 2020. The R package ‘learnrbook’ available through CRAN contains data and the R code scripts and chunks from the book. The book has now its own BOOK WEBSITE

ISSN 2343-3248 (Helsinki, Finland)

My book on R was published on 28 July 2020. The R package ‘learnrbook’ available through CRAN contains data and the R code scripts and chunks from the book. The book has now its own BOOK WEBSITE

Version 0.4.3 contain the bug fix implemented in version 0.4.2-2, which did not make it to CRAN. (See bug listed under 1. in the post ggpmisc 0.4.2.)

- Add
`stat_ma_line()`

and`stat_ma_eq()`

implementing support for major axis (MA), standard major axis (SMA), ranged major axis (RMA) and ordinary least squares (OLS) using function`lmodel2()`

from package ‘lmodel2’.

*Documentation web site at http://docs.r4photobiology.info/ggpmisc/ includes all help pages, with output from all examples, vignettes as well as a changelog in HTML format.*

NOTE: Version 0.4.3 is on its way to CRAN.

Please raise issues concerning bugs or enhancements to this package through GitHub https://github.com/aphalo/ggpmisc/issues

During the major updates to 0.4.0 and 0.4.1 some bugs slipped through various tests. Versions 0.4.2, 0.4.2-1 and 0.4.2-2 contain fixes to these bugs. The bug fixed in 0.4.2-2 triggered an error only when R had been built with specific compilers.

- Error in
`stat_poly_eq()`

and`stat_quant_eq()`

under some Linux builds of R, including when used in RStudio Cloud. This bug did not affect Windows. - Failure to find
`after_stat()`

when instead of attaching the package with`library(ggpmisc)`

statistics were called using the`ggpmisc::<name>`

notation. - Remove or convert to suggests some dependencies no longer needed after the split of ‘ggpp’.

*Documentation web site at http://docs.r4photobiology.info/ggpmisc/ includes all help pages, with output from all examples, vignettes as well as a changelog in HTML format.*

**NOTE: Version 0.4.2-2 addressing all three bugs was not submitted to CRAN but instead the fix was released in version 0.4.3 now in CRAN.
**

Please raise issues concerning bugs or enhancements to this package through GitHub https://github.com/aphalo/ggpmisc/issues

*Documentation web site at http://docs.r4photobiology.info/ggpmisc/ includes all help pages, with output from all examples, vignettes as well as a changelog in HTML format.*

This update is special in that it was built with the input of excellent ideas and code contributions from users. I learnt a lot myself and these improvements have made ‘ggpmisc’ more useful in general and for myself. Support for quantile regression is now, I hope, close to its final shape. Support for the new ‘ggplot2’ feature: *orientation* is implemented in the statistics where it is most useful, and can be also be changed, more intuitively, through the model formula. Of the planned enhancements, implementing support for major axis regression, remains in the to do list. Both `stat_poly_eq()`

and `stat_quant_eq()`

now return additional labels, plus some numeric values to facilitate conditional display. Much of the code used to generate the text labels has been improved, and markdown formatting tested.

The suggestion from Mark Neal of adding support for quantile regression partly addressed in ggpmisc 0.4.0 has lead to additional enhancements in this version. The idea of supporting confidence bands for quantile regression came from Samer Mouksassi who also provided code and examples for different types of quantile regression. Additional suggestions from Mark Neal, Carl and other users have lead to bug fixes as well as to an interface with better defaults for arguments (see issue #1).

- Support robust regression using
`rlm`

and the use of`function`

objects as argument to`method`

in`stat_poly_eq()`

. - Support in
`stat_poly_eq()`

and`stat_quant_eq()`

`formula = x ~ y`

and other models in which the explanatory variable is`y`

in addition to models with`x`

as explanatory variable. `stat_poly_eq()`

and`stat_quant_eq()`

now pass to the geom by default a suitable value as argument to`parse`

depending on`output.type`

(enhancement suggested by*Mark Neal*in issue #11).`stat_poly_eq()`

and`stat_quant_eq()`

return the coefficient estimates as`numeric`

columns in`data`

when`output.type = "numeric"`

(problem with`coefs.ls`

reported by*cgnolte*in issue #12).`stat_poly_eq()`

now supports optional use of lower case for*r*^{2}and*p*-value.- Revise
`stat_poly_eq()`

and`stat_quant_eq()`

so that by default they keep trailing zeros according to the numbers of significant digits given by`coef.digits`

. A new parameter`coef.keep.zeros`

can be set to`FALSE`

to restore the deletion of trailing zeros. Trailing zeros in the equation will be rendered to the plot only if`output.type`

is other than`"expression"`

. - Add
`stat_poly_line()`

, a new interface to`ggplot2::stat_smooth()`

accepting`formula = x ~ y`

and other models in which the explanatory variable is`y`

rather than`x`

or setting`orientation = "y"`

. In contrast to`ggplot2::stat_smooth()`

,`stat_poly_line()`

has always`"lm"`

as default`method`

. - Add
`stat_quant_line()`

which is an adaptation of`ggplot2::stat_smooth()`

and`ggplot2::stat_quantile()`

accepting`formula = x ~ y`

and other models in which the explanatory variable is`y`

rather than`x`

or setting`orientation = "y"`

to fit models with`x`

as explanatory variable. This change makes it possible to add to a plot a*double quantile regression*.`stat_quant_line()`

supports plotting of confidence bands for quantile regression using`ggplot2::geom_smooth()`

to create the plot layer. - Add
`stat_quant_band()`

which plots quantile regressions for three quantiles as a band plus a line, accepting`formula = x ~ y`

and other models in which the explanatory variable is`y`

rather than`x`

or setting`orientation = "y"`

to fit models with`x`

as explanatory variable. - Add support for quantile regression
`rq`

, robust regression`rlm`

, and resistant regression`lqs`

and`function`

objects to`stat_fit_residuals()`

and`stat_fit_deviations()`

. - Support use of
`stat_fit_residuals()`

and`stat_fit_deviations()`

with`formula = x ~ y`

and other models in which the explanatory variable is`y`

in addition to models with`x`

as explanatory variable. - Add
`weights`

to returned values by`stat_fit_residuals()`

and`stat_fit_deviations()`

and add support for the`weight`

aesthetic as input for parameter`weights`

of model fit functions.

- Fix bug in
`stat_poly_eq()`

and`stat_quant_eq()`

resulting in mishandling of formulas using the`+ 0`

notation (reported by*orgadish*in issue #10). - Fix bug in
`stat_poly_eq()`

and`stat_quant_eq()`

resulting in bad/non-syntactical character strings for`eq.label`

when`output.type`

was different from its default of`"expression"`

.

*Documentation web site at http://docs.r4photobiology.info/gginnards/ includes all help pages, with output from all examples, vignettes as well as a changelog in HTML format.*

**NOTE: The new version of the package is on its way to CRAN.
**

Please raise issues concerning bugs or enhancements to this package through GitHub https://github.com/aphalo/gginnards/issues

*Documentation web site at http://docs.r4photobiology.info/ggpp/ includes all help pages, with output from all examples, vignettes as well as a changelog in HTML format.*

The initial implementation and user interface of three apply statistics first introduced in ‘ggpmisc’ 0.3.6 has been revised to expand their usefulness and to make them less error-prone, while the

fourth one is now defunct.

- Update
`stat_apply_group()`

to support summary functions like`quantile()`

that return vectors with more than one value but shorter than the original number of observations. - Update
`stat_summary_xy()`

and`stat_apply_group()`

to return`NA`

`x`

and/or`y`

when`.fun.x`

or`.fun.y`

are not passed anargument. This is a code breaking change with respect to the previous (unstable) version. - Update
`stat_summary_xy()`

and`stat_centroid()`

to support functions that return a one row data frame, like those defined in ‘ggplot2’ to be passed as argument to parameter`fun.data`

of`ggplot2::stat_summary()`

, such as`mean_se`

,`mean_cl_boot`

, etc.

- Fix bug in
`stat_centroid()`

,`stat_summary_xy()`

and`stat_apply_group()`

resulting in the return of a long data frame with`NA`

values instead of a data frame with fewer rows. - Remove
`stat_apply_panel()`

, as it was redundant. (Grouping can be modified per layer when needed.)

The default argument for `geom`

in `stat_centroid()`

is likely to change in the near future. Otherwise, the three statistics can be considered now stable.

Please raise issues concerning bugs or enhancements to this package through GitHub at https://github.com/aphalo/ggpp/issues. Pull requests are also welcome.

**NOTE:** The updated ‘ggpp’ (0.4.2) is on its way to CRAN. The latest development version of the package can be installed from GitHub.

remotes::install_github("aphalo/ggpp")