My new book was published on 28 July. Within the next few days I will make available on-line supplementary material, and explain how I created in R the word cloud in the front cover of the book. The word list I used is that for the R index from the book. I typeset the book using LaTeX. It is currently available from the publisher through the book’s web page.
Package ‘ggpmisc’ focuses mainly on plot annotations. The new version adds features that I hope will be found useful.
Package documentation web site at: https://docs.r4photobiology.info/ggpmisc/
Changes from version 0.3.6 the most recent CRAN release, are:
Three new statistics stat_dens1d_filter(), stat_dens1d_filter_g() and stat_dens1d_labels() are 1D versions of the existing 2D functions which received a minor update.
stat_dens1d_filter(), stat_dens1d_filter_g() and stat_dens1d_labels(), to complement existing stat_dens2d_filter(), stat_dens2d_filter_g() and stat_dens2d_labels().stat_dens2d_filter(), stat_dens2d_filter_g() and stat_dens2d_labels() adding formal parameters keep.sparse and invert.selection, as available in the new 1D versions.stat_dens2d_labels() to accept not only character strings but also functions as argument to label.fill as the new stat_dens1d_labels() does.Updates to statistics stat_fmt_tb() and stat_fit_tb() add flexibility.
stat_fit_tb() to support renaming of terms/parameter names in the table (Suggested by Big Old Dave and Z. Lin). In addition implement selection, reordering and renaming of columns and terms/parameters using positional indexes and pattern matching of truncated names in addition to whole names. Improve formatting of small P-values.stat_fmt_tb() to support the same expanded syntax as stat_fit_tb().Here is an example for an ANOVA table inset.
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()
Acknowlegement: This update was encouraged by recent questions at stackoverflow. The tag [ggpmisc] is in use at stackoverflow for questions related to this package.
Documentation web site at http://docs.r4photobiology.info/ggpmisc/ includes all help pages, with output from all examples, and vignettes 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 Bitbucket https://bitbucket.org/aphalo/ggpmisc/issues.
A minor update to R was released recently as version 4.0.3. As always it is recommended to keep R up-to-date. R for photobiology packages and their dependencies are not affected by this are previous updates and pass checks on R >= 3.6.0.
Package documentation web site at: https://docs.r4photobiology.info/photobiologySensors/
The main changes from version 0.4.0, the current CRAN version:
diffusers.lst.Code breaking because of renaming of members of the collection of sensor response spectra.
NOTE: The updated package has been submitted to CRAN.
Please raise issues concerning bugs or enhancements to this package through Bitbucket at https://bitbucket.org/aphalo/photobiologySensors/issues
Package documentation web site at: https://docs.r4photobiology.info/photobiologyFilters/
The main changes from version 0.5.1, the current CRAN version, released 10 months ago are:
Use of package ‘photobiologyReflectors’, which was never released through CRAN, is deprecated as the same data are now contained in ‘photobiologyFilters’ (>= 5.2).
NOTE: The updated package has been submitted to CRAN.
Please raise issues concerning bugs or enhancements to this package through Bitbucket at https://bitbucket.org/aphalo/photobiologyfilters/issues