ggpmisc 0.4.6

Version 0.4.6 fixes a significant bug occasionally affecting highlighting and labelling of peaks and valleys. Even though when triggered the problem is easily detectable by looking at the plot, please, update.

Changes compared to version 0.4.5, the previous version in CRAN are:

  • Fix bug in stat_peaks() and stat_valleys(). They could return wrong values for peaks and valleys if the rows in data in the ggplot object were not sorted by the value of x.

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 0.4.6 is on its way to CRAN.

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

photobiologyWavebands 0.4.5

Package documentation web site at: https://docs.r4photobiology.info/photobiologyWavebands/

The main changes from version 0.4.4 the previous CRAN release, are:

  • Major bug fixed! The definitions of UVA1() and UVA2()  were swapped.
  • Git repository moved to GitHub

NOTE: The updated package is on its way to CRAN.

Please raise issues concerning bugs or enhancements to this package through Bitbucket at https://github.com/aphalo/photobiologyWavebands/issues/

 

ggpmisc 0.4.5

Version 0.4.5 includes a minor bugfix and an edit in vignette examples, to ensure compatibility with the upcoming version of package ‘ggpp’.

Changes compared to version 0.4.4, the previous version in CRAN are:

  • Decrease version of ‘gginnards’ in suggests.
  • Edit vignette examples to allow renaming a geom exported by ‘ggpp’ (retaining backwards compatibility).

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 0.4.5 is on its way to CRAN.

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

ggpmisc 0.4.4

Version 0.4.4 includes enhancements. An issue raised in GitHub and a question in StackOverflow asked for the possibility of changing how fitted lines are plotted based on the “goodness” of the fit. In addition an old question in StackOverflow highlighted the need of more intuitive support for annotations based on stats::cor.test(). In addition to implementing these enhancements we continued adding support for flipping of statistics controlled through formal parameter orientation as implemented in ‘ggplot2’ since version 3.3.0.

Changes compared to version 0.4.3, the previous version in CRAN are:

  • Add new function stat_correlation() to annotate plots with correlation estimates, their P-value, a test statistic and n computed with stats::cor.test(). In addition to formatted character strings, numeric values are included in the returned data frame to facilitate conditional display.
  • Update stat_poly_line() to optionally add columns n, p.value, r.squared , adj.r.squared and method to the returned data frame. Code breaking: This statistic no longer supports fitting of splines with methods such as loess . This could potentially break user code, in which case the solution is to use stat_smooth().
  • Update stat_ma_line() to optionally add columns n, p.value, r.squared and method to the returned data frame. (As only a slope can be fitted, adj.r.squared is irrelevant.)
  • Update stat_quant_line() and stat_quant_band() to optionally add n and method columns to the returned data frame. (No exact equivalent of r.squared exists for quantile regression.)
  • Update stat_fit_residuals() to optionally return weighted
    residuals.
  • Update stat_peaks() and stat_valleys() to allow flipping with new parameter orientation.

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 0.4.4 is on its way to CRAN.

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

photobiologyInOut 0.4.23

Fifteen months since the previous release, this update adds support for one new spectrometer from LI-COR and multiple spectrometers from Wasatch Photonics. Updates to the tidyverse were triggering deprecation notices which are now avoided. Updates to package ‘photobiology’ revealed some minor bugs that are also fixed. Unnecessary progress messages during file realing and text scanning have been silenced.

The main changes from version 0.4.22 the previous CRAN release, are:

  • Add parameters na and ... to read_csi_data().
  • Add function read_wasatch_csv() supporting data import from long form CSV spectrum files saved by Wasatch’s Enlighten program.
  • Add function read_li180_txt() supporting data import from files saved by the LI-180 handheld array spectrometer from LI-COR.
  • Avoid spurious progress messages when reading files.
  • Track various changes in the tidyverse that deprecated functions used in this package.
  • Fix bugs in some imports from ‘photobiology’.
  • Move git repository from Bitbucket to Github.
  • Set up Github action for CRAN-checks on Windows, OS X and Ubuntu.
  • Documentation web site at http://docs.r4photobiology.info/photobiologyInOut/.

NOTE: This version of the package is on its way to CRAN.

Please raise issues concerning bugs or enhancements to this package through Bitbucket at https://github.com/aphalo/photobiologyinout/issues

ggpmisc 0.4.3

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.)

New

  1. 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

ggpmisc 0.4.2

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.

Bugs fixed

  1. 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.
  2. Failure to find after_stat() when instead of attaching the package with library(ggpmisc) statistics were called using the ggpmisc::<name> notation.
  3. 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

ggpmisc 0.4.1

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.

Overview

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).

Changes compared to ‘ggpmisc’ 0.4.0

Enhancements

  • 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 r2 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.

Bugs fixed

  • 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

ggpp 0.4.2

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.

Overview

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.

Changes compared to ‘ggpp’ 0.4.1

Enhancements

  • 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.

Bugs fixed

  • 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.)

Warning

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")

ggpp 0.4.1

The package documentation web site at: https://docs.r4photobiology.info/ggpp/ includes a changelog.

Compared to ‘ggpp’ 0.4.0, the following changes have been introduced.

  • Update compute_just2D() and compute_just() to work with any value for the angle aesthetic, as in ‘ggplot2’ (>= 3.3.5).
  • Fix bug in geom_table() that would cause text left or right justified to be clipped when the text in a cell was very long (reported by dryguy). (Cell padding still needs improvement.)

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’ (0.4.1) is on its way to CRAN. The latest development version of the package can be installed from GitHub.

remotes::install_github("aphalo/ggpp")

ggpmisc 0.4.0 and ggpp 0.4.0

The main change is the split of package ‘ggpmisc’ into two packages. Starting from this version, package ‘ggpmisc’ focuses only on plot annotations related to model fits and statistical summaries. The new package ‘ggpp’ contains generally useful extensions to the grammar of graphics such as new geometries and position functions. As package ‘ggpmisc’ loads package ‘ggpp’ the migration should have minimal if any impact on users’ code. Package ‘ggpp’ will be most useful to authors of packages that currently import ‘ggpmisc’, including myself. It is good to be aware that the split almost exactly follows the subject matter of the two vignettes previously part of ‘ggpmisc’. When using ‘ggpmisc’, users will notice that some functions have migrated only when consulting the documentation.

‘ggpp’

The package documentation web site at: https://docs.r4photobiology.info/ggpp/ includes a changelog.

Compared to ‘ggpmisc’ 0.3.9, the following changes have been introduced. New justification styles have being implemented to complement position_nudge_center(). They are supported in geom_text_linked(), geom_plot(), geom_table(), geom_grob() and geom_marging_grob(). In the current implementation all rows in data should contain the same hjust or vjust value when using the new types of justification described here, this seems reasonable as they compute the individual justification values from the data. All other justification values, either numeric or character do not have this restriction and can be used as in geoms from ‘ggplot2’. These new features may change in the near future.

  • Rename geom_text_linked().
  • Implement justifications "outward_mean", "inward_mean", "outward_median" and "inward_median" so that outward and inward are with respect to the centroid of the data instead of to the middle of the x or y scales. This should be useful in combination with position_nudge_center().
  • Implement justifications "outward_nnn" and "inward_nnn" so that outward and inward are with respect to the number resulting from applying as.numeric() to the characters that replace nnn. For example strings like "outward_0.5", "inward_3e5" or "outward_-3e-2" are supported. This should be useful when manual tweaking is desired. As special cases "outward_0" and "inward_0" apply justification outward and inward with respect to the origin. This should be useful for biplots used for PCA and similar cases with arrows radiating out of the origin. (The "outward" and "inward" justification implemented in ‘ggplot2’ is relative to the middle of the x or y scales.)
  • Revise compute_npcx() and compute_npcy() to support multiple steps per group (needed in ‘ggpmisc’).
  • Fix problem related to "outward" and "inward" justification of text labels when angle aesthetic takes values < -45 or > 45 degrees. This code change alters how old plots are rendered if text labels have been rotated by more than 45 degrees.
  • [‘ggplot2’, ‘ggrepel’] The problem with angle was a “bug” in ‘ggplot2’ also present in ‘ggrepel’. A pull request for ggplot2::geom_text() has been submitted and merged. This is now in the ‘ggplot2’ 3.3.4 milestone retaining consistent behaviour between ‘ggplot2’, ‘ggrepel’, ‘ggpp’ and ‘ggpmisc’.

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

‘ggpmisc’

The package documentation web site at: https://docs.r4photobiology.info/ggpmisc/ includes a changelog.

Changes from ‘ggpmisc’ version 0.3.9, the most recent CRAN release, are:

  • Add stat_quant_eq() based on quantile regression as implemented in package ‘quantreg’. (enhancement suggested by Mark Neal)
  • Add n.label and n to the values returned by stat_poly_eq() and stat_quant_eq(). (enhancement suggested by a question from ganidat)
  • Add r.squared, adj.r.squared, p.value and n as numeric to values returned in addition to the corresponding character labels when stat_poly_eq() is called with output.type other than numeric. Similarly for n and rho in the case of stat_quant_eq(). (enhancement suggested by a question from Tiptop)
  • Fix bug in stat_poly_eq() leading to empty returned value when data contains too few observations to fit the model. (reported by ganidat)
  • Add support for quantile regression rq, robust regression rlm, and resistant regression lqs and function objects to stat_fit_deviations().

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

Acknowlegements: I thank Kamil Slowikowski for his contributions of ideas and for his willingness to keep the development of our packages coordinated. This update was done in part to address questions raised and reports of bugs by users, which I thank. The tag [ggpmisc] is in use at stackoverflow for questions related to the use these two packages. I thank Mark Neal for the suggestion that lead to the new statistic stat_quant_eq() and for his help during its testing. I thank Stackoverflow users ganidat and Tiptop for enhancement ideas.

Documentation web sites at https://docs.r4photobiology.info/ggpmisc/ and https://docs.r4photobiology.info/ggpp/ include all help pages, with output from all examples, and vignettes in HTML format. The online vignettes include the output of all code examples, while the vignettes as included in the package, contain the output of only a subset of the code examples so as to keep the documentation at a reasonable size for package distribution.

NOTE: The new package ‘ggpp’ (0.4.0)  is in CRAN and updated ‘ggpmisc’ (0.4.0) is on its way to CRAN. The latest development versions of both packages can be installed from GitHub.

remotes::install_github("aphalo/ggpp")
remotes::install_github("aphalo/ggpmisc")

photobiology 0.10.6

The package documentation web site at https://docs.r4photobiology.info/photobiology/ includes a changelog with information for each release since version 0.1.0.

This release includes fixes to several minor bugs that were reported during the last seven months. In addition to these fixes new specializations of existing methods were added to improve the consistency across the different methods and classes. The Git repository was recently moved from Bitbucket to GitHub and continuous integration using Git actions set up.

Changes from version 0.10.5 the most recent CRAN release, are:

  • Fix boundary-condition bug in msmsply().
  • Fix handling of na.rm = TRUE in find_peaks().
  • Revise the computation of the default for dyn.range in cps2Tfr() and cps2Rfr() so that it takes into account the relative signal in
    the reference spectrum.
  • Add parameter missing.pixs to cps2irrad() so that corrupted too-short spectra can be converted if the location of missing pixels
    is known.
  • Add row-wise summaries for raw_mspct and cps_mspct objects.
  • Add support of multiple spectra in long form to irrad(), e_irrad(), q_irrad(), q_ratio(), e_ratio(), qe_ratio(), eq_ratio(), absorbance(), absorptance(), trasmittance(), reflectance() methods.
  • Add warning for handling of multiple spectra in long form to integrate_spct() method.

NOTE: The updated package is on its way to CRAN.

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