ggspectra 0.3.4

The changes from version 0.3.3-1, the current CRAN release, are:

  • Update for compatibility with code-breaking changes in ‘tidyr’ (>= 1.0.0).
  • Minor bug fixes.
  • Make autoplot.object_spct() robust to bad-input data to avoid artefacts on stacked Rfr/Afr/Tfr plots. Data are now sanitized with a warning when off-range.

Documentation web site at

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

Please raise issues concerning bugs or enhancements to this package through Bitbucket at

Code breaking changes in ‘tidyr’ 1.0.0

Three of my packages needed updates due to code-breaking changes introduced in ‘tidyr’ 1.0.0, an update which will soon be submitted to CRAN by its authors. I received warning e-mails only about ‘photobiologyInOut’, which I have updated and submitted to CRAN some time ago. More recently I have noticed that ‘ggspectra’ had also to be updated and this required an update to ‘photobiology’. ‘ggspectra’  0.3.4 is ready to be submitted to CRAN as soon as ‘photobiology’ 0.9.29 is built on CRAN servers.

If you have been using ‘tidyr’ in your scripts, you may need to update them. The required edits are rather small but can come as a surprise, which is why I am writing this post.

photobiology 0.9.29

Changes from version 0.9.28 the most recent CRAN release, are:

  • Add clean.object_spct(), na.rm.object_spct() and na.omit.object_spct() methods, which were missing and needed for updating package ‘ggspectra’.
  • Correct small off-range error in Ler_leaf.spct data:Tfr + Rfr = 1.0028 in the worse case instead of Tfr + Rfr <= 1.

Documentation web site at includes all help pages, with output from all examples, and vignettes in HTML format.
NOTE: The updated package is in CRAN.

Please raise issues concerning bugs or enhancements to this package through Bitbucket

Tidy time series: ‘tsibble’ and ‘feasts’

This post is not to announce something related to my own packages, but to highlight Rob Hyndman’s new packages for working with time-series data using a tidy approach. If you have to deal with time series in R, you should have a look at these packages and read the posts with examples of their use at the Hyndsight blog.

I suspect more is to come soon, but for the time being have a look at what Rob Hyndman wrote yesterday and today in his blog.