New metadata() function. Added at the end of a
pipeline, it writes a provenance summary of the whole download: the
source URLs and the date each file was downloaded, the variables
produced, the temporal period they refer to, the native resolution of
every source dataset, the resolution, CRS and extent of the output, and
the processing settings used (those of par_set() and the
arguments of every dataset function called). The summary is written as a
readable report (envar_metadata.txt) and as a table
(envar_metadata.csv) and is also returned with the pipeline
object. As in corr_check(), an interactive session asks at
the console where to store the files, so a pipeline containing both
functions asks once for each of them.
The correlation plot written by corr_check() is no
longer a fixed 2000 x 2000 pixel image: its side now grows with the
number of variables, from 1200 up to 2000 pixels at 300 dpi, so that
small sets of variables are not drawn on a mostly empty sheet.
par_set() no longer writes to the user’s home
filespace without permission. The cache argument now
defaults to NULL, which asks once per interactive session
whether the persistent download cache (in
tools::R_user_dir()) may be used, and always answers “no”
in non-interactive sessions, where a session temporary directory is used
instead. Pass cache = TRUE/FALSE, or set
options(envar.cache = ), to skip the question.
chelsa() now fails with an informative message when
vars = "bio" (which asks in the console which bioclimatic
variables to download) is used in a non-interactive session, instead of
failing later with an unrelated error. The corresponding example has
been removed from the documentation.
roads() gains the two aggregated variables
"primary" (sum of road classes 4 and 5) and
"other" (sum of road classes 1, 2 and 3), alongside the
existing "all" and the five single classes. Because
"primary" now names the aggregated group, the single class
2 is requested with "class2" or
"primary class". The download links of the aggregated
layers were updated.
The roads() documentation and the “Available
variables” article now describe how the road classes are derived from
OpenStreetMap and what the aggregated layers contain.
The land mask example in the “Package overview” article now uses
a CHELSA climatology, which provides values over the sea, so that the
effect of land = TRUE is actually visible.