A lot has happened since my last post (and really, I mean A LOT). However, in the spirit of that last post, one good thing that has happened is that in the upcoming 1.3-0 release of the
oce package on CRAN we have changed the default colormap (or “palette” as it’s often referred in the R world) to something not nearly sucky as the classic “jet” colormap (originally made popular by Matlab).
The purpose of this post is just to highlight the difference between the old and new defaults, and also to show many other great colormaps for plotting oceanographic data.
Jet vs viridis
imagep() function was first added to
oce, there was a limited selection of palettes that could be used. For a long time, the default of the
imagep() function itself has been a palette called
oceColorsPalette() (which is a blue-through-white-to-red palette most useful for diverging colormaps), however many of the internal functions that use colors defaulted to the
oceColorsJet() palette, including: the
plot,section-method, and more importantly, the
colormap() function itself (which I wrote about here).
## Loading required package: gsw
## Loading required package: testthat
## Loading required package: sf
## Linking to GEOS 3.8.1, GDAL 3.1.1, PROJ 6.3.1
data(volcano) par(mfrow=c(1, 3)) imagep(volcano, col=oceColorsPalette, main='oceColorsPalette()') imagep(volcano, col=oceColorsJet, main='oceColorsJet') imagep(volcano, col=oceColorsViridis, main='oceColorsViridis()')
The palette on the left,
oceColorsPalette() has been retained as the default for the
plot,adp-method (is velocities are typically diverging). The palette in the middle, Jet, is still available but is no longer the default for any plots. The
viridis palette on the right is now the default for all palette-related functions.
Other, even better palettes
Kristen Thyng is author the author of the
cmocean series of oceanographic colormaps, originally created for python/matplotlib. There is now a
cmocean package on CRAN, which makes all the python colormaps available in R.
library(ocedata) library(cmocean) data(levitus) par(mfrow=c(1, 2)) with(levitus, imagep(longitude, latitude, SST, col=cmocean('thermal'), main="sea surface temperature")) with(levitus, imagep(longitude, latitude, SST, col=cmocean('haline'), main="sea surface salinity"))
There is also the
viridis package itself, which makes the series of palettes developed for matplotlib available in R.
## Loading required package: viridisLite