Map an environmental variable

A quick look at what came back from accessEnvDat() — the first thing worth doing after a download, and the fastest way to catch a bounding box that came out somewhere unintended, a variable that is all NA, or a depth range that returned the wrong level.

Usage

plot_env(env_dat, var = NULL, time = 1, palette = "viridis", main = NULL, ...)

Arguments

env_dat

an sf POINT object from accessEnvDat()

var

which variable to map; NULL uses the first covariate column

time

which time step, as an index or a named vector of YEAR/MONTH/DAY values

palette

a grDevices::hcl.colors() palette name. The default is perceptually uniform and readable in greyscale, which matters more for a field of continuous values than the usual rainbow does.

main

plot title; the default names the variable and the time step

...

passed to terra::plot()

Value

the SpatRaster that was plotted, invisibly

Details

The data are rendered as a raster rather than as points, so gaps read as holes rather than as absent dots, and cell size is visible.

Time steps

Environmental data carries many time steps and a map shows one. time picks it: a number is an index into the steps present, and a list or vector names the step directly (c(YEAR = 2010, MONTH = 6)). The default is the first step, and the step being shown is written into the title so a map is never ambiguous about which month it is.

See also

plot_coverage() for where the gaps are, plot_series() for how a variable moves through time

Examples

if (FALSE) { # \dontrun{
env <- accessEnvDat(vars = c("SST", "CHL"), years = 2010, months = 1:12,
                    bounding_box = bb)

plot_env(env)                                  # first variable, first step
plot_env(env, "CHL", time = c(MONTH = 6))      # June chlorophyll
} # }