# Loading example data
data(australia)
spectra(australia) <- sr_no ~ ... ~ 350:2500
# Default plotting method
plot(australia[1:5,])
# Default plot using ggplot2
plot(australia[1:5,], gg = TRUE)
if (FALSE) {
# Managing gaps in the spectra
s <- cut(australia, wl =c(-1*450:500, -1*1800:2050))
plot(s, gaps = TRUE)
plot(s, gaps = FALSE)
# passing various options to matplot
plot(
  australia, 
  lty = 1:5, 
  col = 'blue', 
  xlab = 'foo', ylab = 'bar', 
  ylim = c(0.4,0.6), 
  main = 'my plot'
)
# Using colour ramps
plot(
  australia, 
  lty = 1, 
  col = rainbow(10), 
  main = "It is possible to create really ugly visualisations"
)
# Example using colours given by ColorBrewer (http://colorbrewer2.org/)
library(RColorBrewer)
plot(australia, lty = 1, col = brewer.pal(n = 8, name = "Set2"))
# Using an attribute to group spectra
# Generate some kind of factor
australia$fact <- sample(
  LETTERS[1:3], 
  size = nrow(australia), 
  replace = TRUE
) 
s <- aggregate_spectra(australia, fun = mean, id = 'fact')
plot(s, gg = TRUE, attr = 'fact')
}
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