inst/doc/tuning.R

In colorist: Coloring Wildlife Distributions in Space-Time

## ---- include = FALSE---------------------------------------------------------
knitr::opts_chunk$set(warning = FALSE, 
                      message = FALSE,
                      collapse = TRUE,
                      comment = "#>",
                      out.width = "\\textwidth", 
                      fig.height = 4, 
                      fig.width = 7, 
                      fig.align = "center")
BUILD_VIGNETTES <- isTRUE(as.logical(Sys.getenv("BUILD_VIGNETTES")))

## ----libraries----------------------------------------------------------------
library(colorist)
library(ggplot2)
library(RColorBrewer)

## ----hues-fiespa-default, dpi = 150, eval = BUILD_VIGNETTES-------------------
#  # pull metrics, generate default palette, map layers
#  m1 <- metrics_pull(fiespa_occ)
#  p1 <- palette_timecycle(12)
#  map_multiples(m1, p1, labels = names(fiespa_occ), ncol = 4)

## ----dpi = 150, echo = FALSE--------------------------------------------------
# pull metrics, generate default palette, map layers
m1 <- metrics_pull(fiespa_occ)
p1 <- palette_timecycle(12)

## ----hues-fiespa-custom, dpi = 150, eval = BUILD_VIGNETTES--------------------
#  # change palette start position on color wheel
#  p1_custom <- palette_timecycle(12, start_hue = 60)
#  
#  # map layers
#  map_multiples(m1, p1_custom, labels = names(fiespa_occ), ncol = 4)

## ----hues-elephant-default----------------------------------------------------
# pull metrics, generate default palette, map layers
m2 <- metrics_pull(elephant_ud)
p2 <- palette_set(2)
map_multiples(m2, p2, labels = c("'Purple People-eater'", "'Jolly Green Giant'"), ncol = 2)

## ----hues-elephant-custom-----------------------------------------------------
# use custom_hues argument to make specific hue choices
p2_custom <- palette_set(2, custom_hues = c(280, 120))

# map layers
map_multiples(m2, p2_custom, labels = c("'Purple People-eater'", "'Jolly Green Giant'"), ncol = 2)

## ----opacity-intensity-plot, echo = FALSE, fig.cap = "**Cell opacity as a function of intensity values and `lambda_i`.**"----
# describe modulus function
modulus <- function(y, lambda) {
  if (lambda != 0) {
    y_t <- sign(y) * ((abs(y) + 1) ^ lambda - 1) / lambda
  } else {
    y_t = sign(y) * log(abs(y) + 1)
  }
  return(y_t)
}

# create data for plotting
d <- data.frame(y = seq(0, 1, .01), lambda = rep(seq(-12, 12, 3), each = 101)) 
for (i in 1:nrow(d)) {
  d$y_t[i] <- modulus(d$y[i], d$lambda[i] + 1) / modulus(1, d$lambda[i] + 1)
}

# plot data describing effects of adjustments to lambda
ggplot(d, aes(y, y_t, group = lambda, color = factor(lambda))) +
  geom_path() +
  scale_color_brewer(type = "div", palette = "BrBG", direction = 1, 
                     name = "lambda_i") +
  xlab("intensity") +
  ylab("opacity (apparent intensity)") +
  theme(panel.background = element_blank(),
        panel.border = element_rect(color = "black", fill = NA),
        panel.grid = element_blank(),
        aspect.ratio = 1,
        legend.key = element_blank())

## ----opacity-elephant-default-------------------------------------------------
# map one layer
map_single(m2, p2_custom, layer = 2)

## ----opacity-elephant-custom--------------------------------------------------
# map one layer with adjustment to lambda_i
map_single(m2, p2_custom, layer = 2, lambda_i = -12)

## ----opacity-fisher-default---------------------------------------------------
# pull metrics, generate default palette, map layers
m3 <- metrics_pull(fisher_ud)
p3 <- palette_timeline(fisher_ud)
map_multiples(m3, p3, labels = names(fisher_ud))

## ----opacity-fisher-custom----------------------------------------------------
# map layers with adjustment to lambda_i
map_multiples(m3, p3, labels = names(fisher_ud), lambda_i = 12)

## ----chroma-specificity-plot, echo = FALSE, fig.cap = "**Cell chroma as a function of specificity values and `lambda_s`.**"----
# get colors from palette and edit
cols <- brewer.pal(9, "RdGy")
cols[5] <- "#F5F5F5"
names(cols) <- seq(-12, 12, 3)

# plot data describing effects of adjustments to lambda_s
ggplot(d, aes(100 * y, 100 * y_t, group = lambda, color = factor(lambda))) +
  geom_path() +
  scale_color_manual(values = cols, name = "lambda_s") +
  xlab("specificity") +
  ylab("chroma (apparent specificity)") +
  theme(panel.background = element_blank(),
  panel.border = element_rect(color = "black", fill = NA),
  panel.grid = element_blank(),
  aspect.ratio = 1,
  legend.key = element_blank())

## ----chroma-fisher-default----------------------------------------------------
# distill metrics, visualize metrics in a single map, create legend
m3_distill <- metrics_distill(fisher_ud)
map_single(m3_distill, p3, lambda_i = -5)
legend_timeline(p3, time_labels = c("April 7", "April 15"))

## ----chroma-fisher-custom-----------------------------------------------------
# visualize metrics in a single map with adjustment to lambda_s
map_single(m3_distill, p3, lambda_i = -5, lambda_s = 12)

## ----chroma-fiespa-default----------------------------------------------------
# distill metrics, visualize metrics in a single map, create legend
m1_distill <- metrics_distill(fiespa_occ)
map_single(m1_distill, p1)
legend_timecycle(p1, origin_label = "Jan 1")

## ----chroma-fiespa-custom-----------------------------------------------------
# visualize metrics in a single map with adjustment to lambda_s
map_single(m1_distill, p1, lambda_s = -12)