###Vorlesung 25.1. Farbwahl 
library(vcd)
### sehr gut für Visualisierung von nominale und ordinalen  daten
?hsv
?hcl
pie(1,col=hsv(h = 0.5, s = 0.5, v = 0.5))
library(colorspace)
pie(rep(1, 12), col = sapply(1:12/12, function(x) hsv(h=x,s=1,v=1)))
pie(rep(1, 30), col = sapply(1:30/30, function(x) hsv(h=0.5,s=x,v=1)))
pie(rep(1, 12), col = sapply(1:12/12, function(x) hsv(h=0.8,s=0.5,v=x)))
pie(rep(1, 12), col = sapply((1:12)*30, function(x) hcl(h=x,c=100,l=100)))
pie(rep(1, 12), col = sapply((1:12)*100/12, function(x) hcl(h=180,c=x,l=50)))
pie(rep(1, 12), col = sapply((1:12)*100/12, function(x) hcl(h=270,c=50,l=x)))


##  Verschiedene Varianten

##wheel <- function(col, radius = 1, ...)pie(rep(1, length(col)), col = col, radius = radius, ...) 

pal <- function(col, border = "light gray")
{
  n <- length(col)
  plot(0, 0, type="n", xlim = c(0, 1), ylim = c(0, 1), axes = FALSE, xlab = "", ylab = "")
  rect(0:(n-1)/n, 0, 1:n/n, 1, col = col, border = border)
}

pal(diverge_hcl(7))
pal(diverge_hcl(7, h = c(246, 50), c = 96, l = c(65, 90)))
pal(diverge_hcl(7, h = c(130, 43), c = 100, l = c(70, 90)))
pal(diverge_hcl(7, h = c(180, 70), c = 70, l = c(90, 95)))
pal(diverge_hcl(7, h = c(180, 330), c = 59, l = c(75, 95)))
pal(diverge_hcl(7, h = c(128, 330), c = 98, l = c(65, 90)))
pal(diverge_hcl(7, h = c(255, 330), l = c(40, 90)))
pal(diverge_hcl(7, c = 100, l = c(50, 90), power = 1))
pal(rainbow_hcl(7))







data("Arthritis")
#Format
#A data frame with 84 observations and 5 variables.
#ID patient ID.
#Treatment factor indicating treatment (Placebo, Treated).
#Sex factor indicating sex (Female, Male).
#Age age of patient.
#Improved ordered factor indicating treatment outcome (None, Some, Marked).


art <- xtabs(~ Treatment + Improved, data = Arthritis, subset = Sex == "Female")
art
mosaic(art, gp = shading_hsv)
mosaic(art, gp = shading_max)
mosaic(Improved ~ Treatment | Sex, data = Arthritis, zero_size = 0, highlighting_direction = "right")
?mosaic
mosaic(HairEyeColor, gp = shading_hcl)

require(graphics)

# Single Color
hist(rnorm(1000), col = hcl(140,c=50))
hist(rnorm(1000), col = hcl(140,c=50,l=))

# The Foley and Van Dam PhD Data.
csd <- matrix(c( 4,2,4,6, 4,3,1,4, 4,7,7,1,
                 0,7,3,2, 4,5,3,2, 5,4,2,2,
                 3,1,3,0, 4,4,6,7, 1,10,8,7,
                 1,5,3,2, 1,5,2,1, 4,1,4,3,
                 0,3,0,6, 2,1,5,5), nrow = 4)

csphd <- function(colors)
  barplot(csd, col = colors, ylim = c(0,30),
          names = 72:85, xlab = "Year", ylab = "Students",
          legend = c("Winter", "Spring", "Summer", "Fall"),
          main = "Computer Science PhD Graduates", las = 1)

# The Original (Metaphorical) Colors (Ouch!)
csphd(c("blue", "green", "yellow", "orange"))

# A Color Tetrad (Maximal Color Differences)
csphd(hcl(h = c(30, 120, 210, 300)))

# Same, but lighter and less colorful
# Turn off automatic correction to make sure
# that we have defined real colors.
csphd(hcl(h = c(30, 120, 210, 300),
          c = 20, l = 90, fixup = FALSE))

# Analogous Colors
# Good for those with red/green color confusion
csphd(hcl(h = seq(60, 240, by = 60)))

# Metaphorical Colors
csphd(hcl(h = seq(210, 60, length = 4)))

# Cool Colors
csphd(hcl(h = seq(120, 0, length = 4) + 150))

# Warm Colors
csphd(hcl(h = seq(120, 0, length = 4) - 30))