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In almost three weeks, the (FIFA) World Cup will start, in Brazil. I have to admit that I am not a big fan of soccer, so I will not talk to much about it. Actually, I wanted to talk about colors, and variations on some colors. For instance, there are a lot of blues. In order to visualize standard blues, let us consider the following figure, inspired by the well known chart of R colors,

```BLUES=colors()[grep("blue",colors())]
RGBblues=col2rgb(BLUES)
library(grDevices)
HSVblues=rgb2hsv( RGBblues[1,], RGBblues[2,], RGBblues[3,])
HueOrderBlue=order( HSVblues[1,], HSVblues[2,], HSVblues[3,] )
SetTextContrastColor=function(color) ifelse( mean(col2rgb(color)) > 127, "black", "white")
TextContrastColor=unlist( lapply(BLUES, SetTextContrastColor) )
c=11
l=6
plot(0, type="n", ylab="", xlab="",axes=FALSE, ylim=c(0,11), xlim=c(0,6))
for (j in 1:11){
for (i in 1:6){
k=(j-1)*6 + i
rect(i-1,j-1,i,j, border=NA, col=BLUES[ HueOrderBlue[k] ])
text(i-.5,j-.5,paste(BLUES[k]), cex=0.75, col=TextContrastColor[ HueOrderBlue[k] ])}}```

All the color names that contain “blue” in it are here.

Having the choice between several possible colors is interesting, but it can also be interesting to get a palette of blue colors, What we can get is the following

```library(RColorBrewer)
blues=colorRampPalette(brewer.pal(9,"Blues"))(100)```

In order to illustrate the use of palette colors, consider some data, on soccer players (officially registered). The dataset – lic-2012-v1.csv – can be downloaded from http://data.gouv.fr/fr/dataset/… (I will also use a dataset we have on location of all towns, in France, with latitudes and longitudes)

```base1=read.csv(
"http://freakonometrics.free.fr/popfr19752010.csv",
header=TRUE)
base1\$cp=base1\$dep*1000+base1\$com
base2=read.csv("lic-2012-v1.csv", header=TRUE)
base2=base2[base2\$fed_2012==111,]
names(base2)[1]="cp"
base2\$cp=as.numeric(as.character(base2\$cp))```

The problem with France (I should probably say one of the many problems) is that regions and departements are not well coded, in the standard functions. To explain where départements are, let us use the dept.rda file, and then, we can get a matching between R names, and standard (administrative) ones,

```base21=base2[,c("cp","l_2012","pop_2010")]
base21\$dpt=trunc(base21\$cp/1000)
library(maps)
load("dept.rda")
base21\$nomdpt=dept\$dept[match(as.numeric(base21\$dpt),dept\$CP)]
L=aggregate(base21\$l_2012,by=list(Category=base21\$nomdpt),FUN=sum)
P=aggregate(base21\$pop_2010,by=list(Category=base21\$nomdpt),FUN=sum)
base=data.frame(D=P\$Category,Y=L\$x/P\$x,C=trunc(L\$x/P\$x/.0006))
france=map(database="france")
matche=match.map(france,base\$D,exact=TRUE)
map(database="france", fill=TRUE,col=blues[base\$C[matche]],resolution=0)```

Here are the rates of soccer players (with respect to the total population) It is also possible to look at rate not by département, but by town,

```base10=base1[,c("cp","long","lat","pop_2010")]
base20=base2[,c("cp","l_2012")]
base=merge(base10,base20)
Y=base\$l_2012/base\$pop_2010
QY=as.numeric(cut(Y,c(0,quantile(Y,(1:99)/100),10),labels=1:100))
library(maps)
map("france",xlim=c(-1,1),ylim=c(46,48))
points(base\$long,base\$lat,cex=.4,pch=19,col=blues[QY])```

The darker the dot, the more player, We can also zoom in, to get a better understanding, in the northern part of France, for instance, or in the Southern part,

We can obtain a map which is not (too) far away from the one mentioned a few months ago on http://slate.fr/france/78502/.

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