World Cup 2010 Heatmap

I am reading Visualize This by Yau at the moment. It is full of really pretty visualization ideas and examples. One it has is creating a heatmap of NBA players. To practice this visualization I have made one of World Cup 2010 players. The dataset I got from the Gardian Data blog 'World Cup 2010 statistics: every match and every player in data'. The data only has 5 qualities quantified but that is good enough to practice making heatmaps.

The R Package code I used is below

library(RColorBrewer)
#save the guardian data to world.csv and load it
players2<-read.csv('World.csv', sep=',', header=TRUE)
players2[1:3,]
#players with the same name (like Torres) meant I had to merge surnames and countries
players2$Name <-paste(players2[,1], players2[,2])
rownames(players2) <- players2$Name
###I removed one player by hand
###I now do not need these columns
players2$Position <- NULL
players2$Player.Surname <- NULL
players2$Team <- NULL
players3 <-players2[order(players2$Total.Passes, decreasing=TRUE),]
### or to order by time played
###players3 <-players2[order(players2$Time.Played, decreasing=TRUE),]
players3 <- players3[,1:5]
players4<-players3[1:50,]
players_matrix <-data.matrix(players4)
###change names of columns to make graph readable
colnames(players_matrix )[1] <- "played"
colnames(players_matrix )[2] <- "shots"
colnames(players_matrix )[3] <- "passes"
colnames(players_matrix )[4] <- "tackles"
colnames(players_matrix )[5] <- "saves"
players_heatmap <- heatmap(players_matrix, Rowv=NA, Colv=NA, col = brewer.pal(9, 'Blues'), scale='column', margins=c(5,10), main="World Cup 2010")
dev.print(file="SoccerPassed.jpeg", device=jpeg, width=600)       
#players_heatmap <- heatmap(players_matrix, Rowv=NA, Colv=NA, col = brewer.pal(9, 'Greens'), scale='column', margins=c(5,10), main="World Cup 2010")
#dev.print(file="SoccerPlayed.jpeg", device=jpeg, width=600) 
dev.off()

Nothing very fancy here. Just showing that with a good data source and some online tutorials it is easy enough to knock up a picture in a fairly short time.