A heatmap is basically a table that has colors in place of numbers. Colors correspond to the level of the measurement. Each column can be a different metric like above, or it can be all the same like this one. It’s useful for finding highs and lows and sometimes, patterns.
From Nathan Yau | Visualize This
In order to visualize trends within large sets of data, it is useful consider to create a data heat map with color instead of number allowing display highs and lows.
If it true that the accuracy is lost for the lack of the numbers, but a wide vision about trends is obtained in exchange.
The colors used within the table, belong a spectrum of colors based on its distance from the statistical mean, so, in that way, intuitively darker colors means one thing and lighter colors another thing facilitating a quick evaluation about patterns, maximum and minimum values.
As I read the book “Visualize This” from Nathan Yau, I was analyzing which projects could implement the ideas presented. And one of the graphics that came back to my mind again and again was the Heatmap.
library(RColorBrewer) america <- read.csv("AmericaCupData.csv", sep=",") america <- america[order(america$Title, decreasing = FALSE),] row.names(america) <- america$Team america <- america[,2:17] america_matrix <- data.matrix(america_titles) america_heatmap <- heatmap(america_matrix, Rowv=NA, Colv=NA, col = brewer.pal(9, "Blues"), scale="column", margins=c(5,10))