Until now we have observed frequencies of the relationship between categorical membership (nominal attributes) and frequencies or means. It is also useful to have a look at relationships between numerical attributes. We will rely on scatterplots for this purpose. This will require a little scripting again, as we will examine the relationships between proportions. Let me first introduce the function proportions() which will generate the proportions for us, for all of our nominal attributes. This function takes one argument, DF, and call our attributes() function by default. We could instead give as an argument the data frame with the numbers we have previously drawn and the attributes.
The body of the function computes and returns the transpose of the means of each nominal attributes:
1 proportions = function(n = 100) {
2 DF=attributes(n)
3 return(data.frame(t(colMeans(DF[3:ncol(DF)]))))
4 }The body of this function calls our attributes() function and passes...
