One of the great things about following blogs on R is seeing what others are doing & being able to replicate and try out things on my own data sets.
For example, some great links on rapidly creating heat maps using R.
The basic steps in the process are (i) to scale the numeric data using the
scale function, (ii) create a Euclidean distance matrix using the
dist function and then (iii) plotting the heat map with the
“Simplicity is about subtracting the obvious and adding the meaningful.” – John Maeda
R is a simple and elegant language but I’ve always struggled to use it simply and elegantly. Why ? Two main reasons.
- The power and flexibility of R can make difficult things easy but also easy things hard, and by hard I mean complex, counter intuitive and difficult
- Documentation is dense, thorough and complete but usually unusable. Most documentation reflects the software’s statistical analysis roots and makes minimal reference to data preparation and transformation that usually takes up to 80% of the effort in real life modelling exercises
Together this makes programming in R an exercise in Googling and looking for help on what should be straight forward. The purpose of these posts is to document and share what I’ve learnt in trying to use R in real exercises.