Deep Research and Development

Let us change our traditional attitude to the construction of programs: Instead of imagining that our main task is to instruct a computer what to do, let us concentrate rather on explaining to humans what we want the computer to do.               Donald E. Knuth, Literate Programming, 1984 Overview and Motivation So what is dynamic documentation and why do we need it. As opposed to usual programming, R programs were intended to used as report for not development oriented folks, whether they are data scientists, statisticians or managers. Moreover by nature, R programs don't tend to be huge spanning across hundreds of thousands of code lines. All these led to a huge demand good documentation framework. But how do you document a report? One may of course is to write a passage and then paste a copied graph into it, however once something changes one must re-copy all the graph, which is of course very tedious and non-rewarding procedure...

The history of technology is the history of the invention of tools and techniques, and is similar in many ways to the history of humanity. And since data scientists are mere mortals, they also need tools to make their work more productive and even enjoyable, but that's just me. In this article we'll be talking about main languages and tools used by data scientists. For ones who have recently entered this field of science, it will be a great overview about mostly used tools. R A great advantage of R is that scientists adopted it as their de facto standard. As a consequence, the latest cutting-edge techniques are first available in R. It also seems to be the preference of most Kaggle competition winners . Most of R practitioners use R Studio , and while they do offer a free community version, enterprise edition is a bit expensive. I use the community version only when I compete in Kaggle, haven't won anything yet :( Commercially I find  Sublime Text  a good alternat...