In this part of the book, you'll enrich your programming skills. Programming is a cross-cutting skill needed for all data science work. You must use a computer; you cannot do it in your head, nor with paper and pencil. And to work efficiently, you will need to know how to program in a computer language, such as R.
Code is a tool of communication, not just to the computer, but to other people. This is important because every project you undertake is fundamentally collaborative. Even if you're not working with other people, you'll definitely be working with future-you. You want to write clear code so that future-you doesn't curse present-you when you look at a project again after several months have passed.
Improving your communication skills is a key part of mastering R as a programming language. Over time, you want your code to become increasingly clear and easier to write. Removing duplication is an important part of expressing yourself clearly because it lets the reader (i.e. future-you!) focus on what's different between operations rather than what's the same. The goal is not just to write better functions or to do things that you couldn't do before, but to code with more "ease". As you internalise the ideas in this part of the book, you should find it easier to re-tackle problems that you've struggled to solve in the past.
Writing code is similar in many ways to writing prose. One parallel which I find particularly useful is that in both cases rewriting is the key to clarity. The first expression of your ideas is unlikely to be particularly clear, and you may need to rewrite multiple times. After solving a data analysis challenge, it's often worth looking at your code and thinking about whether or not it's obvious what you've done. If you spend a little time rewriting your code while the ideas are fresh, you can save a lot of time later trying to recreate what your code did. But this doesn't mean you should rewrite every function: you need to balance what you need to achieve now with saving time in the long run. (But the more you rewrite your functions the more likely you'll first attempt will be clear.)
The goal of these chapters is to teach you the minimum about programming that a practicising data scientist must know. It turns out this is a reasonable amount, and I strongly believe it's worth investing even more in your programming skills. Learning more about programming is a long-term investment. It won't pay off immediately, but over time it will allow you to solve new problems more quickly, and reuse your insights from previous problems in new scenarios.
To learn more you need to study R as a programming language, not just an interactive environment for data science. We have written two books that will help you do so:
