on Tools & Techniques

Today we look at how to draw simple flowcharts in R.

I think I am not far off when I say that flowcharts are an essential tool in the engineering toolbox. They provide a visual way to describe a set of activities, or a a process if you will. This can range from listing sequential steps in a manufacturing process to laying out a project plan to describing a decision making process (think decision trees).

“If you can’t describe what you are doing as a process, you don’t know what you’re doing.”

 – W. Edwards Deming

It comes as no surprise that engineers love to use flowcharts to describe or document stuff. If you’re like me, you’ve probably used various Microsoft Office applications to draw flowcharts. I, for example, have mastered creating flowcharts in PowerPoint over the years. Some prefer Visio or maybe some other application (Figma, Miro and the like).

Today we look at a couple different ways to visualize the distribution of your data.

Understanding the distribution of your data can be useful for engineers undertaking various tasks. The fact of the matter is that there are many different ways in which one can get an idea of the distribution of the data they’re interested in, one of which is density curves.

This is the sixth edition of the R for Engineering newsletter, and today we look at the ultimate prioritization tool – Pareto charts!

Pareto charts are a core tool for anyone who makes decisions, whether it is selecting a project or problem to solve, combing through last year’s spend or deciding on what equipment to purchase this year. The list goes on; bottom line is that Pareto charts simply allow you to focus on what’s important and cut through what may be interesting but unimportant.

In the last edition of R for Engineering, we learned how to draw small multiple plots in R and harness the power of comparison. We went from a busy graph to being able to use ggplot’s faceting functions to create a small multiples plot. If you need a recap, here’s a link to the last edition.

That is to say, nature’s laws are causal; they reveal themselves by comparison and difference, and they operate at every multi-variate space-time point.

– Edward Tufte

Small multiples have many uses in engineering, but the one I personally use them the most for is characterization and diagnosis. In my line of work, which is quality engineering, the ability to diagnose problems in physical systems (both product and machine/process-related) is a critical skill, and I will go as far as to say that diagnosing problems is a critical skill in any engineering discipline.

In this week’s edition, I introduce you to the concept of small multiples, and, more importantly, how to make them in R. This is one of those really low effort-super high return kind of features of R that can make you look like a rock star of data visualization. So, without further ado, let’s jump right into it!

In this week’s edition, we dig into a scenario you’ve probably run across when working in Excel or other software, for example Minitab —at least I have, many times.

Say you have a complete dataset. The data has been collected, and you’re now getting ready to run plot it or run some sort of analysis on it. It should be plug and play, but it ends up not being the case as the data is not formatted in the right way, and you’re not able to run your analysis (it happens pretty frequently if you ask me).

If this is your first time reading my newsletter: I am thrilled that you decided to give it try!

If this is not your first time: I’m glad you’re still here!

We’ve got a few things to go through in this week’s edition.

However, before we get into the cool stuff, that is showcasing useful functionality and interesting use cases, I feel it would behoove me to lay down some of the foundational things you’ll need to do to get you started in R, should you be interested.