While it will be important to focus on theory, I want to explain the ggplot2 package because I will be using it throughout the rest of this series. Knowing how it works will keep the focus on the results rather than the code. It’s an incredibly powerful package and once you wrap your head around what it’s doing, your life will change for the better! There are a lot of tools out there which provide better charts, graphs and ease of use (i.e. plot.ly, d3.js, Qlik, Tableau), but ggplot2 is still a fantastic resource and I use it all of the time.
Introduction to Data Visualization – Theory, R & ggplot2
The topic of data visualization is very popular in the data science community. The market size for visualization products is valued at $4 Billion and is projected to reach $7 Billion by the end of 2022 according to Mordor Intelligence. While we have seen amazing advances in the technology to display information, the understanding of how, why, and when to use visualization techniques has not kept up. Unfortunately, people are often taught how to make a chart before even thinking about whether or not it’s appropriate.
In short, are you adding value to your work or are you simply adding this to make it seem less boring? Let’s take a look at some examples before going through the Stoltzmaniac Data Visualization Philosophy.
I am asked this question regularly, both online and in person. There is a simple answer: it doesn’t matter. There are pros and cons to both which have been written about extensively so I won’t reinvent the wheel by making a list here (do a quick search in Google and you’ll find tens of thousands of relevant results).
There is a plethora of classification algorithms available to people who have a bit of coding experience and a set of data. A common machine learning method is the random forest, which is a good place to start.