A scatterplot shows how one variable is distributed with respect to another variable and is the simplest form of bivaria

We can work with traditional methods to avoid taking risks in large-scale projects. At some point, each of our designs looks the same (even if it’s not). Yes, I know we’ve been working on each of them individually, but that’s all.

Violin plot smooths out “rough edges” of swarm plot but in this case, you cannot see the individual data points. However, you may notice that there is a boxplot inside each of the violin plots — which certainly adds value.

Second, when you are learning visualization, you should first focus on how to communicate information using visualization as a medium. Styles and aesthetics will come naturally later on but that’s the cherry on top. It’s also easy to get lost when your one-liner code becomes a complex one.

So basically, multivariate plots take 2D plots one step further by adding variables via parameters such as hue, style, bubble size etc. Although, in theory, you can represent more variables, but it can quickly become crowded and difficult to read as we will see shortly.

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was released, it felt like Apple was convinced that developers would flock to the opportunity to build all-new apps for iPad. Unfortunately, history has largely demonstrated otherwise, which is why I’m still looking at sideways apps in 2020.

And finally, adding more variables in the plot is possible, but may not be desirable, as the figure quickly becomes crowded with too much information that is difficult to interpret. The purpose of data visualization is communication, so the simpler the better.

In two-dimensional plots, you really don’t have that many options to visualize multi-dimensional values. Multivariate plots are nothing but a representation of one or more additional variables in bivariate plots.

I touched upon a few different things, so I felt I need to summarise them. First, simplicity matters; it puts less pressure on the audience and forces the creators to explain complex information in a digestible way.

Designing sites suitable for scanning does not require much effort. Our basic principle is to act according to user habits. Design according to habits: -Not always but usually- the logo appears in the upper left, the Search button is located in the upper right (in countries where reading left-to-right). Also, if the texts are divided into titles, the user will scan the page more easily.

And finally, adding more variables in the plot is possible, but may not be desirable, as the figure quickly becomes crowded with too much information that is difficult to interpret. The purpose of data visualization is communication, so the simpler the better.

Boxplots can compare categorical variables and their distribution on a continuous variable. Here I’ve disaggregated tip amount by the time of restaurant visits, and as you can tell, people are more generous during dinner time compared to lunch visits. The variation is also higher during dinner visits with much more outliers values.

When users enter the site, they take a quick look at it. They don’t read, they scan. This skimming is even faster if they know what they’re looking for. If they do not know exactly what they are looking for, they scan the page until they find a meaningful word (trigger words). They skip all the articles we have prepared with care, sorry… Because no matter what they enter the site for, they try to get things done right away. It’s not about laziness, it’s our habit of using the web. For this reason, we must design pages that are suitable for scanning.

But there are legitimate ways to show multiple variables and for that, we can go back to our boxplot, violin plot and swarm plot. They work perfectly in a 2D plot to represent multiple variables.

Boxplot above is great, but swarm plots can show each data point and their distribution. You lose some statistical information as in boxplots but you gain something else! So they can be complementary during exploratory data analysis (EDA).

We can get even crazier and add a “size” parameter for yet another variable. We can go on, but at this point you must be lost if you are trying to interpret what this plot is all about!