The key variable of interest here is the amount of tip people give during their restaurant visits. Other associated vari

A scatterplot shows how one variable is distributed with respect to another variable and is the simplest form of bivariate plots that immediately reveal valuable information that is also visible to “nacked eyes”.

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.

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.

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.

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n begins and the upcoming eclipse season looms on the horizon, ZORA’s resident astrologer Ashleigh D. Jay offers specific recommendations and ideas surrounding the astrology of November 27–29 — the weekend directly following Thanksgiving. Black Friday is not only a day of shining capitalism, but today’s skies highlight the issues of secret spending, so keep a lookout for what this could mean for you, yours, and the country. Meanwhile, Friday morning, Mercury (in Scorpio) collaborates with Pluto (in Capricorn), highlighting how you can transform.

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.

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).

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.

In a two-dimensional space, it’s easy to visualize two variables that people can easily comprehend. These two variables can be any combination of continuous, categorical and integer data types.

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’m not going to explain too much, as these figures are self-explanatory, it should be quite easy for readers to examine codes and compare similarities and differences between the plots.

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!

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.

Scatterplot and linear models are best represented with continuous variables and that’s what the first two figures below are about. Boxplot, violin plot, swarm plot and bar plots are great for presenting categorical variables with continuous and integer data types.

We’ll pick the variable total_bill and see its distribution using different visualization techniques. In a way, all these techniques present similar kinds of information, with just some variation in presentation.