If you haven’t fallen in love with data visualization charts yet – you should. They are unique, colourful, fun, and communicate valuable information that encourages you to optimize your expenses, eliminate profitless campaigns and make data-driven decisions regarding your expansion plans.
Find out how to use data visualization to display frequency distribution in our new article. And if you’ve missed it, here’s a free guide on How to Show Hierarchy with Data Visualization – all for you, dear.
Bubble Chart
Let’s start our list of data visualization methods to visualize frequency distribution with a familiar-looking Bubble Chart. In the data visualization world, it is considered a love child of a Scatter Plot and a Proportional Area Chart, with an XY coordinate system.
In a Bubble Chart, each point (or each bubble) represents a separate label or a category. Here colours can also be utilized in order to distinguish between those categories and act as a third variable. The size of each bubble represents a fourth variable, allowing this visualizing frequency distribution chart type to present a large number of information within a single axis system.
Use it to display a comparison between objects of the same kind, say countries or football teams, in relation to some commensurate values like the number of internet users or, wins in a season. But remember to keep the number of bubbles low or make the Bubble Chart interactive – the name of each bubble has to be easily accessible, otherwise, it will get too crowded.
Multi-set Bar Chart
Moving on to the next way to visualize a frequency distribution – Multi-set Bar Chart. Multi-set Bar Chart is a data visualization method that compares 2 to 4 variables of the same category placed one near the other with a small space in between.
Use Multi-set Bar Charts when it’s time to visualize the frequency of multiple concepts with a couple of variables each. This data visualization method allows you to stack important information next to each other for fast assessment and is incredibly easy to read.
To get the best out of a Multi-set Bar Chart keep the number of columns up to 4, as the more bars you add the more complex it becomes, while this chart type is loved and valued for its simplicity.
Histogram
A Histogram, as you’ve guessed from its name which is forthrightly revealing, is one of the best ways to visualize frequency over a time period. A histogram visualizes a frequency distribution of a single category over an extended history, allowing you to identify its value in a given time interval.
Use Histograms to display the progress of a particular event or an object throughout history. This method of frequency distribution helps you estimate the highs, the lows, the places where your values are concentrated and where they’re scarce.
Refrain from using more than one value in a single Histogram chart to avoid confusion.
Density Plot
Meet the Density Plot – the exact same Histogram chart from the previous section, only covered with a blanket to smooth the plot values out. Here instead of columns, history data is presented in the form of an evened out Line Graph, and you can observe the value’s development process more easily.
So what’s the difference between a Histogram and a Density Plot? Frequency distribution. While the histogram is great at showcasing values at a given point in time, the Density Plot possesses a technical advantage when it comes to showcasing the distribution shape.
The amount of data resulting in 4 columns in the Histogram won’t give you the frequency pattern a Density Plot with the same amount of data will.
Dot Matrix Chart
It’s easier to understand Dot Matrix Charts if we review it’s two components – dots and matrix. Here each dot colour is assigned a specific category and grouped together to form a complete 100% structure or so-called matrix.
By looking at the amount of dots in each category you can see its proportion size in relation to other categories and its distribution in relation to the whole. Dot Matrix Chart enables a quick overview of the topic and its components without having to dive deep into details and figure out what’s what.
Use it to visualize a frequency distribution in a single subject like the company’s income according to product categories. In our example, a 100% matrix is broken down into a hundred dots, forming 5 distinct sections, with each dot representing a single percent of the whole.
Pictogram Chart
Another version of a Dot Matrix Chart, Pictogram Chart takes a whole 100% and visualizes a frequency distribution within it by breaking it down into shapes or images. This data visualization method uses icons in order to convey value allocation in a certain category.
Pictogram Chart presents a bigger opportunity to play around with design and colours than other visualization methods do. Here you can choose which pictogram will be best suited to represent which data set by using associations and broadness of your imagination.
Below is a perfect example of such frequency distribution. Each icon is represented by 10% of the whole share, allowing you to review statistics incredibly clearly. To get the best out of the Pictogram Chart don’t use large amounts of data and more than 10 icons to represent it.
Parallel Sets
Background knowledge is sometimes required in order to understand and decode certain data visualization charts, and the Parallel Sets chart is among them. Let’s review this frequency visualization method using our example representing the statistical breakdown of crash victims on the famous Titanic.
In Parallel Sets each line-group represents a dataset (survived and perished), which then divides into smaller lines according to the categories (sex, age, class). The width of each line represents a fraction of a total, forming a flow that helps you navigate the given information more comfortably.
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For example, if you focus on the radical right blue line coming from the top of the chart and landing at the Female section, you can then see how many females were adults or kids, how many belonged to the first or third class, and a tiny string indicating those few belonging to the crew. To make your job of decoding Parallel Sets easier you can also look at the black lines at the verge of each category. They split into sections helping to estimate the proportion of a value (male, female) to the grand total.
Stem and Leaf Plot
In accordance with the plants’ system analogy, Stem & Leaf Plots represent a set of numbers broken down into categories by a certain parameter. “Stem” is the root, the dominant source of data while the “Leaf” acts as a continuation of the same “Stem” data set.
Values in Stem and Leaf Plot frequency distribution chart are increasing, and are always split into two categories, where “Stem” usually represents thousands, hundreds or tens, while “Leaf” is usually one tear down from it.
To form a Stem & Leaf Plot displayed on the left in our example below, the following data set has been inserted : 9,12,13, 67, 15, 71, 53, 20, 23, 29, 22, 39, 38, 32, 35, 37, 38,19, 38, 49, 43, 42, 46, 58, 59, 50, 55, 66, 18, 65. In order to compare certain number parameters in data visualization, often two Stem and Leaf Plots are displayed side by side.
Tally Chart
Remember those lines prisoners scratch on walls with something metal when counting a number of days spent in the cell of a prison? In the words of visualizing frequency distribution, they are creating a Tally Chart.
What’s peculiar about this data visualization system is its ability to not only visualize the frequency of data but to also record it. Data surrounding a Tally Chart is usually formed into a table, just like the one you can see in the example below.
Each time a counted value occurs, another mark (usually a stick) is then added to the data visualization chart in the relevant row. At the point when the experiment has run to an end, the tallies are counted and usually displayed alongside tally marks in a separate column.
Timeline
Timelines! A common guest of PowerPoint presentations, history books and product development roadmaps, timelines are one of the most commonly used frequency distribution methods out there.
A Timeline represents events placed among one another in chronological order. In most cases, businesses utilize Timelines to show product development progress, a story behind a business or an outcome of an estimated initiative.
The Timeline example below combines events of the past years and future predictions of the global technological advancements that are yet to come. The answer to how to visualize frequency distribution in a Timeline chart is however you please – as long as you provide a map legend you can use a scale or simply place your events in series.
Box and Whisker Plot
We are used to thinking of data visualization as something that turns information into a more straightforward, uncomplicated method of knowledge transmission, but as you’ll see in this and the following chart description, this is not always the case.
Box and Whisker Plot looks as complex as it sounds and is almost impossible to understand without any background knowledge on the subject. The prolonged, pointy lines data scientists call “whiskers” and use them in order perform pattern evaluation: is data frequency distributed asymmetrically, how far away or close is it located, or maybe it is condensed in the certain side of the data visualization chart – and if so which side is it.
In general the Box and Whisker Plot is used to visualize a frequency distribution, recognize patterns and compare data sets with one another. Due to the complexity of its decoding, we recommend keeping this data visualization chart reserved for the use inside the company and avoid showing it to investors, customers or anyone who’s not majored in data science.
Violin Plot
The last item on our list of the best ways to visualize frequency is a Violin Plot. If you’ve been attentive, Violin Plot has an actual Box and Whisker Plot chart inside of it, that is covered with a Density Plot distributed according to the shape of the former.
In a Violin Plot, the grey dot in the very middle represents median value and the thick black bar surrounding it stands for interquartile range. The whiskers showcase the maximum and the minimum adjacent values in the displayed data.
In general, Violin Plot is greatly superior to the Box and Whisker Plot chart when it comes to the best way to visualize frequency distribution. Thanks to its shape Violin Plot conveys distribution details like peaks, drops, and patterns that the Box and Whisker Plot just can’t.
Conclusion
Now that you know the best ways to visualize frequency distribution, take a look at other data visualization articles that will make you fall in love with data a little bit more 🙂
