Dot Plot Chart

Dot Plot Definition

What is a dot plot graph? A dot plot is a graphical depiction of statistical data. The chart uses individual dots to depict data. How the chart arranges these dots shows valuable elements like distribution, variance, magnitude and more.

Like most chart types, dot plots help you recognize trends, shifts, outliers and other notable patterns in your data. It arranges data points along a horizontal axis while the vertical axis measures the value of each item.

Most dot plots function as histograms. Vertical arrangements of dots demonstrate the value of each category, acting the same as bars do in a bar chart. The higher the stack of dots, the higher the magnitude of that category or component.

The more categories or items you have, the wider your dot plot and the more vertical clusters you have.

In some instances, dot plots work better than bar charts because you can visualize more information without overloading your chart. Adding too many bars to a chart becomes distracting, making it hard to compare similar values. You don’t have this problem with dot plots.

Other times, a dots graph functions closer to a scatter plot. There are fewer individual dots because one point is used to measure magnitude.

These two distinct uses for dot plots make them an incredibly versatile and practical chart type. We’ll look more closely at the kinds of dot plots and specific examples in the following sections.

History of Dot Plots

The history of the dot plot is marked by pre-computer and post-computer eras. Since these plots tend to display lots of individual dots, drawing one by hand takes a lot of time. So, there aren’t many examples of this chart type in the pre-computer period.

The earliest example of a hand-drawn dot plot originates from a distribution graph in 1884 by William Stanley Jevons. Jevons was an economist studying the weight of British Sovereign coins by year. He used a dot plot distribution chart to analyze and display the information.

Otherwise, these early dot plots are hard to come by and didn’t attract much attention due to the extensive work required to plot each dot.

Once computers entered the equation, graphing dot plots became much more manageable. Even if you have a massive volume of data to plot, computer systems will chart it instantaneously.

In the 1990s, Leland Wilkinson published a paper on dot plots and statistical computing. This work helped put the dot plot on the map in the charting community.

Wilkinson consulted William S. Cleveland in preparing this paper. Cleveland’s insight was that dot plots could function as both histograms and as one-dimensional, horizontal scatter plots.

Today, the two main types of dot plots (detailed in the next section) are known as a Wilkinson Dot Plot and a Cleveland Dot Plot.

Types of Dot Plots

Like many other chart types, the dot plot has different variations you need to be aware of when making your own visualizations.

Each dot plot variation represents a slightly different method of charting and analyzing data using this visualization.

By understanding how the different types of dot plots operate, you can better decide which one to use for your efforts.

The Wilkinson Dot Plot: This type of chart is a more classic dot plot example. Dots stack to display values for the same category or variable. This style adheres more to the histographic nature of dot plots.

For example, look at the following dataset:

Category A	Category B	Category C	Category D
11	6	7	13

In total, you would have 37 individual dots in your graph. For category A, 11 would appear stacked on top of one another, then 6 for category B and so on.

The Wilkinson Dot Plot offers many of the advantages of other histograms, like bar charts. The advantage of the dot plot is you can compare far more data without overloading your chart.

The Cleveland Dot Plot: In a Cleveland Dot Plot, you move away from the histogram-style visualization. This dot plot format is more scatter-plot-like.

Instead of depicting magnitude by the height of each dot stack, the Cleveland Dot Plot shows value through the location of each point.

You’d only have 4 dots on your graph using the same sample dataset as above. The height of each dot would reflect the value of the category.

The Cleveland Dot Plot excels at displaying massive amounts of data, even more than the Wilkinson Dot Plot. It’s also useful when you have overlapping categories or variables because there’s less vertical clutter.

Dot Plot Examples

Dot plots hold a unique space between histograms (bar charts and the like) and scatter plots. Looking at examples is helpful in understanding how these two types of graphs operate. The differences between each dot plot approach become apparent when viewing examples.

Let’s look at some dot plot examples for each type.

Dot Plot Example 1: On a nature hike, a class starts counting the different bird species they spot. When they return to the classroom, they compile the data and find the following results:

Robin	Blue Jay	Chickadee	Woodpecker	Other
22	5	17	2	19

Using a dot plot, the horizontal X-axis displays each bird species. The vertical Y is how many times each bird was seen. So, the dots stack according to how many birds were spotted of each type.

The robin vertical is highest with 22 dots, while the woodpecker is smallest at only 2.

Dot Plot Example 2: A sandwich restaurant wants to know what the most and least popular toppings are. They record each time a customer requests each topping on their sandwich for a week.

The restaurant charts the final results using a dot plot. The horizontal axis shows each topping the restaurant offers, while the vertical axis counts each time a customer orders the topping.

The dot plot is excellent in this case because there are so many topping options. A traditional bar chart histogram would be too overwhelming because of all the items.

Dot Plot Example 3: The same sandwich restaurant wants to measure how many total orders they receive for each day of the week. In this dot plot graph example, the team uses the Cleveland style.

Instead of having individual dots for every order placed throughout the week, they use one point for each day. The location of this dot reflects the value.

So, if Monday had the highest volume of orders, the dot would be highest on the Y-axis.

Have a Goal in Mind for Your Dot Plot

Every journey needs a heading or destination. This final destination dictates which way to head and how to navigate the roads ahead.

Having a goal for your visual analysis projects offers the same advantage. It gives you purpose and direction for your dot plots and other charts.

You don’t want to create your own dot plot just for the sake of making charts. In other words, you need a purpose or point behind your visualization efforts. How do you make a dot plot with no purpose?

So, before you begin wondering how to make a dot graph in Excel or Google Sheets, you should answer the critical question, “Why am I making this chart in the first place?”

There are many goals to choose from when deciding how to construct a dot plot. Do you want to find outliers or anomalies in your data distributions? Alternatively, you may just want to compare results across many different categories or items to find the most significant parts.

The more detailed and specific your goals are, the better. A specific goal is easier to track and follow. You’ll know exactly what data you need and how to approach the analysis. You may even know which chart type is best!

Conversely, broad goals that are not carefully planned lead to adverse results. You’ll have less idea of what data to collect or how to visualize the information best. With an ambiguous goal, you may start with one question and end up answering another.

Gather the Data for Your Dot Plot

Having a goal for your charts is crucial because it dictates what data you’ll need. It will prevent you from gathering more data than you need and including irrelevant data in your dot graph.

When you include irrelevant data in your chart, it distracts from the valuable information pertinent to your goal.

Thus, you need to think critically about your goal. Then, pick the metrics, variables and data components you need to understand the data and thoroughly answer your initial analysis question.

Gathering data may seem easy in theory, but it presents many challenges. These challenges multiply when combining multiple datasets or information from different sources.

The primary issue is that datasets need to be able to “talk” to one another. This means making sure that each piece is error-free, using the same labels, units, etc.

You may also want to isolate the data to plan to use in your chart. This is especially helpful if the data is part of a much larger spreadsheet. When it comes time to use your dot chart maker, you’ll have a much smaller spreadsheet that only includes the data you actually need.

Once you’ve gathered and isolated the data you need, it’s time to chart your results using a dot plot generator and begin the visual analysis phases.

How to Make a Dot Plot in Excel?

The popularity of Excel speaks for itself. However, it does have its shortcomings. For one, there are no dot plots in Excel without a tool like ChartExpo.

Luckily, downloading and using ChartExpo is exceptionally easy. You can find the ChartExpo Excel add-in and dot plot graph maker from the Microsoft App Source. To download it, just follow the steps as they appear on your device’s screen — it’s no different from installing any other tools.

After downloading the ChartExpo add-in, open Excel and go to “Insert” in the top menu. From the Insert dropdown menu, select “My Apps” or “Add-ins” (depending upon your Excel version). This menu allows you to select which add-ins are active.

If you downloaded ChartExpo correctly, you’ll see it in this menu. Make sure it’s activated to begin using the dot plot template for Excel.

Opening ChartExpo brings you to the top of the tool’s visualization options. It starts the process of how to create a dot plot in Excel. You can manually scroll through this list or utilize the search bar to look up the dot chart in Excel.

After selecting your chart type, it’s time to add the data to the online dot plot maker. Again, there are two options. You can click and drag to select a specific part of your Excel spreadsheet. Or, you can manually enter the rows and columns you wish to visualize with your Excel dot plot maker.

The final step is clicking the “Create Chart” button. ChartExpo will instantly make a dot plot in Excel alongside your spreadsheet.

The dot plot graph in Excel can be saved as an image and used elsewhere. You can also change any minor details to your dot plots and frequency tables once your chart appears.

How to Make a Dot Plot in Google Sheets?

How to make a dot plot on Google Sheets is very similar to the above process for an Excel dot chart. Your first step is to download the ChartExpo dot plot maker online for Google Sheets.

You’ll find this data visualization tool in the Google Workspace Marketplace. To access this app store, take the following steps:

• Click “Extensions” from the top toolbar.
• Next, hold your mouse over “Add-ons” to open a dropdown menu.
• From this new list of options, click on “Get add-ons.”

This will open the Google Workspace Marketplace, where you can search for and download the ChartExpo dot plot graph generator.

The next time you use Google Sheets, you’ll find ChartExpo using the same process above. Instead of clicking “Get add-ons,” you’ll click “ChartExpo” under the “Add-ons” menu.

After accessing ChartExpo for Google Sheets, you can begin experiencing how to make a dot graph on Google Sheets in just a few clicks.

First, select your dot plot chart type. Then, add your data using ChartExpo’s straightforward interface. It’s as simple as entering the cell IDs of the data you want to visualize. You can also edit ranges and other details in the Google Sheets dot plot maker.

The final step is always the easiest. Click on “Create Chart” at the bottom of the dot plot graph creator and watch your chart appear before your eyes.

At this point, you can save your Google Sheets dot plot and export it for other purposes.

Start with the Most (and Least) Significant Values in Your Dot Plot

The best starting points for any visual analysis are the most significant values you can see. These are typically the most obvious parts of your chart. In a dot plot, it will be the highest single point or vertical group of dots.

Why do these values matter the most? When you have an extreme high (or low) in your data, it represents the best (or worst) performing item in that dataset.

This insight is incredibly valuable if you don’t already know your best and worst performers. You can begin optimizing your efforts to highlight your best strategies, items, etc.

It’s also crucial that you consistently track results for these items. For example, a business needs to know when a best-selling product loses traction. Similarly, you want to know when a low performer is starting to improve. It could be an emerging opportunity!

Dot plots often reveal your most and least significant values with greater detail than other chart types. For instance, if two neighboring bars have very similar values, it may be hard to gauge which is actually higher and by how much.

Dot plots don’t have this problem because you can identify which vertical has the most circles.

It can be fun and exciting to see high values in your dataset, but don’t neglect the other end of the spectrum. In many cases, identifying your worst performers is actually more valuable than knowing the best.

Depending on the data you’re analyzing, your worst performance may present your most substantial opportunities.

For example, you have a dot plot showing each employee’s sales numbers for the year. While it’s good to recognize and praise your top salesperson(s), it’s actually more crucial to note who is struggling with their sales.

These individuals have the biggest room for improvement, making them your most opportunistic items.

Your Next Step Is Measuring Dot Plot Variability

Looking at the most and least significant items in a dot plot has you looking vertically. This answers, “What stack in the dot plot is the largest?”

The next objective is to look horizontally and answer, “What is dot plot variability?”

An X-axis in a dot plot depicts each variable, category or item you’re charting. If you were charting monthly sales, you’d have each month displayed on this axis, totaling 12 items.

Sometimes, you may not know how many unique categories exist in your data. People often use dot plots when they have lots of data points that they need to map. For this reason, you may have an unknown number of items or variables.

With the dot plot, it’s easy to measure and see how varied your results are. The longer your horizontal axis appears, the more categories/items you’re analyzing.

Datasets with high variance can be overwhelming when charted using other methods. Some data users will break up large data sets into more manageable chunks to chart, which isn’t very efficient.

This is one of the benefits of using a dot plot. Charts with many items allow you to pack more details and potential insights into one place.

Plus, the insights you discover require less testing or proving because they come from a larger sample size.

Look for Patterns in Your Dot Plot Distribution

Aside from variability and significance, dot plots also shine in helping users assess the distribution of their data.

Distribution matters for several reasons. It depends mostly on the type of data you’re analyzing. It’s the basis for how your results break down across each category or item.

Part of identifying your most and least significant components is seeing the highs and lows in the data’s distribution.

However, you don’t want to only know your best and worst. All of the values in the middle matter too!

Another crucial component of distribution analysis is when you’re charting time-related data. For example, you’re measuring orders for each hour of the day to see which times are most busy.

In this example, looking for concentrations of dots will reveal the busiest times for your business throughout the day. That’s helpful knowledge when planning strategies and allocating resources.

Sometimes, your distribution may have repeating patterns that help you identify valuable trends and other details.

Your dot plot distribution may also be skewed to one side. A school teacher might use the dot plot to look at test scores across each class. If most people aced the test, the distribution would be skewed heavily to the right.

Most of your dot plot insights will come from analyzing this distribution and its patterns.

Have a Plan for Your Dot Plot Analysis

The final piece of your dot plot analysis is creating a plan for how and when you’ll use the insights you’ve acquired.

Without this plan, you risk making the devastating mistake of neglecting to use the intel you worked so hard to gain.

This happens more often than you think, especially in a competitive business environment. It’s easy to become sidetracked by other projects or unexpected issues and forget your analysis.

By the time you finish resolving the distraction, you may fail to return to your analysis and use the new intelligence.

A plan prevents this mistake by putting together a concrete foundation for implementing data-driven decisions and changes to your strategies. No matter what distractions happen, you’ll be able to return to this plan and resume work.

Your dot plot analysis plan needs to include a few key points:

• What are the primary insights? A statement regarding what data changed or any discoveries made due to the analysis should headline your plan. It will summarize the information for anyone looking at the document.
• What actions are necessary? Clearly identify the decisions or changes that need to be made as a result of the data.
• Who is responsible for making any new changes? Selecting a specific person (or persons) will mean someone is accountable if changes aren’t made.
• When will changes be made? You also want to set a deadline for action. This date should be sooner rather than later. After all, data moves very quickly!
• When will you review changes? Another date should be set weeks or months in the future to review the changes you make. Did they improve results as expected? Are there unexpected effects you didn’t anticipate?

Creating this plan for each analysis project will guarantee that you never leave actionable insights left on the table.

Dot Plots in Bioinformatics

Dot plots have found an interesting niche in bioinformatics. Since the 1980s, dot plots have been the go-to method for comparing sequences in biology.

Bioinformatics is an interesting field of study that combines biology and chemistry with math and computer science. Essentially, it’s the practice of collecting and analyzing biological data, genetic codes and other things.

What is a dot plot in bioinformatics? Dot plots work well for bioinformatics because these datasets are often massive and complex.

The simplicity of the dot plot visualization allows you to compare two DNA or protein sequences with relative ease.

Due to the large sizes of DNA and protein sequences, other chart types become too cluttered or overwhelming to analyze the data properly. The simpler structure of the dot plot allows scientists in this interdisciplinary field to map and understand these sequences.

Dot plots in bioinformatics use the Cleveland method in a matrix that compares each part of the sequences.

While it takes time to analyze a bioinformatic dot plot (it’s seriously a lot of data), scientists can use it to detect mutations, find similarities and differences between two sequences and more.

It is arguably the only chart type capable of handling the volume and complexity of this data!

Dot Plots Help You Detect Outliers

Outliers in data can be tricky for a variety of reasons.

The first hurdle is detecting them in the first place. Some outliers are easy to spot — by definition, they should be. However, that isn’t always the case. Sometimes, you can only spot these funny occurrences when viewing your data using certain dimensions.

After an outlier is detected comes the hard part: significance and causation.

Significance dictates whether the outlier matters or not. Not all data points or insights are equal. Some are more valuable than others, especially when it comes to outliers.

For instance, a restaurant may plot sales for each day of the month. The business notices that sales skyrocketed to 500% more than the average on a particular day. Naturally, they want to know what was different about that day and how to replicate this success.

After digging into the data, the owner remembers that this spike in sales resulted from a tour bus breaking down in front of the restaurant. While it is nice to understand the cause, this outlier isn’t very useful because you can’t replicate it (short of sabotaging buses in front of the restaurant).

However, some outliers in your data are valuable and worth exploring. They may represent outlying risks or opportunities that others haven’t noticed yet.

You need a dot plot to quickly detect outliers in your distribution and investigate the causes behind these anomalies.

See All of Your Data in One Place

The most powerful benefit of the dot plot is its ability to effectively visualize huge volumes of data. In the Digital Age, where data is growing at hyperspeed, this is a colossal advantage.

Other chart types struggle with this aspect. For example, a pie chart can only map 5-7 categories before the slices become too skewed and jumbled to compare accurately. Bar charts do a better job but have their own limitations.

Thus, when you’re in a situation where many different chart types don’t work, dot plots are the answer.

The advantage here is two-fold. Not only do you have a tool that’s capable of handling large volumes of information, but you can also view more information in a single chart.

This second advantage is especially valuable when you have fast-moving data streams. You need to analyze your data and discover insights quickly to make data-driven decisions based on current intelligence.

If you’re too slow to respond to data, you may be making decisions based on outdated information.

Including more data into a single chart saves you time and offers a more efficient analysis process. You’ll get more value from a single dot plot than several other histograms combined.

Dot Plots Effortlessly Represent Frequency and Proportions

The versatility of the dot plot chart is another reason why it is a must-have in your visual analysis toolbox.

You can measure frequency, proportions, variability and other beneficial factors using the same chart.

In many ways, the dot plot shows frequency and proportions with greater accuracy than other chart types.

Let’s say you have a bar chart with two neighboring bars that have the values 99 and 100. Since these values are so similar, the size of the bars will also be very close, making it hard to compare. You may have to look very closely to identify which one is the 100 or 99 value.

Dot plots avoid this problem by stacking dots on top of one another to represent the value or frequency of each item. You can quickly tell when one category has more dots than its neighbor, even when the values are almost identical.

As far as histograms go, it is arguably the most accurate when analyzing frequency or comparing proportions. By counting the dots, you can definitively know the value of each category.

Bar, pie and other visualization types make you estimate the values based on the size of each component. That’s not very intuitive or accurate. You need to add labels if you want audiences to know the exact numbers behind each data item.

Create a Dot Plot in Only 3 Steps

The ChartExpo visualization software offers simplicity and accessibility where other charting tools do not.

The team behind ChartExpo understands data and visual analysis thoroughly, particularly how tiring and time-consuming it can be at times. Thus, they aimed to make a tool to alleviate these struggles.

Essentially, ChartExpo removes the need to draw or code charts by hand. The tool instantly handles these steps for you in the background.

This leaves only the parts of the chart creation process that require human intervention. There are only 3 steps in the ChartExpo system to create a dot plot online.

Step 1: Choose your chart. ChartExpo provides many different chart types to users — not just dot plots. The first step is to select which chart you want to use to visualize your data. You can scroll through the different charts options or enter a specific chart name into the tool’s search bar.

Step 2: Select your data. Choosing your chart type will proceed to the next menu. Here you will select the information from your datasets to include in the chart.

In some cases, you may be able to simply click and drag to select a part of your spreadsheet to visualize. Otherwise, you’ll enter which columns and rows in your data set to include.

You may have to enter other variables or components depending on the chart type.

Step 3: Create your chart. Your chart is ready the moment you’re happy with your data selections from step 2. All that’s left is to hit the “Create Chart” button. Instantly, your chart appears alongside the dataset.

After creating the chart, you can make minor edits, like changing colors, fonts, labels and other details.

This straightforward visualization method lets you transform raw data into insight-rich charts in mere minutes. ChartExpo is truly the easiest dot plot online maker to use.

A Codeless Way of Constructing a Dot Plot and Other Charts

The reason that ChartExpo is the easiest and most accessible chart maker comes down to the removal of codes and scripts.

JavaScript is used to add new visualizations into Excel, Google Sheets and other programs. Other data visualization programs offer dot plot templates that are essentially pre-written JavaScript codes.

Users then have to edit these codes and input their own data for the chart template to work. If you don’t have some prior background in code writing, you may be at a loss for how to use these tools.

This makes other data visualization tools far less accessible and straightforward than ChartExpo. They also lack the agility of ChartExpo.

Even if you have the expertise to handle these scripts, it’s not the most efficient approach. Coding takes time, even with lots of experience.

Plus, there’s a higher risk for error. The greatest coders in the world still make mistakes. Accidentally entering the wrong value or mistyping a character can result in problems for your charts.

ChartExpo still utilizes the JavaScript programming language, but you’d hardly know it. All of the coding is done automatically by the tool in the background. So, you never have to edit or look at scripts directly.

This saves you lots of time and opens up charting to everyone on your team, regardless of their coding background or JavaScript capabilities.

And, you never have to worry about mistypes or accidental errors. ChartExpo doesn’t make mistakes!

Chart Types for Any Data Analysis Project

Aside from making data visualization easier and more efficient on time, ChartExpo also provides a versatile array of charting options.

The dots graph is just the tip of the charting iceberg with ChartExpo!

This tool offers an extensive library of different chart types. Many chart types have multiple variations, making the total number of visualization options even higher.

With so many options to utilize, you always have the perfect chart to display your data. It’s just a matter of selecting the best chart from the menu.

Sometimes, multiple charts work for the same data. Thanks to ChartExpo, you're not limited to a handful of charting options. Instead, you have all the options that might help you understand the information and reach those valuable, actionable insights.

As the needs of data users change and evolve, the ChartExpo team responds and develops new visualizations to meet these shifts. For example, with the rise of cryptocurrency investing, ChartExpo began exploring crypto charting.

If you can’t find the proper chart for your data, contact the ChartExpo team. In some situations, the team may be able to create a new custom chart type for you.

That said, with so many visualizations already available to choose from, it’s unlikely you won’t find what you’re looking for here.

View Data from More Angles and Increase Your Insights

Having more charting options isn’t just valuable for variety’s sake. More visualizations lead to more insights and a more complete understanding of the data.

While one visualization may be better than others at displaying data, it doesn’t necessarily mean that other chart types are useless.

Each time you view your data using a new visualization, it’s like gaining a fresh perspective. You’ll identify new trends or patterns that were not apparent in other charts.

With so many different chart types included in ChartExpo, you have all of the visual angles you need to see the entire picture behind your data.

The simple visualization software makes it easy to swap between various chart and graph types. You can easily cycle through each relevant one and view your data from every vantage point.

Including multiple charts of the same data in your reports and presentations help audiences better understand the results you’re showcasing.

You’ll never struggle to get stakeholders or teams to reach the intended conclusions or understand the significance of what you’re presenting.

This advantage highlights how exceptional ChartExpo is at data communication. At a time when data is driving successful businesses, having a tool that can help you convey data findings to others is incredibly valuable.

ChartExpo delivers value to all stages of the visual analysis process!