graph presentation of data

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Graphical Representation of Data

Graphical Representation of Data: Graphical Representation of Data,” where numbers and facts become lively pictures and colorful diagrams . Instead of staring at boring lists of numbers, we use fun charts, cool graphs, and interesting visuals to understand information better. In this exciting concept of data visualization, we’ll learn about different kinds of graphs, charts, and pictures that help us see patterns and stories hidden in data.

There is an entire branch in mathematics dedicated to dealing with collecting, analyzing, interpreting, and presenting numerical data in visual form in such a way that it becomes easy to understand and the data becomes easy to compare as well, the branch is known as Statistics .

The branch is widely spread and has a plethora of real-life applications such as Business Analytics, demography, Astro statistics, and so on . In this article, we have provided everything about the graphical representation of data, including its types, rules, advantages, etc.

Table of Content

What is Graphical Representation

Types of graphical representations, line graphs, histograms , stem and leaf plot , box and whisker plot .

• Graphical Representations used in Maths

Value-Based or Time Series Graphs 

Frequency based, principles of graphical representations, advantages and disadvantages of using graphical system, general rules for graphical representation of data, frequency polygon, solved examples on graphical representation of data.

Graphics Representation is a way of representing any data in picturized form . It helps a reader to understand the large set of data very easily as it gives us various data patterns in visualized form.

There are two ways of representing data,

• Pictorial Representation through graphs.

They say, “A picture is worth a thousand words”.  It’s always better to represent data in a graphical format. Even in Practical Evidence and Surveys, scientists have found that the restoration and understanding of any information is better when it is available in the form of visuals as Human beings process data better in visual form than any other form.

Does it increase the ability 2 times or 3 times? The answer is it increases the Power of understanding 60,000 times for a normal Human being, the fact is amusing and true at the same time.

Check: Graph and its representations

Comparison between different items is best shown with graphs, it becomes easier to compare the crux of the data about different items. Let’s look at all the different types of graphical representations briefly: 

A line graph is used to show how the value of a particular variable changes with time. We plot this graph by connecting the points at different values of the variable. It can be useful for analyzing the trends in the data and predicting further trends. 

A bar graph is a type of graphical representation of the data in which bars of uniform width are drawn with equal spacing between them on one axis (x-axis usually), depicting the variable. The values of the variables are represented by the height of the bars. 

This is similar to bar graphs, but it is based frequency of numerical values rather than their actual values. The data is organized into intervals and the bars represent the frequency of the values in that range. That is, it counts how many values of the data lie in a particular range. 

It is a plot that displays data as points and checkmarks above a number line, showing the frequency of the point.  

This is a type of plot in which each value is split into a “leaf”(in most cases, it is the last digit) and “stem”(the other remaining digits). For example: the number 42 is split into leaf (2) and stem (4).  

These plots divide the data into four parts to show their summary. They are more concerned about the spread, average, and median of the data. 

It is a type of graph which represents the data in form of a circular graph. The circle is divided such that each portion represents a proportion of the whole. 

Graphical Representations used in Math’s

Graphs in Math are used to study the relationships between two or more variables that are changing. Statistical data can be summarized in a better way using graphs. There are basically two lines of thoughts of making graphs in maths: 

• Value-Based or Time Series Graphs

These graphs allow us to study the change of a variable with respect to another variable within a given interval of time. The variables can be anything. Time Series graphs study the change of variable with time. They study the trends, periodic behavior, and patterns in the series. We are more concerned with the values of the variables here rather than the frequency of those values. 

Example: Line Graph

These kinds of graphs are more concerned with the distribution of data. How many values lie between a particular range of the variables, and which range has the maximum frequency of the values. They are used to judge a spread and average and sometimes median of a variable under study.

Also read: Types of Statistical Data

• All types of graphical representations follow algebraic principles.
• When plotting a graph, there’s an origin and two axes.
• The x-axis is horizontal, and the y-axis is vertical.
• The axes divide the plane into four quadrants.
• The origin is where the axes intersect.
• Positive x-values are to the right of the origin; negative x-values are to the left.
• Positive y-values are above the x-axis; negative y-values are below.

• It gives us a summary of the data which is easier to look at and analyze.
• It saves time.
• We can compare and study more than one variable at a time.

Disadvantages

• It usually takes only one aspect of the data and ignores the other. For example, A bar graph does not represent the mean, median, and other statistics of the data. 
• Interpretation of graphs can vary based on individual perspectives, leading to subjective conclusions.
• Poorly constructed or misleading visuals can distort data interpretation and lead to incorrect conclusions.

Check : Diagrammatic and Graphic Presentation of Data

We should keep in mind some things while plotting and designing these graphs. The goal should be a better and clear picture of the data. Following things should be kept in mind while plotting the above graphs: 

• Whenever possible, the data source must be mentioned for the viewer.
• Always choose the proper colors and font sizes. They should be chosen to keep in mind that the graphs should look neat.
• The measurement Unit should be mentioned in the top right corner of the graph.
• The proper scale should be chosen while making the graph, it should be chosen such that the graph looks accurate.
• Last but not the least, a suitable title should be chosen.

A frequency polygon is a graph that is constructed by joining the midpoint of the intervals. The height of the interval or the bin represents the frequency of the values that lie in that interval. 

Question 1: What are different types of frequency-based plots? 

Types of frequency-based plots:  Histogram Frequency Polygon Box Plots

Question 2: A company with an advertising budget of Rs 10,00,00,000 has planned the following expenditure in the different advertising channels such as TV Advertisement, Radio, Facebook, Instagram, and Printed media. The table represents the money spent on different channels. 

Draw a bar graph for the following data. 

• Put each of the channels on the x-axis
• The height of the bars is decided by the value of each channel.

Question 3: Draw a line plot for the following data 

• Put each of the x-axis row value on the x-axis
• joint the value corresponding to the each value of the x-axis.

Question 4: Make a frequency plot of the following data: 

• Draw the class intervals on the x-axis and frequencies on the y-axis.
• Calculate the midpoint of each class interval.

Now join the mid points of the intervals and their corresponding frequencies on the graph. 

This graph shows both the histogram and frequency polygon for the given distribution.

Related Article:

Graphical Representation of Data| Practical Work in Geography Class 12 What are the different ways of Data Representation What are the different ways of Data Representation? Charts and Graphs for Data Visualization

Conclusion of Graphical Representation

Graphical representation is a powerful tool for understanding data, but it’s essential to be aware of its limitations. While graphs and charts can make information easier to grasp, they can also be subjective, complex, and potentially misleading . By using graphical representations wisely and critically, we can extract valuable insights from data, empowering us to make informed decisions with confidence.

Graphical Representation of Data – FAQs

What are the advantages of using graphs to represent data.

Graphs offer visualization, clarity, and easy comparison of data, aiding in outlier identification and predictive analysis.

What are the common types of graphs used for data representation?

Common graph types include bar, line, pie, histogram, and scatter plots , each suited for different data representations and analysis purposes.

How do you choose the most appropriate type of graph for your data?

Select a graph type based on data type, analysis objective, and audience familiarity to effectively convey information and insights.

How do you create effective labels and titles for graphs?

Use descriptive titles, clear axis labels with units, and legends to ensure the graph communicates information clearly and concisely.

How do you interpret graphs to extract meaningful insights from data?

Interpret graphs by examining trends, identifying outliers, comparing data across categories, and considering the broader context to draw meaningful insights and conclusions.

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18 Best Types of Charts and Graphs for Data Visualization [+ Guide]

Published: May 22, 2024

As a writer for the marketing blog, I frequently use various types of charts and graphs to help readers visualize the data I collect and better understand their significance. And trust me, there's a lot of data to present.

In fact, the volume of data in 2025 will be almost double the data we create, capture, copy, and consume today.

This makes data visualization essential for businesses. Different types of graphs and charts can help you:

• Motivate your team to take action.
• Impress stakeholders with goal progress.
• Show your audience what you value as a business.

Data visualization builds trust and can organize diverse teams around new initiatives. So, I'm going to talk about the types of graphs and charts that you can use to grow your business.

And, if you still need a little more guidance by the end of this post, check out our data visualization guide for more information on how to design visually stunning and engaging charts and graphs.  

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Charts vs Graphs: What's the Difference?

A lot of people think charts and graphs are synonymous (I know I did), but they're actually two different things.

Charts visually represent current data in the form of tables and diagrams, but graphs are more numerical in data and show how one variable affects another.

For example, in one of my favorite sitcoms, How I Met Your Mother, Marshall creates a bunch of charts and graphs representing his life. One of these charts is a Venn diagram referencing the song "Cecilia" by Simon and Garfunkle. 

Marshall says, "This circle represents people who are breaking my heart, and this circle represents people who are shaking my confidence daily. Where they overlap? Cecilia."

The diagram is a chart and not a graph because it doesn't track how these people make him feel over time or how these variables are influenced by each other.

It may show where the two types of people intersect but not how they influence one another.

Later, Marshall makes a line graph showing how his friends' feelings about his charts have changed in the time since presenting his "Cecilia diagram.

Note: He calls the line graph a chart on the show, but it's acceptable because the nature of line graphs and charts makes the terms interchangeable. I'll explain later, I promise.

The line graph shows how the time since showing his Cecilia chart has influenced his friends' tolerance for his various graphs and charts. 

Image source

I can't even begin to tell you all how happy I am to reference my favorite HIMYM joke in this post.

Now, let's dive into the various types of graphs and charts. 

Different Types of Graphs for Data Visualization

1. bar graph.

I strongly suggest using a bar graph to avoid clutter when one data label is long or if you have more than 10 items to compare. Also, fun fact: If the example below was vertical it would be a column graph.

Best Use Cases for These Types of Graphs

Bar graphs can help track changes over time. I've found that bar graphs are most useful when there are big changes or to show how one group compares against other groups.

The example above compares the number of customers by business role. It makes it easy to see that there is more than twice the number of customers per role for individual contributors than any other group.

A bar graph also makes it easy to see which group of data is highest or most common.

For example, at the start of the pandemic, online businesses saw a big jump in traffic. So, if you want to look at monthly traffic for an online business, a bar graph would make it easy to see that jump.

Other use cases for bar graphs include:

• Product comparisons.
• Product usage.
• Category comparisons.
• Marketing traffic by month or year.
• Marketing conversions.

Design Best Practices for Bar Graphs

• Use consistent colors throughout the chart, selecting accent colors to highlight meaningful data points or changes over time.

You should also use horizontal labels to improve its readability, and start the y-axis at 0 to appropriately reflect the values in your graph.

2. Line Graph

A line graph reveals trends or progress over time, and you can use it to show many different categories of data. You should use it when you track a continuous data set.

This makes the terms line graphs and line charts interchangeable because the very nature of both is to track how variables impact each other, particularly how something changes over time. Yeah, it confused me, too.

Line graphs help users track changes over short and long periods. Because of this, I find these types of graphs are best for seeing small changes.

Line graphs help me compare changes for more than one group over the same period. They're also helpful for measuring how different groups relate to each other.

A business might use this graph to compare sales rates for different products or services over time.

These charts are also helpful for measuring service channel performance. For example, a line graph that tracks how many chats or emails your team responds to per month.

Design Best Practices for Line Graphs

• Use solid lines only.
• Don't plot more than four lines to avoid visual distractions.
• Use the right height so the lines take up roughly 2/3 of the y-axis' height.

3. Bullet Graph

A bullet graph reveals progress towards a goal, compares this to another measure, and provides context in the form of a rating or performance.

In the example above, the bullet graph shows the number of new customers against a set customer goal. Bullet graphs are great for comparing performance against goals like this.

These types of graphs can also help teams assess possible roadblocks because you can analyze data in a tight visual display.

For example, I could create a series of bullet graphs measuring performance against benchmarks or use a single bullet graph to visualize these KPIs against their goals:

• Customer satisfaction.
• Average order size.
• New customers.

Seeing this data at a glance and alongside each other can help teams make quick decisions.

Bullet graphs are one of the best ways to display year-over-year data analysis. YBullet graphs can also visualize:

• Customer satisfaction scores.
• Customer shopping habits.
• Social media usage by platform.

Design Best Practices for Bullet Graphs

• Use contrasting colors to highlight how the data is progressing.
• Use one color in different shades to gauge progress.

4. Column + Line Graph

Column + line graphs are also called dual-axis charts. They consist of a column and line graph together, with both graphics on the X axis but occupying their own Y axis.

Download our FREE Excel Graph Templates for this graph and more!

Best Use Cases

These graphs are best for comparing two data sets with different measurement units, such as rate and time. 

As a marketer, you may want to track two trends at once.

Design Best Practices 

Use individual colors for the lines and colors to make the graph more visually appealing and to further differentiate the data. 

The Four Basic Types of Charts

Before we get into charts, I want to touch on the four basic chart types that I use the most. 

1. Bar Chart

Bar charts are pretty self-explanatory. I use them to indicate values by the length of bars, which can be displayed horizontally or vertically. Vertical bar charts, like the one below, are sometimes called column charts. 

2. Line Chart 

I use line charts to show changes in values across continuous measurements, such as across time, generations, or categories. For example, the chart below shows the changes in ice cream sales throughout the week.

3. Scatter Plot

A scatter plot uses dotted points to compare values against two different variables on separate axes. It's commonly used to show correlations between values and variables. 

4. Pie Chart

Pie charts are charts that represent data in a circular (pie-shaped) graphic, and each slice represents a percentage or portion of the whole. 

Notice the example below of a household budget. (Which reminds me that I need to set up my own.)

Notice that the percentage of income going to each expense is represented by a slice. 

Different Types of Charts for Data Visualization

To better understand chart types and how you can use them, here's an overview of each:

1. Column Chart

Use a column chart to show a comparison among different items or to show a comparison of items over time. You could use this format to see the revenue per landing page or customers by close date.

Best Use Cases for This Type of Chart

I use both column charts to display changes in data, but I've noticed column charts are best for negative data. The main difference, of course, is that column charts show information vertically while bar charts  show data horizontally.

For example, warehouses often track the number of accidents on the shop floor. When the number of incidents falls below the monthly average, a column chart can make that change easier to see in a presentation.

In the example above, this column chart measures the number of customers by close date. Column charts make it easy to see data changes over a period of time. This means that they have many use cases, including:

• Customer survey data, like showing how many customers prefer a specific product or how much a customer uses a product each day.
• Sales volume, like showing which services are the top sellers each month or the number of sales per week.
• Profit and loss, showing where business investments are growing or falling.

Design Best Practices for Column Charts

• Use horizontal labels to improve readability.
• Start the y-axis at 0 to appropriately reflect the values in your chart .

2. Area Chart

Okay, an area chart is basically a line chart, but I swear there's a meaningful difference.

The space between the x-axis and the line is filled with a color or pattern. It is useful for showing part-to-whole relations, like showing individual sales reps’ contributions to total sales for a year.

It helps me analyze both overall and individual trend information.

Best Use Cases for These Types of Charts

Area charts help show changes over time. They work best for big differences between data sets and help visualize big trends.

For example, the chart above shows users by creation date and life cycle stage.

A line chart could show more subscribers than marketing qualified leads. But this area chart emphasizes how much bigger the number of subscribers is than any other group.

These charts make the size of a group and how groups relate to each other more visually important than data changes over time.

Area charts  can help your business to:

• Visualize which product categories or products within a category are most popular.
• Show key performance indicator (KPI) goals vs. outcomes.
• Spot and analyze industry trends.

Design Best Practices for Area Charts

• Use transparent colors so information isn't obscured in the background.
• Don't display more than four categories to avoid clutter.
• Organize highly variable data at the top of the chart to make it easy to read.

3. Stacked Bar Chart

I suggest using this chart to compare many different items and show the composition of each item you’re comparing.

These charts  are helpful when a group starts in one column and moves to another over time.

For example, the difference between a marketing qualified lead (MQL) and a sales qualified lead (SQL) is sometimes hard to see. The chart above helps stakeholders see these two lead types from a single point of view — when a lead changes from MQL to SQL.

Stacked bar charts are excellent for marketing. They make it simple to add a lot of data on a single chart or to make a point with limited space.

These charts  can show multiple takeaways, so they're also super for quarterly meetings when you have a lot to say but not a lot of time to say it.

Stacked bar charts are also a smart option for planning or strategy meetings. This is because these charts can show a lot of information at once, but they also make it easy to focus on one stack at a time or move data as needed.

You can also use these charts to:

• Show the frequency of survey responses.
• Identify outliers in historical data.
• Compare a part of a strategy to its performance as a whole.

Design Best Practices for Stacked Bar Charts

• Best used to illustrate part-to-whole relationships.
• Use contrasting colors for greater clarity.
• Make the chart scale large enough to view group sizes in relation to one another.

4. Mekko Chart

Also known as a Marimekko chart, this type of chart  can compare values, measure each one's composition, and show data distribution across each one.

It's similar to a stacked bar, except the Mekko's x-axis can capture another dimension of your values — instead of time progression, like column charts often do. In the graphic below, the x-axis compares the cities to one another.

Image Source

I typically use a Mekko chart to show growth, market share, or competitor analysis.

For example, the Mekko chart above shows the market share of asset managers grouped by location and the value of their assets. This chart clarifies which firms manage the most assets in different areas.

It's also easy to see which asset managers are the largest and how they relate to each other.

Mekko charts can seem more complex than other types of charts, so it's best to use these in situations where you want to emphasize scale or differences between groups of data.

Other use cases for Mekko charts include:

• Detailed profit and loss statements.
• Revenue by brand and region.
• Product profitability.
• Share of voice by industry or niche.

Design Best Practices for Mekko Charts

• Vary your bar heights if the portion size is an important point of comparison.
• Don't include too many composite values within each bar. Consider reevaluating your presentation if you have a lot of data.
• Order your bars from left to right in such a way that exposes a relevant trend or message.

5. Pie Chart

Remember, a pie chart represents numbers in percentages, and the total sum of all segments needs to equal 100%.

The image above shows another example of customers by role in the company.

The bar chart  example shows you that there are more individual contributors than any other role. But this pie chart makes it clear that they make up over 50% of customer roles.

Pie charts make it easy to see a section in relation to the whole, so they are good for showing:

• Customer personas in relation to all customers.
• Revenue from your most popular products or product types in relation to all product sales.
• Percent of total profit from different store locations.

Design Best Practices for Pie Charts

• Don't illustrate too many categories to ensure differentiation between slices.
• Ensure that the slice values add up to 100%.
• Order slices according to their size.

6. Scatter Plot Chart

As I said earlier, a scatter plot or scattergram chart will show the relationship between two different variables or reveal distribution trends.

Use this chart when there are many different data points, and you want to highlight similarities in the data set. This is useful when looking for outliers or understanding your data's distribution.

Scatter plots are helpful in situations where you have too much data to see a pattern quickly. They are best when you use them to show relationships between two large data sets.

In the example above, this chart shows how customer happiness relates to the time it takes for them to get a response.

This type of chart  makes it easy to compare two data sets. Use cases might include:

• Employment and manufacturing output.
• Retail sales and inflation.
• Visitor numbers and outdoor temperature.
• Sales growth and tax laws.

Try to choose two data sets that already have a positive or negative relationship. That said, this type of chart  can also make it easier to see data that falls outside of normal patterns.

Design Best Practices for Scatter Plots

• Include more variables, like different sizes, to incorporate more data.
• Start the y-axis at 0 to represent data accurately.
• If you use trend lines, only use a maximum of two to make your plot easy to understand.

7. Bubble Chart

A bubble chart is similar to a scatter plot in that it can show distribution or relationship. There is a third data set shown by the size of the bubble or circle.

In the example above, the number of hours spent online isn't just compared to the user's age, as it would be on a scatter plot chart.

Instead, you can also see how the gender of the user impacts time spent online.

This makes bubble charts useful for seeing the rise or fall of trends over time. It also lets you add another option when you're trying to understand relationships between different segments or categories.

For example, if you want to launch a new product, this chart could help you quickly see your new product's cost, risk, and value. This can help you focus your energies on a low-risk new product with a high potential return.

You can also use bubble charts for:

• Top sales by month and location.
• Customer satisfaction surveys.
• Store performance tracking.
• Marketing campaign reviews.

Design Best Practices for Bubble Charts

• Scale bubbles according to area, not diameter.
• Make sure labels are clear and visible.
• Use circular shapes only.

8. Waterfall Chart

I sometimes use a waterfall chart to show how an initial value changes with intermediate values — either positive or negative — and results in a final value.

Use this chart to reveal the composition of a number. An example of this would be to showcase how different departments influence overall company revenue and lead to a specific profit number.

The most common use case for a funnel chart is the marketing or sales funnel. But there are many other ways to use this versatile chart.

If you have at least four stages of sequential data, this chart can help you easily see what inputs or outputs impact the final results.

For example, a funnel chart can help you see how to improve your buyer journey or shopping cart workflow. This is because it can help pinpoint major drop-off points.

Other stellar options for these types of charts include:

• Deal pipelines.
• Conversion and retention analysis.
• Bottlenecks in manufacturing and other multi-step processes.
• Marketing campaign performance.
• Website conversion tracking.

Design Best Practices for Funnel Charts

• Scale the size of each section to accurately reflect the size of the data set.
• Use contrasting colors or one color in graduated hues, from darkest to lightest, as the size of the funnel decreases.

10. Heat Map

A heat map shows the relationship between two items and provides rating information, such as high to low or poor to excellent. This chart displays the rating information using varying colors or saturation.

Best Use Cases for Heat Maps

In the example above, the darker the shade of green shows where the majority of people agree.

With enough data, heat maps can make a viewpoint that might seem subjective more concrete. This makes it easier for a business to act on customer sentiment.

There are many uses for these types of charts. In fact, many tech companies use heat map tools to gauge user experience for apps, online tools, and website design .

Another common use for heat map charts  is location assessment. If you're trying to find the right location for your new store, these maps can give you an idea of what the area is like in ways that a visit can't communicate.

Heat maps can also help with spotting patterns, so they're good for analyzing trends that change quickly, like ad conversions. They can also help with:

• Competitor research.
• Customer sentiment.
• Sales outreach.
• Campaign impact.
• Customer demographics.

Design Best Practices for Heat Map

• Use a basic and clear map outline to avoid distracting from the data.
• Use a single color in varying shades to show changes in data.
• Avoid using multiple patterns.

11. Gantt Chart

The Gantt chart is a horizontal chart that dates back to 1917. This chart maps the different tasks completed over a period of time.

Gantt charting is one of the most essential tools for project managers. It brings all the completed and uncompleted tasks into one place and tracks the progress of each.

While the left side of the chart displays all the tasks, the right side shows the progress and schedule for each of these tasks.

This chart type allows you to:

• Break projects into tasks.
• Track the start and end of the tasks.
• Set important events, meetings, and announcements.
• Assign tasks to the team and individuals.

I use donut charts for the same use cases as pie charts, but I tend to prefer the former because of the added benefit that the data is easier to read.

Another benefit to donut charts is that the empty center leaves room for extra layers of data, like in the examples above. 

Design Best Practices for Donut Charts 

Use varying colors to better differentiate the data being displayed, just make sure the colors are in the same palette so viewers aren't put off by clashing hues. 

14. Sankey Diagram

A Sankey Diagram visually represents the flow of data between categories, with the link width reflecting the amount of flow. It’s a powerful tool for uncovering the stories hidden in your data.

As data grows more complex, charts must evolve to handle these intricate relationships. Sankey Diagrams excel at this task.

With ChartExpo , you can create a Sankey Chart with up to eight levels, offering multiple perspectives for analyzing your data. Even the most complicated data sets become manageable and easy to interpret.

You can customize your Sankey charts and every component including nodes, links, stats, text, colors, and more. ChartExpo is an add-in in Microsoft Excel, Google Sheets, and Power BI, you can create beautiful Sankey diagrams while keeping your data safe in your favorite tools.

Sankey diagrams can be used to visualize all types of data which contain a flow of information. It beautifully connects the flows and presents the data in an optimum way.

Here are a few use cases:

• Sankey diagrams are widely used to visualize energy production, consumption, and distribution. They help in tracking how energy flows from one source (like oil or gas) to various uses (heating, electricity, transportation).
• Businesses use Sankey diagrams to trace customer interactions across different channels and touchpoints. It highlights the flow of users through a funnel or process, revealing drop-off points and success paths.
• I n supply chain management, these diagrams show how resources, products, or information flow between suppliers, manufacturers, and retailers, identifying bottlenecks and inefficiencies.

Design Best Practices for Sankey Diagrams 

When utilizing a Sankey diagram, it is essential to maintain simplicity while ensuring accuracy in proportions. Clear labeling and effective color usage are key factors to consider. Emphasizing the logical flow direction and highlighting significant flows will enhance the visualization.

How to Choose the Right Chart or Graph for Your Data

Channels like social media or blogs have multiple data sources, and managing these complex content assets can get overwhelming. What should you be tracking? What matters most?

How do you visualize and analyze the data so you can extract insights and actionable information?

1. Identify your goals for presenting the data.

Before creating any data-based graphics, I ask myself if I want to convince or clarify a point. Am I trying to visualize data that helped me solve a problem? Or am I trying to communicate a change that's happening?

A chart or graph can help compare different values, understand how different parts impact the whole, or analyze trends. Charts and graphs can also be useful for recognizing data that veers away from what you’re used to or help you see relationships between groups.

So, clarify your goals then use them to guide your chart selection.

2. Figure out what data you need to achieve your goal.

Different types of charts and graphs use different kinds of data. Graphs usually represent numerical data, while charts are visual representations of data that may or may not use numbers.

So, while all graphs are a type of chart, not all charts are graphs. If you don't already have the kind of data you need, you might need to spend some time putting your data together before building your chart.

3. Gather your data.

Most businesses collect numerical data regularly, but you may need to put in some extra time to collect the right data for your chart.

Besides quantitative data tools that measure traffic, revenue, and other user data, you might need some qualitative data.

These are some other ways you can gather data for your data visualization:

• Interviews 
• Quizzes and surveys
• Customer reviews
• Reviewing customer documents and records
• Community boards

Fill out the form to get your templates.

4. select the right type of graph or chart..

Choosing the wrong visual aid or defaulting to the most common type of data visualization could confuse your viewer or lead to mistaken data interpretation.

But a chart is only useful to you and your business if it communicates your point clearly and effectively.

Ask yourself the questions below to help find the right chart or graph type.

Download the Excel templates mentioned in the video here.

5 Questions to Ask When Deciding Which Type of Chart to Use

1. do you want to compare values.

Charts and graphs are perfect for comparing one or many value sets, and they can easily show the low and high values in the data sets. To create a comparison chart, use these types of graphs:

• Scatter plot

2. Do you want to show the composition of something?

Use this type of chart to show how individual parts make up the whole of something, like the device type used for mobile visitors to your website or total sales broken down by sales rep.

To show composition, use these charts:

• Stacked bar

3. Do you want to understand the distribution of your data?

Distribution charts help you to understand outliers, the normal tendency, and the range of information in your values.

Use these charts to show distribution:

4. Are you interested in analyzing trends in your data set?

If you want more information about how a data set performed during a specific time, there are specific chart types that do extremely well.

You should choose one of the following:

• Dual-axis line

5. Do you want to better understand the relationship between value sets?

Relationship charts can show how one variable relates to one or many different variables. You could use this to show how something positively affects, has no effect, or negatively affects another variable.

When trying to establish the relationship between things, use these charts:

Featured Resource: The Marketer's Guide to Data Visualization

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Graphical Representation of Data

Graphical representation of data is an attractive method of showcasing numerical data that help in analyzing and representing quantitative data visually. A graph is a kind of a chart where data are plotted as variables across the coordinate. It became easy to analyze the extent of change of one variable based on the change of other variables. Graphical representation of data is done through different mediums such as lines, plots, diagrams, etc. Let us learn more about this interesting concept of graphical representation of data, the different types, and solve a few examples.

Definition of Graphical Representation of Data

A graphical representation is a visual representation of data statistics-based results using graphs, plots, and charts. This kind of representation is more effective in understanding and comparing data than seen in a tabular form. Graphical representation helps to qualify, sort, and present data in a method that is simple to understand for a larger audience. Graphs enable in studying the cause and effect relationship between two variables through both time series and frequency distribution. The data that is obtained from different surveying is infused into a graphical representation by the use of some symbols, such as lines on a line graph, bars on a bar chart, or slices of a pie chart. This visual representation helps in clarity, comparison, and understanding of numerical data.

Representation of Data

The word data is from the Latin word Datum, which means something given. The numerical figures collected through a survey are called data and can be represented in two forms - tabular form and visual form through graphs. Once the data is collected through constant observations, it is arranged, summarized, and classified to finally represented in the form of a graph. There are two kinds of data - quantitative and qualitative. Quantitative data is more structured, continuous, and discrete with statistical data whereas qualitative is unstructured where the data cannot be analyzed.

Principles of Graphical Representation of Data

The principles of graphical representation are algebraic. In a graph, there are two lines known as Axis or Coordinate axis. These are the X-axis and Y-axis. The horizontal axis is the X-axis and the vertical axis is the Y-axis. They are perpendicular to each other and intersect at O or point of Origin. On the right side of the Origin, the Xaxis has a positive value and on the left side, it has a negative value. In the same way, the upper side of the Origin Y-axis has a positive value where the down one is with a negative value. When -axis and y-axis intersect each other at the origin it divides the plane into four parts which are called Quadrant I, Quadrant II, Quadrant III, Quadrant IV. This form of representation is seen in a frequency distribution that is represented in four methods, namely Histogram, Smoothed frequency graph, Pie diagram or Pie chart, Cumulative or ogive frequency graph, and Frequency Polygon.

Advantages and Disadvantages of Graphical Representation of Data

Listed below are some advantages and disadvantages of using a graphical representation of data:

• It improves the way of analyzing and learning as the graphical representation makes the data easy to understand.
• It can be used in almost all fields from mathematics to physics to psychology and so on.
• It is easy to understand for its visual impacts.
• It shows the whole and huge data in an instance.
• It is mainly used in statistics to determine the mean, median, and mode for different data

The main disadvantage of graphical representation of data is that it takes a lot of effort as well as resources to find the most appropriate data and then represent it graphically.

Rules of Graphical Representation of Data

While presenting data graphically, there are certain rules that need to be followed. They are listed below:

• Suitable Title: The title of the graph should be appropriate that indicate the subject of the presentation.
• Measurement Unit: The measurement unit in the graph should be mentioned.
• Proper Scale: A proper scale needs to be chosen to represent the data accurately.
• Index: For better understanding, index the appropriate colors, shades, lines, designs in the graphs.
• Data Sources: Data should be included wherever it is necessary at the bottom of the graph.
• Simple: The construction of a graph should be easily understood.
• Neat: The graph should be visually neat in terms of size and font to read the data accurately.

Uses of Graphical Representation of Data

The main use of a graphical representation of data is understanding and identifying the trends and patterns of the data. It helps in analyzing large quantities, comparing two or more data, making predictions, and building a firm decision. The visual display of data also helps in avoiding confusion and overlapping of any information. Graphs like line graphs and bar graphs, display two or more data clearly for easy comparison. This is important in communicating our findings to others and our understanding and analysis of the data.

Types of Graphical Representation of Data

Data is represented in different types of graphs such as plots, pies, diagrams, etc. They are as follows,

Related Topics

Listed below are a few interesting topics that are related to the graphical representation of data, take a look.

• x and y graph
• Frequency Polygon
• Cumulative Frequency

Examples on Graphical Representation of Data

Example 1 : A pie chart is divided into 3 parts with the angles measuring as 2x, 8x, and 10x respectively. Find the value of x in degrees.

We know, the sum of all angles in a pie chart would give 360º as result. ⇒ 2x + 8x + 10x = 360º ⇒ 20 x = 360º ⇒ x = 360º/20 ⇒ x = 18º Therefore, the value of x is 18º.

Example 2: Ben is trying to read the plot given below. His teacher has given him stem and leaf plot worksheets. Can you help him answer the questions? i) What is the mode of the plot? ii) What is the mean of the plot? iii) Find the range.

Solution: i) Mode is the number that appears often in the data. Leaf 4 occurs twice on the plot against stem 5.

Hence, mode = 54

ii) The sum of all data values is 12 + 14 + 21 + 25 + 28 + 32 + 34 + 36 + 50 + 53 + 54 + 54 + 62 + 65 + 67 + 83 + 88 + 89 + 91 = 958

To find the mean, we have to divide the sum by the total number of values.

Mean = Sum of all data values ÷ 19 = 958 ÷ 19 = 50.42

iii) Range = the highest value - the lowest value = 91 - 12 = 79

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Practice Questions on Graphical Representation of Data

Faqs on graphical representation of data, what is graphical representation.

Graphical representation is a form of visually displaying data through various methods like graphs, diagrams, charts, and plots. It helps in sorting, visualizing, and presenting data in a clear manner through different types of graphs. Statistics mainly use graphical representation to show data.

What are the Different Types of Graphical Representation?

The different types of graphical representation of data are:

• Stem and leaf plot
• Scatter diagrams
• Frequency Distribution

Is the Graphical Representation of Numerical Data?

Yes, these graphical representations are numerical data that has been accumulated through various surveys and observations. The method of presenting these numerical data is called a chart. There are different kinds of charts such as a pie chart, bar graph, line graph, etc, that help in clearly showcasing the data.

What is the Use of Graphical Representation of Data?

Graphical representation of data is useful in clarifying, interpreting, and analyzing data plotting points and drawing line segments , surfaces, and other geometric forms or symbols.

What are the Ways to Represent Data?

Tables, charts, and graphs are all ways of representing data, and they can be used for two broad purposes. The first is to support the collection, organization, and analysis of data as part of the process of a scientific study.

What is the Objective of Graphical Representation of Data?

The main objective of representing data graphically is to display information visually that helps in understanding the information efficiently, clearly, and accurately. This is important to communicate the findings as well as analyze the data.

• Math Article

Graphical Representation

Graphical Representation is a way of analysing numerical data. It exhibits the relation between data, ideas, information and concepts in a diagram. It is easy to understand and it is one of the most important learning strategies. It always depends on the type of information in a particular domain. There are different types of graphical representation. Some of them are as follows:

• Line Graphs – Line graph or the linear graph is used to display the continuous data and it is useful for predicting future events over time.
• Bar Graphs – Bar Graph is used to display the category of data and it compares the data using solid bars to represent the quantities.
• Histograms – The graph that uses bars to represent the frequency of numerical data that are organised into intervals. Since all the intervals are equal and continuous, all the bars have the same width.
• Line Plot – It shows the frequency of data on a given number line. ‘ x ‘ is placed above a number line each time when that data occurs again.
• Frequency Table – The table shows the number of pieces of data that falls within the given interval.
• Circle Graph – Also known as the pie chart that shows the relationships of the parts of the whole. The circle is considered with 100% and the categories occupied is represented with that specific percentage like 15%, 56%, etc.
• Stem and Leaf Plot – In the stem and leaf plot, the data are organised from least value to the greatest value. The digits of the least place values from the leaves and the next place value digit forms the stems.
• Box and Whisker Plot – The plot diagram summarises the data by dividing into four parts. Box and whisker show the range (spread) and the middle ( median) of the data.

General Rules for Graphical Representation of Data

There are certain rules to effectively present the information in the graphical representation. They are:

• Suitable Title: Make sure that the appropriate title is given to the graph which indicates the subject of the presentation.
• Measurement Unit: Mention the measurement unit in the graph.
• Proper Scale: To represent the data in an accurate manner, choose a proper scale.
• Index: Index the appropriate colours, shades, lines, design in the graphs for better understanding.
• Data Sources: Include the source of information wherever it is necessary at the bottom of the graph.
• Keep it Simple: Construct a graph in an easy way that everyone can understand.
• Neat: Choose the correct size, fonts, colours etc in such a way that the graph should be a visual aid for the presentation of information.

Graphical Representation in Maths

In Mathematics, a graph is defined as a chart with statistical data, which are represented in the form of curves or lines drawn across the coordinate point plotted on its surface. It helps to study the relationship between two variables where it helps to measure the change in the variable amount with respect to another variable within a given interval of time. It helps to study the series distribution and frequency distribution for a given problem.  There are two types of graphs to visually depict the information. They are:

• Time Series Graphs – Example: Line Graph
• Frequency Distribution Graphs – Example: Frequency Polygon Graph

Principles of Graphical Representation

Algebraic principles are applied to all types of graphical representation of data. In graphs, it is represented using two lines called coordinate axes. The horizontal axis is denoted as the x-axis and the vertical axis is denoted as the y-axis. The point at which two lines intersect is called an origin ‘O’. Consider x-axis, the distance from the origin to the right side will take a positive value and the distance from the origin to the left side will take a negative value. Similarly, for the y-axis, the points above the origin will take a positive value, and the points below the origin will a negative value.

Generally, the frequency distribution is represented in four methods, namely

• Smoothed frequency graph
• Pie diagram
• Cumulative or ogive frequency graph
• Frequency Polygon

Merits of Using Graphs

Some of the merits of using graphs are as follows:

• The graph is easily understood by everyone without any prior knowledge.
• It saves time
• It allows us to relate and compare the data for different time periods
• It is used in statistics to determine the mean, median and mode for different data, as well as in the interpolation and the extrapolation of data.

Example for Frequency polygonGraph

Here are the steps to follow to find the frequency distribution of a frequency polygon and it is represented in a graphical way.

• Obtain the frequency distribution and find the midpoints of each class interval.
• Represent the midpoints along x-axis and frequencies along the y-axis.
• Plot the points corresponding to the frequency at each midpoint.
• Join these points, using lines in order.
• To complete the polygon, join the point at each end immediately to the lower or higher class marks on the x-axis.

Draw the frequency polygon for the following data

Mark the class interval along x-axis and frequencies along the y-axis.

Let assume that class interval 0-10 with frequency zero and 90-100 with frequency zero.

Now calculate the midpoint of the class interval.

Using the midpoint and the frequency value from the above table, plot the points A (5, 0), B (15, 4), C (25, 6), D (35, 8), E (45, 10), F (55, 12), G (65, 14), H (75, 7), I (85, 5) and J (95, 0).

To obtain the frequency polygon ABCDEFGHIJ, draw the line segments AB, BC, CD, DE, EF, FG, GH, HI, IJ, and connect all the points.

Frequently Asked Questions

What are the different types of graphical representation.

Some of the various types of graphical representation include:

• Line Graphs
• Frequency Table
• Circle Graph, etc.

Read More:  Types of Graphs

What are the Advantages of Graphical Method?

Some of the advantages of graphical representation are:

• It makes data more easily understandable.
• It saves time.
• It makes the comparison of data more efficient.

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Very useful for understand the basic concepts in simple and easy way. Its very useful to all students whether they are school students or college sudents

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August 15th, 2024

29 Best Types of Charts and Graphs for Data Visualization

By: Alysha Gullion · 8 min read

Selecting the right chart is crucial for effective data presentation. The choice depends on your data type, audience, and intended message. For example, line charts work well for time trends, while pie charts show proportions. Complex visualizations like correlation heat maps may not suit audiences unfamiliar with data science. This article will outline various graph types and their typical uses, noting that some graphs may fit multiple categories but will be mentioned only once for simplicity. By understanding these options, you can choose the most impactful way to present your data.

How to Find Data for Graphs and Charts

Trying to find high-quality, interesting data for creating charts and graphs is always difficult. We used the following open-source repo of datasets for all of the graphs and charts in this post: vincentarelbundock.github.io . Other options for finding datasets include Kaggle , which is a prominent data science community and data repository, or the UC Irvine Machine Learning Repository .

How to Create Charts and Graphs

Various tools cater to different needs in chart and graph creation. Excel is widely used in business for its simplicity. Tableau is favored by data analysts for interactive visualizations. Researchers often use SPSS for complex statistical graphs, while data scientists prefer R for its programming flexibility. For those seeking a more intuitive approach, Julius offers a unique alternative. Supporting both Python and R, Julius allows users to generate graphs using plain language descriptions, making it accessible to both beginners and experienced users. When choosing a tool, consider your technical skills and visualization requirements.

Comparison Charts

Comparison charts or graphs are used to compare quantities across different categories. Their purpose is to highlight the differences and similarities within data sets, making it easier for viewers to draw conclusions about the variations amongst various groups.

You can find the code associated with these charts by visiting our community forum . 

1. Bar/Column charts

Bar and column charts provide clear comparisons between discrete categories (i.e., car models) based on a quantitative measure (e.g., miles per gallon, MPG). They are widely used as they offer a quick and effective way to visualize differences amongst categorical variables. The difference between bar and column charts is based on their orientation: bar charts display their bars horizontally, while column charts display them vertically.

The data used in this visualization can be accessed here . This data frame consists of 32 observations on 11 numeric variables and was collected in 1974 from Motor Trend US magazine. It details fuel consumption of 10 different motor vehicles. We will create a bar chart to compare miles per gallon between each car model. 

Python Example

The images above compare the fuel efficiency of each car model. The graph shows that the Mercedes-Benz 240D outperforms its counterparts in terms of miles per gallon.

2. Grouped/Clustered Bar Chart

Grouped or clustered bar charts are used to compare frequencies, counts, or other measures across multiple categories and groups. 

For this visualization, we will be using a dataset from the College Scorecard, which contains college-by-year data on how students are doing after graduation, available here . This data frame contains 48,445 rows and 8 variables. We will create a grouped bar chart to compare the counts of working vs. not working for five institutions in the year 2007.

In the images above, we can see that graduates from ASA college tended to have a substantially higher count of ‘working’ individuals compared to the other institutions.

3. Dumbbell Plot

Often mistaken for a type of bar chart, the dumbbell plot differs by displaying two values for each category rather than one. It shows two points connected by a line, which displays the minimum and maximum values of data points for each category. Dumbbell plots are useful for displaying variability, distributions, and confidence intervals within categories. 

For this visualization, we will be using a dataset that contains daily temperatures (minimum and maximum) for Clemson, South Carolina from January 1st, 1930 to December 31st, 2020 (33,148 observations). The dataset can be accessed here .

For simplicity, we will focus on the year 1930 and 2020, which contains 365 observations each. We will plot the average minimum and maximum temperature for each month in the year 1930 and 2020.

Overall, the trend suggests that 2020 experienced higher temperatures compared to 1930. For yearly averages, 2020 had a higher average minimum temperature (52.43°F vs 48.68°F in 1930) but a slightly lower average maximum temperature (72.77°F vs 73.90°F in 1930).

4. Radar Chart

Radar charts are useful for displaying multivariate data in a way that is easy to compare across different variables. However, some users may find this chart difficult to interpret depending on the information and message presented. 

For this example, we are going to plot the fitness scores of five individuals. The assessed fitness components included: cardiovascular endurance, muscle strength, flexibility, body composition, balance and nutrition. Each component was ranked from a scale of 1 to 10, with 10 being the highest and 1 being the worst. The dataset can be accessed here .

These radar charts show how each individual's fitness varies across the six components, providing an overall comparison on a single plot.

5. Dot Plot

Dot plots show one or more qualitative values for each category, allowing for comparison across multiple values within and between categories. They provide an informative visualization, effectively condensing information in an easy to read format. 

For this visualization, we will use a dataset containing the stats of starter Pokémon and from Generations I through VI (19 entries). This dataset can be accessed here .

In the images above, we can see the different stats for the starters from generations I through VI. Who will you choose? I always choose Mudkip, he is my favourite. 

Correlation Charts

Correlation graphs are used to visualize relationships between variables, showing how one variable changes in relation to another. They show the strength and direction of these relationships, which is important in fields like statistics, economics, and data science.

6. Heatmap & Correlation Matrices

Heatmaps and correlation matrices are great visualizations that are simple for readers to understand. They use a colour gradient to represent the value of variables in a two-dimensional space. They are good tools for identifying patterns, variable-variable relationships, and anomalies in complex datasets. 

For this visualization, we will use a dataset called ‘cerebellum_gene_expression2, accessible here . We will randomly choose 20 genes and create a correlation matrix to visualize gene expression rates via a heatmap. 

The original dataset can be accessed through this file , which is an example dataset provided by the tissueGeneExpression package from the genomicsclass GitHub repository. It contains 500 genes, randomly selected from a dataset of 22,215 entries. 

The image above displays the correlation matrix for 20 randomly selected genes. In the matrix, yellow indicates a strong positive correlation (both variables increase or decrease together), while dark blue indicates a strong negative correlation (as one increases the other decreases). Green represents a weak correlation or no correlation.

7. Bubble Chart

A bubble chart is a data visualization technique that displays multiple dimensions of data within a two-dimensional plot. The ‘bubbles’ represent data points, with their positions determined by two variables, and the size representing the third variable. 

The dataset used to create this graph was from the 2000 US census, and can be accessed here . It contains 437 entries and 28 columns representing various demographic measurements. We will visualize the relationship between education level, poverty, total population and population density in the top 15 counties from Illinois.

The R and Python graphs follow the same formatting. Each bubble represents one of the top 15 counties in Illinois. The size of the bubble corresponds to the total population density of the county, the colour indicates the population density (with lighter colours representing higher density). Each bubble is labeled with the county abbreviation. 

8. Scatter Plot

A scatter plot is a type of data visualization technique that displays values for two variables for a set of data points. It shows how one variable is affected by another, which can reveal relationships between them. Each point on the plot represents an individual data point, with its position along the x-axis representing one variable and its position on the y-axis indicating another variable. 

For this visualization, we are using a dataset called ‘insurance’, which can be accessed here . This dataset includes data on monthly quotes and television advertising expenditure from a US-based insurance company, collected from January 2002 to April 2002. This dataset contains 40 entries and 3 columns. The visualization will examine the relationship between TV advertisements and quotes given. A trendline will be added to help visualize the relationship. 

Python Example 

A positive relationship was observed between increases in TV advertisement and quotes given, as displayed by the increasing trendline.

9. Hexagonal binning

Hexagonal binning is a technique used for large, complex datasets with continuous numerical data in two dimensions. It displays the distribution and density of points, which is particularly useful when over-plotting occurs.

For this visualization, we will use a dataset containing daily observations made for the S&P 500 stock market from 1950 to 2018. The dataset includes 17,346 observations and 7 variables. It can be accessed here . The visualization will be plotting the volume by closing price.  

The yellow hexagon at the lower left corner indicates a clustering of points (high density of points here) that represents low closing price and trading volume. Here, the closing price was equal to $44.64 per share, and the volume of trade is ≤ 2.5 million shares. This specific point makes up ~8.0% of the total dataset.

10. Contour plot + Surface Plot

This is another technique that is used for visualizing data distributions and densities within a two dimensional field. It is oftentimes used to create topographic maps of data. For simplicity, we are going to plot the function Z = sin(sqrt(X^2 + Y^2)).

You can manipulate the surface plot directly within Julius itself to examine different angles, allowing for an in-depth exploration of the plotted points.

Part-to-Whole & Hierarchical Charts

Part-to-Whole visualizations show how individual portions contribute to the whole. Hierarchical graphs represent data in a tree-like structure, displaying relationships between different levels of data.

11. Stacked Bar Graphs

Stacked bar graphs show the composition of different categories within a dataset. Each bar represents the total amount, with segments within the bar representing the categories and their proportion to the total. 

For this example, we will use data from a 2020 Financial Independence (FI) Survey conducted on Reddit. This dataset examined people’s finances and the changes experienced during the pandemic. The full dataset can be accessed here , which contains 1998 rows and 65 variables. We will be using a cleaned version of the full dataset, that contains the same number of rows but only 3 variables. This dataset can be accessed here . 

The visualization focuses on the columns pan_inc_chg (pandemic income change), pan_exp_chg (pandemic expense change), and pan_fi_chg (pandemic financial independence change), as they contain multiple categories relevant to the analysis.

The results show that the pandemic had varying effects on income, leading to reductions in expenses for many individuals. The combination of stable or increased income, along with decreased expenses, may have contributed to a slight improvement in the financial independence for some people.

12. Dendrogram

Dendrograms are tree-like diagrams that show the arrangement of clusters formed by a hierarchical structure. They are commonly used in fields such as biology, bioinformatics, and machine learning to visualize the relationships between data points. 

For this visualization, we will use a dataset called ‘cerebellum_gene_expression2’, which can be accessed here . We are only going to plot the first 20 genes for this visualization. 

The original dataset can be accessed through this file . This example dataset, provided by the ‘tissueGeneExpression’ package from the genomicsclass GitHub repository, includes 500 genes randomly selected from a larger dataset containing 22,215 entries.

Genes grouped together at lower heights in this dendrogram have more similar expression patterns across samples. Additionally, the higher the branching point between two pairs of genes or clusters, the more dissimilar they are. For example, x.MAML1 and x.FIBP are clustered closely together, suggesting similar expression patterns.

13. Pie Chart

A pie chart is a circular statistical graph divided into slices to show the relative proportions of different categories within a dataset. Each slice represents a category, and the size of the slice corresponds to the proportion of that category in relation to the whole. 

For this visualization, we will use a dataset from a 2010 poll on whether airports should use full-body scanners. The poll collected a total of 1137 responses and included two factors. The dataset can be accessed here .

Both visualizations show group responses regarding body scanner use in airports for security purposes, with an overall trend suggesting that people approve of their use.

14. Donut Chart

Donut charts are similar to pie charts, but they have a hole in the center of the circle, giving them their name. This inner circle’s removal allows for the additional information to be shown in the chart. The length of each arc corresponds to the proportion of the category it represents. 

For this visualization, we will use a dataset detailing the chemical composition (Aluminum, Iron, Magnesium, Calcium, and Sodium) found at four different archaeological sites in Great Britain (26 entries). We will compare the different chemical composition of pottery amongst the four sites. The dataset can be accessed here .

Across all four different sites, we can observe variations in the chemical composition of the pottery. Aluminum, the primary chemical compound, constitutes the highest percentage in composition of each pottery sample, but its percentages vary amongst sites.  

15. Population Pyramid

Also known as age-sex pyramids, population pyramids are visualizations that display the gender distribution of a population. They are typically presented as a bar chart, with age cohorts displayed horizontally to the left or right. One side represents males, while the other side shows females. 

For this visualization, we will use a dataset containing male and female birth rates in London from 1962 to 1710 (82 rows; 7 variables). For simplicity, we will only plot male and female data for the first 20 years. The dataset can be accessed here . 

The population distribution between males and females appears steady amongst the years, showing a slight decrease in births for both sexes from 1641 to 1648. 

Data Over Time (Temporal) Charts

Temporal charts are used to display data over time, revealing trends, patterns, and changes. They are essential for time series analysis and can be presented in multiple different forms depending on the type of data and the message intended to be conveyed.

You can find the code associated with these charts by visiting our community forum .

16. Area Chart

Area charts are a type of data visualizations used to represent quantitative data and show how values change over a period of time. They plot a continuous variable and are great at showing the magnitude of change over time or visualizing cumulative effects. 

We will be using the London dataset (82 rows; 7 variables) to visualize the mortality rate and plague deaths over time. The dataset can be accessed here . 

These charts visualize the impact of the plague on mortality rates. We can see a peak between 1660 and 1670, during which the majority of deaths were due to plague.

17. Line chart

Line charts are among the most commonly used types of charts worldwide. They are great at showing overall trends or progress over time. The x-axis typically represents the continuous variables (usually time), while the y-axis displays the dependent variable, showing how its value changes.

For this visualization, we will use a dataset called ‘trump_tweet’, which tracks the number of tweets by Mr. Trump from 2009 to 2017. The full dataset can be accessed here (20,761 rows; 8 variables), while the condensed dataset used for this visualization is available here (9 rows; one variable).

This line chart displays the number of tweets made by Mr. Trump over an eight year period. The lowest number of tweets was recorded in 2009 (~43 tweets/year), while his highest was in 2013 (~5,616 tweets/year). 

18. Candlestick Chart

A candlestick chart is a financial visualization used to analyze price movements of an asset, derivative, or currency. It is commonly used in technical analysis to predict market trends. The chart displays the high, low, opening, and closing prices of a product within a specific time frame. 

For this chart, we will use the S&P 500 stock market dataset. This dataset includes daily observations from 1950 to 2018, with a total of 17,346 entries and 7 variables. The original dataset can be accessed here , while the one we are using for the visualization is here . For this chart, we are only focusing on a short timeframe, specifically March 1974 high, low, opening, closing prices and volume. 

The green candlesticks indicate the days when the closing price was higher than the opening price, suggesting buyer pressure. Red candlesticks indicate days where the closing price was lower than the opening price, suggesting selling pressure. Candlesticks with small bodies, where the opening and closing prices are close together, suggest market indecision. 

Overall, this chart shows that the market started positively (as indicated by many green candlesticks), experienced a brief mid-month dip (indicated by the red candlesticks), and then recovered slightly, as shown by some green candlesticks.

19. Stream graph

A stream graph displays changes in the magnitude of categorical data over time. It is a variation of the stacked area bar graph, where the baseline is not anchored to a singular point but rather moves up or down, allowing the to display a natural flow. 

For this visualization, we will use a dataset that measures air pollutants in Leeds (UK) from 1994 to 1998 (Heffernan and Tawn, 2004). The winter dataset includes measurements between November to February of the various air pollutants (532 rows with 5 variables). The dataset can be accessed here .

The images shows how the composition of the pollutants change over time, with peaks and dips of pollutants illustrated throughout the season.

20. Gantt chart

A Gantt chart is a visual tool used in project management to plan and track the progress of tasks. It displays individual tasks or activities along a timeline, highlighting their scheduled start and end dates. Gantt charts are a great way for visualizing sequences of tasks, duration, and the dependencies between tasks. 

For this visualization, we will use a dataset showing task allocation between start and end dates of my Master’s program. The dataset can be accessed here (contains 17 rows, with 4 columns).

R Example 

Distribution Charts

Distribution charts are meant to show the spread of data across various categories or values. They help readers understand the frequency, range, and the overall shape of the data’s distribution. In addition, it can help readers understand the patterns, central tendency, and variations within their dataset.

21. Density plot

A density plot measures the probability distribution of a continuous variable. By providing a smooth curve that represents the distribution of data points over a range, it helps readers to identify patterns, trends, and the overall shape of the distribution. Density plots are useful for visualizing the distribution, identifying modes, and comparing distributions between multiple groups.

For this visualization, we will use the “iris” dataset (151 rows, 5 columns). This is a common dataset that contains information on petal width, petal length, sepal width and sepal length of three different iris species (Setosa, Versicolour, and Virginica). It is often used as an introductory model for clustering algorithms in machine learning. For this visualization, we will be using it to compare how flower features differ between species. The dataset can be accessed by simply asking Julius to retrieve it in Python or R, or it can be accessed here . 

The density plot reveals the following observations: For Setosa, the distribution of petal width and length is generally on the lower end compared to the other species of iris’s, suggesting that Setosa would be easily distinguished by its smaller petal dimensions. 

Versicolor shows some overlap with Virginica regarding sepal length and width, but exhibits less variation and tends to concentrate around 5.5cm (sepal length) and 3.0cm (sepal width).Vericolor can be identified by its intermediate petal size – larger than Setosa but smaller than Virginica. Virginica, on the other hand, displays the largest petal length and width, though it does show some high variability due to the spread of points along the x-axis.

22. Histogram

A histogram is used to display the distribution of a dataset by dividing it into intervals, or bins, and counting the data points that fall into each bin. The height of each bar represents the frequency of data points falling into that specific interval. Histograms are commonly used to display frequency distribution of a continuous variable.  

For this visualization, we will use a dataset comparing thermometer readings between Mr. Trump and Mr. Obama (3,081 rows, 3 columns). We will visualize the frequencies of scores between Mr. Trump and Mr. Obama. The dataset can be found here .

The dataset shows a non-normal distribution, as evident by the multiple peaks observed in the trendline.

23. Jitter Plot

A jitter plot is similar to scatter plot but introduces intentional random dispersions of points – referred to as ‘jittering’ – along one axis to prevent overlapping. This technique reveals the density and distribution of data points that would otherwise overlap. This is useful when your data points may have the same values or relatively close values across categories.    

For this visualization, we will use a dataset comparing dried plant weight yields (30 observations) under three different conditions (control, treatment 1, and treatment 2). The dataset can be accessed here .

Both images demonstrate how a jitter plot effectively prevents overlapping between points with identical or nearly identical values.

24. Beeswarm Chart

A beeswarm chart visualizes data points along a single axis, with dots representing each individual datapoint. This method does slightly rearrange the points to avoid overlapping.  

We will use the same plant growth dataset from the jitter plot visualization to illustrate how the data points appear in comparison to the jitter plot. The dataset can be accessed here .

The beeswarm plot is more appealing with a larger sample size, but this example provides a general idea of its format. Unlike the jitter plot, data points in a beeswarm plot are positioned in a vertical line, with slight dispersion when multiple points overlap. Although some beeswarm plots do not include boxplot and box-and-whiskers plot, adding these can help visualize interquartile ranges. 

From a general observation, treatment 2 appears to have a slightly higher overall weight compared to the control and treatment 1. However, it is important to note that outliers in treatment 1 and the control can skew this range.

25. Boxplot (Box-and-whisker plot)

A boxplot, or box-and-whiskers plot, is a standardized method for displaying the distribution of a dataset. It highlights five key aspects: the minimum value, the first quartile (Q1), median, third quartile (Q3), and the maximum value. This allows the reader to examine the spread of the data, central tendency, and identify potential outliers, making it a great tool for exploratory data analysis. 

For this visualization, we will use a dataset from Baumann & Jones, as reported by Moore & McCabe (1993). The dataset examines whether three different teaching methods – traditional (Basal), innovative 1 (DRTA), and innovative 2 (Strat) – affected reading comprehension in students. The data frame has 66 rows with 6 columns: group, pretest.1, pretest.2, post.test.1, post.test.2, post.test.3. The dataset can be accessed here .

The visualization was created by averaging the scores between the two pre-tests and three post-tests by teaching methods, and then plotting them.

From quick observation, there appears to be differences in test performance associated with teaching methods. The Basal method seems to show the lowest median test score in comparison to the DRTA and Strat. However, these initial observations should be confirmed through further statistical testing.

Geospatial & Other

Geospatial visualizations are designed to represent data with geographic information, such as coordinates, GPS, longitude, and latitude. Their purpose is to communicate spatial patterns and relationships. Also included in this section are flow charts and network diagrams, which show how ideas or concepts are related to one another.

26. Geographic Heat Map

A geographic heat map shows where points are most concentrated within a specific geographic location by using colours to represent density. This type of map is useful for highlighting patterns, trends, and hotspots in spatial data. 

For this visualization, we will use a dataset that includes the locations of 1000 seismic events near Fiji since 1964. This dataset, part of the Harvard PRIM-H project dataset, was obtained by Dr. John Woodhouse from the department of Geophysics. This dataset can be accessed here . 

27. Choropleth map

A choropleth map is a thematic map where areas are shaded (or patterned) based on the values of a variable, such as population density, income level, or election results. Colours are used to represent different densities or magnitudes, which provides a comparative visual between spatial data distributions. 

For this visualization, we will use data from the 2017 American Census Society. It has 3221 entries, with 37 columns detailing various demographic information. This dataset can be accessed here .

28. Network diagram

A network diagram is a visualization tool used to show connections between multiple different elements, illustrating how different entities (nodes) are connected to one another. 

For this visualization, we will use a document that outlines the sequence of tasks in a project. It defines the nodes (tasks), dependencies, and gives a short description of the dependencies. This document can be accessed here and the google sheet can be accessed here . 

Network diagrams are great ways to organize your thoughts and visualize how events are connected to one another.

29. Flowchart

A flowchart is a visual representation of a process, workflow, or system. It uses symbols and arrows to signify a sequence of steps, decisions, or actions. Flowcharts are similar to network diagrams, as they clearly illustrate how different activities or steps are connected, making it easy to understand the flow of activities involved in the process. 

For this example, we will create a flowchart outlining the process of online purchases. The Google document can be accessed here , which contains all the information you need to create the flowchart. You can simply copy and paste the text into the chat box. 

This article has served as a visual guide to 29 diverse chart and graph types, each designed to address specific data presentation needs. From simple bar charts to complex network diagrams, we've explored a range of visualization options to help you choose the right tool for your data story. Understanding these different graph types empowers you to communicate your insights more effectively, regardless of your audience or data complexity.

Throughout this journey, we've used Julius to generate our examples, showcasing how it seamlessly supports both R and Python users. Julius's ability to create these visualizations through simple, natural language commands demonstrates how data visualization tools are evolving to become more accessible. As you continue to explore and apply these chart types in your own work, consider how platforms like Julius can streamline your process, allowing you to focus on the story your data tells rather than the technicalities of graph creation.

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• Graphic Presentation of Data

Apart from diagrams, Graphic presentation is another way of the presentation of data and information. Usually, graphs are used to present time series and frequency distributions. In this article, we will look at the graphic presentation of data and information along with its merits, limitations , and types.

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Construction of a graph.

The graphic presentation of data and information offers a quick and simple way of understanding the features and drawing comparisons. Further, it is an effective analytical tool and a graph can help us in finding the mode, median, etc.

We can locate a point in a plane using two mutually perpendicular lines – the X-axis (the horizontal line) and the Y-axis (the vertical line). Their point of intersection is the Origin .

We can locate the position of a point in terms of its distance from both these axes. For example, if a point P is 3 units away from the Y-axis and 5 units away from the X-axis, then its location is as follows:

Browse more Topics under Descriptive Statistics

• Definition and Characteristics of Statistics
• Stages of Statistical Enquiry
• Importance and Functions of Statistics
• Nature of Statistics – Science or Art?
• Application of Statistics
• Law of Statistics and Distrust of Statistics
• Meaning and Types of Data
• Methods of Collecting Data
• Sample Investigation
• Classification of Data
• Tabulation of Data
• Frequency Distribution of Data
• Diagrammatic Presentation of Data
• Measures of Central Tendency
• Mean Median Mode
• Measures of Dispersion
• Standard Deviation
• Variance Analysis

Some points to remember:

• We measure the distance of the point from the Y-axis along the X-axis. Similarly, we measure the distance of the point from the X-axis along the Y-axis. Therefore, to measure 3 units from the Y-axis, we move 3 units along the X-axis and likewise for the other coordinate .
• We then draw perpendicular lines from these two points.
• The point where the perpendiculars intersect is the position of the point P.
• We denote it as follows (3,5) or (abscissa, ordinate). Together, they are the coordinates of the point P.
• The four parts of the plane are Quadrants.
• Also, we can plot different points for a different pair of values.

General Rules for Graphic Presentation of Data and Information

There are certain guidelines for an attractive and effective graphic presentation of data and information. These are as follows:

• Suitable Title – Ensure that you give a suitable title to the graph which clearly indicates the subject for which you are presenting it.
• Unit of Measurement – Clearly state the unit of measurement below the title.
• Suitable Scale – Choose a suitable scale so that you can represent the entire data in an accurate manner.
• Index – Include a brief index which explains the different colors and shades, lines and designs that you have used in the graph. Also, include a scale of interpretation for better understanding.
• Data Sources – Wherever possible, include the sources of information at the bottom of the graph.
• Keep it Simple – You should construct a graph which even a layman (without any exposure in the areas of statistics or mathematics) can understand.
• Neat – A graph is a visual aid for the presentation of data and information. Therefore, you must keep it neat and attractive. Choose the right size, right lettering, and appropriate lines, colors, dashes, etc.

Merits of a Graph

• The graph presents data in a manner which is easier to understand.
• It allows us to present statistical data in an attractive manner as compared to tables. Users can understand the main features, trends, and fluctuations of the data at a glance.
• A graph saves time.
• It allows the viewer to compare data relating to two different time-periods or regions.
• The viewer does not require prior knowledge of mathematics or statistics to understand a graph.
• We can use a graph to locate the mode, median, and mean values of the data.
• It is useful in forecasting, interpolation, and extrapolation of data.

Limitations of a Graph

• A graph lacks complete accuracy of facts.
• It depicts only a few selected characteristics of the data.
• We cannot use a graph in support of a statement.
• A graph is not a substitute for tables.
• Usually, laymen find it difficult to understand and interpret a graph.
• Typically, a graph shows the unreasonable tendency of the data and the actual values are not clear.

Types of Graphs

Graphs are of two types:

• Time Series graphs
• Frequency Distribution graphs

Time Series Graphs

A time series graph or a “ histogram ” is a graph which depicts the value of a variable over a different point of time. In a time series graph, time is the most important factor and the variable is related to time. It helps in the understanding and analysis of the changes in the variable at a different point of time. Many statisticians and businessmen use these graphs because they are easy to understand and also because they offer complex information in a simple manner.

Further, constructing a time series graph does not require a user with technical skills. Here are some major steps in the construction of a time series graph:

• Represent time on the X-axis and the value of the variable on the Y-axis.
• Start the Y-value with zero and devise a suitable scale which helps you present the whole data in the given space.
• Plot the values of the variable and join different point with a straight line.
• You can plot multiple variables through different lines.

You can use a line graph to summarize how two pieces of information are related and how they vary with each other.

• You can compare multiple continuous data-sets easily
• You can infer the interim data from the graph line

Disadvantages

• It is only used with continuous data.

Use of a false Base Line

Usually, in a graph, the vertical line starts from the Origin. However, in some cases, a false Base Line is used for a better representation of the data. There are two scenarios where you should use a false Base Line:

• To magnify the minor fluctuation in the time series data
• To economize the space

Net Balance Graph

If you have to show the net balance of income and expenditure or revenue and costs or imports and exports, etc., then you must use a net balance graph. You can use different colors or shades for positive and negative differences.

Frequency Distribution Graphs

Let’s look at the different types of frequency distribution graphs.

A histogram is a graph of a grouped frequency distribution. In a histogram, we plot the class intervals on the X-axis and their respective frequencies on the Y-axis. Further, we create a rectangle on each class interval with its height proportional to the frequency density of the class.

Frequency Polygon or Histograph

A frequency polygon or a Histograph is another way of representing a frequency distribution on a graph. You draw a frequency polygon by joining the midpoints of the upper widths of the adjacent rectangles of the histogram with straight lines.

Frequency Curve

When you join the verticals of a polygon using a smooth curve, then the resulting figure is a Frequency Curve. As the number of observations increase, we need to accommodate more classes. Therefore, the width of each class reduces. In such a scenario, the variable tends to become continuous and the frequency polygon starts taking the shape of a frequency curve.

Cumulative Frequency Curve or Ogive

A cumulative frequency curve or Ogive is the graphical representation of a cumulative frequency distribution. Since a cumulative frequency is either of a ‘less than’ or a ‘more than’ type, Ogives are of two types too – ‘less than ogive’ and ‘more than ogive’.

Scatter Diagram

A scatter diagram or a dot chart enables us to find the nature of the relationship between the variables. If the plotted points are scattered a lot, then the relationship between the two variables is lesser.

Solved Question

Q1. What are the general rules for the graphic presentation of data and information?

Answer: The general rules for the graphic presentation of data are:

• Use a suitable title
• Clearly specify the unit of measurement
• Ensure that you choose a suitable scale
• Provide an index specifying the colors, lines, and designs used in the graph
• If possible, provide the sources of information at the bottom of the graph
• Keep the graph simple and neat.

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Choosing the Right Chart or Graph for Your Data: A Comprehensive Guide

By   STC

August 12, 2023

Data is everywhere. We use it to make decisions, communicate, to persuade, and to learn. But data alone is not enough. We need to present it in a way that makes sense, that tells a story, that reveals insights. That’s where charts and graphs come in.

Charts and graphs are visual representations of data that help us to understand, analyze, and communicate complex information. They can show patterns, trends, relationships, comparisons, and more. But not all charts and graphs are created equal. Some are better suited for certain types of data than others. Some are more effective at conveying a message than others. Some are more appealing to the eye than others.

How do you choose the right chart or graph for your data? How do you make sure that your visualizations are clear, accurate, and engaging? How do you avoid common pitfalls and mistakes that can confuse or mislead your audience? These are the questions that this comprehensive guide will answer.

In this guide, you will learn:

• The basic principles of data visualization and why they matter.
• The different types of charts and graphs and how to use them for different purposes.
• The best practices and tips for creating effective and attractive charts and graphs.
• The tools and resources that can help you create stunning visualizations.

By the end of this guide, you will be able to choose the right chart or graph for your data and create visualizations that will wow your audience. Whether you are a student, a teacher, a researcher, a marketer, a journalist, or anyone who works with data, this guide is for you.

So buckle up and get ready for a journey into the world of data visualization. It’s going to be fun, informative, and eye-opening. Let’s get started!

Choosing the Right Chart

Are you comparing sales across different regions? A bar chart might be your answer.

Bar charts are one of the most common and simple types of charts that you can use to visualize your data. They consist of rectangular bars with lengths proportional to the values that they represent. Bar charts are ideal for comparing individual groups or categories. For example, if you want to compare sales across different regions, a bar chart might be your answer. You can easily see which region has the highest or lowest sales, and how the regions differ from each other. A bar chart can also show the distribution of data across categories, such as the frequency or percentage of each category. Bar charts are versatile and easy to understand, making them a great choice for many situations.

Line Charts

Line charts are best for showing trends over time. Want to see the growth of your website’s traffic over a year? Line charts can present this data cleanly.

Line charts are another popular and simple type of chart that you can use to visualize your data. They consist of a series of points connected by straight lines, forming a line that shows the change in values over time. Line charts are best for showing trends over time. For example, if you want to see the growth of your website’s traffic over a year, line charts can present this data cleanly. You can easily see the ups and downs, the peaks and valleys, and the overall direction of your traffic. A line chart can also show the relationship between two or more variables over time, such as the correlation between temperature and ice cream sales. Line charts are useful and intuitive, making them a great choice for many situations.

Pie charts are great for displaying a part-to-whole relationship. Need to illustrate a budget? Pie charts give a clear picture.

Pie charts are another common and simple type of chart that you can use to visualize your data. They consist of a circular shape divided into slices, each representing a proportion of the whole. The size of each slice is proportional to the percentage of the total value that it represents. Pie charts are great for displaying a part-to-whole relationship. For example, if you need to illustrate a budget, pie charts give a clear picture of how much money is allocated to each category, and how each category compares to the others. A pie chart can also show the composition of a population, such as the age groups, genders, or ethnicities. Pie charts are colorful and easy to read, making them a great choice for many situations.

Scatter Plots

Scatter plots are perfect for showing relationships between two variables. Looking to find correlation between age and income? This is the graph for you.

Scatter plots consist of a collection of points on a two-dimensional plane, each representing the values of two variables for a single observation. Scatter plots are perfect for showing relationships between two variables. For example, if you are looking to find correlation between age and income, this is the graph for you. You can easily see how the two variables vary together, and whether there is a positive, negative, or no correlation. A scatter plot can also show outliers, clusters, and gaps in your data. Scatter plots are powerful and insightful, making them a great choice for many situations.

Area Charts

Area charts can illustrate trends and are particularly useful for showing cumulative totals. Interested in how savings accumulate over time? Consider an area chart.

Area charts are similar to line charts, but they have a shaded area below the line that shows the magnitude of the values. Area charts can illustrate trends and are particularly useful for showing cumulative totals. For example, if you are interested in how savings accumulate over time, consider an area chart. You can easily see how much money you have saved at any point in time, and how the savings rate changes over time. An area chart can also show the contribution of different components to a total, such as the sources of revenue or the types of expenses. Area charts are expressive and informative, making them a great choice for many situations

Histograms are used for displaying frequency distributions. Studying the distribution of customer satisfaction scores? A histogram will serve you well.

Histograms are similar to bar charts, but they have no gaps between the bars and they show the frequency of values in a continuous variable. Histograms are used for displaying frequency distributions. For example, if you are studying the distribution of customer satisfaction scores, a histogram will serve you well. You can easily see how many customers gave a certain score, and how the scores are spread across the range. A histogram can also show the shape of the distribution, such as whether it is symmetric, skewed, or bimodal. Histograms are descriptive and revealing, making them a great choice for many situations.

Graph Types

Time series.

Time series graphs are vital for tracking changes over periods. Monitoring stock prices? Time series graphs are the way to go.

Time series graphs are similar to line charts, but they show the change in values over a specific period of time, such as days, months, or years. Time series graphs are vital for tracking changes over periods. For example, if you are monitoring stock prices, time series graphs are the way to go. You can easily see how the prices fluctuate over time, and how they respond to external events, such as news, earnings, or market trends. A time series graph can also show the seasonality, cycles, and trends of your data. Time series graphs are dynamic and insightful, making them a great choice for many situations.

Correlation

Correlation graphs help in identifying patterns between two variables. Investigating how temperature affects sales? Use this graph.

Correlation graphs are another type of chart that you can use to visualize your data. They are similar to scatter plots, but they show the strength and direction of the linear relationship between two variables. Correlation graphs help in identifying patterns between two variables. For example, if you are investigating how temperature affects sales, use this graph. You can easily see if there is a positive correlation (higher temperature leads to higher sales), a negative correlation (higher temperature leads to lower sales), or no correlation (temperature has no effect on sales). A correlation graph can also show the correlation coefficient, which is a numerical measure of how closely the variables are related. Correlation graphs are helpful and informative, making them a great choice for many situations.

Distribution

Distribution graphs depict how variables are spread out. Analyzing product quality? This is your choice.

Distribution graphs are similar to histograms, but they show the probability density of a continuous variable, rather than the frequency. Distribution graphs depict how variables are spread out. For example, if you are analyzing product quality, this is your choice. You can easily see the mean, median, mode, standard deviation, and range of your data. You can also see the shape of the distribution, such as whether it is normal, skewed, or uniform. A distribution graph can also show the confidence intervals, which indicate how certain you are about the true value of the mean. Distribution graphs are descriptive and analytical, making them a great choice for many situations.

Comparison graphs are used to contrast different data sets. Comparing marketing channels? Select this type.

Comparison graphs are similar to bar charts, but they show two or more data sets side by side, or stacked on top of each other, to highlight the differences and similarities between them. Comparison graphs are used to contrast different data sets. For example, if you are comparing marketing channels, select this type of graph. You can easily see how each channel performs in terms of reach, engagement, conversion, and revenue. You can also see how the channels compare to each other, and to the overall average. A comparison graph can also show the variance, which indicates how much the data varies from the mean. Comparison graphs are useful and informative, making them a great choice for many situations.

Common Mistakes in Data Visualization

Data visualization is a powerful tool that can help you communicate your data in a clear and compelling way. But it can also backfire if you make some common mistakes that can distort, confuse, or mislead your audience. Here are some of the pitfalls that you should avoid when creating charts and graphs:

• Misleading Scales: Scales are the numbers that show the range of values on the axes of your chart. They can easily misrepresent data if not chosen wisely. For example, if you use a scale that is too large or too small, you can make the differences between data points look bigger or smaller than they really are. Or if you use a scale that is not consistent across charts, you can make unfair comparisons between data sets. To avoid misleading scales, you should always choose a scale that is appropriate for your data, and that is consistent and transparent for your audience.
• Too Many Colors: Colors are a great way to add visual interest and contrast to your chart. But too many colors can lead to confusion rather than clarity. For example, if you use too many colors to represent different categories, you can make it hard for your audience to distinguish between them. Or if you use colors that are too similar or too different, you can make it hard for your audience to see the patterns or trends in your data. To avoid too many colors, you should always use a color scheme that is suitable for your data, and that is simple and intuitive for your audience.
• Overcomplicating: Simplicity often wins. Ever felt overwhelmed by a complex chart? You’re not alone. Sometimes, we try to cram too much information or detail into our chart, thinking that more is better. But this can make our chart look cluttered and confusing, and distract our audience from the main message. To avoid overcomplicating, you should always focus on the key point that you want to convey with your chart and eliminate any unnecessary or redundant elements that might obscure it.

You have reached the end of this comprehensive guide on choosing the right chart or graph for your data. We hope that you have learned a lot from this guide and that you are now ready to create your own stunning and effective visualizations.

Choosing the right chart or graph for your data is crucial for accurate and effective data presentation. By understanding the differences and uses of various charts and avoiding common mistakes, you can create compelling and insightful visualizations that will capture the attention and interest of your audience. You can also communicate your data in a clear and concise way, and reveal the hidden stories and insights that lie within your data.

We hope that this guide has been helpful and informative for you and that you have enjoyed reading it as much as we have enjoyed writing it. Data visualization is a fascinating and rewarding field, and we encourage you to explore it further and apply it to your own projects. Remember, a picture is worth a thousand words, but a good chart or graph is worth even more. Happy visualizing!

About the author

We are passionate about the power of visual storytelling and believe that charts can convey complex information in a captivating and easily understandable way. Whether you're a data enthusiast, a business professional, or simply curious about the world around you, this page is your gateway to the world of data visualization.

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6 Data Visualization Examples To Inspire Your Own

• 12 Jan 2017

Data informs virtually every business decision an organization makes. Because of this, it’s become increasingly important for professionals of all backgrounds to be adept at working with data.

While data can provide immense value, it’s important that professionals are able to effectively communicate the significance of the data to stakeholders. This is where data visualization comes into play. By transforming raw data into engaging visuals using various data visualization tools , it’s much easier to communicate insights gleaned from it.

Here are six real-world examples of data visualization that you can use to inspire your own.

What Is Data Visualization?

Data visualization is the process of turning raw data into graphical representations.

Visualizations make it easy to communicate trends in data and draw conclusions. When presented with a graph or chart, stakeholders can easily visualize the story the data is telling, rather than try to glean insights from raw data.

There are countless data visualization techniques , including:

• Scatter plots

The technique you use will vary based on the type of data you’re handling and what you’re trying to communicate.

6 Real-World Data Visualization Examples

1. the most common jobs by state.

Source: NPR

National Public Radio (NPR) produced a color-coded, interactive display of the most common jobs in each state in each year from 1978 to 2014. By dragging the scroll bar at the bottom of the map, you’re able to visualize occupational changes over time.

If you’re trying to represent geographical data, a map is the best way to go.

2. COVID-19 Hospitalization Rates

Source: CDC

Throughout the COVID-19 pandemic, the Centers for Disease Control and Prevention (CDC) has been transforming raw data into easily digestible visuals. This line graph represents COVID-19 hospitalization rates from March through November 2020.

The CDC tactfully incorporated color to place further emphasis on the stark increase in hospitalization rates, using a darker shade for lower values and a lighter shade for higher values.

3. Forecasted Revenue of Amazon.com

Source: Statista

Data visualizations aren’t limited to historical data. This bar chart created by Statista visualizes the forecasted gross revenue of Amazon.com from 2018 to 2025.

This visualization uses a creative title to summarize the main message that the data is conveying, as well as a darker orange color to spike out the most important data point.

4. Web-Related Statistics

Source: Internet Live Stats

Internet Live Stats has tracked web-related statistics and pioneered methods for visualizing data to show how different digital properties have ebbed and flowed over time.

Simple infographics like this one are particularly effective when your goal is to communicate key statistics rather than visualizing trends or forecasts.

5. Most Popular Food Delivery Items

Source: Eater

Eater, Vox’s food and dining brand, has created this fun take on a “pie” chart, which shows the most common foods ordered for delivery in each of the United States.

To visualize this data, Eater used a specific type of pie chart known as a spie chart. Spie charts are essentially pie charts in which you can vary the height of each segment to further visualize differences in data.

6. Netflix Viewing Patterns

Source: Vox

Vox created this interesting visualization depicting the viewing patterns of Netflix users over time by device type. This Sankey diagram visualizes the tendency of users to switch to streaming via larger device types.

Visualizing Data to Make Business Decisions

The insights and conclusions drawn from data visualizations can guide the decision-making and strategic planning processes for your organization.

To ensure your visualizations are relevant, accurate, and ethical, familiarize yourself with basic data science concepts . With a foundational knowledge in data science, you can maintain confidence in your data and better understand its significance. An online analytics course can help you get started.

Are you interested in improving your data science and analytical skills? Download our Beginner’s Guide to Data & Analytics to learn how you can leverage the power of data for professional and organizational success.

This post was updated on February 26, 2021. It was originally published on January 12, 2017.

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• v.1(9); 2020 Dec 11

Principles of Effective Data Visualization

Stephen r. midway.

1 Department of Oceanography and Coastal Sciences, Louisiana State University, Baton Rouge, LA 70803, USA

We live in a contemporary society surrounded by visuals, which, along with software options and electronic distribution, has created an increased importance on effective scientific visuals. Unfortunately, across scientific disciplines, many figures incorrectly present information or, when not incorrect, still use suboptimal data visualization practices. Presented here are ten principles that serve as guidance for authors who seek to improve their visual message. Some principles are less technical, such as determining the message before starting the visual, while other principles are more technical, such as how different color combinations imply different information. Because figure making is often not formally taught and figure standards are not readily enforced in science, it is incumbent upon scientists to be aware of best practices in order to most effectively tell the story of their data.

The Bigger Picture

Visuals are an increasingly important form of science communication, yet many scientists are not well trained in design principles for effective messaging. Despite challenges, many visuals can be improved by taking some simple steps before, during, and after their creation. This article presents some sequential principles that are designed to improve visual messages created by scientists.

Many scientific visuals are not as effective as they could be because scientists often lack basic design principles. This article reviews the importance of effective data visualization and presents ten principles that scientists can use as guidance in developing effective visual messages.

Introduction

Visual learning is one of the primary forms of interpreting information, which has historically combined images such as charts and graphs (see Box 1 ) with reading text. 1 However, developments on learning styles have suggested splitting up the visual learning modality in order to recognize the distinction between text and images. 2 Technology has also enhanced visual presentation, in terms of the ability to quickly create complex visual information while also cheaply distributing it via digital means (compared with paper, ink, and physical distribution). Visual information has also increased in scientific literature. In addition to the fact that figures are commonplace in scientific publications, many journals now require graphical abstracts 3 or might tweet figures to advertise an article. Dating back to the 1970s when computer-generated graphics began, 4 papers represented by an image on the journal cover have been cited more frequently than papers without a cover image. 5

Regarding terminology, the terms graph , plot , chart , image , figure , and data visual(ization) are often used interchangeably, although they may have different meanings in different instances. Graph , plot , and chart often refer to the display of data, data summaries, and models, while image suggests a picture. Figure is a general term but is commonly used to refer to visual elements, such as plots, in a scientific work. A visual , or data visualization , is a newer and ostensibly more inclusive term to describe everything from figures to infographics. Here, I adopt common terminology, such as bar plot, while also attempting to use the terms figure and data visualization for general reference.

There are numerous advantages to quickly and effectively conveying scientific information; however, scientists often lack the design principles or technical skills to generate effective visuals. Going back several decades, Cleveland 6 found that 30% of graphs in the journal Science had at least one type of error. Several other studies have documented widespread errors or inefficiencies in scientific figures. 7 , 8 , 9 In fact, the increasing menu of visualization options can sometimes lead to poor fits between information and its presentation. These poor fits can even have the unintended consequence of confusing the readers and setting them back in their understanding of the material. While objective errors in graphs are hopefully in the minority of scientific works, what might be more common is suboptimal figure design, which takes place when a design element may not be objectively wrong but is ineffective to the point of limiting information transfer.

Effective figures suggest an understanding and interpretation of data; ineffective figures suggest the opposite. Although the field of data visualization has grown in recent years, the process of displaying information cannot—and perhaps should not—be fully mechanized. Much like statistical analyses often require expert opinions on top of best practices, figures also require choice despite well-documented recommendations. In other words, there may not be a singular best version of a given figure. Rather, there may be multiple effective versions of displaying a single piece of information, and it is the figure maker's job to weigh the advantages and disadvantages of each. Fortunately, there are numerous principles from which decisions can be made, and ultimately design is choice. 7

The data visualization literature includes many great resources. While several resources are targeted at developing design proficiency, such as the series of columns run by Nature Communications , 10 Wilkinson's The Grammar of Graphics 11 presents a unique technical interpretation of the structure of graphics. Wilkinson breaks down the notion of a graphic into its constituent parts—e.g., the data, scales, coordinates, geometries, aesthetics—much like conventional grammar breaks down a sentence into nouns, verbs, punctuation, and other elements of writing. The popularity and utility of this approach has been implemented in a number of software packages, including the popular ggplot2 package 12 currently available in R. 13 (Although the grammar of graphics approach is not explicitly adopted here, the term geometry is used consistently with Wilkinson to refer to different geometrical representations, whereas the term aesthetics is not used consistently with the grammar of graphics and is used simply to describe something that is visually appealing and effective.) By understanding basic visual design principles and their implementation, many figure authors may find new ways to emphasize and convey their information.

The Ten Principles

Principle #1 diagram first.

The first principle is perhaps the least technical but very important: before you make a visual, prioritize the information you want to share, envision it, and design it. Although this seems obvious, the larger point here is to focus on the information and message first, before you engage with software that in some way starts to limit or bias your visual tools. In other words, don't necessarily think of the geometries (dots, lines) you will eventually use, but think about the core information that needs to be conveyed and what about that information is going to make your point(s). Is your visual objective to show a comparison? A ranking? A composition? This step can be done mentally, or with a pen and paper for maximum freedom of thought. In parallel to this approach, it can be a good idea to save figures you come across in scientific literature that you identify as particularly effective. These are not just inspiration and evidence of what is possible, but will help you develop an eye for detail and technical skills that can be applied to your own figures.

Principle #2 Use the Right Software

Effective visuals typically require good command of one or more software. In other words, it might be unrealistic to expect complex, technical, and effective figures if you are using a simple spreadsheet program or some other software that is not designed to make complex, technical, and effective figures. Recognize that you might need to learn a new software—or expand your knowledge of a software you already know. While highly effective and aesthetically pleasing figures can be made quickly and simply, this may still represent a challenge to some. However, figure making is a method like anything else, and in order to do it, new methodologies may need to be learned. You would not expect to improve a field or lab method without changing something or learning something new. Data visualization is the same, with the added benefit that most software is readily available, inexpensive, or free, and many come with large online help resources. This article does not promote any specific software, and readers are encouraged to reference other work 14 for an overview of software resources.

Principle #3 Use an Effective Geometry and Show Data

Geometries are the shapes and features that are often synonymous with a type of figure; for example, the bar geometry creates a bar plot. While geometries might be the defining visual element of a figure, it can be tempting to jump directly from a dataset to pairing it with one of a small number of well-known geometries. Some of this thinking is likely to naturally happen. However, geometries are representations of the data in different forms, and often there may be more than one geometry to consider. Underlying all your decisions about geometries should be the data-ink ratio, 7 which is the ratio of ink used on data compared with overall ink used in a figure. High data-ink ratios are the best, and you might be surprised to find how much non-data-ink you use and how much of that can be removed.

Most geometries fall into categories: amounts (or comparisons), compositions (or proportions), distributions , or relationships . Although seemingly straightforward, one geometry may work in more than one category, in addition to the fact that one dataset may be visualized with more than one geometry (sometimes even in the same figure). Excellent resources exist on detailed approaches to selecting your geometry, 15 and this article only highlights some of the more common geometries and their applications.

Amounts or comparisons are often displayed with a bar plot ( Figure 1 A), although numerous other options exist, including Cleveland dot plots and even heatmaps ( Figure 1 F). Bar plots are among the most common geometry, along with lines, 9 although bar plots are noted for their very low data density 16 (i.e., low data-ink ratio). Geometries for amounts should only be used when the data do not have distributional information or uncertainty associated with them. A good use of a bar plot might be to show counts of something, while poor use of a bar plot might be to show group means. Numerous studies have discussed inappropriate uses of bar plots, 9 , 17 noting that “because the bars always start at zero, they can be misleading: for example, part of the range covered by the bar might have never been observed in the sample.” 17 Despite the numerous reports on incorrect usage, bar plots remain one of the most common problems in data visualization.

Examples of Visual Designs

(A) Clustered bar plots are effective at showing units within a group (A–C) when the data are amounts.

(B) Histograms are effective at showing the distribution of data, which in this case is a random draw of values from a Poisson distribution and which use a sequential color scheme that emphasizes the mean as red and values farther from the mean as yellow.

(C) Scatterplot where the black circles represent the data.

(D) Logistic regression where the blue line represents the fitted model, the gray shaded region represents the confidence interval for the fitted model, and the dark-gray dots represent the jittered data.

(E) Box plot showing (simulated) ages of respondents grouped by their answer to a question, with gray dots representing the raw data used in the box plot. The divergent colors emphasize the differences in values. For each box plot, the box represents the interquartile range (IQR), the thick black line represents the median value, and the whiskers extend to 1.5 times the IQR. Outliers are represented by the data.

(F) Heatmap of simulated visibility readings in four lakes over 5 months. The green colors represent lower visibility and the blue colors represent greater visibility. The white numbers in the cells are the average visibility measures (in meters).

(G) Density plot of simulated temperatures by season, where each season is presented as a small multiple within the larger figure.

For all figures the data were simulated, and any examples are fictitious.

Compositions or proportions may take a wide range of geometries. Although the traditional pie chart is one option, the pie geometry has fallen out of favor among some 18 due to the inherent difficulties in making visual comparisons. Although there may be some applications for a pie chart, stacked or clustered bar plots ( Figure 1 A), stacked density plots, mosaic plots, and treemaps offer alternatives.

Geometries for distributions are an often underused class of visuals that demonstrate high data density. The most common geometry for distributional information is the box plot 19 ( Figure 1 E), which shows five types of information in one object. Although more common in exploratory analyses than in final reports, the histogram ( Figure 1 B) is another robust geometry that can reveal information about data. Violin plots and density plots ( Figure 1 G) are other common distributional geometries, although many less-common options exist.

Relationships are the final category of visuals covered here, and they are often the workhorse of geometries because they include the popular scatterplot ( Figures 1 C and 1D) and other presentations of x - and y -coordinate data. The basic scatterplot remains very effective, and layering information by modifying point symbols, size, and color are good ways to highlight additional messages without taking away from the scatterplot. It is worth mentioning here that scatterplots often develop into line geometries ( Figure 1 D), and while this can be a good thing, presenting raw data and inferential statistical models are two different messages that need to be distinguished (see Data and Models Are Different Things ).

Finally, it is almost always recommended to show the data. 7 Even if a geometry might be the focus of the figure, data can usually be added and displayed in a way that does not detract from the geometry but instead provides the context for the geometry (e.g., Figures 1 D and 1E). The data are often at the core of the message, yet in figures the data are often ignored on account of their simplicity.

Principle #4 Colors Always Mean Something

The use of color in visualization can be incredibly powerful, and there is rarely a reason not to use color. Even if authors do not wish to pay for color figures in print, most journals still permit free color figures in digital formats. In a large study 20 of what makes visualizations memorable, colorful visualizations were reported as having a higher memorability score, and that seven or more colors are best. Although some of the visuals in this study were photographs, other studies 21 also document the effectiveness of colors.

In today's digital environment, color is cheap. This is overwhelmingly a good thing, but also comes with the risk of colors being applied without intention. Black-and-white visuals were more accepted decades ago when hard copies of papers were more common and color printing represented a large cost. Now, however, the vast majority of readers view scientific papers on an electronic screen where color is free. For those who still print documents, color printing can be done relatively cheaply in comparison with some years ago.

Color represents information, whether in a direct and obvious way, or in an indirect and subtle way. A direct example of using color may be in maps where water is blue and land is green or brown. However, the vast majority of (non-mapping) visualizations use color in one of three schemes: sequential , diverging , or qualitative . Sequential color schemes are those that range from light to dark typically in one or two (related) hues and are often applied to convey increasing values for increasing darkness ( Figures 1 B and 1F). Diverging color schemes are those that have two sequential schemes that represent two extremes, often with a white or neutral color in the middle ( Figure 1 E). A classic example of a diverging color scheme is the red to blue hues applied to jurisdictions in order to show voting preference in a two-party political system. Finally, qualitative color schemes are found when the intensity of the color is not of primary importance, but rather the objective is to use different and otherwise unrelated colors to convey qualitative group differences ( Figures 1 A and 1G).

While it is recommended to use color and capture the power that colors convey, there exist some technical recommendations. First, it is always recommended to design color figures that work effectively in both color and black-and-white formats ( Figures 1 B and 1F). In other words, whenever possible, use color that can be converted to an effective grayscale such that no information is lost in the conversion. Along with this approach, colors can be combined with symbols, line types, and other design elements to share the same information that the color was sharing. It is also good practice to use color schemes that are effective for colorblind readers ( Figures 1 A and 1E). Excellent resources, such as ColorBrewer, 22 exist to help in selecting color schemes based on colorblind criteria. Finally, color transparency is another powerful tool, much like a volume knob for color ( Figures 1 D and 1E). Not all colors have to be used at full value, and when not part of a sequential or diverging color scheme—and especially when a figure has more than one colored geometry—it can be very effective to increase the transparency such that the information of the color is retained but it is not visually overwhelming or outcompeting other design elements. Color will often be the first visual information a reader gets, and with this knowledge color should be strategically used to amplify your visual message.

Principle #5 Include Uncertainty

Not only is uncertainty an inherent part of understanding most systems, failure to include uncertainty in a visual can be misleading. There exist two primary challenges with including uncertainty in visuals: failure to include uncertainty and misrepresentation (or misinterpretation) of uncertainty.

Uncertainty is often not included in figures and, therefore, part of the statistical message is left out—possibly calling into question other parts of the statistical message, such as inference on the mean. Including uncertainty is typically easy in most software programs, and can take the form of common geometries such as error bars and shaded intervals (polygons), among other features. 15 Another way to approach visualizing uncertainty is whether it is included implicitly into the existing geometries, such as in a box plot ( Figure 1 E) or distribution ( Figures 1 B and 1G), or whether it is included explicitly as an additional geometry, such as an error bar or shaded region ( Figure 1 D).

Representing uncertainty is often a challenge. 23 Standard deviation, standard error, confidence intervals, and credible intervals are all common metrics of uncertainty, but each represents a different measure. Expressing uncertainty requires that readers be familiar with metrics of uncertainty and their interpretation; however, it is also the responsibility of the figure author to adopt the most appropriate measure of uncertainty. For instance, standard deviation is based on the spread of the data and therefore shares information about the entire population, including the range in which we might expect new values. On the other hand, standard error is a measure of the uncertainty in the mean (or some other estimate) and is strongly influenced by sample size—namely, standard error decreases with increasing sample size. Confidence intervals are primarily for displaying the reliability of a measurement. Credible intervals, almost exclusively associated with Bayesian methods, are typically built off distributions and have probabilistic interpretations.

Expressing uncertainty is important, but it is also important to interpret the correct message. Krzywinski and Altman 23 directly address a common misconception: “a gap between (error) bars does not ensure significance, nor does overlap rule it out—it depends on the type of bar.” This is a good reminder to be very clear not only in stating what type of uncertainty you are sharing, but what the interpretation is. Others 16 even go so far as to recommend that standard error not be used because it does not provide clear information about standard errors of differences among means. One recommendation to go along with expressing uncertainty is, if possible, to show the data (see Use an Effective Geometry and Show Data ). Particularly when the sample size is low, showing a reader where the data occur can help avoid misinterpretations of uncertainty.

Principle #6 Panel, when Possible (Small Multiples)

A particularly effective visual approach is to repeat a figure to highlight differences. This approach is often called small multiples , 7 and the technique may be referred to as paneling or faceting ( Figure 1 G). The strategy behind small multiples is that because many of the design elements are the same—for example, the axes, axes scales, and geometry are often the same—the differences in the data are easier to show. In other words, each panel represents a change in one variable, which is commonly a time step, a group, or some other factor. The objective of small multiples is to make the data inevitably comparable, 7 and effective small multiples always accomplish these comparisons.

Principle #7 Data and Models Are Different Things

Plotted information typically takes the form of raw data (e.g., scatterplot), summarized data (e.g., box plot), or an inferential statistic (e.g., fitted regression line; Figure 1 D). Raw data and summarized data are often relatively straightforward; however, a plotted model may require more explanation for a reader to be able to fully reproduce the work. Certainly any model in a study should be reported in a complete way that ensures reproducibility. However, any visual of a model should be explained in the figure caption or referenced elsewhere in the document so that a reader can find the complete details on what the model visual is representing. Although it happens, it is not acceptable practice to show a fitted model or other model results in a figure if the reader cannot backtrack the model details. Simply because a model geometry can be added to a figure does not mean that it should be.

Principle #8 Simple Visuals, Detailed Captions

As important as it is to use high data-ink ratios, it is equally important to have detailed captions that fully explain everything in the figure. A study of figures in the Journal of American Medicine 8 found that more than one-third of graphs were not self-explanatory. Captions should be standalone, which means that if the figure and caption were looked at independent from the rest of the study, the major point(s) could still be understood. Obviously not all figures can be completely standalone, as some statistical models and other procedures require more than a caption as explanation. However, the principle remains that captions should do all they can to explain the visualization and representations used. Captions should explain any geometries used; for instance, even in a simple scatterplot it should be stated that the black dots represent the data ( Figures 1 C–1E). Box plots also require descriptions of their geometry—it might be assumed what the features of a box plot are, yet not all box plot symbols are universal.

Principle #9 Consider an Infographic

It is unclear where a figure ends and an infographic begins; however, it is fair to say that figures tend to be focused on representing data and models, whereas infographics typically incorporate text, images, and other diagrammatic elements. Although it is not recommended to convert all figures to infographics, infographics were found 20 to have the highest memorability score and that diagrams outperformed points, bars, lines, and tables in terms of memorability. Scientists might improve their overall information transfer if they consider an infographic where blending different pieces of information could be effective. Also, an infographic of a study might be more effective outside of a peer-reviewed publication and in an oral or poster presentation where a visual needs to include more elements of the study but with less technical information.

Even if infographics are not adopted in most cases, technical visuals often still benefit from some text or other annotations. 16 Tufte's works 7 , 24 provide great examples of bringing together textual, visual, and quantitative information into effective visualizations. However, as figures move in the direction of infographics, it remains important to keep chart junk and other non-essential visual elements out of the design.

Principle #10 Get an Opinion

Although there may be principles and theories about effective data visualization, the reality is that the most effective visuals are the ones with which readers connect. Therefore, figure authors are encouraged to seek external reviews of their figures. So often when writing a study, the figures are quickly made, and even if thoughtfully made they are not subject to objective, outside review. Having one or more colleagues or people external to the study review figures will often provide useful feedback on what readers perceive, and therefore what is effective or ineffective in a visual. It is also recommended to have outside colleagues review only the figures. Not only might this please your colleague reviewers (because figure reviews require substantially less time than full document reviews), but it also allows them to provide feedback purely on the figures as they will not have the document text to fill in any uncertainties left by the visuals.

What About Tables?

Although often not included as data visualization, tables can be a powerful and effective way to show data. Like other visuals, tables are a type of hybrid visual—they typically only include alphanumeric information and no geometries (or other visual elements), so they are not classically a visual. However, tables are also not text in the same way a paragraph or description is text. Rather, tables are often summarized values or information, and are effective if the goal is to reference exact numbers. However, the interest in numerical results in the form of a study typically lies in comparisons and not absolute numbers. Gelman et al. 25 suggested that well-designed graphs were superior to tables. Similarly, Spence and Lewandowsky 26 compared pie charts, bar graphs, and tables and found a clear advantage for graphical displays over tabulations. Because tables are best suited for looking up specific information while graphs are better for perceiving trends and making comparisons and predictions, it is recommended that visuals are used before tables. Despite the reluctance to recommend tables, tables may benefit from digital formats. In other words, while tables may be less effective than figures in many cases, this does not mean tables are ineffective or do not share specific information that cannot always be displayed in a visual. Therefore, it is recommended to consider creating tables as supplementary or appendix information that does not go into the main document (alongside the figures), but which is still very easily accessed electronically for those interested in numerical specifics.

Conclusions

While many of the elements of peer-reviewed literature have remained constant over time, some elements are changing. For example, most articles now have more authors than in previous decades, and a much larger menu of journals creates a diversity of article lengths and other requirements. Despite these changes, the demand for visual representations of data and results remains high, as exemplified by graphical abstracts, overview figures, and infographics. Similarly, we now operate with more software than ever before, creating many choices and opportunities to customize scientific visualizations. However, as the demand for, and software to create, visualizations have both increased, there is not always adequate training among scientists and authors in terms of optimizing the visual for the message.

Figures are not just a scientific side dish but can be a critical point along the scientific process—a point at which the figure maker demonstrates their knowledge and communication of the data and results, and often one of the first stopping points for new readers of the information. The reality for the vast majority of figures is that you need to make your point in a few seconds. The longer someone looks at a figure and doesn't understand the message, the more likely they are to gain nothing from the figure and possibly even lose some understanding of your larger work. Following a set of guidelines and recommendations—summarized here and building on others—can help to build robust visuals that avoid many common pitfalls of ineffective figures ( Figure 2 ).

Overview of the Principles Presented in This Article

The two principles in yellow (bottom) are those that occur first, during the figure design phase. The six principles in green (middle) are generally considerations and decisions while making a figure. The two principles in blue (top) are final steps often considered after a figure has been drafted. While the general flow of the principles follows from bottom to top, there is no specific or required order, and the development of individual figures may require more or less consideration of different principles in a unique order.

All scientists seek to share their message as effectively as possible, and a better understanding of figure design and representation is undoubtedly a step toward better information dissemination and fewer errors in interpretation. Right now, much of the responsibility for effective figures lies with the authors, and learning best practices from literature, workshops, and other resources should be undertaken. Along with authors, journals play a gatekeeper role in figure quality. Journal editorial teams are in a position to adopt recommendations for more effective figures (and reject ineffective figures) and then translate those recommendations into submission requirements. However, due to the qualitative nature of design elements, it is difficult to imagine strict visual guidelines being enforced across scientific sectors. In the absence of such guidelines and with seemingly endless design choices available to figure authors, it remains important that a set of aesthetic criteria emerge to guide the efficient conveyance of visual information.

Acknowledgments

Thanks go to the numerous students with whom I have had fun, creative, and productive conversations about displaying information. Danielle DiIullo was extremely helpful in technical advice on software. Finally, Ron McKernan provided guidance on several principles.

Author Contributions

S.R.M. conceived the review topic, conducted the review, developed the principles, and wrote the manuscript.

Steve Midway is an assistant professor in the Department of Oceanography and Coastal Sciences at Louisiana State University. His work broadly lies in fisheries ecology and how sound science can be applied to management and conservation issues. He teaches a number of quantitative courses in ecology, all of which include data visualization.

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Praxis Core Math

Course: praxis core math   >   unit 1, data representations | lesson.

• Data representations | Worked example
• Center and spread | Lesson
• Center and spread | Worked example
• Random sampling | Lesson
• Random sampling | Worked example
• Scatterplots | Lesson
• Scatterplots | Worked example
• Interpreting linear models | Lesson
• Interpreting linear models | Worked example
• Correlation and Causation | Lesson
• Correlation and causation | Worked example
• Probability | Lesson
• Probability | Worked example

What are data representations?

• How much of the data falls within a specified category or range of values?
• What is a typical value of the data?
• How much spread is in the data?
• Is there a trend in the data over time?
• Is there a relationship between two variables?

What skills are tested?

• Matching a data set to its graphical representation
• Matching a graphical representation to a description
• Using data representations to solve problems

How are qualitative data displayed?

• A vertical bar chart lists the categories of the qualitative variable along a horizontal axis and uses the heights of the bars on the vertical axis to show the values of the quantitative variable. A horizontal bar chart lists the categories along the vertical axis and uses the lengths of the bars on the horizontal axis to show the values of the quantitative variable. This display draws attention to how the categories rank according to the amount of data within each. Example The heights of the bars show the number of students who want to study each language. Using the bar chart, we can conclude that the greatest number of students want to study Mandarin and the least number of students want to study Latin.
• A pictograph is like a horizontal bar chart but uses pictures instead of the lengths of bars to represent the values of the quantitative variable. Each picture represents a certain quantity, and each category can have multiple pictures. Pictographs are visually interesting, but require us to use the legend to convert the number of pictures to quantitative values. Example Each represents 40 ‍   students. The number of pictures shows the number of students who want to study each language. Using the pictograph, we can conclude that twice as many students want to study French as want to study Latin.
• A circle graph (or pie chart) is a circle that is divided into as many sections as there are categories of the qualitative variable. The area of each section represents, for each category, the value of the quantitative data as a fraction of the sum of values. The fractions sum to 1 ‍   . Sometimes the section labels include both the category and the associated value or percent value for that category. Example The area of each section represents the fraction of students who want to study that language. Using the circle graph, we can conclude that just under 1 2 ‍   the students want to study Mandarin and about 1 3 ‍   want to study Spanish.

How are quantitative data displayed?

• Dotplots use one dot for each data point. The dots are plotted above their corresponding values on a number line. The number of dots above each specific value represents the count of that value. Dotplots show the value of each data point and are practical for small data sets. Example Each dot represents the typical travel time to school for one student. Using the dotplot, we can conclude that the most common travel time is 10 ‍   minutes. We can also see that the values for travel time range from 5 ‍   to 35 ‍   minutes.
• Histograms divide the horizontal axis into equal-sized intervals and use the heights of the bars to show the count or percent of data within each interval. By convention, each interval includes the lower boundary but not the upper one. Histograms show only totals for the intervals, not specific data points. Example The height of each bar represents the number of students having a typical travel time within the corresponding interval. Using the histogram, we can conclude that the most common travel time is between 10 ‍   and 15 ‍   minutes and that all typical travel times are between 5 ‍   and 40 ‍   minutes.

How are trends over time displayed?

How are relationships between variables displayed.

• (Choice A)   A
• (Choice B)   B
• (Choice C)   C
• (Choice D)   D
• (Choice E)   E
• Your answer should be
• an integer, like 6 ‍  
• a simplified proper fraction, like 3 / 5 ‍  
• a simplified improper fraction, like 7 / 4 ‍  
• a mixed number, like 1   3 / 4 ‍  
• an exact decimal, like 0.75 ‍  
• a multiple of pi, like 12   pi ‍   or 2 / 3   pi ‍  
• a proper fraction, like 1 / 2 ‍   or 6 / 10 ‍  
• an improper fraction, like 10 / 7 ‍   or 14 / 8 ‍  

Things to remember

• When matching data to a representation, check that the values are graphed accurately for all categories.
• When reporting data counts or fractions, be clear whether a question asks about data within a single category or a comparison between categories.
• When finding the number or fraction of the data meeting a criteria, watch for key words such as or , and , less than , and more than .

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Blog Data Visualization 10 Data Presentation Examples For Strategic Communication

10 Data Presentation Examples For Strategic Communication

Written by: Krystle Wong Sep 28, 2023

Knowing how to present data is like having a superpower. 

Data presentation today is no longer just about numbers on a screen; it’s storytelling with a purpose. It’s about captivating your audience, making complex stuff look simple and inspiring action. 

To help turn your data into stories that stick, influence decisions and make an impact, check out Venngage’s free chart maker or follow me on a tour into the world of data storytelling along with data presentation templates that work across different fields, from business boardrooms to the classroom and beyond. Keep scrolling to learn more! 

Click to jump ahead:

10 Essential data presentation examples + methods you should know

What should be included in a data presentation, what are some common mistakes to avoid when presenting data, faqs on data presentation examples, transform your message with impactful data storytelling.

Data presentation is a vital skill in today’s information-driven world. Whether you’re in business, academia, or simply want to convey information effectively, knowing the different ways of presenting data is crucial. For impactful data storytelling, consider these essential data presentation methods:

1. Bar graph

Ideal for comparing data across categories or showing trends over time.

Bar graphs, also known as bar charts are workhorses of data presentation. They’re like the Swiss Army knives of visualization methods because they can be used to compare data in different categories or display data changes over time. 

In a bar chart, categories are displayed on the x-axis and the corresponding values are represented by the height of the bars on the y-axis. 

It’s a straightforward and effective way to showcase raw data, making it a staple in business reports, academic presentations and beyond.

Make sure your bar charts are concise with easy-to-read labels. Whether your bars go up or sideways, keep it simple by not overloading with too many categories.

2. Line graph

Great for displaying trends and variations in data points over time or continuous variables.

Line charts or line graphs are your go-to when you want to visualize trends and variations in data sets over time.

One of the best quantitative data presentation examples, they work exceptionally well for showing continuous data, such as sales projections over the last couple of years or supply and demand fluctuations. 

The x-axis represents time or a continuous variable and the y-axis represents the data values. By connecting the data points with lines, you can easily spot trends and fluctuations.

A tip when presenting data with line charts is to minimize the lines and not make it too crowded. Highlight the big changes, put on some labels and give it a catchy title.

3. Pie chart

Useful for illustrating parts of a whole, such as percentages or proportions.

Pie charts are perfect for showing how a whole is divided into parts. They’re commonly used to represent percentages or proportions and are great for presenting survey results that involve demographic data. 

Each “slice” of the pie represents a portion of the whole and the size of each slice corresponds to its share of the total. 

While pie charts are handy for illustrating simple distributions, they can become confusing when dealing with too many categories or when the differences in proportions are subtle.

Don’t get too carried away with slices — label those slices with percentages or values so people know what’s what and consider using a legend for more categories.

4. Scatter plot

Effective for showing the relationship between two variables and identifying correlations.

Scatter plots are all about exploring relationships between two variables. They’re great for uncovering correlations, trends or patterns in data. 

In a scatter plot, every data point appears as a dot on the chart, with one variable marked on the horizontal x-axis and the other on the vertical y-axis.

By examining the scatter of points, you can discern the nature of the relationship between the variables, whether it’s positive, negative or no correlation at all.

If you’re using scatter plots to reveal relationships between two variables, be sure to add trendlines or regression analysis when appropriate to clarify patterns. Label data points selectively or provide tooltips for detailed information.

5. Histogram

Best for visualizing the distribution and frequency of a single variable.

Histograms are your choice when you want to understand the distribution and frequency of a single variable. 

They divide the data into “bins” or intervals and the height of each bar represents the frequency or count of data points falling into that interval. 

Histograms are excellent for helping to identify trends in data distributions, such as peaks, gaps or skewness.

Here’s something to take note of — ensure that your histogram bins are appropriately sized to capture meaningful data patterns. Using clear axis labels and titles can also help explain the distribution of the data effectively.

6. Stacked bar chart

Useful for showing how different components contribute to a whole over multiple categories.

Stacked bar charts are a handy choice when you want to illustrate how different components contribute to a whole across multiple categories. 

Each bar represents a category and the bars are divided into segments to show the contribution of various components within each category. 

This method is ideal for highlighting both the individual and collective significance of each component, making it a valuable tool for comparative analysis.

Stacked bar charts are like data sandwiches—label each layer so people know what’s what. Keep the order logical and don’t forget the paintbrush for snazzy colors. Here’s a data analysis presentation example on writers’ productivity using stacked bar charts:

7. Area chart

Similar to line charts but with the area below the lines filled, making them suitable for showing cumulative data.

Area charts are close cousins of line charts but come with a twist. 

Imagine plotting the sales of a product over several months. In an area chart, the space between the line and the x-axis is filled, providing a visual representation of the cumulative total. 

This makes it easy to see how values stack up over time, making area charts a valuable tool for tracking trends in data.

For area charts, use them to visualize cumulative data and trends, but avoid overcrowding the chart. Add labels, especially at significant points and make sure the area under the lines is filled with a visually appealing color gradient.

8. Tabular presentation

Presenting data in rows and columns, often used for precise data values and comparisons.

Tabular data presentation is all about clarity and precision. Think of it as presenting numerical data in a structured grid, with rows and columns clearly displaying individual data points. 

A table is invaluable for showcasing detailed data, facilitating comparisons and presenting numerical information that needs to be exact. They’re commonly used in reports, spreadsheets and academic papers.

When presenting tabular data, organize it neatly with clear headers and appropriate column widths. Highlight important data points or patterns using shading or font formatting for better readability.

9. Textual data

Utilizing written or descriptive content to explain or complement data, such as annotations or explanatory text.

Textual data presentation may not involve charts or graphs, but it’s one of the most used qualitative data presentation examples. 

It involves using written content to provide context, explanations or annotations alongside data visuals. Think of it as the narrative that guides your audience through the data. 

Well-crafted textual data can make complex information more accessible and help your audience understand the significance of the numbers and visuals.

Textual data is your chance to tell a story. Break down complex information into bullet points or short paragraphs and use headings to guide the reader’s attention.

10. Pictogram

Using simple icons or images to represent data is especially useful for conveying information in a visually intuitive manner.

Pictograms are all about harnessing the power of images to convey data in an easy-to-understand way. 

Instead of using numbers or complex graphs, you use simple icons or images to represent data points. 

For instance, you could use a thumbs up emoji to illustrate customer satisfaction levels, where each face represents a different level of satisfaction. 

Pictograms are great for conveying data visually, so choose symbols that are easy to interpret and relevant to the data. Use consistent scaling and a legend to explain the symbols’ meanings, ensuring clarity in your presentation.

Looking for more data presentation ideas? Use the Venngage graph maker or browse through our gallery of chart templates to pick a template and get started! 

A comprehensive data presentation should include several key elements to effectively convey information and insights to your audience. Here’s a list of what should be included in a data presentation:

1. Title and objective

• Begin with a clear and informative title that sets the context for your presentation.
• State the primary objective or purpose of the presentation to provide a clear focus.

2. Key data points

• Present the most essential data points or findings that align with your objective.
• Use charts, graphical presentations or visuals to illustrate these key points for better comprehension.

3. Context and significance

• Provide a brief overview of the context in which the data was collected and why it’s significant.
• Explain how the data relates to the larger picture or the problem you’re addressing.

4. Key takeaways

• Summarize the main insights or conclusions that can be drawn from the data.
• Highlight the key takeaways that the audience should remember.

5. Visuals and charts

• Use clear and appropriate visual aids to complement the data.
• Ensure that visuals are easy to understand and support your narrative.

6. Implications or actions

• Discuss the practical implications of the data or any recommended actions.
• If applicable, outline next steps or decisions that should be taken based on the data.

7. Q&A and discussion

• Allocate time for questions and open discussion to engage the audience.
• Address queries and provide additional insights or context as needed.

Presenting data is a crucial skill in various professional fields, from business to academia and beyond. To ensure your data presentations hit the mark, here are some common mistakes that you should steer clear of:

Overloading with data

Presenting too much data at once can overwhelm your audience. Focus on the key points and relevant information to keep the presentation concise and focused. Here are some free data visualization tools you can use to convey data in an engaging and impactful way. 

Assuming everyone’s on the same page

It’s easy to assume that your audience understands as much about the topic as you do. But this can lead to either dumbing things down too much or diving into a bunch of jargon that leaves folks scratching their heads. Take a beat to figure out where your audience is coming from and tailor your presentation accordingly.

Misleading visuals

Using misleading visuals, such as distorted scales or inappropriate chart types can distort the data’s meaning. Pick the right data infographics and understandable charts to ensure that your visual representations accurately reflect the data.

Not providing context

Data without context is like a puzzle piece with no picture on it. Without proper context, data may be meaningless or misinterpreted. Explain the background, methodology and significance of the data.

Not citing sources properly

Neglecting to cite sources and provide citations for your data can erode its credibility. Always attribute data to its source and utilize reliable sources for your presentation.

Not telling a story

Avoid simply presenting numbers. If your presentation lacks a clear, engaging story that takes your audience on a journey from the beginning (setting the scene) through the middle (data analysis) to the end (the big insights and recommendations), you’re likely to lose their interest.

Infographics are great for storytelling because they mix cool visuals with short and sweet text to explain complicated stuff in a fun and easy way. Create one with Venngage’s free infographic maker to create a memorable story that your audience will remember.

Ignoring data quality

Presenting data without first checking its quality and accuracy can lead to misinformation. Validate and clean your data before presenting it.

Simplify your visuals

Fancy charts might look cool, but if they confuse people, what’s the point? Go for the simplest visual that gets your message across. Having a dilemma between presenting data with infographics v.s data design? This article on the difference between data design and infographics might help you out. 

Missing the emotional connection

Data isn’t just about numbers; it’s about people and real-life situations. Don’t forget to sprinkle in some human touch, whether it’s through relatable stories, examples or showing how the data impacts real lives.

Skipping the actionable insights

At the end of the day, your audience wants to know what they should do with all the data. If you don’t wrap up with clear, actionable insights or recommendations, you’re leaving them hanging. Always finish up with practical takeaways and the next steps.

Can you provide some data presentation examples for business reports?

Business reports often benefit from data presentation through bar charts showing sales trends over time, pie charts displaying market share,or tables presenting financial performance metrics like revenue and profit margins.

What are some creative data presentation examples for academic presentations?

Creative data presentation ideas for academic presentations include using statistical infographics to illustrate research findings and statistical data, incorporating storytelling techniques to engage the audience or utilizing heat maps to visualize data patterns.

What are the key considerations when choosing the right data presentation format?

When choosing a chart format , consider factors like data complexity, audience expertise and the message you want to convey. Options include charts (e.g., bar, line, pie), tables, heat maps, data visualization infographics and interactive dashboards.

Knowing the type of data visualization that best serves your data is just half the battle. Here are some best practices for data visualization to make sure that the final output is optimized. 

How can I choose the right data presentation method for my data?

To select the right data presentation method, start by defining your presentation’s purpose and audience. Then, match your data type (e.g., quantitative, qualitative) with suitable visualization techniques (e.g., histograms, word clouds) and choose an appropriate presentation format (e.g., slide deck, report, live demo).

For more presentation ideas , check out this guide on how to make a good presentation or use a presentation software to simplify the process.  

How can I make my data presentations more engaging and informative?

To enhance data presentations, use compelling narratives, relatable examples and fun data infographics that simplify complex data. Encourage audience interaction, offer actionable insights and incorporate storytelling elements to engage and inform effectively.

The opening of your presentation holds immense power in setting the stage for your audience. To design a presentation and convey your data in an engaging and informative, try out Venngage’s free presentation maker to pick the right presentation design for your audience and topic. 

What is the difference between data visualization and data presentation?

Data presentation typically involves conveying data reports and insights to an audience, often using visuals like charts and graphs. Data visualization , on the other hand, focuses on creating those visual representations of data to facilitate understanding and analysis. 

Now that you’ve learned a thing or two about how to use these methods of data presentation to tell a compelling data story , it’s time to take these strategies and make them your own. 

But here’s the deal: these aren’t just one-size-fits-all solutions. Remember that each example we’ve uncovered here is not a rigid template but a source of inspiration. It’s all about making your audience go, “Wow, I get it now!”

Think of your data presentations as your canvas – it’s where you paint your story, convey meaningful insights and make real change happen. 

So, go forth, present your data with confidence and purpose and watch as your strategic influence grows, one compelling presentation at a time.

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10 Methods of Data Presentation That Really Work in 2024

Leah Nguyen • 20 August, 2024 • 13 min read

Have you ever presented a data report to your boss/coworkers/teachers thinking it was super dope like you’re some cyber hacker living in the Matrix, but all they saw was a pile of static numbers that seemed pointless and didn't make sense to them?

Understanding digits is rigid . Making people from non-analytical backgrounds understand those digits is even more challenging.

How can you clear up those confusing numbers and make your presentation as clear as the day? Let's check out these best ways to present data. 💎

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Data Presentation - What Is It?

The term ’data presentation’ relates to the way you present data in a way that makes even the most clueless person in the room understand. 

Some say it’s witchcraft (you’re manipulating the numbers in some ways), but we’ll just say it’s the power of turning dry, hard numbers or digits into a visual showcase that is easy for people to digest.

Presenting data correctly can help your audience understand complicated processes, identify trends, and instantly pinpoint whatever is going on without exhausting their brains.

Good data presentation helps…

• Make informed decisions and arrive at positive outcomes . If you see the sales of your product steadily increase throughout the years, it’s best to keep milking it or start turning it into a bunch of spin-offs (shoutout to Star Wars👀).
• Reduce the time spent processing data . Humans can digest information graphically 60,000 times faster than in the form of text. Grant them the power of skimming through a decade of data in minutes with some extra spicy graphs and charts.
• Communicate the results clearly . Data does not lie. They’re based on factual evidence and therefore if anyone keeps whining that you might be wrong, slap them with some hard data to keep their mouths shut.
• Add to or expand the current research . You can see what areas need improvement, as well as what details often go unnoticed while surfing through those little lines, dots or icons that appear on the data board.

Methods of Data Presentation and Examples

Imagine you have a delicious pepperoni, extra-cheese pizza. You can decide to cut it into the classic 8 triangle slices, the party style 12 square slices, or get creative and abstract on those slices. 

There are various ways to cut a pizza and you get the same variety with how you present your data. In this section, we will bring you the 10 ways to slice a pizza - we mean to present your data - that will make your company’s most important asset as clear as day. Let's dive into 10 ways to present data efficiently.

#1 - Tabular 

Among various types of data presentation, tabular is the most fundamental method, with data presented in rows and columns. Excel or Google Sheets would qualify for the job. Nothing fancy.

This is an example of a tabular presentation of data on Google Sheets. Each row and column has an attribute (year, region, revenue, etc.), and you can do a custom format to see the change in revenue throughout the year.

When presenting data as text, all you do is write your findings down in paragraphs and bullet points, and that’s it. A piece of cake to you, a tough nut to crack for whoever has to go through all of the reading to get to the point.

• 65% of email users worldwide access their email via a mobile device.
• Emails that are optimised for mobile generate 15% higher click-through rates.
• 56% of brands using emojis in their email subject lines had a higher open rate.

(Source: CustomerThermometer )

All the above quotes present statistical information in textual form. Since not many people like going through a wall of texts, you’ll have to figure out another route when deciding to use this method, such as breaking the data down into short, clear statements, or even as catchy puns if you’ve got the time to think of them.

#3 - Pie chart

A pie chart (or a ‘donut chart’ if you stick a hole in the middle of it) is a circle divided into slices that show the relative sizes of data within a whole. If you’re using it to show percentages, make sure all the slices add up to 100%.

The pie chart is a familiar face at every party and is usually recognised by most people. However, one setback of using this method is our eyes sometimes can’t identify the differences in slices of a circle, and it’s nearly impossible to compare similar slices from two different pie charts, making them the villains in the eyes of data analysts.

#4 - Bar chart

The bar chart is a chart that presents a bunch of items from the same category, usually in the form of rectangular bars that are placed at an equal distance from each other. Their heights or lengths depict the values they represent.

They can be as simple as this:

Or more complex and detailed like this example of data presentation. Contributing to an effective statistic presentation, this one is a grouped bar chart that not only allows you to compare categories but also the groups within them as well.

#5 - Histogram

Similar in appearance to the bar chart but the rectangular bars in histograms don’t often have the gap like their counterparts.

Instead of measuring categories like weather preferences or favourite films as a bar chart does, a histogram only measures things that can be put into numbers.

Teachers can use presentation graphs like a histogram to see which score group most of the students fall into, like in this example above.

#6 - Line graph

Recordings to ways of displaying data, we shouldn't overlook the effectiveness of line graphs. Line graphs are represented by a group of data points joined together by a straight line. There can be one or more lines to compare how several related things change over time. 

On a line chart’s horizontal axis, you usually have text labels, dates or years, while the vertical axis usually represents the quantity (e.g.: budget, temperature or percentage).

#7 - Pictogram graph

A pictogram graph uses pictures or icons relating to the main topic to visualise a small dataset. The fun combination of colours and illustrations makes it a frequent use at schools.

Pictograms are a breath of fresh air if you want to stay away from the monotonous line chart or bar chart for a while. However, they can present a very limited amount of data and sometimes they are only there for displays and do not represent real statistics.

#8 - Radar chart

If presenting five or more variables in the form of a bar chart is too stuffy then you should try using a radar chart, which is one of the most creative ways to present data.

Radar charts show data in terms of how they compare to each other starting from the same point. Some also call them ‘spider charts’ because each aspect combined looks like a spider web.

Radar charts can be a great use for parents who’d like to compare their child’s grades with their peers to lower their self-esteem. You can see that each angular represents a subject with a score value ranging from 0 to 100. Each student’s score across 5 subjects is highlighted in a different colour.

If you think that this method of data presentation somehow feels familiar, then you’ve probably encountered one while playing Pokémon .

#9 - Heat map

A heat map represents data density in colours. The bigger the number, the more colour intensity that data will be represented.

Most US citizens would be familiar with this data presentation method in geography. For elections, many news outlets assign a specific colour code to a state, with blue representing one candidate and red representing the other. The shade of either blue or red in each state shows the strength of the overall vote in that state.

Another great thing you can use a heat map for is to map what visitors to your site click on. The more a particular section is clicked the ‘hotter’ the colour will turn, from blue to bright yellow to red.

#10 - Scatter plot

If you present your data in dots instead of chunky bars, you’ll have a scatter plot. 

A scatter plot is a grid with several inputs showing the relationship between two variables. It’s good at collecting seemingly random data and revealing some telling trends.

For example, in this graph, each dot shows the average daily temperature versus the number of beach visitors across several days. You can see that the dots get higher as the temperature increases, so it’s likely that hotter weather leads to more visitors.

5 Data Presentation Mistakes to Avoid

#1 - assume your audience understands what the numbers represent.

You may know all the behind-the-scenes of your data since you’ve worked with them for weeks, but your audience doesn’t.

Showing without telling only invites more and more questions from your audience, as they have to constantly make sense of your data, wasting the time of both sides as a result.

While showing your data presentations, you should tell them what the data are about before hitting them with waves of numbers first. You can use interactive activities such as polls , word clouds , online quizzes and Q&A sections , combined with icebreaker games , to assess their understanding of the data and address any confusion beforehand.

#2 - Use the wrong type of chart

Charts such as pie charts must have a total of 100% so if your numbers accumulate to 193% like this example below, you’re definitely doing it wrong.

Before making a chart, ask yourself: what do I want to accomplish with my data? Do you want to see the relationship between the data sets, show the up and down trends of your data, or see how segments of one thing make up a whole?

Remember, clarity always comes first. Some data visualisations may look cool, but if they don’t fit your data, steer clear of them. 

#3 - Make it 3D

3D is a fascinating graphical presentation example. The third dimension is cool, but full of risks.

Can you see what’s behind those red bars? Because we can’t either. You may think that 3D charts add more depth to the design, but they can create false perceptions as our eyes see 3D objects closer and bigger than they appear, not to mention they cannot be seen from multiple angles.

#4 - Use different types of charts to compare contents in the same category

This is like comparing a fish to a monkey. Your audience won’t be able to identify the differences and make an appropriate correlation between the two data sets. 

Next time, stick to one type of data presentation only. Avoid the temptation of trying various data visualisation methods in one go and make your data as accessible as possible.

#5 - Bombard the audience with too much information

The goal of data presentation is to make complex topics much easier to understand, and if you’re bringing too much information to the table, you’re missing the point.

The more information you give, the more time it will take for your audience to process it all. If you want to make your data understandable and give your audience a chance to remember it, keep the information within it to an absolute minimum. You should end your session with open-ended questions to see what your participants really think.

What are the Best Methods of Data Presentation?

Finally, which is the best way to present data?

The answer is…

There is none! Each type of presentation has its own strengths and weaknesses and the one you choose greatly depends on what you’re trying to do. 

For example:

• Go for a scatter plot if you’re exploring the relationship between different data values, like seeing whether the sales of ice cream go up because of the temperature or because people are just getting more hungry and greedy each day?
• Go for a line graph if you want to mark a trend over time. 
• Go for a heat map if you like some fancy visualisation of the changes in a geographical location, or to see your visitors' behaviour on your website.
• Go for a pie chart (especially in 3D) if you want to be shunned by others because it was never a good idea👇

Frequently Asked Questions

What is a chart presentation.

A chart presentation is a way of presenting data or information using visual aids such as charts, graphs, and diagrams. The purpose of a chart presentation is to make complex information more accessible and understandable for the audience.

When can I use charts for the presentation?

Charts can be used to compare data, show trends over time, highlight patterns, and simplify complex information.

Why should you use charts for presentation?

You should use charts to ensure your contents and visuals look clean, as they are the visual representative, provide clarity, simplicity, comparison, contrast and super time-saving!

What are the 4 graphical methods of presenting data?

Histogram, Smoothed frequency graph, Pie diagram or Pie chart, Cumulative or ogive frequency graph, and Frequency Polygon.

Leah Nguyen

Words that convert, stories that stick. I turn complex ideas into engaging narratives - helping audiences learn, remember, and take action.

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Present Your Data Like a Pro

• Joel Schwartzberg

Demystify the numbers. Your audience will thank you.

While a good presentation has data, data alone doesn’t guarantee a good presentation. It’s all about how that data is presented. The quickest way to confuse your audience is by sharing too many details at once. The only data points you should share are those that significantly support your point — and ideally, one point per chart. To avoid the debacle of sheepishly translating hard-to-see numbers and labels, rehearse your presentation with colleagues sitting as far away as the actual audience would. While you’ve been working with the same chart for weeks or months, your audience will be exposed to it for mere seconds. Give them the best chance of comprehending your data by using simple, clear, and complete language to identify X and Y axes, pie pieces, bars, and other diagrammatic elements. Try to avoid abbreviations that aren’t obvious, and don’t assume labeled components on one slide will be remembered on subsequent slides. Every valuable chart or pie graph has an “Aha!” zone — a number or range of data that reveals something crucial to your point. Make sure you visually highlight the “Aha!” zone, reinforcing the moment by explaining it to your audience.

With so many ways to spin and distort information these days, a presentation needs to do more than simply share great ideas — it needs to support those ideas with credible data. That’s true whether you’re an executive pitching new business clients, a vendor selling her services, or a CEO making a case for change.

• JS Joel Schwartzberg oversees executive communications for a major national nonprofit, is a professional presentation coach, and is the author of Get to the Point! Sharpen Your Message and Make Your Words Matter and The Language of Leadership: How to Engage and Inspire Your Team . You can find him on LinkedIn and X. TheJoelTruth

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A Guide to Effective Data Presentation

Key objectives of data presentation, charts and graphs for great visuals, storytelling with data, visuals, and text, audiences and data presentation, the main idea in data presentation, storyboarding and data presentation, additional resources, data presentation.

Tools for effective data presentation

Financial analysts are required to present their findings in a neat, clear, and straightforward manner. They spend most of their time working with spreadsheets in MS Excel, building financial models , and crunching numbers. These models and calculations can be pretty extensive and complex and may only be understood by the analyst who created them. Effective data presentation skills are critical for being a world-class financial analyst .

It is the analyst’s job to effectively communicate the output to the target audience, such as the management team or a company’s external investors. This requires focusing on the main points, facts, insights, and recommendations that will prompt the necessary action from the audience.

One challenge is making intricate and elaborate work easy to comprehend through great visuals and dashboards. For example, tables, graphs, and charts are tools that an analyst can use to their advantage to give deeper meaning to a company’s financial information. These tools organize relevant numbers that are rather dull and give life and story to them.

Here are some key objectives to think about when presenting financial analysis:

• Visual communication
• Audience and context
• Charts, graphs, and images
• Focus on important points
• Design principles
• Storytelling
• Persuasiveness

For a breakdown of these objectives, check out Excel Dashboards & Data Visualization course to help you become a world-class financial analyst.

Charts and graphs make any financial analysis readable, easy to follow, and provide great data presentation. They are often included in the financial model’s output, which is essential for the key decision-makers in a company.

The decision-makers comprise executives and managers who usually won’t have enough time to synthesize and interpret data on their own to make sound business decisions. Therefore, it is the job of the analyst to enhance the decision-making process and help guide the executives and managers to create value for the company.

When an analyst uses charts, it is necessary to be aware of what good charts and bad charts look like and how to avoid the latter when telling a story with data.

Examples of Good Charts

As for great visuals, you can quickly see what’s going on with the data presentation, saving you time for deciphering their actual meaning. More importantly, great visuals facilitate business decision-making because their goal is to provide persuasive, clear, and unambiguous numeric communication.

For reference, take a look at the example below that shows a dashboard, which includes a gauge chart for growth rates, a bar chart for the number of orders, an area chart for company revenues, and a line chart for EBITDA margins.

To learn the step-by-step process of creating these essential tools in MS Excel, watch our video course titled “ Excel Dashboard & Data Visualization .”  Aside from what is given in the example below, our course will also teach how you can use other tables and charts to make your financial analysis stand out professionally.

Learn how to build the graph above in our Dashboards Course !

Example of Poorly Crafted Charts

A bad chart, as seen below, will give the reader a difficult time to find the main takeaway of a report or presentation, because it contains too many colors, labels, and legends, and thus, will often look too busy. It also doesn’t help much if a chart, such as a pie chart, is displayed in 3D, as it skews the size and perceived value of the underlying data. A bad chart will be hard to follow and understand.

Aside from understanding the meaning of the numbers, a financial analyst must learn to combine numbers and language to craft an effective story. Relying only on data for a presentation may leave your audience finding it difficult to read, interpret, and analyze your data. You must do the work for them, and a good story will be easier to follow. It will help you arrive at the main points faster, rather than just solely presenting your report or live presentation with numbers.

The data can be in the form of revenues, expenses, profits, and cash flow. Simply adding notes, comments, and opinions to each line item will add an extra layer of insight, angle, and a new perspective to the report.

Furthermore, by combining data, visuals, and text, your audience will get a clear understanding of the current situation,  past events, and possible conclusions and recommendations that can be made for the future.

The simple diagram below shows the different categories of your audience.

  This chart is taken from our course on how to present data .

Internal Audience

An internal audience can either be the executives of the company or any employee who works in that company. For executives, the purpose of communicating a data-filled presentation is to give an update about a certain business activity such as a project or an initiative.

Another important purpose is to facilitate decision-making on managing the company’s operations, growing its core business, acquiring new markets and customers, investing in R&D, and other considerations. Knowing the relevant data and information beforehand will guide the decision-makers in making the right choices that will best position the company toward more success.

External Audience

An external audience can either be the company’s existing clients, where there are projects in progress, or new clients that the company wants to build a relationship with and win new business from. The other external audience is the general public, such as the company’s external shareholders and prospective investors of the company.

When it comes to winning new business, the analyst’s presentation will be more promotional and sales-oriented, whereas a project update will contain more specific information for the client, usually with lots of industry jargon.

Audiences for Live and Emailed Presentation

A live presentation contains more visuals and storytelling to connect more with the audience. It must be more precise and should get to the point faster and avoid long-winded speech or text because of limited time.

In contrast, an emailed presentation is expected to be read, so it will include more text. Just like a document or a book, it will include more detailed information, because its context will not be explained with a voice-over as in a live presentation.

When it comes to details, acronyms, and jargon in the presentation, these things depend on whether your audience are experts or not.

Every great presentation requires a clear “main idea”. It is the core purpose of the presentation and should be addressed clearly. Its significance should be highlighted and should cause the targeted audience to take some action on the matter.

An example of a serious and profound idea is given below.

To communicate this big idea, we have to come up with appropriate and effective visual displays to show both the good and bad things surrounding the idea. It should put emphasis and attention on the most important part, which is the critical cash balance and capital investment situation for next year. This is an important component of data presentation.

The storyboarding below is how an analyst would build the presentation based on the big idea. Once the issue or the main idea has been introduced, it will be followed by a demonstration of the positive aspects of the company’s performance, as well as the negative aspects, which are more important and will likely require more attention.

Various ideas will then be suggested to solve the negative issues. However, before choosing the best option, a comparison of the different outcomes of the suggested ideas will be performed. Finally, a recommendation will be made that centers around the optimal choice to address the imminent problem highlighted in the big idea.

This storyboard is taken from our course on how to present data .

To get to the final point (recommendation), a great deal of analysis has been performed, which includes the charts and graphs discussed earlier, to make the whole presentation easy to follow, convincing, and compelling for your audience.

CFI offers the Business Intelligence & Data Analyst (BIDA)® certification program for those looking to take their careers to the next level. To keep learning and developing your knowledge base, please explore the additional relevant resources below:

• Investment Banking Pitch Books
• Excel Dashboards
• Financial Modeling Guide
• Startup Pitch Book
• See all business intelligence resources
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Home Blog Design Chart vs. Graph: Understanding the Graphical Representation of Data

Chart vs. Graph: Understanding the Graphical Representation of Data

Presenters face a common question when representing data whether to pick graphs vs. charts. Which one do you need to visualize your information? Well, the short answer is: it depends.

The trick to confidently knowing if you need a chart or a graph when visualizing information is knowing how the two are related to each other hierarchically.

Did you know that all graphs are charts, but not all charts are graphs? The two terms are often used interchangeably but do have unique distinctions. Knowing these characteristics will help you create and present better data presentations .

Let’s go deeper into the semantics of it all.

Difference Between a Chart and a Graph

As a person that creates presentations, it’s important that you know the relationship between charts and graphs and how they support the visual and graphical representation of data. The differences between a chart and a graph lie in semantics and hierarchy.

In biology, we have systems of classification to understand the hierarchical relationship between animal genera, kingdoms, and species. In the same way, charts are graphs are two parts of a larger hierarchical classification system of data visualizations. 

Think of a tree diagram (or organizational chart) with a Data Visualization label at the top. The next tier underneath includes labels like charts , infographics , and dashboards. The next tier branches from these terms into their subcategories. Underneath charts, you find; graphs, flowcharts, organizational charts , Gantt charts, maps, and diagrams . 

Charts and graphs are related hierarchically. When you define the term “chart,” graphs are inherently included in that definition.

A chart is a visual representation of information or data. The purpose of a chart is to help viewers understand and analyze information easily with the help of visuals. Charts can be stand-alone visuals or grouped to create infographics, dashboards, and other more complex data visualizations. 

A graph is a chart that uses mathematical equations to visualize data and analyze relationships and trends. For a chart to be a graph—and not another type of chart—it must involve a mathematical analysis, generally using the x and y axis to plot data points. Common graphs use lines and bars to visualize data quantities, relationships, and trends. 

Edward Tufte, a pioneer in the field of data visualization, has profoundly influenced how we understand and present data. His principles of clarity, efficiency, and truthful communication in data presentation serve as foundational guidelines for creating more effective and insightful visual representations. Tufte advocates for the use of high-resolution data displays, the integration of words, numbers, and images into a single narrative, and the elimination of non-data ink to focus attention on the data itself.

Good design is clear thinking made visible, bad design is stupidity made visible. Edward Tufte

By applying Tufte’s principles to the design of charts and graphs, presenters can significantly enhance their ability to convey complex information in an accessible and engaging manner. Whether you’re working with PowerPoint, Google Slides, Microsoft Excel, Tableau, R or any other tool, incorporating Tufte’s insights can elevate the clarity of your data visualizations in presentations and book reports.

Types of Graphs & Types of Charts 

The tree diagram above shows the big picture: graphs are a subset of charts. Now let’s look at the details. 

Charts can be separated into two main categories; numerical and non-numerical. Graphs are the numerical type. Here’s an outline to help you visualize the hierarchy of these terms.

• Line graphs
• Gantt Charts
• Network Diagrams
• Venn Diagrams

When to use a Chart or Graph

This is not a question of choosing between a chart or a graph. Because inherently, If you choose a graph, you are also choosing to use a chart. So let’s rephrase it.

Question: When is a good time to use a chart or—more specifically—a graph while creating a presentation ?

Answer: If you want to steer away from blocks of text and visualize information across slides engagingly, then you should use a chart. 

Now the question is, which one? The chart you use depends on the nature of your data, so you’ll have to choose according to that.

Here are some questions to help make a decision:

• What type of data do you need to visualize?
• Is the data numerical (quantitative) or categorical (qualitative)?
• compare values or represent relationships between variables?
• show patterns or trends over time?
• show proportions or parts of a whole ?
• show distribution or frequency of data?
• highlight specific values or data points?
• What is the message you want to convey with the chart?
• What is the target audience , and what charts are they familiar with?
• What is the context, and how is it relevant to your audience?

How to create Charts and Graphs in PowerPoint and Google Slides?

Creating charts and graphs in PowerPoint and Google Slides is easy. Here are instructions to guide you.

How to add charts inside PowerPoint

1. Open your PowerPoint presentation and navigate to the slide where you want to add the chart.

2. In the top menu bar, click on Insert > Charts. The popup window will show you chart options, most of which are graphs. Select the type you want to use by selecting it and clicking ok.

3. Input the data in the worksheet that pops up for your graph. 

4. Customize the design with colors and fonts for the chart’s legend.

How to add charts inside Google Slides

1. Open your Google Slides presentation and navigate to the slide where you want to add the chart.

2. In the top menu bar, click on Insert > Charts. The dropdown offers four simple graphs; choose one by clicking on it. You can also insert a graph from a Google Sheet that you own or have access to. 

3. A default graph is placed into your slide, offering a prompt to edit the data in Sheets. For every new graph, Google creates a separate sheet for the data. The Sheet remains linked to the graph in the presentation unless you unlink it.

4. Customize the design with colors and fonts for the visualization and the legend.

You can browse more detailed information on how to make charts & graphs in Google Slides in this article on “ How to Make a Graph in Google Slides .”

A better way to add charts and graphs to PowerPoint and Google Slides

Yes, it’s easy to add charts and graphs to presentations in PowerPoint and Google Slides, but there’s a way to make them look better easily. 

Download chart and graph templates from SlideModel and give your presentations a more refined look. In the SlideModel template repository, you’ll find plenty of professionally designed charts. Most of which are compatible with both PowerPoint and Google Slides.

Start with a comprehensive slide deck template with various ppt charts and graphs to choose from. Or download individual ppt graph templates to mix and match freely.

You’ll also find charts—that aren’t graphs—like flowcharts, tree diagrams, bubble charts , and concept maps . 

After downloading from SlideModel, you can select either PowerPoint or Google Slides as your presentation design software, and customize the data, colors, and fonts to match the rest of your presentation.

How to Present Charts & Graphs

What is the purpose of graphs, charts, and diagrams? To help people understand and analyze information visually and effectively. We use them in presentations, reports, proposals, and other business communication.

The most efficient way to present charts and graphs is to follow this formula:

• Introduce the graph.
• Identify the variables.
• Highlight key info.
• Share conclusions.

This formula works for presentation slides and your speech during a keynote or talk.

Let’s break it down.

Introduce the Graph

Add a descriptive title at the top of the slide. The title must say exactly what the chart is about, and an optional subtitle to reinforce the message. While presenting, seamlessly say the chart’s name as you speak and tell a two-sentence story about it, like an elevator pitch.

Identify the variables

Identify the variables by using color, size, and location concerning the chart. While presenting, speak about each variable succinctly and mention its visual characteristic. For example, “As you can see, telephone calls—here in the blue line—have increased yearly.”

Highlight key info

Highlight key info using subtle and not-so-subtle design techniques to bring attention to specific areas of the chart. Use icons to visualize the most critical variables in the chart legend. Add noticeable colors and bigger fonts. During your talk, speak about these data points and then about the others as support. 

Share conclusions

Share conclusions in small text boxes below the chart legend. These don’t necessarily need to be in your slides, but they do need to appear in your speech. The conclusions are what bring everything full circle with the introduction.

Charts and Graphs in Action

All charts—including graphs—help you share information and data across presentations, reports , proposals , scientific posters , etc. 

Including charts and graphs in slides is a necessary step in creating analytic presentations, so why not maximize their efficiency by taking the time to customize them visually according to your brand or project?

Discover all the chart and graph options in the SlideModel template library. Browse flowcharts, Gantt charts, and all types of graphs for your slides. 

And remember, charts and graphs are different and related, all rolled into one.

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Business Presentations, Data Visualization, Presentation Approaches Filed under Design , Presentation Ideas

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Gdp up by 0.3% and employment up by 0.2% in the euro area, announcement.

Following recommendations for a harmonised European revision policy for national accounts and balance of payments , EU countries are carrying out a benchmark revision of their national accounts estimates in 2024. The purpose of this benchmark revision is to implement changes introduced by the amended ESA 2010 regulation , and to incorporate new data sources and other methodological improvements. Most of the revised quarterly and annual country data are expected to be released by Eurostat between June and October 2024, and will be progressively integrated in European estimates. The impact of these revisions is expected to be limited, but still noticeable for some European aggregates and more pronounced for certain Member States. For further details, please consult the available documentation on Eurostat’s website .

GDP growth in the euro area and EU

In the second quarter of 2024, seasonally adjusted GDP increased by 0.3% in both the euro area and the EU , compared with the previous quarter, according to a flash estimate published by Eurostat, the statistical office of the European Union . In the first quarter of 2024, GDP had also grown by 0.3% in both zones.

Compared with the same quarter of the previous year, seasonally adjusted GDP increased by 0.6% in the euro area and by 0.8% in the EU in the second quarter of 2024, after +0.5% in the euro area and +0.6% in the EU in the previous quarter.

During the second quarter of 2024, GDP in the United States increased by 0.7% compared to the previous quarter (after +0.4% in the first quarter of 2024 ). Compared with the same quarter of the previous year, GDP increased by 3.1% (after +2.9% in the previous quarter).

Employment growth in the euro area and EU

The number of employed persons increased by 0.2% in both the euro area and the EU in the second quarter of 2024, compared with the previous quarter. In the first quarter of 2024, employment had grown by 0.3% in both zones.

Compared with the same quarter of the previous year, employment increased by 0.8% in the euro area and by 0.7% in the EU in the second quarter of 2024, after +1.0% in the euro area and +0.9% in the EU in the first quarter of 2024 .

These data provide a picture of labour input consistent with the output and income measures of national accounts.

Notes for users

The reliability of GDP and employment flash estimates was tested by dedicated working groups and revisions of subsequent estimates are continuously monitored. Further information can be found on Eurostat website .

With these flash estimates, euro area and EU employment and GDP figures for earlier quarters are not revised.

The flash GDP estimates of the second quarter 2024 are based on Member States’ data covering 99% of the EA and the EU GDP, while flash employment estimates are based on Member States’ data covering 96% of the EA and 93% of the EU total employment.

A preliminary flash estimate of GDP growth was published in the News Release issued on 30 July 2024. This was based on GDP estimates for eighteen Member States.

The EA and EU estimates for the last quarter were revised as presented in the following table:

All figures presented in this release may be revised with Eurostat’s regular estimates of GDP and main aggregates (including employment) scheduled for 6 September 2024 and 18 October 2024, which will reflect the impact of countries’ benchmark revisions as available.

Release schedule

Comprehensive estimates of European main aggregates (including GDP and employment) are based on countries regular transmissions and published around 65 and 110 days after the end of each quarter. To improve the timeliness of key indicators, Eurostat also publishes flash estimates for GDP (after around 30 and 45 days) and employment (after around 45 days). Their compilation is based on estimates provided by EU Member States on a voluntary basis.

This news release presents flash estimates for euro area and EU GDP and employment growth after around 45 days.

Methods and definitions

European quarterly national accounts are compiled in accordance with the European System of Accounts 2010 (ESA 2010). They include key policy indicators of GDP and employment.

Gross domestic product (GDP) at market prices measures the production activity of resident production units. Growth rates are based on chain-linked volumes.

Employment covers employees and self-employed working in resident production units (domestic concept). While employment flash estimates are limited to total employment in persons, regular estimates also cover hours worked and industry breakdowns.

The method used for compilation of European GDP and employment estimates is the same as for previous releases.

Geographical information

Euro area (EA20): Belgium, Germany, Estonia, Ireland, Greece, Spain, France, Croatia, Italy, Cyprus, Latvia, Lithuania, Luxembourg, Malta, the Netherlands, Austria, Portugal, Slovenia, Slovakia and Finland.

European Union (EU27): Belgium, Bulgaria, Czechia, Denmark, Germany, Estonia, Ireland, Greece, Spain, France, Croatia, Italy, Cyprus, Latvia, Lithuania, Luxembourg, Hungary, Malta, the Netherlands, Austria, Poland, Portugal, Romania, Slovenia, Slovakia, Finland and Sweden.

For more information   

Website section on national accounts , notably information on European GDP and employment estimates

Database section on national accounts and metadata on quarterly national accounts

Statistics Explained articles on measuring quarterly GDP and presentation of updated quarterly estimates

Country specific metadata

Country specific metadata on the recording of Ukrainian refugees in main aggregates of national accounts

European System of Accounts 2010

Euro indicators dashboard

Release calendar for Euro indicators

European Statistics Code of Practice                                                                                                                             

Get in touch

Media requests

Eurostat Media Support

Phone: (+352) 4301 33 408

E-mail: [email protected]

Further information on data

Thierry COURTEL (GDP)

E-mail: [email protected]

Véronique DENEUVILLE (Employment)

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