data representation on graphs

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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17 Data Visualization Techniques All Professionals Should Know

• 17 Sep 2019

There’s a growing demand for business analytics and data expertise in the workforce. But you don’t need to be a professional analyst to benefit from data-related skills.

Becoming skilled at common data visualization techniques can help you reap the rewards of data-driven decision-making , including increased confidence and potential cost savings. Learning how to effectively visualize data could be the first step toward using data analytics and data science to your advantage to add value to your organization.

Several data visualization techniques can help you become more effective in your role. Here are 17 essential data visualization techniques all professionals should know, as well as tips to help you effectively present your data.

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What Is Data Visualization?

Data visualization is the process of creating graphical representations of information. This process helps the presenter communicate data in a way that’s easy for the viewer to interpret and draw conclusions.

There are many different techniques and tools you can leverage to visualize data, so you want to know which ones to use and when. Here are some of the most important data visualization techniques all professionals should know.

Data Visualization Techniques

The type of data visualization technique you leverage will vary based on the type of data you’re working with, in addition to the story you’re telling with your data .

Here are some important data visualization techniques to know:

• Gantt Chart
• Box and Whisker Plot
• Waterfall Chart
• Scatter Plot
• Pictogram Chart
• Highlight Table
• Bullet Graph
• Choropleth Map
• Network Diagram
• Correlation Matrices

1. Pie Chart

Pie charts are one of the most common and basic data visualization techniques, used across a wide range of applications. Pie charts are ideal for illustrating proportions, or part-to-whole comparisons.

Because pie charts are relatively simple and easy to read, they’re best suited for audiences who might be unfamiliar with the information or are only interested in the key takeaways. For viewers who require a more thorough explanation of the data, pie charts fall short in their ability to display complex information.

2. Bar Chart

The classic bar chart , or bar graph, is another common and easy-to-use method of data visualization. In this type of visualization, one axis of the chart shows the categories being compared, and the other, a measured value. The length of the bar indicates how each group measures according to the value.

One drawback is that labeling and clarity can become problematic when there are too many categories included. Like pie charts, they can also be too simple for more complex data sets.

3. Histogram

Unlike bar charts, histograms illustrate the distribution of data over a continuous interval or defined period. These visualizations are helpful in identifying where values are concentrated, as well as where there are gaps or unusual values.

Histograms are especially useful for showing the frequency of a particular occurrence. For instance, if you’d like to show how many clicks your website received each day over the last week, you can use a histogram. From this visualization, you can quickly determine which days your website saw the greatest and fewest number of clicks.

4. Gantt Chart

Gantt charts are particularly common in project management, as they’re useful in illustrating a project timeline or progression of tasks. In this type of chart, tasks to be performed are listed on the vertical axis and time intervals on the horizontal axis. Horizontal bars in the body of the chart represent the duration of each activity.

Utilizing Gantt charts to display timelines can be incredibly helpful, and enable team members to keep track of every aspect of a project. Even if you’re not a project management professional, familiarizing yourself with Gantt charts can help you stay organized.

5. Heat Map

A heat map is a type of visualization used to show differences in data through variations in color. These charts use color to communicate values in a way that makes it easy for the viewer to quickly identify trends. Having a clear legend is necessary in order for a user to successfully read and interpret a heatmap.

There are many possible applications of heat maps. For example, if you want to analyze which time of day a retail store makes the most sales, you can use a heat map that shows the day of the week on the vertical axis and time of day on the horizontal axis. Then, by shading in the matrix with colors that correspond to the number of sales at each time of day, you can identify trends in the data that allow you to determine the exact times your store experiences the most sales.

6. A Box and Whisker Plot

A box and whisker plot , or box plot, provides a visual summary of data through its quartiles. First, a box is drawn from the first quartile to the third of the data set. A line within the box represents the median. “Whiskers,” or lines, are then drawn extending from the box to the minimum (lower extreme) and maximum (upper extreme). Outliers are represented by individual points that are in-line with the whiskers.

This type of chart is helpful in quickly identifying whether or not the data is symmetrical or skewed, as well as providing a visual summary of the data set that can be easily interpreted.

7. Waterfall Chart

A waterfall chart is a visual representation that illustrates how a value changes as it’s influenced by different factors, such as time. The main goal of this chart is to show the viewer how a value has grown or declined over a defined period. For example, waterfall charts are popular for showing spending or earnings over time.

8. Area Chart

An area chart , or area graph, is a variation on a basic line graph in which the area underneath the line is shaded to represent the total value of each data point. When several data series must be compared on the same graph, stacked area charts are used.

This method of data visualization is useful for showing changes in one or more quantities over time, as well as showing how each quantity combines to make up the whole. Stacked area charts are effective in showing part-to-whole comparisons.

9. Scatter Plot

Another technique commonly used to display data is a scatter plot . A scatter plot displays data for two variables as represented by points plotted against the horizontal and vertical axis. This type of data visualization is useful in illustrating the relationships that exist between variables and can be used to identify trends or correlations in data.

Scatter plots are most effective for fairly large data sets, since it’s often easier to identify trends when there are more data points present. Additionally, the closer the data points are grouped together, the stronger the correlation or trend tends to be.

10. Pictogram Chart

Pictogram charts , or pictograph charts, are particularly useful for presenting simple data in a more visual and engaging way. These charts use icons to visualize data, with each icon representing a different value or category. For example, data about time might be represented by icons of clocks or watches. Each icon can correspond to either a single unit or a set number of units (for example, each icon represents 100 units).

In addition to making the data more engaging, pictogram charts are helpful in situations where language or cultural differences might be a barrier to the audience’s understanding of the data.

11. Timeline

Timelines are the most effective way to visualize a sequence of events in chronological order. They’re typically linear, with key events outlined along the axis. Timelines are used to communicate time-related information and display historical data.

Timelines allow you to highlight the most important events that occurred, or need to occur in the future, and make it easy for the viewer to identify any patterns appearing within the selected time period. While timelines are often relatively simple linear visualizations, they can be made more visually appealing by adding images, colors, fonts, and decorative shapes.

12. Highlight Table

A highlight table is a more engaging alternative to traditional tables. By highlighting cells in the table with color, you can make it easier for viewers to quickly spot trends and patterns in the data. These visualizations are useful for comparing categorical data.

Depending on the data visualization tool you’re using, you may be able to add conditional formatting rules to the table that automatically color cells that meet specified conditions. For instance, when using a highlight table to visualize a company’s sales data, you may color cells red if the sales data is below the goal, or green if sales were above the goal. Unlike a heat map, the colors in a highlight table are discrete and represent a single meaning or value.

13. Bullet Graph

A bullet graph is a variation of a bar graph that can act as an alternative to dashboard gauges to represent performance data. The main use for a bullet graph is to inform the viewer of how a business is performing in comparison to benchmarks that are in place for key business metrics.

In a bullet graph, the darker horizontal bar in the middle of the chart represents the actual value, while the vertical line represents a comparative value, or target. If the horizontal bar passes the vertical line, the target for that metric has been surpassed. Additionally, the segmented colored sections behind the horizontal bar represent range scores, such as “poor,” “fair,” or “good.”

14. Choropleth Maps

A choropleth map uses color, shading, and other patterns to visualize numerical values across geographic regions. These visualizations use a progression of color (or shading) on a spectrum to distinguish high values from low.

Choropleth maps allow viewers to see how a variable changes from one region to the next. A potential downside to this type of visualization is that the exact numerical values aren’t easily accessible because the colors represent a range of values. Some data visualization tools, however, allow you to add interactivity to your map so the exact values are accessible.

15. Word Cloud

A word cloud , or tag cloud, is a visual representation of text data in which the size of the word is proportional to its frequency. The more often a specific word appears in a dataset, the larger it appears in the visualization. In addition to size, words often appear bolder or follow a specific color scheme depending on their frequency.

Word clouds are often used on websites and blogs to identify significant keywords and compare differences in textual data between two sources. They are also useful when analyzing qualitative datasets, such as the specific words consumers used to describe a product.

16. Network Diagram

Network diagrams are a type of data visualization that represent relationships between qualitative data points. These visualizations are composed of nodes and links, also called edges. Nodes are singular data points that are connected to other nodes through edges, which show the relationship between multiple nodes.

There are many use cases for network diagrams, including depicting social networks, highlighting the relationships between employees at an organization, or visualizing product sales across geographic regions.

17. Correlation Matrix

A correlation matrix is a table that shows correlation coefficients between variables. Each cell represents the relationship between two variables, and a color scale is used to communicate whether the variables are correlated and to what extent.

Correlation matrices are useful to summarize and find patterns in large data sets. In business, a correlation matrix might be used to analyze how different data points about a specific product might be related, such as price, advertising spend, launch date, etc.

Other Data Visualization Options

While the examples listed above are some of the most commonly used techniques, there are many other ways you can visualize data to become a more effective communicator. Some other data visualization options include:

• Bubble clouds
• Circle views
• Dendrograms
• Dot distribution maps
• Open-high-low-close charts
• Polar areas
• Radial trees
• Ring Charts
• Sankey diagram
• Span charts
• Streamgraphs
• Wedge stack graphs
• Violin plots

Tips For Creating Effective Visualizations

Creating effective data visualizations requires more than just knowing how to choose the best technique for your needs. There are several considerations you should take into account to maximize your effectiveness when it comes to presenting data.

Related : What to Keep in Mind When Creating Data Visualizations in Excel

One of the most important steps is to evaluate your audience. For example, if you’re presenting financial data to a team that works in an unrelated department, you’ll want to choose a fairly simple illustration. On the other hand, if you’re presenting financial data to a team of finance experts, it’s likely you can safely include more complex information.

Another helpful tip is to avoid unnecessary distractions. Although visual elements like animation can be a great way to add interest, they can also distract from the key points the illustration is trying to convey and hinder the viewer’s ability to quickly understand the information.

Finally, be mindful of the colors you utilize, as well as your overall design. While it’s important that your graphs or charts are visually appealing, there are more practical reasons you might choose one color palette over another. For instance, using low contrast colors can make it difficult for your audience to discern differences between data points. Using colors that are too bold, however, can make the illustration overwhelming or distracting for the viewer.

Related : Bad Data Visualization: 5 Examples of Misleading Data

Visuals to Interpret and Share Information

No matter your role or title within an organization, data visualization is a skill that’s important for all professionals. Being able to effectively present complex data through easy-to-understand visual representations is invaluable when it comes to communicating information with members both inside and outside your business.

There’s no shortage in how data visualization can be applied in the real world. Data is playing an increasingly important role in the marketplace today, and data literacy is the first step in understanding how analytics can be used in business.

Are you interested in improving your analytical skills? Learn more about Business Analytics , our eight-week online course that can help you use data to generate insights and tackle business decisions.

This post was updated on January 20, 2022. It was originally published on September 17, 2019.

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80 types of charts & graphs for data visualization (with examples).

Ask any dataviz expert and they will tell you there aren’t many things as annoying as the wrong use of data visualizations. Well, duh. It’s easy to say if your job is to know all about it. But what about the rest of us? What about those who don’t make a face when they look at a simple pie chart? How do we know when to pick the right chart type and avoid disapproval from the entire community of dataviz geeks and lovers?

First and foremost, ask yourself what is it you actually want to show and who is your audience? Sounds simple, I know. But remember, you can’t please everyone. And sometimes, a pie chart is really fine. We don’t hate pie charts and actually, there are cases when they’re quite appropriate charts to use to communicate data. 

Yes, you can try to explore variations and alternatives to different chart types, it is encouraged. But before you gather all of your data and start creating beautiful graphs and visualizations, take a step back for a second and think. Who do you want to show your data to? Are the viewers equally knowledgeable about dataviz best practices? It’s very likely that you just want to present your information to someone who needs to easily understand it.

For this reason, it’s equally important to consider the right type of data visualization for you.

Read this article if you want to learn about the way you can display your data and how to tell your data story to your specific audience.

Now, if you want to include different charts and graphs in your final product, it’s a great next step to explore your options. There are many, many chart types and we won’t be able to cover all of them. In this article, we will show you some of the most important charts that can effectively convey a message and communicate your data, creating engaging data storytelling for your readers. Below, you might find charts you are familiar with and some that are less common. Either way, we hope you explore all chart types and find the most suitable ones for you and your data visualization project. The list consists of eighty types of charts and graphs, many of which you can create online for free with Datylon Online , or with our chart maker plug-in Datylon for Illustrator .

We divided the charts below into six categories that vary per use case. Sometimes, some of the charts can fall under multiple categories, so to make it easier, we only listed them once.

1. Comparison

Alternative name: Bar graph

One of the most common chart types out there. A bar chart is a set of rectangles with a length proportional to the values it represents. Each rectangle – the bar, is a representation of one category. Bar charts are great for comparison. The differences in bar length are easier to perceive, than, for example, differences in size and color.

Bar charts are commonly used charts due to their simplicity. Viewers mostly need to decode their bars' length and position, making bar charts very easy to understand. The general public is fairly capable of reading bar charts, so no additional dataviz expertise is necessary. For this reason, bar charts are doing their job really well. That's why, if the data structure and the actual message you're trying to convey allow for it, you should consider using bar charts in your data visualization.

It’s worth noting that to be really correct, bar charts display the bars horizontally. If you turn them 90 degrees, you will get a column chart. But, remember that long labels don’t suit column charts because of easy overlapping. You don’t have that issue in a bar chart.

If you want to improve your dataviz skills and design the best bar chart, we recommend you read this article about bar charts . But you can also check our bar chart resource page and discover even more pro design tips. You can also find some bar chart examples on our inspiration page .

Column chart

Alternative names: Column graph , Vertical bar chart

Long story short, you can say that a column chart is the same thing as a bar chart, turned by 90 degrees. Indeed, a column chart is a type of chart that resembles a bar graph with bars positioned vertically. They are often considered the same type of chart but from the dataviz point of view, that’s wrong. The main difference between a column chart and a bar chart is in the usage of categorical labels. Long labels don't suit column charts because of easy overlapping. But it might be useful if the labels are short and don’t take up a lot of horizontal space. Still, when it comes to design recommendations, you can use our bar chart resource page to learn how to greatly improve the readability of your column chart as well. You can find column chart examples on the inspiration page .

Grouped bar/column chart

Alternative names: Paired bar/column chart , Clustered bar/column chart

Made with Datylon - Edit

A grouped bar chart (or a grouped column chart if the bars are positioned vertically) is a multi-series variation of a bar/column chart where every category is represented by several columns communicating different aspects of the main category. Columns of each category are separated from the other categories using spacing. We use this type of chart to compare multiple series. Opposite to a basic bar chart, which doesn’t require any data to be formatted, to create a grouped bar/column chart, the data must be first organized. You can find more grouped bar chart examples on inspiration page .

Lollipop chart

Alternative name: Lollipop plot

A lollipop chart can be a sweet alternative to a regular bar chart if you are dealing with a lot of categories and want to make optimal use of space. It shows the relationship between a numeric and a categorical variable. This type of chart consists of a line, which represents the magnitude, and ends with a dot, or a circle, which highlights the data value. So it probably suffices to say that it is designed to resemble a bunch of lollipops. You can find more examples of lollipop charts on inspiration page .

Bullet chart

Alternative name: Bullet graph

A bullet chart is a type of chart designed to benchmark against a target value and ranges. It’s a very space-efficient chart used primarily for displaying performance data. Visually, bullet charts resemble a combination of bar/column charts and progress bars. The results are shown in a single bar or column. The ranges bar is constructed based on values from a category that comparison will be based on (for example competitor sales figures). All these values are then divided into a certain number of sub-ranges (in most cases it’s quartiles). Target shows the value which is aimed for. And the bar shows the actual figures. You can find more examples of bullet charts on inspiration page .

Alternative name: Dot chart

A dot plot (shows one or more quantitative values per category by plotting one or more dots per category on a numerical (or date-time) axis. A dot plot with only one value per category makes a comparison between those categories very easy. When the dot plot has multiple values per category, you can also compare within the categories. This results in a chart type that packs a lot of information in a small space. This chart may need gridlines that turn a dot plot into a chart with a proper context. We wrote a very interesting article about dot plots.

Make sure to also check our dot plot resource page and discover pro design tips. You can find more examples of dot plot on inspiration page .

Alternative names: Dumbbell plot , Dumbbell chart , Connected dot plot , Dumbbell dot plot , DNA chart , Barbell chart

A dumbbell is a type of dot plot with two connected values per category. Use it when you want to emphasize the delta (change) between the two values (data points, i.e. two points in time) and to compare and visualize this size in a difference between these two values across all categories. A dumbbell consists of dots (or circles) and connectors (or lines). Not adding marks and only leaving the connector makes it a range chart. We mentioned dumbbells throughout deep dive article about dot plots . You can find more examples of dumbbell charts on inspiration page .

Alternative names: Pictorial chart , Proportional unit chart , Picture graph

A pictogram chart is a type of chart that uses icons or symbols, or even small images, to represent data. Each of these icons corresponds to a certain category. Pictogram charts to some extent resemble bar charts, but instead of using a bar, they show icons. Some data visualization experts might argue this type of chart is very basic, to the point that it’s widely used in schools and kindergarten. While this is true, it’s also very important to keep in mind that using a pictogram chart helps overcome language barriers and it’s really easy to interpret. Moreover, it makes your data story memorable!

Alternative name: Proportional area chart

An icon chart will be a perfect choice if the position of the marks is not driven by data. Values can be bound to the color and size of the icons. The icon chart uses area rather than length to visualize values, which allows it to display a larger range of values in a compact way. But keep in mind, if you’re planning to use an icon chart in your visualization, it’s important to use the area and not the radius to present your value. This helps better compare the icons visually, as the difference between the categories will be much bigger if you use the radius. This will be misleading to your readers. See other icon chart examples on the inspiration pagehere .

Alternative name: Range chart

A range plot sometimes looks like a bar chart. The difference is that a range plot shows two values of a category, instead of just one. A range plot shows two points with a connecting line between them. This line indicates the difference, or a gap, between these points and suggests a direction of such change. So using this type of chart is great if you want to highlight this difference, rather than the values themselves. A use case example is any sort of demographical gap, i.e. gender pay gap. See examples of similar charts on our inspiration page .

Radial bar chart

Alternative name: Circular bar chart

A radial bar chart is simply a variation of a regular bar chart with the main difference being the circular shape of the chart. The chart itself is plotted on what is called a polar coordinates system. It means that each bar appears in a circle. The larger the value, the longer the bar. What's really great about radial bar charts is they are really beautiful, even impressive charts that can be used to compare key metrics in your data. The challenge that comes with using radial bar charts is that they're not the easiest to interpret. Some websites refer to radial bar charts as multilayered donut charts or multi-level doughnut charts but it's worth pointing out that it's not the same type of chart. You can find more details about this chart type on Data Viz Project .

Parallel coordinates

Alternative names: Parallel plot , Parallel coordinates plot

The parallel coordinates chart resembles a line chart, but instead of time values, categories are plotted on the horizontal axis. It allows you to plot a multitude of categories/dimensions without compromising the readability in a simple 2d space - all of the dimensions follow the same pattern. A dimension can have both a separate axis or just one of the gridlines if all the dimensions share the same data range. The simplicity of the chart, however, adds some limitations. Maximum two neighboring dimensions relationships can be followed at a time, so the ordering plays a crucial role in this chart.

Radar chart

Alternative names: Spider chart , Spider graph , Web chart , Spider web chart , Star chart , Star plot , Cobweb chart , Irregular polygon , Kiviat diagram

A radar chart shows a comparison between multiple data points or groups (minimum of three). It consists of several axes, all coming from the same point in the center (which resembles a spider web). Although it’s a very interesting chart to use, it’s important to keep in mind that it is harder to read. As it is designed in a circular fashion, it requires extra visual perception, in contrast to the more common linear types of charts and graphs. It is often easier to replace it with another type of chart. If all axes in your chart have the same scale, then a bar chart or sometimes a lollipop will suffice. If the axes have a different scale, it’s good to use parallel coordinates.

Nightingale chart

Alternative names: Nightingale's graph , Nightingale rose chart , Rose diagram , Coxcomb chart ,  Polar area chart

This chart is visually similar to a pie chart, but a Nightingale chart does not communicate a part-to-whole relationship. It compares values between categories like a bar chart does, only this one is radial.

Waterfall chart

Alternative names: Flying bricks chart , Mario chart , Bridge chart , Cascade chart

A waterfall chart is a type of graph that usually shows positive and negative values of change between two points, which helps in understanding the cumulative effect of these changes (so the net change). This chart does not only look at the starting value and the ending value of your data set but also visualized each individual positive or negative change that happened. As you can imagine, this type of chart is quite useful in financial sectors or human resources, but also in other industries (think of inventories, revenue tracking, etc.). Last but not least, the waterfall chart takes its name from the fact it looks like a waterfall. In the chart, the first value (column) typically starts from the baseline of zero, as does the ending value. They are connected by a number of seemingly floating shorter bars (that represent the said changes). The whole shape of the chart resembles then a waterfall.

Matrix chart

Alternative name: Matrix diagram

A matrix chart is a very common type of chart that helps in visualizing the relationship between two or more variables in a data set. Specifically, it shows the presence and strengths of such relationships and it does so in a grid format. It can have six different forms (shapes) depending on how many groups must be compared (L, T, Y, X, C, R, and roof-shaped). This chart usually presents a huge amount of data, so its visual display is limited. A matrix chart is very suitable for (but not limited to) project managers.

Small multiples

Alternative name: Trellis chart , Lattice chart , Panel chart

Unlike all the other graphs in this article, Small multiples are more of a visualization concept than a graph itself. That is because Small multiples use the same type of chart in it and multiply it within a grid to show different slices of the data set. The main advantage of using small multiples is the possibility of showing three or (usually) more variables presenting different values in the same graph without confusing your audience. If you go for this type of data visualization, make sure not to apply multiple colors in the charts as it might decrease the readability. You can find more Small multiples examples on our inspiration page .

Alternative name: Tag cloud , word collage , wordle

A word cloud is not a typical type of chart but it deserves its place in this list as it still is an instrument used to visualize qualitative (text) data. A word cloud is nothing more than a visual cluster of different words which vary in size accordingly to their frequency within the data set. In other words, the more often a certain word (or a keyword) appears in the text, the bigger (and perhaps bolder) it will be in a cloud. This type of chart is quite common across so many industries and segments. It can be a great visualization tool for students working on their dissertation who want to analyze their interviews. But just so you know, there are much more creative ways to show qualitative data.

Slope chart

Alternative name: Slopegraph

A slope chart is a chart that emphasizes the evolution between two values by using the angle of the slope to communicate the difference. It can be a change over time or a transition. A slope chart can be a good alternative for a line chart, grouped- or stacked bar chart, if we only have two points in time we want to address. See other slope chart examples See other slope chart examples on inspiration page .

Table chart

A table chart is a chart that helps visually represent data that is arranged in rows and columns. Throughout all forms of communication and research, tables are used extensively to store, analyze, compare, and present data.

Categorical scatter plot

A categorical scatter plot differs from a regular scatter plot by the presence of a categorical axis. It can be just one categorical axis or both of them. A categorical scatter plot can be quite similar to a dot plot. See other scatter plot examples See other scatter plot examples on our inspiration page .

Quadrant chart

Alternative names: matrix diagram , matrix chart , 4-quadrant matrix chart

A quadrant chart is very similar to a scatter plot but it’s divided into four equal parts (quadrants) in a 2x2 matrix. It is useful if we want to group distinctly data marks for some specific type of analysis. One of the best and most well-known examples of using the quadrant chart is for a SWOT analysis.

2. Correlation (relational)

Alternative names: Heat map , Heat table , Density table

A heatmap shows data variances, such as patterns, trends, and correlations. It does this by using color, hue, or intensity, as well as data labels, as a direct representation of the values. By adding a date or a time scale on the x-axis it shows how the values evolve over time. The data in a heatmap is structured as a table. Using a heatmap as a chart lets you explore the data and gives hints on where to look for outliers, other viewpoints, or specific angles. If you would like to explore the fascinating world of heatmaps, we definitely recommend you this article.

Also, make sure to check our heatmap resource page and discover pro tips on how to design the best heatmap chart yourself. You can find more heatmaps examples on the inspiration page.

Bubble chart

Alternative name: Bubble plot

Deriving from a scatter plot, a bubble chart is a chart that looks at a relation between three (numeric) variables. Two of those variables are represented by dots located between axes. The third value is represented by the size of a bubble. But with some expansions, a bubble chart can represent up to seven variables at once. But as it’s very easy to overwhelm a reader with too much information, it’s better not to plot too many variables. Being really popular among researchers and analysts, a bubble chart is also a chart with one of the best data/space ratios. One of the most interesting things about bubble charts is that they can be colored in many different ways. Make sure to check out a blog post taking a closer look at bubble charts .

Also, refer to our bubble chart resource page and discover pro tips on how to design the best bubble chart yourself. And if you want to see other bubble chart examples, find them on the inspiration page .

Scatter plot

Alternative names: Scatterplot , Scatter chart , Scattergram , Scatter diagram , Scatter graph

A scatter plot shows values for two numerical variables by plotting them as dots between horizontal and vertical axes. Simple one-sized data marks give a clear view of every observation’s positioning in a two-variable plane. A scatter plot is often used to show correlations between numeric variables and identify patterns. Being a swiss knife among the charts, a scatter plot is usually the first one for data exploration. It is a chart with one of the best data/space ratios. A scatter plot is also known for its versatility. It gives a lot of inspiration to infographic designers and data visualization specialists. It can be turned into almost any chart: heatmap, dot plot, icon chart, tilemap, or some hybrid chart. On the inspiration page you will find more scatter plot examples .

Connected scatter plot

Once upon a time, a line chart fell in love with a scatter plot. Were they to have a baby, it would look exactly like a connected scatter plot. This type of chart consists of a scatter plot with two variables and a line drawn between the dots in a continuous path. See other scatter plot examples on the inspiration page .

Hexagonal binning

Alternative names: hexagonal plot , hexagonal bin plot

A hexagonal binning is a method that uses hexagons in order to show the density of the data points. It is a good alternative to a scatter plot if the data gets too dense to interpret. The hexagons are binned into the area of the chart, and the color or hue (color intensity) is assigned accordingly to the number of observations it covers.

Contour plot

A contour plot allows you to visualize three-dimensional data in a two-dimensional plot/plane. Contour plots are typically used in cartography, as their contour lines can nicely indicate elevations. But they can also be used in meteorology, astrology, and similar scientific fields, where the contour lines would represent density or temperature.

3. Part-to-whole & hierarchical

Stacked bar chart & stacked column chart.

Being a variant of a bar chart (or a column chart, if plotted vertically), a stacked bar/column chart shows a relation of stacks to the whole bar or column and relations between whole bars/columns. The whole bar/column can be also presented as 100%. In this case, the stacks show a relative part to the whole bar/column in percentages. You can find more examples of bar chart on inspiration page .  

Diverging (stacked) bar/column chart

A diverging bar chart (or, if plotted vertically, a diverging column chart) is a chart that resembles a regular bar chart. However, a crucial difference is a baseline located in the middle (usually corresponding to a zero) and the bars extending to both sides of this midpoint. Often used to display results of a questionnaire or a survey, but definitely not limited to this use case, as seen in the example above. In a diverging bar chart, we use contrasting colors to show the categories being compared. A very common variation of this chart is called a ‘diverging stacked bar chart’, which adds additional segments. In other words, it’s very similar to a regular stacked bar chart but with an extra baseline in the middle. But a diverging stacked bar is a very good alternative to a stacked bar chart since it is easier to compare the stacks with it. That is because the stacks here share the same baseline, which makes comparison much easier. See more variations of bar charts on inspiration page .

Population pyramid

Alternative names: Age-sex pyramid , Age structure diagram

Very similar to a diverging bar chart, a population pyramid is a type of chart that specifically visualizes the age and gender distribution across populations. Typically used by demographers, population pyramids can be a very simple and nice addition to many reports. You can find other bar chart examples You can find other bar chart examples on the inspiration page .

Alternative name: Pictograph

An icon array is a graph that clearly visualizes a proportion of a unit. Icon arrays use a matrix of icons, usually a 100. Each one of those icons represents a unit of something (i.e. people). A portion of the icons is then colored to represent a numerical value in our data. The rest of the icons can be greyed out or even absent. A very common type of graph, icon arrays are extremely easy to interpret. You can see more icon array examples You can find more icon array examples on inspiration page .

Waffle chart

Alternative names: Square pie chart , Square area chart , Gridplot

A waffle chart is very similar to an icon array. However, instead of using different icons, it consists of a grid of 100 square (or even round) cells. Each cell represents 1%. This grid pattern typically displays progress towards a target (or a completion percentage) but can be also used to show parts-to-whole contribution. Waffle charts are often called a square alternative to a pie chart and are very easy to interpret. And they do look like waffles. See examples of similar charts on inspiration page .

Alternative names: Pie graph , Pizza chart, Circle chart

Arguably the most popular type of chart, a pie chart is a circular graph that visualizes a part-to-whole relationship. It shows how the data is divided into categories with a certain value (the slices), but it always keeps the link between the value of one category and the total sum of those categories (the pie). This means that the slices should add up to a logical sum. If the data is in percentages, the total should round up to a hundred. If the data is in absolute values, for example in dollars, the categories should form a meaningful total. A pie chart works best with only a few categories, otherwise, the chart becomes an unreadable clutter. It is also very suitable when one category is very big or very small compared to the other categories. Pie charts are often ridiculed by dataviz specialists. Read the deep dive pie chart article to see our arguments for using pie charts. And if you want to create a really good pie chart yourself, don’t miss out on the pie chart resource page full of pro design tips. Also you can find more pie chart examples on inspiration page .

Donut chart

Alternative names: Doughnut chart

A donut chart is practically the same thing as a pie chart, with an obvious difference of an empty round hole in the middle, making it resemble a donut. However, the data-ink ratio of a donut chart is better than that of a pie chart and the data is depicted by the length of the sectors, rather than the surface, which is easier to interpret. Another advantage of a donut chart is that the space in the center can be used to add a title or a significant value derived from the data. For your convenience, we also created a donut chart resource page with valuable design tips for your next donut chart. On inspiration page you will find more examples of pie and donut charts.

Semicircle donut chart

Alternative name: Half moon chart , Half donut chart , Semi-circle doughnut chart

This chart works the same as a normal pie or donut chart, only the sum of all categories results in half a circle instead of a full circle. It can serve as a basis for a gauge chart, by using the slices to show progress or by adding a pointer. We have more pie and donut chart examples on the inspiration page .

Marimekko chart

Alternative names: Mekko chart , Mosaic chart , Mosaic plot

A Marimekko chart is a type of two-dimensional stacked chart that depicts data through varying heights of different segments and widths of columns. These columns are scaled to fill up the entire available chart area. They can be hard to read, especially if there are many segments. Although Marimekko charts can be used to visualize different types of data, they are most commonly used for analyzing marketing and sales data.

Treemap charts come in handy when you are dealing with large numbers of categories with a hierarchical structure. A treemap consists of multiple categories and each category in the treemap is given a rectangle. The categories could be subdivided into smaller rectangles if you are dealing with subcategories in the data. The size of the area of the rectangles communicates the value. Therefore, treemaps are very useful charts in finding relationships fastly, both within and between categories. Another benefit of a treemap is the efficient use of space which makes it easy to show a lot of data at the same time. If you’re curious about the history and different features of a treemap chart, you can’t miss the deep dive article . We also have a very elaborate treemap resource page for you to check out before you start making your own treemap.

Circular treemap

Alternative name: Circular packing , Circle packing

This type of treemap consists of circles instead of squares, which makes them a bit less space-efficient. Though, because of the space in between the circles, the groups and subgroups are presented very neatly. Moreover, when designed properly, the circular treemap could be really pleasing to look at.

Convex treemap

Alternative names: Voronoi treemap , Polygonal partition

A convex treemap is essentially the same thing as a regular treemap but with convex polygons instead of rectangles. With this type of treemap, it is possible to create treemaps within arbitrary shapes like circles, triangles, or any shape you can think of. Convex treemaps are great if you wish to show grouping and relations instead of the hierarchical structure typically found in a regular treemap. We presented a very nice example of such a treemap in this article that generally looks closely at treemaps.

Alternative name: Phylogenetic tree

To put it simply, a dendrogram is a diagram representing a tree or a network structure. Consisting of stacked branches, it is used to visualize taxonomic relationships (hierarchical relationships between objects). Dendrograms are commonly used in biology to show the clustering of genes but they can illustrate any type of grouped data.

Venn diagram

Alternative name: Set diagram , Logic diagram

Originating in the 1800s, Venn diagrams are widely used within different industries to illustrate relationships (i.e. commonalities or differences) between two or more sets. This type of graph is commonly used in presentations and reports. They are closely related (and similar) to Euler diagrams with the difference that the Euler diagram will omit a set if no relationship exists.

Euler diagram

Euler diagrams are very similar to Venn diagrams, so it’s not surprising that people may occasionally confuse the two. The main difference is that the Euler diagram (which is pronounced Oy-ler) will omit a set if no relationship exists. What does it mean? A Venn diagram shows all possible logical relationships between a collection of sets, while an Euler diagram will only show the relationships that actually exist in real world. If you’re curious to understand it better, we recommend this article that explains the difference between mentioned charts .

Circular gauge

Alternative names: Angular gauge , Radial gauge chart

A circular gauge is a type of chart that uses a circular or half-circular scale with a needle indicating a value on that circular scale. For this reason, it resembles a speedometer or even an analog clock. The interesting thing about circular gauges is that they are so easy to customize and can take so many different, visually interesting forms. This type of chart is extremely useful in all sorts of dashboards.

Sunburst chart

Alternative names: Multi-level pie chart , Multilayer pie chart , Sunburst graph , Ring chart , Radial treemap

A sunburst chart has many names but whatever you call it, it’s still a spectacular type of graph. It shows a hierarchical dataset through a series of concentric outward rings. Each of those rings corresponds to a different hierarchy level. The inner circle looks like a donut chart, but each outer ring can be sliced up depending on its relationship to the inner (parent) circle. Sunburst charts are often a good alternative to treemaps, but if you do opt for this type of chart, keep in mind that its radial layout takes more space than a rectangular shape of a treemap.

Pyramid chart & Funnel chart

Alternative name: Triangle chart

If you work in sales or marketing, this type of chart definitely won’t be new to you. A pyramid chart and a funnel chart are visually almost the same - if you flip a pyramid chart, you get a funnel chart. Funnel charts are very commonly used to visualize the flow of users through a business or sales process. This information is usually paired with the revenue or potential revenue amount at each stage of the funnel. They are widely used in infographics and business presentations or dashboards. In the pyramid chart, each level of the pyramid indicates a different level of hierarchy (among the topics).

4. Data over time (temporal)

An area chart is similar to a line chart. Data values are plotted in a similar way, and connected with lines. The difference is that the area between these lines and the x-axis is filled with a color. This helps in visualizing the change in volume over time. It doesn’t focus on specific data values but more on showing a general change that occurs over a period of time. You will find more area chart examples on inspiration page .

Stacked area chart

Alternative name: Stacked area graph

A stacked area chart is a variation of an area chart. It visualized the evolution of multiple data series (value of several groups) over time. See other stacked area chart examples on the inspiration page .

Stream graph

Alternative names: Streamgraph , ThemeRiver

A stream graph is undoubtedly one of the most beautiful chart types available. This stunning type of chart derives from a stacked area chart, from which it differs by using a central baseline rather than a fixed axis. A stream graph then visualizes different values (compound volumes) around the baseline. This creates a visualization that resembles a river-like stream. The shape of the stream, which consists of peaks and troughs referencing different values over time, can also indicate seasonal patterns. See more similar chart examples on our inspiration page .

A bump chart is a very good choice if you’re interested in showing rankings over time. Since every step in ranking has the same size, this type of chart is not useful in showing the data precisely. See other bump charts and line charts examples on the inspiration page .

Bump area chart

A bump area chart (or an area bump chart) is a variation of a bump chart that instead of only displaying the ranking over time also shows the values on the y-axis. This helps in visualizing the number of different categories over time and their ranking. If you were to compare this chart to a stream graph, they’re actually visually not so far from each other. However, a bump area chart sorts the categories based on their ranking. So in other words, a bump area chart shows both magnitude and rank. And it’s also a stunning chart.

Alternative names: Line graph , Line plot

A line chart is a type of chart that comes in very handy when showing overall trends or progress. Line charts are among the oldest types of charts and are still one of the most popular. They are versatile, simple, and easy to understand. They can show a lot of information at once. What’s really nice about line charts is that they can be also very easily applied onto or merged with other charts like the bar chart or the area chart. In a line chart, the data points represent two variables and are connected by a line to show the changing trend of the data. The x-axis or independent axis shows a continuous variable (usually time) and the y-axis or dependent axis contains a numerical value for a metric of interest. If you’d like to design really stunning line charts, make sure to see our line chart resource page full of great tips and more line chart examples.

Spline chart

Alternative names: Spline graph , Curve chart

A spline chart is functionally the same thing as a line chart. The only difference is that a spline chart connects data points using a smooth curve, whereas a regular line chart uses a straight line to join those points. For this reason, a spline is also known as a curve chart. A combination of an area chart with a spline chart creates a variation called a spline area chart. Find the examples of similar charts on inspiration page .

Step line chart

Alternative names: Step chart , Stepped line graph

The step line chart only uses horizontal and vertical lines to connect the data points. It is convenient to use when you want to highlight the exact moment in time when the data changes and is, therefore, helpful when you must deal with data that changes in irregular intervals. See more examples of similar charts on the inspiration page .

Candlestick chart

Alternative name: Japanese candlestick chart

A candlestick chart is a chart typically used in the financial industry. It helps visualize the price movements over a period of time. For this reason, it helps detect and predict market trends. This type of chart is almost exclusively associated with stock price information. If you’re interested in designing a candlestick chart and adding it to your financial report, it’s possible to create it with Datylon for Illustrator. You can read more about creating a candlestick chart in our article .

Gantt chart

A Gantt chart is a graph that typically shows activities or tasks performed against time: a project plan over time. Used in project management, it helps in tracking project progress, schedule, changes, etc. In other words, a Gantt chart shows what has been done and what still needs to be done. However, it’s worth noting that although this type of graph is most commonly used in project management, it is definitely not limited to it. The idea behind this chart is that it visualizes the start and end time in form of period blocks. Therefore, it can be also used to illustrate seasonal occurrences, such as the availability of different fruits and vegetables throughout the year, or the appearance of mosquitoes in different months of the year.

Barcode chart

Barcode charts are used when one of the dimensions of the dataset is extensive while the space is limited. Barcode charts can be created in several ways. The first is to place a row of thin bars along the horizontal axis. It can be useful as an alternative to a strip plot when the density of data marks is too high and individual elements can be hardly recognized. The second way is to use the thickness of the bar for binding an additional dimension. The color is also often used to show a few states of the bar. In most cases the number of colors is limited due to bar width - it’s hard to recognize a wide range of colors when the bar is very thin.

The OHLC chart’s name stands for Open-High-Low-Close Chart. This type of chart is nearly exclusively used in the financial sector. It helps visualize price changes over time, typically in a trading stock market.

5. Distribution

Density plot.

Alternative names: Kernel density plot , Density trace graph

A density plot is a type of chart that helps us visualize how the numeric data is being distributed over a period of time. Density plots somewhat resemble smooth peaks and valleys plotted between two axes. These correspond to a higher or lower concentration of values. A density plot is a variation of a histogram. However, it is visually more appealing, as it loses the sharp edges typical for histograms and adds a smooth continuous curve. Find more examples on the inspiration page .

Ridgeline plot

Alternative names: Joy plot , Joyplot

A ridgeline plot is a somewhat special type of chart. A ridgeline plot shows the distribution of a numeric value for several groups of a category. It is done by illustrating partially overlapping line plots (that can be made of density plots or histograms), which then can resemble a mountain range. This beautiful chart can be useful to visualize distribution over time or space. But what is the most interesting about it is its history! The alternative name for a ridgeline plot is a joy plot because this very example above appeared on the first album cover of the British band Joy Division (‘Unknown Pleasures’ from 1979). See other examples of similar charts other examples of similar charts on the inspiration page .

Horizon chart

The horizon chart is for some an unfamiliar chart. Though, it is definitely worth getting to know this type of chart. When you are dealing with a lot of categories and you want to make efficient use of space, this chart is the way to go. It is perfect to show time series data on the horizontal axis and with colored bands, the values are represented on the vertical axis. The use of colored bands makes it possible to show great precision of the values. With the use of a diverging color scheme, it is even possible to show both positive and negative values. The difference with other charts is that both the positive and negative values are shown above the baseline, instead of showing negative values under the baseline. This allows you to show a lot of data in a very condensed manner.

Alternative names: Frequency distribution graph, Frequency distribution chart

A histogram is a type of chart that visually resembles a column chart. It’s a graph that consists of vertical rectangles (columns), whose length is proportional to the frequency of a variable (data items). The main visual difference between a histogram and a column chart is that there is no empty space between each rectangle. That’s because, unlike in column charts, in a histogram, the numbers are grouped into ranges. Then the columns have different heights because they correspond to the frequency of each group - meaning, how many items fall in a certain range.

Radial histogram

Alternative names: Angular histogram, Circular histogram, Polar histogram

A radial histogram is simply a variation of a histogram (see above) but with columns wrapped around a circle. It functions the same way as a regular histogram. And it’s very likely going to grab your readers’ attention. See examples of similar charts on the inspiration page .

Alternative names: Individual value plot, Single-axis scatter plot

A strip plot is a type of scatter plot but it only has one categorical and one numerical axis. It is a chart used to illustrate the distribution of many individual one-dimensional values. These values look like dots located along a single (category) axis in this chart. If some of the dots have the same value, they can overlap, creating something that looks like a strip.

Jitter plot

Alternative names: Jittered strip plot , Jittered individual value plot

A jitter plot is an alternative to a strip plot (see above). It is used to visualize the relationship between a measurement variable and a categorical variable. The main difference from a strip plot is that the dots used in the charts are shifted on the horizontal y-axis, to avoid overlapping (overplotting), which in turn allows avoiding lack of clarity.

One dimensional heatmap

If you want to zoom in on one category and focus on the evolution of that variable, you can use heatmaps in only one dimension. These charts are very popular in climate communication and often visualize temperatures.

Beeswarm chart

Alternative name: Swarm plot

A beeswarm chart is like a dot plot with a lot of values per category. These values are each represented by one dot, and the swarm of dots represents the distribution found in the data. Instead of packing them in bins, the dots are scattered around each other and plotted on one single axis. This kind of chart is very useful when you want to display a lot of data points at once.

Alternative names: Box plot , Boxplot , Box-and-whisker plot/chart , Whisker plot

A box chart uses boxes and lines to depict the distributions of one or more groups of numeric data. They are meant to provide a high level of information at glance - a summary of data. In a box plot, boxes are the main part of the chart, and they represent the range of the central 50% (middle portion) of the data. There is also a line visible within the boxes that indicates the median value. The remaining half of the data is visualized with the lines (whiskers) extending out of each box. This type of graph is quite popular in the research and financial fields. See similar chart examples here .

Violin plot

A box chart (above) can be useful for comparing summary statistics (such as range and quartiles), but it doesn't let you see variations in the data - unlike a violin plot. This type of chart is a hybrid of a box plot and a density plot. Thanks to this, a violin plot depicts distributions of numeric data for one or more groups using density curves. Of course, visually, it resembles a violin, hence its name. 

6. Geospatial & other charts

Geographic heatmap.

Alternative names: hot spot map , geo heat map , density heatmap

A geographic heatmap is a geographical representation of data that demonstrates where something occurs, specifying the areas of data’s high and low density. Unlike a choropleth map, a geo heatmap does not limit displaying geospatial data to specified boundaries. Therefore, using the data’s location radius, it can cover a small and specific geographic area, as well as large regions, such as oceans or coasts. It uses color to highlight the areas of occurrence.

Choropleth map

A choropleth map is a type of map in which different administrative areas are colored (or shaded) according to the magnitude of their numeric value. The main difference between a choropleth map and a geographic heatmap is that a choropleth map uses border-defined areas, such as countries, states, or neighborhoods. A common example of the use of choropleth maps can be a visualization of population density.

A tile map is a type of geographical map where a larger area (usually a country or a continent) is visualized by multiple equal-size and shape tiles, often square rectangles. Each tile represents a different region. A simple example of a tile map can be a collection of tiles forming the shape of the United States, where each tile corresponds to a state. What is important about tile maps is that all tiles don’t vary in size, meaning that larger regions can’t dominate the visualization and smaller regions are not harder to read.

Chord diagram

A chord diagram is used for showing the structure of paired connections between the instances of the same level. Every instance is represented by an arc. Every connection is shown as a band with various start and end widths which depicts differences in input and output. Common examples of chord diagrams vary from international trade flows to text and script analysis.

Arc diagram

An arc diagram in its essence is similar to a chord diagram. While the chord diagram focuses mostly on the quantitative aspect of the connection, the arc diagram is more focused on the existence of the link. The arc diagram shows the connections between points that are placed on the line axis with the arcs. Arcs could be placed on both sides of the axis showing the different aspects of the connection. Although the focus of the arc diagram is to show the existence of the connection it can also be used to show the quantitative aspect of the connection using the thickness of the arc.

A Sankey diagram is a type of visualization that allows you to display flows from one set of values to another. It shows entities that represent the values and connects them by links, or flows. Each flow has a varying height, which depends on its quantity. They can also differ in color. For this reason, it’s really common to use Sankey diagrams in visualizing supply chains, engineering and production processes, energy efficiency, etc. A known example is Google Analytics, which uses Sankey to depict the customer journey between pages of a website., The disadvantage of using this otherwise really beautiful graph is that inexperienced users will find it difficult to digest this visualization. Sankey diagrams are very often also called Alluvial diagrams. For an untrained eye, they will indeed appear to be the same chart. There is, however, a bit of a difference between the two. If you’re interested in learning more, we found this post quite a nice resource .

Network diagram

Alternative names: Network graph, Network mapping, Network visualization A network diagram is used to show the connections between multiple elements. The structure of the data and the purpose is somehow similar to the arc diagram. But while in the arc diagram, all of the points are placed on the same line, in the network diagram positioning of the peaks can vary. In some variations of the network diagram, the position of the point depends on the number of connections this point has and the group it belongs to. Network diagrams are often used to show the clusters of members based on the intensity of the connections.

A flowchart is a visualization of a workflow. It’s a diagram that depicts subsequent steps in the process. In other words, it shows what steps need to be followed to complete an action. A flowchart uses connecting lines and arrows to allow viewers to follow the process. It has many organizational use cases and can be a good tool to map out the customer journey, and step-by-step instructions. It’s also popular in project management.

Charts in Illustrator

As mentioned at the beginning, many of the charts and graphs listed in this post can be made with Datylon. Currently, we offer 130+ chart templates in our Chart Library. You can sign up for free and try it for yourself.

What is even more interesting, a lot of charts from this list can be designed in Adobe ® Illustrator ® . Of course, Illustrator has a built-in graphing tool but unfortunately for many graphic designers and data visualization experts, it is seriously limited .  Check out the walk-through for our graph maker by "Yes I'm a Designer".

With Datylon for Illustrator , you get full freedom of chart design. It's a chart maker plug-in for Adobe Illustrator with extraordinary features that will help you make the most captivating chart design! Hey, did anyone say fully resizable charts?

➡️ Create an account and don't forget to download Datylon for Illustrator with a free 14-day trial (no credit card needed) and supercharge your data visualization!

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Global citizen, world traveler, content creator, marketing specialist, can't sing to save his life. In his free time, he's mastering Datylon for Illustrator for no reason.

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Data Structures and Algorithms

Chapter 10 graphs.

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10. 1. Chapter Introduction: Graphs ¶

10. 1.1. graph terminology and implementation ¶.

Graphs provide the ultimate in data structure flexibility. A graph consists of a set of nodes, and a set of edges where an edge connects two nodes. Trees and lists can be viewed as special cases of graphs.

Graphs are used to model both real-world systems and abstract problems, and are the data structure of choice in many applications. Here is a small sampling of the types of problems that graphs are routinely used for.

Modeling connectivity in computer and communications networks.

Representing an abstract map as a set of locations with distances between locations. This can be used to compute shortest routes between locations such as in a GPS routefinder.

Modeling flow capacities in transportation networks to find which links create the bottlenecks.

Finding a path from a starting condition to a goal condition. This is a common way to model problems in artificial intelligence applications and computerized game players.

Modeling computer algorithms, to show transitions from one program state to another.

Finding an acceptable order for finishing subtasks in a complex activity, such as constructing large buildings.

Modeling relationships such as family trees, business or military organizations, and scientific taxonomies.

The rest of this module covers some basic graph terminology. The following modules will describe fundamental representations for graphs, provide a reference implementation, and cover core graph algorithms including traversal, topological sort, shortest paths algorithms, and algorithms to find the minimal-cost spanning tree. Besides being useful and interesting in their own right, these algorithms illustrate the use of many other data structures presented throughout the course.

A graph \(\mathbf{G} = (\mathbf{V}, \mathbf{E})\) consists of a set of vertices \(\mathbf{V}\) and a set of edges \(\mathbf{E}\) , such that each edge in \(\mathbf{E}\) is a connection between a pair of vertices in \(\mathbf{V}\) . 1 The number of vertices is written \(|\mathbf{V}|\) , and the number of edges is written \(|\mathbf{E}|\) . \(|\mathbf{E}|\) can range from zero to a maximum of \(|\mathbf{V}|^2 - |\mathbf{V}|\) .

Some graph applications require that a given pair of vertices can have multiple or parallel edges connecting them, or that a vertex can have an edge to itself. However, the applications discussed here do not require either of these special cases. To simplify our graph API, we will assume that there are no duplicate edges.

A graph whose edges are not directed is called an undirected graph , as shown in part (a) of the following figure. A graph with edges directed from one vertex to another (as in (b)) is called a directed graph or digraph . A graph with labels associated with its vertices (as in (c)) is called a labeled graph . Associated with each edge may be a cost or weight . A graph whose edges have weights (as in (c)) is said to be a weighted graph .

Figure 10.2.1: Some types of graphs.

An edge connecting Vertices \(a\) and \(b\) is written \((a, b)\) . Such an edge is said to be incident with Vertices \(a\) and \(b\) . The two vertices are said to be adjacent . If the edge is directed from \(a\) to \(b\) , then we say that \(a\) is adjacent to \(b\) , and \(b\) is adjacent from \(a\) . The degree of a vertex is the number of edges it is incident with. For example, Vertex \(e\) below has a degree of three.

In a directed graph, the out degree for a vertex is the number of neighbors adjacent from it (or the number of edges going out from it), while the in degree is the number of neighbors adjacent to it (or the number of edges coming in to it). In (c) above, the in degree of Vertex 1 is two, and its out degree is one.

A sequence of vertices \(v_1, v_2, ..., v_n\) forms a path of length \(n-1\) if there exist edges from \(v_i\) to \(v_{i+1}\) for \(1 \leq i < n\) . A path is a simple path if all vertices on the path are distinct. The length of a path is the number of edges it contains. A cycle is a path of length three or more that connects some vertex \(v_1\) to itself. A cycle is a simple cycle if the path is simple, except for the first and last vertices being the same.

An undirected graph is a connected graph if there is at least one path from any vertex to any other. The maximally connected subgraphs of an undirected graph are called connected components . For example, this figure shows an undirected graph with three connected components.

A graph with relatively few edges is called a sparse graph , while a graph with many edges is called a dense graph . A graph containing all possible edges is said to be a complete graph . A subgraph \(\mathbf{S}\) is formed from graph \(\mathbf{G}\) by selecting a subset \(\mathbf{V}_s\) of \(\mathbf{G}\) ’s vertices and a subset \(\mathbf{E}_s\) of \(\mathbf{G}\) ‘s edges such that for every edge \(e \in \mathbf{E}_s\) , both vertices of \(e\) are in \(\mathbf{V}_s\) . Any subgraph of \(V\) where all vertices in the graph connect to all other vertices in the subgraph is called a clique .n

A graph without cycles is called an acyclic graph . Thus, a directed graph without cycles is called a directed acyclic graph or DAG .

A free tree is a connected, undirected graph with no simple cycles. An equivalent definition is that a free tree is connected and has \(|\mathbf{V}| - 1\) edges.

10. 1.1.1. Graph Representations ¶

There are two commonly used methods for representing graphs. The adjacency matrix for a graph is a \(|\mathbf{V}| \times |\mathbf{V}|\) array. We typically label the vertices from \(v_0\) through \(v_{|\mathbf{V}|-1}\) . Row \(i\) of the adjacency matrix contains entries for Vertex \(v_i\) . Column \(j\) in row \(i\) is marked if there is an edge from \(v_i\) to \(v_j\) and is not marked otherwise. The space requirements for the adjacency matrix are \(\Theta(|\mathbf{V}|^2)\) .

The second common representation for graphs is the adjacency list . The adjacency list is an array of linked lists. The array is \(|\mathbf{V}|\) items long, with position \(i\) storing a pointer to the linked list of edges for Vertex \(v_i\) . This linked list represents the edges by the vertices that are adjacent to Vertex \(v_i\) .

Here is an example of the two representations on a directed graph. The entry for Vertex 0 stores 1 and 4 because there are two edges in the graph leaving Vertex 0, with one going to Vertex 1 and one going to Vertex 4. The list for Vertex 2 stores an entry for Vertex 4 because there is an edge from Vertex 2 to Vertex 4, but no entry for Vertex 3 because this edge comes into Vertex 2 rather than going out.

Figure 10.2.7: Representing a directed graph.

Both the adjacency matrix and the adjacency list can be used to store directed or undirected graphs. Each edge of an undirected graph connecting Vertices \(u\) and \(v\) is represented by two directed edges: one from \(u\) to \(v\) and one from \(v\) to \(u\) . Here is an example of the two representations on an undirected graph. We see that there are twice as many edge entries in both the adjacency matrix and the adjacency list. For example, for the undirected graph, the list for Vertex 2 stores an entry for both Vertex 3 and Vertex 4.

Figure 10.2.8: Representing an undirected graph.

The storage requirements for the adjacency list depend on both the number of edges and the number of vertices in the graph. There must be an array entry for each vertex (even if the vertex is not adjacent to any other vertex and thus has no elements on its linked list), and each edge must appear on one of the lists. Thus, the cost is \(\Theta(|\mathbf{V}| + |\mathbf{E}|)\) .

Sometimes we want to store weights or distances with each each edge, such as in Figure 10.2.1 (c). This is easy with the adjacency matrix, where we will just store values for the weights in the matrix. In Figures 10.2.7 and 10.2.8 we store a value of “1” at each position just to show that the edge exists. That could have been done using a single bit, but since bit manipulation is typically complicated in most programming languages, an implementation might store a byte or an integer at each matrix position. For a weighted graph, we would need to store at each position in the matrix enough space to represent the weight, which might typically be an integer.

The adjacency list needs to explicitly store a weight with each edge. In the adjacency list shown below, each linked list node is shown storing two values. The first is the index for the neighbor at the end of the associated edge. The second is the value for the weight. As with the adjacency matrix, this value requires space to represent, typically an integer.

Which graph representation is more space efficient depends on the number of edges in the graph. The adjacency list stores information only for those edges that actually appear in the graph, while the adjacency matrix requires space for each potential edge, whether it exists or not. However, the adjacency matrix requires no overhead for pointers, which can be a substantial cost, especially if the only information stored for an edge is one bit to indicate its existence. As the graph becomes denser, the adjacency matrix becomes relatively more space efficient. Sparse graphs are likely to have their adjacency list representation be more space efficient.

Example 10.2.1

Assume that a vertex index requires two bytes, a pointer requires four bytes, and an edge weight requires two bytes. Then, each link node in the adjacency list needs \(2 + 2 + 4 = 8\) bytes. The adjacency matrix for the directed graph above requires \(2 |\mathbf{V}^2| = 50\) bytes while the adjacency list requires \(4 |\mathbf{V}| + 8 |\mathbf{E}| = 68\) bytes. For the undirected version of the graph above, the adjacency matrix requires the same space as before, while the adjacency list requires \(4 |\mathbf{V}| + 8 |\mathbf{E}| = 116\) bytes (because there are now 12 edges represented instead of 6).

The adjacency matrix often requires a higher asymptotic cost for an algorithm than would result if the adjacency list were used. The reason is that it is common for a graph algorithm to visit each neighbor of each vertex. Using the adjacency list, only the actual edges connecting a vertex to its neighbors are examined. However, the adjacency matrix must look at each of its \(|\mathbf{V}|\) potential edges, yielding a total cost of \(\Theta(|\mathbf{V}^2|)\) time when the algorithm might otherwise require only \(\Theta(|\mathbf{V}| + |\mathbf{E}|)\) time. This is a considerable disadvantage when the graph is sparse, but not when the graph is closer to full.

10. 1.2. Graph Terminology Questions ¶

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What is: Data Representation

Understanding data representation.

Data representation refers to the methods and techniques used to visually or symbolically depict data. This can include various formats such as graphs, charts, tables, and diagrams. Effective data representation is crucial for data analysis and data science, as it allows for easier interpretation and communication of complex information. By transforming raw data into a more understandable format, stakeholders can make informed decisions based on insights derived from the data.

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Types of Data Representation

There are several types of data representation, each suited for different types of data and analysis. Common forms include numerical representation, categorical representation, and temporal representation. Numerical representation involves using numbers to convey information, while categorical representation uses categories or groups. Temporal representation focuses on data over time, often visualized through time series graphs. Understanding these types is essential for selecting the appropriate method for data visualization.

The Importance of Visual Representation

Visual representation of data plays a vital role in data analysis. It enhances the ability to identify trends, patterns, and outliers within datasets. By utilizing visual tools like bar charts, pie charts, and scatter plots, analysts can quickly convey complex information in a digestible format. This not only aids in analysis but also facilitates communication with non-technical stakeholders, ensuring that insights are accessible to a broader audience.

Common Tools for Data Representation

Several tools and software applications are widely used for data representation in the fields of statistics and data science. Popular tools include Tableau, Microsoft Power BI, and Google Data Studio. These platforms provide users with the ability to create interactive and dynamic visualizations, allowing for real-time data analysis and exploration. Additionally, programming languages like Python and R offer libraries such as Matplotlib and ggplot2, which enable custom visualizations tailored to specific analytical needs.

Best Practices in Data Representation

When creating data representations, adhering to best practices is essential for clarity and effectiveness. This includes choosing the right type of visualization for the data at hand, ensuring that visualizations are not cluttered, and using appropriate scales and labels. Additionally, color choices should enhance readability and accessibility, avoiding combinations that may confuse or mislead viewers. Following these guidelines helps ensure that the data representation communicates the intended message accurately.

Challenges in Data Representation

Despite its importance, data representation comes with challenges. One significant challenge is the risk of misrepresentation, where visualizations may distort the data or lead to incorrect conclusions. This can occur due to inappropriate scaling, selective data presentation, or biased visual choices. Analysts must be vigilant in ensuring that their representations are truthful and accurately reflect the underlying data, as misleading visuals can have serious implications for decision-making.

Data Representation in Machine Learning

In the realm of machine learning, data representation is critical for model performance. The way data is represented can significantly impact the effectiveness of algorithms. Feature engineering, which involves selecting and transforming variables into a suitable format for modeling, is a key aspect of this process. Proper data representation can enhance the model’s ability to learn from the data, leading to better predictions and insights.

Interactive Data Representation

Interactive data representation has gained popularity in recent years, allowing users to engage with data in real-time. Tools that support interactive visualizations enable users to filter, zoom, and manipulate data, providing a more immersive experience. This interactivity fosters deeper exploration and understanding of the data, making it easier for users to uncover insights that may not be immediately apparent in static representations.

Future Trends in Data Representation

As technology continues to evolve, so too does the field of data representation. Emerging trends include the use of augmented reality (AR) and virtual reality (VR) for data visualization, offering new dimensions for understanding complex datasets. Additionally, advancements in artificial intelligence are enabling automated data representation, where algorithms can generate visualizations based on data patterns without human intervention. These innovations promise to enhance the way data is represented and understood in the future.

21 Best Data Visualization Types: Examples of Graphs and Charts Uses

Those who master different data visualization types and techniques (such as graphs, charts, diagrams, and maps) are gaining the most value from data.

Why? Because they can analyze data and make the best-informed decisions.

Whether you work in business, marketing, sales, statistics, or anything else, you need data visualization techniques and skills.

Graphs and charts make data much more understandable for the human brain.

On this page:

• What are data visualization techniques? Definition, benefits, and importance.
• 21 top data visualization types. Examples of graphs and charts with an explanation.
• When to use different data visualization graphs, charts, diagrams, and maps?
• How to create effective data visualization?
• 10 best data visualization tools for creating compelling graphs and charts.

What Are Data V isualization T echniques? Definition And Benefits.

Data visualization techniques are visual elements (like a line graph, bar chart, pie chart, etc.) that are used to represent information and data.

Big data hides a story (like a trend and pattern).

By using different types of graphs and charts, you can easily see and understand trends, outliers, and patterns in data.

They allow you to get the meaning behind figures and numbers and make important decisions or conclusions.

Data visualization techniques can benefit you in several ways to improve decision making.

Key benefits:

• Data is processed faster Visualized data is processed faster than text and table reports. Our brains can easily recognize images and make sense of them.
• Better analysis Help you analyze better reports in sales, marketing, product management, etc. Thus, you can focus on the areas that require attention such as areas for improvement, errors or high-performing spots.
• Faster decision making Businesses who can understand and quickly act on their data will gain more competitive advantages because they can make informed decisions sooner than the competitors.
• You can easily identify relationships, trends, patterns Visuals are especially helpful when you’re trying to find trends, patterns or relationships among hundreds or thousands of variables. Data is presented in ways that are easy to consume while allowing exploration. Therefore, people across all levels in your company can dive deeper into data and use the insights for faster and smarter decisions.
• No need for coding or data science skills There are many advanced tools that allow you to create beautiful charts and graphs without the need for data scientist skills . Thereby, a broad range of business users can create, visually explore, and discover important insights into data.

How Do Data Visualization Techniques work?

Data visualization techniques convert tons of data into meaningful visuals using software tools.

The tools can operate various types of data and present them in visual elements like charts, diagrams, and maps.

They allow you to easily analyze massive amounts of information, discover trends and patterns in data and then make data-driven decisions .

Why data visualization is very important for any job?

Each professional industry benefits from making data easier to understand. Government, marketing, finance, sales, science, consumer goods, education, sports, and so on.

As all types of organizations become more and more data-driven, the ability to work with data isn’t a good plus, it’s essential.

Whether you’re in sales and need to present your products to prospects or a manager trying to optimize employee performance – everything is measurable and needs to be scored against different KPI s.

We need to constantly analyze and share data with our team or customers.

Having data visualization skills will allow you to understand what is happening in your company and to make the right decisions for the good of the organization.

Before start using visuals, you must know…

Data visualization is one of the most important skills for the modern-day worker.

However, it’s not enough to see your data in easily digestible visuals to get real insights and make the right decisions.

• First : to define the information you need to present
• Second: to find the best possible visual to show that information

Don’t start with “I need a bar chart/pie chart/map here. Let’s make one that looks cool” . This is how you can end up with misleading visualizations that, while beautiful, don’t help for smart decision making.

Regardless of the type of data visualization, its purpose is to help you see a pattern or trend in the data being analyzed.

The goal is not to come up with complex descriptions such as: “ A’s sales were more than B by 5.8% in 2018, and despite a sales growth of 30% in 2019, A’s sales became less than B by 6.2% in 2019. ”

A good data visualization summarizes and presents information in a way that enables you to focus on the most important points.

Let’s go through 21 data visualization types with examples, outline their features, and explain how and when to use them for the best results.

21 Best Types Of Data Visualization With Examples And Uses

1. Line Graph

The line graph is the most popular type of graph with many business applications because they show an overall trend clearly and concisely.

What is a line graph?

A line graph (also known as a line chart) is a graph used to visualize the values of something over a specified period of time.

For example, your sales department may plot the change in the number of sales your company has on hand over time.

Data points that display the values are connected by straight lines.

When to use line graphs?

• When you want to display trends.
• When you want to represent trends for different categories over the same period of time and thus to show comparison.

For example, the above line graph shows the total units of a company sales of Product A, Product B, and Product C from 2012 to 2019.

Here, you can see at a glance that the top-performing product over the years is product C, followed by Product B.

2. Bar Chart

At some point or another, you’ve interacted with a bar chart before. Bar charts are very popular data visualization types as they allow you to easily scan them for valuable insights.

And they are great for comparing several different categories of data.

What is a bar chart?

A bar chart (also called bar graph) is a chart that represents data using bars of different heights.

The bars can be two types – vertical or horizontal. It doesn’t matter which type you use.

The bar chart can easily compare the data for each variable at each moment in time.

For example, a bar chart could compare your company’s sales from this year to last year.

When to use a bar chart?

• When you need to compare several different categories.
• When you need to show how large data changes over time.

The above bar graph visualizes revenue by age group for three different product lines – A, B, and C.

You can see more granular differences between revenue for each product within each age group.

As different product lines are groups by age group, you can easily see that the group of 34-45-year-old buyers are the most valuable to your business as they are your biggest customers.

3. Column Chart

If you want to make side-by-side comparisons of different values, the column chart is your answer.

What is a column chart?

A column chart is a type of bar chart that uses vertical bars to show a comparison between categories.

If something can be counted, it can be displayed in a column chart.

Column charts work best for showing the situation at a point in time (for example, the number of products sold on a website).

Their main purpose is to draw attention to total numbers rather than the trend (trends are more suitable for a line chart).

When to use a column chart?

• When you need to show a side-by-side comparison of different values.
• When you want to emphasize the difference between values.
• When you want to highlight the total figures rather than the trends.

For example, the column chart above shows the traffic sources of a website. It illustrates direct traffic vs search traffic vs social media traffic on a series of dates.

The numbers don’t change much from day to day, so a line graph isn’t appropriate as it wouldn’t reveal anything important in terms of trends.

The important information here is the concrete number of visitors coming from different sources to the website each day.

4. Pie Chart

Pie charts are attractive data visualization types. At a high-level, they’re easy to read and used for representing relative sizes.

What is a pie chart?

A Pie Chart is a circular graph that uses “pie slices” to display relative sizes of data.

A pie chart is a perfect choice for visualizing percentages because it shows each element as part of a whole.

The entire pie represents 100 percent of a whole. The pie slices represent portions of the whole.

When to use a pie chart?

• When you want to represent the share each value has of the whole.
• When you want to show how a group is broken down into smaller pieces.

The above pie chart shows which traffic sources bring in the biggest share of total visitors.

You see that Searches is the most effective source, followed by Social Media, and then Links.

At a glance, your marketing team can spot what’s working best, helping them to concentrate their efforts to maximize the number of visitors.

5. Area Chart 

If you need to present data that depicts a time-series relationship, an area chart is a great option.

What is an area chart?

An area chart is a type of chart that represents the change in one or more quantities over time. It is similar to a line graph.

In both area charts and line graphs, data points are connected by a line to show the value of a quantity at different times. They are both good for showing trends.

However, the area chart is different from the line graph, because the area between the x-axis and the line is filled in with color. Thus, area charts give a sense of the overall volume.

Area charts emphasize a trend over time. They aren’t so focused on showing exact values.

Also, area charts are perfect for indicating the change among different data groups.

When to use an area chart?

• When you want to use multiple lines to make a comparison between groups (aka series).
• When you want to track not only the whole value but also want to understand the breakdown of that total by groups.

In the area chart above, you can see how much revenue is overlapped by cost.

Moreover, you see at once where the pink sliver of profit is at its thinnest.

Thus, you can spot where cash flow really is tightest, rather than where in the year your company simply has the most cash.

Area charts can help you with things like resource planning, financial management, defining appropriate storage space, and more.

6. Scatter Plot

The scatter plot is also among the popular data visualization types and has other names such as a scatter diagram, scatter graph, and correlation chart.

Scatter plot helps in many areas of today’s world – business, biology, social statistics, data science and etc.

What is a Scatter plot?

Scatter plot is a graph that represents a relationship between two variables . The purpose is to show how much one variable affects another.

Usually, when there is a relationship between 2 variables, the first one is called independent. The second variable is called dependent because its values depend on the first variable.

But it is also possible to have no relationship between 2 variables at all.

When to use a Scatter plot?

• When you need to observe and show relationships between two numeric variables.
• When just want to visualize the correlation between 2 large datasets without regard to time.

The above scatter plot illustrates the relationship between monthly e-commerce sales and online advertising costs of a company.

At a glance, you can see that online advertising costs affect monthly e-commerce sales.

When online advertising costs increase, e-commerce sales also increase.

Scatter plots also show if there are unexpected gaps in the data or if there are any outlier points.

7. Bubble chart

If you want to display 3 related dimensions of data in one elegant visualization, a bubble chart will help you.

What is a bubble chart?

A bubble chart is like an extension of the scatter plot used to display relationships between three variables.

The variables’ values for each point are shown by horizontal position, vertical position, and dot size.

In a bubble chart, we can make three different pairwise comparisons (X vs. Y, Y vs. Z, X vs. Z).

When to use a bubble chart?

• When you want to depict and show relationships between three variables.

The bubble chart above illustrates the relationship between 3 dimensions of data:

• Cost (X-Axis)
• Profit (Y-Axis)
• Probability of Success (%) (Bubble Size).

Bubbles are proportional to the third dimension – the probability of success. The larger the bubble, the greater the probability of success.

It is obvious that Product A has the highest probability of success.

8. Pyramid Graph

Pyramid graphs are very interesting and visually appealing graphs. Moreover, they are one of the most easy-to-read data visualization types and techniques.

What is a pyramid graph?

It is a graph in the shape of a triangle or pyramid. It is best used when you want to show some kind of hierarchy. The pyramid levels display some kind of progressive order, such as:

• More important to least important. For example, CEOs at the top and temporary employees on the bottom level.
• Specific to least specific. For example, expert fields at the top, general fields at the bottom.
• Older to newer.

When to use a pyramid graph?

• When you need to illustrate some kind of hierarchy or progressive order

Image Source: Conceptdraw

The above is a 5 Level Pyramid of information system types that is based on the hierarchy in an organization.

It shows progressive order from tacit knowledge to more basic knowledge. Executive information system at the top and transaction processing system on the bottom level.

The levels are displayed in different colors. It’s very easy to read and understand.

9. Treemaps

Treemaps also show a hierarchical structure like the pyramid graph, however in a completely different way.

What is a treemap?

Treemap is a type of data visualization technique that is used to display a hierarchical structure using nested rectangles.

Data is organized as branches and sub-branches. Treemaps display quantities for each category and sub-category via a rectangle area size.

Treemaps are a compact and space-efficient option for showing hierarchies.

They are also great at comparing the proportions between categories via their area size. Thus, they provide an instant sense of which data categories are the most important overall.

When to use a treemap?

• When you want to illustrate hierarchies and comparative value between categories and subcategories.

Image source: Power BI

For example, let’s say you work in a company that sells clothing categories: Urban, Rural, Youth, and Mix.

The above treemap depicts the sales of different clothing categories, which are then broken down by clothing manufacturers.

You see at a glance that Urban is your most successful clothing category, but that the Quibus is your most valuable clothing manufacturer, across all categories.

10. Funnel chart

Funnel charts are used to illustrate optimizations, specifically to see which stages most impact drop-off.

Illustrating the drop-offs helps to show the importance of each stage.

What is a funnel chart?

A funnel chart is a popular data visualization type that shows the flow of users through a sales or other business process.

It looks like a funnel that starts from a large head and ends in a smaller neck. The number of users at each step of the process is visualized from the funnel width as it narrows.

A funnel chart is very useful for identifying potential problem areas in the sales process.

When to use a funnel chart?

• When you need to represent stages in a sales or other business process and show the amount of revenue for each stage.

Image Source: DevExpress

This funnel chart shows the conversion rate of a website.

The conversion rate shows what percentage of all visitors completed a specific desired action (such as subscription or purchase).

The chart starts with the people that visited the website and goes through every touchpoint until the final desired action – renewal of the subscription.

You can see easily where visitors are dropping out of the process.

11. Venn Diagram 

Venn diagrams are great data visualization types for representing relationships between items and highlighting how the items are similar and different.

What is a Venn diagram?

A Venn Diagram is an illustration that shows logical relationships between two or more data groups. Typically, the Venn diagram uses circles (both overlapping and nonoverlapping).

Venn diagrams can clearly show how given items are similar and different.

Venn diagram with 2 and 3 circles are the most common types. Diagrams with a larger number of circles (5,6,7,8,10…) become extremely complicated.

When to use a Venn diagram?

• When you want to compare two or more options and see what they have in common.
• When you need to show how given items are similar or different.
• To display logical relationships from various datasets.

The above Venn chart clearly shows the core customers of a product – the people who like eating fast foods but don’t want to gain weight.

The Venn chart gives you an instant understanding of who you will need to sell.

Then, you can plan how to attract the target segment with advertising and promotions.

12. Decision Tree

As graphical representations of complex or simple problems and questions, decision trees have an important role in business, finance, marketing, and in any other areas.

What is a decision tree?

A decision tree is a diagram that shows possible solutions to a decision.

It displays different outcomes from a set of decisions. The diagram is a widely used decision-making tool for analysis and planning.

The diagram starts with a box (or root), which branches off into several solutions. That’s why it is called a decision tree.

Decision trees are helpful for a variety of reasons. Not only they are easy-to-understand diagrams that support you ‘see’ your thoughts, but also because they provide a framework for estimating all possible alternatives.

When to use a decision tree?

• When you need help in making decisions and want to display several possible solutions.

Imagine you are an IT project manager and you need to decide whether to start a particular project or not.

You need to take into account important possible outcomes and consequences.

The decision tree, in this case, might look like the diagram above.

13. Fishbone Diagram

Fishbone diagram is a key tool for root cause analysis that has important uses in almost any business area.

It is recognized as one of the best graphical methods to understand and solve problems because it takes into consideration all the possible causes.

What is a fishbone diagram?

A fishbone diagram (also known as a cause and effect diagram, Ishikawa diagram or herringbone diagram) is a data visualization technique for categorizing the potential causes of a problem.

The main purpose is to find the root cause.

It combines brainstorming with a kind of mind mapping and makes you think about all potential causes of a given problem, rather than just the one or two.

It also helps you see the relationships between the causes in an easy to understand way.

When to use a fishbone diagram?

• When you want to display all the possible causes of a problem in a simple, easy to read graphical way.

Let’s say you are an online marketing specialist working for a company witch experience low website traffic.

You have the task to find the main reasons. Above is a fishbone diagram example that displays the possible reasons and can help you resolve the situation.

14. Process Flow Diagram

If you need to visualize a specific process, the process flow diagram will help you a lot.

What is the process flow diagram?

As the name suggests, it is a graphical way of describing a process, its elements (steps), and their sequence.

Process flow diagrams show how a large complex process is broken down into smaller steps or tasks and how these go together.

As a data visualization technique, it can help your team see the bigger picture while illustrating the stages of a process.

When to use a process flow diagram?

• When you need to display steps in a process and want to show their sequences clearly.

The above process flow diagram shows clearly the relationship between tasks in a customer ordering process.

The large ordering process is broken down into smaller functions and steps.

15. Spider/Radar Chart

Imagine, you need to rank your favorite beer on 8 aspects (Bitterness, Sweetness, Sourness, Saltiness, Hop, Malt, Yeast, and Special Grain) and then show them graphically. You can use a radar chart.

What is a radar chart?

Radar chart (also called spider, web, and polar bar) is a popular data visualization technique that displays multivariate data.

In can compare several items with many metrics of characteristics.

To be effective and clear, the radar chart should have more than 2 but no more than 6 items that are judged.

When to use a radar chart?

• When you need to compare several items with more than 5 metrics of characteristics.

The above radar chart compares employee’s performance with a scale of 1-5 on skills such as Communications, Problem-solving, Meeting deadlines, Technical knowledge, Teamwork.

A point that is closer to the center on an axis shows a lower value and a worse performance.

It is obvious that Mary has a better performance than Linda.

16. Mind Map

Mind maps are beautiful data visuals that represent complex relationships in a very digestible way.

What is a mind map?

A mind map is a popular diagram that represents ideas and concepts.

It can help you structure your information and analyze, recall, and generate new ideas.

It is called a mind map because it is structured in a way that resembles how the human brain works.

And, best of all, it is a fun and artistic data visualization technique that engages your brain in a much richer way.

When to use a mind map?

• When you want to visualize and connect ideas in an easy to digest way.
• When you want to capture your thoughts/ideas and bring them to life in visual form.

Image source: Lucidchart

The above example of a mind map illustrates the key elements for running a successful digital marketing campaign.

It can help you prepare and organize your marketing efforts more effectively.

17. Gantt Chart

A well-structured Gantt chart aids you to manage your project successfully against time.

What is a Gantt chart?

Gantt charts are data visualization types used to schedule projects by splitting them into tasks and subtasks and putting them on a timeline.

Each task is listed on one side of the chart. This task also has a horizontal line opposite it representing the length of the task.

By displaying tasks with the Gantt chart, you can see how long each task will take and which tasks will overlap.

Gantt charts are super useful for scheduling and planning projects.

They help you estimate how long a project should take and determine the resources needed.

They also help you plan the order in which you’ll complete tasks and manage the dependencies between tasks.

When to use a Gantt chart?

• When you need to plan and track the tasks in project schedules.

Image Source: Aha.io

The above example is a portfolio planning Gantt Chart Template that illustrates very well how Gantt Charts work.

It visualizes the release timeline for multiple products for an entire year.

It shows also dependencies between releases.

You can use it to help team members understand the release schedule for the upcoming year, the duration of each release, and the time for delivering.

This helps you in resource planning and allows teams to coordinate implementation plans.

18. Organizational Charts

Organizational charts are data visualization types widely used for management and planning.

What is an organizational chart?

An organizational chart (also called an org chart) is a diagram that illustrates a relationship hierarchy.

The most common application of an org chart is to display the structure of a business or other organization.

Org charts are very useful for showing work responsibilities and reporting relationships.

They help leaders effectively manage growth or change.

Moreover, they show employees how their work fits into the company’s overall structure.

When to use the org chart?

• When you want to display a hierarchical structure of a department, company or other types of organization.

Image Source: Organimi

The above hierarchical org chart illustrates the chain of command that goes from the top (e.g., the CEOs) down (e.g., entry-level and low-level employees) and each person has a supervisor.

It clearly shows levels of authority and responsibility and who each person reports to.

It also shows employees the career paths and chances for promotion.

19. Area Map

Most business data has a location. Revenue, sales, customers, or population are often displayed with a dimensional variable on a map.

What is an area map?

It is a map that visualizes location data.

They allow you to see immediately which geographical locations are most important to your brand and business.

Image Source: Infogram

The map above depicts sales by location and the color indicates the level of sales (the darker the blue, the higher the sales).

These data visualization types are very useful as they show where in the world most of your sales are from and where your most valuable sales are from.

Insights like these illustrate weaknesses in a sales and marketing strategy in seconds.

20. Infographics

In recent years, the use of infographics has exploded in almost every industry.

From sales and marketing to science and healthcare, infographics are applied everywhere to present information in a visually appealing way.

What is an infographic?

Infographics are specific data visualization types that combine images, charts, graphs, and text. The purpose is to represent an easy-to-understand overview of a topic.

However, the main goal of an infographic is not only to provide information but also to make the viewing experience fun and engaging for readers.

It makes data beautiful—and easy to digest.

When you want to represent and share information, there are many data visualization types to do that – spreadsheets, graphs, charts, emails, etc.

But when you need to show data in a visually impactful way, the infographic is the most effective choice.

When to use infographics?

• When you need to present complex data in a concise, highly visually-pleasing way.

Image Source: Venngage

The above statistical infographic represents an overview of Social Buzz’s biggest social platforms by age and geography.

For example, we see that 75% of active Facebook users are 18-29 years old and 48% of active users live in North America.

21. T-Chart

If you want to compare and contrast items in a table form, T-Chart can be your solution.

What is a T-Chart?

A T-Chart is a type of graphic organizer in the shape of the English letter “T”. It is used for comparison by separating information into two or more columns.

You can use T-Chart to compare ideas, concepts or solutions clearly and effectively.

T-Charts are often used for comparison of pros and cons, facts and opinions.

By using T-Chart, you can list points side by side, achieve a quick, at-a-glance overview of the facts, and arrive at conclusions quickly and easily.

When to use a T-Chart?

• When you need to compare and contrast two or more items.
• When you want to evaluate the pros and cons of a decision.

The above T-Chart example clearly outlines the cons and pros of hiring a social media manager in a company.

10 Best Data Visualization Tools

There is a broad range of data visualization tools that allow you to make fascinating graphs, charts, diagrams, maps, and dashboards in no time.

They vary from BI (Business Intelligence) tools with robust features and comprehensive dashboards to more simple software for just creating graphs and charts.

Here we’ve collected some of the most popular solutions. They can help you present your data in a way that facilitates understanding and decision making.

1. Visme is a data presentation and visualization tool that allows you to create stunning data reports. It provides a great variety of presentation tools and templates for a unique design.

2. Infogram is a chart software tool that provides robust diagram-making capabilities. It comes with an intuitive drag-and-drop editor and ready-made templates for reports. You can also add images for your reports, icons, GIFs, photos, etc.

3. Venngage is an infographic maker. But it also is a great chart software for small businesses because of its ease of use, intuitive design, and great templates.

4. SmartDraw is best for those that have someone graphic design skills. It has a slightly more advanced design and complexity than Venngage, Visme, and Infogram, … so having some design skills is an advantage. It’s a drawing tool with a wide range of charts, diagrams, maps, and well-designed templates.

5. Creately is a dynamic diagramming tool that offers the best free version. It can be deployed from the cloud or on the desktop and allows you to create your graphs, charts, diagrams, and maps without any tech skills.

6. Edraw Max is an all-in-one diagramming software tool that allows you to create different data visualization types at a high speed. These include process flow charts, line graphs, org charts, mind maps, infographics, floor plans, network diagrams, and many others. Edraw Max has a wide selection of templates and symbols, letting you to rapidly produce the visuals you need for any purpose.

7. Chartio is an efficient business intelligence tool that can help you make sense of your company data. Chartio is simple to use and allows you to explore all sorts of information in real-time.

8. Sisense – a business intelligence platform with a full range of data visualizations. You can create dashboards and graphical representations with a drag and drop user interface.

9. Tableau – a business intelligence system that lets you quickly create, connect, visualize, and share data seamlessly.

10. Domo is a cloud business intelligence platform that helps you examine data using graphs and charts. You can conduct advanced analysis and create great interactive visualization.

Data visualization techniques are vital components of data analysis, as they can summarize large amounts of data effectively in an easy to understand graphical form.

There are countless data visualization types, each with different pros, cons, and use cases.

The trickiest part is to choose the right visual to represent your data.

Your choice depends on several factors – the kind of conclusion you want to draw, your audience, the key metrics, etc.

I hope the above article helps you understand better the basic graphs and their uses.

When you create your graph or diagram, always remember this:

A good graph is the one reduced to its simplest and most elegant form without sacrificing what matters most – the purpose of the visual.

About The Author

Silvia Valcheva

Silvia Valcheva is a digital marketer with over a decade of experience creating content for the tech industry. She has a strong passion for writing about emerging software and technologies such as big data, AI (Artificial Intelligence), IoT (Internet of Things), process automation, etc.

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44 Types of Graphs Perfect for Every Top Industry

Written by: Samantha Lile

Graphs are a great way to visualize data and display numbers and statistics. In fact, they're essential to help your audience understand your points or key findings. 

They can help you visualize growth in a sales report , showcase demographics in a pitch deck or share industry statistics in an infographic . 

But with so many popular types of charts and graphs including line graphs, bar graphs, pie charts, bubble charts, scatter plots, and histograms, how do you know what are the best options for your industry, project, or data?

Picking the right chart or graph  doesn't have to be difficult. To help make your choice easier we’ve compiled a list of 44 types of graphs and charts, many of which can be made right in Visme. 

Find your industry below and check out the graph options available to you, then click the button below each template to start inputting your data and customizing it for your project.

Table of Contents

• Business & Finance

Engineering and Technology

Political science and sociology, meteorology and environment.

And, if you’re short on time check out this  video tutorial on the ultimate guide to data visualization

44 Types of Graphs and Charts

Line graphs.

Line charts, or line graphs, are powerful visual tools that illustrate trends in data over a period of time or a particular correlation. For example, one axis of the graph might represent a variable value, while the other axis often displays a timeline.

Each value is plotted on the chart, then the points are connected to display a trend over the compared time span. Multiple trends can be compared by plotting lines of various colors.

For example, the interest of digital marketing over time can be visually shown with ease through the use of a line graph. Simply plot each number of searches along the timeline to view the trend.

The simplest and most straightforward way to compare various categories is the classic bar graph. The universally-recognized graph features a series of bars of varying lengths.

One axis of a bar graph features the categories being compared, while the other axis represents the value of each. The length of each bar is proportionate to the numerical value or percentage that it represents. 

For example, in the template below, each social media platform is represented by a bar. With just one quick glance, audiences can learn exactly which social media platform has the highest and lowest usage during the allotted time frame.

Bar graphs work great for visually presenting nearly any type of data, but they hold particular power in the marketing industry. The graphs are ideal for comparing any sort of numeric value, including group sizes, inventories, ratings and survey responses.

If you like this chart but you want to make it your own, you can easily customize your charts with your personal or company brand. You can use the AI-powered Brand Wizard : simply input your URL, and the wizard will create a brand kit of your colors, logos, and fonts, which you can easily access in any Visme project. 

Alternatively, you can add them manually. The upside to the wizard is that it also provides templates that match your brand, making it faster to find a chart that aligns with your brand.

Customize this pie chart template and make it your own! Edit and Download

Pie charts are the simplest and most efficient visual tool for comparing parts of a whole. For example, a pie chart can quickly and effectively compare various budget allocations, population segments or market-research question responses.

Marketing content designers frequently rely on pie charts to compare the size of market segments. For example, a simple pie graph can clearly illustrate how the most popular mobile-phone manufacturers compare based on the sizes of their user-bases.

Audiences are able to quickly understand that stock photography is the most-used visual in marketing, with original graphics – like those that can be created with Visme – coming in as a close second.  The beauty of using Visme is that you can create animated charts to engage your audience and make complex information digestible.

Mosaic or Mekko Charts

Basic line, bar and pie charts are excellent tools for comparing one or two variables in a few categories, but what happens when you need to compare multiple variables or multiple categories at the same time?

What if all those variables aren’t numeric even? A mosaic – or Mekko – chart plot might be the better choice.

Perhaps a production analyst wants to compare the amount of furniture produced, analyze the production percentages for multiple best-selling types of furniture, review their year-over-year growth of sales to decide how to allocate their production budget effectively.

A mosaic chart would allow said analysts to illustrate all the variables in a clear and straightforward manner.

In the above example, one axis of the chart represents the categories being compared – Tables, chairs, sofas, beds, bedroom sets, the year over year review – while the other axis lists various percentages.

The size and color of each cross-section of the chart corresponds with the market segment it represents, as depicted in the chart's legend.

Population Pyramids

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Market segments are often divided based on age and gender, and a population pyramid is an ideal visual representation of the two groups.

The graph classically takes on the shape of a pyramid when a population is healthy and growing -- the largest groups are the youngest, and each gender dwindles somewhat equally as the population ages, leaving the smallest groups at the top of the graph.

A population pyramid that veers away from its classic shape might indicate an irregularity in a population during a particular period, such as a famine or an economic boom that led to an increase in deaths or births.

Of course, population pyramids aren’t always used to compare populations by age, and therefore don’t always take on the graph’s namesake shape.

A marketer, for example, might use the design to compare a population by income, weight or IQ, in which the smallest groups will often be at both the top and bottom. Regardless, the graph clearly depicts population trends, while it compares the sizes of two related groups.

Spider Charts

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When a statistician needs to visually compare three or more quantitative variables, he or she might choose to use a  radar chart , also known as a spider or star chart.

The chart usually consists of a series of radii, each representing a different category, that splay out from a center point like spokes.

The length of each “spoke” is proportionate to the value being compared. For each category, the spokes are then connected with a line of a designated pattern or color, forming a star-like shape with points equal to the number of categories.

The result is a graphic representation that can reveal trends and compare categories all at the same time.

With Visme, you can not only easily populate your charts with pre-existing data from your Google Sheets, but also ensure that when values change in your Google Sheet, your Visme chart will be updated whenever the project is refreshed. This not only helps keep your charts up to date but also allows seamless design collaboration between team members in or outside of Visme.

Want to create your own radar chart?

• Upload an Excel file or sync with live data from Google sheets
• Choose from 16+ types of charts, from bar and line graphs to pyramid and Mekko charts
• Customize anything, from backgrounds and placement of labels to font style and color

Business and Finance

Stock charts.

Image source

One of the most vital of all financial graphs, stock charts help investors track the markets to determine profits and loss, as well as make buying and selling decisions.

While a variety of graphs are used to represent market changes, the most common is likely the basic line graph turned histogram.

The lines simply tracks changes in a particular stock’s or overall market’s value over a period of time. Multiple stocks can be tracked and compared at the same time by transforming the line graph into a stacked area chart or simply using multiple lines of various colors.

Whether you need to create stock charts for potential shareholders or whip up a quick visualization for your team, you can add hotspots or link documents to your charts to add additional reports. This lets you share more information at once rather than sending long emails or cluttering your chart design. 

This is just one of the many all-in-one approaches Visme provides that most data visualization tools fail to offer. Not only does it allow you to create professionally designed charts or graphs with little to no design experience, but it expands its use case to help you work more efficiently. 

But don’t take our word for it, see what MacKenzie Stonis, Economic Research Analyst at Greater Memphis Chamber had to say:

MacKenzie Stonis

Economic Research Analyst

Flow Charts

Oftentimes in business – as well as other industries – a process must be diagrammed. A flow chart allows a process to be sequenced step-by-step, from beginning to end, for the purpose of analyzing, designing, documenting or managing it.

These flow charts can even feature multiple beginnings and ends, with countless pathways and journeys in between.

While a simple flow chart can certainly document a basic process from A to B to C, the diagrams are more frequently used to illustrate more complex sequences with multiple decisions or conditions along the way.

Each time a condition is met, the chart diagrams the various options, then the path continues following each choice.

Flowcharts are commonly used for organizational charts, training materials, customer onboarding, planning and execution, as well as SOPs, and more. 

So, whether you plan to share your flowcharts internally or externally, Visme allows you to publish and share them as live links. This means that viewers can review your charts like a website. And if you make any edits to your chart, simply refresh the page, and the changes are updated instantly.

Want to create your own flowchart?

• Get a head start with pre-made flowchart blocks
• Easily snap lines and objects together
• Dozens of shapes and lines styles to choose from

Gantt Charts

Gantt charts are special types of bar graphs used to diagram projects and schedules. The use of colored bars of varying lengths reflect not only a project’s start and end dates, but also important events, tasks, milestones and their timeframes.

Modern Gantt charts can also illustrate activities’ dependency relationships.

If Team 3’s completion of task C, for example, is dependent upon the prior completion of task B by Team 2, the chart can not only reflect that relationship but also the scheduled dates and deadlines for each. Easily create beautiful Gantt charts to visualize your project schedule and streamline project management with Visme's Gantt Chart Maker .

Control Charts

Also commonly known as a process-behavior chart, a control chart helps determine if a data set falls within a mean or predetermined control range.

Frequently used in quality control processes, a typical control chart consists of points plotted on two axes, representing sample measurements.

The mean of each point is calculated, and a center line across the graph at the mean value. Then, a standard deviation from the mean is calculated using each sample.

Finally, upper and lower control limits are determined and diagrammed to reflect the points at which deviation is beyond the expected standard.

Waterfall Charts

Waterfall is one of the most commonly used data visualization techniques used in business. This chart is particularly useful in accounting and qualitative analysis; waterfall charts illustrate how an initial value is affected positively and negatively by various factors.

For example, a waterfall chart could clearly and efficiently communicate how an opening balance changes month by month over the course of a year.

Because they often appear as though bars are floating throughout the graph, waterfall charts are sometimes referred to as floating bricks or Mario charts.  

If your team uses a wide range of tools to gather data for charts like these, keep in mind that Visme offers a more integrated approach for all your data visualization needs. Integrate with apps like Tableau, Salesforce, Hubspot and Google Analytics and so much more. 

Hierarchy Diagrams

Similar in appearance to a flow chart, a hierarchical diagram, also known as an organizational chart or an organigram, illustrates the structure of an organization, as well as the relationships within it.

A typical company organigram, for example, lists the CEO at the top, followed by presidents, vice presidents, managers and so on.

An organizational chart can illustrate the chain of command from any employee all the way to the top. Hierarchy diagrams are similarly used to represent pedigrees, scientific classifications, demographics and any data set with a similar breakdown.

Take the above diagram as an example, where a project team is organized in an organizational hierarchy chart so that everyone knows who their supervisor is in a project.

Remember that if you’re using this for organizational purposes, it has to be updated regularly. To assist with this, you can add dynamic fields in Visme, for top positions in your hierarchy diagrams. 

With dynamic fields, you can instantly update names, positions, or business information across all projects with just one click. This significantly cuts down on the time that would otherwise be spent manually editing your chart .

Want to create your own organizational diagram?

• Get a head start with pre-made diagram blocks

Scatter Plots

Also known as a scattergram , the graph consists of two axes, each representing a set of data. For example, o ne axis might represent the percentage of profits , while the second axis displays the total budget in millions.

For each budget and profit made is represented by a dot and plotted onto the graph. Once multiple dots are plotted, trends can be spotted and samples can be compared. The chart ultimately states that higher the budget the greater the possibility of increased profits.

A bubble chart is another variation of scatter plots. While both of them visualize relationships between two variables, a bubble chart adds an extra dimension by incorporating the size of the bubbles to represent a third variable.

Trellis Plots

Sometimes a statistician will need to compare more data sets than can be  represented by a single graph. What if, for example, a graph needs to compare not only miles driven and gallons used, but also the number of gears and cylinders contained in each vehicle sample?

A trellis plot, also called a lattice graph or plot, can display and compare all of those variables. While the above example uses a series of scatter charts, trellis plots commonly feature series of bar or line graphs, as well.

Function Plots

Mathematicians, engineers and statisticians often need to determine the value of an equation by graphing its result. The graph of a function is the set of all points whose coordinates satisfy the equation.  

Therefore, the function of an equation with variables of x and y would be drawn on a graph with an x and y axis. Likewise, an equation that also included a variable of z would need to be drawn on a three-dimensional graph with a third axis.

Function graphs of common shapes are visually associated with their corresponding algebraic formulas.

Binary Decision Diagrams

A binary decision is a choice between two alternatives, so a binary-decision diagram illustrates the path from one decision to another.

In computer science, binary decisions make up the Boolean data type, in which two values are associated with different actions within a process flow.

Outside of computer science, a binary-decision diagram can still be used to illustrate any process by which actions are based on a decision between two values, whether those conditions be yes or no, true or false, 1 or 0 or any other opposing choices.

Ultimately, the path taken will diagram how the process flowed, from beginning to end.

Circuit Diagrams

Just as its name implies, a circuit diagram is a visual representation of an electrical circuit. Using simple shapes and images, the diagram illustrates the components and interconnections of a circuit, from start to finish.

While the pathways and connections are accurate, the diagram does not necessarily represent a proportionate spatial construction of the circuit. In computer science, circuit diagrams are useful in depicting data related to both hardware and software.

The graphics not only visualize the pathways of a circuit in the literal sense, but they are also closely related to the aforementioned binary-decision diagram -- both are used to diagram programming process flows.

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Customize this timeline template and make it your own! Edit and Download

Possibly the most self-explanatory of data visualizations, a timeline tracks data over a time period. Significant dates and events are highlighted at the point at which they appear on a chronological scale. Timelines can be used alone or in conjunction with other visualizations.

This History of Vincent Van Gogh timeline infographic is a great example of how you can create a timeline chart right in Visme.

Condensing historical timelines can be tough , especially when you have to cram a lot of  facts or events into a bit-sized timeline. The last thing you want to do is overwhelm your reader. To do the heavy lifting for you use Visme’s AI Writer . This tool can help you to  summarize your lengthy historical details and present them in a timeline bullet points. 

Plus, use it to proofread or revise your changes or additional writing so that it's grammatically correct and ready to be shared  with the world.

Want to create your own timeline?

Tree diagrams.

A form of hierarchical diagram, a genealogical tree illustrates the structure of a family. It can either begin with an ancestor, then diagram his or her descendants, their siblings, marriages and children, and so on.

A pedigree chart, on the other hand, begins with an individual and charts their ancestry, from parents to grandparents, and continues up.

Sunburst Charts

A type of multi-level pie chart, a sunburst chart is used to illustrate hierarchical data using concentric circles. Each ring of the “sunburst” represents a level in the hierarchy, with the root node represented by the center circle, and the hierarchy moving outward.

While a sunburst chart can be used to illustrate a familiar or company hierarchy, it can also break data down by time periods, creating a historical hierarchy.

Various branches of an organization can be represented by designated hues, with different levels often taking on varying shades of the same color family. Rings can also be divided further to represent multiple divisions within the same organizational level.

In fact, a traditional, complex color wheel, such as that used by paint stores, is another form of sunburst chart.

Customize this line graph template and make it your own! Edit and Download

If a timeline is a form of graph, then it only makes sense that historians often employ it in displaying other data. By plotting immigration levels against a timeline, the resulting histogram illustrates population trends over a century or longer with a basic line graph.

Stacked Area Charts

Stacked area charts are frequently used to diagram changes of multiple variables across time. Multiple lines can be drawn, for example, to track the population changes of various states across time.

The area below each line can be colored a different hue to represent the state it signifies, resulting in a graph that clearly represents population trends, while at the same time displaying each state’s data in order from least to most populous.

Stacked Bar Graphs

When studying groups of people, it’s common to compare multiple variables at once. After all, it’s enormously more useful to examine racial backgrounds, ages and gender in addition to total population.

A stacked bar graph combines elements of the traditional bar graph and the pie graph to communicate totals, trends and proportions in a single illustration.

Rather than simply illustrating changes in global population over time with a traditional column bar graph, a stacked bar graph can also represent the racial makeup of the population during each year and how those proportions have changed during the same period.

Want to create your own stacked bar chart?

Trellis bar graphs.

When presenting data with three variables, a designer might try and create a three-dimensional bar graph, but adding an additional axis can sometimes appear cluttered and unclear, especially in printed form.

Instead, additional variables can be presented in a trellis – or lattice – format.

By combining a series of bar graphs in a modular design, additional sets of data can be easily compared. For example, a single bar graph could illustrate the political breakdown of Poland’s national elections over a period of five years.

But a trellis bar graph could depict the same data set for 16 European nations.

Stacked area charts are ideal for comparing values that would normally require multiple line graphs. Each line represents a different category, and the area below each line is generally shaded a designated color so each data set can be easily compared.

For example, an area chart with one axis that represents a numeric value, and another axis that serves as a timeline, data for various categories over time can be tracked and compared with a single graphic. 

Multi-level Pie Charts

All too often a designer finds him or herself with more sets of data than can be presented in a single standard graph. Fortunately, in the case of a pie chart, multiple layers of data can be presented without the need for multiple images or a trellis design.

A multi-level pie chart, for example, consists of tiers, with each layer representing a separate set of data, and can be the perfect solution.

So while it would take three traditional pie graphs to illustrate the various sources of recorded words for three different decades, a multi-level pie graph can not only take the place of all three, but it also offers a clearer visual comparison of each year’s results.

Venn Diagrams

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The classic Venn diagram , also known as a logic diagram, illustrates all possible logical relationships between a designated collection of sets.

For example, the overlap of two or more circles – in this case there are three – visually represents the similarities and differences between the social, economical and environmental areas of sustainable development.

The more circles used, the more logical conclusions that can be represented by their overlap. The combined set of all data in the diagram is known as the union, while the areas that overlap are called intersections.

A Venn diagram in which the relative size and area of each shape is proportional to the size of the group it represents is known as an area-proportional or scaled Venn diagram.

Scattergrams

Scattergrams, also known as scatter plots, are graphs that show the relationship between two or more variables. The plots use mathematical coordinates to represent two variables of a data set.

Data is displayed in a scattergram as a collection of points, each representing the value variables plotted on the horizontal and vertical axes. If points are color-coded, an additional variable can be represented in a single chart.

By plotting certain data sets, scientists can discover trends of which they might not otherwise be aware. For example, a scattergram might allow a doctor to plot patients’ resting heart rates against their body-mass index figures.

The resulting graph reveals that a higher heart rate correlates with a higher BMI.

Trellis Line Graphs

Trellis graphs allow scientists to examine complex, multi-variable data sets, comparing a greater deal of information at once.

While a single line graph can illustrate monthly UFO sightings in Tennessee over an 18-year period, a trellis line graph will display the same data for all 50 states in a single graphic.

A trellis line graph is based on the same principle as its simpler counterpart, plotting trends in a dataset consisting of two variables – numbers of UFO sightings and dates – through use of connecting points on two axes.

But by combining multiple line graphs in a modular format, an additional variable – location – is represented.

Pareto Charts

You might be wondering what type of graph is this ? Well, sometimes a basic graph doesn’t display enough information to draw the necessary conclusion. A Pareto chart combines a bar graph with a line graph to illustrate not only categories’ individual values, but also the cumulative total of the entire set.

Pareto charts are designed to highlight the most important of a set of factors.

In a Pareto chart that tracks the type and frequency of food defects, the bars illustrate each type of defects’ total occurrences – as reported on one of the charts’ axes – while the line charts the cumulative frequency of all categories, from most to least prevalent.

The result is a graph that clearly reflects the most common food defects and what percentage of the whole each represents.

Radar Charts

A radar chart, also commonly referred to as a spider chart or a star chart, displays data sets consisting of three or more variables on a two-dimensional graphic. Each variable’s quantitative value is reflected across an axis that usually starts in the chart’s center point.

As each item’s variables are charted, a line connects the points on each axis, forming an irregular polygon that may or may not resemble a star or spider web.

Multiple data sets can be compared on a single radar graph by representing each with a different color, identified by labels or in an accompanying key.

A radar chart can, for example, clearly compare and illustrate the costs and outcomes of various medical procedures as they relate to multiple conditions – all in a single graphic.

Spherical Contour Graphs

Plotting planetary conditions on a basic two-axis graph can pose a problem. The Earth, after all, is a sphere. Instead, data can be plotted on a three-axis field using variables of x, y and z. The resulting plot, if completed, will take the form of a sphere.

A spherical plot can, for example, reveal global temperature or rainfall trends by assigning each value range with a particular color, then plotting the data with points of the corresponding hue.

Health and Wellness

Multi-line graphs.

Just as medical symptoms are rarely isolated, neither is the analysis of biometric data. After all, rarely does one statistic paint the entire medical picture.

Line graphs can reflect multiple data sets with lines of varying patterns or color. For example, a multi-line graph can illustrate changes in life expectancies of not just the population in general, but for each gender and multiple racial backgrounds.

Stacked bar graphs aren’t useful only in illustrating parts of of a whole. They can also be used to display additional variables.

While a basic bar graph could represent what portion of a population is classified as overweight over a designated time period, a stacked bar graph can also track how much of the total is obese.

Customize this flow chart template and make it your own! Edit and Download

Following the proper process is probably more important in medicine than in any other field. After all, if the surgeon forgets a step, you might very well bleed to death while you sleep.

Flow charts are frequently used by hospitals, clinics and other medical facilities to ensure proper procedures are uniformly followed. There are different types of graphs that can be used but these are the most commonly used. 

In a pictogram, or pictograph, images and symbols are used to illustrate data. For example, a basic pictogram might use an image of the sun to signify each fair-weather day in a month and a rain cloud to symbolize each stormy day.

Because images are known to hold more emotional power than raw data, pictograms are often used to present medical data.

An illustration that shades five of 20 person symbols to represent a 20-percent death rate carries a more powerful message, for example, than a bar, line or pie that illustrates the same data.

Anatomical Diagrams

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Medical diagrams are often used to illustrate anatomy, treatments or disease pathology in order to explain treatments for patients and others without an extensive biomedical background.

While medical diagrams are considered a combination of science and art, they can be just as technical as any other quantitative graph. And no matter how detailed the drawing, anatomical diagrams are designed to clearly and efficiently present data.

And just as with a complex contour diagram, the diagrams focus on key information, even if it was selected from voluminous amounts of medical or scientific data.

Multi-Pie Charts

Just as in the cases of multi-level pie graphs, stacked bar graphs and trellis plots, multi-pie graphs paint a more detailed portrait of the data set it illustrates.

While a single pie chart can display what portion of the total population has a particular condition, a multi-pie graph can break those statistics down to illustrate not only the portion of men and the portion of women, but also how the two groups compare.

Want to create your own pie chart?

• Upload an Excel file or synch with live data from Google sheets

It can be difficult to graphically represent medical data sets that consist of hundreds -- or more -- patients, as is the case in most medical studies.

But a scatter plot allows for the representation of each subject, plotted on the graph according to the variables on the chart’s two axes.

The pattern formed by the plotted dots can clearly determine trends in the data. By analyzing a scatter plot, for example, a researcher could easily identify a correlation between longer life expectancy and higher household income.

Contour Plots

Contour plots allow for the analysis of three variables in a two-dimensional format. Instead of plotting data along two main axes, the graph also presents a third value that is based on shading or color.

Just as a topographical map plots longitude, latitude and elevation in a two-dimensional design, a contour graph illustrates values of x , y and z .

With a contour graph, for example, a climatologist can not only plot ocean’s salinity on different dates, but its salinity at various depths on those dates.

A type of contour graph, a heat map specifically charts varying temperatures at different geographical points. While the graph’s two axes are a map’s latitude and longitude, the third variable – temperature – is represented by a spectrum of color.

There are different types of graphs you can use to show the varying temperatures on a global scale, such as bar graphs or line graphics. However, most people find heatmaps more effective and quicker to read than bars or line graphs.

That being said, while heat maps are most commonly used to illustrate weather, they can also represent web traffic, user engagement or behavior, financial indicators, and almost any other three-dimensional data.

Scatter-Line Combo

By combining a line graph with a scatter plot, meteorologists and other statisticians can illustrate the relationship between two data sets.

For example, the high and low temperatures of each day in a month can be displayed in a scatter plot, then a line graph can be added to plot the historic average high and low temperatures over the same period.

The resulting combination graph clearly displays how the temperature range each day compares to the historic average, and it even indicates how those measurements trend over the examined time period.

Technology now allows statisticians to display multi-dimensional data sets in true form. Three-dimensional graphs created with specialized software reflect the relationship between three variables plotted across three axes.

A meteorologist can, for example, graph the wind field of a hurricane.

By definition, a histogram is a special type of vertical bar graph that presents numeric data and its frequency distribution.

As its name suggests, the distribution is often illustrated across time, but the data could also be plotted based on any chronological scale, such as temperature, elevation or monetary value.

While histograms are typically a form of bar graph, the concept can also be applied to line graphs and other designs relying on plotting two axes.

Choose From These Types of Graphs to Create

Now that we've walked through the different charts and graphs used for data visualization, you have a better idea of how to select one based on your industry or needs.

Now it's time to take the next step: create your own.

Visme goes beyond simply being a data visualization tool with over 30+ design widgets and a wide variety of data visualization templates. It's an intuitive platform that allows you to plan, strategize, and translate statistics and figures into stunning and meaningful visuals.

You can seamlessly create reports, projects, presentations, visual content, and more, all while saving on cost, empowering your team with the power of design, and streamlining your content creation process.

Interested to see what Visme can do for you and your team? Request a demo or give Visme a free test run today and begin experiencing all that you can accomplish.

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About the Author

Samantha Lile is a web content creator with a journalism and mass media degree from Missouri State University. She contributes news and feature articles to various web publications, such as the Huffington Post. Currently, she resides in the beautiful Ozarks with her husband, four dogs and two cats.

DSA Tutorial

Linked lists, stacks & queues, hash tables, shortest path, minimum spanning tree, maximum flow, time complexity, dsa reference, dsa examples.

A Graph is a non-linear data structure that consists of vertices (nodes) and edges.

A vertex, also called a node, is a point or an object in the Graph, and an edge is used to connect two vertices with each other.

Graphs are non-linear because the data structure allows us to have different paths to get from one vertex to another, unlike with linear data structures like Arrays or Linked Lists.

Graphs are used to represent and solve problems where the data consists of objects and relationships between them, such as:

• Social Networks: Each person is a vertex, and relationships (like friendships) are the edges. Algorithms can suggest potential friends.
• Maps and Navigation: Locations, like a town or bus stops, are stored as vertices, and roads are stored as edges. Algorithms can find the shortest route between two locations when stored as a Graph.
• Internet: Can be represented as a Graph, with web pages as vertices and hyperlinks as edges.
• Biology: Graphs can model systems like neural networks or the spread of diseases.

Graph Properties

Use the animation below to get an understanding of the different Graph properties, and how these properties can be combined.

A weighted Graph is a Graph where the edges have values. The weight value of an edge can represent things like distance, capacity, time, or probability.

A connected Graph is when all the vertices are connected through edges somehow. A Graph that is not connected, is a Graph with isolated (disjoint) subgraphs, or single isolated vertices.

A connected Graph depending on if it is directed or not:

• We have an undirected connected Graph is if you can go along the edges in the Graph from one node, to every other node in the Graph.
• A strongly connected directed Graph is if you can start at any node and reach every other node in the Graph along the directed edges.
• A weakly connected directed Graph is if you can reach all nodes from any node, when disregarding the direction of the edges.

A directed Graph, also known as a digraph, is when the edges between the vertex pairs have a direction. The direction of an edge can represent things like hierarchy or flow.

A cyclic Graph is defined differently depending on whether it is directed or not:

• A directed cyclic Graph is when you can follow a path along the directed edges that goes in circles. Removing the directed edge from F to G in the animation above makes the directed Graph not cyclic anymore.
• An undirected cyclic Graph is when you can come back to the same vertex you started at without using the same edge more than once. The undirected Graph above is cyclic because we can start and end up in vertes C without using the same edge twice.

A loop , also called a self-loop, is an edge that begins and ends on the same vertex. A loop is a cycle that only consists of one edge. By adding the loop on vertex A in the animation above, the Graph becomes cyclic.

Graph Representations

A Graph representation tells us how a Graph is stored in memory.

Different Graph representations can:

• take up more or less space.
• be faster or slower to search or manipulate.
• be better suited depending on what type of Graph we have (weighted, directed, etc.), and what we want to do with the Graph.
• be easier to understand and implement than others.

Below are short introductions of the different Graph representations, but Adjacency Matrix is the representation we will use for Graphs moving forward in this tutorial, as it is easy to understand and implement, and works in all cases relevant for this tutorial.

Graph representations store information about which vertices are adjacent, and how the edges between the vertices are. Graph representations are slightly different if the edges are directed or weighted.

Two vertices are adjacent, or neighbors, if there is an edge between them.

Adjacency Matrix Graph Representation

Adjacency Matrix is the Graph representation (structure) we will use for this tutorial.

How to implement an Adjacency Matrix is shown on the next page.

The Adjacency Matrix is a 2D array (matrix) where each cell on index (i,j) stores information about the edge from vertex i to vertex j .

Below is a Graph with the Adjacency Matrix representation next to it.

The adjacency matrix above represents an undirected Graph, so the values '1' only tells us where the edges are. Also, the values in the adjacency matrix is symmetrical because the edges go both ways (undirected Graph).

To create a directed Graph with an adjacency matrix, we must decide which vertices the edges go from and to, by inserting the value at the correct indexes (i,j) . To represent a weighted Graph we can put other values than '1' inside the adjacency matrix.

Below is a directed and weighted Graph with the Adjacency Matrix representation next to it.

In the adjacency matrix above, the value 3 on index (0,1) tells us there is an edge from vertex A to vertex B, and the weight for that edge is 3 .

As you can see, the weights are placed directly into the adjacency matrix for the correct edge, and for a directed Graph, the adjacency matrix does not have to be symmetric.

Adjacency List Graph Representation

In case we have a 'sparse' Graph with many vertices, we can save space by using an Adjacency List compared to using an Adjacency Matrix, because an Adjacency Matrix would reserve a lot of memory on empty Array elements for edges that don't exist.

A 'sparse' Graph is a Graph where each vertex only has edges to a small portion of the other vertices in the Graph.

An Adjacency List has an array that contains all the vertices in the Graph, and each vertex has a Linked List (or Array) with the vertex's edges.

In the adjacency list above, the vertices A to D are placed in an Array, and each vertex in the array has its index written right next to it.

Each vertex in the Array has a pointer to a Linked List that represents that vertex's edges. More specifically, the Linked List contains the indexes to the adjacent (neighbor) vertices.

So for example, vertex A has a link to a Linked List with values 3, 1, and 2. These values are the indexes to A's adjacent vertices D, B, and C.

An Adjacency List can also represent a directed and weighted Graph, like this:

In the Adjacency List above, vertices are stored in an Array. Each vertex has a pointer to a Linked List with edges stored as i,w , where i is the index of the vertex the edge goes to, and w is the weight of that edge.

Node D for example, has a pointer to a Linked List with an edge to vertex A. The values 0,4 means that vertex D has an edge to vertex on index 0 (vertex A), and the weight of that edge is 4 .

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Introduction to Graphs

Table of Contents

15 December 2020                 

Read time: 6 minutes

Introduction

What are graphs?

What are the different types of data?

What are the different types of graphical representations?

The graph is nothing but an organized representation of data. It helps us to understand the data. Data are the numerical information collected through observation.

The word data came from the Latin word Datum which means “something given”

After a research question is developed, data is being collected continuously through observation. Then it is organized, summarized, classified, and then represented graphically.

Differences between Data and information: Data is the raw fact without any add on but the information is the meaning derived from data.

Introduction to Graphs-PDF

The graph is nothing but an organized representation of data. It helps us to understand the data. Data are the numerical information collected through observation. Here is a downloadable PDF to explore more.

• Line and Bar Graphs Application
• Graphs in Mathematics & Statistics

What are the different Types of Data?

There are two types of Data :

Quantitative

The data which are statistical or numerical are known as Quantitive data. Quantitive data is generated through. Quantitative data is also known as Structured data. Experiments, Tests, Surveys, Market Report.

Quantitive data is again divided into Continuous data and Discrete data.

Continuous Data

Continuous data is the data which can have any value. That means Continuous data can give infinite outcomes so it should be grouped before representing on a graph.

• The speed of a vehicle as it passes a checkpoint
• The mass of a cooking apple
• The time taken by a volunteer to perform a task

Discrete Data

Discrete data can have certain values. That means only a finite number can be categorized as discrete data.

• Numbers of cars sold at a dealership during a given month
• Number of houses in certain block
• Number of fish caught on a fishing trip
• Number of complaints received at the office of airline on a given day
• Number of customers who visit at bank during any given hour
• Number of heads obtained in three tosses of a coin

Differences between Discrete and Continuous data

• Numerical data could be either discrete or continuous
• Continuous data can take any numerical value (within a range); For example, weight, height, etc.
• There can be an infinite number of possible values in continuous data
• Discrete data can take only certain values by finite ‘jumps’, i.e., it ‘jumps’ from one value to another but does not take any intermediate value between them (For example, number of students in the class)

Qualitative

Data that deals with description or quality instead of numbers are known as Quantitative data. Qualitative data is also known as unstructured data. Because this type of data is loosely compact and can’t be analyzed conventionally.

Different Types of Graphical Representations

There are many types of graph we can use to represent data. They are as follows,

A bar graph or chart is a way to represent data by rectangular column or bar. The heights or length of the bar is proportional to the values.

A line graph is a type of graph where the information or data is plotted as some dots which are known as markers and then they are added to each other by a straight line.

The line graph is normally used to represent the data that changes over time.

A histogram graph is a graph where the information is represented along with the height of the rectangular bar. Though it does look like a bar graph, there is a fundamental difference between them. With the histogram, each column represents a range of quantitative data when a bar graph represents categorical variables.

The other name of the pie chart is a circle graph. It is a circular chart where numerical information represents as slices or in fractional form or percentage where the whole circle is 100%.

• Stem and leaf plot

The stem and leaf plot is a way to represents quantitative data according to frequency ranges or frequency distribution.

In the stem and leaf plot, each data is split into stem and leaf, which is 32 will be split into 3 stems and 2 leaves.

Frequency table: Frequency means the number of occurrences of an event. A frequency distribution table is a graph or chart which shows the frequency of events. It is denoted as ‘f’ .

Pictograph or Pictogram is the earliest way to represents data in a pictorial form or by using symbols or images. And each image represents a particular number of things.

According to the above-mentioned Pictograph, the number of Appels sold on Monday is 6x2=12.

• Scatter diagrams

Scatter diagram or scatter plot is a way of graphical representation by using cartesian coordinates of two variables. The plot shows the relationship between two variables. Below there is a data table as well as a Scattergram as per the given data.

What is the meaning of Graphical representation?

Graphical representation is a way to represent and analyze quantitive data. 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.

Principles of graphical representation

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 X-axis and y-axis intersected each other at the origin it divides the plane into four parts which are called Quadrant I, Quadrant II, Quadrant III, Quadrant IV.

The location on the coordinate plane is known as the ordered pair and it is written as (x,y). That means the first value will be on the x-axis and the second one is on the y-axis. When we will plot any coordinate, we always have to start counting from the origin and have to move along the x-axis, if it is positive then to the right side, and if it is negative then to the left side. Then from the x-axis, we have to plot the y’s value, which means we have to move up for positive value or down if the value is negative along with the y-axis.

In the following graph, 1st ordered pair (2,3) where both the values of x and y are positive and it is on quadrant I. 2nd ordered pair (-3,1), here the value of x is negative and value of y is positive and it is in quadrant II. 3rd ordered pair (-1.5, -2.5), here the value of x as well as y both are Negative and in quadrant III.

Methods of representing a frequency distribution

There are four methods to represent a frequency distribution graphically. These are,

• Smoothed Frequency graph
• Cumulative frequency graph or Ogive.
• Pie diagram.

Advantages and Disadvantages of 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.

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 represents it graphically.

You may also like:

• Graphing a Quadratic Function
• Empirical Relationship Between Mean, Median, and Mode

Not only in mathematics but almost in every field the graph is a very important way to store, analyze, and represents information. After any research work or after any survey the next step is to organize the observation or information and plotting them on a graph paper or plane. The visual representation of information makes the understanding of crucial components or trends easier.

A huge amount of data can be store or analyze in a small space.

The graphical representation of data helps to decide by following the trend.

A complete Idea: Graphical representation constitutes a clear and comprehensive idea in the minds of the audience. Reading a large number (say hundreds) of pages may not help to make a decision. Anyone can get a clear idea just by looking into the graph or design.

Graphs are a very conceptual topic, so it is essential to get a complete understanding of the concept. Graphs are great visual aids and help explain numerous things better, they are important in everyday life. Get better at graphs with us, sign up for a free trial . 

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Frequently Asked Questions (FAQs)

What is data.

Data are characteristics or information, usually numerical, that are collected through observation.

How do you differentiate between data and information?

Data is the raw fact without any add on but the information is the meaning derived from data.

What are the types of data?

There are two types of Data:

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, organisation and analysis of data as part of the process of a scientific study.

- 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, organisation and analysis of data as part of the process of a scientific study.

What are the different types of graphs?

Different types of graphs include:

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Graph Data Stucture

A graph data structure is a collection of nodes that have data and are connected to other nodes.

Let's try to understand this through an example. On facebook, everything is a node. That includes User, Photo, Album, Event, Group, Page, Comment, Story, Video, Link, Note...anything that has data is a node.

Every relationship is an edge from one node to another. Whether you post a photo, join a group, like a page, etc., a new edge is created for that relationship.

All of facebook is then a collection of these nodes and edges. This is because facebook uses a graph data structure to store its data.

More precisely, a graph is a data structure (V, E) that consists of

• A collection of vertices V
• A collection of edges E, represented as ordered pairs of vertices (u,v)

In the graph,

• Graph Terminology
• Adjacency : A vertex is said to be adjacent to another vertex if there is an edge connecting them. Vertices 2 and 3 are not adjacent because there is no edge between them.
• Path : A sequence of edges that allows you to go from vertex A to vertex B is called a path. 0-1, 1-2 and 0-2 are paths from vertex 0 to vertex 2.
• Directed Graph : A graph in which an edge (u,v) doesn't necessarily mean that there is an edge (v, u) as well. The edges in such a graph are represented by arrows to show the direction of the edge.
• Graph Representation

Graphs are commonly represented in two ways:

1. Adjacency Matrix

An adjacency matrix is a 2D array of V x V vertices. Each row and column represent a vertex.

If the value of any element a[i][j] is 1, it represents that there is an edge connecting vertex i and vertex j.

The adjacency matrix for the graph we created above is

Since it is an undirected graph, for edge (0,2), we also need to mark edge (2,0); making the adjacency matrix symmetric about the diagonal.

Edge lookup(checking if an edge exists between vertex A and vertex B) is extremely fast in adjacency matrix representation but we have to reserve space for every possible link between all vertices(V x V), so it requires more space.

2. Adjacency List

An adjacency list represents a graph as an array of linked lists.

The index of the array represents a vertex and each element in its linked list represents the other vertices that form an edge with the vertex.

The adjacency list for the graph we made in the first example is as follows:

An adjacency list is efficient in terms of storage because we only need to store the values for the edges. For a graph with millions of vertices, this can mean a lot of saved space.

• Graph Operations

The most common graph operations are:

• Check if the element is present in the graph
• Graph Traversal
• Add elements(vertex, edges) to graph
• Finding the path from one vertex to another

Table of Contents

• Introduction

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Title: multi-object event graph representation learning for video question answering.

Abstract: Video question answering (VideoQA) is a task to predict the correct answer to questions posed about a given video. The system must comprehend spatial and temporal relationships among objects extracted from videos to perform causal and temporal reasoning. While prior works have focused on modeling individual object movements using transformer-based methods, they falter when capturing complex scenarios involving multiple objects (e.g., "a boy is throwing a ball in a hoop"). We propose a contrastive language event graph representation learning method called CLanG to address this limitation. Aiming to capture event representations associated with multiple objects, our method employs a multi-layer GNN-cluster module for adversarial graph representation learning, enabling contrastive learning between the question text and its relevant multi-object event graph. Our method outperforms a strong baseline, achieving up to 2.2% higher accuracy on two challenging VideoQA datasets, NExT-QA and TGIF-QA-R. In particular, it is 2.8% better than baselines in handling causal and temporal questions, highlighting its strength in reasoning multiple object-based events.

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Introduction to Graph Data Structure

Graph Data Structure is a non-linear data structure consisting of vertices and edges. It is useful in fields such as social network analysis, recommendation systems, and computer networks. In the field of sports data science, graph data structure can be used to analyze and understand the dynamics of team performance and player interactions on the field.

Table of Content

What is Graph Data Structure?

Components of graph data structure.

• Types Of Graph Data Structure
• Representation of Graph Data Structure
• Adjacency Matrix Representation of Graph Data Structure
• Adjacency List Representation of Graph
• Basic Operations on Graph Data Structure
• Difference between Tree and Graph
• Real-Life Applications of Graph Data Structure
• Advantages of Graph Data Structure
• Disadvantages of Graph Data Structure
• Frequently Asked Questions(FAQs) on Graph Data Structure

Graph is a non-linear data structure consisting of vertices and edges. The vertices are sometimes also referred to as nodes and the edges are lines or arcs that connect any two nodes in the graph. More formally a Graph is composed of a set of vertices( V ) and a set of edges( E ). The graph is denoted by G(V, E).

Imagine a game of football as a web of connections, where players are the nodes and their interactions on the field are the edges. This web of connections is exactly what a graph data structure represents, and it’s the key to unlocking insights into team performance and player dynamics in sports.

• Vertices: Vertices are the fundamental units of the graph. Sometimes, vertices are also known as vertex or nodes. Every node/vertex can be labeled or unlabelled.
• Edges: Edges are drawn or used to connect two nodes of the graph. It can be ordered pair of nodes in a directed graph. Edges can connect any two nodes in any possible way. There are no rules. Sometimes, edges are also known as arcs. Every edge can be labelled/unlabelled.

Types Of Graphs in Data Structure and Algorithms

1. null graph.

A graph is known as a null graph if there are no edges in the graph.

2. Trivial Graph

3. Undirected Graph

A graph in which edges do not have any direction. That is the nodes are unordered pairs in the definition of every edge. 

4. Directed Graph

A graph in which edge has direction. That is the nodes are ordered pairs in the definition of every edge.

5. Connected Graph

The graph in which from one node we can visit any other node in the graph is known as a connected graph. 

6. Disconnected Graph

The graph in which at least one node is not reachable from a node is known as a disconnected graph.

7. Regular Graph

The graph in which the degree of every vertex is equal to K is called K regular graph.

8. Complete Graph

9. Cycle Graph

The graph in which the graph is a cycle in itself, the minimum value of degree of each vertex is 2. 

10. Cyclic Graph

A graph containing at least one cycle is known as a Cyclic graph.

11. Directed Acyclic Graph

A Directed Graph that does not contain any cycle. 

12. Bipartite Graph

A graph in which vertex can be divided into two sets such that vertex in each set does not contain any edge between them.

13. Weighted Graph

•   A graph in which the edges are already specified with suitable weight is known as a weighted graph. 
•  Weighted graphs can be further classified as directed weighted graphs and undirected weighted graphs. 

Representation of Graph Data Structure:

There are multiple ways to store a graph: The following are the most common representations.

• Adjacency Matrix
• Adjacency List

Adjacency Matrix Representation of Graph Data Structure:

In this method, the graph is stored in the form of the 2D matrix where rows and columns denote vertices. Each entry in the matrix represents the weight of the edge between those vertices. 

Below is the implementation of Graph Data Structure represented using Adjacency Matrix:

Adjacency List Representation of Graph:

This graph is represented as a collection of linked lists. There is an array of pointer which points to the edges connected to that vertex. 

Below is the implementation of Graph Data Structure represented using Adjacency List:

Comparison between Adjacency Matrix and Adjacency List

When the graph contains a large number of edges then it is good to store it as a matrix because only some entries in the matrix will be empty. An algorithm such as Prim’s and Dijkstra adjacency matrix is used to have less complexity.

Basic Operations on Graph Data Structure:

Below are the basic operations on the graph:

• Add and Remove vertex in Adjacency List representation of Graph
• Add and Remove vertex in Adjacency Matrix representation of Graph
• Add and Remove Edge in Adjacency List representation of a Graph
• Add and Remove Edge in Adjacency Matrix representation of a Graph
• Searching in Graph Data Structure- Search an entity in the graph.
• Traversal of Graph Data Structure- Traversing all the nodes in the graph.

Difference between Tree and Graph:

Tree is a restricted type of Graph Data Structure, just with some more rules. Every tree will always be a graph but not all graphs will be trees. Linked List , Trees , and Heaps all are special cases of graphs. 

Real-Life Applications of Graph Data Structure:

Graph Data Structure has numerous real-life applications across various fields. Some of them are listed below:

• If we recall all the previous data structures that we have studied like array, linked list, tree, etc. All these had some restrictions on structure (mostly linear and tree hierarchical which means no loops). Graph allows random connections between nodes which is useful in many real world problems where do have restrictions of previous data structures.
• Used heavily in social networks. Everyone on the network is a vertex (or node) of the graph and if connected, then there is an edge. Now imagine all the features that you see, mutual friends, people that follow you, etc can seen as graph problems.
• Used to represent the topology of computer networks, such as the connections between routers and switches.
• Used to represent the connections between different places in a transportation network, such as roads and airports.
• Neural Networks: Vertices represent neurons and edges represent the synapses between them. Neural networks are used to understand how our brain works and how connections change when we learn. The human brain has about 10^11 neurons and close to 10^15 synapses.
• Compilers: Graph Data Structure is used extensively in compilers. They can be used for type inference, for so-called data flow analysis, register allocation, and many other purposes. They are also used in specialized compilers, such as query optimization in database languages.
• Robot planning: Vertices represent states the robot can be in and the edges the possible transitions between the states. Such graph plans are used, for example, in planning paths for autonomous vehicles.
• Dependencies in a software project (or any other type of project) can be seen as graph and generating a sequence to solve all tasks before dependents is a standard graph topological sorting algorithm.
• For optimizing the cost of connecting all locations of a network. For example, minimizing wire length in a wired network to make sure all devices are connected is a standard Graph problem called Minimum Spanning Tree.

Advantages of Graph Data Structure:

• Graph Data Structure used to represent a wide range of relationships as we do not have any restrictions like previous data structures (Tree cannot have loops and have to be hierarchical. Arrays, Linked List, etc are linear)
• They can be used to model and solve a wide range of problems, including pathfinding, data clustering, network analysis, and machine learning.
• Any real world problem where we certain set of items and relations between them can be easily modeled as a graph and a lot of standard graph algorithms like BFS, DFS, Spanning Tree, Shortest Path, Topological Sorting and Strongly Connected
• Graph Data Structure can be used to represent complex data structures in a simple and intuitive way, making them easier to understand and analyze.

Disadvantages of Graph Data Structure:

• Graph Data Structure can be complex and difficult to understand, especially for people who are not familiar with graph theory or related algorithms.
• Creating and manipulating graphs can be computationally expensive, especially for very large or complex graphs.
• Graph algorithms can be difficult to design and implement correctly, and can be prone to bugs and errors.
• Graph Data Structure can be difficult to visualize and analyze, especially for very large or complex graphs, which can make it challenging to extract meaningful insights from the data.

Frequently Asked Questions(FAQs) on Graph Data Structure:

1. what is a graph.

A graph is a data structure consisting of a set of vertices (nodes) and a set of edges that connect pairs of vertices.

2. What are the different types of Graph Data Structure?

Graph Data Structure can be classified into various types based on properties such as directionality of edges (directed or undirected), presence of cycles (acyclic or cyclic), and whether multiple edges between the same pair of vertices are allowed (simple or multigraph).

3. What are the applications of Graph Data Structure?

Graph Data Structure has numerous applications in various fields, including social networks, transportation networks, computer networks, recommendation systems, biology, chemistry, and more.

4. What is the difference between a directed graph and an undirected graph?

In an undirected graph, edges have no direction, meaning they represent symmetric relationships between vertices. In a directed graph (or digraph), edges have a direction, indicating a one-way relationship between vertices.

5. What is a weighted graph?

A weighted graph is a graph in which each edge is assigned a numerical weight or cost. These weights can represent distances, costs, or any other quantitative measure associated with the edges.

6. What is the degree of a vertex in a graph?

The degree of a vertex in a graph is the number of edges incident to that vertex. In a directed graph, the indegree of a vertex is the number of incoming edges, and the outdegree is the number of outgoing edges.

7. What is a path in a graph?

A path in a graph is a sequence of vertices connected by edges. The length of a path is the number of edges it contains.

8. What is a cycle in a graph?

A cycle in a graph is a path that starts and ends at the same vertex, traversing a sequence of distinct vertices and edges in between.

9. What are spanning trees and minimum spanning trees?

A spanning tree of a graph is a subgraph that is a tree and includes all the vertices of the original graph. A minimum spanning tree (MST) is a spanning tree with the minimum possible sum of edge weights.

10. What algorithms are commonly used to traverse or search Graph Data Structure?

Common graph traversal algorithms include depth-first search (DFS) and breadth-first search (BFS). These algorithms are used to explore or visit all vertices in a graph, typically starting from a specified vertex. Other algorithms, such as Dijkstra’s algorithm and Bellman-Ford algorithm, are used for shortest path finding.

More Resources of Graph:

• Recent Articles on Graph
• Practice problems on Graph
• Algorithms on Graphs

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