graphic representation bar chart

A complete guide to bar charts

Posted by: mike yi.

One of the most fundamental chart types is the bar chart, and one of your most useful tools when it comes to exploring and understanding your data.

What is a bar chart?

A bar chart (aka bar graph, column chart) plots numeric values for levels of a categorical feature as bars. Levels are plotted on one chart axis, and values are plotted on the other axis. Each categorical value claims one bar, and the length of each bar corresponds to the bar’s value. Bars are plotted on a common baseline to allow for easy comparison of values.

This example bar chart depicts the number of purchases made on a site by different types of users. The categorical feature, user type, is plotted on the horizontal axis, and each bar’s height corresponds to the number of purchases made under each user type. We can see from this chart that while there are about three times as many purchases from new users who create user accounts than those that do not create user accounts (guests), both are dwarfed by the number of purchases made by repeating users.

When you should use a bar chart

A bar chart is used when you want to show a distribution of data points or perform a comparison of metric values across different subgroups of your data. From a bar chart, we can see which groups are highest or most common, and how other groups compare against the others. Since this is a fairly common task, bar charts are a fairly ubiquitous chart type.

The primary variable of a bar chart is its categorical variable. A categorical variable takes discrete values, which can be thought of as labels. Examples include state or country, industry type, website access method (desktop, mobile), and visitor type (free, basic, premium). Some categorical variables have ordered values, like dividing objects by size (small, medium, large). In addition, some non-categorical variables can be converted into groups, like aggregating temporal data based on date (eg. dividing by quarter into 20XX-Q1, 20XX-Q2, 20XX-Q3, 20XX-Q4, etc.) The important point for this primary variable is that the groups are distinct.

In contrast, the secondary variable will be numeric in nature. The secondary variable’s values determine the length of each bar. These values can come from a great variety of sources. In its simplest form, the values may be a simple frequency count or proportion for how much of the data is divided into each category – not an actual data feature at all. For example, the following plot counts pageviews over a period of six months. You can see from this visualization that there was a small peak in June and July before returning to the previous baseline.

Other times, the values may be an average, total, or some other summary measure computed separately for each group. In the following example, the height of each bar depicts the average transaction size by method of payment. Note that while the average payments are highest with checks, it would take a different plot to show how often customers actually use them.

Example of data structure

Data rendered as a bar chart might come in a compact form like the above table, with one column for the categories and the second column for their values. Other times, data may come in its unaggregated form like the below table snippet, with the visualization tool automatically performing the aggregation at the time of visualization creation.

For a count-based bar chart, just the first column is needed. For a summary-based bar chart, group by the first column, then compute the summary measure on the second.

Best practices for using bar charts

Use a common zero-valued baseline.

First and foremost, make sure that all of your bars are being plotted against a zero-value baseline. Not only does that baseline make it easier for readers to compare bar lengths, it also maintains the truthfulness of your data visualization. A bar chart with a non-zero baseline or some other gap in the axis scale can easily misrepresent the comparison between groups since the ratio in bar lengths will not match the ratio in actual bar values.

By cutting 90 points out of the vertical axis, a small 4-point difference can be exaggerated to look like a 1:3 ratio.

Maintain rectangular forms for your bars

Another major no-no is to mess with the shape of the bars to be plotted. Some tools will allow for the rounding of the bar caps, rather than just have straight edges. This rounding means that it’s difficult for the reader to tell where to read the actual value: from the top of the semicircle, or somewhere in the middle? A little bit of rounding of the corners can be okay, but make sure each bar is flat enough to discern its true value and provide an easy comparison between bars.

Similarly, you should avoid including 3-d effects on your bars. As with heavy rounding, this can make it harder to know how to measure bar lengths, and as a bonus, might cause baselines to not be aligned (see the above point).

Consider the ordering of category levels

One consideration you should have when putting together a bar chart is what order in which you will plot the bars. A standard convention to take is to sort the bars from longest to shortest: while it is always possible to compare the bar lengths no matter the order, this can reduce the burden on the reader to make those comparisons themselves. The major exception to this is if the category labels are inherently ordered in some way. In cases like that, the inherent ordering usually takes precedence.

The district codes aren’t inherently ordered, so a better representation is to sort by value.

Use color wisely

Another consideration is on how you should use color in your bar charts. Certain tools will color each bar differently by default, but this can distract the reader by implying additional meaning where none exists. Instead, color should be used with purpose. For example, you might use color to highlight specific columns for storytelling. Colors can also be used if they are meaningful for the categories posted (e.g. to match company or team colors).

The rainbow colors on the left don’t add anything meaningful to interpretation of the plot. On the right side, most bars are a neutral gray to highlight the comparison of the two colored bars.

Common misuses

Replacing bars with images.

It may be tempting to replace bars with pictures that depict what is being measured (e.g. bags of money for money amounts), be careful that you do not misrepresent your data in this way. If your choice of symbol scales both width and height with value, differences will look much larger than they actually are, since people will end up comparing the areas of the bars rather than just their widths or heights. In the example below, there is a 58% growth in downloads from 2018 to 2019. However, this growth is exaggerated with the icon-based representation, since the surface area of the 2019 icon is more than 2.5 times the size of the 2018 icon.

If you feel the need to use icons to depict value, then a better – though still not great – option is to use the pictogram chart type instead. In a pictogram chart , each category’s value is indicated by a series of icons, with each icon representing a certain quantity. In a certain sense, this is like changing the texture of its corresponding bar to a repeating image. One major caution with this chart type is that it can make values harder to read, since the reader needs to perform some mental mathematics to gauge the relative values of each category.

Common bar chart options

Horizontal bars vs. vertical bars.

A common bar chart variation is whether or not the bar chart should be oriented vertically (with categories on the horizontal axis) or horizontally (with categories on the vertical axis). While the vertical bar chart is usually the default, it’s a good idea to use a horizontal bar chart when you are faced with long category labels. In a vertical chart, these labels might overlap, and would need to be rotated or shifted to remain legible; the horizontal orientation avoids this issue.

If the bars from a previous example were vertically oriented, the Team tick labels would need to be rotated in order to be readable.

Include value annotations

A common addition to bar charts are value annotations. While it is fairly easy for readers to compare bar lengths and gauge approximate values from a bar chart, exact values aren’t necessarily easy to state. Annotations can report these values where they are important, and are usually placed in the middle of the bar or at their ends.

Include variability whiskers

When the numeric values are a summary measure, a frequent consideration is whether or not to include error bars in the plot. Error bars are additional whiskers added to the end of each bar to indicate variability in the individual data points that contributed to the summary measure. Since there are many choices for uncertainty measure (e.g. standard deviation, confidence interval, interquartile range) it is important that when you display error bars, that you note in an annotation or comment what the error bars represent.

Alternatively, you may wish to depict variance within each category with a different chart type such as the  box plot  or  violin plot . While these plots will have more elements for a reader to parse, they provide a deeper understanding of the distribution of values within each group.

Error bars indicate the standard deviation for transaction amounts for each payment type. The variability is lower for credit and debit cards compared to the others.

Lollipop chart

One variation of the bar chart is the lollipop chart. It presents exactly the same information as a bar chart, but with different aesthetics. Instead of bars, we have lines topped by dots at their endpoints. A lollipop chart is most useful when there are a lot of categories and their values are fairly close together. By changing the aesthetic form of the plotted values, it can make the chart much easier to read.

Related plots

If the values in a bar chart represent parts of a whole (the sum of bar lengths totals the number of data points or 100%), then an alternative chart type you could use is the  pie chart . While the pie chart is much-maligned, it still  fills a niche  when there are few categories to plot, and the parts-to-whole division needs to be put front and center. Still, in general you are most likely to use a bar chart in general usage, as it’s easier to make comparisons between categories.

Histograms  are a close cousin to bar charts that depict frequency values. While a bar chart’s primary variable is categorical in nature, a histogram’s primary variable is continuous and numeric. The bars in a histogram are typically placed right next to each other to emphasize this continuous nature: bar charts usually have some space between bars to emphasize the categorical nature of the primary variable.

For bar charts that depict summary statistics, the  line chart  is the closest relative. Like the relationship from the bar chart to a histogram, a line chart’s primary variable is typically continuous and numeric, emphasized by the continuous line between points. Shading the region between the line and a zero baseline generates an  area chart , which can be thought of as a combination of the bar chart and line chart.

Alternatively, when we have summary statistics over a categorical primary variable, we might choose a dot plot, or Cleveland dot plot, instead of a bar chart. A dot plot is essentially a line plot without line segments connecting each point. This frees it up to be used with categorical levels, rather than a continuous progression. The biggest advantage a dot plot has over a bar chart is that values are indicated by position rather than length, so we don’t necessarily need a zero-baseline. When the necessary baseline on a bar chart interferes with perception of changes or differences between bars, then a line chart or dot plot can be a good alternative choice.

Stacked bar chart and grouped bar chart

Bar charts can be extended when we introduce a second categorical variable to divide each of the groups in the original categorical variable. If the bar values depict group frequencies, the second categorical variable can divide each bar’s count into subgroups. Applied to the original bars, this results in a  stacked bar chart , seen on the left in the figure below. Alternatively, if we move the different subgroups’ bars to the baseline, the resulting chart type is the  grouped bar chart , seen on the right. We also use the grouped bar chart when we compute statistical summary measures across levels of two categorical variables.

Visualization tools

Most tools that can create visualizations, whether they be spreadsheets, programming libraries, or business intelligence tools, should be capable of creating basic vertical bar charts. Sometimes, options need to be checked or modified in order to follow best practices. However, for basic data exploration needs, any tool should be sufficient. Other variations like horizontal bars, error bars, and annotations may not always be possible. In particular, the lollipop chart variation is not normally considered a default chart type, and will usually require specialized tweaking with programmatic tools instead.

The bar chart is one of many different chart types that can be used for visualizing data. Learn more from our articles on  essential chart types ,  how to choose a type of data visualization , or by browsing the full collection of  articles in the charts category .

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ChartExpo Survey

Master Bar Charts: A Step-by-Step Guide

By ChartExpo Content Team

The bar chart emerges as a pivotal player in the realm of data representation. More than a standard numerical display, the bar chart—also referred to as a bar graph—transforms into a medium of storytelling, trend revelation, and simplification of complex information for both experts and novices.

Its effectiveness in representing categorical data, facilitating efficient comparisons, and adapting to diverse presentation styles emphasizes its crucial role across various industries.

In this exploration of the bar chart universe, we delve into its properties, applications, and the art of harnessing its potential for compelling storytelling. Welcome to the dynamic world of bar charts, where data transforms into narratives, and insights unfold through the simplicity of bars.

Table of Content:

What is a bar chart, historical context and evolution of bar graphs, importance of bar charts in data visualization, crafting engaging narratives with bar charts, what are the components of a bar chart, design considerations, step-by-step guide to creating a bar graph, purpose and utility of bar graphs, key characteristics of bar graphs, applications and examples of bar graphs, what are the benefits of a bar chart, what are the different types of bar charts, evolution of bar charts: specialized bar charts.

• Bar Chart vs Histogram: A Comprehensive Comparison

How to Make a Bar Chart?

When to use a bar chart, when not to use a bar chart, pitfalls, mistakes, and common misconceptions of bar chart, technical tips and best practices for using bar charts, using bar charts to depict part-to-whole relationships, types of bar charts for part-to-whole representation, practical examples and use cases, how to choose the right bar chart type based on the data.

Definition: A bar chart, also known as a bar graph, is a graphical representation of data using bars of different heights or lengths to show the frequency, distribution, or comparison of categories.

Each bar in a bar chart represents a category, and the length or height of the bar corresponds to the value it represents.

Bar charts are commonly used for displaying categorical data and making comparisons between different groups.

Bar charts have a rich history dating back to the 18th century when they were first introduced by William Playfair, a Scottish engineer and economist. Playfair is credited with inventing several types of charts and graphs , including the bar chart, as a means of visualizing economic data. His work laid the foundation for modern data visualization techniques.

Over time, bar charts have evolved to become one of the most widely used tools in data visualization. With advancements in technology and the availability of powerful software tools, creating bar charts has become easier and more accessible to a wider audience.

Today, bar charts are used across various industries and disciplines to analyze data, identify trends, and make informed decisions.

Bar charts play a crucial role in data visualization due to several key reasons:

Clarity and Simplicity

Bar charts are easy to understand and interpret, making them suitable for both experts and non-experts alike. The visual simplicity of bar charts allows viewers to quickly grasp the underlying trends or patterns in the data.

Effective Comparison

Bar charts enable efficient comparison between different categories or groups. Whether it’s comparing sales figures for different products or analyzing demographic data across regions, bar charts provide a clear visual representation of the relationships between variables.

Flexibility and Versatility

Bar charts offer flexibility in terms of customization and presentation options. From simple vertical bar charts to more complex stacked or grouped bar charts, there are various options available to visualize different types of data effectively.

Insightful Decision-Making

By visually summarizing large datasets, bar charts help stakeholders make informed decisions based on data-driven insights. Whether it’s identifying outliers, spotting trends, or tracking progress over time , bar charts serve as valuable tools for data analysis and decision-making.

Bar charts, beyond their conventional role as data visualization tools , serve as powerful storytellers when wielded with finesse. In this exploration, we delve into the art of crafting narratives using bar charts, elevating them from mere graphical representations to compelling storytelling devices.

Introduction to Storytelling in Data Visualization

Bar charts are not just about displaying data; they’re powerful tools for data storytelling . Understanding how to leverage them effectively can transform your data into a compelling narrative. Let’s delve into the role of bar charts in storytelling and explore how you can craft engaging narratives using them.

The Power of Narrative in Data Presentation

Data alone can be dry and difficult to comprehend. By weaving a narrative around your data using bar charts, you can bring it to life, making it more engaging and understandable for your audience. Whether you’re presenting sales figures, survey results , or any other type of data, storytelling adds context and meaning, making the information easier to digest and remember.

Role of Bar Charts in Storytelling

Bar charts are one of the most commonly used types of data visualization due to their simplicity and effectiveness. They allow you to compare different categories or groups easily, making them ideal for telling stories with data. Whether you’re highlighting trends over time, comparing quantities, or showing distribution, bar charts provide a clear and intuitive way to convey information.

Choosing the Right Bar Chart Type

Your story’s essence guides your choice. Whether it’s a simple comparison or a complex distribution, selecting the appropriate type (horizontal, vertical, stacked, or grouped) ensures your narrative’s clarity.

Enhancing the Narrative with Bar Chart Options

Every element in a bar chart (color, order, axis labels) adds depth to your story. Use these options thoughtfully to guide your audience through the data journey.

A Bar Chart is a visual representation of data, and its effectiveness lies in the clarity and precision with which information is conveyed. Understanding the components of a Bar Chart is essential for creating meaningful visualizations. Here are the key elements that make up a Bar Chart:

The title of a bar chart succinctly summarizes the data being represented. It provides context and clarity to the viewer.

The bars in a bar chart represent the data values corresponding to different categories or groups. The length or height of each bar is proportional to the value it represents.

• X-Axis (Horizontal Axis):

The horizontal axis of a bar chart typically displays the categories or groups being compared. It provides a reference point for interpreting the data .

• Y-Axis (Vertical Axis):

The vertical axis of a bar chart displays the scale or values being measured. It helps viewers understand the magnitude of each data point.

The scale on the axes determines the range of values displayed on the chart. It ensures accurate representation of data and facilitates comparisons between different categories.

Labels are used to identify specific data points or categories on the chart. They enhance readability and comprehension for the viewer.

If multiple data series are present in the bar chart, a legend may be included to clarify which color or pattern corresponds to each series.

Creating an effective and visually compelling Bar Chart involves careful consideration of various design elements. These considerations play a crucial role in ensuring that the chart not only accurately represents the data but also engages and informs the audience effectively. Here are key design considerations for crafting impactful Bar Charts:

Bar Width and Spacing

The width of the bars and the spacing between them can impact the visual clarity and interpretation of the chart. Optimal width and spacing ensure that the data is presented clearly without overcrowding.

Color and Patterns

Choosing appropriate colors and patterns for the bars can aid in differentiating between data categories or series. Consistent use of color enhances readability and helps convey meaning effectively.

Creating a bar chart is a straightforward process that involves a series of organized steps to ensure accurate representation and effective communication of your data. Follow this step-by-step guide to seamlessly craft a compelling bar chart:

Gather Your Data

Before diving into creating a bar chart, ensure you have all the necessary data collected and organized. This includes the categories or groups you want to represent on the x-axis and their corresponding values or frequencies for the y-axis.

Choose Your Software

Select a software or tool that suits your preferences and needs for creating bar charts. Options range from simple spreadsheet programs like Microsoft Excel or Google Sheets to more advanced data visualization software like ChartExpo .

Input Your Data

Once you’ve chosen your software, input your data into the designated fields. Make sure your data is correctly formatted to match the requirements of the software you’re using. Typically, you’ll enter your categories or groups along with their corresponding values.

Select the Bar Chart Option

Find and select the option to create a bar chart within your chosen software. This may be located in the “Insert” or “Charts” menu depending on the program you’re using. Choose the basic bar chart type to start.

Customize Your Chart

After inserting the basic bar chart, you can customize it to better suit your needs and preferences. This includes adjusting colors, labels, titles, and axis scales. You may also explore additional features such as adding data labels or changing the chart layout.

Interpret and Share the Chart

Once your bar chart is complete, take some time to interpret the data it represents. Identify any trends, patterns, or insights that the chart reveals. Once you’ve analyzed the data, you can then share the chart with others through various means such as exporting it as an image or embedding it in a presentation.

Bar graphs serve various purposes and offer utility in data visualization. Here are the key aspects highlighting the purpose and utility of bar graphs:

Simplifying Complex Data Presentation

Bar graphs streamline the presentation of intricate data sets into easily digestible visual formats.

Facilitating Comparisons Between Different Data Sets

By visually comparing the lengths or heights of bars, it becomes simpler to discern variations between distinct data categories.

Demonstrating Relationships Between Categories and Values

Bar graphs effectively showcase how different categories relate to corresponding values through their respective bar lengths or heights.

Highlighting Significant Changes in Data Over Time

Over time, bar graphs can illustrate fluctuations or trends in data, making them invaluable for trend analysis and forecasting.

Bar graphs exhibit key characteristics that make them effective tools for data visualization. Here are the essential characteristics of bar graphs:

Two Axes Representation

Bar graphs typically feature two axes—the X-axis representing categories and the Y-axis representing values.

Length or Height of Bars Indicating Value

The values associated with each category are represented by the length or height of the bars, providing a clear visual indicator.

Clarity in Data Presentation and Ease of Understanding:

Bar graphs are designed to present data clearly and are easily comprehensible to a wide audience.

Bar graphs find diverse applications across various industries, serving as valuable tools for visualizing data and data-driven decision-making . Here are applications and examples of bar graphs in different fields:

In the corporate world, bar graphs are commonly utilized to illustrate financial data , market trends, and performance metrics. For instance, a company might create a bar chart to compare sales figures for different product categories over a specific period.

Manufacturing

Manufacturing companies often employ bar graphs to monitor production output, track inventory levels, and analyze efficiency metrics. An example could be a bar graph illustrating the distribution of defects across different production lines.

Real Estate

In real estate, bar graphs can be used to showcase housing market statistics, such as average home prices, rental rates, or property sales by neighborhood. An example might be a bar chart comparing the median home prices in various suburbs.

In the tech industry, bar graphs are valuable for visualizing data related to user engagement, website traffic , and software performance. For instance, a tech startup may create a bar graph to display monthly user acquisition numbers across different marketing channels.

Banking/Finance

Bar graphs play a crucial role in banking and finance for displaying financial statements , investment portfolios, and market trends. An example could be a bar chart illustrating the distribution of assets in a mutual fund.

In healthcare, bar graphs are used to present patient demographics, treatment outcomes, and epidemiological data. For example, a healthcare provider might create a bar graph to compare the prevalence of various diseases in different age groups.

Bar charts are a powerful tool for data visualization, offering unparalleled clarity, versatility, and ease of use. Whether you’re a seasoned analyst or a novice presenter, leveraging bar charts can significantly enhance the impact and effectiveness of your data communication.

Visual Clarity and Impact

Bar charts excel in providing visual clarity, and simplifying data interpretation for all viewers. With their straightforward presentation, understanding complex data becomes effortless.

Screen-Friendly Format

One of the key benefits of bar charts lies in their adaptability to various presentation sizes and styles. Their minimal labeling requirements make them particularly suitable for digital content, enhancing readability and engagement.

Flexibility in Structure and Data Representation

Bar charts boast a flexible structure that accommodates almost any kind of data. Whether it’s organizing data by categories or layers, bar charts offer versatile options for comprehensive comparisons.

Comparative Analysis and Trends Demonstration

Comparative analysis across different categories or over time is made easy with bar charts. By arranging bars sequentially, they effectively demonstrate data trends, enabling audiences to grasp insights swiftly.

Handling Large Data Sets

Bar charts are capable of accommodating and simplifying the presentation of large data sets . They are ideal for representing nominal and small ordinal variable data, ensuring clarity without overwhelming the audience.

Multivariate Data Representation

With bar charts, representing multivariate data becomes a breeze. Their versatility allows for the simultaneous visualization of multiple variables, facilitating a deeper understanding of complex datasets.

Versatility in Types and Uses

From horizontal to vertical, stacked to grouped, bar charts offer various types to suit specific data presentation needs. Each type has its advantages, ensuring optimal visualization for diverse datasets.

Ease of Creation and Customization

Creating bar charts is hassle-free with the availability of bar chart maker tools and software. These tools offer customization options, allowing users to tailor charts to their exact specifications effortlessly.

Comparisons Made Easy

The inherent design of bar charts simplifies the comparison of data variables or categories. Whether using grouped or stacked formats, bar charts make it easy to draw comparisons across multiple categories and variables.

Identification of Key Data Points

Bar charts facilitate the identification of key data points, offering immediate insight into prominent categories or items. This allows for quick data analysis and identification of areas requiring attention or improvement.

Universal Familiarity and Adoption

Bar charts enjoy universal familiarity, making them an ideal choice for reports and presentations. Their widespread adoption ensures that audiences can quickly grasp and analyze the presented data with ease.

Bar charts serve as indispensable tools in data visualization, aiding in the comparison and analysis of various datasets.

Understanding the nuances of each bar chart type empowers you to select the most appropriate visualization method for effectively conveying your data insights.

Bar charts come in various forms, each serving specific purposes in data visualization. Let’s explore these types in detail:

Vertical Bar Chart

Vertical bar charts use vertical bars along the x-axis to represent data categories. Each bar’s height corresponds to the value it represents.

Usage : Commonly used for comparing discrete data categories or showing changes over time, especially when the number of categories is limited.

Horizontal Bar Chart

Horizontal bar charts flip the orientation of vertical bar charts, displaying bars along the y-axis. They are particularly useful when labels are long or when comparing data across different groups.

Usage : Suitable for presenting ranked data or emphasizing differences in magnitude.

Stacked Bar Chart

Stacked bar charts represent data with bars stacked on top of each other, illustrating the total value while showing the contribution of each subgroup.

Usage : Useful for visualizing part-to-whole relationships and identifying trends within each category.

100% Stacked Bar Chart

Similar to stacked bar charts, bars represent percentages rather than absolute values, making it easier to compare the relative proportions of each subgroup.

Usage : Valuable for comparing the relative distribution of data categories and identifying percentage contributions.

Clustered Bar Chart

Clustered bar charts group bars by category, with bars within each group displayed adjacent to each other, facilitating direct comparison.

Usage : Ideal for comparing multiple datasets across different categories while maintaining clarity and organization.

Comparison Bar Chart

A Comparison Bar Chart is a visual representation that utilizes rectangular bars to illustrate and compare individual items or categories across different groups. The lengths of the bars are proportional to the values they represent, offering a straightforward and effective way to highlight disparities or similarities within the data.

Usage : Employ a comparison bar chart to benchmark and compare the performance of various entities, be it products, departments, or competitors. This visualization simplifies the process of identifying strengths, weaknesses, and areas for improvement clearly and concisely.

Overlapping Bar Chart

An Overlapping Bar Chart , also known as a clustered bar chart, displays multiple sets of data with bars positioned side by side for direct comparison. In this visualization, each group of bars represents a distinct category, enabling an immediate visual contrast between different datasets.

Usage : Efficiently compare and analyze multiple variables or data series within specific categories, offering a clear visual representation of their contributions and variations.

Bar charts have evolved beyond their conventional forms to cater to diverse data visualization needs. These specialized variations provide unique insights and enhance the presentation of data by offering innovative ways to showcase complex information. Explore the evolution of bar charts through these specialized forms and discover how they bring a new dimension to the world of visuals.

Radial (Circular) Bar Chart

A radial bar chart , also known as a circular bar chart, is a unique variation of the traditional bar chart. Instead of bars being arranged horizontally or vertically, they radiate outward from a central point. Each bar’s length corresponds to the data it represents, making it visually appealing and useful for displaying cyclical or periodic data.

Radial Bar Chart Example

A radial bar chart can be used to display sales data for different regions, with each bar representing a region and the length indicating the sales volume. The circular layout makes it easy to compare sales across different regions at a glance.

Pareto Bar Chart

A Pareto bar chart is a combination of both bar and line graphs, used to highlight the most significant factors among a set of data. The bars represent individual categories, sorted in descending order of frequency or impact, while the line graph shows the cumulative total. This chart helps identify the “vital few” from the “trivial many” based on the Pareto Principle.

Pareto Bar Chart Example

In management, a Pareto bar chart can show the frequency of different types of things, helping identify which issues to prioritize for improvement efforts.

You can create a Bar Chart in your favorite spreadsheet. Follow the steps below to create a Bar Chart.

Steps to Make Bar Chart in Microsoft Excel:

• Open your Excel Application.
• Install ChartExpo Add-in for Excel from Microsoft AppSource to create interactive visualizations.
• Select Comparison Bar Chart from the list of charts.
• Select your data
• Click on the “Create Chart from Selection” button.
• Customize your chart properties to add header, axis, legends, and other required information.

The following video will help you to create a Bar Chart in Microsoft Excel.

Steps to Make a Bar Chart in Google Sheets:

• Open your Google Sheets Application.
• Install ChartExpo Add-in for Google Sheets from Google Workspace Marketplace.
• Select Bar Chart from the list of charts.
• Fill in the necessary fields.
• Click on the Create Chart button.
• Export your chart and share it with your audience.

The following video will help you to create a Bar Chart in Google Sheets.

Steps to Make Bar Chart in Power BI:

• Open your Power BI Desktop or Web.
• From the Power BI Visualizations pane, expand three dots at the bottom and select “Get more visuals”.
• Search for “ Comparison Bar Chart by ChartExpo ” on the AppSource.
• Add the custom visual.
• Select your data and configure the chart settings to create the chart.
• Share the chart with your audience.

The following video will help you to create a Bar Chart in Microsoft Power BI.

Choosing the right type of data visualization is crucial for effectively conveying information. Bar charts are particularly useful in the following scenarios:

Comparing Categories

Bar graphs are ideal for comparing different categories or groups. If you have data that falls into distinct categories, such as different products, cities, or years, a bar graph allows you to easily compare the values associated with each category.

Tracking Changes Over Time

Bar graphs can also be used to track changes over time. By plotting data points along a timeline, such as months or years, you can visualize trends and patterns. This makes bar graphs useful for showing growth, decline, or consistency over time.

Highlighting Distribution

When you want to emphasize the distribution of data within a category or group, a bar graph is a great choice. Whether you’re showcasing the frequency of certain outcomes or the distribution of responses to a survey question , a bar graph can effectively illustrate this information.

Simplifying Complex Data

Bar graphs excel at simplifying complex data sets. Even if you have large amounts of data or multiple variables to compare, a well-designed bar graph can make the information more digestible and easier to understand at a glance.

When to Use Horizontal Bar Charts?

Horizontal bar charts are ideal when comparing categories or items with longer names. They are particularly useful when you have a lot of data points to display and want to prevent labels from overlapping. Here are some scenarios where a horizontal bar chart might be the best option:

• Comparing Categories: When you have categories with lengthy labels, such as product names or project titles, a horizontal bar chart allows for better readability.
• Time Comparisons: If you’re comparing data over periods and want to emphasize the chronological aspect, a horizontal bar chart can provide a clear visual representation.
• Limited Space: In cases where you have limited space to display the chart horizontally, such as in reports or presentations, a horizontal bar chart can fit more data without sacrificing clarity.

When to Use Vertical Bar Charts?

Vertical bar charts are commonly used for straightforward comparisons between different categories or groups. They are easy to read and interpret, making them suitable for various scenarios:

• Comparing Values : When comparing values across different categories, a vertical bar chart offers a simple and intuitive way to visualize the data.
• Frequency Distribution: For displaying frequency distributions or discrete data sets, such as survey results or demographic information, vertical bar charts are effective.
• Space Efficiency: In situations where vertical space is not a constraint, vertical bar charts provide a compact and concise way to present information without compromising clarity.
• Sales Performance: Compare the sales performance of different products or regions using either horizontal or vertical bar charts, depending on the length of the category names and the amount of data.
• Budget Allocation: Visualize budget allocations across various departments or expense categories with vertical bar charts to easily identify areas of focus or concern.
• Survey Analysis: Present survey responses or demographic data using vertical bar charts to showcase trends or patterns among different groups or variables.

Bar charts are versatile and widely used for data visualization, there are certain situations where alternative methods may be more appropriate. Understanding the limitations of bar charts and knowing when to utilize other visualization techniques will help you effectively communicate your data insights to your audience.

Continuous Data Representation

When data is continuous, showing gradual change over a range, a bar chart may not be the most effective option.

Bar charts are best suited for discrete categories or groups, where each category is distinct and separate.

For instance, if you’re analyzing temperature fluctuations throughout the day, a line graph would better depict the continuous nature of the data, whereas a bar chart might misrepresent the data’s continuity.

Complex Data Sets with Many Categories

Bar charts become less effective as the number of categories or groups increases. In such cases, the bars may become too narrow or numerous, making it difficult for viewers to interpret the information accurately.

For example, if you’re comparing sales performance across hundreds of products, a bar chart would likely be overwhelming and impractical. Instead, consider using other visualization methods like a heatmap or a treemap to display complex data sets more effectively.

Illustrating Trends Over Time

While bar charts can display data over time, they are not optimal for illustrating trends. Line graphs are typically better suited for this purpose because they visually connect data points, making it easier for viewers to identify patterns and trends.

If you want to showcase how a particular variable changes over time, such as stock prices or population growth, a line graph would provide clearer insights than a bar chart.

Showing Relationships Between Variables

Bar charts are primarily used to compare individual categories or groups, making them unsuitable for depicting relationships between variables.

If you need to visualize correlations or dependencies between different factors, other types of graphs, such as scatter plots or bubble charts, would be more appropriate.

These graphs allow you to examine how variables interact with each other, providing deeper insights into underlying relationships.

Displaying Geospatial Data

When dealing with geospatial data, such as maps or geographic regions, bar charts are not the ideal choice for visualization.

Geospatial data often requires specialized mapping techniques to accurately represent spatial relationships and distributions. Instead of using bar charts, consider employing geographic information system (GIS) software or mapping tools to create interactive maps or choropleth maps, which offer a more comprehensive view of spatial data.

By avoiding the following misconceptions, mistakes, and pitfalls, you can create more accurate, effective, and trustworthy bar charts that enhance understanding and facilitate data-driven decision-making.

Starting the y-axis at a Non-Zero Value Can Be Misleading

Bar charts are designed to visually represent data by using bars of varying lengths. The length of each bar corresponds to the magnitude of the data it represents. However, when the y-axis does not start at zero, it can distort the visual perception of these magnitudes, leading to misinterpretations.

Inaccurate Scaling

Inaccurate scaling is a frequent error when creating bar charts. It occurs when the intervals on the y-axis are not correctly proportionate to the data being represented. This can lead to misinterpretation of the data and exaggeration of differences between categories.

Overcrowding

Overcrowding happens when there are too many bars or categories on the chart, making it difficult to distinguish between them. This can occur due to a large amount of data or insufficient space on the chart. It results in a cluttered and confusing visualization, undermining its effectiveness.

Mislabeling or Lack of Labels

Mislabeling or omitting labels on the axes or bars can lead to confusion about what the chart represents. Labels are essential for providing context and understanding the data. Without proper labeling, viewers may misinterpret the information or be unable to interpret it at all.

Inappropriate Chart Type Selection

Selecting an inappropriate chart type can hinder understanding and misrepresent the data. For example, using a bar chart to represent data that is better suited for a line chart can lead to confusion. It’s crucial to choose the most appropriate chart type based on the nature of the data and the intended message.

Misleading Data Representation

Misleading data representation occurs when the design of the bar chart distorts or exaggerates the data. This can include using truncated axes, omitting relevant data points, or altering the scale to emphasize certain trends. Such practices can manipulate the viewer’s perception and compromise the integrity of the visualization.

Overuse of Design Elements

Overusing design elements such as colors, patterns, or effects can distract from the data and make the chart difficult to read. While some visual enhancements can improve aesthetics, excessive use can overwhelm the viewer and detract from the chart’s effectiveness in conveying information.

Lack of Context or Comparative Data

Presenting data without context or comparative data can make it challenging for viewers to interpret its significance. Providing context allows viewers to understand the implications of the data and make informed decisions. Without it, the data may lack relevance or meaning.

Incorrect Use of Bar Charts for Non-Categorical Data

Using bar charts for non-categorical data, such as continuous or sequential data, is a common mistake. Bar charts are designed to display categorical data, where each bar represents a distinct category or group. Using them for non-categorical data can result in misleading visualizations and misinterpretation of the data.

  Choosing Between Horizontal and Vertical Bars

The choice between horizontal and vertical bar charts depends on factors such as the length of category names, the type of comparison being made, and the available space for displaying the chart. By considering these factors, you can select the most appropriate option to effectively communicate your data.

Unlock the full potential of bar charts with these technical tips and best practices. Dive into the nuances of effective data visualization, ensuring your bar charts deliver clarity and impact.

Designing Bar Charts for Readability and Impact

Creating a visually appealing and informative bar chart involves several key considerations to ensure readability and impact.

Effective Use of Colors and Patterns

Carefully select colors and patterns that enhance the readability and visual appeal of your bar chart. Use contrasting colors to differentiate between bars, making it easier for viewers to interpret the data.

Avoid using colors that may be difficult for color-blind individuals to distinguish. In addition to colors, consider incorporating patterns for added clarity, especially in stacked or grouped bar charts.

Patterns can help distinguish between categories or subgroups, even when printed in black and white.

Data Labeling and Annotation

Accurate labeling is essential for clarity and understanding. Ensure that each bar is clearly labeled with the corresponding data value, either directly on the bar or adjacent to it.

Avoid cluttering the chart with excessive labels by using strategic placement or utilizing tooltips for additional information.

Consider using descriptive labels that provide context and meaning to the data, making it easier for viewers to interpret the chart at a glance.

Optimizing Chart Layout and Spacing

Proper spacing between bars, axis labels, and other chart elements is crucial for readability. Avoid overcrowding the chart by adjusting the width of the bars and the margins. Maintain consistency in spacing to create a balanced and visually appealing layout.

Pay attention to the alignment of axis labels and gridlines to ensure they are evenly distributed and visible. Experiment with different layouts and configurations to find the optimal arrangement that maximizes clarity and impact.

Bar charts are powerful tools for illustrating part-to-whole relationships within datasets. By leveraging the structure of bar charts, where bars can represent individual components of a whole, you can effectively communicate the contribution of each part to the overall dataset. Whether visualizing the percentage distribution of sales across product categories or showcasing the market share of different segments, bar charts offer a clear and concise way to convey how each part relates to the entirety. Explore the versatility of bar charts in capturing the nuances of part-to-whole relationships and enhance your data visualization capabilities.

Bar charts are versatile tools, and when it comes to illustrating part-to-whole relationships, stacked bar charts stand out as an invaluable choice.

Stacked Bar Charts: Introduction and Basic Concept

Stacked bar charts display multiple datasets stacked on top of each other, showcasing both the individual parts and the total composition. Each bar represents the whole, while segments within the bar represent the proportions of different categories.

100% Stacked Bar Charts: Specialized for Depicting Total Composition by Percentage

In 100% stacked bar charts, each bar represents 100% of the data, and the segments within the bar represent the proportion of each category as a percentage of the whole. This type of chart is particularly useful for comparing the relative distribution of categories across different groups.

Comparison between Stacked Bar Charts and 100% Stacked Bar Charts

While both types of bar charts visualize part-to-whole relationships, stacked bar charts emphasize absolute values, whereas 100% stacked bar charts focus on relative proportions.

Let’s uncover the practical applications of bar charts and gain valuable insights into their role in various use cases.

Real-World Examples Showcasing Part-to-Whole Data Visualization

For instance, imagine a bar chart representing the distribution of expenses in a household budget. Each bar could depict categories such as groceries, utilities, rent, etc., providing a clear visualization of how each expense contributes to the total budget.

Double-Bar Graphs

Double bar graphs are ideal for comparing two sets of data side by side, such as comparing the sales performance of two different products over multiple months.

Clustered Bar Chart and Grouped Bar Charts

Clustered bar charts group similar categories together, allowing for easy comparison within each group, whereas grouped bar charts separate categories into distinct clusters, providing a broader overview of the data.

Understanding the different types of bar charts and when to use them is essential for effective data visualization. Whether you’re comparing a single variable or analyzing complex datasets with multiple variables, there’s a bar chart option suitable for your needs. Experiment with different types to find the best way to present your data clearly and accurately.

Bar Chart With 1 Variable

A bar chart with one variable, also known as a simple bar chart, displays the frequency or count of a single category. For example, you might create a bar chart showing the number of students in each grade level.

Bar Chart With 2 Variables

When you have two variables to compare, you can create a grouped or clustered bar chart. This type of chart allows you to visualize the relationship between two different categories. For instance, you could compare the sales performance of two products over several months.

Bar Chart with 3 Variables

Introducing more than two variables to your bar chart can be challenging but informative. One option is a grouped stacked bar chart. This type of chart is useful for illustrating the composition of a whole while comparing multiple categories simultaneously.

What are the Pros and Cons of Using Bar Charts?

• Easy to Understand: Bar charts are simple and easy to understand, making them accessible to a wide audience, including those with little statistical knowledge.
• Effective for Comparison: They are particularly effective for comparing data across different categories or groups.
• Flexible: Bar charts can be used to represent both categorical and numerical data, and they can accommodate a wide range of data types and formats.
• Visual Impact: The visual impact of bar charts can help highlight patterns, trends, and differences in the data.
• Limited for Continuous Data: Bar charts are not well-suited for representing continuous data or data with many distinct values. In such cases, histograms or other types of charts may be more appropriate.
• Space Consumption: If there are many categories or groups to compare, bar charts can become crowded and difficult to read, especially when labels need to be displayed.
• Misleading Scaling: If the scaling on the axis is not appropriate, bar charts can be misleading in conveying the true proportions or relationships between data points.
• Lack of Precision: Bar charts may not be suitable for displaying precise numerical values, as they only provide a visual approximation of the data.

Are there any alternatives to bar charts?

• Pie Charts : Suitable for showing proportions of a whole, but can be less effective than bar charts for precise comparisons.
• Line Charts : Ideal for showing trends over time or continuous data.
• Scatter Plots : Useful for visualizing relationships between two variables.
• Histograms : Great for displaying the distribution of continuous data.
• Box Plots : Effective for comparing distributions of different groups.

Heatmaps : Useful for displaying data with two categorical variables using colors.

How can I ensure the accuracy of data represented in a bar chart?

• Verify the source of your data : Ensure that the data you’re using is from a reliable and credible source.
• Check for errors : Review the data for any inconsistencies, inaccuracies, or missing values. Double-check calculations if necessary.
• Validate data entry : If the data has been entered manually, ensure accuracy in transcription and avoid typos.
• Use appropriate measurement units : Make sure that all data points are in the same units and are correctly represented on the chart.
• Consider the context : Understand the context in which the data was collected and interpreted to ensure its relevance and accuracy in the chart.
• Cross-reference with other sources : If possible, compare the data with other reliable sources to validate its accuracy.
• Label axes and bars clearly : Clearly label the axes and bars on the chart to avoid misinterpretation of data.

Bar charts offer a clear, simple way to present data. They help us compare, understand trends, and make decisions.

Whether you’re just starting or seeking deeper insights, bar charts are tools that turn numbers into stories. They remind us that behind every data point, there’s a narrative waiting to unfold. So, dive into the world of bar charts.

Uncover the stories your data tells. It’s not just about graphs; it’s about giving voice to the silent numbers. Remember, every bar chart you create is a step towards unlocking the power of data. Let your data speak.

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A bar graph, also called a bar chart, represents data graphically in the form of bars. The height of the bars corresponds to the data they represent. Like all graphs, bar graphs are also presented on a coordinate plane having an x-axis and a y-axis.

The different parts of a bar graph are:

• x-axis label
• y-axis label

How to Draw a Bar Graph

Let us consider an example. Sam went to the vegetable market and bought some vegetables. He bought 6 kg of potatoes, 8 kg of onions, 5 kg of tomatoes, and 3 kg of capsicum. He now wants to display the data as a bar graph.

To create the bar graph in an Excel sheet, he needs to follow the following steps:

• Giving a title to the graph, for example, ‘Vegetables Bought.’
• Drawing a horizontal x-axis and a vertical y-axis
• Labeling the axes: The x-axis is ‘Types of Vegetables’, which is an independent variable, and the y-axis is ‘Weights of Vegetables’, which is a dependent variable
• Naming the vegetables: Potatoes, onions, tomatoes, and capsicum, and giving an equal gap between each bar on the horizontal axis.
• Scaling the graph. For example, it is written as 1 unit = 1 kg
• Drawing the bars corresponding to the available data.

Following the steps, if we plot the above data, the bar graph will look as shown.

The key properties of a bar graph are:

• It represents numerical data by rectangles of equal width but varying height
• The height of the bars depends on the value it represents
• The gap between the bars is uniform
• It can be vertical or horizontal

Bar graphs are used to represent the frequencies of categorical variables. They are mainly of two types: vertical and horizontal.

Vertical Bar Graphs

Let us assume that Rob has taken a survey of his classmates to find which kind of sports they prefer and noted the result in the form of a table.

When we represent the above data in the form of a vertical bar graph, it shapes up like this:

This form of representation is most commonly used in statistics.

Horizontal Bar Graphs

However, bar graphs can also be presented horizontally. If we represent the above data horizontally, it looks like as shown below:

Sometimes we need to present data representing a group. Such data are presented as either vertical or stacked bar graphs.

Other Types

Grouped Bar Graphs

Also known as the clustered bar graph, it plots numeric values for levels of 2 or more categorical variables instead of one side-by-side. Here, the rectangular bars are grouped by position for levels of one categorical variable, with the same colors indicating the secondary category level within each group.

A grouped bar graph showing 2 sets of data is called a double bar graph. It can be both vertical and horizontal.

Let us represent the given data using a vertical double-bar graph.

This is an example of a double bar graph from which we can quickly identify the sport that is most popular of all and the least popular one. It also shows the relative sizes of the things under study.

Stacked Bar Graphs

Also known as the segmented or composite bar graph, it divides the whole graph into different parts. Each part of the bar represents a particular category with a different color. Thus, a bar represents the whole, and each segment is a part of the whole.

A stacked bar graph can be both vertical and horizontal.

Let us represent the data of a farm producing apples, oranges, bananas, and mangoes for the years 2018, 2019, 2020, 2021, and 2022 in the form of a stacked bar graph.

What is a Bar Graph Used For

Students widely use bar graphs to represent numeric data in mathematics and statistics. However, it is also used in various industries for business and finance. Some of its uses are:

• Comparison between 2 or more variables is easy
• It is prepared without much effort
• It helps to determine a pattern in data collected over a long period
• Represents data that are grouped into categories. For example, it is used to show the difference in the votes obtained by the winning candidate compared to the rest.
• It also estimates the percentage of some quantity compared to the rest.

Bar Graph vs. Histogram

The main difference is that a bar graph represents ungrouped data. In contrast, a histogram is used to represent grouped data. Again, the bars are not adjacent in a bar graph, whereas in a histogram, the bars are adjacent.  

Solved Examples

E.g.1. Draw a bar graph of the number of students newly admitted to a school in different grades.

E.g.2. A survey of 50 students about their favorite season of the year is listed. Prepare a bar graph to show which season is most popular among them.

E.g.3. Draw the horizontal bar graph for the given data set between the number of people and their preferred mode of transport.

E.g. 4. Represent the given data in a double-bar graph.

E.g.5. The table shows the number of students newly admitted to St Paul’s and St Xavier’s schools in the following years. Represent your data in the form of a horizontal segmented bar graph.

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A Complete Guide To Bar Charts With Examples, Benefits, And Different Types 

Table of Contents

1) What Are Bar Charts & Graphs?

2) Pros & Cons Of Bar Charts

3) When To Use A Bar Graph

4) Types Of Bar Charts

5) Bar Graphs & Charts Best Practices

6) Bar Chart Examples

In today’s fast-paced analytical landscape, data visualization has become one of the most powerful tools organizations can benefit from to be successful with their analytical efforts. By using different types of graphs and charts, businesses can make their data more understandable which also makes it easier to extract powerful insights from it. 

At datapine, we believe that in order to successfully utilize the various data visualizations we have available, it is necessary to identify the advantages and disadvantages of each graphic to make sure you are using them in the correct way. For that purpose, we are creating a series of blog posts that will take an in-depth look at the most common types of graphs and charts out there and explore their main uses through insightful business examples. We started this series with gauge charts , now it’s the turn of one of the most common charts: the bar chart.

Here, you’ll learn the definition, its advantages in a business context, common types and their use cases as well as an insightful list of examples for different functions and industries. Let’s dive in with the definition.  

What Are Bar Charts & Graphs?

A bar graph is a graphical representation that uses rectangular bars with diverse sizes to compare different values of categorical data. The bars on a bar chart can be horizontal or vertical, but the vertical version is most commonly known as a column chart. 

As mentioned above, bar graphs can be plotted using horizontal or vertical bars. For the purpose of this post, we will only focus on horizontal bars. As vertical ones are a different type of visual known as a column chart and we will do an in-depth analysis on that one as a standalone chart soon. 

Typically, a bar chart displays a categorical variable on the y-axis (vertical) with comparable numerical values that are displayed on the x-axis (horizontal). The categories are usually qualitative data such as products, years, product categories, countries, etc. that are being compared based on specific criteria. This is represented by the example above, in which we can see the top 10 products by revenue where the length of the horizontal bars are corresponding to the size of the values. Making it a great visual to extract conclusions about product development. 

Disadvantages & Advantages Of Bar Graphs

Just like any data analysis technique, bar graphs have advantages and disadvantages to them. It is important to recognize and understand these as they will enable you to gain a deeper understanding of when it is appropriate to benefit from this visual. Let’s begin with two key advantages of bar graphs. 

• Summarize large data sets : Due to their horizontal orientation, a bar graph enables users to easily integrate longer labels in a visually appealing way. Plus, they have enough space to plot as many categories as you need without cluttering the graph, making them way more efficient than column charts when it comes to analyzing multiple categories of data. 
• Performance tracking: Looking at it from a business context, these charts are great visuals to monitor and analyze performance in multiple areas. For example, you can use a bar diagram to display sales by employee and sort the chart from largest to smallest. This way, you’ll be able to see which employees are performing well and which ones might need some help. Likewise, you can track sales by products and identify which ones are lacking and decide if you want to allocate more resources to them. 
• Accessible to all audiences: Due to their massive use in media, politics, and business, the bar chart is a visual that is recognized and understood by most audiences. This makes them the perfect tool to show important information to non-technical audiences in various contexts, especially in business. Plus, it is a simple visual that can be understood at a glance due to the different bar lengths, something that can be considered a disadvantage depending on the use case. We will discuss this in more detail below.   

Now let’s look at two disadvantages or roadblocks of horizontal bar graphs: 

• Too simple: As mentioned, the simplicity of a bar graph can be considered an advantage and a disadvantage depending on the use case. It can be great when you are trying to compare different values, but it falls short when looking for extra insights such as further context or causes for a specific scenario. That is not to say that they are useless in providing insights. They prove to be invaluable comparison tools that have been widely used for decades in multiple contexts and in the modern landscape, they have become more dynamic than ever (more on this later). 
• Too easily manipulated: Just like many other chart types, bar graphs can be used in unethical manners to mislead audiences. This is a common practice in the media, advertising, and politics, where values are manipulated to make the audiences believe certain conclusions. 

When To Use A Bar Graph

Now that you can recognize the main advantages and disadvantages of these visuals, it is time to dive into what is a bar graph used for. For this purpose, it is necessary to consider the goals of your analysis, the type of data you are trying to represent, and, of course, the audience. 

As you’ve probably already learned, the main use case for bar graphs is to compare categorical data within different groups. These groups can be anything from countries, payment methods, product categories, or even time periods like years, quarters, months, and the list can go on and on. The important criterion is that these groups should be distinct and comparable with each other.  The groups are compared based on a second variable which is numerical. This can be anything from sales amounts, page views, clicks, energy consumption, survey answers, and many more.  

So, if the aim of your analysis is to represent differences between groups as the ones we mentioned above, then a bar graph is the best way to go about it. That said, there are multiple types of bar graphs that maintain the main goal of comparison but go a bit deeper with it or serve the purpose in a different way. We will discuss each of them below.  

Types Of Bar Charts

Bar charts are versatile charts that can be used in multiple shapes and forms depending on the aim of the analysis, the questions you are trying to answer as well as the type of data you are representing. Below we go into depth into different types of bar graphs with examples.

1. Horizontal bar chart 

The (horizontal) bar chart, is the primary bar graph from which all the others are born. It basically uses horizontal bars to display different values of categorical data. As mentioned previously, for this type of visual the y-axis displays the categories, and the x-axis the numerical values. 

**click to enlarge**

It is recommended to use the horizontal bar chart when you want to display long category names or multiple categories that don’t fit on another type of comparison chart as the horizontal orientation makes it easier to fit in more information without overcrowding the graph. 

For instance, in our example above, we can see that some product names are on the longer side. This would make it impossible to visualize this data in a column chart as the labels would not fit on the vertical axis, as seen in the example below.

Tools such as datapine, give users some design options to use a column chart with longer labels by changing the orientation of the labels. This can be seen in the example below which shows even the top 15 products with labels on the bottom. That said, this is still not perfect as it makes the chart visually busy. Thus, making the horizontal bar chart the best option to make the chart visually harmonic and easier to understand. 

2. Grouped bar chart

A grouped bar chart, also known as a clustered bar chart, is a variation of the traditional horizontal bar but instead of displaying one categorical variable, it displays two or more. When it comes to the design of this chart, the bars are displayed using different colors that represent the different categories. This can be seen in the example below, where the total customer service tickets by each channel are compared with the solved ones using different colors to easily identify the two values. 

This type of bar graph is mostly used to show data distribution or comparison between categories and it can provide more detailed insights than the traditional type of bar chart as the categories can be compared within a specific group or across groups. Going back to our example, this graph can be used to extract conclusions from a specific channel, but also to compare the number of solved tickets within channels and drive conclusions that can lead to improving the service being provided. 

3. Stacked bar chart

The stacked bar graph is a great tool to show how different subcategories influence a larger category. The way this is represented is by plotting all subcategories on top of each other forming a horizontal bar. The length of the horizontal bar will be determined by the total value of the larger category, while the length of the subcategories will be determined by their contribution to the larger one. 

This is represented graphically in our example below. In which the answers to a customer survey on a company’s brand image are displayed. In this case, the length of the horizontal bar is represented by 100% of the answers and each subgroup is represented by a type of answer and the percentage of respondents that identified the brand with that specific characteristic. This is a great way to visually analyze if the brand is being perceived as expected by consumers or if some attributes need to be reinforced using promotional campaigns or other methods. 

Stacked bar graphs are great visuals if you are trying to extract conclusions from categorical proportions within a group (as we saw previously with our example) or when you have data that is naturally divided into components such as sales by country, by quarter, by product, or others. 

4. Dynamic bar chart - Interactive bar graph

Remember when we mentioned that one of the disadvantages of bar graphs was their simple nature? Well, this is not necessarily the case and our next type of bar graph will show you why.

The interactive (or dynamic) bar chart is basically a traditional bar chart that can be explored in real-time using interactive dashboard filters. This enables users to go into lower or higher levels of the data and extract more detailed conclusions from it. 

Our example above is a video that shows an interactive bar chart that uses a drill down filter to go into lower levels of customer data. We first see the number of customers by country and then, by clicking on a specific country, we can see the number of customers by city. As mentioned previously, a horizontal bar graph is the best way to visualize this data as the lower level includes multiple cities that would likely not fit into another type of visual. 

Bar Graphs & Charts Best Practices

Now that you know the most common bar chart types, let’s look into some best practices and tips on how to create them. 

While it might sound fairly easy to gather some data and put it together in a bar chart, the process has its complexities and requirements in order to be successful. This is the case with any type of visual that you are trying to create. Each of them has a purpose and specific design requirements.

• Assess key considerations first 

Before generating any type of visual, it is necessary to revisit your goals. Remember, that a bar chart is mainly used to compare categorical data, so, if your goal is not comparison then you should stop and think of another type of chart. Once that is out of the way, you can move on to other important considerations such as the context you will need to provide to make the data understandable. 

When talking about context in data visualization we mean labels, titles, icons, and any other form of relevant information that makes the data more understandable for the user. To provide context you should make sure you are writing engaging titles and using extra legends only when necessary and in a way that will not overcrowd the graph or make the analysis process tedious. 

In that regard, using a professional KPI dashboard is a great way to provide context and tell a complete data story. Dashboards enable you to integrate multiple charts in a centralized location, so you can generate extra charts to provide context and tell a story instead of overcrowding just one visualization. 

• Use axes correctly

A very common mistake that happens when plotting data using bar charts is the incorrect use of axes. This means, starting them in any other value that is not 0. This can not only make the differences between bars harder to understand, but it can also affect the truthfulness of the chart. Believe it or not, this is a practice that is widely used in the media, advertising, and politics as a way to mislead audiences into believing certain things that are not necessarily true. We already discuss this topic in our misleading statistics blog post in which we provided an example of a misleading bar chart by KFC. See the example below: 

Source : Reddit “Data Is Ugly” 

The issue here is that the numerical axis starts at 590 instead of 0, making it seem that KFC’s wrap has half the calories as the ones from Taco Bell, Burger King, or Wendy’s when is actually just 70 calories less.

• Keep a minimal design for bars

Some tools such as Excel offer users the possibility to get creative with the shape of the bars adding 3D effects, rounding the corners, or adding thick borders to them. In our experience, this is not the best course of action as the key to create a bar chart that is successful lies in simplicity. The more noise you add to your design, the more confusing and harder to understand it will be for the people that will have to work with it later. 

This also applies when choosing the colors for the bars. Here, you want to avoid going crazy with fluorescent colors or colors that are visually harder to read such as bright red, brown, or even black. You should also avoid using multiple colors when it is not necessary. We recommend using variations of the same color to represent different categories when possible.  That said, if you really need to differentiate between the categories, then pick a color palette that means something to your business or to your audience. Using colors that are already familiar or have some type of meaning behind them will make the audience perceive the graph in a more positive way. 

Another important design tip is to be mindful of the spaces between the bars. Here, you should consider a space of roughly half of the width of each bar. As a result, the graphic will look more harmonious and more categories can be fit into the available space.

• Be mindful of the way categories are organized  

While there is no rule of thumb to organize your categories, there are guidelines you can consider to make sure they are organized in a way that makes sense for the audience and for the purpose of the chart itself. For instance, if your goal is to show comparisons and your data is not sorted by time or other criteria that have a mandatory chronological order, then sorting them from highest to lowest or lowest to highest values will make the differences between each category more visually obvious for the audience. 

Top 5 Bar Chart Examples For Different Business Functions & Industries

As you’ve probably learned by now, bar charts are powerful visuals that make data more accessible and understandable for everyone. To keep putting the value of this graphical tool into perspective, we will go through 5 bar chart examples for different business functions and industries generated with a professional bar graph maker. 

1. Marketing

The first one in our list of bar graphs examples is a critical paid marketing KPI, the cost per click or CPC tracks the amount of money a business spends every time a person clicks on an ad. In this case, the CPC for the top and bottom keywords is displayed using a grouped bar chart with the quality score as the second category. This is valuable information as the average between the CPC and the quality score enables you to determine the position of an ad. Therefore, the grouped chart is a great tool to display this data. 

2. Human Resources

The next bar chart template is the dismissal rate. It is a valuable human resources KPI that enables the HR department to compare their turnover rates by employment period. Since the goal of this metric is comparison, the bar chart is a great choice to represent the data. In this case, the bars are organized from the smallest to the largest employment period so it is not possible to organize them in ascending or descending order. Regardless, it is not a critical matter as there are only 4 subcategories so the data is easily understandable at a glance. 

3. Customer Service

The cost per resolution is a great example of when to use a bar chart. This customer service KPI tracks the costs of resolving an issue through the different support channels. It is a great way to compare which channels are more efficient and which ones are not. In this case, complementing the information in a customer service dashboard can enable you to extract deeper conclusions. For example, you can see that the cost per resolution on the phone is the highest, by looking into another graph displaying the number of solved issues by channel you can see that the phone has the highest resolution rate, meaning costs are justified. 

4. Healthcare

This next bar graph example is for healthcare analytics and it shows how this type of visual can enhance the service of a healthcare facility. The stacked bar chart above is tracking the average waiting time in minutes within two subgroups: time to see a doctor, and time to get treatment. The chart goes even further by providing a target of the maximum time a person should be waiting, making it possible to extract deeper conclusions. For instance, we can observe that three disciplines are above average, which is something that needs to be looked into to ensure high patient satisfaction levels.  

5. Market Research

Our final example was generated with a bar chart maker and it shows data that is valuable for any type of organization no matter the industry or size: the number of customers by gender. In this case, the stacked bar chart is displaying data for the last 5 years with the subgroups showing the percentages of females and males completing 100% of customers for that year. What makes this visual so valuable is the fact that you can extract conclusions from specific years as well as compare them with each other. For example, we can see that from 2018 to 2022 the share of female and male customers went from 40-60% to almost 50-50%. 

For more templates like these ones, visit our KPI examples library. We have more than 100+ templates for different industries, functions, and platforms. 

Key Takeaways From Bar Charts 

Throughout this insightful guide on the power of good bar charts, we hope you were able to grasp the value of these visual tools. Bar graphs and charts are a great means to represent selected KPIs to support your decision-making process across all areas of your business. As you saw from our examples on different industries and departments including marketing, HR, customer service, finances, and more, bar charts are versatile graphics that can be used in many contexts. That said, their value increases even more when combined with other types of charts and visualized together in an interactive business dashboard . 

By relying on the right tools and data visualization techniques you stand to be extremely successful with your analytical efforts. If you are ready to start generating stunning visuals to represent your most important business data, then try our professional online data visualization software for a 14-day free trial today! 

What is a Bar Chart?

A bar chart is a graphical representation used to display and compare discrete categories of data through rectangular bars, where the length or height of each bar is proportional to the frequency or value of the corresponding category.

From capturing trends across time and mapping geographic distributions to interpreting frequency distributions, there's seemingly no end to a bar chart’s applicability. Though they come in different variants, such as stacked , horizontal, grouped, and more, the common ground lies in how they reveal patterns and allow for quick, effective comparisons.

Anatomy of a Bar Chart

Decoding a bar chart begins with understanding its structure. While the simplicity of the bar chart resides in its visual clarity, each component of its anatomy has a pivotal role to play in interpreting the data it presents.

Just think of each bar as a category and its length as the magnitude or frequency. By glancing at a bar chart, the length of each bar gives a quick idea of the quantity for each category, letting the viewer compare and contrast them swiftly. Its scales, titles, and labels are among these integral components that lend clarity to the data represented.

Here is a detailed look at the integral parts of a bar chart:

The title is the explanatory heading of the bar chart. It describes what the chart represents, aiding readers in understanding the purpose of the chart instantly. It usually sits at the top, is easily viewable, and should be clear, concise, and descriptive.

The bars are the main components of the chart. They represent categories or groups of data. Each bar's height (or length, in the case of a horizontal bar chart) represents the frequency or the quantity of the category it represents. The space between the bars is usually equal to demonstrate accurate comparisons.

X & Y Axes

The bar chart is divided into two axes – X, the horizontal axis, and Y, the vertical one.

• The X-Axis (Horizontal Axis): It's often where the different categories are represented. In the case of a time-series bar chart, this axis represents the timeline.
• The Y-Axis (Vertical Axis): This is the numerical scale that represents the quantities or frequencies associated with each category. The length (or height) of the corresponding bar indicates this value.

The scale is effectively marked along the Y-axis. It needs to be carefully chosen since it provides the measurement for the bar lengths. The scale should be chosen in a way that doesn't skew the outlook of the data and delivers a truthful representation.

In some bar charts, especially where multiple datasets are being compared, a legend communicates what each bar represents. It uses colors, patterns, or textures and correlating labels to differentiate between data sets.

Labels detail what each axis and bar represent. They show the values of individual bars. The Y-axis is generally labeled with numerical values, while the X-axis is labeled with the categories or groups in question.

Each of these elements works in unison to create a coherent and understandable image of the data at hand. When perusing a bar chart, paying attention to these elements will ensure a precise understanding of the information being shared.

The Power of Simplicity: Advantages of Using a Bar Chart

Universally recognized and adopted as an essential tool for data visualization , bar charts come with an array of inherent advantages appreciated by both the data scientists preparing them and the audiences interpreting them. Here, we delve into some of the key benefits of using bar charts, shedding light on why they are an indispensable tool in the realm of data representation.

Visual Clarity

Bar charts, through their minimalist design, offer a remarkable level of visual clarity. By transforming numerical or categorical data into a series of bars with varying lengths, they allow our brains to swiftly process and compare different data pieces visually, making interpretation faster and effortless.

Comparative Analysis Made Easy

One of the core strengths of bar charts is their ability to facilitate comparative analysis of data across different categories. This superiority in comparison clarity makes bar charts a commonly used tool in business presentations, academic research, and marketing research, where quick comparisons are essential.

Effective Communication of Data Trends

For a time-series dataset, bar charts elucidate trends and patterns over time efficiently. The sequence of bars provides a chronological representation, making it easier to discern patterns, peaks, and troughs in the data.

Versatile Variations

The bar chart isn't a one-size-fits-all solution but lends itself to multiple variations: horizontal bar charts, stacked bar charts, grouped bar charts, and more. These different formats cater to different needs, providing flexibility in choosing how to best represent your data.

Accommodate Large Data Sets

Bar charts can comfortably handle large datasets. Even when dealing with a broad range of categories or groups, they can conveniently codify the data in a manner that is easy on the eye and straightforward to interpret.

Ideal for Nominal and Small Ordinal Variable Data

Bar charts shine when dealing with nominal or small ordinal variable data, where categories are mutually exclusive and have no order or priority. They offer the ability to distinguish between different categories easily, making the information interpretation process simple.

Immediate Impact

A well-crafted bar chart delivers immediate impact in presentations or reports. It allows for the effective communication of key points and can spur discussion around the data, its trends, and its implications.

Multivariate Data Representation

Bar charts, specifically stacked and grouped variations, have the potential to represent multiple data series simultaneously in one chart. They show the part-to-whole relationships, segmenting individual data points into sub-parts under the umbrella of a total bar. This feature aids in encompassing multiple dimensions of data in one graphic representation.

Easy to Create and Understand

Finally, bar charts are straightforward both to create and interpret, irrespective of the audience’s statistical knowingness. Numerous software and online tools offer user-friendly interfaces for the creation of bar charts, aiding their proliferation.

While the choice of data representation form should always be tailored to the specific data set and the story you wish to tell, these manifold advantages make the humble bar chart a formidable contender for consideration. In the world of data visualization, where less is often more, the simplicity, impact, and effectiveness of bar charts have stood the test of time.

Exploring Alternative Data Visualization Techniques

The landscape of data visualization is diverse and expansive, offering numerous alternatives to bar charts. Each alternative brings its unique strengths and weaknesses and becomes suitable under varying data circumstances. Below, we'll discuss some of these alternatives, with a focus on their comparative features to a bar chart.

Line Graphs

Line graphs shine when visualizing changes over a time series, trend analysis, or when the dataset contains many variables. With data points plotted over an interval connected by lines, it becomes easier to identify trends, movements, fluctuations, and patterns.

However, they may not be ideal for categorical comparisons, where bar charts traditionally excel. They are also less optimal for sparse datasets and can be difficult to interpret when too many variables lead to overlapping lines.

Pie charts excel at showing data's proportional parts to the whole, often expressed as percentages. Like the stacked variant of a bar chart, they effectively visualize part-to-whole comparisons within a dataset.

However, pie charts can become confusing and ineffective when used with too many categories, leading to a cluttered mess of pie slices. In contrast, bar charts can handle multiple categories more efficiently.

Often confused with bar charts, histograms also use rectangular bars for representation. However, they are primarily used for showcasing the distribution of continuous rather than categorical data. Unlike bar charts, histograms place bars adjacent to each other, indicating that there are no discrete groups.

On the downside, histograms may not convey meanings as clearly as bar charts when it comes to comparing categorical data, where each category acts independently.

Scatter Plots

Scatter plots are a brilliant way to understand relationships between two numerical variables, plotting dots according to their coordinate values. They are beneficial for correlation analysis, outlier detection, and regression analysis.

However, for discrete categories or part-to-whole relations, scatter plots aren't as effective or intuitive as bar charts.

Area Charts

Area charts , similar to line graphs, are remarkable for tracking changes over time for one or more categories. They fill the area under the line, enhancing the visual impact of quantity comparisons and trends.

However, with multiple categories, they can become confusing or misleading due to overlapping areas. In such cases, a bar chart could deliver a clearer comparison.

Box-and-Whisker Plots

Box-and-whisker plots or box plots provide a view of data distribution, including median, quartiles, and potential outliers, in a five-number summary format. They provide insights into data spread and skewness at a glance.

However, they are not as intuitive for simple comparison tasks due to their more technical nature, where the bar chart remains a straightforward solution.

Ultimately, the best alternative to bar charts depends on the specific nature of your data, what you intend to present, and how your audience will understand it. Each visualization type carries its peculiar value, and determining the correct fit is an essential aspect of data storytelling.

Creating a Bar Chart – A Step-by-Step Guide

Creating a bar chart is a straightforward process, and with numerous software options available, you can make a bar chart easily, regardless of your technical proficiency. Here, we provide a generic, step-by-step guide, which you can modify and adapt according to the specific software or tool you're using.

Gather Your Data

Your first task is to collect the data you plan to represent in a bar chart. It could be anything from sales data, survey responses, demographic statistics, or any other kind of relevant quantitative or categorical data. Ensure that you have accurate data for both your categories (X-axis) and the corresponding values (Y-axis).

Choose Your Software

Numerous software and tools, such as Jaspersoft, has great tools for bar chart creation. Most tools provide a user-friendly interface, making the chart creation process relatively straightforward.

Input Your Data

Input your data into the chosen software. This step varies in style as different software has different interfaces for data input. Usually, you will enter your categorical data (bar labels) in one column and corresponding numerical data (bar lengths) in the adjacent column.

Select the Bar Chart Option

Choose the option to create a bar chart from the list of charts offered by the software. You may also find variations such as vertical or horizontal bar charts, stacked bar charts, grouped bar charts, etc. Select the one that fits your data presentation needs the best.

Customize Your Chart

After generating the basic chart, you’ll likely need to refine and customize it. This could entail adding or editing the title, setting your axis labels, choosing a color theme, adjusting the scale on the Y-axis, adding data labels, or any number of other modifications. Ensure each customization enhances the chart's clarity and aesthetic appeal rather than compromising it.

Interpret and Share the Chart

After finalizing the design, share your chart with your peers, colleagues, or intended audience. The most crucial part is to interpret the chart, extract insights, and drive discussions from it. Remember, your goal is not only to create a visually appealing chart but also to tell a compelling data story to enhance understanding or drive decision-making.

Creating a bar chart is relatively simple, but creating an effective one necessitates a good grasp of the data you’re presenting and the story you want to tell. By following the above steps, you'll be well on your way to crafting powerful bar charts that will communicate your data succinctly, provoking thought and facilitating informed decisions.

History and Origin of the Bar Chart

The bar chart, or bar graph as some may call it, has been a beacon of simplicity and clarity in data visualization for centuries. Notwithstanding its simple demeanor, the bar chart has an intriguing history.

The earliest known use of the bar chart dates back to the 14th century. It was a chart depicting the fluctuating prices of wheat, displaying variations over a span of time. The conventional horizontal layout was utilized, and by varying the length of bars, the information was clearly presented.

However, the advent of modern times brings us to William Playfair, an engineer, economist, and pioneer of graphical methods of statistics. He is widely regarded in the annals of statistical history for introducing the bar chart to the world in a more refined form in the late 18th century. Alongside bar charts, he is also credited with the invention of the line and pie charts.

The field of data visualization is brimming with diverse tools and charts, with one of the most widely utilized being the bar chart. Despite its simplicity, the bar chart is an effective and versatile tool for presenting data, providing visual clarity, facilitating comparative analysis, demonstrating data trends, and accommodating large data sets.

Alternative chart styles, such as line graphs, pie charts, histograms, scatter plots, area charts, and box-and-whisker plots, all have their unique strengths and potential applications, yet none universally outperforms the others. The choice depends on your distinct data and what story you want to tell.

Bar Charts with Jaspersoft

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Bar Graph – Definition, Types, Examples, Practice Problems, Facts

What is a bar graph, elements of a bar graph, types of bar graph, solved examples on bar graph, practice problems on bar graph, frequently asked questions on bar graph.

A bar graph can be defined as a graphical representation of data, quantities, or numbers using bars or strips. They are used to compare and contrast different types of data, frequencies, or other measures of distinct categories of data. For example,

The above graph shows how many students like which season. The seasons are listed as spring, summer, fall, or winter on the bottom horizontal axis of the graph. The number of students is written on the vertical axis as 0, 1, 2…

The rectangles drawn on the bar charts are called “bars”. The bars display the number of items under particular categories.

There are two axes present on the graph. One axis is used to represent the numerical values whereas the other represents the categorical data against which the numerical data is plotted.

In the same example, 

The seasons on the x-axis represent the categorical data and the number of students on the y-axis represents the numerical possible values. And the blue bars represent the number of students pertaining to each category or season.

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Properties of Bar Graph

• A bar graph is the representation of numerical data by rectangles (or bars) of equal width and varying height. 
• The gap between one bar and another should be uniform throughout. 
• It can be either horizontal or vertical. 
• The height or length of each bar relates directly to its value.

Horizontal: Here, the bars are drawn horizontally from left to right. The data categories are placed on the vertical axis and numerical values are placed on the graph’s horizontal axis.

Vertical: Here, the bars are drawn vertically from down to top. The data categories are placed on the horizontal axis, and the numerical values are placed on the graph’s vertical axis.  

Grouped: This graph represents related sets of data. Each set of data is graphed separately but on the same graph. The key explains which set of data is shown by the graph.

For example, in the above graph, the blue bar represents the number of students in Class A that prefer a particular fruit, the orange bar represents the number of students in Class B, and the green bar represents the number of students in Class C that prefer a particular fruit.

How to make a Bar Graph?

Let’s try representing the given data on a graph.

Step 1: Decide the scale and interval for your numerical values. The scale refers to the counting sequence we will place on the axis. For smaller values, we simply go with the counting sequence 1, 2, 3… but for larger values, we can also pick a counting sequence with some interval of numbers between each value. For example, 5, 10, 15… 

Step 2: Label the horizontal and vertical axes to describe the information.

Here, we are drawing a vertical bar graph , so we will place the categorical data on the x-axis and the numerical values on the y-axis.

Step 3: Draw bars corresponding to each category. For example, since there are 8 pencils, draw a rectangle over the “pencil category” with the length of the rectangle as 8 units. Similarly, draw other bars corresponding to each category with the same breadth as the first bar.

Step 4: Label the graph with a title.

Example 1: Draw the horizontal bar graph for the given data set between the number of people and their preferred beverage.

Example 2: Create a bar graph for the given data set.

Example 3: Draw a bar graph for the given data set.

Example 4: Read the following graph and answer the questions.

• Which is the most popular club?
• How many more students than the math club does the science club have?
• How many students are in the sports club?
• Find the total number of students in the clubs. 
• The movie club is the most popular club.
• Number of students in Science Club $= 21$

Number of students in Math Club $= 15$

$21$ $–$ $15 = 6$

So, 6 more students are in the science club than in the math club.

21 students are in the sports club

Total number of students $= 15 + 21 + 16 + 25 + 23 = 100$

Bar Graph - Definition with Examples

Attend this quiz & Test your knowledge.

Use the given graph to find out the topping most liked by the students.

The given graph shows the data about the sale of the dresses over four months. One bar is missing from the graph. If 78 dresses are sold in these four months, which bar shows the number of dresses sold in July?

Which sport is liked by the least number of people?

The average marks scored by four different classes of boys and girls are shown in the given graph. What is the average score of Class C?

Why do we need to learn data representation?

Data representation gives a clear idea of what the information means by giving it visual context through graphs. It makes the data easy to understand.

What is the advantage of a bar graph?

It represents the data set in a visual form, which helps to observe and analyze the data easily.

What are some other types of pictorial charts?

• Histogram, etc.

Why are titles and labels necessary in a bar graph?

The title and labels give information about the number of items under certain categories which is essential for reading the graph .

RELATED POSTS

• Horizontal – Definition with Examples
• Relative Change Formula: Definition, Facts, Examples, FAQs
• Perpendicular Lines – Definition with Examples
• Vertices – Definition with Examples
• Line – Definition, Types of Line, Examples, Practice Problems

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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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Have you noticed the display of some tables during the telecast of a cricket match or any sports match? These tables tell about the players or their comparison with others. Few tables have some block-like structure . Some blocks are higher than the others while some are of the same height. These tables-like representation of the information are the graphs . Here, we will study graphs, their types and the ways of construction. We will also study pictograph , barchart, pie chart etc.

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Representation of data.

Suppose we have hundreds of thousands of collected data. How can we represent these data in an interesting way? This can be done by making pictograph, bar graph and pie-chart.

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• Tally Marks and Frequency Distribution

In pictograph, we use pictures or symbols to represent data. A pictograph is an interesting way of making a comparison and analyzing data. A picture can have any numerical value. We can represent half of the value by making half pictures or symbols. Consider a data collection of the number of items sold during a sale in a week of a shop.

‘#’ = 50 items ‘=’ = 25 items. Without doing the calculation, we say that the number of items sold was the maximum on Sunday. What was the number of items sold? The number = 6 # = 6 × 50 = 300. The number of the least sold items = (# + =) = 50 + 25 = 75.

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A pictograph can be a time-consuming representation of data if the data are large in number. To overcome this problem, bar graphs are used. A barchart is the simplest representation of data . It is a graphical representation of the data in the form of rectangular bars or columns of equal width.

Properties of Bar Graphs

• Each bar or column in a barchart is of equal width.
• All bars have a common base.
• The height of the bar corresponds to the value of the data.
• The distance between each bar is the same.

Construction of a Bar Graph

• Draw two perpendicular lines intersecting each other at a point O. The vertical line is the y-axis and the horizontal is the x-axis.
• Choose a suitable scale to determine the height of each bar.
• On the horizontal line, draw the bars at equal distance with corresponding heights.
• The space between the bars should be equal.

The barchart for the example of the number of items sold during the sale is

A pie chart is a circular representation of data. Here, data is represented by sector of a circle . The sectors of the circle are like the slices of a pie or pizza. The only difference is that in a pie chart the sectors are of different sizes. The size of each sector is related to its frequency.

Properties of Pie Charts

• The total angle of a circle is 360° .
• Each sector of the circle shows various observations of the data.
• The sum of the values of the observation is represented by the whole circle.
• The angle of the particular sector is calculated as ((Value of the component) ÷ Total value) × 360° .
• If the data is in percentage form, the angle of the sector becomes ((Percentage value of component) ÷ 100) × 360°. It shows the percentage distribution of the observations.

Construction of a Pie Chart

• Draw a circle of any radius.
• Calculate the angles of each sector by the above formula.
• Draw the various sectors.
• Colour each sector of your choice.

The pie chart for the example of the number of items sold during the sale is

We can calculate the angle of the sector as:

Solved Examples for You

Question 1: What is the frequency of 20 marks if 15 students obtained 20 marks in a test? Answer : The frequency of 20 marks is 15.

Problem: From the given bar-graph answer the following questions:

• In which subject the student scored the highest?
• What is the difference between the highest and the lowest marks?
• In how many subjects, the student got less than 90 marks?
• What is the total mark scored by the students?

Solution: From the bar graph,

• The student scored the highest marks in maths.
• The difference between the highest and the lowest marks = 95 − 90 = 10.
• Two subjects.
• The total marks = 85 + 90 + 95 + 90 + 85 + 90 = 535.

Question 3: Explain what is a bar graph in maths?

Answer: A bar graph refers to a chart that makes use of bars to show the various comparisons between categories of data. The bars can be either vertical or horizontal. Bar graphs with vertical bars are also known as vertical bar graphs.

Question 4: What are the characteristics that make a good bar graph?

Answer: The characteristics that make a good bar graph are as follows:

• Easy comparisons between different variables
• Clarity in displaying trends in data
• Easy determination in the value of a variable

Question 5: What are the various things that a bar graph must have?

Answer: The various things that a bar graph must have are as follows:

• visual structures
• axes and background
• scales and tick marks

Question 6: Explain what is a simple bar graph?

Answer: A simple bar graph or chart is one that represents data involving only one variable classified on a temporal, quantitative, or spatial basis. In a simple bar chart, we make bars of equal width but the length is variable. Furthermore, the representation of a quantity’s magnitude takes place by the length or height of the bars.

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Graphical Representation of Data

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

Definition of Graphical Representation of Data

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

Representation of Data

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

Principles of Graphical Representation of Data

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

Advantages and Disadvantages of Graphical Representation of Data

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

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

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

Rules of Graphical Representation of Data

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

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

Uses of Graphical Representation of Data

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

Types of Graphical Representation of Data

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

Related Topics

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

• x and y graph
• Frequency Polygon
• Cumulative Frequency

Examples on Graphical Representation of Data

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

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

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

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

Hence, mode = 54

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

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

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

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

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Practice Questions on Graphical Representation of Data

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

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

What are the Different Types of Graphical Representation?

The different types of graphical representation of data are:

• Stem and leaf plot
• Scatter diagrams
• Frequency Distribution

Is the Graphical Representation of Numerical Data?

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

What is the Use of Graphical Representation of Data?

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

What are the Ways to Represent Data?

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

What is the Objective of Graphical Representation of Data?

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

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In this chapter, you will study numerical and graphical ways to describe and display your data. This area of statistics is called "Descriptive Statistics." You will learn how to calculate, and even more importantly, how to interpret these measurements and graphs.

• 2.1: Introduction In this chapter, you will study numerical and graphical ways to describe and display your data. This area of statistics is called "Descriptive Statistics." You will learn how to calculate, and even more importantly, how to interpret these measurements and graphs. In this chapter, we will briefly look at stem-and-leaf plots, line graphs, and bar graphs, as well as frequency polygons, and time series graphs. Our emphasis will be on histograms and box plots.
• 2.2: Stem-and-Leaf Graphs (Stemplots), Line Graphs, and Bar Graphs A stem-and-leaf plot is a way to plot data and look at the distribution, where all data values within a class are visible. The advantage in a stem-and-leaf plot is that all values are listed, unlike a histogram, which gives classes of data values. A line graph is often used to represent a set of data values in which a quantity varies with time. These graphs are useful for finding trends.  A bar graph is a chart that uses either horizontal or vertical bars to show comparisons among categories.
• 2.3: Histograms, Frequency Polygons, and Time Series Graphs A histogram is a graphic version of a frequency distribution. The graph consists of bars of equal width drawn adjacent to each other. The horizontal scale represents classes of quantitative data values and the vertical scale represents frequencies. The heights of the bars correspond to frequency values. Histograms are typically used for large, continuous, quantitative data sets. A frequency polygon can also be used when graphing large data sets with data points that repeat.
• 2.4: Using Excel to Create Graphs Using technology to create graphs will make the graphs faster to create, more precise, and give the ability to use larger amounts of data. This section focuses on using Excel to create graphs.
• 2.5: Graphs that Deceive It's common to see graphs displayed in a misleading manner in social media and other instances. This could be done purposefully to make a point, or it could be accidental. Either way, it's important to recognize these instances to ensure you are not misled.
• 2.E: Graphical Representations of Data (Exercises) These are homework exercises to accompany the Textmap created for "Introductory Statistics" by OpenStax.

Contributors and Attributions

Barbara Illowsky and Susan Dean (De Anza College) with many other contributing authors. Content produced by OpenStax College is licensed under a Creative Commons Attribution License 4.0 license. Download for free at http://cnx.org/contents/[email protected] .

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• Math Article

Graphical Representation

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

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

General Rules for Graphical Representation of Data

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

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

Graphical Representation in Maths

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

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

Principles of Graphical Representation

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

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

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

Merits of Using Graphs

Some of the merits of using graphs are as follows:

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

Example for Frequency polygonGraph

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

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

Draw the frequency polygon for the following data

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

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

Now calculate the midpoint of the class interval.

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

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

Frequently Asked Questions

What are the different types of graphical representation.

Some of the various types of graphical representation include:

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

Read More:  Types of Graphs

What are the Advantages of Graphical Method?

Some of the advantages of graphical representation are:

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

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

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Graphical Representation of Data| Practical Work in Geography Class 12

Geography Class 12 Chapter 3 talks about the Graphical Representation of Data. It includes all types of representation processes of data through different types of graphs like line, bar, pie, dot, and isopleth maps. Graphical representation gives us a visual of the raw data which helps us to understand to analyze it through different numeric formations.

In this article, we are going to discuss the Practical Work in Geography Class 12 with the Chapter called Graphical Representation of Data.

Graphical Representation of Data means to analyze the numerical data sources through different types of graphs. It creates a relation between the data set with a diagram. The graphical representation is simple and easy to understand which is a part of the important learning technique. The Graphical Representation process is totally dependent process on data sources.

Let us discuss different types of Graphical Representation of Data as mentioned below.

• Line Graphs: Line graphs are one type of linear graph that examines the continuous data sources to predict the future.
• Histograms: The histograms use the bar formations to represent the data as the frequency of the numerical data sources. Here the intervals are present in an equal manner.
• Bar Graphs: The bar graphs are used to depict the different categories and compare the data by using solid bars of quantities.
• Frequency Table: The frequency table represents the data by following a proper time interval.
• Line Plot: The line plot shows the data in a manner of frequency that is written as a line number.
• Circle Graph: The circle graph is also known as the pie chart. It shows the relationships between the data parts. The circle holds 100 % data by mentioning the data portions in percentages.
• Scatter Plots: The scatter plots depict the data to establish the relationship between two data sets.
• Venn Diagram: The Venn diagram is a process graph where the set is important. The inner part of the circle makes and shows the representation of graphs.
• Stem and Leaf Plots: They are the representation of the least and highest value of a particular data set. The lowest value is known as the leaf and the highest value is the stem.
• Box and Whisker Plot: This is the process of summarizing the data into four different parts. It majorly represents the spread and median of the different data sets.

Rules for Graphical Representation of Data

There are some major rules to make the Graphical Representation of Data as mentioned below.

• Give a Title: Give a suitable title for the graph which presents the subject.
• Scale: The scale needs to be used efficiently in an accurate way.
• Mention the Measurement Units: It is important to mention the measurement units to represent the dataset as a graph in a proper way.
• Index Formation: It is needed to apply the different types of colors, shades, and designs to make the related graph more understanding with more information.
• Data Sources: Include the proper sources of data at the bottom of the graph when it is necessary to make it more authentic.
• Make It Simple: A simple graph is more understandable than a hard one. You need to make it more easy for the readers.

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• Spatial and Geographical data

Graphical Representation of Data- FAQs

What are the 4 different types of graphical representation.

There are four most widely used graphs namely histogram, pie diagram, frequency polygon, and ogive frequency graph.

What is a graphical form of representation?

Graphical Representation is a way of analysing numerical data. It exhibits the relation between data, ideas, information and concepts in a diagram. It is easy to understand and it is one of the most important learning strategies. It always depends on the type of information in a particular domain.

What are the graphical displays of data?

Two common types of graphic displays are bar charts and histograms. Both bar charts and histograms use vertical or horizontal bars to represent the number of data points in each category or interval.

What is a graphical display?

Graphical displays communicate comparisons, relationships, and trends. They emphasize and clarify numbers. To choose the appropriate type of display, first define the purpose of the report, and then identify the most effective display to suit that purpose. For example, you can use a multiline display to show trends.

What is a graphical representation called?

The method of presenting these numerical data is called a chart. There are different kinds of charts such as a pie chart, bar graph, line graph, etc, that help in clearly showcasing the data.

Why are statistical graphs important?

Raw data might contain hidden patterns and relationships that you cannot identify by just looking at the raw data. These will be revealed using a picture. A display of data will help you identify the most significant features of your data.

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Charles Minard pioneered several thematic mapping techniques. The maps in this image depict the origin and amount of cotton imported into Europe in 1858, 1864, and 1865.

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The Underappreciated Man Behind the “Best Graphic Ever Produced”

He’s known for his acclaimed depiction of Napoleon’s ill-fated invasion of Russia. But Charles Minard was full of innovative visualizations.

Charles Joseph Minard’s name is synonymous with an outstanding 1869 graphic depicting the horrific loss of life that Napoleon’s army suffered in 1812 and 1813, during its invasion of Russia and subsequent retreat. The graphic (below), which is often referred to simply as “Napoleon’s March” or “the Minard graphic,” rose to its prominent position in the pantheon of data visualizations largely thanks to praise from one of the field’s modern giants, Edward Tufte . In his 1983 classic text, “The Visual Display of Quantitative Information,” Tufte declared that Napoleon’s March “may well be the best statistical graphic ever produced.”

Today Minard is revered in the data-visualization world, commonly mentioned alongside other greats such as John Snow , Florence Nightingale , and William Playfair . But Minard’s legacy has been almost completely dominated by his best-known work. In fact, it may be more accurate to say that Napoleon’s March is his only widely known work. Many fans of the March have likely never even seen the graphic that Minard originally paired it with: a visualization of Hannibal’s famous military campaign in 218 BC, as seen in the image below.

On its face, it may not seem remarkable that Minard is remembered for this one piece of work; after all, many people owe their fame to a single great achievement, and the Napoleon graphic is certainly worthy of its reputation. But Minard was most definitely not a one-hit wonder.

Charles Minard’s renowned graphic of Napoleon’s 1812 march on Moscow (lower image) is paired here with a visualization of Hannibal’s famous military campaign of 218 BC (upper image). The tremendous numbers of casualties suffered by both armies are shown by the thinning of the lines (1 millimeter of thickness is equal to 10,000 men), which chartrepresent their routes through space and time.

Minard made scores of other graphics and charts, as well as nearly 50 maps. He pioneered several important thematic mapping techniques and perfected others, such as using flow lines on a map. A great example of this is the trio of maps in the graphic at the top of the post, which depict cotton imports to Europe.

It wasn’t until I met R.J. Andrews, the visual storyteller behind the website Infowetrust.com , that I learned about Minard’s prolific career. Andrews has been studying the history of data visualization and writing a series of posts about some of the field’s “sacred cows.” When he started looking into Minard, he found a trove of his work in the digital archives of the École Nationale des Ponts et Chaussées (National School of Bridges and Roads), where Minard was an instructor.

Minard made some of his charts and maps during his engineering career, which culminated in his appointment as inspector general of the school. But it was after he retired, at age 70, that he really poured himself into crafting his “graphic tables and figurative maps,” as he called them. The Napoleon and Hannibal graphics were among the very last he made, at age 88.

Minard wasn’t the first to put flow lines on a map , but he really raised the bar for doing so. The maps are all designed to tell a story—to “speak to they eyes,” in Minard’s words.* He mapped the flow of everything from coal and wine to people and languages. He always prioritized the data, often distorting the underlying geography to accommodate it.

In some cases, such as the cotton import maps, he charted the same data over time. In the video above, Andrews explains how the cotton maps tell the story of an emerging global economy and the impacts of civil war.

The invention of the pie chart is credited to Playfair, but again Minard took an existing idea and vastly improved it. He was the first to use pie charts on a map, and he added his own innovation: turning the pie charts into proportional symbols.

One of his first pie-chart maps shows the origin of butcher’s meats supplied to Paris markets in 1858 from each of the country’s departments. The size of the pies indicates how much total meat came from each location. The colors indicate which type of meat: black for beef, red for veal, green for mutton. All the departments in yellow contributed some meat, and the meatless areas are tan colored. A century and a half later, cartographers are still using the technique .

A flow map of global emigration in 1858. Colors indicate the country of origin of migrants, and the thickness of the lines represents the number of migrants moving (1 millimeter is equal to 1,500 people).

Andrews also discovered that, unlike now, Minard’s work was widely known and appreciated in his day, at least among government officials. Buried in a 15-year-old scholarly paper, Andrews found an intriguing passage of Minard’s obituary translated from the original French. It said that from around 1850 to 1860, all the ministers of public works in France made a point of having their portraits painted with one of Minard’s charts in the background. This sent Andrews down a rabbit hole in search of those portraits. You can read Andrews’ account of the hunt and what he found out.

*Friendly, Michael (2002). Visions and Re-Visions of Charles Joseph Minard. Journal of Educational and Behavioral Statistics, Vol. 27, No. 1, pp. 31-51.

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Ribosome decision graphs for the representation of eukaryotic RNA translation complexity

• Jack A.S. Tierney 1 , 2 , 8 ,
• Michał Świrski 3 , 8 ,
• Håkon Tjeldnes 1 , 4 ,
• Jonathan M. Mudge 5 ,
• Joanna Kufel 3 ,
• Nicola Whiffin 6 ,
• Eivind Valen 4 , 7 and
• Pavel V. Baranov 1
• 1 School of Biochemistry and Cell Biology, University College Cork, Cork T12 K8AF, Ireland;
• 2 SFI Centre for Research Training in Genomics Data Science, University College Cork, Cork T12 K8AF, Ireland;
• 3 Institute of Genetics and Biotechnology, Faculty of Biology, University of Warsaw, 02-106 Warsaw, Poland;
• 4 Computational Biology Unit, Department of Informatics, University of Bergen, NO-5020 Bergen, Norway;
• 5 European Molecular Biology Laboratory, European Bioinformatics Institute, Wellcome Genome Campus, Hinxton CB10 1SD, Cambridge, United Kingdom;
• 6 The Big Data Institute and Wellcome Centre for Human Genetics, University of Oxford, Oxford OX3 7BN, United Kingdom;
• 7 Department of Biosciences, University of Oslo, 0316 Oslo, Norway

↵ 8 These authors contributed equally to this work.

• Corresponding author: p.baranov{at}ucc.ie

The application of ribosome profiling has revealed an unexpected abundance of translation in addition to that responsible for the synthesis of previously annotated protein-coding regions. Multiple short sequences have been found to be translated within single RNA molecules, within both annotated protein-coding and noncoding regions. The biological significance of this translation is a matter of intensive investigation. However, current schematic or annotation-based representations of mRNA translation generally do not account for the apparent multitude of translated regions within the same molecules. They also do not take into account the stochasticity of the process that allows alternative translations of the same RNA molecules by different ribosomes. There is a need for formal representations of mRNA complexity that would enable the analysis of quantitative information on translation and more accurate models for predicting the phenotypic effects of genetic variants affecting translation. To address this, we developed a conceptually novel abstraction that we term ribosome decision graphs (RDGs). RDGs represent translation as multiple ribosome paths through untranslated and translated mRNA segments. We termed the latter “translons.” Nondeterministic events, such as initiation, reinitiation, selenocysteine insertion, or ribosomal frameshifting, are then represented as branching points. This representation allows for an adequate representation of eukaryotic translation complexity and focuses on locations critical for translation regulation. We show how RDGs can be used for depicting translated regions and for analyzing genetic variation and quantitative genome-wide data on translation for characterization of regulatory modulators of translation.

[Supplemental material is available for this article.]

Article published online before print. Article, supplemental material, and publication date are at https://www.genome.org/cgi/doi/10.1101/gr.278810.123 .

Freely available online through the Genome Research Open Access option.

• Received December 4, 2023.
• Accepted April 1, 2024.
• © 2024 Tierney et al.; Published by Cold Spring Harbor Laboratory Press

This article, published in Genome Research , is available under a Creative Commons License (Attribution 4.0 International), as described at http://creativecommons.org/licenses/by/4.0/ .

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Computer Science > Machine Learning

Title: subgraph2vec: a random walk-based algorithm for embedding knowledge graphs.

Abstract: Graph is an important data representation which occurs naturally in the real world applications \cite{goyal2018graph}. Therefore, analyzing graphs provides users with better insights in different areas such as anomaly detection \cite{ma2021comprehensive}, decision making \cite{fan2023graph}, clustering \cite{tsitsulin2023graph}, classification \cite{wang2021mixup} and etc. However, most of these methods require high levels of computational time and space. We can use other ways like embedding to reduce these costs. Knowledge graph (KG) embedding is a technique that aims to achieve the vector representation of a KG. It represents entities and relations of a KG in a low-dimensional space while maintaining the semantic meanings of them. There are different methods for embedding graphs including random walk-based methods such as node2vec, metapath2vec and regpattern2vec. However, most of these methods bias the walks based on a rigid pattern usually hard-coded in the algorithm. In this work, we introduce \textit{subgraph2vec} for embedding KGs where walks are run inside a user-defined subgraph. We use this embedding for link prediction and prove our method has better performance in most cases in comparison with the previous ones.

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