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A bar chart is a chart with rectangular bars with lengths proportional to the values that they represent. The bars can be plotted vertically or horizontally. Bar charts can be used to show comparisons among categories.

The bar chart below shows how the average U.S. diet compares with recommended dietary percentages.

source:ers.usda.gov

Are there any food groups the average person in the U.S. eats too little of (compared to the recommended amount)? Show answer Solution: Find any bars that do not reach the “MyPlate Recommendations” line (which is at 100% on the y-axis). Vegetables, dairy, and fruit do not reach this bar, and this means that the average person in the U.S. does not meet recommended amounts for these food groups.

Making a Bar Chart

To make a bar chart, decide what columns and rows to include. The chart above relates five food groups (food group is the x-axis variable) to a percentage (the y-axis variable). However, bar charts can display more than just two variables. Generally, if multiple things are being compared, one axis will be a variable that links all other parameters.

For example, the chart below shows vegetable type on the x-axis, and shows both pounds per person and whether the food was canned, frozen, fresh, etc. on the y-axis. This graph is able to display two variables on one axis because it uses color to distinguish the state of the food.

To read a bar chart, determine which parameters are being compared by reading the components of the x and y-axes and taking note of how parameters are related. Some parameters are related by space (a taller bar might mean there is more of that element than a shorter bar) while others could be related by color. In the chart above, for example, all frozen foods are denoted by the color red.

Using the chart above, determine the pounds of fresh romaine and leaf lettuce consumed per person. Show answer Solution: We can tell from the key above the bars that the color green indicates a fresh vegetable. Looking at the romaine and leaf lettuce column, we can see that a little more than 5 pounds of fresh lettuce was consumed per person.

What is the third most popular kind of fruit among U.S. consumers according to this bar chart?

source: ers.usda.gov

Good practices for making bar charts Title the chart. Use labels on the axes and to denote categories. Use consistent color unless intentionally differentiating elements. Use consistent spacing between numerical increments. Take care in choosing a scale that best displays the data.

What is wrong with this chart? source: wikipedia Show answer Answer: The scale is too large and the reader cannot discern differences between the bars. Here is the chart with a better scale. source: wikipedia

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Statistics By Jim

Making statistics intuitive

Bar Charts: Using, Examples, and Interpreting

By Jim Frost 4 Comments

Use bar charts to compare categories when you have at least one categorical or discrete variable. Each bar represents a summary value for one discrete level, where longer bars indicate higher values. Types of summary values include counts, sums, means, and standard deviations. Bar charts are also known as bar graphs.

Use bar charts to do the following:

• Compare counts by categories.
• Display a variable function (sum, average, standard deviation) by categories.
• Understand relationships between categorical variables.

Unlike histograms, the bars in bar charts have spaces between them to emphasize that each bar represents a discrete value, whereas histograms are for continuous data . For more information about the difference between bar charts and histograms, please read my Guide to Histograms .

At a minimum, bar charts require one categorical variable but frequently use two of them. To learn about other graphs, read my Guide to Data Types and How to Graph Them . If you’re mainly interested in comparing and contrasting qualitive properties of different groups, consider using a Venn diagram .

A Pareto chart is a special type of bar chart that identifies categories that contribute the most to all outcomes. Please read my post about Pareto charts .

Example Bar Chart

A delivery service promises that deliveries will occur within a specified time. The service wants to determine how well they are meeting this promise during peak hours and non-peak hours.

The dataset for this graph uses two categorical variables, each having two values, which produces four possible combinations that observations can fall within:

Bar charts typically contain the following elements:

• Y-axis representing counts, variable function (average, sum, standard deviation), or other summary value.
• Categories or discrete values on the x-axis.
• Vertical bars representing the value for each category.
• Optionally, the bars can be clustered in groups and/or stacked to facilitate comparisons.

For the delivery data, the bars indicate the counts of observations having each of the four possible combinations of categorical values. The graph shows that more deliveries occur during peak hours than off-peak hours. Late deliveries are rare during off-peak hours. However, the number of late deliveries increases markedly during peak hours. The service should focus on improving delivery times during peak hours.

Because this chart has two categorical variables, it helps us understand the relationship between them.

Learn more about the X and Y Axis .

Interpreting Bar Charts and Comparing Categories

Bar charts often compare categories, but that’s not always the case. You just need a discrete variable for the horizontal X-axis. For instance, the bar chart below uses a five-point Likert scale for satisfaction. Likert scale data are ordinal and have discrete values. Learn more about Likert Scale: Survey Use & Examples and Ordinal Data: Definition, Examples & Analysis .

Assess the differences between bars to evaluate how the metric changes between discrete values. Identify the groups that have the highest and lowest values. The service provider must be pleased with the results!

Using clustering and stacking, you can compare groups within groups. To understand relationships between categorical variables, assess how the proportions of subgroups change between groups. In the plot of ice cream flavor preferences, females prefer chocolate, males prefer vanilla, and they equally enjoy strawberry.

Keep in mind that the length of the bars can represent different characteristics, such as counts, total, average, and so on. Be sure to notice which metric the graph displays while interpreting it.

Bar charts are also a fantastic way to display cumulative frequency , relative frequency distributions , and can really make contingency tables pop! In fact, the preceding graph is based on a contingency table in my post, Contingency Table: Definition, Examples & Interpreting .

Use Bar Charts with the Appropriate Hypothesis Tests

You can use bar charts to compare summary values between categories or understand the relationships between categorical variables. However, if you want to use your sample to infer the properties of a larger population , be sure to perform the appropriate hypothesis tests to determine statistical significance.

Related post : Descriptive versus Inferential Statistics

Graphs are somewhat subjective because statistical software allows you to edit their properties, such as the graph’s scaling. Changing these settings can alter the appearance of bar charts and the conclusions you draw from them. Conversely, hypothesis tests provide an objective assessment of statistical significance. These tests also account for the possibility of random error explaining the observed patterns.

The hypothesis tests that you can use with bar charts depend on whether you are comparing summary statistics between groups or exploring the relationship between categorical variables.

When you are comparing summary statistics, consider the following hypothesis tests:

• t-Tests for one or two groups
• ANOVA for at least three groups
• Variances tests that assess variability between groups

When you’re assessing the relationship between categorical variables, consider using the chi-square test of independence .

Click the link to see how I use the chi-square test to assess the data in the graph below! I determine whether there is a relationship between uniform color and survival status in the original Star Trek TV series.

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Reader Interactions

November 21, 2021 at 7:03 am

Please what theory supports the use of bar charts for hypothesis testing if you don’t want to use the chi-square to test your hypothesis?

November 21, 2021 at 2:47 pm

When you just want to describe a sample and you’re not using your sample to infer properties of a population, then using only bar charts is fine.

However, if you want to draw conclusions about a population, you’ll need to use a hypothesis test. In this scenario, bar charts can help illustrate your results but they can’t draw conclusions themselves.

It basically comes down to whether you’re performing descriptive or inferential statistics . You don’t need hypothesis tests for descriptive statistics but you do need them for inferential.

June 11, 2021 at 11:18 am

I have a data set and I am just struggling to find out the right way to present it. I have exposed larvae to say 500 particles until they develop into their final larval form. During this period, I am checking mortalities each day and removing dead nauplii to investigate if they ingested particles. Now for mortalities, I have taken sum of larve dead each day to get the cumulative mortalities. I am interested to relate mortality with no. of particles ingested by dead nauplii. I have no. of particles ingested/ dead larvae for each day like 2 particles/dead larve for day 1, 0 particles/dead larvae for day 2 and so on. My question is Should I take the average or sum of no. of particles/dead larvae over days and then relate to cumulative mortality? Note my unit is no. of particles per dead larvae

Thanks in advance for being so helpful.

June 14, 2021 at 9:54 pm

I wonder if you should use a line chart with a line for each batch. Number of dead for the y-axis a days along the x-axis. You would then record the number of deaths per day for each batch. Then compare the lines on the chart. Is one line steeper than others? That sounds promising if your main goal is to compare batches. But, you’d also see if the death rate changes over time.

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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 Contents:

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, including the Side-By-Side Bar Chart , 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, like the Segmented Bar Graph , 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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• Math Article

Bar graphs are the pictorial representation of data (generally grouped), in the form of vertical or horizontal rectangular bars, where the length of bars are proportional to the measure of data. They are also known as bar charts. Bar graphs are one of the means of data handling  in statistics.

The collection, presentation, analysis, organization, and interpretation of observations of data are known as statistics. The statistical data can be represented by various methods such as tables, bar graphs, pie charts, histograms, frequency polygons, etc.  In this article, let us discuss what is a bar chart, different types of bar graphs, uses, and solved examples.

What is Bar Graph?

The pictorial representation of grouped data, in the form of vertical or horizontal rectangular bars, where the lengths of the bars are equivalent to the measure of data, are known as bar graphs or bar charts.

The bars drawn are of uniform width, and the variable quantity is represented on one of the axes. Also, the measure of the variable is depicted on the other axes. The heights or the lengths of the bars denote the value of the variable, and these graphs are also used to compare certain quantities. The frequency distribution tables can be easily represented using bar charts which simplify the calculations and understanding of data.

The three major attributes of bar graphs are:

• The bar graph helps to compare the different sets of data among different groups easily.
• It shows the relationship using two axes, in which the categories are on one axis and the discrete values are on the other axis.
• The graph shows the major changes in data over time.

What Constitutes a Bar Graph?

Following are the many parts of a bar graph:

• Vertical axis
• Horizontal axis
• The bar graph’s title informs the reader of its purpose.
• The title of the horizontal axis indicates the information that is shown there.
• The title of the vertical axis indicates the data it is used to display.
• The categories on the particular axis indicate what each bar represents.
• The bar graph’s scale demonstrates how numbers are used in the data. It is a system of markings spaced at specific intervals that aid in object measurement. For instance, the scale of a graph may be stated as 1 unit = 10 fruits

Types of Bar Graphs

The bar graphs can be vertical or horizontal. The primary feature of any bar graph is its length or height. If the length of the bar graph is more, then the values are greater than any given data.

Bar graphs normally show categorical and numeric variables arranged in class intervals. They consist of an axis and a series of labelled horizontal or vertical bars. The bars represent frequencies of distinctive values of a variable or commonly the distinct values themselves. The number of values on the x-axis of a bar graph or the y-axis of a column graph is called the scale.

The types of bar charts are as follows:

• Vertical bar chart
• Horizontal bar chart

Even though the graph can be plotted using horizontally or vertically, the most usual type of bar graph used is the vertical bar graph. The orientation of the x-axis and y-axis are changed depending on the type of vertical and horizontal bar chart. Apart from the vertical and horizontal bar graph, the two different types of bar charts are:

Grouped Bar Graph

Stacked bar graph.

Now, let us discuss the four different types of bar graphs.

Vertical Bar Graphs

When the grouped data are represented vertically in a graph or chart with the help of bars, where the bars denote the measure of data, such graphs are called vertical bar graphs. The data is represented along the y-axis of the graph, and the height of the bars shows the values.

Horizontal Bar Graphs

When the grouped data are represented horizontally in a chart with the help of bars, then such graphs are called horizontal bar graphs, where the bars show the measure of data. The data is depicted here along the x-axis of the graph, and the length of the bars denote the values.

The grouped bar graph is also called the clustered bar graph, which is used to represent the discrete value for more than one object that shares the same category. In this type of bar chart, the total number of instances are combined into a single bar. In other words, a grouped bar graph is a type of bar graph in which different sets of data items are compared. Here, a single colour is used to represent the specific series across the set. The grouped bar graph can be represented using both vertical and horizontal bar charts.

The stacked bar graph is also called the composite bar chart, which divides the aggregate into different parts. In this type of bar graph, each part can be represented using different colours, which helps to easily identify the different categories. The stacked bar chart requires specific labelling to show the different parts of the bar. In a stacked bar graph, each bar represents the whole and each segment represents the different parts of the whole.

Properties of Bar Graph

Some of the important properties of a bar graph are as follows:

• All the bars should have a common base.
• Each column in the bar graph should have equal width.
• The height of the bar should correspond to the data value.
• The distance between each bar should be the same.

Applications of Bar Graphs

Bar graphs are used to match things between different groups or to trace changes over time. Yet, when trying to estimate change over time, bar graphs are most suitable when the changes are bigger.

Bar charts possess a discrete domain of divisions and are normally scaled so that all the data can fit on the graph. When there is no regular order of the divisions being matched, bars on the chart may be organized in any order. Bar charts organized from the highest to the lowest number are called Pareto charts .

Real-Life Applications of Bar Graph

Bar graphs are a visual representation of data. They are used to show the relationship between two or more sets of data. They are mostly used in business and finance, but they can also be found in other contexts. Bar graphs are used in many real-life situations. For example, a bar graph can be used to show the distribution of different types of food in a restaurant. The height of each rectangle would represent how many orders were placed for that type of food. 

Bar graphs are also often used to represent the data grouped into categories, such as how many people have voted for each candidate in an election or how much money was spent by each department. The bars on this type of graph represent the number or percentage of people or money spent and are usually stacked on top of one another so that they can be easily compared to one another.

Advantages and Disadvantages of Bar Chart

Advantages:

• Bar graph summarises the large set of data in simple visual form.
• It displays each category of data in the frequency distribution.
• It clarifies the trend of data better than the table.
• It helps in estimating the key values at a glance.

Disadvantages:

• Sometimes, the bar graph fails to reveal the patterns, cause, effects, etc.
• It can be easily manipulated to yield fake information.

Difference Between Bar Graph and Histogram

The bar graph and the histogram look similar. But it has an important difference. The major difference between them is that they plot different types of data. In the bar chart, discrete data is plotted, whereas, in the histogram, it plots the continuous data. For instance, if we have different categories of data like types of dog breeds, types of TV programs, the bar chart is best as it compares the things among different groups. For example, if we have continuous data like the weight of the people, the best choice is the histogram.

Difference Between Bar Graph and Pie Chart

A pie chart is one of the types of graphical representation . The pie chart is a circular chart and is divided into parts. Each part represents the fraction of a whole. Whereas, bar graph represents the discrete data and compares one data with the other data.

Difference Between Bar Graph and Line Graph

The major difference between bar graph and line graph are as follows:

• The bar graph represents the data using the rectangular bars and the height of the bar represents the value shown in the data. Whereas a line graph helps to show the information when the series of data are connected using a line.
• Understanding the line graph is a little bit confusing as the line graph plots too many lines over the graph. Whereas bar graph helps to show the relationship between the data quickly.

Important Notes:

Some of the important notes related to the bar graph are as follows:

• In the bar graph, there should be an equal spacing between the bars.
• It is advisable to use the bar graph if the frequency of the data is very large.
• Understand the data that should be presented on the x-axis and y-axis and the relation between the two.

How to Draw a Bar Graph?

Let us consider an example, we have four different types of pets, such as cat, dog, rabbit, and hamster and the corresponding numbers are 22, 39, 5 and 9 respectively.

In order to visually represent the data using the bar graph, we need to follow the steps given below.

• Step 1: First, decide the title of the bar graph.
• Step 2: Draw the horizontal axis and vertical axis. (For example, Types of Pets)
• Step 3: Now, label the horizontal axis.
• Step 4: Write the names on the horizontal axis, such as Cat, Dog, Rabbit, Hamster.
• Step 5: Now, label the vertical axis. (For example, Number of Pets)
• Step 6: Finalise the scale range for the given data.
• Step 7: Finally, draw the bar graph that should represent each category of the pet with their respective numbers.

Bar Graph Solved Examples

To understand the above types of bar graphs, consider the following examples:

In a firm of 400 employees, the percentage of monthly salary saved by each employee is given in the following table. Represent it through a bar graph.

The given data can be represented as

This can also be represented using a horizontal bar graph as follows:

A cosmetic company manufactures 4 different shades of lipstick. The sale for 6 months is shown in the table. Represent it using bar charts.

The graph given below depicts the following data

The variation of temperature in a region during a year is given as follows. Depict it through the graph (bar).

As the temperature in the given table has negative values, it is more convenient to represent such data through a horizontal bar graph.

Practice Problem

A school conducted a survey to know the favorite sports of the students. The table below shows the results of this survey.

From this data,

1. Draw a graph representing the sports and the total number of students.

2. Calculate the range of the graph.

3. Which sport is the most preferred one?

4. Which two sports are almost equally preferred?

5. List the sports in ascending order.

Frequently Asked Questions on Bar Graph

What is meant by a bar graph.

Bar graph (bar chart) is a graph that represents the categorical data using rectangular bars. The bar graph shows the comparison between discrete categories.

What are the different types of bar graphs?

The different types of bar graphs are: Vertical bar graph Horizontal bar graph Grouped bar graph Stacked bar graph

When is a bar graph used?

The bar graph is used to compare the items between different groups over time. Bar graphs are used to measure the changes over a period of time. When the changes are larger, a bar graph is the best option to represent the data.

When to use a horizontal bar chart?

The horizontal bar graph is the best choice while graphing the nominal variables.

When to use a vertical bar chart?

The vertical bar graph is the most commonly used bar chart, and it is best to use it while graphing the ordinal variables.

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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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What is a bar graph used for, bar graph vs line graph, when to use a bar graph, how to create a bar graph, types of bar graphs, with smartdraw, you can create more than 70 types of diagrams, charts, and visuals, what is a bar graph used for.

A bar graph (also known as a bar chart or bar diagram) is a visual tool that uses bars to compare data among categories. A bar graph may run horizontally or vertically. The important thing to know is that the longer the bar, the greater its value.

Bar graphs consist of two axes. On a vertical bar graph, as shown above, the horizontal axis (or x-axis) shows the data categories. In this example, they are years. The vertical axis (or y-axis) is the scale. The colored bars are the data series.

Bar graphs have three key attributes:

• A bar diagram makes it easy to compare sets of data between different groups at a glance.
• The graph represents categories on one axis and a discrete value in the other. The goal is to show the relationship between the two axes.
• Bar charts can also show big changes in data over time.

Bar graphs display data in a way that is similar to line graphs. Line graphs are useful for displaying smaller changes in a trend over time. Bar graphs are better for comparing larger changes or differences in data among groups.

Bar graphs are an effective way to compare items between different groups. This bar graph shows a comparison of numbers on a quarterly basis over a four-year period of time. Users of this chart can compare the data by quarter on a year-over-year trend, and also see how the annual sales are distributed throughout each year.

Bar graphs are an extremely effective visual to use in presentations and reports. They are popular because they allow the reader to recognize patterns or trends far more easily than looking at a table of numerical data.

When presenting data visually, there are several different styles of bar graphs to consider.

Vertical Bar Graph

The most common type of bar graph is the vertical bar graph. It is very useful when presenting a series of data over time.

One disadvantage of vertical bar graphs is that they don't leave much room at the bottom of the chart if long labels are required.

Horizontal Bar Graph

Converting the vertical data to a horizontal bar chart solves this problem. There is plenty of room for the long label along the vertical axis, as shown below.

Stacked Bar Graph

The stacked bar graph is a visual that can convey a lot of information.

Make sure that you select the type of graph that best presents the data you want to emphasize.

How to Create a Bar Graph

Step one is making sure you have data formatted the correct way for a bar graph.

In the Insert tab in SmartDraw, click on Graph and choose a type of Bar Graph .

Choose your data file to import and SmartDraw will automatically generate your bar graph.

Once imported, you can easily change the title, legend placement and even the quickly change the graph type using the Edit Graph options or just double-click on your imported chart.

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Bar Graph: Definition, Examples and How to Create One

Written by: Mahnoor Sheikh

A bar graph is a great way to deal with complex and confusing data.

Visualizing data makes it easier to extract knowledge and draw conclusions from a large swath of information. And a bar graph is one of the best ways to do that.

From students and researchers to business professionals, anyone can use a bar graph to effectively communicate data and findings.

In this post, we’ll introduce you to the different types of bar graphs and how you can easily create one to use for your own data.

Let’s get started!

What is a bar graph?

A bar graph , or bar chart, is a visual representation of data using bars of varying heights or lengths.

It is used to compare measures (like frequency, amount, etc) for distinct categories of data. A typical bar graph will have a label, scales, axes and bars.

A bar graph is usually plotted across two axes; one axis shows the categories being compared and the other presents the measured value, such as percentages or numbers, via bars of different lengths.

Create a bar graph using the Visme Graph Engine! Try It For Free

Take a look at the bar graph example above.

This basic bar chart shows the top 10 podcast publishers in the US. Here, the “distinct categories of data” are the ten different podcast publishers and the “measured value” is the unique monthly US audience.

Like the example above, most bar graphs put an independent variable on the x-axis and a dependent variable on the y-axis while graphing data.

Bar graphs may be used to map just about any type of data, from crop yields to participation in school activities to household median income for a country during a period of time.

Types of bar graphs.

When it comes to bar graphs, there’s no such thing as one-size-fits-all. The example above is very simple, but bar graphs can be modified to represent data sets of varying complexities.

Bar graphs are of many different types. They may be horizontal or vertical, they may make use of colors, and they may take on a grouped or stacked appearance.

Below are some of the major types of bar graphs.

1. Vertical

The most commonly used bar chart is like the one seen above. A vertical bar chart is simple and easy to understand—the taller the bar, the larger the category.

Check out the example below.

Customize this bar graph template and make it your own! Try It For Free

It is obvious that the greatest number of people in Britain between 1999-2000 visited the cinema when compared to any other cultural event. The vertical bar graph above tells you this at first glance.

2. Horizontal

The vertical bar graph works for most data types, but it becomes challenging to manage when the distinct categories on the vertical axis have long titles that would be difficult to fit on the bottom, or when there are simply too many categories to fit at the bottom.

This is when horizontal bar graphs are useful.

Here’s an example of a horizontal bar graph.

A horizontal bar chart is almost the same as a vertical bar chart. The only difference is that the categories in a horizontal bar graph are on the vertical axis.

In a grouped bar chart, each categorical group has two or more bars.

This helps show information about the different sub-groups belonging to the categorical group. A consistent color scheme is employed so that each bar represents a sub-group throughout the chart.

Below is an example of a grouped bar graph.

Customize this bar graph template and make it your own!

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

Let’s dig deeper into how the bar graph above was plotted.

An American store owner named Adam has three stores (A, B, and C) and wishes to graphically represent the profit that he has generated from each of them in the first four months of 2017.

Adam’s independent categories are the months: January, February, March, and April. He plots these on the x-axis, while the profits generated during these months (in thousands of dollars) are plotted on the y-axis.

Adam’s categorical group of ‘months’ is further divided into subcategories, each represented by a distinct and consistent color. The resulting graph above makes it easy to see and interpret the data.

A grouped bar graph may be horizontal or vertical depending on the nature of the data.

A stacked bar graph is another way to show information about sub-groups within a main categorical group.

In a stacked bar graph, each bar in the chart represents a category and segments in the bar represent parts of that category.

This type of graph is a good way to represent the discrete values that make up a whole group. It can numerically represent the sub-groups that make up a category.

Take a look at the stacked bar graph example below.

Here’s how the bar graph above was plotted.

The owner of a hardware store wishes to graphically represent the items her employees have sold in the month of January 2019.

There are four employees: Andy, Stacey, Charles, and Marvin. This independent variable can fit accurately along the x-axis, while the dependent variable, the number of items sold, is plotted along the y-axis.

The resulting graph is shown above.

The things the owner wishes to measure sales for are locks, pliers and hammers. Notice how a distinct color is assigned to each and the graph clearly shows which employee sold the most items, as well as the amount sold for each sub-category.

This stacked bar graph, therefore, becomes an example of a visual representation of data where the subcategories can be seen making up the whole.

We can easily infer from this graph that Andy had the most sales overall (11 items) while Charles sold the least amount of pliers (only 1) in the month of January.

Like grouped bar graphs, stacked bar graphs can be plotted horizontally or vertically.

How to make a bar graph in Visme.

Now that you’re aware of the different types of bar graphs out there, let’s find out how to create one for your own data.

Before online design tools like Visme, creating bar graphs on your computer was a pain. And even if you did design one in Microsoft Word or Excel, it ended up looking plain and generic.

Visme is a cloud-based design software, which means it lets you create graphs, charts and other graphics right in your browser.

You don’t even need any design or technical skills to use it. All you need is your laptop, data set and a good internet connection.

There are two ways to make a bar graph in Visme:

• Using a bar graph template
• Using the graph maker

Method 1: Using a bar graph template

To get started, sign in to your Visme account and open up your dashboard. Navigate over to the left-hand sidebar and click on “Create.”

Click on “Infographics” and type “bar graphs” into the search box.

Ta-da! You’ll see a large collection of editable bar graph templates , all designed by professionals and ready to use for any kind of data or project.

Create a bar graph using your favorite template! Try It For Free

Hover on your favorite bar graph template and click on “Edit” to customize it for your own project.

Once you’re inside the Visme editor, you can easily edit the bar graph data and replace the values with your own.

Click on the bar graph, and then click on “Settings” at the top to get inside the graph engine.

If you don’t want to enter your data manually, you can easily import it directly from an Excel file or Google sheet.

As you feed data into the graph engine, you’ll be able to see a live preview of what your bar graph will look like on the left.

Once you’ve uploaded your data, customize the bar graph colors, labels, fonts and more to create your ideal bar graph. You can also enable animation to make your bar graph look even better. This is especially useful if you plan on publishing your bar graph online.

Finally, click on “Update” to insert the bar graph into your visual.

Next, change the background and title. Edit, add or remove any other text or graphic elements around your bar graph as you see fit.

When you’re happy with the way your bar graph looks, download it in image or PDF format by clicking on the “Download” button on the top-right corner of the screen.

You can also share your bar graph with specific people using a link or embed it on your website or blog by clicking on the “Share” button.

Method 2: Using the graph maker

If you don’t find any bar graph template in Visme that works for you, you can always create your own bar graph from scratch.

Sign in to your Visme account and click on “Create” on the left-hand side of your dashboard.

Instead of choosing a ready-made template, click on “Custom Size.”

Enter in the dimensions that work for you or choose one of the commonly used sizes below. For this tutorial, we will use the Presentation (4:3) size.

Once inside the Visme editor, navigate over to “Data” in the left-hand sidebar and click on “Charts.”

The first thing you’ll see is a vertical bar graph on the left and customizable settings on the right. This is where you can design your unique bar graph.

Enter in your data manually or upload it directly from an Excel or Google sheet .

Edit the bar graph colors, labels, fonts, sizes and more until you’re completely happy with the way it looks.

When you’re done creating your bar graph, click on “Insert” to add it to your visual.

Create your own bar graph using Visme!

Place the bar graph wherever you want on the canvas and add a title, subtitle and other text content by clicking on “Basics” on the left.

You can also add icons , shapes, images and other graphic elements. Customize colors and fonts, or add a background to make your graph look even better.

When you’re done creating your perfect bar graph, download it in your favorite format, like JPG, PNG or PDF, and use it on it’s own or as part of your presentation or report.

You can also share it online using a link or embed it on your website or blog using a responsive code by clicking on “Share.”

That’s it! You’re done.

Ready to make your own bar graph?

Bar graphs are one of the most visually appealing and effective ways to present complex data and explain research findings to your audience.

Creating a bar graph for your next presentation , report or research is a breeze with Visme’s graph engine.

Literally anyone can use it—students, designers, business professionals and more!

Share your findings on social media by making mini-infographics in Visme. Or create full, long-form infographics with bar graphs to publish on your blog. You can also download your bar graphs in high-resolution image or PDF format to print out as hard copies.

Sign up for a free Visme account now and start creating your bar graph online in minutes.

If you want to read up more on data visualization, here are some articles from our Visual Learning Center that you might find useful:

• How and When to Use a Circle Graph
• 44 Types of Graphs Perfect for Every Top Industry
• Data Visualizations: A Beginner’s Guide to Finding Stories In Numbers

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About the Author

Mahnoor Sheikh is the content marketing manager at Visme. She has years of experience in content strategy and execution, SEO copywriting and graphic design. She is also the founder of MASH Content and is passionate about tea, kittens and traveling with her husband. Get in touch with her on LinkedIn .

A Bar Graph (also called Bar Chart) is a graphical display of data using bars of different heights.

Imagine you do a survey of your friends to find which type of movie they like best:

We can show that on a bar graph like this:

It is a really good way to show relative sizes: we can see which types of movie are most liked, and which are least liked, at a glance.

We can use bar graphs to show the relative sizes of many things, such as what type of car people have, how many customers a shop has on different days and so on.

Example: Nicest Fruit

A survey of 145 people asked them "Which is the nicest fruit?":

And here is the bar graph:

That group of people think Blueberries are the nicest.

Bar Graphs can also be Horizontal , like this:

Example: Student Grades

In a recent test, this many students got these grades:

You can create graphs like that using our Data Graphs (Bar, Line, Dot, Pie, Histogram) page.

Histograms vs Bar Graphs

Bar Graphs are good when your data is in categories (such as "Comedy", "Drama", etc).

But when you have continuous data (such as a person's height) then use a Histogram .

It is best to leave gaps between the bars of a Bar Graph, so it doesn't look like a Histogram.

Bar Chart / Bar Graph: Examples, Excel Steps & Stacked Graphs

What is a bar chart.

• Bar Chart vs. Histogram
• Excel 2007-2016 (includes stacked).

A bar chart is a graph with rectangular bars. The graph usually compares different categories. Although the graphs can be plotted vertically (bars standing up) or horizontally (bars laying flat from left to right), the most usual type of bar graph is vertical.

Bar charts can also represent more complex categories with stacked bar charts or grouped bar charts. For example, if you had two houses and needed budgets for each, you could plot them on the same x-axis with a grouped bar chart, using different colors to represent each house. See types of bar graphs below.

Back to Top

Difference Between a Histogram and a Bar Chart

Although they look the same, bar charts and histograms have one important difference: they plot different types of data. Plot discrete data on a bar chart, and plot continuous data on a histogram What’s the difference between discrete and continuous data? ).

A bar chart is used for when you have categories of data: Types of movies, music genres, or dog breeds. It’s also a good choice when you want to compare things between different groups. You could use a bar graph if you want to track change over time as long as the changes are significant (for example, decades or centuries). If you have continuous data , like people’s weights or IQ scores, a histogram is best.

Bar Graph Examples (Different Types)

A bar graph compares different categories . The bars can be vertical or horizontal. It doesn’t matter which type you use—it’s a matter of choice (and perhaps how much room you have on your paper!).

List of Types

1. grouped bar graph.

When there are only two sub-groups (as in the above image), the graph is called a double bar graph. It’s possible to have as many sub-groups as you like, although too many can make the graph look cluttered.

2. Stacked Bar Chart

A stacked bar chart also shows sub-groups, but the sub-groups are stacked on the same bar.

Each bar shows the total for sub-groups within each individual category.

Like the double bar chart, different colors represent different sub-groups. This type of chart is a good choice if you:

• Want to show the total size of groups.
• Are interested in showing how the proportions between groups related to each other, in addition to the total of each group.
• Sales by district.
• Book sales by type of book.

Stacked bar charts can also show negative values ; negative values are displayed below the x-axis. Back to Top

3. Segmented Bar Graph.

A type of stacked bar chart where each bar shows 100% of the discrete value. They should represent 100% on each of the bars or else it’s going to be an ordinary stacked bar chart. For more on this particular type of graph, see: Segmented Bar Charts .

How to Make a Bar Chart

Need help with a homework question? Check out our tutoring page!

If you’re just starting to learn how to make a bar graph, you’ll probably be asked to draw one by hand on graph paper at first. Here’s how to do it.

Example problem: Make a bar graph that represents exotic pet ownership in the United States. There are:

• 8,000,000 fish,
• 1,500,000 rabbits,
• 1,300,000 turtles,
• 1,000,000 poultry
• 900,000 hamsters.

Optional: In the above graph, I chose to write the actual numbers on the bars themselves. You don’t have to do this, but if you have numbers than don’t fall on a line (i.e. 900,000), then it can help make the graph clearer for a viewer. Tips:

• Line the numbers up on the lines of the graph paper, not the spaces.
• Make all your bars the same width.

How to Make a Bar Chart in Excel

• Stacked charts in Excel

Excel 2007-2010

Example problem : Create a bar chart in Excel that illustrates the following data for the tallest man-made structures in the world (as of January, 2013):

Step 1: Type your data into a new Excel worksheet. Place one set of values in column A and the next set of values in column B. For this example problem, place the building names in column A and the heights of the towers in column B.

Step 2: Highlight your data: Click in the top left (cell A1 in this example) and then hold and drag to the bottom right.

Step 3: Click the “Insert” tab and then click on the arrow below “Column.” Click the type of chart you would like (for example, click “2D column).

That’s it!

Tip: To widen the columns in Excel, mouse over the column divider, click and drag the divider to the width you want. Back to Top

Excel 2016-2013

A bar graph in statistics is usually a graph with vertical bars. However, Excel calls a bar graph with vertical bars a column graph .

Step 1: Click the “Insert” tab on the ribbon.

Step 2: Click the down arrow next to the bar chart icon.

Step 3: Select a chart icon. For example, select a simple bar chart.

How to make a bar graph in Excel 2016/2013: Formatting tips

• Change the width of the bars : Click on a bar so that handles appear around the bars. Right click, then choose Format Data Series. Under Format Data Series, click the down arrow and choose “Series Options.” Click the last choice (“Series…”). Then move the “Gap Width” slider to change the bar width.
• If you want to remove the title or the data labels, select the “Chart Elements icon.” The Chart Elements icon is the first icon showing at the upper right of the graph area (the + symbol).
• Change the style or color theme for your chart by selecting the Chart Styles icon. The Chart Styles icon is the second icon showing at the upper right of the graph (the pencil).
• Edit what names or data points are visible on the chart by selecting the filter icon. The filter icon is the third icon at the top right of the chart area.

How to Make a Stacked Bar Chart in Excel

Step 1: Select the data in your worksheet. The names for each bar are in column A. The “stacked” portion of each bar are in the rows.

Step 2: Click the “Insert” tab, then click “Column.”

Step 3: Choose a stacked chart from the given options. For example, the second chart listed (under 2-D column) is a good choice.

If you want to modify the layout of your chart, click the chart area of the chart to display the Chart Tools in the ribbon. Then click the “Design” tab for options. Back to Top

Make a Bar Graph in Minitab

Minitab is a statistical software package distributed by Minitab, Inc. The software is used extensively, especially in in education. It has a spreadsheet format, similar in feel to Microsoft Excel. However, where it differs from Excel is that the toolbar is set up specifically for creating statistical graphs and distributions. Creating a bar graph in Minitab is as simple as entering your data into the spreadsheets and performing a couple of button clicks.

Step 1: Type your data into columns in a Minitab worksheet. For most bar graphs, you’ll probably enter your data into two columns (x-variables in one column and y-variables in another). Make sure you give your data a meaningful name in the top (unnumbered) row, because your variables will be easier to recognize in Step 5, where you build the bar graph.

Step 2: Click “Graph,” then click “Bar Chart.”

Step 3: Select your variable type from the Bars Represent drop down menu. For most charts, you’ll probably select “Counts of Unique Variables” unless your data is in groups or from a function.

Step 4: Click “OK.”

Step 5: Select a variable from the left window, then click the “Select” button to move your variable over to the Variables window.

Step 6: Click “OK.” A bar graph in Mintab appears in a separate window.

Tip: If you want to label your graph, click the “Label” button at the bottom of the window in Step 5.

Check out our YouTube channel for more stats tips!

How to Make a Bar Chart in SPSS

When you make a bar chart in SPSS , the x-axis is a categorical variable and the y-axis represents summary statistics such as means , summations or counts. Bar charts are accessed in SPSS through the Legacy dialogs command or through the Chart Builder.

How to Make a Bar Chart in SPSS: Steps

Step 1: Open the file you want to work with in SPSS or type the data into a new worksheet.

Step 3: Click on an image for the type of bar graph you want (Simple, Clustered (a.k.a. grouped), or Stacked) and then click the appropriate radio button to tell SPSS what type of data is in your variables lists:

• Summaries for groups of cases,
• Summaries of separate variables, or
• Values of individual cases.

When you have made your selections, click the “Define” button.

Step 4: Click a radio button in the Bars Represent area to choose what you would like the bars to represent. For example, if you want the bars to represent the number of cases, click the “N of cases” radio button.

Step 5: Click a variable in the left-hand window in the “Define Simple Bar” pop-up window and then transfer those variables by clicking the appropriate center arrow. For this simple example, click “Grade Point Average” and then click the arrow to the left of “Category Axis.” When you have made your selections, click “OK.” This will produce a graph of the number (counts) of each grade point average.

Dodge, Y. (2008). The Concise Encyclopedia of Statistics . Springer. Gonick, L. (1993). The Cartoon Guide to Statistics . HarperPerennial. Wheelan, C. (2014). Naked Statistics . W. W. Norton & Company

Presentation And Display Of Quantitative Data: Graphs, Tables, Scatter Grams And Bar Charts

March 8, 2021 - paper 2 psychology in context | research methods.

Many people find it easier to understand quantitative data when it is presented in pictorial form. The specification requires that you  look at 3 types of pictorial presentations of data.

Graph example taken from Wiki Educator.org

These are mainly used to present frequency distributions of interval data. The horizontal axis is a continuous scale, (e.g. time in seconds). On a histogram, there are no spaces between bars, because the bars are not considered separate categories. In the example of the graph to the right, the data shows how many students have achieved between a score of 20-30, 30-40, 40-50 etc marks on a test. The data is interval (i.e. between 20-30 marks) and is continuous.

A line chart or line graph is a type of chart which displays information as a series of data points called ‘markers’ connected by straight line segments. It is similar to a scatter graph except that the measurement points are ordered and joined with straight line segments (showing that the data is continuous).

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• CBSE Class 11 Statistics for Economics Notes

Chapter 1: Concept of Economics and Significance of Statistics in Economics

• Statistics for Economics | Functions, Importance, and Limitations

Chapter 2: Collection of Data

• Methods of Data Collection
• Sources of Data Collection | Primary and Secondary Sources
• Direct Personal Investigation: Meaning, Suitability, Merits, Demerits and Precautions
• Indirect Oral Investigation : Suitability, Merits, Demerits and Precautions
• Difference between Direct Personal Investigation and Indirect Oral Investigation
• Information from Local Source or Correspondents: Meaning, Suitability, Merits, and Demerits
• Questionnaires and Schedules Method of Data Collection
• Difference between Questionnaire and Schedule
• Qualities of a Good Questionnaire and Types of Questionnaires
• What are the Published Sources of Collecting Secondary Data?
• What Precautions should be taken before using Secondary Data?
• Two Important Sources of Secondary Data: Census of India and Reports & Publications of NSSO
• What is National Sample Survey Organisation (NSSO)?
• What is Census Method of Collecting Data?
• Sample Method of Collection of Data
• Methods of Sampling
• Father of Indian Census
• What makes a Sampling Data Reliable?
• Difference between Census Method and Sampling Method of Collecting Data
• What are Statistical Errors?

Chapter 3: Organisation of Data

• Organization of Data
• Objectives and Characteristics of Classification of Data
• Classification of Data in Statistics | Meaning and Basis of Classification of Data
• Concept of Variable and Raw Data
• Types of Statistical Series
• Difference between Frequency Array and Frequency Distribution
• Types of Frequency Distribution

Chapter 4: Presentation of Data: Textual and Tabular

• Textual Presentation of Data: Meaning, Suitability, and Drawbacks
• Tabular Presentation of Data: Meaning, Objectives, Features and Merits
• Different Types of Tables
• Classification and Tabulation of Data

Chapter 5: Diagrammatic Presentation of Data

• Diagrammatic Presentation of Data: Meaning , Features, Guidelines, Advantages and Disadvantages
• Types of Diagrams

Bar Graph | Meaning, Types, and Examples

• Pie Diagrams | Meaning, Example and Steps to Construct
• Histogram | Meaning, Example, Types and Steps to Draw
• Frequency Polygon | Meaning, Steps to Draw and Examples
• Ogive (Cumulative Frequency Curve) and its Types
• What is Arithmetic Line-Graph or Time-Series Graph?
• Diagrammatic and Graphic Presentation of Data

Chapter 6: Measures of Central Tendency: Arithmetic Mean

• Measures of Central Tendency in Statistics
• Arithmetic Mean: Meaning, Example, Types, Merits, and Demerits
• What is Simple Arithmetic Mean?
• Calculation of Mean in Individual Series | Formula of Mean
• Calculation of Mean in Discrete Series | Formula of Mean
• Calculation of Mean in Continuous Series | Formula of Mean
• Calculation of Arithmetic Mean in Special Cases
• Weighted Arithmetic Mean

Chapter 7: Measures of Central Tendency: Median and Mode

• Median(Measures of Central Tendency): Meaning, Formula, Merits, Demerits, and Examples
• Calculation of Median for Different Types of Statistical Series
• Calculation of Median in Individual Series | Formula of Median
• Calculation of Median in Discrete Series | Formula of Median
• Calculation of Median in Continuous Series | Formula of Median
• Graphical determination of Median
• Mode: Meaning, Formula, Merits, Demerits, and Examples
• Calculation of Mode in Individual Series | Formula of Mode
• Calculation of Mode in Discrete Series | Formula of Mode
• Grouping Method of Calculating Mode in Discrete Series | Formula of Mode
• Calculation of Mode in Continuous Series | Formula of Mode
• Calculation of Mode in Special Cases
• Calculation of Mode by Graphical Method
• Mean, Median and Mode| Comparison, Relationship and Calculation

Chapter 8: Measures of Dispersion

• Measures of Dispersion | Meaning, Absolute and Relative Measures of Dispersion
• Range | Meaning, Coefficient of Range, Merits and Demerits, Calculation of Range
• Calculation of Range and Coefficient of Range
• Interquartile Range and Quartile Deviation
• Partition Value | Quartiles, Deciles and Percentiles
• Quartile Deviation and Coefficient of Quartile Deviation: Meaning, Formula, Calculation, and Examples
• Quartile Deviation in Discrete Series | Formula, Calculation and Examples
• Quartile Deviation in Continuous Series | Formula, Calculation and Examples
• Mean Deviation: Coefficient of Mean Deviation, Merits, and Demerits
• Calculation of Mean Deviation for different types of Statistical Series
• Mean Deviation from Mean | Individual, Discrete, and Continuous Series
• Mean Deviation from Median | Individual, Discrete, and Continuous Series
• Standard Deviation: Meaning, Coefficient of Standard Deviation, Merits, and Demerits
• Standard Deviation in Individual Series
• Standard Deviation in Discrete Series
• Standard Deviation in Frequency Distribution Series
• Combined Standard Deviation: Meaning, Formula, and Example
• How to calculate Variance?
• Coefficient of Variation: Meaning, Formula and Examples
• Lorenz Curveb : Meaning, Construction, and Application

Chapter 9: Correlation

• Correlation: Meaning, Significance, Types and Degree of Correlation
• Methods of Measurements of Correlation
• Scatter Diagram Correlation | Meaning, Interpretation, Example
• Spearman's Rank Correlation Coefficient in Statistics
• Karl Pearson's Coefficient of Correlation | Assumptions, Merits and Demerits
• Karl Pearson's Coefficient of Correlation | Methods and Examples

Chapter 10: Index Number

• Index Number | Meaning, Characteristics, Uses and Limitations
• Methods of Construction of Index Number
• Unweighted or Simple Index Numbers: Meaning and Methods
• Methods of calculating Weighted Index Numbers
• Fisher's Index Number as an Ideal Method
• Fisher's Method of calculating Weighted Index Number
• Paasche's Method of calculating Weighted Index Number
• Laspeyre's Method of calculating Weighted Index Number
• Laspeyre's, Paasche's, and Fisher's Methods of Calculating Index Number
• Consumer Price Index (CPI) or Cost of Living Index Number: Construction of Consumer Price Index|Difficulties and Uses of Consumer Price Index
• Methods of Constructing Consumer Price Index (CPI)
• Wholesale Price Index (WPI) | Meaning, Uses, Merits, and Demerits
• Index Number of Industrial Production : Characteristics, Construction & Example
• Inflation and Index Number

Important Formulas in Statistics for Economics

• Important Formulas in Statistics for Economics | Class 11

Bar graphs are one of the most common and versatile types of charts used to represent categorical data visually. They display data using rectangular bars, where the length or height of each bar corresponds to the value it represents. Bar graphs are widely used in various fields such as business, education, and research to compare different categories or track changes over time. This article explores the different types of bar graphs, their uses, and how to create and interpret them.

Table of Content

What is a Bar Graph/Bar Diagram?

Types of bar graphs or bar diagrams.

• Simple Bar Graph

Example of Simple Bar Graph

• Multiple Bar Graph

Example of Multiple Bar Graph

• Sub-Divided Bar Graph

Example of Sub-Divided Bar Graph

• Percentage Bar Graph

Example of Percentage Bar Graph

• Broken-Scale Bar Graph

Example of Broken-Scale Bar Graph

• Deviation Bar Graph

Example of Deviation Bar Graph

Creating a bar graph, features of bar graph, advantages of bar graph, disadvantages of a bar graph.

A Bar graph is a type of data-handling method that is popularly used in statistics. A bar graph or bar chart is a visual presentation of a group of data that is made up of vertical or horizontal rectangular bars with lengths that are equal to the measure of the data. 

The different types of Bar Graphs are as follows:

• Sub-Divided Bar Graph or Component Bar Graph

1. Simple Bar Graph

A diagram in which each class or category of data is represented by a group of rectangular bars of equal width is known as a Simple Bar Diagram . It is the simplest type of bar diagram. In this diagram, each bar represents one figure only. The number of bars will be equal to the number of figures. These diagrams show only one characteristic of the data, such as sales, production, or population figures for various years.

The magnitude of data is determined by the bar’s height (or length). The lower end of the bar touches the base line; therefore, the height of a bar starts from the zero unit. These diagrams can be vertical or horizontal in layout:

• Vertical Bar Graph: The diagram in which the magnitude of the data is presented vertically, i.e., along the Y-axis, is a Vertical Bar Diagram.
• Horizontal Bar Graph: The diagram in which the magnitude of the data is presented horizontally; i.e., along the X-axis is a Horizontal Bar Diagram.

The bars of a bar diagram can be visually compared by their relative height, and data can be easily comprehended accordingly.

The following table shows the percentage of monthly salary saved by each employee in a 100-person company. Create a vertical and a horizontal bar diagram to represent it.

2. Multiple Bar Graph

The Multiple Bar Diagram is used to compare two or more variables such as revenue and expenditure, import and export for different years, marks obtained in different subjects in different grades, and so on. It is often referred to as a Compound Bar Diagram . The method for creating multiple bar diagrams is the same as for making a Simple Bar Diagram. However, to distinguish the bars from each other, different bars are differentiated by different shades or colours.

A company manufactures three varieties of soap. Represent the following information showing the quarterly sales (three months) using a multiple-bar diagram.

Solution:  

3. Sub-Divided Bar Graph

In these diagrams, the bar corresponding to each phenomenon is divided into several components. Each part or component occupies a proportional part of the bar to its share in the total.  For example, the bar corresponding to the number of students enrolled in a course can be further sub-divided into boys and girls.

• When preparing a sub-divided bar diagram, the various components in each bar should be kept in the same sequence.
• It is important to use different colours or shades to differentiate between different components.
• A suitable index should explain these various colours or shades.
• These diagrams are quite useful for comparing the sizes of various parts and throwing light on the relationship between these integral parts. For instance , such diagrams are used to present data such as sales profits from various products, a family’s expenditure pattern, the budget outlay for receipts and expenditures, and so on.

Represent the following information using a sub-divided bar diagram, showing the quarterly sales of three varieties of soap manufactured by a company.

4. Percentage Bar Graph

A Percentage Bar Diagram is a sub-divided bar diagram that indicates the total percentage of each component rather than the magnitude. The absolute magnitudes of several components are presented using a subdivided diagram. These magnitudes can be converted into relative values by describing them as a percentage of the total.

Each data component is expressed as a percentage of the corresponding total. Thus, in a percentage bar diagram, all of the bars are of height 100, while the different segments of the bar representing the various components vary in size depending on their % value of the total. Just like in the sub-divided bar diagram, in the percentage bar diagram, different components can be differentiated by different shades or colours.

The table below displays the number of newspapers sold out by the company, out of the total printed newspaper from 2016 to 2021.

5. Broken-Scale Bar Graph

This diagram is used when the value of one variable is extremely high or extremely low compared to others. Larger bars may be broken to make space for the smaller bars of the series. Every bar has its value written on the top of the bar.

When the majority of the data figures are of low magnitude and one or more of the figures are of unusually large magnitude, a broken-scale diagram is used to present the data.

Prepare an appropriate diagram using the following data.

6. Deviation Bar Graph

These diagrams are used to represent net changes in data such as net profit, net loss, net exports, net imports, etc.

• In these diagrams, only changes are shown, not the original data.
• The values in these diagrams might be both positive and negative.
• Positive values are displayed above the X-axis (Base line), while negative values are below it.

The following table shows the data relating to the net exports of a company in different years. Use a deviation bar diagram to represent the profit/loss made by the firm.

• 1. Collecting Data: Gather the data you want to represent, ensuring it is categorical and each category has a corresponding value.
• 2. Choosing the Type of Bar Graph : Select the appropriate type of bar graph based on your data and the comparison you want to make.
• 3. Drawing the Axes: Draw the x-axis (horizontal) and y-axis (vertical) on a graph. Label the axes with the categories and values, respectively.
• 4. Plotting the Bars: Draw bars for each category. Ensure the bars are of uniform width and equally spaced. The length or height of each bar should correspond to its value.
• 5. Adding Labels and Titles: Label each bar with its category and value. Add a title to the graph to describe what it represents.
• In this diagram, the magnitude of the characteristics is shown by the length or height of the bar.
• The width of a bar is arbitrarily set to make the constructed diagram more elegant and attractive.
• The length and height of the bars vary depending on the variable value. However, the width of the bar remains constant.
• The width of a bar is also determined by the number of bars that must be accommodated in the diagrams.
• The bars must be equidistant from one another.
• Bars can be drawn horizontally or vertically. They are, however, usually in vertical form.
• The side or vertical axis of a bar graph is called the y-axis and the bottom or horizontal axis of a bar graph is known as the x-axis.
• The y-axis represents data value, while the x-axis represents data type.

Related Articles:

Bar Graph – Definition, Examples & How To Draw a Bar Graph Bar Graphs and Histograms Real Life Applications of Bar Graph

Due to their accessibility and suitability for visual data representation, bar diagrams are used across several industries. Following are some of the advantages of bar diagrams:

1. It is simple to create and easy to understand

Bar diagrams are simple to create on paper and in computer applications. Before choosing the type of bar diagram, one just needs the necessary data for comparison. The vertical bar diagram is ideal for a few categories. In contrast, the horizontal bar diagram works better for multiple categories. In addition, a stacked bar diagram can be used to segment categories, or a grouped bar diagram to show data over time. It is simple to label the x-axis and y-axis and to draw the bars at corresponding values. A bar diagram is widely utilised for visually presenting data.

2. It is frequently used in a variety of industries

Around the world, a bar diagram is utilised in a variety of industries. For instance, it can be used in the field of epidemiology to better understand disease transmission and its control. Bar graphs are frequently used by businesses to analyse their finances and sales. It can also be used to keep a record of personal finances.

3. It makes data comparison easier

Various types of bar graphs can precisely represent data visually. This simplifies the comparison-making process. For instance, the stacked bar diagram/sub-divided bar diagram can be utilised to compare product sales for an online and offline business over different months. The bar diagram shows monthly sales from both online and offline retailers. With a bar diagram, businesses can compare how each of their stores performs and where they need to place their attention.

4. It provides a visual summary of a large data set

A bar diagram is used to represent the large data set. The graphical representation makes the analysis of the data clear. Additionally, gaps between the bars, highlight how each bar represents an individual value. For instance, a business would be interested in knowing how long deliveries take during peak and off-peak periods. The grouped bar diagram can be used to visually summarise the data and identify areas for improvement.

5. It facilitates pattern analysis

The bar diagram helps identify patterns and trends by visually representing data that changes over time. When compared to a table of numerical data, using a bar diagram is easier to understand and use. A bar diagram allows anyone with basic knowledge to read the label and identify patterns and trends, but a table of numerical data requires an expert to identify trends and patterns. In this way, studying patterns and highlighting trends is made simple by the visual representation of data provided by a bar diagram.

Bar diagrams are inappropriate for large projects because they cannot be utilised to evaluate the effects of unforeseen events. Following are some of the disadvantages of a bar diagram:

1. It needs further explanation

Bar diagrams are frequently used in presentations to visually present the data. However, using only the bar diagram is not always effective. Even though the represented data is explained by labels along both axes, an additional example is still required to understand. That is a disadvantage of a bar diagram, especially when presenting complex data. It is insufficient to describe the data set by using the bar diagram.

2. It can be modified

In the digital era, people prefer a visual presentation of data for which a bar diagram is used. But unfortunately, given how simple data is to access and how fast it can be spread on social media, its misuse is unavoidable. These days, bar diagrams have been modified in several different ways that include manipulating the x-axis or y-axis and mishandling the standard. This can ultimately result in the manipulation of the readers.

3. It fails to show the relationship between activities

A bar diagram can be used to estimate the amount of time and work needed for different project activities. Still, it cannot indicate how these activities are related to each other. As a result, it can’t be used as an instrument of control. It is challenging to manage projects with bar diagrams since they can not show the relationships between the activities.

4. It fail to show progress

A bar graph enables the systematic presentation and arrangement of various project activities. However, a bar diagram cannot show the progress of these activities while monitoring them. This makes it an unsuitable tool because there is a need to move fast in a dynamic world. It also makes it challenging to identify the delays in activities.

FAQs on Bar Graph | Meaning, Types, and Examples

What is the main difference between a grouped bar graph and a stacked bar graph.

A grouped bar graph groups multiple bars for each category to compare sub-categories side-by-side, while a stacked bar graph stacks sub-categories within a single bar to show the composition of the main category.

Can bar graphs be used to represent continuous data?

No, bar graphs are typically used to represent categorical data. Continuous data is better represented using histograms or line graphs.

How can I make my bar graph more visually appealing?

You can make your bar graph more visually appealing by using consistent colors, adding labels and titles, ensuring bars are evenly spaced, and using a clean and simple design.

Why are bar graphs preferred over pie charts for comparing categories?

Bar graphs are preferred over pie charts because they make it easier to compare the lengths or heights of bars directly, providing a clearer visual comparison of categories.

What should I do if my bar graph has too many categories to fit neatly on the axis?

If there are too many categories, you can group similar categories together, use a horizontal bar graph, or create a separate graph for each group of categories.

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Represent Data on a Bar Graph

We will learn how to represent data on a bar graph. A bar graph is a simple way of presenting data. Bar graph is a method of presenting data by drawing rectangular bars of equal width and with equal space between the two bars. Bar graphs can be drawn using horizontal or vertical bars.

A bar graph must be drawn on a graph sheet. A bar graph must have a tittle written above the bar graph.

The information in a bar graph is represented along the horizontal and vertical axis. the horizontal axis generally represents the periods or intervals and vertical axis represents the quantity.

Each axis has a label. The label depicts the information represented on each axis. The bars can be shaded or colored suitably. A convenient scale is chosen to decided about the width of bars, and marking the numbers or values on the vertical axis.

We have seen how pictograph represents a pattern of information through numerical data in general. But such representation takes much time and sometimes, drawing picture symbols make it more difficult. So, instead of using picture symbols, it is easier to use bars with equal width (rectangles) to represent the number of buses.

Look at the following bar graph representation.

Such a representation of data with the help of bars is called a bar graph. The bars can be shaded or coloured.

Types of bar graphs

There are two types of bar graphs:

(i) Horizontal bar graph                  (ii) Vertical bar graph

Horizontal Bar Graph

Let us consider an example:       

Draw a horizontal bar graph based on the following data.

The bar graph gives us the following information.

(i) The number of students passed in different subjects.

(ii) The maximum number of students passed in Music.

(iii) The minimum number of students passed in English and Maths.

(iv) The student performed best in Music and worst in English and Maths.

Vertical Bar Graph

Let us take some examples.

1. Draw a vertical bar graph based on the following data.

The graph gives us the following information.

(i) The number of different brand of T. V. Sets manufactured on a certain day.

(ii) The production of Sony TV sets was maximum on that day.

(iii) The production of Prince TV sets was minimum on that day.

We can say that a bar graph is a pictorial representation of the numerical data by a number of bars (rectangles) having the same width drawn horizontally or vertically with equal space between them.

2. Look at the bar graph given below. What information does the bar graph depicts?

The bar graph depicts here the number of saplings planted by students of classes from 1 to 5. The title of the bargraph of saplings planted. The scale used here is 1 square = 10 saplings.

Now, observe the above bar graph and answer the questions that follow.

(i) How many saplings were planted in all?

     50 + 60 + 40 + 90 + 50 = 290

(ii) Write the scale of the above bar graph.

     1 square = 10 saplings

(iii) Which two classes planted the same number of saplings?

      Class I and Class V

(iv) Given 2 reasons why we should grow plants ____________________

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55+ Bar Chart Templates for PowerPoint and Google Slides

Download Bar Chart PowerPoint Templates . Making bar charts and dashboards with complex data was never easy. With SlideModel’s professionally crafted bar chart templates you can easily create amazing bar charts to present your data in the form of easy to grasp slides.

These unique slide designs are easy to edit, with the option to comprehensively customize the very basic aspects of each sample slide, including objects within the slide designs. Pick a bar chart template from our collection to represent your data in an impressive layout.

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A bar chart is also known as a bar graph. It is a visual tool used in representing the grouping of data into separate groups. It can be represented vertically or horizontally; it represents ordinal and nominal variables, respectively. It is a versatile visual representation tool that represents data across various industries. William Playfair invented the bar chart in the late 18th century. Bar graph templates are simple to understand, widely used, and display changes over time. A bar graph, on the other hand, can only be used with discrete data.

The Bar Graph templates represent various statistical discrete data across industries. These templates are used by academic institutions, financial organizations, health institutions, marketers, and the science and technology industries. Also, it helps to compare discrete data. For example, you can use the Finance Growth Metaphor PowerPoint Template to visually represent your organization’s financial reports. You will be privileged to compare the past and present reports to know the progress status of the organization.

These creative PowerPoint Bar chart templates are also used in health institutes to inform the audience about medical statistics and epidemiological information. The rate of bacteria and viral infections are represented using this template and can be equally compared. We at SlideModel got 100% downloadable and editable bar chart templates that can illustrate your data and information nicely. Please browse through our array of innovative PowerPoint Bar Chart slide designs to visually communicate your data. These templates are professionally designed considering colors, icons, and other elements that make them visually distinctive and appealing to your respective audience. For more information check our article about how to make a presentation graph and data presentations .

How Do I Apply A Chart Template In Powerpoint?

To apply a Chart Template in PowerPoint, follow the steps outlined below:

• To open the Insert Chart window, click a Chart button on the Insert tab of the ribbon.
• On the left sidebar, select the Templates tab.
• A gallery of your Chart Templates will appear. Choose which one you want to use to make the chart, and then click on the OK icon.

When you insert a chart template into PowerPoint, the formatting options change and adjust to the inserted template.

How Do You Use A Chart Template?

To use a chart template in PowerPoint, follow these steps: Insert the icon; select Illustrations; and then chart. Then, in the top left, click the Templates folder. Choose your chart template from the gallery and press the OK icon.

How Do I Copy A Chart Style In Powerpoint?

Here are the steps for quickly copying the chart format and pasting it into the new chart:

• Copy the formatting of the chart by right-clicking on it and selecting copy.
• Go to Home; Clipboard; Paste; Special Paste. It will bring up the Paste Special dialog box.
• Select Formats in the paste special dialogue box and click OK.

It would copy the formatting from one chart to the other instantly. Alternatively, you can use the following keyboard command, which is faster:

• Using the following keyword commands: Choose the formatting you want to copy and then press Control + C and Control + P to paste it on the new chart.

How Do I Apply A Chart Template To An Existing Chart?

For you to apply a chart template to an existing chart, you need to follow the following procedures carefully:

• Right-click an existing chart and select Change Chart Type to add a template.
• From the window that appears, select the Templates folder.
• Then choose a template and click OK. It will then be evident on the chart.

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Data Presentation: Bar Graphs Advantages and Disadvantages

Bar graphs are good for showing how data change over time.

Disadvantages Online Bar Chart Maker (up to 100 categories) Histogram or Bihistogram Creator Percentage or Divided Bar Chart Creator Stacked Bar Chart or Bar Graph Maker Got any suggestions? We want to hear from you! Send us a message and help improve Slidesgo Top searches Trending searches meet the teacher 31 templates weekly planner 54 templates 90 templates welcome back 88 templates 79 templates 178 templates Bar Charts Infographics It seems that you like this template, free google slides theme, powerpoint template, and canva presentation template. Bar charts are very adaptable. No matter what you want to represent: if you have some numbers, data and percentages, use these diagrams. We have designed many of them for you: simple bars, cylindrical, pyramidal, arrows… Choose one! Features of these infographics Many styles of bar charts, so you can choose the most suitable one for your project 100% editable and easy to modify 31 different infographics to boost your presentations Include icons and Flaticon’s extension for further customization Designed to be used in Google Slides, Canva, and Microsoft PowerPoint and Keynote 16:9 widescreen format suitable for all types of screens Include information about how to edit and customize your infographics How can I use the infographics? Am I free to use the templates? How to attribute the infographics? Attribution required If you are a free user, you must attribute Slidesgo by keeping the slide where the credits appear. How to attribute? Register for free and start downloading now Related posts on our blog. How to Add, Duplicate, Move, Delete or Hide Slides in Google Slides How to Change Layouts in PowerPoint How to Change the Slide Size in Google Slides Related presentations. Premium template Unlock this template and gain unlimited access Alternatives 10 Methods of Data Presentation That Really Work in 2024 Leah Nguyen • 15 July, 2024 • 13 min read Have you ever presented a data report to your boss/coworkers/teachers thinking it was super dope like you’re some cyber hacker living in the Matrix, but all they saw was a pile of static numbers that seemed pointless and didn't make sense to them? Understanding digits is rigid . Making people from non-analytical backgrounds understand those digits is even more challenging. How can you clear up those confusing numbers and make your presentation as clear as the day? Let's check out these best ways to present data. 💎 How many type of charts are available to present data? 7 How many charts are there in statistics? 4, including bar, line, histogram and pie. How many types of charts are available in Excel? 8 Who invented charts? William Playfair When were the charts invented? 18th Century More Tips with AhaSlides Marketing Presentation Survey Result Presentation Types of Presentation Start in seconds. Get any of the above examples as templates. Sign up for free and take what you want from the template library! Data Presentation - What Is It? The term ’data presentation’ relates to the way you present data in a way that makes even the most clueless person in the room understand.  Some say it’s witchcraft (you’re manipulating the numbers in some ways), but we’ll just say it’s the power of turning dry, hard numbers or digits into a visual showcase that is easy for people to digest. Presenting data correctly can help your audience understand complicated processes, identify trends, and instantly pinpoint whatever is going on without exhausting their brains. Good data presentation helps… Make informed decisions and arrive at positive outcomes . If you see the sales of your product steadily increase throughout the years, it’s best to keep milking it or start turning it into a bunch of spin-offs (shoutout to Star Wars👀). Reduce the time spent processing data . Humans can digest information graphically 60,000 times faster than in the form of text. Grant them the power of skimming through a decade of data in minutes with some extra spicy graphs and charts. Communicate the results clearly . Data does not lie. They’re based on factual evidence and therefore if anyone keeps whining that you might be wrong, slap them with some hard data to keep their mouths shut. Add to or expand the current research . You can see what areas need improvement, as well as what details often go unnoticed while surfing through those little lines, dots or icons that appear on the data board. Methods of Data Presentation and Examples Imagine you have a delicious pepperoni, extra-cheese pizza. You can decide to cut it into the classic 8 triangle slices, the party style 12 square slices, or get creative and abstract on those slices.  There are various ways to cut a pizza and you get the same variety with how you present your data. In this section, we will bring you the 10 ways to slice a pizza - we mean to present your data - that will make your company’s most important asset as clear as day. Let's dive into 10 ways to present data efficiently. #1 - Tabular  Among various types of data presentation, tabular is the most fundamental method, with data presented in rows and columns. Excel or Google Sheets would qualify for the job. Nothing fancy. This is an example of a tabular presentation of data on Google Sheets. Each row and column has an attribute (year, region, revenue, etc.), and you can do a custom format to see the change in revenue throughout the year. When presenting data as text, all you do is write your findings down in paragraphs and bullet points, and that’s it. A piece of cake to you, a tough nut to crack for whoever has to go through all of the reading to get to the point. 65% of email users worldwide access their email via a mobile device. Emails that are optimised for mobile generate 15% higher click-through rates. 56% of brands using emojis in their email subject lines had a higher open rate. (Source: CustomerThermometer ) All the above quotes present statistical information in textual form. Since not many people like going through a wall of texts, you’ll have to figure out another route when deciding to use this method, such as breaking the data down into short, clear statements, or even as catchy puns if you’ve got the time to think of them. #3 - Pie chart A pie chart (or a ‘donut chart’ if you stick a hole in the middle of it) is a circle divided into slices that show the relative sizes of data within a whole. If you’re using it to show percentages, make sure all the slices add up to 100%. The pie chart is a familiar face at every party and is usually recognised by most people. However, one setback of using this method is our eyes sometimes can’t identify the differences in slices of a circle, and it’s nearly impossible to compare similar slices from two different pie charts, making them the villains in the eyes of data analysts. #4 - Bar chart The bar chart is a chart that presents a bunch of items from the same category, usually in the form of rectangular bars that are placed at an equal distance from each other. Their heights or lengths depict the values they represent. They can be as simple as this: Or more complex and detailed like this example of data presentation. Contributing to an effective statistic presentation, this one is a grouped bar chart that not only allows you to compare categories but also the groups within them as well. #5 - Histogram Similar in appearance to the bar chart but the rectangular bars in histograms don’t often have the gap like their counterparts. Instead of measuring categories like weather preferences or favourite films as a bar chart does, a histogram only measures things that can be put into numbers. Teachers can use presentation graphs like a histogram to see which score group most of the students fall into, like in this example above. #6 - Line graph Recordings to ways of displaying data, we shouldn't overlook the effectiveness of line graphs. Line graphs are represented by a group of data points joined together by a straight line. There can be one or more lines to compare how several related things change over time.  On a line chart’s horizontal axis, you usually have text labels, dates or years, while the vertical axis usually represents the quantity (e.g.: budget, temperature or percentage). #7 - Pictogram graph A pictogram graph uses pictures or icons relating to the main topic to visualise a small dataset. The fun combination of colours and illustrations makes it a frequent use at schools. Pictograms are a breath of fresh air if you want to stay away from the monotonous line chart or bar chart for a while. However, they can present a very limited amount of data and sometimes they are only there for displays and do not represent real statistics. #8 - Radar chart If presenting five or more variables in the form of a bar chart is too stuffy then you should try using a radar chart, which is one of the most creative ways to present data. Radar charts show data in terms of how they compare to each other starting from the same point. Some also call them ‘spider charts’ because each aspect combined looks like a spider web. Radar charts can be a great use for parents who’d like to compare their child’s grades with their peers to lower their self-esteem. You can see that each angular represents a subject with a score value ranging from 0 to 100. Each student’s score across 5 subjects is highlighted in a different colour. If you think that this method of data presentation somehow feels familiar, then you’ve probably encountered one while playing Pokémon . #9 - Heat map A heat map represents data density in colours. The bigger the number, the more colour intensity that data will be represented. Most US citizens would be familiar with this data presentation method in geography. For elections, many news outlets assign a specific colour code to a state, with blue representing one candidate and red representing the other. The shade of either blue or red in each state shows the strength of the overall vote in that state. Another great thing you can use a heat map for is to map what visitors to your site click on. The more a particular section is clicked the ‘hotter’ the colour will turn, from blue to bright yellow to red. #10 - Scatter plot If you present your data in dots instead of chunky bars, you’ll have a scatter plot.  A scatter plot is a grid with several inputs showing the relationship between two variables. It’s good at collecting seemingly random data and revealing some telling trends. For example, in this graph, each dot shows the average daily temperature versus the number of beach visitors across several days. You can see that the dots get higher as the temperature increases, so it’s likely that hotter weather leads to more visitors. 5 Data Presentation Mistakes to Avoid #1 - assume your audience understands what the numbers represent. You may know all the behind-the-scenes of your data since you’ve worked with them for weeks, but your audience doesn’t. Showing without telling only invites more and more questions from your audience, as they have to constantly make sense of your data, wasting the time of both sides as a result. While showing your data presentations, you should tell them what the data are about before hitting them with waves of numbers first. You can use interactive activities such as polls , word clouds , online quizzes and Q&A sections , combined with icebreaker games , to assess their understanding of the data and address any confusion beforehand. #2 - Use the wrong type of chart Charts such as pie charts must have a total of 100% so if your numbers accumulate to 193% like this example below, you’re definitely doing it wrong. Before making a chart, ask yourself: what do I want to accomplish with my data? Do you want to see the relationship between the data sets, show the up and down trends of your data, or see how segments of one thing make up a whole? Remember, clarity always comes first. Some data visualisations may look cool, but if they don’t fit your data, steer clear of them.  #3 - Make it 3D 3D is a fascinating graphical presentation example. The third dimension is cool, but full of risks. Can you see what’s behind those red bars? Because we can’t either. You may think that 3D charts add more depth to the design, but they can create false perceptions as our eyes see 3D objects closer and bigger than they appear, not to mention they cannot be seen from multiple angles. #4 - Use different types of charts to compare contents in the same category This is like comparing a fish to a monkey. Your audience won’t be able to identify the differences and make an appropriate correlation between the two data sets.  Next time, stick to one type of data presentation only. Avoid the temptation of trying various data visualisation methods in one go and make your data as accessible as possible. #5 - Bombard the audience with too much information The goal of data presentation is to make complex topics much easier to understand, and if you’re bringing too much information to the table, you’re missing the point. The more information you give, the more time it will take for your audience to process it all. If you want to make your data understandable and give your audience a chance to remember it, keep the information within it to an absolute minimum. You should end your session with open-ended questions to see what your participants really think. What are the Best Methods of Data Presentation? Finally, which is the best way to present data? The answer is… There is none! Each type of presentation has its own strengths and weaknesses and the one you choose greatly depends on what you’re trying to do.  For example: Go for a scatter plot if you’re exploring the relationship between different data values, like seeing whether the sales of ice cream go up because of the temperature or because people are just getting more hungry and greedy each day? Go for a line graph if you want to mark a trend over time.  Go for a heat map if you like some fancy visualisation of the changes in a geographical location, or to see your visitors' behaviour on your website. Go for a pie chart (especially in 3D) if you want to be shunned by others because it was never a good idea👇 Frequently Asked Questions What is a chart presentation. A chart presentation is a way of presenting data or information using visual aids such as charts, graphs, and diagrams. The purpose of a chart presentation is to make complex information more accessible and understandable for the audience. When can I use charts for the presentation? Charts can be used to compare data, show trends over time, highlight patterns, and simplify complex information. Why should you use charts for presentation? You should use charts to ensure your contents and visuals look clean, as they are the visual representative, provide clarity, simplicity, comparison, contrast and super time-saving! What are the 4 graphical methods of presenting data? Histogram, Smoothed frequency graph, Pie diagram or Pie chart, Cumulative or ogive frequency graph, and Frequency Polygon. Leah Nguyen Words that convert, stories that stick. I turn complex ideas into engaging narratives - helping audiences learn, remember, and take action. Tips to Engage with Polls & Trivia More from AhaSlides Data Visualization: How Do You Create A Bar Graph? Examples and Bar Graph Definition Explore the art of data visualization with our guide on creating impactful bar graphs. Learn the essentials, discover key features, and gain insights through practical examples, empowering you to effectively convey information visually. December 21, 2023 What's Inside? What is Bar Graph? A bar graph is a graphical data representation where values for several categories are shown using rectangular bars or columns. The height or length of each bar reflects the value it represents, making it simple to compare categories visually. Bar graphs' salient characteristics include: Categories (X-Axis): Labels or categories are usually displayed along the horizontal axis (X-axis) in bar graphs. These categories stand for various sets of objects under comparison. Values (Y-Axis): The numerical values corresponding to each category are represented by the vertical axis (Y-axis). These values are reflected in the bars' height or length. Bars: It is simple to compare the values visually because the bars are drawn perpendicular to the axis. The value represented by a bar increases with its length. Usage Areas Of Bar Graphs Bar graphs are versatile graphic types that can be used in various contexts. Here are some common places where bar graphs are frequently utilized: Marketing and Sales Analysis: Bar graphs are often used to visualize marketing and sales data, such as product sales, market share, and customer preferences. Financial Analysis: Bar graphs serve as effective tools for comparing income and expenses, budget analysis, and representing financial performance. Human Resources Management: Bar graphs are employed to visualize human resources data, including staff numbers, training budgets, and performance evaluations. Education and Academic Analysis: Bar graphs are used to represent educational and academic data, such as exam results, student performance, and education budgets. Demographic Analysis: Bar graphs are useful for visualizing demographic data, such as city populations and employment rates by age groups. Social Science Research: Bar graphs are employed in social science research to visualize survey results and societal opinions on specific topics. Public Health and Epidemiology: Bar graphs are utilized to represent health data, including disease spread and vaccination rates. Project Management: Bar graphs help in understanding project-related data, such as project progress and task completion times. Business Performance Analysis: Bar graphs are used to visualize business-related data, including employee performance evaluations and goal attainment rates. Investment and Financial Markets: Bar graphs are commonly used to represent financial market data, such as stock performance and index comparisons. The benefits and conveniences of using bar graphs Ease of Comparison: Bar graphs are ideal for quickly and clearly comparing values between different categories. The length or height of bars facilitates visual comparison of values. Representation of Categorical Data: Bar graphs are effective tools for representing categorical data. Each bar corresponds to a category, making them suitable for illustrating relationships between specific groups. Simple and Understandable Visualization: Due to their straightforward design, bar graphs appeal to a wide audience. Their uncomplicated structure aids in the easy understanding and communication of information. Distribution Representation: Bar graphs can be used to visualize the distribution of data in a dataset. Understanding how values are distributed over a specific period or situation is made easy. Trend Analysis: Bar graphs can be used to showcase changes and trends over time. For instance, a bar graph displaying monthly sales data is effective in highlighting seasonal trends or growth patterns Clear and Direct Value Representation: The length or height of each bar directly represents its value, providing a clear and immediate indication of the data. Wide Application Range: Bar graphs find applications in various fields, from financial analysis to marketing strategies, educational data, and health statistics. We use bar graphs extensively in our daily lives, providing us with valuable insights for understanding data. Do you also incorporate bar graphs when giving presentations? If your answer is no, we have a fantastic suggestion for you! Try Decktopus AI now! We strongly recommend trying Decktopus AI for creating presentations that are not only enriched with artificial intelligence but can also be done swiftly within minutes. Give it a try for a seamless and impressive presentation experience! Let's take a look together at how you can add your bar graph to create fantastic presentations. Join us to explore the process of seamlessly incorporating your bar graph into your presentation for an impressive outcome. Step-by-Step Guide to Adding a Bar Graph to Your Presentation Step 1: open dectopus ai. You can start creating your deck by registering to Decktopus . Step 2: Choose a Bar Graph Template A basic bar graph template with customizable colors and labels. A stacked bar chart template to compare multiple categories. A grouped bar graph template for comparing multiple groups within each category. Step 3: Enter Data Decktopus allows you to easily customize the titles of the X and Y axes, providing flexibility in tailoring your chart's appearance to suit your presentation needs. You have the option to hide or reveal the titles of both X and Y axes, giving you control over the visual elements of your chart and ensuring a clean and focused presentation. When you click on 'Edit Data,' Decktopus opens the Data screen, enabling you to modify and update the information in your chart effortlessly. By simply clicking on cells, you can add or remove rows and columns, providing a user-friendly interface for adjusting the structure of your data directly within Decktopus. Also easy data updates, allowing you to modify and refresh information seamlessly, ensuring your chart reflects the most recent and relevant data. Step 4: Change The Colours By accessing the 'Design' section and clicking on 'Color Palette,' users can effortlessly update and customize the graph's colors, providing the flexibility to match the visual aesthetics of the presentation or convey specific themes effectively. Step 5: Finalize and Save Create your bar graph now! Creating impressive and beautiful presentations with Decktopus AI is as easy as this. If you'd like to explore further, you can check out our presentation. Common Types Of Bar Graphs Common Types Of Bar Graphs Horizontal Bar Graphs: Categories are displayed along the horizontal axis, and horizontal bars represent the values for each category. This type is especially useful for long category labels. Clustered Bar Graphs: Bars are grouped closely together, with each group representing different values for a specific category. Stacked Bar Graphs: Bars have stacked layers representing the total value for the category. The total length of each bar represents the total value within the category. Dodged Bar Graphs: Bars representing two or more categorical variables are separated, allowing for comparisons between different groups. Clustered Stacked Bar Graphs: This type has a two-tier structure, organizing bars into groups and including stacked layers within each bar. Differentiated Bar Graphs: Bars differentiate through color, pattern, or shape. This type enables quick visual distinction between different categories. Vertical Bar Graphs: The most commonly used type, where categories are displayed along the vertical axis, and vertical bars represent the values for each category. Colored Bar Graphs: Each bar is a different color, enhancing the visual distinction between categories. 3D Bar Graph:Depth to the traditional two-dimensional bar chart, providing a visually engaging representation of data. Frequently Asked Questions 1) what does a bar graph explain. A bar graph visually represents data through rectangular bars, where the length or height of each bar corresponds to the quantity it represents. It effectively communicates comparisons, distributions, and trends within different categories or data sets. 2) Is bar graph easy? Yes, bar graphs are generally easy to create and interpret. They provide a straightforward visual representation of data, making it easy for individuals to understand and compare values across different categories. 3) How do you explain a bar graph to students? Explaining a bar graph to students can be done in a step-by-step manner: Introduction: Start by introducing the concept of a bar graph as a visual representation of data using rectangular bars. Components: Explain the key components: horizontal/vertical axis, bars, and labels. The horizontal axis represents categories, while the vertical axis represents values. Data Input: Demonstrate how to input data into a bar graph. Each category gets a bar, and the length or height of the bar corresponds to the value it represents. Labeling: Emphasize the importance of labeling. Clearly label axes and provide a title to the graph. Comparison: Highlight that bar graphs are excellent for comparing quantities between different categories. Longer bars represent larger values. Interpretation: Show students how to interpret the graph. Discuss trends, highs, lows, and any patterns visible in the data. Real-Life Examples: Provide real-life examples relevant to students' interests. This helps in connecting the abstract concept to practical applications. Practice: Allow students to practice creating their own bar graphs with simple datasets. Encourage them to interpret the graphs they create. Review: Summarize the main points and encourage questions. Review the key concepts to ensure understanding. Application: Discuss situations where bar graphs are commonly used, such as in newspapers, reports, or scientific studies. Right at this point, recommending Decktopus AI to your students can facilitate their use of bar graphs effortlessly, without intimidating them. This enables them to easily incorporate bar graphs into real-life scenarios. Latest Articles July 29, 2024 Your Questions Answered We were delighted to receive a host of great reviews and solid questions from the community, giving us a significant reminder of why we do what we do. To make it easier for you, we gathered the most frequently asked questions in this blog and shared the answers gathered by our expert team. Top Virtual Team-Building Activities: Boost Work Morale in 2024 Connect your virtual teams like never before with dynamic building activities designed for 's remote workforce. The Crucial Role of Teamwork in Achieving Success: Why is Teamwork Important? Explore why teamwork is crucial for success Learn the key benefits of collaborative efforts. Don't waste your time designing your presentations by yourself! Type your content and let our platform design your presentations automatically. No more wasting time for your presentations. Use hundreds of presentation templates to impress your audience. This is the only tool you need to prepare presentations. Try our Presentation Builder today >> Don’t waste your time by trying to make a website for all your content Place your content links and let our platform design your bio link automatically. No more wasting time for your social content distribution. Use hundreds of presentation biolink to impress your audience. This is the only tool you need to prepare good-looking bio links. Try our Bio Link Builder today >> Do You Want To Create a Presentation? Sign up for our newsletter to stay up-to-date on the latest news and tips from Decktopus. Let’s create a form here to get visitors’ email addresses. Ready to dive in? Start your free trial today. If you're seeing this message, it means we're having trouble loading external resources on our website. If you're behind a web filter, please make sure that the domains *.kastatic.org and *.kasandbox.org are unblocked. To log in and use all the features of Khan Academy, please enable JavaScript in your browser. Course: 2nd grade   >   Unit 7 Creating a bar graph. Reading bar graphs: bikes Make bar graphs Solve problems with bar graphs Want to join the conversation? Upvote Button navigates to signup page Downvote Button navigates to signup page Flag Button navigates to signup page Video transcript How can I create a professional and visually appealing bar graph in PowerPoint for my presentation? June 25, 2024 / To create a professional and visually appealing bar graph in PowerPoint for your presentation, follow these steps: Open PowerPoint and select the slide where you want to insert the bar graph. Click on the “Insert” tab in the top menu and choose “Chart” from the options. In the Chart dialog box, select the “Bar” category and choose the desired bar graph type (e.g., clustered bar, stacked bar, etc.). Click “OK” to insert a default bar graph onto your slide. Double-click on the graph to open the Excel spreadsheet associated with it. Replace the default data with your own by typing or pasting it into the Excel sheet. Customize the appearance of your bar graph by selecting the graph elements (bars, axes, labels, etc.) and using the formatting options available in the “Chart Design” and “Format” tabs. Once you are satisfied with the design, close the Excel spreadsheet. Resize and reposition the bar graph on your slide as needed. Finally, save your PowerPoint presentation to preserve the changes made to your bar graph. View Our Presentation Portfolio Get a Quote on a Custom Designed Presentation How can I create a professional-looking bar chart in PowerPoint for my presentation? Popular Posts Common Challenges in Tailoring Presentations—and Solutions Dos and Don’ts of Pre-Seed Pitch Deck Creation How to Write a Teaser Pitch Deck that Captivates Tips for a Persuasive How It Works Slide What Not to Do When Presenting Funding History Search by keyword Gdp up by 0.3% in both the euro area and the eu, announcement. Following recommendations for a harmonised European revision policy for national accounts and balance of payments , EU countries will carry out a benchmark revision of their national accounts estimates in 2024. The purpose of this benchmark revision is to implement changes introduced by the amended ESA 2010 regulation , and to incorporate new data sources and other methodological improvements. Most of the revised quarterly and annual country data are expected to be released by Eurostat between June and October 2024, and will be progressively integrated in European estimates. The impact of these revisions is expected to be limited, but still noticeable for some European aggregates and more pronounced for certain Member States. For further details, please consult the available documentation on Eurostat’s website . In the second quarter of 2024, seasonally adjusted GDP increased by 0.3% in both the euro area and the EU , compared with the previous quarter, according to a preliminary flash estimate published by Eurostat, the statistical office of the European Union . In the first quarter of 2024, GDP had also grown by 0.3% in both zones. These preliminary GDP flash estimates are based on data sources that are incomplete and subject to further revisions. Compared with the same quarter of the previous year, seasonally adjusted GDP increased by 0.6% in the euro area and by 0.7% in the EU in the second quarter of 2024, after +0.5% in the euro area and +0.6% in the EU in the previous quarter. Among the Member States for which data are available for the second quarter of 2024, Ireland (+1.2%) recorded the highest increase compared to the previous quarter, followed by Lithuania (+0.9%) and Spain (+0.8%). The highest declines were recorded in Latvia (-1.1%), Sweden (-0.8%) and Hungary (-0.2%). The year on year growth rates were positive for eight countries and negative for three. Published growth rates of GDP in volume up to 2024Q2   (based on seasonally adjusted* data) Percentage change compared with the previous quarter Percentage change compared with the same quarter of the previous year 2023Q3 2023Q4 2024Q1 2024Q2 2023Q3 2023Q4 2024Q1 2024Q2 Euro area 0.0 0.0 0.3 0.1 0.2 0.5 EU 0.1 0.0 0.3 0.2 0.4 0.6 Belgium 0.3 0.3 0.3 1.3 1.3 1.3 Czechia -0.4 0.3 0.2 -0.4 0.0 0.3 Germany 0.2 -0.4 0.2 -0.3 -0.2 -0.1 Ireland -1.7 -1.5 0.7 -8.3 -9.8 -4.0 Spain 0.5 0.7 0.8 1.9 2.2 2.6 France 0.1 0.4 0.3 0.9 1.3 1.5 Italy 0.3 0.1 0.3 0.6 0.7 0.6 Latvia -0.3 0.3 0.8 0.2 -0.2 0.8 Lithuania -0.1 -0.2 0.9 0.1 0.1 3.0 Hungary 0.8 0.0 0.7 -0.2 0.5 1.6 Austria -0.2 0.1 0.2 -1.7 -1.3 -1.3 Portugal -0.2 0.7 0.8 1.9 2.1 1.5 Sweden** 0.2 0.3 0.5 -0.7 -0.1 0.7 *  Growth rates to the previous quarter and to the same quarter of the previous year presented in this table are both based on seasonally and calendar adjusted figures, except where indicated. Unadjusted data are not available for all Member States that are included in GDP flash estimates. **  Percentage change compared with the same quarter of the previous year calculated from calendar adjusted data. Source dataset: The next estimates for the second quarter of 2024 will be released on 14 August 2024. Notes for users The reliability of GDP flash estimates was tested by dedicated working groups and revisions of subsequent estimates are continuously monitored . Further information can be found on Eurostat website . With this preliminary flash estimate, euro area and EU GDP figures for earlier quarters are not revised. All figures presented in this release may be revised with the GDP t+45 flash estimate scheduled for 14 August 2024 and subsequently by Eurostat’s regular estimates of GDP and main aggregates (including employment) scheduled for 6 September 2024 and 18 October 2024, which will reflect the impact of countries’ benchmark revisions as available. The preliminary flash estimate of GDP growth for the second quarter of 2024 presented in this release is based on the data of 18 Member States, covering 96% of euro area GDP and 94% of EU GDP. Release schedule Comprehensive estimates of European main aggregates (including GDP and employment) are based on countries regular transmissions and published around 65 and 110 days after the end of each quarter. To improve the timeliness of key indicators, Eurostat also publishes flash estimates for GDP (after around 30 and 45 days) and employment (after around 45 days). Their compilation is based on estimates provided by EU Member States on a voluntary basis. This news release presents preliminary flash estimates for euro area and EU after around 30 days. Methods and definitions European quarterly national accounts are compiled in accordance with the European System of Accounts 2010 (ESA 2010). Gross domestic product (GDP) at market prices measures the production activity of resident production units. Growth rates are based on chain-linked volumes. Two statistical working papers present the preliminary GDP flash methodology for the European estimates and Member States estimates . The method used for compilation of European GDP is the same as for previous releases. Geographical information Euro area (EA20): Belgium, Germany, Estonia, Ireland, Greece, Spain, France, Croatia, Italy, Cyprus, Latvia, Lithuania, Luxembourg, Malta, the Netherlands, Austria, Portugal, Slovenia, Slovakia and Finland. European Union (EU27): Belgium, Bulgaria, Czechia, Denmark, Germany, Estonia, Ireland, Greece, Spain, France, Croatia, Italy, Cyprus, Latvia, Lithuania, Luxembourg, Hungary, Malta, the Netherlands, Austria, Poland, Portugal, Romania, Slovenia, Slovakia, Finland and Sweden. For more information Website section on national accounts , and specifically the page on quarterly national accounts Database section on national accounts and metadata on quarterly national accounts Statistics Explained articles on measuring quarterly GDP and presentation of updated quarterly estimates Country specific metadata Country specific metadata on the recording of Ukrainian refugees in main aggregates of national accounts European System of Accounts 2010 Euro indicators dashboard Release calendar for Euro indicators European Statistics Code of Practice Get in touch Media requests Eurostat Media Support Phone: (+352) 4301 33 408 E-mail: [email protected] Further information on data Thierry COURTEL Johannes BUCK E-mail: [email protected] Share the release An official website of the United States government Here’s how you know Official websites use .gov A .gov website belongs to an official government organization in the United States. Secure .gov websites use HTTPS A lock ( Lock Locked padlock icon ) or https:// means you’ve safely connected to the .gov website. Share sensitive information only on official, secure websites. Header - Callout In Crisis? Call or Text 988 Data Source 2023 national survey on drug use and health (nsduh) releases. Conducted annually, the National Survey on Drug Use and Health (NSDUH) provides nationally representative data on the use of tobacco, alcohol, and drugs; substance use disorders; mental health issues; and receipt of substance use and mental health treatment among the civilian, noninstitutionalized population aged 12 or older in the United States. NSDUH estimates allow researchers, clinicians, policymakers, and the general public to better understand and improve the nation’s behavioral health. These reports and detailed tables present national estimates from the 2023 National Survey on Drug Use and Health (NSDUH). Jump to: Highlights Annual National Report Detailed Tables Methodology Companion Infographic Reports For the 2023 NSDUH release, two companion infographic reports are provided. Both offer user-friendly, visual representations of findings. First, a NSDUH report on key national indicators (PDF) that compares outcomes from 2021, 2022, and 2023. Second, a report on important indicators broken out by race and ethnicity   (PDF) . The report uses the combined data from the 2021 through 2023 NSDUH to increase precision for smaller groups. They cover selected indicators of substance use, substance use disorders, mental health issues, suicidality, and recovery from substance use problems or mental health issues among the civilian, noninstitutionalized U.S. population aged 12 or older. 2023 NSDUH Frequently Asked Questions General Questions 1. Are NSDUH data from 2023 able to be compared with data from 2021 and 2022? Many (but not all) NSDUH estimates from 2023 may be compared with estimates from 2022 and updated 2021 estimates that take into account the proportions of web and in-person interviews. Selected updated 2021 estimates are available in the 2022 Detailed Tables and in the 2023 Companion Infographic Report . Additionally, the 2021 Public Use File and Restricted Use File have been revised to allow data users to produce updated estimates for 2021. The 2021 Key Substance Use and Mental Health Indicators report, and 2021 Detailed Tables should not be used to compare estimates from 2021 with those from 2022 or 2023 because these 2021 products do not present the updated estimates. SAMHSA provides more information about NSDUH restricted use files . Adjustment to these targeted proportions also was included as part of the 2022 and 2023 weighting procedures to remove the potential for bias in the comparison of estimates across years due to changes in the proportions of interviews that were completed in each mode. See section 2.3.4 of the 2023 Methodological Summary and Definitions report for more details. 2. Why does SAMHSA not use the term “trends” to compare estimates from 2021 to 2023 in the 2023 Companion Infographic Report? SAMHSA has indicated in the 2023 Companion Infographic Report that statistical tests for overall trends from the baseline year to the current year will not be conducted until at least four comparable NSDUH data points are available. Although the 2023 Companion Infographic Report compares estimates between years for 2021, 2022, and 2023, SAMHSA does not consider comparisons involving only 3 years of data to be sufficient for drawing conclusions about longer-term trends for substance use and mental health outcomes. 3. Is it appropriate to combine 2023 data with previous years’ data? If data users wish to create pooled estimates that include data from 2021, the 2021 updated weights should be used (see response to FAQ #1 ). A revised 2021 NSDUH Public Use File was released in January 2024 with the updated weight. A revised 2021 Restricted Use File also is available with the updated weight. See the 2022 Statistical Inference Report (SIR) for details on the procedures for creating annual average estimates using pooled NSDUH data. SAMHSA expects to release the 2023 SIR in early 2025 as part of the 2023 Methodological Resource Book. As with the 2021 and 2022 NSDUH data, the 2023 NSDUH data should not be combined with data from 2020 or prior years for a variety of methodological reasons. A full description of the analyses that were conducted to investigate the effects of methodological changes can be found in chapter 6 of the 2021 Methodological Summary and Definitions report. Some estimates should not be compared between years because of changes in the NSDUH questionnaire in 2022 and 2023. Information related to variable comparability between 2021 and 2022 can be found in the Variable Crosswalk Chart available with the 2022 Public Use File . The Variable Crosswalk Chart for the 2023 Public Use File will be available later in 2024. 4. Is SAMHSA planning to release state-level and substate-level estimates using 2023 data? State estimates are generally produced with 2 years of comparable data, and substate estimates are generally produced with 3 years of comparable data. State estimates using combined 2022 and 2023 data are expected to be released later in 2024. Substate-level estimates using combined data from 2021 to 2023 are expected to be released in 2025. What's New/What's Changed This section lists notable changes for the 2023 NSDUH. A full list of questionnaire changes is available as part of the 2023 NSDUH Questionnaire . Also, see the 2023 Methodological Summary and Definitions report for more information about methods for the 2023 NSDUH. 1. Front-End Demographics Questions about sex, Hispanic origin, and race that are used to create estimates for reports and tables were moved to this new section from the core demographics section, which was interviewer administered for in-person respondents in 2022. The question about respondents’ sex from 2022 was also revised to ask about sex assigned at birth instead of asking whether respondents were male or female. Investigation of the effects of changes to the questions for sex, Hispanic origin, and race led SAMHSA to conclude that substance use and mental health estimates in 2023 by sex and by race/ethnicity may be compared with corresponding estimates from 2021 and 2022. New questions were included to ask about respondents’ gender identity. Consequently, estimates for gender identity do not exist for prior years. 2. Sexual Attraction and Identity Beginning in 2023, questions about sexual attraction and identity were asked of all respondents. Before 2023, these questions were asked only of adults aged 18 or older. The question about sexual identity was also revised to be more inclusive. Additional response choices were provided, and respondents could specify other terms that they use to describe their sexual identity. Therefore, estimates of sexual identity among adults should not be compared between 2023 and prior years. 3. Medication-Assisted Treatment Questions that measured medication-assisted treatment (MAT) for alcohol and opioid use were removed from the emerging issues section Since 2022, NSDUH has used MAT questions that were added to the alcohol and drug treatment section to estimate the receipt of MAT. Questions for the receipt of MAT from this section did not change for 2023. Therefore, estimates for the receipt of MAT from the alcohol and drug treatment section may be compared between 2022 and 2023. 4. Modes of Marijuana Use Beginning in 2023, all variables for the ways in which respondents used marijuana in the past year and past month (smoking; vaping; dabbing waxes, shatter, or concentrates; eating or drinking; putting drops, strips, lozenges, or sprays in their mouth or under their tongue; applying lotion, cream, or patches to their skin; taking pills; or some other way) were statistically imputed to replace missing data. For estimates of modes of marijuana use that were presented in 2022 NSDUH reports and tables, only the variables for marijuana vaping were statistically imputed. For the 2023 NSDUH, these revised methods were also applied to produce imputed variables for all modes of marijuana use for 2022, including revised marijuana vaping variables. Therefore, the 2022 estimates for modes of marijuana use in the 2023 Detailed Tables may differ from previously published estimates. However, the revised 2022 estimates for modes of marijuana use may be compared with estimates in 2023. 2023 NSDUH Highlights Overall Highlights for 2023 NSDUH (PDF) Highlights for 2023 NSDUH by Race and Ethnicity (PDF) Annual National Report Key substance use and mental health indicators in the united states. NSDUH’s latest annual report shows indicators of substance use and mental health in the United States based on 2023 NSDUH data. All reported indicators meet rigorous criteria for statistical precision. The 2023 Key Substance Use and Mental Health Indicators report summarizes the following: Substance use (tobacco, alcohol, vaping, marijuana and other illicit drug use, as well as the use and misuse of prescription drugs) Initiation of substance use by type Substance use disorders (SUDs) Major depressive episode (MDE), any mental illness, and serious mental illness Mental illness and MDE co-occurring with substance use and SUDs Suicidal thoughts, plans, and attempts Substance use treatment and mental health treatment Estimates are presented by age group and by race/ethnicity for selected measures. Also view slides based on the figures in the Annual National Report: Key Substance Use and Mental Health Indicators in the United States ( PDF | PPT ). Detailed Tables The 2023 NSDUH Detailed Tables present national estimates of substance use, mental health, and treatment in the United States. They present indicators for youths aged 12 to 17 and adults aged 18 or older (separately or combined) on drug, alcohol, and tobacco use, risk and availability of substance use, substance initiation, substance use disorder (SUD), mental illness and major depressive episode, suicidality, and treatment, along with some other miscellaneous health topics. The tables include estimates from 2022 and 2023 where appropriate, including statistical tests of differences between the two years. Please refer to the Methodological Summary and Definitions report for more information on the NSDUH survey. Sections of the 2023 Detailed Tables: Please refer to the related README file for instructions on how to use the Table of Contents and download files for faster viewing. Clickable Table of Contents Section 1: Illicit Drug Use/Misuse Tables - 1.1 to 1.134 Section 2: Tobacco Product Use, Nicotine Vaping, and Alcohol Use Tables - 2.1 to 2.47 Section 3: Risk and Protective Factor Tables - 3.1 to 3.18 Section 4: Incidence Tables - 4.1 to 4.11 Section 5: Substance Use Disorder and Treatment Tables - 5.1 to 5.35 Section 6: Adult Mental Health Tables - 6.1 to 6.87 Section 7: Youth Mental Health Tables - 7.1 to 7.40 Section 8: Miscellaneous Tables - 8.1 to 8.43 Section 9: Sample Size and Population Tables - 9.1 to 9.8 Appendix A: Glossary Appendix B: List of Tables Appendix C: List of Contributors Methodology The 2023 Methodological Summary and Definitions  report summarizes the information users need to properly interpret NSDUH estimates related to substance use and mental health. This report accompanies the annual detailed tables and National Report and provides information on overall methodology, key definitions for measures and terms used in 2023 NSDUH reports and tables, along with some analysis of these measures and of the survey as a whole. The report is organized into four chapters: Chapter 1 is an introduction to the report. Chapter 2 describes the survey, including information about the sample design; data collection procedures and questionnaire changes; and key aspects of data processing, such as development of analysis weights. Chapter 3 presents technical details on the statistical methods and measurement, such as suppression criteria for unreliable estimates, statistical testing procedures, and measurement issues for selected substance use and mental health measures. Chapter 4 covers special methodological topics related to prescription psychotherapeutic drugs. 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Your monthly guide to all the topics, technologies and techniques that every professional needs to know about. Subscribe for free. InfoQ Homepage Presentations LIquid: a Large-Scale Relational Graph Database LIquid: a Large-Scale Relational Graph Database Scott Meyer discusses LIquid, the graph database built to host LinkedIn, serving a ~15Tb graph at ~2M QPS. Scott Meyer has 40 years of adventures in software: computer graphics, networking, GUI application development, object-oriented databases, a Java language implementation and now he has settled on graph databases. He led the storage team at Metaweb. In 2014 he moved to LinkedIn to start a next-generation relational graph database project called LIquid. About the conference Software is changing the world. QCon empowers software development by facilitating the spread of knowledge and innovation in the developer community. A practitioner-driven conference, QCon is designed for technical team leads, architects, engineering directors, and project managers who influence innovation in their teams. Meyer: Peter Boncz is one of the preeminent database researchers on the planet. He basically gave the modern shared nothing column store its form. This is him being somewhat critical of graph database systems. I'm a graph database implementer, so a little bit nervous making. This is a great talk if you're a database person. However, it's quite technical, so I thought I would unpack it a little bit for you guys. How many people out here have teenage children? A graph database is like teenage sex. They're all talking about it. Some of them are doing it. The ones who are doing it are not doing a very good job. I'm here as your own graph database teenager. I'm going to talk about LIquid. LIquid is a relational graph database that we built at LinkedIn, runs about 2 million QPS. It's a pretty standard, large-scale Silicon Valley database system. As is usual with these things, I didn't build this by myself. I have a wonderful and talented team of probably 40 people now who make this thing a reality. I'm shamelessly going to claim credit for their work. Browsing a Large Graph First question is, why did LinkedIn build a graph database? The recruiter who brought me to LinkedIn, explained LinkedIn to me. He said, the magic of LinkedIn is in the second degree. Your first degree, all your connections, you already know who they are, like you have your Rolodex and your email. It's not very valuable. Your second degree is all of the connections that you could have, but maybe don't yet. Maybe your next job is in your first degree, maybe you'll go work with a friend, but probably your next job is in your second degree. What we're doing fundamentally, when we're looking at LinkedIn is we're browsing a large graph, and we're figuring out what about that graph is interesting to us. Here's some of the stuff that comes out of the graph database. You notice, these are all second degree connections. What about a graph is interesting to me? It's some sort of second degree connection, like maybe we worked at the same employer, maybe we went to the same school, maybe we know some of the same people. Graph Workloads This is just one of three graph workloads. What we're doing is graph serving. Database people would call this as complex OLTP. What we're doing is we're browsing a graph, this is the predominant application workload. Even things like PowerPoint or Word, what they're doing is they're browsing a graph of stuff almost all the time. You do a little bit of editing. The important thing about serving is we need to work in human real time, like about a quarter of a second. Let it be half a second, but it's got to be like that fast. You can also do graph analytics. Database people would just say complex OLAP, or maybe just OLAP. Where you have an analyst typing in a query, and they're happy to wait for a few minutes, or maybe they're running a dashboard, and it runs once an hour and comes up with a graph for somebody to look at. There, we're looking at something that scans a whole bunch of the graph and summarizes it. It's going to take minutes probably. Then there's a third workload, which is graph computation, things like Page Rank, Bellman-Ford, where I want to run an algorithm over the whole graph. These things typically take minutes or even hours, depending on the size of the graph and the algorithm. What we're doing is just graph serving. Handy rule of thumb in software development, never invent something that's in a textbook. All of the background for this talk and this product is basically textbook computer science, literally. Specifically, the relational model and datalog. This is actually the real textbook that we use as a reference on the team. Relational Graph Data Relational graph data, I'm going to throw some SQL at you. I'll speak it slowly. Basically, relational graph data is two tables. I have a table of vertices. This is just mapping strings to integers, that's all it's doing. A vertex is represented by a string. Inside the database we'd like to represent it with a fixed length integer. That's what this does. Now the exciting part, I have a table of edges. An edge is just three vertices. This is a compound primary key. The edge, you specify it by value, and it either exists or it doesn't exist. Very simple. Pause right here, this is like 90% of the content of this talk right here. It just so happens, like a lot of things in software, that the remaining 10% of the talk will take the other 90% of the time. Let's look at some graph data. What we're talking about is basically just three columns of integers like this. I've shown these guys, if this was a normal SQL database, they'd be sorted like this because of the primary key constraint. Let's think a bit about what it's going to be like to work with this. Suppose you start with a bunch of things, and you want to transform them, you want to navigate through an edge, one edge for each thing, or multiple edges for each thing. You notice the things are all sorted, and the subjects are all sorted, so this is great. We do a merge join. What we get in the output is no longer sorted, it's not even a set. If we were doing a complex query, where we do multiple hops through a path, we would have to resort this thing in order to keep on going, or do random access. There's another aspect to this. This is really small, but any real database, the edge does not contain a lot of information, so you're going to have a lot of edges, think a trillion edges. I said our workload is browsing, so I have to produce a screen full of data for someone to look at. I have to do this in about a quarter of a second. What I can tell you then, is, if I only have a quarter of a second, and I'm looking at a trillion things, I can't actually scan very many things. Maybe I can scan a few hundred thousand, maybe a million things, a million edges. We're very limited in the amount of data that we can have as an intermediate result, just by the nature of the domain. Now think about it like, so I have an intermediate result, it's just going to be a bunch of edges. Everything is edges. I have some intermediate result. It's got like 100,000 things in it. I'm doing a lookup into a table of a trillion things. It's just incredibly improbable that a page that I read for one thing would contain the answer for the next thing. Just by the law of large numbers, what you're doing when you work with graph data is random access. The key insight here is we're doing random access at scale. There's absolutely no way around this. The index structures that we're using are hash tables. The performance domain that we're working in, we're going to be counting L3 cache misses, because we're doing random access into memory. The entire graph is held in hash tables in memory. Why only 3 things? Why not 2, why not 4? Seems like an arbitrary choice. The first reason, it's tractable to index everything, like 3 factorial a 6, 6x is a reasonable write amplification if you're careful. We can just index everything and say, edges are fast, there's no create index. They're just uniformly fast, they deliver random access. That's a guarantee that we can make. That's super handy. Second of all, as subject, predicate, object would lead you to believe, this is how people encode knowledge. We are building database machinery that deals with knowledge the way people like to encode it, which seems like it ought to be a good idea. There's a subset of people, computer programmers, and they like to use structs and pointers when they're building a model of the world in an application. Because the whole point of having a database is to build applications with it. Let's go look at that world a little bit. You have a triple, like Herman Melville wrote Moby Dick, just the fact. If you're an application programmer, you're probably going to model this as like, I have a struct author, and here's the one for Herman Melville. He wrote a book. He could have written many books so there's a vector right there. Then, one of the books that he wrote is Moby Dick. Similarly, the Moby Dick structure, it's a book. Books were written by an author, and there's the author. When you're programming with structs, and pointers, what you're really doing is you're working with a hand-built inverted index of the underlying relation. Some good news, this is just a relational database, we can do this with SQL. We have a much better syntax. Interestingly, it's been sitting there in a textbook for 30 years, it's datalog. This datalog that you see on the screen is the same query that the SQL was doing. You see, it has a natural decomposition into rules. It's a basic social graph type of a thing. I'm looking for employers and skills of friends of friends, employers and skills in my second degree. These are datalog rules. In datalog, anything with a period is a statement of fact. In our datalog, the only thing you can assert is an edge at the bottom. Everything is assertions with edges. Here I'm saying, user 1, their name is Scott. Pretty straightforward. Anything with a question mark is a query. Underbar is a variable, which just means, I'm different from all other variables. Every underbar is different. All you're doing here is you're asking, what is the name of user 1? Pretty simple. I can define a rule, like, here's a rule called named. The rule just evaluates the right-hand side, same sort of thing. I can ask, who has this name? Or, what is the name of this thing? Just by supplying different variable bindings. A cool thing that you can do with datalog and edges is you can say, user 1, what's up with that? Like all the inbound edges on user 1. This is a very typical first step, when you're exploring a new database with a schema that you're not familiar with. You find something that you know, typically yourself, and then you go explore just by looking at the inbound edges. It's something that's very difficult to do in SQL. Our two-table representation actually lost some valuable information. We need to add this information back. We're going to do this by formally defining predicates. Here's a rule called DefPred. You have to use DefPred to introduce the predicate. This is a predicate for title, like the title of a book, and what the definition says. Says, the subject side, a book only has one title, so it's cardinality 1. The type of a book, it's just a node. On the object side, there's a string, a UTF-8 string, so our type is a string. Of course, many books can have the same title, so 0 meaning unlimited cardinality. All you have to do to start using a new predicate is put this DefPred in, and then you can immediately start using it. For example, book 1 title is Moby Dick. The predicate itself is just a node in the graph like any other node, so you can add your own metadata to predicates. For example, here I'm saying there's a kind of a predicate that's like a name like thing. A title is like a name. Name is like a name. I'm going to identify these as being of type Known As, just writing edges in. Now I can ask queries like this. What we're saying is there's a thing, u:1, I don't know much else about it. It's referred to by an edge with some kind of a predicate. The kind of predicate is anything referred to by an edge with the type Known As. I'm able to define what a name like thing is, and I'm able to ask for it in a query. If you look at what an application does, very often this is exactly what they need. I have a little card, it has a name, it has a picture, and so forth. I use this to display anything. I don't care. As long as it has a name, and a picture, and maybe some descriptive text. That's predicates. N-Ary Relationships We're not quite done yet, because the predicate is a simple binary relationship. Most relationships in the real world are not simple binary relationships. Let me give you some examples. Grammar is more than subject, predicate, and object. Here are some real relationships that are not subject, predicate, and object. You work for a company, that's employer, employee, and a start date. If I don't have the start date, then I can't represent a common situation in the world where you work for a company, and then you quit and you go work for another company. Then you quit and you go back and work for the first company. That's actually a super important piece of data. LinkedIn calls this a boomerang. Similarly, a marriage, spouse, spouse, start date. You have people like Elizabeth Taylor, who are married a lot, sometimes to the same people. Again, if you want to model the data, you need more than a binary relationship. Another one, actor, role, and film, endorser, endorsee, skill. N-Ary relationships happen all over the place. We need support for these guys in a graph database. One more thing, a lot of relationships have attributes that describe them that are not part of the identity, like a super common one in our world is a score. This relationship exists, but how important is it? Herman Melville wrote a book, but what's his most important book? Which one should you show first? Other things might be like a stop date, or maybe a title, something like that. The basic intuition here is like, if I have an edge, and my edge traversal is fast, then it's not really objectionable just to add an extra hop. If going across one edge is going to cost you two L3 cache misses, going across two is going to cost you four. That's not outrageous. Honestly represents the structure here. Your big question is, what do I use for the identity of this central node? If I just make one up, now this N-Ary relationship doesn't behave like an edge, you can get duplicates. If you have duplicates, then you might add interesting data to the wrong one. It's not really an effective way to work. We can actually solve this problem. We're just going to have a function which generates us a string, which is the identity of the central node. You can read the string and get how this works. Like, I'm going to take all pairs of object and predicate, and I'm going to sort them. That's the identity of this hub node. Once we figure out that, the way edges work, now N-Ary relationships work just like edges, so we get this for free. You can annotate. You can do anything you want with a central node. It's just a node, it's not going to bite anybody. You can put whatever data you want on there. The data is just edges, we don't need to come up with some fancy schema to talk about this stuff. LIquid does what we call compound predicates. This is just the syntax of that working. What is this thing that we built? This has been known to relational database types for a long time. I think in the '70s, Peter Chen came up with this notion of Entity-Relationship modeling. This thing that we've come up with is just a relationship table. It says, there's a compound primary key of actor, role, and film. Then you have some attribute columns, whatever you want. This thing that we invented is actually completely normal, well understood 40-year-old SQL. There's a nice bonus that you get out of this. Suppose we wanted to do a symmetric relationship, like a mutual friend. You'd start off in SQL. It's like, I have two friends, like friend1 and friend2. Now I have Bob and Jane, they're mutual friends. Is it Bob and Jane, or is it Jane and Bob? There are two different ways to represent that. Pretty quickly, you're going to reach for your PL/SQL hammer, and you're going to say, friend1 has got to be less than friend2. That's the way we know the identity of the mutual friendship. If we're in this N-Ary relationship world, what I'd observe is like, if you just had two friend edges, it's in fact structurally perfectly symmetrical. The structure expresses exactly what we want to express. Furthermore, our little algorithm for generating the primary key works exactly right. We sorted the predicates of the same Bob sorts ahead of Jane, so now we have a primary key. That's pretty cool. This is actually something SQL can't do. Here's a little bit of datalog. I'm showing you, I'm asserting the edge twice in both different orders. At the end, I ask for the inbound edge as Bob. You can see there's only one inbound edge. There's just a unique mutual friendship. OODB/Ontologies A question you might ask here is like, what about nodes? We've only talked about edges. That's on purpose. Nodes are just immutable strings, like they're primary keys. They represent entities. That's it. Sometimes they parse. You could have a node that's like 7, 7 has a meaning as like an integer. When we give that node a type like int, it just means I want something that parses as an integer. A question is like, why not more? Why don't we put a bunch more stuff inside our node? If you have something that has node properties, a question to ask is like, why even have edges? I can have a node be like an author and another node be like a book, and the book would have a little vector of author identities in there. Why not just do stuff like that? We tried this back in the '90s. I was I was involved with this, OODBs. They don't work very well. In this world, say, we have a person. That's a base class, people have a name or something like that. Then we have a person who's an author, that's a subclass. We're just working on structure extension here, subclassing. No one could object to that. What if I have a person who's an editor? I can solve it the same way. What if I have a person who's an editor and an author? Now I have a problem, because either I figure out some multiple inheritance thing, which never worked very well. Or, I have two different identities, two different structs, one for person as an author, and one for person as an editor. Equality doesn't work the way you think it ought to work. It gets worse. Like, you're making an ontological assumption that all authors are people. That's not actually the case. Back in my history, starting with graph databases, worked at Metaweb, kept discovering that the Beatles, a rock group, were getting typed as a person. We were trying to do an inheritance-based type system, we said, an author that's a subclass of a person. It turns out the Beatles authored a book, like there are books in the world. They have a title and a name, and the author's name says the Beatles. It's a fact. I'm not making this up. This is Amazon right now. The notion that you're going to have a single ontology that is going to support everything is really badly broken. There's another interesting problem with it, which is, the root of your hierarchy becomes a gigantic single point of failure. You do some clever editing up at the root, and suddenly two-thirds of your database just disappears. The nice thing about the graph structure is it doesn't care, like we're just recording facts. The triples don't need to have a particular order of structure fields, they just exist. They don't do anybody any harm. Authors, editors, astronauts, all can cluster around the same identity without any conflict at all. That's the data model. Query Evaluation I want to talk a little bit about query evaluation, just a couple details. The first problem is cross-products. The second problem is static planning. These are both endemic to SQL. Worst case, optimal joins. Super interesting, big line topic. You can go search for that on Google Scholar or something, and read about it. It's very relevant to a social graph, kind of a workload. Let's look at a cross-product. I'm going to use a predicate like knows, A knows B, B knows A, so there's an edge. Now I'm going to define a rule that's bidirectional. I just ask, knows x, y is either x knows y, or y knows x. I'm going to say, show me who knows who in the whole database? I get two results, A knows B, and B knows A. The simplest cross-product that I could come up with. If I look at the edges, there's this one edge. Why is this a big deal? We're doing complex queries. We're doing complex queries of many to many relationships. Every time you introduce a cross-product, you're multiplying the size of your result by some constant factor, 2, 3, 5, 10, something like that. It's really easy to get results that are 100 times the size of the actual edges, if you're doing a complex query. Why do we care? Inside the database, we do lots of clever stuff to avoid materializing cross-products, because it's really hard. If you have n times m things, you wind up doing n times m work, and that turns out to be slow. A key point here is like, the application has exactly the same problem. They don't want to do n times m work, either. How does this play out? What we need is a subgraph return. Typically, in SQL, SQL only returns you a single table. A subgraph return, I just want to return multiple tables. You can think of this as, I want to return one row in every relationship table that matched. I can stitch this stuff together. It's a relational algebra tree, like what we have inside a SQL engine. Really, I only need two things in the tree. I need cross-products and I need outer joins. If you're processing this data as an application developer, you're building up a graph of people and skills and employers. You're going to have some sort of a loop where you say, person 23, do I have one for that? No. I'm going to make one. Skill 7, do I have one for those? I'm going to create one. The struct for 23 is going to point at the struct for 7 in the correct offset. That thing that you're doing with the hash table lookup to see if you have one, that's an outer join. That's all it is. It's nothing mysterious. Dynamic planning. Let's look at a really simple query. This is graph distance is 3. It's very relevant to our workload. It's just three hops. A is connected to B is connected to C. That's all it is. I want to know if Alice is 3 hops from Bob. There are only four possible plans in a sequential world. I can go left, left, left from Alice. I can go left, left, right. I can go left, right, right, or I can go right, right, right. It's the only ways you can do this query. None of these plans are optimal for all data. We're doing self-joins, all of our statistics to our statistics, each edge constraint in this query looks identical. They're all the same. What makes them different is like what data is in the graph. An observation here is, unlike in a sorted storage world, in a hash table storage world, our set sizes are available in constant time. I can do a hash lookup, and I can know how big the set that I'm going to read is. If I'm storing stuff in a B-tree, like I B search to find the beginning of it, and then I have to B search to find the end, or I can just scan it. It's far more expensive to find out how big a set is. What does this look like? We just hand coded the four plans. You can see them on the chart there. You can see there's this scallopy thing with four scallops. That's where each plan is optimal. The dynamic plan, you can see it's not quite as good as perfectly optimal, because it costs you a little bit to figure out how to use it. We're going to say, how big is the fanout from Bob? How big is the fanout from Alice? Pick the smaller one, expand that. Did that get super huge? We have to do some work to figure things out. We can do that work. It's tractable to do that work in this indexing domain. You get a query evaluation performance that is pretty close to optimal in all four domains. What is a Graph Database? What I want to leave you with is a formal definition for what a graph database is. Graph database is an implementation of the relational model with four properties. All relationships are equal, everything is an edge. If you think about like a typical SQL database, if you're a relationship that is in one table, you're first class. It's super nice, like I do a B search, I get the row, and I have this whole relationship right there basically for free. If you're a relationship that exists across tables, like I have to do a join, you're really second class. Joining is super slow, so second class that way. Semantically, SQL doesn't really keep track of what joins were intended by the user. SQL will happily say, yes, you're joining depth and fathoms to degree Celsius. Probably ok. In the graph database, everything is an edge, and the edge is exactly what the user intended. Those are the joins that the user intended. If everything is going to be an edge, you're going to have a lot of edges, it better be fast. If it's not constant time, and fast, and by that I mean three L3 cache misses, as your database gets bigger, your performance gets slower. Ultimately, it's not going to be worthwhile having a big graph. You want a smaller graph that performs better. That turns into harder to manage. Query results are a subgraph. If I'm going to have a big graph with a lot of edges, I want to ask complex queries of it. I need to return structured results that the application actually wants to consume. Lastly, schema change is constant time. If you think of it like a general model for a serving system, it's a materialized view. Like I did some fancy query. Then, when you ask something, I just look up a row, and give it back to you. What we're saying with this is, no, you can't heat. I need to be able to show up with a new predicate and just start using it at full speed. This can't turn into any kind of like ALTER TABLE fire drill underneath the surface in the implementation. That's my proposed definition for a graph database. Clearly, I disagree with a lot of the world. We'll see how that turns out. Graph Database vs. SQL, RDF, and Property Graphs I want to do a brief comparison walkthrough. First of all, versus SQL. There's no denormalization. A graph is perfectly normalized. It is in fifth normal form. You can't express denormalization. There are no nulls or trinary logic. Edges either exist or they don't. If you introduce an unbound variable, we're going to force you to give it a binding. If you have a default quantity, you get to decide, is it 0? Is it min int? Is it max int? Is it 1? Depends which is appropriate. Depends on what you're doing with it. We force you to make that decision explicitly, so now there's no trinary logic, which is a big pain. We can do constrain through the predicate. In SQL, a predicate is typically a column, roughly. You're welcome to use the catalog table, but it's hard to use the catalog table meaningfully in a query. Typically, to do something like this in SQL, you need to query the catalog, figure it out, and then you generate another SQL query. Constraining through the predicate is like, this predicate is a name like thing. You can explore all the incident edges on any entity. You have a ticker symbol, (MSFT) Microsoft, it's in the graph somewhere. You can go start exploring. You can do symmetric relationships. You have subgraph results. RDF, the relational model, that's pretty cool. It's been 40 years, we've had 20 years of NoSQL. There really isn't much of a contender. We did not invent a query language. I highly recommend that. We have a much simpler edge. We have first class schema that is much simpler than OWL. You've seen all the moving pieces in the schema in this talk. We can do N-Ary relationships. We can do symmetric relationships. We have composable rules. Lastly, versus property graphs. Relational model. Didn't invent a query language. We have a first-class schema for everything. You can constrain through the predicate. There's no separate property schema. Everything is just edges. There's no OO node problems, so we just make the decision for you. You can't put any stuff in a node, just use edges. N-Ary relationships, again, super common. Composable rules. Single-Query Applications What's the future here? A lot of people think that the future of data is big graphs. If the future of data is big graphs, then I think the future of applications is big queries. You ought to be able to say, here's all the data that I want to put on a page, here's a description of it, go get it for me. Even if I need to do this, I'm doing thousands of joins in human real time. Questions and Answers Participant 1: There is a feature in [inaudible 00:45:25] where you can have a representation like, I am 80% good in Java, and we'll be doing JavaScript or something like that. How do you have that built in these edges? Meyer: You would have a compound relationship with a skill, so you have that hop in the middle. Then you just add a score edge, whatever you want, a floating point, or an integer score. Anand: Do you put any limits on fanout, either on the query edge part or in the representation? Meyer: There are no limits on the fanout. Naturally in a social graph, you're going to experience skew. I don't have very many followers, a few hundred. Bill Gates has 20 million followers. That's not a normal distribution, it'd be a ZIP distribution. That's a benefit of the graph representation is you can represent stuff like that. The implication is, your query eval better be able to deal with skew. If you have a bunch of people, and you're looking at the fanout from followers, it might be 200, 700, 900, 200, 20 million. When you get to 20 million, probably time for a different query plan. Participant 2: I want to understand, in what use cases will you manage to use graph database. We maybe have different options or approaches there, like GraphQL or big data, which also help us to search faster with [inaudible 00:47:33] of the data joins. Each may have its pros and cons, like for graph database basically we're looking for failing use cases that these could be the optimal approach. Meyer: What are the use cases? What are the pros and cons? For the data model itself, for how to model data, I don't think there are pros and cons. I think the graph is just better. It's isomorphic to entity relationships. If you wanted to do normalized entity relationship data modeling in SQL, that's great. It turns right into a graph. For querying, obviously, it really just depends on the workload. The observation I'd make is serving is a new workload. People typically handle this with a cache in front of a conventional database. Really, the difference between a cache and an index is just the index is complete. It has some consistency properties that you can state, whereas the cache is just whatever the application put in there. If you have an Object Relational Mapper, it's running a cache of objects. I think that's pretty undesirable. I think indexing is much better. There's no warmup time. You know how much things are going to cost. It's immediately usable at full speed. The previous system at LinkedIn was a cache system. The current system, LIquid, is uncached. The SREs love that. See more presentations with transcripts Recorded at: Jun 07, 2024 Scott Meyer Related Sponsored Content Designing data intensive applications (by o'reilly), related sponsor. ScyllaDB is the database for data-intensive apps requiring high throughput + low latency. Achieve extreme scale with the lowest TCO. Learn More . This content is in the AI, ML & Data Engineering topic Related topics:. AI, ML & Data Engineering QCon San Francisco 2023 Graph Database Transcripts QCon Software Development Conference Performance & Scalability Related Editorial Popular across infoq, cloudflare introduces advanced load balancing to eliminate hardware dependency, ci/cd beyond yaml, thinking like an architect, communication patterns for architects and engineers with jacqui read, google introduces free trial for alloydb postgresql database, java news roundup: wildfly 33, spring cloud data flow, apache tomee, langchain4j, micronaut. Sullivan & Cromwell M&A Chief Charts Rise of Take-Private Deals By Mahira Dayal Companies having second thoughts about going public are fueling a surge in go-private deals, according to Melissa Sawyer, global head of Sullivan & Cromwell’s mergers and acquisitions group. “Companies that are already public are feeling like living up to the requirements of being a public company—the costs associated with that, the quarterly reporting, the pressure from shareholder activists—is all becoming too much,” said Sawyer. Global take-private deals reached $127 billion through July, according to Bloomberg data. That’s just shy of the total for all of last year. Several large take-privates emerged in the deals market this year. In February, Novo Holdings announced its $16.2 billion deal to take health tech provider Catalent Inc. private. In April, Silver Lake Management announced plans to take Endeavor Group Holdings, which owns Ultimate Fighting Championship and World Wrestling Entertainment, private for $10 billion. Some companies find after going public that capital is pricier than they expected, Sawyer said. They’re turning instead to private equity shops, where dry powder reached a record $2.59 trillion last year as a slow deals market limited opportunities to deploy capital, according to data from S&P Global Market Intelligence and Preqin. Others are opting against going public in the first place. Initial public offerings dropped to $91.7 billion last year, the lowest level in a decade, according to Bloomberg data. “Some public companies feel like they have a target on their backs, and the number of IPOs has been declining steadily over the years,” Sawyer said. Deals Market Sullivan & Cromwell has been among Big Law’s top dealmakers under Sawyer. The firm’s lawyers advised on $79.5 billion worth of M&A transactions in the first half of this year, according to Bloomberg data. Sawyer often leads that work. She is guiding Boeing Co. in its $8.3 billion pending acquisition of parts manufacturer Spirit AeroSystems Holdings Inc., a deal announced this month. She also guided Seagen Inc. in its $43 billion acquisition by Pfizer Inc. last year. Health care has been among the bright spots for deals during a slow stretch in the last two years. Sawyer said regulatory pressure, including from the Federal Trade Commission under Lina Khan, is continuing to chill transaction activity across sectors. “There are a couple of large serial acquirers in health care who I think are more hesitant to do routine transactions because they’re saving their firepower for transformational deals, because they know every deal is hard to get through,” said Sawyer. The FTC has its eye on several mega deals. In July, the agency began probing executives from oil giants including Hess Corp. and Diamondback Energy over communications with OPEC officials amid deals under FTC review. That kind of scrutiny has extended deal timelines, creating financing headaches and adding to costs for companies. “You need your debt commitments to last for 18 months,” said Sawyer. “My sense from recent transactions that I’ve been involved in is that banks are willing to let their commitments hang out there for more than a 12 month drop dead date, which is a little bit of a change in past practice.” The November election could impact the deals landscape, though any impact will take several months to trickle into practice, according to Sawyer. “If there is a change at the top, it will probably be in the number of litigations that are brought and the success rate on those litigations,” said Sawyer. AI in Dealmaking Sawyer is also keeping her eye on the rise of artificial intelligence technology. AI tools in the market today still lack the accuracy and functionality to make them useful for dealmaking beyond speeding up commoditized tasks, she said. “The tools that are available are not that reliable yet, and the accuracy ratings are not great,” Sawyer said. “The more complex deal work involves judgment, experience and creativity and the AI just isn’t there yet.” As the tools improve, however, they could disrupt the way Big Law firms bill their clients, she said. By making lawyers more efficient in routine tasks, it may encourage firms to rethink the billable hour, long the industry standard. “It puts pressure on saying that an hour of your time spent paging through a due diligence document is worth the same as an hour of your time spent counseling a board on a very difficult judgment call,” said Sawyer. To contact the reporter on this story: Mahira Dayal in New York at [email protected] To contact the editors responsible for this story: Chris Opfer at [email protected] ; Alessandra Rafferty at [email protected] Learn more about Bloomberg Law or Log In to keep reading: Learn about bloomberg law. AI-powered legal analytics, workflow tools and premium legal & business news. Already a subscriber? Log in to keep reading or access research tools. 101 IMAGES Bar Graph Basic Bar Graphs Solution Bar Chart Template for PowerPoint Sample Bar Chart PowerPoint Template and Keynote slide Bar Chart Data Analysis PowerPoint Template and Keynote Bar Graph / Bar Chart VIDEO Bar Graph Presentation by Class IX students Mini graph presentation #bar graph presentation# Bar / Column Chart data bar graph in excel #computertricks #shortvideo #exceltricks #viralvideo #trending #subscribe What is Bar chart/Bar graph? COMMENTS Data Presentation A bar chart is a chart with rectangular bars with lengths proportional to the values that they represent. The bars can be plotted vertically or horizontally. Bar charts can be used to show comparisons among categories. The bar chart below shows how the average U.S. diet compares with recommended dietary percentages. To make a bar chart, decide what columns and rows to include. The chart above ... Bar Charts: Using, Examples, and Interpreting At a minimum, bar charts require one categorical variable but frequently use two of them. To learn about other graphs, read my Guide to Data Types and How to Graph Them.If you're mainly interested in comparing and contrasting qualitive properties of different groups, consider using a Venn diagram.. A Pareto chart is a special type of bar chart that identifies categories that contribute the ... A Complete Guide to Bar Charts 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 ... Master Bar Charts: A Step-by-Step Guide Simplifying Complex Data Presentation; Bar graphs streamline the presentation of intricate data sets into easily digestible visual formats. ... 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. Bar Graph Bar graphs are the pictorial representation of data (generally grouped), in the form of vertical or horizontal rectangular bars, where the length of bars are proportional to the measure of data. They are also known as bar charts. Bar graphs are one of the means of data handling in statistics.. The collection, presentation, analysis, organization, and interpretation of observations of data are ... Bar Graph (Chart) 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. Parts. The different parts of a bar graph are: Title; Bars; Categories; x-axis; x-axis label; y ... Bar Graph A bar graph (also known as a bar chart or bar diagram) is a visual tool that uses bars to compare data among categories. A bar graph may run horizontally or vertically. The important thing to know is that the longer the bar, the greater its value. Bar graphs consist of two axes. On a vertical bar graph, as shown above, the horizontal axis (or x ... Bar Graph: Definition, Examples and How to Create One A bar graph, or bar chart, is a visual representation of data using bars of varying heights or lengths. It is used to compare measures (like frequency, amount, etc) for distinct categories of data. A typical bar graph will have a label, scales, axes and bars. Bar Graphs A Bar Graph (also called Bar Chart) is a graphical display of data using bars of different heights. Bar Graphs. A Bar Graph ... Histograms vs Bar Graphs. Bar Graphs are good when your data is in categories (such as "Comedy", "Drama", etc). But when you have continuous data (such as a person's height) then use a Histogram. Bar chart Bar graphs/charts provide a visual presentation of categorical data. Categorical data is a grouping of data into discrete groups, such as months of the year, age group, shoe sizes, and animals. These categories are usually qualitative. Bar Chart / Bar Graph: Examples, Excel Steps & Stacked Graphs Step 1: Open the file you want to work with in SPSS or type the data into a new worksheet. Step 2: Click "Graphs," then click "Legacy Dialogs" and then click "Bar" to open the Bar Charts dialog box. Step 3: Click on an image for the type of bar graph you want (Simple, Clustered (a.k.a. grouped), or Stacked) and then click the ... Bar Graph A bar graph is a visual presentation of data using rectangular bars. The bars can be vertical or horizontal, and their lengths are proportional to the data they represent. Bar graphs can compare items or show how something changes over time. Bar graphs are also known as bar charts or bar diagrams. In this article, we will discuss what is bar graph, Presentation And Display Of Quantitative Data: Graphs, Tables, Scatter A simple and effective way of presenting and comparing data, particularly nominal data. This is because each bar represents a different category of data, and this is denoted by the spaces between them (it is important to leave a gap/space between each bar on the graph in order to indicate that the bars represent 'separate' data rather than 'continuous' data. Bar Graph What is a Bar Graph/Bar Diagram? A Bar graph is a type of data-handling method that is popularly used in statistics. A bar graph or bar chart is a visual presentation of a group of data that is made up of vertical or horizontal rectangular bars with lengths that are equal to the measure of the data. Represent Data on a Bar Graph A bar graph is a simple way of presenting data. Bar graph is a method of presenting data by drawing rectangular bars of equal width and with equal space between the two bars. Bar graphs can be drawn using horizontal or vertical bars. A bar graph must be drawn on a graph sheet. A bar graph must have a tittle written above the bar graph. 55+ Bar Chart Templates for PowerPoint and Google Slides To apply a Chart Template in PowerPoint, follow the steps outlined below: To open the Insert Chart window, click a Chart button on the Insert tab of the ribbon. On the left sidebar, select the Templates tab. A gallery of your Chart Templates will appear. Choose which one you want to use to make the chart, and then click on the OK icon. Data Presentation: Bar Charts Advantages and Disadvantages Data Presentation: Bar Graphs Advantages and Disadvantages. Bar graphs are good for showing how data change over time. Example: Advantages. show each data category in a frequency distribution. display relative numbers or proportions of multiple categories. summarize a large data set in visual form. clarify trends better than do tables. Bar Charts Google Slides theme and PowerPoint template Bar Charts Infographics. Free Google Slides theme, PowerPoint template, and Canva presentation template. Bar charts are very adaptable. No matter what you want to represent: if you have some numbers, data and percentages, use these diagrams. We have designed many of them for you: simple bars, cylindrical, pyramidal, arrows…. 10 Methods of Data Presentation That Really Work in 2024 Histogram, Smoothed frequency graph, Pie diagram or Pie chart, Cumulative or ogive frequency graph, and Frequency Polygon. Tags: Types of Presentation. Struggling to make your data sing in 2024? Click here for the lowdown and unlock 10 impactful data presentation methods that will grab attention. Diagrammatic Presentation of Data: Bar Diagrams, Pie Charts etc. Bar Diagrams. As the name suggests, when data is presented in form of bars or rectangles, it is termed to be a bar diagram. Features of a Bar. The rectangular box in a bar diagram is known as a bar. It represents the value of a variable. These bars can be either vertically or horizontally arranged. Bars are equidistant from each other. Data Visualization: How Do You Create A Bar Graph? Examples and Bar Explaining a bar graph to students can be done in a step-by-step manner: Introduction: Start by introducing the concept of a bar graph as a visual representation of data using rectangular bars. Components: Explain the key components: horizontal/vertical axis, bars, and labels. The horizontal axis represents categories, while the vertical axis ... Creating a bar graph (video) The x-axis is the horizontal (side-to-side) line that goes from left to right. In this bar graph, the x-axis is labeled with courses: physics, chemistry, geometry, history, and language. The y-axis if the vertical (up-and-down) line that goes from bottom to top. The y-axis is labeled with numbers to show the number of teachers. Diagrammatic Representation of Data: Bar Diagram, Line Graphs etc. The horizontal bar diagram is used for qualitative data. The vertical bar diagram is used for the quantitative data or time series data. Let us take an example of a bar graph showing the comparison of marks of a student in all subjects out of 100 marks for two tests. With the bar graph, we can also compare the marks of students in each subject ... 2.2: Stem-and-Leaf Graphs (Stemplots), Line Graphs, and Bar Graphs Figure \(\PageIndex{6}\): This is a bar graph that matches the supplied data. The x-axis shows Park City voting districts, and the y-axis shows the percentages of the registered voter population. Summary. A stem-and-leaf plot is a way to plot data and look at the distribution. In a stem-and-leaf plot, all data values within a class are visible. How to Create a Bar Chart in PowerPoint for Your Presentation Design Choose the "Bar" category from the left sidebar and select the desired bar chart type. Click on the "OK" button to insert the chart into your slide. Customize the chart by adding or removing data, changing colors, or adjusting the chart layout. Update the chart's data by clicking on the "Edit Data" button in the top menu. How can I create a professional and visually appealing bar graph in Click on the "Insert" tab in the top menu and choose "Chart" from the options. In the Chart dialog box, select the "Bar" category and choose the desired bar graph type (e.g., clustered bar, stacked bar, etc.). Click "OK" to insert a default bar graph onto your slide. GDP up by 0.3% in both the euro area and the EU Announcement Following recommendations for a harmonised European revision policy for national accounts and balance of payments, EU countries will carry out a benchmark revision of their national accounts estimates in 2024. The purpose of this benchmark revision is to implement changes introduced by the amended ESA 2010 regulation, and to incorporate new data sources and other methodological ... 2023 National Survey on Drug Use and Health (NSDUH) Releases The 2023 NSDUH data may be combined with 2022 data or with data from 2021 and 2022 for pooled estimates. If data users wish to create pooled estimates that include data from 2021, the 2021 updated weights should be used (see response to FAQ #1). A revised 2021 NSDUH Public Use File was released in January 2024 with the updated weight. LIquid: a Large-Scale Relational Graph Database Let's look at some graph data. What we're talking about is basically just three columns of integers like this. I've shown these guys, if this was a normal SQL database, they'd be sorted like this ... Sullivan & Cromwell M&A Chief Charts Rise of Take-Private Deals The firm's lawyers advised on $79.5 billion worth of M&A transactions in the first half of this year, according to Bloomberg data. Sawyer often leads that work. She is guiding Boeing Co. in its $8.3 billion pending acquisition of parts manufacturer Spirit AeroSystems Holdings Inc., a deal announced this month. essay homework paper phd review speech study thesis