What Is a Bar Chart?

Data visualizations play an important role in allowing you to find outliers within a data set, conduct in-depth data analysis, and summarize key trends and relationships found in a data group. Visualizations also make it easier to recognize key features of a data set that other models or means of analysis may overlook.

Bar charts, sometimes called “bar graphs,” are among the most common data visualizations. It’s a helpful tool that showcases or summarizes the content within your data set in a visual form. In turn, using it empowers your audience to understand the insights and ideas suggested by the data. 

Bar charts use one quantitative measure, expressed as a number, and a qualitative measure, known as the categorical variable or label. This variable represents each category or subcategory receiving a numeric amount. These two types of measures go on opposite axes on the graph.

For example, your qualitative measure or label may be every shirt size that a clothing store sells (small, medium, and large), and your quantitative measure is how many shirts of each size the store has sold. To effectively utilize bar charts to showcase important data, you may find it helpful to understand the different types of bar charts, their various uses, their pros and cons, and how you can start learning more about creating them. 

Types of bar charts

In general, bar charts exist in two standard forms: horizontal and vertical. However, other variations exist. Each subset of bar charts has nuances and differs on the type of data best suits it.

Vertical

In a vertical bar chart, the x-axis serves as the beginning point for the bars. Vertical bar charts are typically the default and a basic option. You might also see this type of graph called a column chart. 

Horizontal

Opposite from a vertical bar chart, the horizontal bar chart has the starting point for the bars on the y-axis. These types of charts prove useful when the names of your categories are lengthy because the horizontal setup allows for the full names to fit in place and not squeeze in at the bottom, below the x-axis.

Clustered

Multiple series of data appear in clustered bar charts. It’s critical to lay this type of chart out in an organized fashion to make it easy to read and understand. Clustered bars can be designed with a horizontal or vertical layout. An example of this type of chart could be a graph showing the amount of apples and oranges each of your friends has. One axis would have the name of your friends, and two different bars, one for apples and one for oranges, would appear for each name, representing the number of fruits they possess. 

Stacked

Similar to clustered bars, stacked bar charts can be vertical or horizontal. These types of charts stack multiple subcategories on top of each other, creating one large bar representing the entire category. Taking our clustered bars example, a stacked bar could use the same data, but the bars for apples and oranges would connect to each other, creating one long bar for each name. 

100 percent stacked

Like the standard stacked bar chart, the 100 percent stacked variation showcases the relative percentage of a data grouping rather than the total amount. This type of graph creates two consistent endlines, on the left and right, enabling you to compare and contrast the data based on how much of each side fills in. 

For example, if data grouping includes three groups of students and their preference for chocolate or vanilla ice cream, each of the three bars would have an equivalent total length. However, the percentage of the bar colored brown for chocolate and white for vanilla would vary based on the relative percentage of each option for each group of students. 

Waterfall

The waterfall variation of the bar chart is more complex and less common than the others. This graph's first and last bars represent the starting and ending points for some data grouping. All of the bars in between the start and end showcase the change as time progresses. The bars in the middle are visually smaller in length because they do not represent the total value but rather the amount that a value has fluctuated. 

For example, a waterfall bar chart can represent your bank account from the beginning of the month to the end. The first bar shows your starting balance on the first of the month. Then, each bar in between would represent some fluctuations to your total, such as receiving a paycheck, paying your credit card, and depositing cash into your account. The last bar considers the sum of all the changes and represents your final balance at the end of the month.

What is a bar chart used for?

Bar charts are helpful in many situations, including those that call for analyzing or graphing categorical data. Bar charts are simple to understand and process for audiences. In any situation where data is split into multiple groups, a bar chart may be an excellent way to represent it visually. 

Depending on your specific data set, you can use a bar graph or chart to represent numbers or percentages. Larger numerical values appear as longer bars within the graph to create a simple comparison between the frequency of data within each grouping. When creating a bar chart, the base or beginning value of each bar should start at zero, and the width of each bar should remain consistent. Doing so allows an interpreter of your chart to compare the lengths of the bars to one another to extract insights.

Bar charts can showcase data and its fluctuations over a set period. For example, each category or grouping is a year, month, or day. As mentioned, the qualitative variable resides on either the y-axis or x-axis, and the quantitative variable, or numeric variable, goes on the other, allowing the chart to reflect any patterns or relationships between the two variables. 

Who uses bar charts?

Various industries and professions rely on bar charts as a means to visualize data for sales, investments, forecasts, and company budgets. 

Professionals within the business and financial industries often utilize bar charts for multiple purposes to succinctly showcase complex information and data. For example, a volume chart can depict fluctuations in trading volume over time. Due to the complexity of data these industries deal with and the need to pass along information clearly and as fast as possible, bar charts provide an excellent option to simplify the data and make it easier to understand.  

Bar charts appear in medical contexts, depicting one isolated variable and multiple categories or groupings. These groupings may represent different subsets of patients in a hospital, and the variable could be the overall concentration of some substance present in the body, for example.  

Bar charts appear in many other professional contexts, as well. A small business owner can use a bar chart to break down their monthly costs. A brick-and-mortar store may create multiple bar graphs to analyze the sales they receive within each department over the course of a calendar year. 

Pros and cons of using bar charts

Bar charts enable professionals to showcase data in an appealing visual format when created correctly. However, the pros and cons of using bar charts may help you learn these factors.

Pros of using bar charts

The pros of bar charts as a data visualization include:

• Easily understood by diverse audiences.

• Users can quickly compare groupings of data by the height of each bar

• Showcases the main trends within a set of data clearly

• Tracks the fluctuations in a data set through time

• Takes large data sets and makes the information easier to understand

Cons of using bar charts

The cons of bar charts as a data visualization include:

• Additional commentary may be necessary to explain patterns seen within the data fully

• Audiences may misinterpret the data shown in a bar chart

• Including too many bars could lead to the bar chart becoming overcrowded

• Confusion may result from not properly sorting the groupings of data

• The causation of a trend or pattern in a set of data is not evident

How to get started with learning about bar charts

Beginning to utilize bar charts in your profession may help you identify the tools you can use to build them, the best practices to keep in mind, and any common mistakes related to utilizing this visual. 

Tools for creating bar charts

In general, any visualization tool or software you use to build graphs from a data set should have a built-in capability to construct bar charts. Common types of tools include spreadsheets, such as Microsoft Excel, Google Sheets, and Airtable, and business intelligence (BI) tools. While many popular BI tools exist in the marketplace today, PowerBI and Tableau are popular among many professionals.

Many programming libraries can create bar charts. Many programming languages allow you to create data visualizations, but Python is one of the most popular and frequently used in data science. Common programming libraries in Python for data science are Matplotlib, Seaborn, and Plotly. 

Best practices for creating bar charts

Following best practices ensures that your visualizations are coherent and well-received by your audience. A few best practices to keep in mind include:

Remain consistent with setting the baseline for your quantities at zero. Doing this lets your audience compare and contrast each bar quickly and efficiently. If starting with another number, label the axis clearly.

Place your bars in order based on their represented quantity. For example, if you are creating a bar graph to show the population of each state in the US, placing each state in order from largest to smallest population allows your audience to interpret the graph and understand the overall trend quickly. 

Always make the bars on your graph rectangular in shape rather than using rounded edges. Additionally, be conscious about your use of color and make any changes to the colors of the bars. Since varied colors could capture your viewer's attention, only use them to highlight key information related to the main trend you aim to showcase. 

Getting started on Coursera

If you’re interested in learning more about bar charts and other common types of graphs utilized in statistics, check out the Data Analysis and Visualization Foundations Specialization by IBM. This four-course series allows you to gain an in-depth understanding of basic data analysis and visualization skills used by successful data analysts today. 

Another relevant course worth checking out is Data Analysis with R Programming by Google. This course introduces you to the R programming language and the various options to create visualizations with data. It can help you understand the key concepts of R, such as the existing functions and the various data types, variables, pipes, and vectors available.