Which Chart Should I Use?

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As a consultant and professor I review or develop charts on a weekly basis. While many of them are fairly intuitive, few of us have enrolled in a Charts 101 class! So when I received a recent white paper from Tableau about different charts and when to use them I was excited! Below is a summary of some of their great work. Keep an eye out for the most misused chart style!


Bar charts are one of the most common ways to visualize data. Why? barIt’s quick to compare information, revealing highs and lows at a glance. Bar charts are especially effective when you have numerical data that splits nicely into different categories so you can quickly see trends within your data.

When to use bar charts:  Comparing data across categories. Examples: Volume of shirts in different sizes, website traffic by origination site, percent of spending by department.


Line charts are right up there with bars and pies as one of theline most frequently used chart types. Line charts connect individual numeric data points. The result is a simple, straightforward way to visualize a sequence of values. Their primary use is to display trends over a period of time.

When to use line charts: Viewing trends in data over time. Examples: stock price change over a five year period, website page views during a month, revenue growth by quarter.


Pie charts should be used to show relative proportions – or percentages pie-chart– of information. That’s it. Despite this narrow recommendation for when to use pies, they are made with abandon. As a result, they are the most commonly misused chart type. If you are trying to compare data, leave it to bars or stacked bars. Don’t ask your viewer to translate pie wedges into relevant data or compare one pie to another. Key points from your data will be missed and the viewer has to work too hard.

When to use pie charts: Showing proportions. Examples: percentage of budget spent on different departments, response categories from a survey, breakdown of how Americans spend their leisure time.


Scatter plot Looking to dig a little deeper into some data, but notscatter-plot quite sure how – or if – different pieces of information relate? Scatter plots are an effective way to give you a sense of trends, concentrations and outliers that will direct you to where you want to focus your investigation efforts further.

When to use scatter plots: Investigating the relationship between different variables. Examples: Male versus female likelihood of having lung cancer at different ages, technology early adopters’ and laggards’ purchase patterns of smart phones, shipping costs of different product categories to different regions.


Gantt charts excel at illustrating the start and finish dates elements ganttof a project. Hitting deadlines is paramount to a project’s success. Seeing what needs to be accomplished – and by when – is essential to make this happen. This is where a Gantt chart comes in. While most associate Gantt charts with project management, they can be used to understand how other things such as people or machines vary over time. You could use a Gantt, for example, to do resource planning to see how long it took people to hit specific milestones, such as a certification level, and how that was distributed over time.

When to use Gantt charts: • Displaying a project schedule. Examples: illustrating key deliverables, owners, and deadlines. • Showing other things in use over time. Examples: duration of a machine’s use, availability of players on a team.


Bubbles are not their own type of visualization but instead should bubblebe viewed as a technique to accentuate data on scatter plots or maps. People are drawn to using bubbles because the varied size of circles provides meaning about the data.

When to use bubbles: Showing the concentration of data along two axes. Examples: sales concentration by product and geography, class attendance by department and time of day.


Use histograms when you want to see how your data arehistorgram distributed across groups. Say, for example, that you’ve got 100 pumpkins and you want to know how many weigh 2 pounds or less, 3-5 pounds, 6-10 pounds, etc. By grouping your data into these categories then plotting them with vertical bars along an axis, you will see the distribution of your pumpkins according to weight. And, in the process, you’ve created a histogram. At times you won’t necessarily know which categorization approach makes sense for your data. You can use histograms to try different approaches to make sure you create groups that are balanced in size and relevant for your analysis.

When to use histograms: Understanding the distribution of your data. Examples: Number of customers by company size, student performance on an exam, frequency of a product defect.


Heat maps are a great way to compare data across two categories using color. heatThe effect is to quickly see where the intersection of the categories is strongest and weakest.

When to use heat maps: Showing the relationship between two factors. Examples: segmentation analysis of target market, product adoption across regions, sales leads by individual rep.

Happy charting!

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Jeff Suderman is a futurist, consultant, and professor who works in the field of organizational development. He partners with clients to improve culture, leadership, teamwork, organizational alignment, strategy and organizational future-readiness. He resides in Palm Desert, California. Twitter: @jlsuderman Email: jeff@jeffsuderman.com

Source: Tableau


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