Which Type of Chart or Graph is Right for You?

You’ve got data and you’ve got questions, but what's the best way to visualize that data to get the answers you need? Transforming data into an effective visualization or dashboard is the first step towards making your data make an impact.

As Henry D. Hubbard, Creator of the Periodic Table of Elements said, “There is magic in graphs. The profile of a curve reveals in a flash a whole situation — the life history of an epidemic, a panic, or an era of prosperity. The curve informs the mind, awakens the imagination, convinces.”

Bar charts are one of the most common data visualizations. You can use them to quickly compare data across categories, highlight differences, show trends and outliers, and reveal historical highs and lows at a glance. Bar charts are especially effective when you have data that can be split into multiple categories. For example, volume of shirts in different sizes, website traffic by referrer, or percent of spending by department.

The line chart, or line graph, connects several distinct data points, presenting them as one continuous evolution. Use line charts to view trends in data, usually over time (like stock price changes over five years or website page views for the month). The result is a simple, straightforward way to visualize changes in one value relative to another. But line charts aren’t limited to time. Any dimension—like date types, time intervals, and other ordinal data—can be used as the horizontal axis.

Pie charts are powerful for adding detail to other visualizations. Alone, a pie chart doesn’t give the viewer a way to quickly and accurately compare information. Since the viewer has to create context on their own, key points from your data are missed. Instead of making a pie chart the focus of your dashboard, try using them to drill down on other visualizations. This approach uses the pie chart’s simplicity to add information, without distracting from the larger picture.

Maps are a no-brainer for visualizing any kind of location information, whether it’s postal codes, state abbreviations, country names, or your own custom geocoding. If you have geographic information associated with your data, maps are a simple and compelling way to show how location correlates with trends in your data. For example, insurance claims by state, product export destinations by country, car accidents by zip code, and custom sales territories.

Density maps reveal patterns or relative concentrations that might otherwise be hidden due to an overlapping mark on a map—helping you identify locations with greater or fewer numbers of data points. Density maps are most effective when working with a data set containing many data points in a small geographic area.

Scatter plots are an effective way to investigate the relationship between different variables, showing if one variable is a good predictor of another, or if they tend to change independently. A scatter plot presents lots of distinct data points on a single chart. The chart can then be enhanced with analytics like cluster analysis or trend lines. For example, you could use this chart to visualize technology early-adopters’ and laggards’ purchase patterns or shipping costs of different product categories to different regions.

Gantt charts display a project schedule or show changes in activity over time. A Gantt chart shows steps that need to be completed before others can begin, along with resource allocation. But Gantt charts aren’t limited to projects. You can represent any data related to a time series with this chart type, like the duration of a machine’s use or availability of players on a team, for example.

Although bubbles aren’t technically their own type of visualization, using them as a technique adds detail to scatter plots or maps to show the relationship between three or more measures. Varying the size and color of circles creates visually compelling charts that present large volumes of data at once.

Histograms show how your data is distributed across distinct groups. Histograms group your data into specific categories (also known as “bins”), then assign a bar that is proportional to the number of records in each category. You could use this chart type to visualize things like number of customers by company size, student performance on an exam, or frequency of a product defect.

With bullet charts, quickly compare progress against a goal. At its core, a bullet graph is a variation of a bar chart. Designed to replace dashboard gauges, meters, and thermometers, a bullet chart shows more information and provides more points of comparison, while using less space. Because it doesn’t display history, this chart is best suited for quick “how are we doing” dashboards, rather than deep analysis.

Highlight tables take heat maps one step further. A highlight table uses color to grab the viewer’s attention, while still presenting precise figures. For example, segmentation analysis of target market, product adoption across regions, and sales leads by individual representative.

Treemaps relate different segments of your data to the whole. As the name of the chart suggests, each rectangle in a treemap is subdivided into smaller rectangles, or sub-branches, based on its proportion to the whole. They make efficient use of space to show percent total for each category.

Box-and-whisker plots, or boxplots, are a common way to show distributions of data. The name refers to the two parts of the diagram: the box, which contains the median of the data along with the 1st and 3rd quartiles (25% greater and less than the median), and the whiskers, which typically represent data within 1.5 times the interquartile range (the difference between the 1st and 3rd quartiles). The whiskers can also be used to show the maximum and minimum points within the data.

Though candlestick charts may remind you of box-and-whisker plots, they mean different things. Candlestick charts are commonly used for financial analysis to show metrics about a financial instrument over a period of time. This chart type shows the open, close, high, and low values of an instrument over time, in an easy to understand format.