Which Type of Chart or Graph is Right for You?

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.

Tips:

• Add color to bars for more impact. Color adds a quick, visual identifier that makes comparisons easy.
• Use stacked bars or side-by-side bars. Stacked and side-by-side bar charts let you break down your data even further, giving more depth to your analysis.
• Combine bar charts with maps. Maps are a powerful and intuitive way to visualize data. Using a map as a filter lets viewers drill down and find detailed answers.
• Put bars on both sides of an axis. Plotting positive and negative data points along the same axis makes trends and outliers stand out.

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.

Tips:

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.

Tips:

• Use cluster analysis to identify segments. Cluster analysis groups data points into distinct segments, based on the variables you select.
• Use highlight actions. By adding a highlight action to your scatter plots, quickly see which points have attributes in common, without losing sight of the rest of the dataset.
• Use custom mark types. Custom marks add a quick visual cue to your chart, clearly distinguishing different groups of points.

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.

Tips

• Add color. Color can add more dimension to a bubble chart, while creating an attractive visual “pop.”
• Overlay bubbles on maps. Bubbles quickly inform a viewer about relative concentration of data. Using these as an overlay puts geographically-related data in context for the viewer.

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.

Tips

• Use color to illustrate achievement thresholds. Including color as a backdrop gives the viewer another reference to better understand how performance is measured against goals.
• Add bullets to dashboards for summary insights. Combining bullets with other chart types into a dashboard supports productive discussions around where to focus efforts to accomplish objectives.

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.

Tips

• Color the rectangles by category. Because treemaps are such a dense chart type, color makes it easier for the viewer to distinguish one category from another.
• Combine treemaps with bar charts. The bar chart lets the viewer quickly compare items through the bar’s length, while also showing the breakdown of categories within each bar.

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.

Tips

• Present multiple separate candlestick charts. Trying to layer too many data points on a single chart can be confusing and difficult to navigate. Creating clear distinctions helps the viewer to analyze the charts separately.
• Use highlight actions to correlate data. Time-series data can be difficult to keep track of. Highlight actions allow a user to focus on a single date, while still seeing the historical information in the rest of the chart.