Selecting an effective visualization

An area chart builds upon characteristics of other Cartesian charts, the Bar chart and the Line chart. Like line charts, area charts highlight continuous data over time in a linear formation. However, these charts utilize a filled color feature similar to a bar chart to display quantity through the data. This allows for the viewer to clearly see how quantities adjust over time.

Area charts convey overall trends rather than individual data points. Area charts are better when you are comparing a smaller number of trends, due to the color-filled area components. For highlighting data with larger amounts of trends, consider using a Line chart instead.

For information on Area configuration options, see Area visualization.

Bar charts display data in a similar way to Column charts, but through horizontal alignment. Commonly in bar charts, the y-axis represents a data category, while the x-axis represents a numerical value.

If your data contains particularly long category titles, bar charts would be favorable over column charts. Through the alignment on the y-axis, the labels on bar charts optimize space and improve readability. Additionally, bar charts are typically better at representing larger amounts of categories due to spacing alignment as opposed to column charts.

For information on Bar configuration options, see Bar visualization.

Column charts are vertical Cartesian charts that display information in rectangular, vertical shapes, where the length of the column corresponds to the data value. Typical column charts include data categories on the x-axis and data values on the y-axis.

If your data contains only a couple of categories, a column chart is ideal. If your data contains a larger number of categories, Bar charts often work better because they provide more space for axes labels. Because negative values are displayed with a downward direction, column charts can also be a useful way to depict data that include negative values.

For information on Column configuration options, see Column visualization.

Donut multiples let you create a series of donut charts to visualize your data in an interconnected formation. These charts omit the center of the circle, forming arc divisions instead of slice divisions. The added blank space in the middle of the chart allows for further labels and descriptions of your data.

When you create donut multiples charts, make sure there is uniformity and cohesive patterns across categories to highlight their relationship. Additionally, to ensure clarity and viewer understanding, include clear, cumulative material in the center of the chart to highlight the nuance of each particular donut multiples chart.

For information on Donut multiples configuration options, see Donut Multiples visualization.

Funnel charts are progression charts that highlight sequential stages. This type of chart has similarities with Bar charts, which also represent data through horizontal, rectangular visualizations. This chart creates a funnel shape through the stacked visualizations.

For an effective funnel chart, make sure that the data includes at least four stages. This will ensure a strong visual impact and highlight the process represented as a whole. If you have fewer than four components, consider using another type of visualization, such as a Pie chart.

For information on Funnel configuration options, see Funnel visualization.

In a line chart, data is displayed through a series of points connected by a straight line. This visualization type specifically highlights continuous data over time.

For clarity in your line chart, the number of lines present remains key. If you're including multiple lines in your chart, use colors to clearly differentiate between the lines. This will allow the viewer to interpret the values separately rather than merging the lines.

For information on Line configuration options, see Line visualization.

Interactive map visualizations apply geographic imagery to represent how your data corresponds to a specific location and region. Interactive maps can reflect many other visualization types by combining design aspects. This can include using points, lines, or areas to signify markers in your visualization.

The design of the overall map can also be customized. Map styles include Light, Dark, and Satellite options. Each of these options have a no labels feature as well. This setting omits key details like city and street names to focus more specifically on the data than on the specifics of the map. When you're choosing a map design, consider the important details for the user to consider, and choose a design that best reflects those details.

For information on Map (Legacy) configuration options, see Map visualization

A pie chart refers to a complete circular chart that is divided into slices based on categories of information. Through these slice divisions, a focus becomes not specifically on the exact percentage amount, but on how the outlined proportions relate to each other and impact the overall goal of the chart.

If you are working to emphasize the importance of the connections between proportional values, pie charts effectively communicate these relationships. If you are working with more than five categories of data, consider selecting a different visualization chart to highlight the information, such as a Bar or Column chart. With bars and column charts, it is often easier for viewers to perceive individual differences.

For information on Pie configuration options, see Pie visualization.

A scatterplot chart is a form of Cartesian chart that highlights the relationship between two variables. Each plotted point represents a value on the x-axis and y-axis that provides insight about the data. These types of charts particularly highlight trends and patterns that emerge in data.

If your data contains two variables that correlate, a scatterplot can be an ideal visualization method to find and explore correlations. This could be positive correlation, which means that while the x variable increases, the y variable increases. This could also include negative correlation, meaning that while one variable increases, the other decreases. Correlation can also be null, meaning that there is no correlation between the two chosen variables. Awareness of potential data correlation can lead to greater insight into your data and can even guide predictions of future data behavior.

The layout and structure of a scatterplot are key to its effectiveness. The plotted points on scatterplots can also be customized through sizing and color use to identify additional variables or categories for the viewer. Trend lines can also be used with scatterplots; these lines highlight connections between the data that emerge for the viewer. Through customization, ensure that these design choices highlight the overall goal of illustrating a relationship and providing a chance to examine potential patterns, correlations, and trends.

For information on Scatterplot configuration options, see Scatterplot visualization.

A single value chart highlights an individual value from your data. Visualizing a value in this way highlights its significance and importance to the complete data.

When creating a single value chart, select a value that has significance to the audience and reflects your goals for the visualization. Additionally, ensure that the font family and size customization emphasize the value rather than distracting from or minimizing the data.

For information on Single value configuration options, see Single Value visualization.

Similar to single value charts, single record charts also highlight selected limited data from the complete data to communicate a certain message. Single record charts contain more information than a single value chart, however. This visualization can provide an example from the complete data.

Choosing an effective and relevant single record for this type of chart will highlight an example from the complete data. This chart can be customized for readability and clarity through font family and size and color usage.

For information on Single record configuration options, see Single Record visualization.

Static maps by region chart how a particular region is impacted by data. Because the map is static, it cannot change or adjust based on user interaction. This type of visualization is helpful for portraying a distinct circumstance rather than a changing, evolving process over time.

For more information on Static Map (Regions) configuration options, see Static Map (Regions) visualization.

Timeline charts highlight the progression of time by including key events and markers over a set duration. While timeline charts often relate to time, this chart structure can also be applied to numbers and amounts as well.

With the customization of color, multiple timelines can be used on one graph to show how multiple factors vary through progression. For timeline patterns, color customization can vary by palette. Your timeline can have a continuous palette, which reflects a gradient option with two variables on either portion of the gradient. You can also have a categorical palette, meaning that each color represents a category in the data.

For information on Timeline configuration options, see Timeline visualization.

Waterfall charts highlight the relationship between positive and negative values through a sequence. These charts show how a starting value evolves due to various factors. Waterfall charts mirror design elements of a Bar chart. Like many other visualization types, time-based markers or category-based markers can structure waterfall charts, depending on your particular data.

As waterfall charts work specifically with positive and negative values, clear definition between these two categories is essential. Through color use and text labels, make sure that the visualization clearly differentiates the values in your data.

For information on Waterfall configuration options, see Waterfall visualization.

Word clouds are data visualizations that display the frequency of data through the customization of font type, size, and color. The key structure of a word cloud is that the higher frequency of a particular word in the data, the larger the font size. Even with a simple glance or a quick scan from a viewer, a word cloud conveys relevant, recurring information in the data through a strong visual impact.

Customization of spacing and horizontal and vertical alignment type can achieve this visual impact. In some word clouds, creators group similar thematic words by a certain color, highlighting the connected nature of certain elements. This grouping of words by color can also help contextualize the content for the reader and understand the information being provided.

For information on Word cloud configuration options, see Word Cloud visualization.