Tips include:. A graph with a simple design strives for a clean, uncluttered look. Simplicity in design does not mean simplicity in data however; well-designed graphs can represent rich data. Good graphs support accurate estimation of the quantities represented. To estimate quantities, the reader needs to understand the scale used to represent quantity on the graph.
The popular vote in the US Presidential election provides a simple example of a transposition:. Data labels show specific value information for each data point on the chart. The purpose of graphs is to visually showcase patterns in the data and not to communicate numerical precision; tables are much better suited for this goal.
Therefore, as tick marks on a numerical scale and grid lines already allow to quickly reference data points to their values, data labels are redundant and add visual clutter to the chart. In the rare case when numerical data are presented on a chart without a quantitative axis, data labels can be used.
In such instances, ensure that numbers are rounded up to reduce precision when it is not necessary, and position data labels relative to the data points in a way that makes it easy to read and reference the information quickly without cluttering the chart.
See element N on the figure above. Many options in terms of chart type, layout, style, and supporting elements are available. Here we illustrate a couple use cases for poor and improved graph design. Axis and chart titles on the right side graph provide focused information about what the data show. Specifically, the good graph specifies that preference ratings were measured on a scale from 0 to Additionally, the title on the right summarizes data in a single takeaway message.
As shown on the left side graph, slanted category labels can create a rough visual edge, especially when label length varies, and can be harder to read than horizontally placed labels; whenever possible, opt for the latter. Because only three data points are represented, it is easy to extract their values by referencing the numerical axis. In the example on the left, the grid lines add unnecessary visual clutter and can be completely removed from the graph.
Additionally, the scale on the numerical axis can be simplified and include tick marks for every 2 rather than every 1 point. Finally, error bars on the right side graph are less visually salient than on the left, but still provide information about variability in preference ratings in the sample. As in the previous example, the graph on the right has more descriptive axis and chart titles that provide focused information about presented data.
A reference line on the right indicates occurrence of a business event that provides additional context within which the audience can interpret data. Data labels clutter the chart on the left side and are not really necessary. As shown on the right side, each data point can be quickly aligned with its value by using the grid lines. The scale on the numerical axis can also be adjusted to show larger scale increments. Tick marks on the categorical axis, as shown on the left, serve no purpose and can be removed to reduce visual clutter.
Circles on the right graph highlight exactly where on the line the data points are placed, and allow an easy reference between a point and a month it is associated with on the categorical axis. Finally, labeling data directly on the chart removes the need for a legend and reduces visual clutter. Once the appropriate chart type to present data visually is selected and supporting details about the data are provided, the last step in effective visual communication with graphs is to carefully design these charts.
Design ensures not only that the graphs look aesthetically appealing, but also, and more importantly, that the intended message is communicated. For example, color or texture can be used to highlight a particular data value from a larger set. Similarly, a color gradient can emphasize change in data, such as increase or decrease in cost of operations at a business, over time.
A poorly designed chart may not only fail to communicate the intended message, but may also mislead the audience. In the business realm, visualized data are used to inform decisions; hence, graphs that are unclear or misleading can negatively impact operations and the bottom line.
Successful data visualization stems from the synthesis of appropriate research methods, analysis, and keen application of design principles. Keep up with new insights from industry leaders on digital transformation, mobile app development, enterprise architecture, and tech innovation topics. Core elements Axis, category and unit labels Axes reflect what kind of information is presented and ought to be labeled.
Scale, numerical axis and tick marks A scale divides an axis into equal segments, and tick marks denote those even segments of the scale. Legend When more than one category of data is presented in a chart, a legend informs what different colors or patterns represent.
Chart title The title communicates what information is presented in the chart. Garnishments Graphs occasionally include additional elements, such as confidence intervals or data labels, that may not be necessary in every instance.
Area graphs are very similar to line graphs. They can be used to track changes over time for one or more groups. Area graphs are good to use when you are tracking the changes in two or more related groups that make up one whole category for example public and private groups. X-Y plots are used to determine relationships between the two different things. The x-axis is used to measure one event or variable and the y-axis is used to measure the other.
If both variables increase at the same time, they have a positive relationship.
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