For example, you can investigate possible correlations between number of stores, sales made, and cost. For three quantitative variables, bubble charts or parallel coordinates are good choices. The most useful visualization for displaying the relationship between 2 quantitative variables is a scatter chart. CorrelationĬorrelation charts are used to investigate the relationship between 2 or more variables. If you can, use simple single values to display metrics as they communicate data in a straightforward way. Or if you would also like gauge against the amount required to match costs, and highlight the profit: To see how a single element measures up to a threshold, or multiple thresholds you may use a single value radial or gauge.įor example, display the current sales compared to the sales goal for the year:
SPLUNK SHOWHIDE PANEL SIDEVIEW SERIES
Only use a pie chart if you have a single series and would like to highlight how the partial categorical elements add up to a whole.įor example, we can highlight the percentage Mary contributed to sales last year: For example, comparing sales across products, with an additional breakdown by person: If you’d like to add a second dimension the `chart` command is useful here. For example, if you’d like determine which product had the most sales last year: By being on an axis, each category is more easily compared using a common baseline.Ī basic categorical chart can be displayed using the `stats` command. If we don’t care to see the total number of sales, but want to clearly compare who is making the most sales per day, we can use a stacked chart because the accumulation of all data adds to a whole in this case:Īn area chart is the best time series chart to understand continuous quantitiesīar charts are typically used to compare data of one period or point in time across multiple categories. Taking the example above, we can use this configuration to understand the charts above in one both how many sales are made in total each day, with an additional breakdown by user for further investigation on the trends:Ī column chart is the best time series chart to understand discrete data points, especially summed If there’s a data series of central importance, position it directly on the axis in order to best see its development over time. Stacked charts represent the accumulation of more than one data series.
To determine whether a certain person had any noticeable differences in sales:Ī line chart is the best time series chart to understand and compare trends. For example, if you’d like to investigate which days of the week have the most sales:Ī line chart is the best time series chart to understand continuous trends. The visualization represents data over a period of time and is useful to understand trends, highlight anomalies, and possibly compare multiple series.Ī basic time series chart can be displayed using the `timechart` command. TimeseriesĬharts based on the horizontal axis typically display time series data. If there is too much unnecessary information on the page it can be overwhelming and focus can be misdirected to unimportant details. In general, keep your visualizations as simple and straightforward as possible to avoid distraction and highlight only the most important information. There are many visualization types and configurations available to choose from. Visualization color palette types to effectively communicate your story.Various visualization types and the best ways to configure them for your use case, and.In this Part 2, we’ll be walking through: In our Part 1 of Dashboard Design, we reviewed dashboard layout design and provided some templates to get started.
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