Selecting the best chart type for your data

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There are over 20 visualizations to choose from and over 500 ways to customize. This article will help you select the best chart type according to your data and the reports you would like to create. If you are looking for a full list of the available chart options, see Visualization types reference .

Comparing different items

The charts in the section are useful for comparing different products, items, or any attribute values. These charts function best when analyzing primary differences in results. You can use these charts with multiple attributes and metrics, but your data will look best with a limited number.

  • Column chart : Column charts are a quick way to compare three to four values. Column charts will compare your values side-by-side, so you can view the scale of your results. For information on customization options, see Column charts .

  • Bar chart : Similar to column charts, bar charts are a great way to compare values side-by-side. Bar charts work best when analyzing more than ten values. For information on customization options, see Bar charts .

  • Bubble pack : Bubble packs are useful for quickly identifying the values with the largest number of results. Your results will be displayed in bubbles varying in size, so you can easily locate the values containing the largest and smallest number. For information on customization options, see Bubble packs .

  • Word cloud : Word clouds are used commonly for comparing social media data, such as keywords, hashtags, and search results. Like a bubble pack, more frequently used words will appear larger. For information on customization options, see Word clouds .

  • Picto charts : A picto chart is a good option for comparing multiple values in a visual way. You can compare your attribute values using different images and colors. A picto chart is most commonly used for comparing gender or product related data. For information on customization options, see Picto charts .

Measuring trends over time

If you want to display data over a time period, you can use these charts to demonstrate the linear change. For more information on viewing trends over time, see Comparing trends over time .
  • Line chart : A line chart is a great way to display results over time when data is non-cyclical and you have several time periods or data points. For example, you can use a line chart to measure growth in a product's sales performance over the course of a few years. For information on customization options, see Line charts .

  • Column chart : A column chart is best used when there are fewer date periods or data points. For example, a column chart is more suited for displaying data over a few months, weeks, or years. For information on customization options, see Column charts .

  • Radar chart : Radar charts are useful for comparing cyclical data or identifying outliers. For example, you can use radar charts to identify the months that received a spike in product sales. For information on customization options see, Radar charts .
    Note: To measure trends over time in a radar chart, you must select Based on categories in Chart configuration > Chart .
  • Waterfall chart : Waterfall charts are best used for results containing both positive and negative values over a time period. Waterfall charts can help you identify the time periods containing lower results. For information on customization options, see Waterfall charts .

  • Sparkline : This chart functions as combination of your KPIs and a line or column chart, depending on the option your select. A sparkline chart is best used for pulling key results and viewing the overall trend line for a large time period. For information on customization options, see Sparkline .

Breaking down totals into individual values

The charts in this section will help you visualize how each value contributes to the total attribute results. For example, you can use these charts to view how each sub-category contributes to the category total or how the results from each time period contribute to the change in results overtime.

Breaking down the components of attributes

These charts will help you visualize how each value in an attribute contributes to an attribute's total results. These charts are helpful for looking at the percentage each value contributes or a visual representation of the difference in values.

  • Pie chart : Pie charts are a good way for analyzing the percentage each value contributes to the whole. Pie charts function best when displaying attributes with three to five values. For information on customization options, see Pie charts .

  • Treemap : Treemaps enable you to view your values in a hierarchy. Treemaps are best for comparing category and subcategory attributes. If you add your category attribute first, your subcategories will be grouped by category. You can use treemaps to easily see how subcategory values contribute to the result of their related category value. For information on customization options, see Treemaps .

  • Funnel charts : Funnel charts are best used for displaying the flow of results. Your metrics will appear in the order you add them to your query. For information on customization options, see Funnel charts .

Breaking down components over time

You can use the charts below to display how the different components of an attribute contribute to the final result over a period of time.
  • Stacked column chart : A stacked column chart is great for showing a change in composition over time or how values of an attribute contribute more or less over a given date period. You can set your column chart as stacked in Chart . To show attribute values over time, you would need to add your date attribute to Columns and the category attribute to Rows . You can then click the attribute name in the Row Selector to see how all values contribute to the end result over time

  • Stacked area chart : Stacked area charts are good for showing the change in composition over several time periods. You can easily analyze the changes in value results through the different shading sizes. For information on customization options, see Area charts .

Viewing how results are distributed

The charts in this section analyze how results are distributed across values. These charts can help you understand how different metrics are divided into different attribute values.

  • Column chart : You can configure column charts as histograms to easily compare how your results are distributed into attribute values. You can view your column chart as a histogram by adding your attribute to Rows , then clicking the attribute name in the Row Selector.

  • Bubble chart : A bubble chart is a great way to analyze the relationship between two metrics. You can identify outliers and view where metric results are closest. For information on customization options, see Bubble charts .

Analyzing the relationships between data points

The charts in this section will help you quickly identify the largest result in your data or the differences between two data points. For some of these charts, you will need to add your metrics as size-encoded (see Adding size encoded metrics ).

  • Size-encoded bubble chart : A bubble chart makes it easy to understand the correlations between data points and identify outliers. Adding your metric as size-encoded will enable you to find the smallest and largest results quickly. For information on customization options, see Bubble charts .

  • Size-encoded relational chart : A relational chart is an easy way to view the difference between data points across multiple attributes. Relational charts are best for using category and subcategory attributes. You can add your metric as size-encoded to help identify results. For information on customization options, see Relational charts .

  • Chord chart : A chord chart is a good way for finding the inter-relationships between your attributes and data. You can highlight over an attribute value label to view the other attributes it is connected to. For information on customization options, see Chord charts .

Analyzing how results compare against targets or goals

The charts in this section can be used to display how close or how far metric results are from a set target or goal. For example, you can see what category sales are below or above your goal number for a time period.

  • Gauge chart : The gauge chart is a quick way to see if viewers are hitting their goals or an estimate of how far away they are. The needle represents the results of your added metric. You can indicate the scale of each target color in Chart configuration > Chart . For information on customization options, see Bullet and gauge charts .

  • KPI chart : A KPI chart can show you just a headline number or how your headline number is trending against a target or goal. A KPI visual can be powerful for comparing month-over-month results and demonstrating percentage change. For information on customization options, see KPI .

  • Bullet chart : A bullet chart is best used for working with multiple attributes and metrics. A bullet chart shows how the values of different attributes trend against a set target metric. For information on customization options, see Bullet and gauge charts.

  • Radar chart : A radar chart is used best with a low number of attribute values. The triangle will stretch towards the attribute with more results, so you can easily analyze how your values are trending against a target. For information on customization options, see Radar charts .

  • Column chart : If you have a target or goal metric, you can see if your results are higher or lower than the target by adding the metric on a trend line (see Adding metrics on a trend line ). If you do not have a fixed target metric, you can create as a benchmark calculated metric (see Benchmarking your results ).

Displaying geographic data

If you dataset contains location values, you can create geographic charts. If you are trying to create a geographic chart, but your data is appearing in the incorrect location, you might need to reset your value locations (see Geocoding values ).

  • Standard map : A standard map is a simple way of showing how data is broken down over regions. Results are depicted as bubbles over each region. For information on customization options, see Maps .

  • Heatmap : A heatmap is a more visual way of showing how a metric is broken down across different locations. Locations will be covered by bubbles that vary in color, depending on the number of results. You can change a standard map to a heat map in Chart configuration > Chart > Rendering mode .

  • Pie chart map : A pie chart map is a good way to view how values contribute to a total result across locations. You can turn a standard map into a pie chart map by adding an attribute to rows, then clicking the attribute name in the Row Selector.

  • Choropleth : A choropleth is a more advanced way to view your geographic results. Instead of showing circles over each location, regions will be displayed in a gradient of colors. For information on customization options, see Choropleths .

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