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Visualization types

Rhiza for Research offers a number of visualization types to help you tell the most compelling story with your data. If you're unsure which visualization type to use, read the sections below and explore in your presentation; try out different kinds of visualizations to see which one works best for the data you want to show.


Tables are a straightforward way to display your data, particularly when you have many results that might otherwise crowd a more visually oriented approach (a column or line chart, for example).

A table showing the default measurements

Figure 41: A default table configured to show only the top 10 rows of data (click to enlarge)

By default, a table shows all of the columns associated with a single target series. You can configure the table to show only the columns you are interested in, to show only a certain number of rows, or to add an optional Net Total row. Note that the net total will always be the total of all the records returned, not just those shown in the table.

Sorting results

When you sort data in the table by clicking on a column header, it sorts all of the series results, not just the items that are shown in the table. You can combine sorting with the Items to Show configuration option to create a table of the top results.

Exporting results

You can download the data in a table as a comma-separated value file (CSV) for use in a spreadsheet or database application. Click the gear menu at the top right of the visualization and click Download CSV from the menu. The exported file contains all of the data available for the data series, regardless of how many rows you've configured your table to show.

For information on how to customize the table visualization, see Configuring tables .


The Research report lets you show your data on a map, which is particularly useful for these kinds of stories:

  • Where specific brands are most popular
  • How many prospective customers live near your business locations
  • Which regions have the most of a certain kind of population (for example, the most vegetarians, or parents of toddlers, or Lexus drivers, or any other information included in your uploaded or syndicated datasets).

The map visualization shows the data from your report plotted against a geographic background image, as seen in the following graphic.

An example of a map visualization showing all Mazda registrations in the Pittsburgh DMA, broken down by ZIP code

Figure 42: A map visualization

Map requirements

If you want to use a map visualization, make sure meet the following requirements:

  • You are using a dataset with geographic information (for example, it contains at least one geographic type, like ZIP Code or County or State).
  • You select a grouping that aggregates your data by geography.

If you don't have geographic data to show or aggregate by, your map will be blank.

Bar and Column Chart

Bar chart and column chart visualizations let you compare categorical data values in your report with a colorful and easy to understand graphic visualization; the height or length of the bars indicate their value. In the Research report, bar charts and column charts have the same configuration and capabilities.

Figure 43: Column chart and bar chart used to visualize the same data

The choice of a vertical or horizontal chart is mainly based on personal preferences. As you can tell from the figure above, some data fits better in one form than another. When deciding which kind of chart to use, consider these issues: 

  • Length of the labels on your data bars. Long labels fit better on a bar chart than a column chart.
  • Type of data. If your figures represent vertical measurements (like elevations, heights, or size), a column chart seems more appropriate. If your figures represent chronological progress, a bar chart might make more sense.
  • Data values. Would you like to show negative values below the zero line (column chart) or to the left of the zero line (bar chart)?

You can hover over a column in the chart to see more data about it:

Hover over a column to see information about the data

Figure 44: Hover over a column to see more data

If you have more than one target series to show on a chart, you can show them as clusters, stacks, or percentages. The following graphic shows the same set of target series configured all three ways. As you can see, the choice you make depends on what part of the data you want to emphasize. For example, do you want to highlight the fact that the Day dealer was the only one to sell the BRZ make? If so, the percentage chart might be your best option. If you are emphasizing visual comparisons between the two dealers' sales of each make, a clustered or stacked chart is a better bet (although some research suggests that clustered charts are visually easier to read).

Clustered, stacked, and percentage chart types

Figure 45: Two target series represented as clustered, stacked, and percent column charts

Note: When using the Stacked or Percent option, it's possible to create a misleading visualization by charting target series that overlap with each other, especially when using a survey-style dataset. For example, let's say you have a target series in which people reported buying Levi's jeans in the last 12 months. You also have a target series in which people responded YES to the question Bought jeans in last 12 months. It's very likely that these two target series contain at least some of the same individual respondents. Showing the overlapping target series in a stacked or percent chart means that some individuals will be counted twice. The Stacked and Percent options should only be used to show data from target series that are mutually exclusive.

Line chart

The line chart visualization records individual data values on a graph and uses a line to connect the data points to visually illustrate changes between each point. It's a good choice to show trends over time.

This visualization type is not fully supported; Rhiza is working on improvements. In the meantime, the workaround is to create a column chart and convert it to a line chart. See Example: Reconfigure a column chart into a line chart.

Scatter Plot

The scatter plot visualization plots data points on a horizontal and a vertical axis to show the relationship between attribute variables.

This visualization type is not fully supported.


Related information

Configuring visualization layout and size

Configuring maps

Configuring tables

Configuring bar charts

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