Analysis charts

Data points appear as vertically-oriented columns. Three different types are available:

Standard Column Charts

These charts have one column for each cell in the analysis, also known as "clustered" charts.

Stacked Column Charts

These charts have one column for each horizontal axis point, and the values for each series are stacked up. The total length of the column represents the total value for its horizontal axis point. Stacked types are useful when the series values add to a total.

100% Stacked Column Charts

These charts are similar to the stacked types except that the values shown are percentages of the total for each horizontal axis point. Thus, every stacked column is the same height: 100%. These chart types are useful for showing contributions to a total from different sources.

Data points appear as horizontally-oriented bars. Three different types are available:

Standard Bar Charts

These have one bar for each cell in the analysis, also known as "clustered" charts.

Stacked Bar Charts

These have one bar for each horizontal axis point, and the values for each series are stacked up. The total length of the bar represents the total value for its horizontal axis point. Stacked types are useful when the series values add to a total.

100% Stacked Bar Charts

These are similar to the stacked types except that the values shown are percentages of the total for each horizontal axis point. Thus, every stacked bar is the same height: 100%. These chart types are useful for showing contributions to a total from different sources.

Area charts look like line charts, but with the area below the line filled in. Three different types are available:

Standard Area Charts

Stacked Area Charts

100% Stacked Area Charts

Line charts display a series of data points connected by straight line segments. The data points appear as vertices between the line segments.

They are particularly useful when there are many horizontal axis points, such as a long time series.

Spline charts, also known as smoothed line charts, are like line charts in that they display a series of data points connected by lines. However, rather than using straight line segments, as line charts do, spline charts use smoothed lines obtained by fitting a cardinal spline curve to the data points. The resulting smoothed curve may offer a more realistic representation of trends in the data than a line chart.

They are particularly useful when there are many horizontal axis points, such as a long time series.

These types of charts are often combined with a standard column or bar chart (via the Series Chart Type button on the Design ribbon). Two different types are available:

Column Marker Charts

This is similar to a standard column chart, but the value is shown as a line corresponding to the top of the filled column.

In the following example, a simple column marker chart is shown.

Column marker charts work effectively when additional series, displayed as stacked columns, are added.

In the following example, the chart's This Year series has been changed to a standard column chart.

Bar Marker Charts

This is similar to a standard bar chart, but the value is shown as a line corresponding to the right of the filled bar.

Bar marker charts work effectively when additional series, displayed as stacked bars, are added. In the following example, the chart's This Year and Remaining series has been changed to stacked bar charts.

Point charts (also known as scatter charts, scattergrams, or XY charts) use individual points to show values for two series simultaneously on a chart. The point locations are determined by the intersection of horizontal and vertical axis data, each axis representing one of the series. The pattern of points indicates the relationship (if any) between the series.

The first measure in each placeholder (Row and Column) is used to determine the series that will be assigned to the horizontal and vertical axis respectively, and hence the location of the points. All other data is ignored.

In the following example chart, the Internet Sales Amount (per product) is shown for two countries: Australia and the United States. Values for Australia are on the horizontal axis, while values for the United States are on the vertical axis (determined by the order of the countries on the Columns placeholder). A point is present for each product, with its location determined by the intersection of its Australia and United States Internet Sales Amount values.

Adding a tab containing a hierarchy or level to a placeholder before the measure used for the series will produce multiple series. Each series is plotted with points of a different color. In the following example, adding a Gender level before the Internet Sales Amount measure produces two versions of the original Internet Sales Amount series: one for Female and one for Male.

Bubble charts are very similar to point charts, in that they use individual points to show values for two series simultaneously on a chart. The point locations are determined by the intersection of horizontal and vertical axis data, each axis representing one series. The pattern of points indicates the relationship (if any) between the series.

However, bubble charts also use a third series to assign an independent value to each point, which determines the diameter of the bubble (the larger the value, the larger the bubble). This allows relationships between any of the three series to be detected.

In the following example chart, the Internet Sales Amount (per product) is shown for two countries: Australia and the United States. Values for Australia are on the horizontal axis, while values for the United States are on the vertical axis (determined by the order of the countries on the Columns placeholder).

A bubble is present for each product, with its location determined by the intersection of its Australia and United States Internet Sales Amount values, with the combined Internet Sales Amount values for the two countries determining the size of the bubble.

Overlapping bubbles are transparent, allowing you to better see bubbles that lay close to one another. Also, each bubble uses a dark border to allow you to better see individual bubble, especially when many bubbles are located close to one another.

The default bubble size for all bubbles is automatically determined when the chart is created. However, you can change the scale of the bubbles, making them all larger or smaller, by using the Size drop-down list on the Design ribbon.

A pie chart represents a single data series as portions (sectors) of a circle. If multiple series are present, only the first series is used to construct the pie chart; the other series are shown in tool tips that appear when hovering on a segment. Multiple pie charts, one for each series, may be displayed using the Small multiples feature.

Legend and Label Behavior

If the pie chart doesn’t have a legend, segment labels also display the segment's name; otherwise, only the segment value is shown.

Label Placement

Whenever possible (if there is enough room), labels are now placed inside sectors.

Segment Ordering

The order of sectors is determined using the order established in the pie chart's underlying pivot table.

In the following example, the Internet Sales Amount column has been sorted in descending order. Pie chart sectors are always presented clockwise from the "12 o'clock" position.

Controlling Segment Label Appearance and Placement

Segment labels are not shown by default, but can be activated using the Show Labels button on the Design ribbon.

Furthermore, you can force all segment labels to display outside of the chart's sectors, even if they fit inside the sectors, using the Pie Labels Outside button on the Design ribbon.

A donut chart resembles a standard pie chart. However, a blank area appears in the middle of the chart, which can be used to display the total value of the data displayed in the chart. You can choose to show or hide this value.

Like pie charts, donut charts are useful when the segments displayed add up to a meaningful whole, such as sales in various territories that add up to the total sales.

With the exception of the central total value display, donut charts can be manipulated in the same way as a pie chart. You can perform the following actions to adjust the appearance of the chart:

A circular gauge chart resembles a standard pie chart. However, a blank area appears in the middle of the chart, which can be used to display the value of the first series. The chart automatically sizes so that the full number can be displayed in the middle, without wrapping. Additional series are displayed as lines within the chart, with the corresponding labels showing the name of the series, the categories, or the values (as specified).

This type of chart is useful in the following scenarios:

You can also use additional series as a way to extend the information displayed on the chart (by placing a "pin" on the chart). For example:

A pyramid chart works like a pie chart, with only one series shown per pyramid. They are useful for displaying values that add to a total.

Funnel charts are basically upside-down pyramid charts. They are often used to show the relationships between leads, opportunities, calls, sales, and invoices.

Treemaps display hierarchical data as a set of nested rectangles. Each member of the hierarchy is given a rectangle, which is then tiled with smaller rectangles representing its children. Each member's rectangle has an area proportional to the value of a specific measure for the member, as defined for the chart.

Treemaps make efficient use of space. They can legibly display many items on the screen simultaneously. They are also great visualizations for spotting outliers and what makes up those outliers.

In Data Hub, treemaps are created using a measure and a level from the Dimension Tree, placed on opposite axes (one in the Series placeholder, the other in the Nodes placeholder), as shown below.

A word cloud is a visual, textual representation of data. The chart is made up of tags, which are usually single words. The importance of each tag is shown by varying the tag's font size and, optionally, its color (in other words, the size of each word is based on the value of the corresponding measure).

This format is useful for quickly perceiving the most prominent members. Word clouds are a great way to display an overall picture of a metric. Further analysis and investigation may then be done.

In Data Hub, word clouds are created using one measure and one dimension item, as shown below.

Two types of map charts are available in Data Hub: map bubbles and map shapes. Both types overlay analysis data on a map, but they do so using different methods (with one using different size bubbles while the other uses various shapes).

Map Bubble Charts

A map bubble chart looks like a bubble chart but overlays the corresponding analysis data on a map. This format is useful for viewing data in terms of specific geographical locations. For example, visualizing stock levels at warehouses, or the severity of overtime delivery incidents at specific customer addresses.

In the following example, chart has been created showing outbreaks of whooping cough in the United States. The bubbles represent both the scale of the outbreak (size of bubble) and year of the outbreak (bubble's color).

Important

For this chart type to function, the selected member properties must contain latitude and longitude details.

Map Shape Charts

A map shapes chart uses predefined shape resources (containing entities such as states, counties, provinces, or postal codes). The analysis data is used to color the corresponding shapes on the map.

In the following example, the zip codes within the state of North Carolina have been used to display population density data. Heat map formatting shows the population density of each zip code from high (green) to low (red).

Note

If labels are enabled (via the Show Labels button on the Design panel), they are displayed unless an individual shape is too small to support the label clearly. In this case, the label is hidden. In the following example, some labels are shown, while those for smaller shapes do not appear.

You can also hover over individual shapes to view all series values.

The waterfall, also known as a bridge or cascade, chart is used to portray how an initial value is affected by a series of intermediate positive or negative values. Usually the initial and the final values (end points) are represented by whole columns, while the intermediate values are shown as floating columns that begin based on the value of the previous column. The columns can be color-coded for distinguishing between positive and negative values.

Like other chart types, the Waterfall chart is fully interactive and supports drill up, drill down and, drill through as well as expanding hierarchy members from the pivot table.

Totals are automatically created based on Totals and/or Subtotals (using the Row and/or Column Summaries on the Rows & Columns section of the Data panel).

This chart type also supports multiple attributes on rows (or columns), however, totals will only show if Row/Column totals are used.

These colors can be changed in the Color Coding section of the Design panel

Use the Show subtotals as totals option in the Chart panel to display the total of expanded parent members or a subtotal as a delta, based on the value of the previous sibling member.

Chart axis labels, when displayed, are intelligently placed in such as way as to avoid overlapping. This placement includes turning the labels horizontally or vertically for a better fit, or truncating the labels with ellipses (...) when the label name cannot be displayed in full.

You can format existing chart axes, (e.g. add titles or shift axis position, add multiple axes (horizontal and vertical) to a chart.

Numerous options allow you to format horizontal and vertical axes on a chart. These options are available from AXIS screen.

Select an axis from the Axes drop-down list (on the left side of the ribbon). Highlighted buttons will dynamically adjust. This means that now all buttons will be available. (Some will be grayed-out.)

Format options:

By default, the first items listed in both the Series and Bar placeholders are used to determine a chart's axes. However, you can assign a series to a new or existing axis using the Axes drop-down lists on the DESIGN ribbon.

To add a new axis to a chart, specify the appropriate series and then add the new axis as a vertical or horizontal axis.

On the chart's legend, click the series that will be represented on the new axis.

You can remove a new axis from your chart using the AXIS ribbon.

Select the ribbon you want to remove from the Axes drop-down list on the left side of the ribbon, then click the Remove button.

You can automatically apply certain pivot table formatting to the chart using the Pivot Table Formatting button on the DESIGN ribbon.

When this feature is enabled (as shown in the above example), any background color formatting (including conditional formatting) applied to cells in a chart's pivot table is automatically applied to the corresponding items in the chart. This feature is enabled by default.

In the following example, with the feature enabled, the background color for one of the chart's table columns has been changed to black (with the text changed to white as well). Notice that the corresponding data in the chart has also been changed.

However, if the same table formatting is performed with the Pivot Table Formatting feature disabled, only the table is updated. The chart retains its default color scheme, as shown below.

You can use multiple chart types within the same chart using the Series Chart Type button on the Design panel.

Using multiple chart types within a chart may improve the appearance and usefulness of a chart. For instance, a chart may be more readable if one or more series are displayed as splines while others are displayed as bars.

All non-selected legend entries, and their corresponding chart series items, become transparent.

Limitations

Since not all chart types support creating combination charts, you should review the following information before attempting to use this feature.

100% Stacked Column Word Cloud

100% Stacked Bar Pie

100% Stacked Area Pyramid

Treemap Funnel

Bubble

Column Line

Stacked Column Spline

Area Point

Stacked Area

A small multiple (also known as a trellis chart, lattice chart, or panel chart) is a grid of small, similar charts, which are arranged for easy comparison.

Proceed to the following subtopics for more information:

Small multiples are useful for viewing "cluttered" charts that contain a large amount of information and are difficult to read. This feature allows you to instead divide the information among several smaller, simpler charts.

An example of small multiples is shown below.

Proceed to the following subtopics for more information that may be helpful when working with small multiples:

General Small Multiple Behavior

When creating small multiples, it is helpful to understand which chart types behave in the same manner.

In ZAP Data Hub, charts can be grouped as follows, based on the behavior of small multiples:

For detailed information on the behavior of these chart types when using small multiples, see About Using Point and Bubble Charts.

For detailed information on the behavior of these chart types when using small multiples, see About Using Pie Charts, Pyramid Charts, Funnel Charts, Treemaps, and Word Clouds.

Horizontal and Vertical Buttons

The Create small multiples buttons, located on the CHART panel, are used to create small multiples. However, the exact behavior of these buttons is affected by the use of the placeholders used to create the initial, single chart.

For more information on using these buttons, see Creating Small Multiples.

The following behavior should be noted before attempting to create small multiples:

In the following example, notice that the charts are the same, even though the contents of the Series and Bars placeholders are reversed, since the charts are created based on the location of the measures.

Number in Image Description

1 Horizontal Small Multiples using Series Placeholder

2 Horizontal Small Multiples using Bars Placeholder

In the following example, notice that the charts are the same, even though the contents of the Series and Bars placeholders are reversed, since the charts are created based on the location of the non-measure items.

Number in Image Description

1 Vertical Small Multiples using Bars Placeholder

2 Vertical Small Multiples using Series Placeholder

In the following example, horizontal small multiples have been created based on the items in the Series placeholder, since it contains the measures. Notice how the charts themselves, based on the alphabetic entries in the Country level, flow horizontally even though the contents of the vertical (Series) placeholder are used to create them.

Similarly, in the following example, vertical small multiples have been created, but the Bars placeholder, which runs horizontally on the application, is used since it does not contain the measures. Notice how the charts themselves, based on the Calendar Year level, flow vertically.

Using a Chart's Pivot Table to Understand Small Multiple Creation

Although in many cases you can determine how small multiples are created simply by examining the Series and Bars placeholders, it is recommended that you view a chart's pivot table to fully understand how ZAP Data Hub creates small multiples.

In the following example, simply examining the Bars placeholder tab allows you to easily see how the small multiples are created. The first tab on the placeholder (Calendar Year) is used to create the charts, since this tab represents a single hierarchy (level). Notice how each chart displays a heading for the year on which it is based.

However, in the following example, the first tab in the Bars placeholder simply represents a member of the hierarchy used in the previous example (the Calendar Year level). By only examining the contents of the placeholder, you may incorrectly assume that only the first member would be used to create the small multiples. Instead, all members from the same hierarchy (the Calendar Year level), if they are added to the Bars placeholder, are used, and the resulting small multiples are exactly the same as previously created using just the Calendar Year level (as shown above).

By examining the chart's pivot table (click the Table button on the VIEW ribbon), you can see that, regardless of whether you use a hierarchy or individual members of that hierarchy, the first column of the table is identical. This column is what is used to create the small multiples. Since this table is the same, whether you use the hierarchy itself (in this example, the Calendar Year level) or the individual members of the hierarchy (the CY 2001, CY 2002, CY 2003, and CY 2004 members), the resulting small multiples are also identical.

Swap Series in Small Multiples

As described in Swapping Rows and Columns for Chart Series, you can swap a chart's horizontal axis and legend entries using the Swap Series button on the CHART ribbon.

However, using this option reverses the placeholder usage behavior when creating small multiples (that is, which placeholders are used when clicking the "Horizontal" or "Vertical" buttons on the CHART ribbon). For more information on the normal, typical behavior of placeholders when creating small multiples, see About the Horizontal and Vertical Buttons.

In the following example, standard small multiples have been created using the "Vertical" button, and are based on the Calendar Year level in the Bars placeholder (the placeholder that does not contain measures). Notice that the charts flow vertically.

If you click the Swap Series button, the same initial chart now creates vertical small multiples based on the Country level in the Bars placeholder (the placeholder that contains measures, which is not the standard behavior). However, the small multiples themselves still flow vertically.

Using Pie Charts, Pyramid Charts, Funnel Charts, Treemaps, and Word Clouds

When creating small multiples using pie, pyramid, or funnel charts, as well as treemaps and word clouds, you may, at times, experience results that can appear to be problems with the application. However, by reviewing the topics in this section, and gaining a better understanding of how small multiples are created when using these types of charts, you will be able to better use the small multiples feature.

The topics in this section are divided based on the type of small multiple you are creating (horizontal, vertical, or both horizontal and vertical at the same time).

Horizontal Small Multiples

If you are using a pie chart, and you create horizontal small multiples when a single measure (or no measure) is defined, the pie chart does not seem to change. The only visible difference is that a title appears above the chart.

This behavior is expected, since horizontal small multiples create charts based on the defined measures, and a single measure will create a single chart, while no measure creates a single chart using the designated default measure.

If you add a second measure, an additional pie chart is created, which better represents the expected behavior when creating small multiples.

Vertical Small Multiples

If you are using a pie chart, and you create vertical small multiples when only a single hierarchy is defined, the result is several pie charts that are not subdivided into sectors.

This behavior is expected, since each chart represents a single data point and does not have multiple data points (sectors) to display. However, seeing these results can initially be confusing.

Adding an additional hierarchy (and a measure, if none is present) makes the small multiples more useful.

Horizontal and Vertical Small Multiples

If you are using a pie chart and you create horizontal and vertical small multiples at the same time, the following behavior should be noted:

In the following example, two measures and two hierarchies are used to create horizontal and vertical small multiples (by clicking both the "Horizontal" and "Vertical" buttons on the CHART ribbon).

It can be helpful to also examine the chart's pivot table (via the Table button on the VIEW ribbon) to see how the small multiples are created.

In the table above, the Customer Geography column is used to define the number of small multiple rows (via the "Vertical" button), while the measures (Internet Sales Amount and Internet Gross Profit) are used to determine the number of columns (via the "Horizontal" button). The second column in the table, Gender, is used to create the individual sectors in each pie chart.

Using Point and Bubble Charts

When creating small multiples using point or bubble charts, it is highly recommended that you use the chart's pivot table to fully understand how the small multiples are being created. Although the overall creation of small multiples with point and bubble charts is similar to the creation when using other chart types, there are subtle differences that, if not understood, may cause confusion.

Important: In the examples in this section, only bubble charts are mentioned specifically. However, the behavior applies to both point and bubble charts. For more information on which chart types behave similarly when using small multiples, see About General Small Multiple Behavior.

Example Chart

The following chart is used as a starting point throughout this section. This chart shows the Internet Sales Amount per product in two countries: Australia and the United States. Values for Australia are on the horizontal axis, while values for the United States are on the vertical axis (determined by the order of the countries on the Columns placeholder). A bubble is present for each product, with its location determined by the intersection of its Australia and United States Internet Sales Amount values, with the combined Internet Sales Amount values for the two countries determining the size of the bubble. You can hover over any bubble, as shown below, to see details about that individual bubble.

Horizontal Small Multiples With a Single Measure

If you are using a point or bubble chart, and you create horizontal small multiples when a single measure (or no measure) is defined, the chart does not seem to change. The only visible difference is that a title appears above the chart.

This behavior is expected, since horizontal small multiple create charts based on the number of specified measures, and a single measure will create a single chart. At the same time, a chart with no measure creates a single chart using the designated default measure.

Tip: When no measure if defined, a default is used and can be determined by examining the chart's pivot table (click the Table button on the VIEW ribbon).

If you add a second measure, an additional chart is created, which better represents the expected behavior when creating small multiples.

Using a Pie/Bubble Chart's Pivot Table With Small Multiples - Horizontal

When creating horizontal small multiples from a point or bubble chart, it is a best practice to use the chart's pivot table to better understand how the charts are generated.

In the following example, based on a slightly altered version of the example chart described in About the Example Chart, horizontal small multiples have been created (with Internet Gross Profit added to the X, Y, Size, Details placeholder). Since two measures are being used, two small multiples are created.

By examining the chart's pivot table (which can be displayed by clicking the Table button on the VIEW ribbon), you can more easily see how the small multiples were created.

A portion of the pivot table is shown below.

The measures, which determine the number of small multiples created, are represented as columns in the table.

If additional columns were present (because more measures were used to create the original chart), more small multiples would be created - one for each additional measure.

Each bubble represents a product in the Product column, with the location determined by the intersection of the corresponding measure values. The size of the bubble is based on the combined value of the corresponding measure values.

If you add an additional DIMENSION TREE item (hierarchy, member, etc.) prior to the measures, an additional level is added to the small multiples.

In the following example, the Gender level has been added to the chart. Notice how the small multiples are now determined using this new level (because it comes first on the placeholder). So, instead of having a chart for each measure, you now have a chart for each gender (one for male and one for female). The measures, which were formerly used to determine the number of small multiples, are now moved to individual series on each small multiple (displayed below the charts).

Again, if you examine the chart's pivot table, you can more easily see the hierarchy, with Gender (Female and Male) coming first.

The measures appear next, with each measure having an entry under each Gender column.

Finally the individual countries appear, with each of the two countries having an entry under each measure.

Using a Pie/Bubble Chart's Pivot Table With Small Multiples - Vertical

When creating vertical small multiples from a point or bubble chart, it is a best practice to use the chart's pivot table to better understand how the charts are generated.

In the following example, based on a slightly altered version of the example chart described in About the Example Chart, vertical small multiples have been created (with Commute Distance replacing Product in the Rows placeholder).

Notice that five small multiples were created. A quick look at the pivot table provides an explanation for this number, since there are five rows (categories) in the Commute Distance column.

If you add an additional DIMENSION TREE item (hierarchy, member, etc.) before the existing item used to create the vertical measure, an additional level is added to the small multiples.

In the following example, the Gender level has been added to the chart. Notice how the small multiples are now determined using this new level (because it comes first on the placeholder). So, instead of having a chart for each of the five categories under Commute Distance, you now have a chart for each gender (one for male and one for female). The Commute Distance categories, which were formerly used to determine the number of small multiples, are now only displayed on the charts as bubbles, with their values accessible via infotips when you hover over a bubble (as shown below).

Once again, a quick look at the chart's pivot table shows you the two categories under the Gender level. Also, the Commute Distance categories have been "pushed to the right," which means they are now used to create the bubbles on the chart .

Legends Replacing an Axis

When creating small multiples where only a single item exists in a placeholder, it is not necessary to show two chart axes. Essentially, the legend for the small multiples replaces the unnecessary axis.

In the following example, a chart has been created with only a single level (Calendar Year) in the Bars placeholder.

If you create vertical small multiples, a chart is created for each year. However, there is no additional value to display on the small multiples' horizontal axes. Instead, the legend provides the necessary information (in this example, which country and measure are represented by each column).

Selection Behavior

If you are using small multiples, the behavior when selecting chart data differs slightly from the behavior exhibited in regular charts (as described in Selecting Chart Data).

When a legend entry, an entire pivot table row or column, or individual pivot table cells are clicked, the related items are selected on each small multiple.

In the following example, the United States series has been selected in the legend. Notice that the United States columns have been selected in each small multiple and in the corresponding pivot table.

Number in Image Description

1 Legend Entry Clicked

2 Columns Automatically Selected

In the following example, the CY 2002 row has been selected in the pivot table. Notice that the corresponding columns in each small multiple have been selected.

Number in Image Description

1 Row Clicked

2 Corresponding Columns Selected

In the following example, the Internet Sales Amount/Germany in CY 2003 has been selected in the pivot table, and the corresponding entry in the Internet Sales Amount small multiple is highlighted. However, notice that the corresponding column in the Internet Gross Profit small multiple (for Germany in CY 2003) is selected as well, but the cell is not highlighted in the table.

Number in Image Description

1 Individual Cell Clicked, and Chart Column Selected

2 Corresponding Column in Other Small Multiple Also Highlighted

When working with treemaps, any expansion done in one chart is automatically repeated in all other charts. In the following example, the CY 2004 rectangle in the Internet Sales Amount chart has been expanded (double-clicked twice - once to display the semester, and once to display the quarters). Notice that the corresponding rectangle in the Internet Gross Profit chart is also expanded.

Creating Small Multiples

Small multiples are created using the buttons in the Small Multiples group on the CHART panel.

Small multiples use the first dimension attribute located on the Columns placeholder for the horizontal multiplier, and/or the first dimension attribute on the Rows placeholder for the vertical multiplier. You can create small multiples using just the horizontal axis, just the vertical axis, or both axes at the same time.

Once you have created or opened the appropriate chart, you can create small multiples using any of the following methods:

In the following example, the small multiples would be created based on the Country level for a bar chart.

In the following example, the small multiples would be created based on the Calendar Year level for a bar chart.

In the following example, the small multiples would be created based on both the Country (horizontal) and Calendar Year (vertical) levels for a bar chart.

Controlling the Displayed Number of Rows and Columns

Once you have created the small multiples, you can control the number of columns and rows used when multiplying.

ZAP Data Hub automatically determines the best number of columns and rows to use. However, if a single multiplier (either horizontal or vertical) is used, the number of columns and rows can be manually changed, if desired.

In the following example, since horizontal multiples are being created, the Columns spin box is accessible.

If resizing a chart to a very small size, it may become difficult to interpret, since the chart's text labels will remain a constant size and could interfere with the display of the chart itself. To prevent this issue, designers can set a minimum size in pixels for the chart using the Set as Current Size button and adjacent Width and Height drop downs under the minimum size section of the Chart design pane.

Legend and Text resizing behavior

Using the Set as Current Size button and corresponding spin boxes doesn't just affect the sizing of the chart's image. It also impacts the text used on a chart's axis, legend, and labels. The exact impact varies depending on whether or not you size your chart more than or less than the set minimum size.

Increasing the chart's size above the minimum size rescales the chart's bars, axes, etc., but the font size used for axis and legend text remains the same.

If the chart size is reduced below the minimum size, the entire chart, including the text, is scaled.

Setting the minimum size

The easiest way to change the minimum size is to resize the chart to an acceptable minimum size, and then click the Set as Current Size button. This action sets the pixel dimensions of the current chart size as the minimum size.

You may also set a minimum size by typing or selecting pixel values directly in the Width and Height spin boxes.

Once a useful minimum size has been set for the chart, reducing the chart's size below the minimum maintains the chart's general appearance, and prevents the text labels from swamping the rest of the chart, as shown below.

Note

Using this technique may result in charts with very small text sizes that may be hard to read. It's useful to note that segment infotips are not resized. Therefore, if a chart is very small and text or labels cannot be read easily, you can still hover over segments of the chart and see their value in the infotips that appear, as shown below.

Example 2. Resizing

In the following example, the chart and associated labels did not resize well, and the chart is very difficult to read. Using the new minimum chart size feature, you can set the minimum size to 384 pixels x 195 pixels (as shown below). Now, when you resize the chart, it will remain readable.

You can remove custom formatting from a chart, returning it to its original state, using the Reset Options button on the CHART panel.

It should be noted that the following items are not affected by this command (and are, therefore, not returned to their original setting):

You can swap a chart's horizontal axis and legend entries using the Swap Series button on the CHART ribbon.

This feature allows you to switch between using the Columns and Rows placeholders to determine series data in a chart.

You can simplify the appearance of a chart by displaying only specific series and series items, hiding all other chart information, by using the Select Data button on the CHART ribbon.

Specifying data on a chart can be performed in two ways: directly on a chart or via a chart's legend. Some charts support both of these selection methods, while other only support one or the other. Data may also be selected from the chart's underlying pivot table for some chart types.

When data is selected in a chart, other data not associated with the selection is made partially transparent, to provide a better visual representation of exactly what information has been selected.