Graphs, Charts, and Plots

A bar chart consists of vertical or horizontal bars that represent quantitative data. Use bar charts to compare data that is aggregated by the distinct values of a category.

You can apply grouping and create data-driven lattices. You can filter or rank your data based on a specified number of top or bottom values.

A targeted bar chart is a variation of the bar chart that has target values. A target value is represented as a triangle with a line at the target value that is determined by the target role.

A waterfall chart (also known as a progressive bar chart) shows how the initial value of a measure increases or decreases during a series of operations or transactions. The first bar begins at the initial value, and each subsequent bar begins where the previous bar ends. The length and direction of a bar indicate the magnitude and type (positive or negative, for example) of the operation or transaction. The resulting chart is a stepped bar showing how incremental changes lead to the final value of the measure.

A line chart shows the relationship of one or more measures over some interval, such as time or a series of ranges. You can measure a single measure (univariate analysis), or you can show the relationships among multiple measures (multivariate analysis), such as the leading or lagging relationship between advertising and sales over time. The category on the X axis of a line chart is discrete; the category on the X axis of a time series plot is continuous.

You can apply grouping and create lattices.

A pie chart displays a part-to-whole relationship in a circle divided into multiple slices for each value of a category data item based on a single measure data item. Each slice represents the relative contribution of each part to the whole. In a pie chart, the legend is sorted by contribution.

In the designer, a pie chart does not show a slice with a missing or zero response.

Effective pie charts limit the number of slices to 5 or 6. In the designer, you can use a rank to reduce the number of slices in a pie chart. For more information, see Adding a New Rank.

A scatter plot is a two-dimensional plot that shows the relationship of two measure data items. Each marker (represented by a symbol such as a dot, a square, or a plus sign) represents an observation. The marker’s position indicates the value for each observation. Use a scatter plot to examine the relationship between numeric data items. You can apply grouping by assigning a category to the Color role.

Scatter plots do not use aggregated data.

A time series plot shows an ordered sequence of values that are observed at equally spaced time intervals. A time series plot requires a date, datetime, or time data item that is continuous on the X axis.

A bubble plot is a variation of a scatter plot in which the markers are replaced with bubbles. A bubble plot displays the relationships among at least three measures. Two measures are represented by the plot axes, and the third measure is represented by the size of the bubbles. Each bubble represents an observation. A bubble plot is useful for data sets with dozens to hundreds of values. You can add categories to the Grouping and Lattice roles.

A treemap displays your data as a set of rectangles (called tiles). Each tile represents a category or a hierarchy node. The color of each tile represents the value of the first measure. The size of each tile represents the value of the second measure. (There are two data roles for measures in a treemap—Size and Color.) For example, a sales data treemap might have tile sizes that represent the number of orders, and it might have tile colors that are derived from color gradients that represent sales.

The layout of the tiles in the treemap is dependent on the size of the display area because it uses a space-filling algorithm to lay the tiles down. This means that the same treemap might appear slightly different in the designer than it does in the viewer or on a mobile device because the aspect ratio and size available in those viewers might be different from what the original report designer sees in the designer.

A dual axis bar chart is a variation of the bar chart that has two measures. A measure is on each axis.

A dual axis line chart is a variation of the line chart that has two measures. A measure is displayed on both the left and right side of the Y axis. The relationship between two measures can be examined on two different scales in a dual axis line chart.

A dual axis bar-line chart is a variation of the bar chart that has two measures. A measure is on each axis, and the bar chart is overlaid by a line chart.

A dual axis time series plot is a variation of the time series plot that has two measures. A measure is displayed on both the left and right side of the Y axis.

For example, a dual axis time series plot can be useful when you need to display two measures that have the same unit of measurement and different scales, such as quantity ordered and returns, or when you need to display two measures that have different units of measurement, such as sales and quantity ordered.

A band plot draws a horizontal band with two Y values for each X value. Or, it draws a vertical band with two X values for each Y value. A band plot is typically used to show confidence, error, prediction, or control limits. The points on the upper and lower band boundaries can be joined to create two outlines. The area between the boundaries can be filled.

A needle plot is a plot in which data points are connected by a vertical line that connects to a horizontal baseline. The baseline intersects the 0 value or the minimum value on the vertical axis.

A step plot consists of a series of horizontal and vertical line segments (giving the appearance of steps) that connect observations of input data.

The following example shows the price trend during a particular time interval:

A schedule chart makes it easy to visualize time lines by representing tasks, start dates, durations, and end dates in cascading horizontal bar charts.