Data distribution

Another very common application for data visualization is to compare values between distinct groups. This is frequently combined with other roles for data visualization, such as showing change over time, or looking at how data is distributed.

Density plot

When you are interested in a frequency distribution, you can visualize the data as a density plot. One of the main advantages of density plots is to display the distribution of a numeric variable's values as series of circles. Each circle typically covers a range of numeric values; a marker size or a color gradient indicates the frequency of data points with a value within the corresponding range.

On the horizontal axis, you need a variable that depicts continuous values that have a regular interval of measurement.

On the vertical axis, you report the value of a second numeric variable for points that fall within each of the intervals defined by the horizontal-axis variable.

Then the number of points within a particular area of the 2D space is counted and represented by a bubble size or a bubble color.

Figure 17: Density plot (bubble size)

To create a density plot, the curve on which you apply the display needs to be set as an XY curve. Refer to the XY curve conversion and XY curve graph type sections for more information.

Once the density plot is created, you can access the options through the graph key. Double-click the graph key to open the window of the curve properties.

Figure 18: Curve properties of density plot

The bubble and the grid settings allow you to customize the display.

For the bubble, you can represent the frequency distribution either by selecting one of the bubble size styles or one of the bubble color styles. If you choose a bubble size style you can pick one color from a palette, alternatively you can pick a colorscale to define the variation of the color gradient. The size ratio allows you to tune the maximal dimension of the bubbles that are drawn on the graph.

For the grid, you can define its global position and the space between the nodes that are used to set the location of the bubble and to count the number of the closest points. So the number of points around a node defines the size or the color of the bubble that is located at the node. The settings origin X and origin Y allow you to move the grid on the 2D area of the graph. The size of the grid is set through the parameters delta X and delta Y. Choice of grid size has an inverse relationship with the number of bubble. The larger the grid sizes, the fewer bubbles there are to cover the whole range of data. With a smaller grid size, the more bubbles there need to be.

Bar chart

A bar chart plots numeric values for levels of a categorical feature as bars. Levels are plotted on one chart axis, and values are plotted on the other axis. Each categorical value claims one bar, and the length of each bar corresponds to the bar’s value. Bars are plotted on a common baseline to allow for easy comparison of values.

A bar chart is used when you want to show a distribution of data points or perform a comparison of metric values across different subgroups of your data. From a bar chart, we can see which groups are highest or most common, and how other groups compare against the others. Since this is a fairly common task, bar charts are a fairly ubiquitous chart type.

The primary variable of a bar chart is its categorical variable. A categorical variable takes discrete values, which can be thought of as labels.

In contrast, the secondary variable will be numeric in nature. The secondary variable’s values determine the length of each bar. These values can come from a great variety of sources. In its simplest form, the values may be a simple frequency count or proportion for how much of the data is divided into each category.

Figure 19: Bar chart (vertical)

To create a bar chart, the curve on which you apply the display needs to be set as an Y animated curve. Refer to the Y animated conversion and Y animated curve graph type sections for more information.

Bar chart is the default display when you deal with cross results. Please refer to the cross results section for more information.

Once the bar chart is created, you can access the options through the graph key. Double-click the graph key to open the window of the curve properties.

Figure 20: Curve properties of bar chart

The filling and the labels settings allow you to customize the display.

You can pick one color from a palette and choose a pattern with which the bar is filled.

You can select whether to display the values that are reached by the bars in labels. Check the box value to add them on the graph. The label's location on the graph is defined by one of the four available positions (outside, inside top, inside center, and inside bottom).

Note

A grouped bar chart extends the bar chart visualization when you have multiple curves, plotting numeric values for levels of two categorical variables instead of one. Bars are grouped by position for levels of one categorical variable, with color indicating the secondary category level within each group. Usually one of the categories corresponds to the variants from a result sets selection.

2D Line chart

Alternatively, when we have summary statistics over a categorical primary variable, we might choose a 2D line chart instead of a bar chart. A 2D line chart is essentially line segments connecting each point. This frees it up to be used with categorical levels, rather than a continuous progression. The biggest advantage a 2D line chart has over a bar chart is that values are indicated by position rather than length, so we do not necessarily need a zero-baseline. When the necessary baseline on a bar chart interferes with perception of changes or differences between bars, then a 2D line chart can be a good alternative choice.

Figure 21: 2D line chart

To create a 2D line chart, the curve on which you apply the display needs to be set as an Y animated curve. Refer to the Y animated conversion and Y animated curve graph type sections for more information.

As for bar chart, 2D line chart is another display when you deal with cross results. Please refer to the cross results section for more information.

Once the 2D line chart is created, you can access the options through the graph key. Double-click the graph key to open the window of the curve properties.

Figure 22: Curve properties of 2D line chart

The line and the symbols settings allow you to customize the display.

For the line, you can pick one color from a palette and choose between line styles and line thicknesses.

For the symbols (by default their visibility is deactivated), you can pick one color from a palette and choose between symbol shapes and symbol size. It is recommended to leave the density to 100% in order to see all the points for this kind of chart.

Note

If you uncheck the visibility of the line and keep the visibility of the symbols, you get the dot plot as another data visualization. The dot plot is like a line plot, except that there are no line segments connecting consecutive points. This addition of line segments restrains the points to their sequential progression, and so the order of points is linked with the variable of the horizontal axis.

Parallel coordinates plot

This type of visualization is used for plotting multivariate, numerical data. Parallel Coordinates Plots are ideal for comparing many variables together and seeing the relationships between them. For example, if you had to compare an array of products with the same attributes (comparing cars specs across different models).

In a Parallel Coordinates Plot, each variable is given its own axis and all the axes are placed in parallel to each other. Each axis can have a different scale, as each variable works off a different unit of measurement, or all the axes can be normalized to keep all the scales uniform. Values are plotted as a series of lines that are connected across all the axes. This means that each line is a collection of points placed on each axis, that have all been connected together.

The order the axes are arranged in can impact how the reader understands the data. One reason for this is that the relationships between adjacent variables are easier to perceive, than for non-adjacent variables. So re-ordering the axes can help in discovering patterns or correlations across variables

Figure 23: Parallel coordinates plot

To create a parallel coordinates plot, the curve on which you apply the display needs to be set as an Y animated curve. Refer to the Y animated conversion and Y animated curve graph type sections for more information.

Once the parallel coordinates plot is created, you can access the options through the graph key. Double-click the graph key to open the window of the curve properties.

Figure 24: Curve properties of parallel coordinates plot

The line and the symbols settings allow you to customize the display.

You can pick one color for the line from a palette and choose between line styles and line thicknesses.

For the symbols (by default their visibility is deactivated), you can pick one color from a palette and choose between symbol shapes and symbol thicknesses. It is recommended to leave the density at 100% in order to see all the points for this kind of chart.

Note

By default the axes of the parallel coordinates plot have the same scale. It is recommended to apply a separate axis on the Y-axes if you want to have all variables axes working independently. Please refer to the Creating multiple Y-axes section for more information.

Spider chart

Spider charts are a way of comparing multiple quantitative variables. This makes them useful for seeing which variables have similar values or if there are any outliers amongst each variable. Spider charts are also useful for seeing which variables are scoring high or low within a data set, making them ideal for displaying performance.

Each variable is provided with an axis that starts from the center. All axes are arranged radially, with equal distances between each other, while maintaining the same scale between all axes. Grid lines that connect from axis-to-axis are often used as a guide. Each variable value is plotted along its individual axis and all the variables in a data set are connected together to form a polygon.

However, there are some major flaws with spider chart:

• Having multiple polygons in one spider chart makes it hard to read, confusing and too cluttered. Especially if the polygons are filled in, as the top polygon covers all the other polygons underneath it. It is recommended not to have more than 5 data sets.

• Spider charts are not ideal for comparing values across each variable. Even with the aid of the spiderweb-like grid guide. Comparing values all on a single straight axis is much easier. You should check that all the variables have the same unit or are scaled to a common score range.

Figure 25: Spider chart

To create a spider chart, the curve on which you apply the display needs to be set as an Y animated curve. Refer to the Y animated conversion and Y animated curve graph type sections for more information.

Once the spider is created, you can access the options through the graph key. Double-click the graph key to open the window of the curve properties.

Figure 26: Curve properties of spider chart (filled polygon)

The line and the symbols settings allow you to customize the display.

For the line, you can pick one color from a palette and choose in between line styles and line thicknesses.

For the symbols (by default their visibility is deactivated), you can pick one color from a palette and choose in between symbol shapes and symbol thicknesses. It is recommended to leave the density to 100% in order to see all the points for this kind of chart.

Note

When you select a 2D spider chart with the area option, the properties window changes slightly since you can adjust the transparency of the color with which the polygon is filled.