Position allows you to set three spatial dimensions (X, Y, Z) that form the foundation for all plot types.

Tips for Working with the Position Dimensions

Adding data features to the X, Y, and Z axes defines the position of the points in your visualizations. At least one axis must have a feature for all plot types. 2D visualizations require any two axes to have features applied, and 3D visualizations require features on all three.

The position dimensions allow all data feature types. The most useful feature type to use on these dimensions depends on the current Plot Type.

Settings

Position settings are found by clicking on the X, Y, and Z axis icons ( ) in the Mapping Panel.

• Scale: Choose between Numerical or Categorical. This shrinks or expands the length of an axis to make individual points easier to see.
• Normalization: Numerical only. (see Normalization)
• Range: Numerical only. This filters out points outside of the two selected values.

• Limits: Numerical only. This limits the size of the plot itself without filtering data values.

• Link with Range: Links the limit and range so any changes to the range also change the limit.

Color allows you to change the color of your points, bins, lines, and surfaces to represent the value of that object for an assigned feature.

Tips for Working with the Color Dimension

Color is a very important dimension and can be used in all Plot Types. This dimension will change the color of your points, bins, lines, and surfaces to represent the value of that object for the assigned feature.

The Legend Panel on the right side of the screen shows what every color represents. This dimension should almost always be used when creating four- and higher-dimensional visualizations.

Color allows all feature types and is equally useful for both numerical and smaller categorical features. When a categorical with many unique values is used on color, the 15 values that appear in the greatest number of rows will be shown, with all other rows grouped into an “Other” category. For this reason, Color may not be useful for categorical features with a very large number of unique values.

Settings

Color settings are found by clicking on the Color dimension icon ( ) in the Mapping Panel.

• Color Bin
  • Color palette selections – Select different colors to represent your bins
    

    Note: The last preset color palette option in the list is designed for users who are color blind.

  • The last 3 color palette options allow you to specify your own colors by clicking on the Edit icon ( ) next to the bin colors.
  • The color bin order can be reversed by selecting Invert Color Bin from the Color settings menu.
• Bin Distribution: Numerical only. Choose bins of an equal number of points or bins of equal range.
• Bins Slider: Numerical only. Set the number of bins displayed.
• Color Gradient: Numerical only. Gives each individual point its own unique color across a color gradient. When this option is selected, rather than data appearing in bins, a slider will appear in the Legend Panel which you can use to filter down the data. Additionally, there is an option to apply Normalization here.

Size allows you to correlate the size of points on a graph based on their values.

Tips for Working with the Size Dimension

Size is a very useful dimension for attracting the viewer’s attention toward certain points and away from other points. Size is an easy dimension for people to process and grasp, even when other dimensions are being used.

Size allows all feature types but is best with numerical features given the continuous nature of the representation. Categorical features can also be used with Size, but for the viewer to be able to differentiate between them, the feature would need to contain very few unique values. Even in this case, the viewer will not know intuitively which size represents which categorical value.

Settings

Size settings are found by clicking on the Size dimension icon ( ) in the Mapping Panel.

• Scale: Adjusts the size of all points to be either bigger or smaller.
  

  Note: This setting can be changed even when there is no feature mapped to size.

• Normalization: Numerical only. (see Normalization)
• Range: Numerical only. This filters out points outside of the two selected values.

Shape allows you to change the shape of each point to represent that point’s value or category for an assigned feature.

Tips for Working with the Shape Dimension

Shape accepts all feature types but is best used with categorical features that have six or fewer unique values. Categorical features with more than six values will have the values with the lowest number of members combined into an Other category. Numerical features will be divided into six bins, although Shape does not have as many bin customization options as the Color dimension.

Settings

Shape settings are found by clicking on the Shape dimension icon ( ) in the Mapping Panel, and allows you to choose between 3 options for the display of data points:

• Default 3D: Default for the best combination of quality and performance.
• Point Cloud: Shapeless objects for each data point.
  • When using Point Cloud, Size and Shape dimensions are not visible on the plot even when features have been mapped to those dimensions.
  • Point Cloud has the lowest performance cost and can be used to reduce machine load when visualizing very large datasets (millions of data points).
  • Point Cloud can also help show Color trends more clearly when there are many data points very close to each other on the plot.

Group By ( ) allows you to select and deselect categories.

Tips for Working with the Group By Dimension

The Group By dimension only accepts categorical features. It is most useful for categorical features with many unique categories – smaller categorical features are better used on other dimensions such as Color or Shape.

After adding a categorical feature to this dimension, the Group By tab on the Legend Panel will populate with every category inside the feature, which makes it easy to show and hide specific categories. The Group By dimension is also used by certain plot types, such as Line Plots, to build a line for each category.

Playback allows you to show points changing in time.

Tips for Working with the Playback Dimension

Playback is a useful dimension for both selecting different categories inside a feature and also for animating a data set. It accepts all data types and is a useful way to show points changing in time. 

Tip: Adding a Date feature creates a dynamic visualization that shows how the structure of the visualization changes in response to time.

After applying a feature to the Playback dimension, the Playback interface can be opened by clicking the Playback dimension icon ( ) in the Mapping Panel.

The interface contains these controls:

• Bin dropdown: Choose from a list of individual categories in the feature.
• Left/Right arrows: Browse between individual categories in the feature.
• Play/Pause: Start automatically moving between categories one by one OR pause animation if already in progress.
• Play slider: Scrub between different categories by dragging the slider left and right.
• Repeat button: Switch between looping and stopping after reaching the last category.
• Stop: Reset play mode and show all categories.
• Speed dropdown: Change play speed.
• Aggregate: Choose whether or not to aggregate points while playing.

Transparency allows you to emphasize or de-emphasize points based on numerical or categorical features.

Tips for Working with the Transparency Dimension

Transparency is a useful dimension for attracting a viewer’s attention toward certain points and away from others.

Transparency allows all feature types and is impactful with numerical features given the continuous nature of the representation. Transparency is also impactful with binary categorical features (categorical with two categories). This dimension is a great way to highlight a small category in a crowded visualization, such as outliers.

While transparency can also be used with larger categorical features, it is not recommended since it is difficult to differentiate between multiple levels of transparency.

Settings

Transparency settings are found by clicking on the Transparency dimension icon ( ) in the Mapping Panel.

• Scale: Adjusts the size of all points to be either bigger or smaller. This setting can be changed even when there is no feature mapped to transparency.
  • By default, all points are slightly transparent — this is to allow the viewer to see when multiple points are overlapping. Points can be made entirely opaque by turning up this setting, which can be useful to make the visualization more vibrant for presentations.
• Normalization: Numerical only. (see Normalization)
• Range: Numerical only. This filters out points outside of the two selected values.

Halo allows you to highlight outliers or other categories of interest by circling points in that category with rings.

Tips for Working with the Halo Dimension

Halos are useful to highlight a category that has very few members to make it even more obvious on the plot. This can be selected using the dropdown menu from the Mapping Panel.

When used on a category with a large number of points, the visualization can become too crowded. In these cases, the dimensions Transparency or Pulsation may be better.

You can also focus on just the haloed points by right-clicking on the plot and selecting “Show Only Haloed” from the Contextual Menu.

Settings

Halo settings are found by clicking on the Halo dimension icon ( ) in the Mapping Panel.

• Scale: Makes the halos larger or smaller.
• On/Off: Turns the visibility of halos on and off without removing the mapped feature.

Pulsation ( ) allows you to highlight outliers or other categories of interest by making points in that category grow larger and smaller repeatedly.

Tips for Working with the Pulsation Dimension

Pulsation is useful to highlight a category that has very few members. This can be selected using the Pulsation dropdown in Mapping Panel. Depending on the distribution and clustering of points, the pulsing points may not be visible – in these cases, the dimensions Transparency or Halo may be better.

Arrow allows you to draw attention to the value of an assigned feature by drawing an arrow pointing out of the point.

Tips for Working with the Arrow Dimension

Arrows that are pointing up on the Y axis indicate a high value, while arrows that are pointing down indicate a low value. Because the arrows can crowd a visualization and potentially obscure other dimensions, use of the arrow dimension is situational.

Arrow allows all feature types but is best with numerical features given the continuous nature of the representation. While arrow can also be used with categorical features, it is generally not recommended since it is difficult for people to differentiate between multiple angles.

Settings

Arrow settings are found by clicking on the Arrow dimension icon ( ) in the Mapping Panel.

Scale: Adjusts the size of all arrows to be either bigger or smaller.

On/Off: Show and hide the arrows without removing the mapped feature.