Boxplot charts help you visualize the distribution and spread of values in your dataset. They can be especially useful for comparing values across categories.

Building a boxplot chart

To create a traditional boxplot, your data should be separated into quartiles, or quarters. Your data values are organized from smallest to largest and then that list is divided into quarters.

The box portion of the chart represents the values between the first and third quartiles, where 50% of your data is contained. The “whisker” portions of the chart, which are the lines that extend vertically from the top and bottom of the box and end at the maximum and minimum values in your data, represent the remaining 50% of values. A horizontal line through the box represents the median value. Each row in the Data table for your query becomes one box in the chart.

After your data is organized, click the ellipsis (…) in the Visualization bar and select Boxplot. Click the gear in the upper right corner of the Visualization bar to edit your visualization settings.

This example shows the values for the Lifetime Orders field based on the Traffic Source dimension:

Display has a median value of 3 lifetime orders per user, with minimum and maximum values of 1 and 14 lifetime orders per user, respectively. It also has a third quartile value of 5 lifetime orders, showing that three quarters of users from the Display traffic source have fewer than 5 lifetime purchases. Compared to other traffic sources like Email, which has a third quartile value of 10 lifetime purchases, users from the Display traffic source tend to make fewer lifetime purchases.

Building a boxplot with five measures

Traditional boxplot visualizations require at least one dimension, and the following five types of measures (which must be in this order, from left to right):

• Minimum: A measure representing the minimum data value. This can be defined in LookML as a measure of type: min.
• 25th percentile: A measure representing the 25th percentile, or the first quartile. One quarter of your data values are less than or equal to this value. This can be defined in LookML as a measure of type: percentile with the value for percentile set to 25.
• Median: A measure representing the median or midpoint of the dataset, or the second quartile. Half of your data values are less than or equal to this value. This can be defined in LookML as a measure of type: median.
• 75th percentile: A measure representing the 75th percentile, or the third quartile. Three quarters of your data values are less than or equal to this value. This can be defined in LookML as a measure of type: percentile with the value for percentile set to 75.
• Maximum: A measure representing the maximum value. This can be defined in LookML as a measure of type: max.

Building a boxplot with fewer than five measures

You can also create a boxplot using minimum, median, and maximum measures, or only minimum and maximum measures, in order from left to right. If your query includes only two or three measures, your boxplot visualization will display only the box portion of the chart, not the whiskers.

This example is based on the Traffic Source dimension and three measures representing the minimum, median, and maximum data values for the Lifetime Orders dimension:

Series menu options

Color configuration

You can define the color palette for a chart in the Color Configuration section.

Collection

A collection lets you create themed visualizations and dashboards that look good together. All of Looker's built-in color collections appear on the Color collections documentation page. Your Looker admin may also create a custom color collection for your organization.

You can choose a color collection from the Collection drop-down menu. The Palette section will update with the first categorical palette from that collection.

Palette

The first color in the palette for the color collection that you have chosen is assigned to your visualization.

Specifying a custom color

To choose a custom color for your visualization, select the Custom tab on the palette picker. You can edit your palette in several ways:

• Click on the first color in the palette to edit it.
• Click EDIT ALL at the bottom right of the menu, then add the desired color to the beginning of the comma-separated list of color values for that palette.

To change a selected color, or edit all colors in a palette at once, you can input hex strings, such as #2ca6cd, or CSS color names, such as mediumblue, into the color value box at the bottom of the picker.

To bring up a color picker to select a color, click the color wheel to the right of the color value box. The corresponding hex value for that color appears in the color value box:

If you click EDIT ALL, the color value box populates with the hex codes of the color palette you've chosen or customized. Copying and pasting this list is the best way to copy custom color palettes from one chart to another.

Reverse colors

Select Reverse colors to apply the last color in the palette to your visualization.

Style menu options

Show full field name

You can determine whether to show the view name along with the field name for each axis title and series name. When Show Full Field Name is turned off, generally only the field name shows; however, measures of type count display only the view name instead.

X menu options

Show axis name

You can toggle the appearance of the x-axis name label.

Custom axis name

You can set the name for the x-axis using any string value. This option is only available when Show Axis Name is ON.

Axis value labels

Axis Value Labels toggles the appearance of value labels on the x-axis.

Gridlines

You can toggle the appearance of gridlines extending from the x-axis. Gridlines are spaced based on the scaling of the x-axis.

Y menu options

Show axis names

You can toggle the appearance of y-axis name labels.

Custom axis names

You can define the label for the y-axis. This option is only available when Show Axis Names is enabled.

Axis value labels

You can toggle the appearance of value labels on the y-axis.

Gridlines

You can toggle the appearance of gridlines extending from the y-axis. Gridlines are spaced based on the scaling of the y-axis.

Minimum values

You can define the minimum value for each y-axis. This parameter accepts a comma-separated list of integers. If there is more than one y-axis, minimum values are assigned to each y-axis in the order of the measures in your query.

Maximum values

You can define the maximum value for each y-axis. This parameter accepts a comma-separated list of integers. If there is more than one y-axis, maximum values will be assigned to each y-axis in the order of the measures in your query.

Tick density

You can set the density of tick marks on the y-axis:

• Default: Sets ticks to the default density.

• Custom: Set ticks with a custom density. A slider bar lets you set the custom density.

Y axis format

You can specify the number format of the y-axis values, independent of the underlying dimension or measure. The parameter accepts Excel style formatting. If no formatting is specified, the value displays in the format of the underlying dimension or measure.

Excel's complete guide explains how to specify these formats in their documentation. At this time, date formatting and color formatting are not supported in Looker.

Some of the most common formatting options are: