0 ×

**KNIME Base Nodes** version **4.3.0.v202012031743** by **KNIME AG, Zurich, Switzerland**

A box plot displays robust statistical parameters: minimum, lower quartile, median, upper quartile, and maximum. These parameters are called robust, since they are not sensitive to extreme outliers.

The conditional box plot partitions the data of a numeric column into classes according to another nominal column and creates a box plot for each of the classes.

A box plot for one numerical attribute is constructed in the following way: The box itself goes from the lower quartile (Q1) to the upper quartile (Q3). The median is drawn as a horizontal bar inside the box. The distance between Q1 and Q3 is called the interquartile range (IQR). Above and below the box are the so-called whiskers. They are drawn at the minimum and the maximum value as horizontal bars and are connected with the box by a dotted line. The whiskers never exceed 1.5 * IQR. This means if there are some data points which exceed either Q1 - (1.5 * IQR) or Q3 + (1.5 * IQR) than the whiskers are drawn at the first value in these ranges and the data points are drawn separately as outliers. For the outliers the distinction between mild and extreme outliers is made. As mild outliers are those data points p considered for which holds: p < Q1 - (1.5 * IQR) AND p > Q1 - (3 * IQR) or p > Q3 + (1.5 * IQR) AND p < Q3 + (3 * IQR). In other words mild outliers are those data points which lay between 1.5 * IRQ and 3 * IRQ. Extreme outliers are those data points p for which holds: p < Q1 - (3 * IQR) or p > Q3 + (3 * IQR). Thus, three times the box width (IQR) marks the boundary between "mild" and "extreme" outliers. Mild outliers are painted as dots, while extreme outliers are displayed as crosses. In order to identify the outliers they can be selected and hilited. This provides a quick overview over extreme characteristics of a dataset.

If the available space is too small to display, all labels (smallest, Q1, median, Q3, largest) are not displayed and the missing information is provided as a tooltip.

The outlier points may be selected by either dragging a rectangle with the mouse over them or by clicking on them. Hold control pressed for multiple selections. The selected outliers may be hilited by either right-click to get the context menu or via the hilite menu in the menu bar. Important: If a row contains outliers in several columns all outliers of that row will be selected and hilited at once, since selection and hiliting are based on data points (rows)!

Move the mouse over the bars of the box to get the exact values for the displayed parameters or over the outliers to get information about the value and the row ID.

Default Settings:

- Mouse Mode: choose "Selection" to select outlier points or "Zooming" to zoom in. If you have zoomed in you may choose "Moving" to navigate in the zoomed display.
- "Fit to screen" fits the display again to the available space.
- "Background color lets you choose the background color of the display.

Column Selection: Choose the classes you want to inspect.

Appearance: Check "Normalize", if all columns should use the available height, uncheck it, if you want to map the boxes to one single y coordinate.

- Nominal Column
- The nominal column used for partitioning
- Numeric Column
- The numeric column which has to be partitioned
- Show missing values
- If selected, missing values in nominal values will form their own class

- DataTable with statistics for each class: minimum, smallest value (non-outlier), Q1, median, Q3, largest value (non-outlier), and maximum.

- Conditional Box Plot
- The box plot displaying the data distribution.

- File Reader (14 %) Streamable
- Numeric Binner (11 %) Streamable
- Box Plot (local) (8 %)
- GroupBy (6 %)
- Color Manager (6 %)
- Show all 240 recommendations

- Color Manager (22 %)
- Scatter Plot (local) (7 %)
- Scatter Matrix (local) (7 %)
- Conditional Box Plot (local) (5 %)
- Statistics (5 %)
- Show all 193 recommendations

- dataAnalysisQs (KNIME Hub)
- hausyann3_Pdaf-6_WT_BG (NodePit Space)
- NucleiClassificationTraining_OMICS2020 (KNIME Hub)
- Official Project 2 (KNIME Hub)

To use this node in KNIME, install KNIME Base nodes from the following update site:

KNIME 4.3

A zipped version of the software site can be downloaded here.

You don't know what to do with this link? Read our NodePit Product and Node Installation Guide that explains you in detail how to install nodes to your KNIME Analytics Platform.

You want to see the source code for this node? Click the following button and we’ll use our super-powers to find it for you.

Do you have feedback, questions, comments about NodePit, want to support this platform, or want your own nodes or workflows listed here as well? Do you think, the search results could be improved or something is missing? Then please get in touch! Alternatively, you can send us an email to mail@nodepit.com, follow @NodePit on Twitter, or chat on Gitter!

Please note that this is only about NodePit. We do not provide general support for KNIME — please use the KNIME forums instead.