Hub
  • Software
  • Blog
  • Forum
  • Events
  • Documentation
  • About KNIME
  • KNIME Hub
  • Nodes
  • Conditional Box Plot
NodeNode / Visualizer

Conditional Box Plot

Views JavaScript
Drag & drop
Like
Copy short link

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.

The node supports custom CSS styling. You can simply put CSS rules into a single string and set it as a flow variable 'customCSS' in the node configuration dialog. You will find the list of available classes and their description on our documentation page .

Node details

Input ports
  1. Type: Table
    Input Data
    Data table containing the categories and values to be plotted in a box plot.
  2. Type: Table
    Color Data
    Data table containing the category names with colors applied. (optional)
Output ports
  1. Type: Image
    Box Plot image
    SVG image of the box plot.

Extension

The Conditional Box Plot node is part of this extension:

  1. Go to item

Related workflows & nodes

  1. Go to item
    Assignment 2, Question 8
    petersonsm99 > ITSCM 774 Team 1 > Assignment 2 > Assignment 2, Question 8
  2. Go to item
    Numeric Outliers simple example
    IQR Numeric Outliers Outlier
    This node shows how KNIME can identify outliers using the standard approach with Interqua…
    scottf > Public > ForumWorkflows > 2020 > 01 > Numeric_Outliers_Simple_Example
  3. Go to item
    00a-KNIME_Introduction
    julianu > KNIME_proteomics_introduction > 00a-KNIME_Introduction
  4. Go to item
    Box Plot Examples
    Univariate Visualization Visualize
    +10
    What is a box plot? A box plot displays robust statistical parameters: minimum, lower qua…
    knime > Examples > 03_Visualization > 02_JavaScript > 05_Example_for_JS_Box_Plot
  5. Go to item
    Segmenting Consumers
    adm > Public > Workflows > Data Analytics Made Easy > Chapter 5 > Segmenting Consumers
  6. Go to item
    GroupAssignment2_Team3_Final
    ramakriss27 > ITSCM-774 - Team3 > GroupAssignment2_Team3_Final
  7. Go to item
    Multiple Conditional Box Plots
    stelfrich > Public > Multiple Conditional Box Plots
  8. Go to item
    Statistics, data distribution and data normalization
    Univariate Visualization Visualize
    +17
    This workflow illustrates how to learn statistical indicators about our data, its distrib…
    barbora > Courses > L4-DV Codeless Data Exploration and Visualization_10.2020 > L4-DV Codeless Data Exploration and Visualization - Demos > Session3_demo > Session_03_charts_demo_boxplot
  9. Go to item
    Statistics, data distribution and data normalization
    Univariate Visualization Visualize
    +17
    This workflow illustrates how to learn statistical indicators about our data, its distrib…
    barbora > Courses > L4-DV Codeless Data Exploration and Visualization_12.2020 > L4-DV Codeless Data Exploration and Visualization - Demos > Session_solutions > Session_02b_demo_distribution
  10. Go to item
    Statistics, data distribution and data normalization
    Univariate Visualization Visualize
    +17
    This workflow illustrates how to learn statistical indicators about our data, its distrib…
    barbora > Courses > L4-DV Codeless Data Exploration and Visualization_06.2021 > L4-DV Codeless Data Exploration and Visualization - Demos > Session_solutions > Session_02b_demo_distribution

No known nodes available

KNIME
Open for Innovation

KNIME AG
Hardturmstrasse 66
8005 Zurich, Switzerland
  • Software
  • Getting started
  • Documentation
  • E-Learning course
  • Solutions
  • KNIME Hub
  • KNIME Forum
  • Blog
  • Events
  • Partner
  • Developers
  • KNIME Home
  • KNIME Open Source Story
  • Careers
  • Contact us
Download KNIME Analytics Platform Read more on KNIME Server
© 2022 KNIME AG. All rights reserved.
  • Trademarks
  • Imprint
  • Privacy
  • Terms & Conditions
  • Credits