Data visualization with KNIME Analytics Platform - Cheat sheet - v5.9

Oct 12, 2026

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Cheat sheet summarizing key data-visualization options in KNIME Analytics Platform. Covers general charts such as pie, bar, line, heatmap, stacked area, sunburst, radar, and parallel coordinates, plus statistical views like histogram, box plot, scatter plot matrix, violin plots, network and statistics viewers. Includes geospatial tools (choropleth maps, Kepler.gl geoview, spatial heatmaps) and model evaluation components such as ROC curve, confusion-matrix based inspectors, and lift/scorer views. Also lists time series and other UI table/tile views and composite component concepts.

LanguageEnglish

CategoryPresentations

FormatPDF

Pages2

Reading Time1 min

AccessFree to download

General visualizations

Multivariate and Univariate charts

Geospatial visualizations

Time series visualizations

Other visualizations

Statistical visualizations

Scatter, histogram, and distributions

Networks and graph views

Components and composite views

Cheat sheet: Data visualization with KNIME Analytics Platform |
General visualizations | | | | | --- | --- | --- | --- | |
Multivariate Univariate | Pie Chart

Bar Chart

Line Plot

Heatmap

Stacked Area Chart

Sunburst Chart

Hierarchical Cluster View

Radar Plot Appender

Parallel Coordinates Plot

| Pie Chart: Visualizes categorical data using circular charts with slices representing categories and sizes indicating proportions.
Bar Chart: Generates graphical representations of categorical data using rectangular bars, providing insights into category frequency or distribution.
Line Plot: Creates line charts to visualize trends, patterns, or correlations between two numeric variables.
Heatmap: Visualizes tabular data using a color-coded matrix, revealing patterns and correlations.
Stacked Area Chart: Displays cumulative contributions of categories or variables, illustrating trends and relative proportions.
Density Plot: Visualizes numeric variable distributions as smooth curves, facilitating comparisons.
Sunburst Chart: Creates hierarchical visualization representing structure and composition of categorical or hierarchical data.
Hierarchical Cluster View: Visualizes hierarchical clustering results.
Radar Plot Appender: Appends radar plot attributes to data based on user-defined rules or mappings. Enables multivariate visualization and comparison.
Parallel Coordinates Plot: Visualizes high-dimensional data with polylines connecting points along different axes. Useful for multivariate analysis and pattern identification. | | | | | | | | | | | | | | | |

| Geospatial visualizations Geospatial View: Visualizes and explores geospatial data on interactive maps. Choropleth Map Choropleth Map: Generates thematic maps by coloring regions based on a specific variable. [Kepler.gl](Kepler.gl) Geoview [Kepler.gl](Kepler.gl) Geoview: Visualizes an interactive mapping interface for exploring and visualizing geographic data in various formats. Spatial Heatmap: Visualizes geospatial data on an interactive heatmap, using a weight column to represent the intensity at each element. This node also allows to customize the color, adjust the intensity, and change the base map. Model scoring ROC Curve ROC Curve: Evaluates and visualizes performance of binary classification models using the Receiver Operating Characteristic (ROC) curve. Binary Classification Inspector: Outputs a view made of four different charts to compare, optimize, and select predictions of different binary classifiers. The view consists of the model's Statistics bar chart, the model's ROC Curve, and each model's Confusion Matrix and Classification Distribution. Lift Chart (JavaScript): Creates a Lift chart that graphically represents the performance of a model compared against a random guess. The higher the difference between the lift line and the base line, the better the model's performance. Scorer (Javascript): Compares two columns by their attribute value pairs and outputs a confusion matrix indicating a classification model's performance. | | | | | | | | | | | | | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | | Statistical visualizations | | | | | | | | | | | | | Scatter Plot:
Visualizes relationships between two numeric variables through points on a plane, identifying correlations, clusters, and patterns.
Violin Plot (Plotly):
Creates violin plots to visualize the distribution of multiple numeric variables using the Plotly library. | | | Histogram: Displays the frequency distribution of a numeric variable, identifying patterns and anomalies.
Box Plot: Displays the distribution of a numeric variable with quartiles, median, and outliers. Useful for comparing distributions and identifying outliers. | | | Scatter Plot Matrix:
Generat

FAQs

Which general charts are covered for categorical, trend, and distribution visualization in KNIME?

The cheat sheet includes pie chart, bar chart, line plot, heatmap, stacked area chart, and sunburst chart, as well as radar plot and hierarchical cluster view for structured comparisons.

What geospatial visualization options are listed?

It lists geospatial view capabilities such as choropleth maps, Kepler.gl Geoview, and spatial heatmap, including how intensity can be mapped via a weight column.

How does the document support evaluating binary classification models visually?

It provides nodes such as ROC curve, Binary Classification Inspector, and Lift chart, plus a scorer that outputs a confusion-matrix-style comparison for classification performance.

Oct 12, 2026

Upload by Terk