The Data Visualisation Catalogue Blog
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The Data Visualization Catalogue, helping you find the right data visualization method for your data. The Data Visualization Catalogue is a project developed by Severino Ribecca to create a library of different information visualization types.
The Data Visualisation Catalogue Blog
1M ago
A diagram that visualises a hierarchical tree structure using a semi-circle format.
The post Chart Snapshot: Genealogy Fan Charts appeared first on The Data Visualisation Catalogue Blog ..read more
The Data Visualisation Catalogue Blog
1M ago
A visualisation tool used in time series analysis to display forecasts and associated uncertainties.
The post Chart Snapshot: Fan Charts appeared first on The Data Visualisation Catalogue Blog ..read more
The Data Visualisation Catalogue Blog
1M ago
A chart that attempts to answer ‘what is this document about?’ through visualising the text-based content.
The post Chart Snapshot: DocuBurst appeared first on The Data Visualisation Catalogue Blog ..read more
The Data Visualisation Catalogue Blog
2M ago
A chart with two Dendrograms displayed side-by-side to show the concordance between two sets of hierarchical clustering.
The post Chart Snapshot: Tanglegrams appeared first on The Data Visualisation Catalogue Blog ..read more
The Data Visualisation Catalogue Blog
3M ago
A variation of a Tree Diagram that illustrates the arrangement of clusters formed by hierarchical clustering.
The post Chart Snapshot: Dendrograms appeared first on The Data Visualisation Catalogue Blog ..read more
The Data Visualisation Catalogue Blog
4M ago
A Graph used to visualise and analyse seasonal patterns within time series data.
The post Chart Snapshot: Cycle Plots appeared first on The Data Visualisation Catalogue Blog ..read more
The Data Visualisation Catalogue Blog
4M ago
Also known as a Jittered Strip Plot.
A Jitter Plot is a Strip Plot / Dot Distribution Plot variation that provides a better view of any overlapping data points by adding a small amount of random shifting to the position of plotted dots. By slightly randomising the positions of data points, Jitter Plots help reduce the obfuscation of data points caused by overlapping, allowing for a clearer view of the data distribution.
The function of a Jitter Plot is to visualise the data distribution across multiple categories by plotting dots along a value axis. Each dot can represent a single data po ..read more
The Data Visualisation Catalogue Blog
5M ago
Also known as a Strip Plot.
A Counts Plot is a variation of the Strip Plot / Dot Distribution Plot that plots circles of varying area size to help provide a better view of any overlapping data points. Like on a Dot Distribution Plot, the function of a Counts Plot is to visualise the data distribution across multiple categories for comparison.
Each circle on a Counts Plot can represent a single data point or a count with the area size being proportional to the count or number (or the aggregate of values) of data points that would overlap at that position on the axis. Alternatively, the area siz ..read more
The Data Visualisation Catalogue Blog
5M ago
As known as a Strip Plot, Dot Strip Plot.
A Dot Distribution Plot visualises the data distribution across multiple categories by plotting dots along an axis. Each dot can represent a single data point or a count.
There are two variations of Dot Distribution Plot: first, the kind that plots a series of dots to compare the distributions between various categories across a single dimension. The other type is an “Instance Chart” that plots dots along a time axis to show the occurrence of multiple categories over time.
Dot Distribution Plots can be displayed either horizontally or vertically. Typic ..read more
The Data Visualisation Catalogue Blog
5M ago
A Dot Plot (also known as a Wilkinson Dot Plot) is a visualisation that uses dots to plot data points along a value scale. Unlike on a Cleveland Dot Plot, which only plots singular dots positioned along a value axis, Wilkinson Dot Plots plot multiple dots for each category, interval, or time point in the dataset.
In other words, the number of dots plotted in this type of Dot Plot is proportional to the quantities or frequencies in the data. Each dot can represent a unit (a single count) or any number of units (e.g. each dot represents a count of 10).
Dot Plots can be handy for displaying simpl ..read more