Spiralize: an R package for visualizing data on spirals
Spiral layout has two major advantages for data visualization. First, it is able to visualize data with long axes, which greatly improves the resolution of visualization. Second, it is efficient for time series data to reveal periodic patterns. Here, we present the R package spiralize that provides...
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| Main Authors: | , |
|---|---|
| Format: | Article (Journal) |
| Language: | English |
| Published: |
1 March 2022
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| In: |
Bioinformatics
Year: 2022, Volume: 38, Issue: 5, Pages: 1434-1436 |
| ISSN: | 1367-4811 |
| DOI: | 10.1093/bioinformatics/btab778 |
| Online Access: | Verlag, kostenfrei, Volltext: https://doi.org/10.1093/bioinformatics/btab778 Verlag, kostenfrei, Volltext: https://www.webofscience.com/api/gateway?GWVersion=2&SrcAuth=DOISource&SrcApp=WOS&KeyAID=10.1093%2Fbioinformatics%2Fbtab778&DestApp=DOI&SrcAppSID=EUW1ED0E8AXtaNVtuJoqQvzdxy8jW&SrcJTitle=BIOINFORMATICS&DestDOIRegistrantName=Oxford+University+Press 
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| Author Notes: | Zuguang Gu and Daniel Huebschmann |
MARC
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| 520 | |a Spiral layout has two major advantages for data visualization. First, it is able to visualize data with long axes, which greatly improves the resolution of visualization. Second, it is efficient for time series data to reveal periodic patterns. Here, we present the R package spiralize that provides a general solution for visualizing data on spirals. spiralize implements numerous graphics functions so that self-defined high-level graphics can be easily implemented by users. The flexibility and power of spiralize are demonstrated by five examples from real-world datasets. | ||
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