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Related Concept Videos

Interpreting R Charts01:22

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R chart, or range chart, is a fundamental tool in statistical process control used to monitor the variability within a process. It complements the X-bar (x̄) chart by focusing on the range of the data, rather than individual values, providing a clear picture of the process dispersion over time.
An R chart plots the range of subsets of measurements collected from a process. Each point on the chart represents the range—defined as the difference between the maximum and minimum...
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In statistical process control, control charts, particularly R charts, are instrumental in monitoring process variations and identifying non-random patterns that run charts might miss. R charts track the variability within process subgroups, which is crucial when standard deviation use is impractical or unknown process variations exist.
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Updated: May 15, 2025

Global and Current Research Trends of Single-Cell Sequencing in Cancer: A Bibliometric and Visualization Study
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CirclizePlus: using ggplot2 feature to write readable R code for circular visualization.

Zheyu Zhang1, Tianze Cao1, Yuexia Huang1

  • 1School of Mathematics, Hangzhou Normal University, Hangzhou, China.

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Summary
This summary is machine-generated.

circlizePlus introduces an object-oriented approach to circular visualization in R, enhancing the popular circlize package. This new framework improves code clarity and reusability for creating complex circular plots.

Keywords:
circlizefunctional programminggeneric functionsggplot2object-oriented

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Area of Science:

  • Bioinformatics
  • Computational Biology
  • Data Visualization

Background:

  • ggplot2 is the standard for rectangular data visualization in R due to its object-oriented nature.
  • The circlize package is widely used for circular visualizations but employs procedural programming.
  • Procedural programming can lead to less clear and reusable code compared to object-oriented approaches.

Purpose of the Study:

  • To introduce circlizePlus, an object-oriented wrapper for the circlize package in R.
  • To enable ggplot2-like syntax for circular visualization.
  • To enhance code readability and reusability in circular plot generation.

Main Methods:

  • Redesigned circular visualization concepts into R S4 classes.
  • Developed additional rules for ggplot2-like drawing techniques.
  • Created circlizePlus as a wrapper for the circlize package, transforming its programming style.

Main Results:

  • circlizePlus facilitates object-oriented programming for circular plots.
  • The package reduces coding effort and improves code readability.
  • Users can leverage familiar ggplot2 concepts for circular data visualization.

Conclusions:

  • circlizePlus offers a more intuitive and efficient way to create circular visualizations in R.
  • The object-oriented design enhances the development process for circular plots.
  • This approach makes complex circular plot generation more accessible.