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Related Concept Videos

Interpreting R Charts01:22

Interpreting R Charts

67
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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The R Chart01:02

The R Chart

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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.
R charts are pivotal for pinpointing shifts in process variability. Stability is indicated when all data points remain within the defined upper and lower...
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Time-Series Graph00:54

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A time-series graph is a line graph with repeated measurements taken at successive intervals of time. It is also called a time series chart. To construct a time-series graph, one must look at both pieces of a paired data set. The horizontal axis is used to plot the time increments, and the vertical axis is used to plot the values of the variable that one is measuring. By using the axes in this way, each point on the graph will correspond to time and a measured quantity. The points on the graph...
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Introduction to R01:11

Introduction to R

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R is a powerful software environment for statistical computing and graphics. Originating as an implementation of the S language, developed at Bell Laboratories, R has evolved into a robust, open-source statistical software favored by statisticians and data scientists worldwide. Its comprehensive suite includes data manipulation, calculation, and graphical display capabilities, making it versatile for data analysis and visualization. Its programming language is at the core of R's...
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Statistical Analysis: Overview01:11

Statistical Analysis: Overview

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When we take repeated measurements on the same or replicated samples, we will observe inconsistencies in the magnitude. These inconsistencies are called errors. To categorize and characterize these results and their errors, the researcher can use statistical analysis to determine the quality of the measurements and/or suitability of the methods.
One of the most commonly used statistical quantifiers is the mean, which is the ratio between the sum of the numerical values of all results and the...
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Multiple Bar Graph01:07

Multiple Bar Graph

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As the name suggests, a multiple bar graph is the same as a bar graph but has multiple bars to depict relationships between different data values. One can include as many parameters as possible. However, each parameter must have the same unit of measurement.
Each bar or column in the multiple bar graph represents a data value. These graphs are used primarily in interrelating two or more sets of data. The categories of different kinds of data are listed along the horizontal or x-axis, whereas...
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Global and Current Research Trends of Single-Cell Sequencing in Cancer: A Bibliometric and Visualization Study
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Simple but powerful interactive data analysis in R with R/LinekdCharts.

Svetlana Ovchinnikova1, Simon Anders2

  • 1Center for Molecular Biology and BioQuant Center of the University of Heidelberg, Heidelberg, Germany.

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|February 5, 2024
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Summary

R/LinkedCharts simplifies exploratory data analysis by linking overview and detail visualizations. This R package allows researchers to easily create interactive, publication-quality data visualizations with minimal code.

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Area of Science:

  • Bioinformatics
  • Data Visualization
  • Computational Biology

Background:

  • Exploratory data analysis (EDA) is essential for data-rich assays.
  • Current EDA methods often require complex, iterative visualization adjustments.
  • Bridging overview and detailed data views can be challenging.

Purpose of the Study:

  • To introduce R/LinkedCharts, a novel R package for streamlined EDA.
  • To facilitate the creation of linked, interactive visualizations.
  • To simplify the generation of publication-quality data exploration tools.

Main Methods:

  • Development of the R/LinkedCharts framework in R.
  • Implementation of a system for linking multiple charts.
  • Focus on minimizing coding effort for complex visualizations.

Main Results:

  • R/LinkedCharts enables the creation of linked charts with minimal code.
  • The framework supports interactive data exploration, linking overview and detail views.
  • Generated visualizations are suitable for polishing to publication quality.

Conclusions:

  • R/LinkedCharts significantly simplifies complex visualization tasks in EDA.
  • The package enhances the efficiency of exploring data-rich assay results.
  • It provides a powerful tool for generating interactive, high-quality scientific graphics.