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Related Concept Videos

Interpreting R Charts01:22

Interpreting R Charts

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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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The R Chart01:02

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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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A bar graph is also called a bar chart and consists of bars that are separated from each other. It either uses horizontal or vertical bars to show comparisons among categories. The bars can be rectangles, or they can be rectangular boxes (used in three-dimensional plots). One axis of the graph represents the specific categories being compared, and the other axis shows a discrete value. In this graph, the length of the bar for each category is proportional to the number or percent of individuals...
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Modified Boxplots00:57

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A standard box and whisker plot informs us about the spread of the data in a given sample. One can identify the minimum value, maximum value, first quartile value, second quartile or median value, and third quartile.
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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.
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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.
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Thinking Outside the Box: Developing Dynamic Data Visualizations for Psychology with Shiny.

David A Ellis1, Hannah L Merdian2

  • 1Department of Psychology, Lancaster University Lancaster, UK.

Frontiers in Psychology
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Summary
This summary is machine-generated.

Psychologists can now create dynamic data visualizations using the R Shiny web application framework, enhancing the communication of research findings across various fields.

Keywords:
RShinyknowledge-exchangeresearch methodsstatisticsvisualization

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Area of Science:

  • Psychology
  • Data Science
  • Human-Computer Interaction

Background:

  • Data visualization is crucial in psychology for communicating complex findings.
  • Traditional static visualizations are often insufficient for modern, data-rich psychological research.
  • Limited adoption of dynamic visualizations in psychology despite technological advancements.

Purpose of the Study:

  • To introduce an accessible method for creating interactive data visualizations in psychology.
  • To demonstrate the utility of the R Shiny framework for psychological research.
  • To encourage the use of dynamic visualizations to improve data communication.

Main Methods:

  • Development of interactive visualizations using the R Shiny web application framework.
  • Utilizing a simple framework that runs within the R statistical platform.
  • No prior knowledge of HTML, CSS, or Java is required.

Main Results:

  • Shiny enables researchers to quickly produce interactive data visualizations.
  • These visualizations can supplement and support current and future publications.
  • The framework facilitates the creation of dynamic representations for psychological data.

Conclusions:

  • The R Shiny framework offers a powerful and accessible tool for psychological data visualization.
  • Interactive visualizations enhance the discoverability, understanding, and presentation of research results.
  • This approach benefits researchers, students, practitioners, and the public by improving data accessibility.