Jove
Visualize
Contact Us
JoVE
x logofacebook logolinkedin logoyoutube logo
ABOUT JoVE
OverviewLeadershipBlogJoVE Help Center
AUTHORS
Publishing ProcessEditorial BoardScope & PoliciesPeer ReviewFAQSubmit
LIBRARIANS
TestimonialsSubscriptionsAccessResourcesLibrary Advisory BoardFAQ
RESEARCH
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchive
EDUCATION
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualFaculty Resource CenterFaculty Site
Terms & Conditions of Use
Privacy Policy
Policies

Related Concept Videos

Interpreting R Charts01:22

Interpreting R Charts

497
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...
497
Residual Plots01:07

Residual Plots

4.7K
A residual plot is a statistical representation of data used to analyze correlation and regression results. It helps verify the requirements for drawing specific conclusions about correlation and regression. To obtain the residual plot, first, the residual for each data value is calculated, which is simply the vertical distance between the observed and the predicted value obtained from the regression equation.
When the residual values are plotted against the variable x, it is called a residual...
4.7K
Scatter Plot01:15

Scatter Plot

8.5K
The most common and easiest way to display the relationship between two variables, x and y, is a scatter plot. A scatter plot shows the direction of a relationship between the variables. A clear direction happens when there is either:
8.5K
Multiple Bar Graph01:07

Multiple Bar Graph

6.3K
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...
6.3K

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by
Same author

Regulatory Guidance for the Return of Raw Genomic Data to Research Participants: A Qualitative Interview Study.

The Journal of law, medicine & ethics : a journal of the American Society of Law, Medicine & Ethics·2026
Same author

Comprehensive Multiplatform Tyrosine Kinase Profiling Reveals Novel Actionable FGFR Aberrations across Sarcomas Affecting the Young.

Molecular cancer therapeutics·2026
Same author

Comprehensive multi-platform tyrosine kinase profiling reveals novel actionable FGFR aberrations across sarcomas affecting the young.

Molecular cancer therapeutics·2026
Same author

Correction: Targeted Therapy of TERT-Rearranged Neuroblastoma with BET Bromodomain Inhibitor and Proteasome Inhibitor Combination Therapy.

Clinical cancer research : an official journal of the American Association for Cancer Research·2026
Same author

Genomic Therapy Matching in Rare and Refractory Cancers.

JAMA oncology·2026
Same author

Integrated Germline and Somatic Molecular Profiling to Detect Cancer Predisposition Has a High Clinical Impact in Poor-Prognosis Pediatric Cancer.

Clinical cancer research : an official journal of the American Association for Cancer Research·2026
Same journal

The Outcome of Cardiac Hydatid Surgery in The Iraqi Center of Heart Diseases.

F1000Research·2026
Same journal

Perception of body donation among the Phase-1 medical students, a questionnaire-based study.

F1000Research·2026
Same journal

Exploring Infertility in Saudi Arabia: Qualitative Insights into IVF Treatment Services and Policy Recommendations.

F1000Research·2026
Same journal

Cyber Military Operations under International Humanitarian Law: Interpreting the Concept of "Attack" and Challenges in Protecting Civilians.

F1000Research·2026
Same journal

Sentiment Analysis of Acceptance TVET Online Courses on the Skill Academy App from Google Play: Leveraging Text Mining with Comparison Machine Learning Model.

F1000Research·2026
Same journal

Emotional intelligence: An important skill to learn now more than ever.

F1000Research·2026
See all related articles

Related Experiment Video

Updated: Apr 24, 2026

ExCYT: A Graphical User Interface for Streamlining Analysis of High-Dimensional Cytometry Data
05:12

ExCYT: A Graphical User Interface for Streamlining Analysis of High-Dimensional Cytometry Data

Published on: January 16, 2019

11.2K

ggEDA: Visualisations for exploratory data analysis using tiled one-dimensional graphics and parallel coordinate

Sam El-Kamand1,2, Julian M W Quinn1, Mark J Cowley1,2

  • 1Computational Biology, Children's Cancer Institute Australia, Sydney, New South Wales, 2052, Australia.

F1000Research
|April 23, 2026
PubMed
Summary
This summary is machine-generated.

The ggEDA R package and interactiveEDA web app simplify multidimensional data exploration. These tools reduce coding and manual review for uncovering complex data relationships and quality issues.

Keywords:
Rexploratory data analysismultidimensionalparallel coordinate plotsvisualisation

More Related Videos

Facilitating the Analysis of Immunological Data with Visual Analytic Techniques
10:58

Facilitating the Analysis of Immunological Data with Visual Analytic Techniques

Published on: January 2, 2011

9.5K
Author Spotlight: Introducing the Tile/SED/Array Interface for Rapid Field of View Positioning in Tissue Imaging
06:15

Author Spotlight: Introducing the Tile/SED/Array Interface for Rapid Field of View Positioning in Tissue Imaging

Published on: September 15, 2023

948

Related Experiment Videos

Last Updated: Apr 24, 2026

ExCYT: A Graphical User Interface for Streamlining Analysis of High-Dimensional Cytometry Data
05:12

ExCYT: A Graphical User Interface for Streamlining Analysis of High-Dimensional Cytometry Data

Published on: January 16, 2019

11.2K
Facilitating the Analysis of Immunological Data with Visual Analytic Techniques
10:58

Facilitating the Analysis of Immunological Data with Visual Analytic Techniques

Published on: January 2, 2011

9.5K
Author Spotlight: Introducing the Tile/SED/Array Interface for Rapid Field of View Positioning in Tissue Imaging
06:15

Author Spotlight: Introducing the Tile/SED/Array Interface for Rapid Field of View Positioning in Tissue Imaging

Published on: September 15, 2023

948

Area of Science:

  • Data Science
  • Bioinformatics
  • Computational Biology

Background:

  • Exploratory Data Analysis (EDA) is crucial for identifying data quality issues and generating hypotheses.
  • Analyzing multidimensional relationships in datasets often requires significant coding and statistical expertise.
  • Current methods for exploring complex data patterns can be time-consuming and require manual inspection.

Purpose of the Study:

  • To introduce the ggEDA R package for streamlining multidimensional data exploration.
  • To provide accessible visualization tools for uncovering multi-feature relationships and data quality issues.
  • To develop an interactive web application (interactiveEDA) for non-programmers to perform data exploration.

Main Methods:

  • Development of the ggEDA R package featuring interactive parallel coordinate plots (PCPs) for large, quantitative datasets.
  • Introduction of tiled one-dimensional plots in ggEDA for revealing missingness and categorical relationships in smaller datasets.
  • Creation of the interactiveEDA web app to enable non-programmers to explore data patterns.

Main Results:

  • ggEDA effectively reduces the coding effort and time needed to detect multi-feature relationships.
  • PCPs in ggEDA are suitable for large datasets with quantitative features.
  • Tiled plots in ggEDA excel at visualizing missing data and categorical associations in smaller datasets.
  • interactiveEDA provides a user-friendly interface for interactive data pattern interpretation.

Conclusions:

  • The ggEDA package and interactiveEDA web app significantly simplify and accelerate multidimensional data exploration.
  • These tools democratize data analysis by reducing the need for extensive programming or statistical modeling.
  • ggEDA and interactiveEDA facilitate the discovery of data quality issues and underlying patterns, aiding hypothesis generation.