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

Introduction to R01:11

Introduction to R

265
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...
265
Interpreting R Charts01:22

Interpreting R Charts

63
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...
63
Steps in Outbreak Investigation01:18

Steps in Outbreak Investigation

125
In the ever-evolving field of public health, statistical analysis serves as a cornerstone for understanding and managing disease outbreaks. By leveraging various statistical tools, health professionals can predict potential outbreaks, analyze ongoing situations, and devise effective responses to mitigate impact. For that to happen, there are a few possible stages of the analysis:
125
Statistical Methods for Analyzing Epidemiological Data01:25

Statistical Methods for Analyzing Epidemiological Data

364
Epidemiological data primarily involves information on specific populations' occurrence, distribution, and determinants of health and diseases. This data is crucial for understanding disease patterns and impacts, aiding public health decision-making and disease prevention strategies. The analysis of epidemiological data employs various statistical methods to interpret health-related data effectively. Here are some commonly used methods:
364
The R Chart01:02

The R Chart

80
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...
80
Statistical Software for Data Analysis and Clinical Trials01:12

Statistical Software for Data Analysis and Clinical Trials

546
Statistical software is pivotal in data analysis and clinical trials by providing tools to analyze data, draw conclusions, and make predictions. These software packages range from simple data management applications to complex analytical platforms, supporting various statistical tests, models, and simulation techniques. Their significance lies in their ability to handle vast amounts of data with precision and efficiency, enabling researchers to validate hypotheses, identify trends, and make...
546

You might also read

Related Articles

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

Sort by
Same author

Awareness and interest in osteopathic manipulative treatment in allopathic medical students.

Journal of osteopathic medicine·2023
Same author

Mental Health Screening in Pediatric Primary Care: Factors Associated With Screening Completion and Elevated Scores.

Clinical pediatrics·2022
Same author

Exact-corrected confidence interval for risk difference in noninferiority binomial trials.

Biometrics·2022
Same author

Targeting WEE1/AKT Restores p53-Dependent Natural Killer-Cell Activation to Induce Immune Checkpoint Blockade Responses in "Cold" Melanoma.

Cancer immunology research·2022
Same author

Clinical Indoor Running Gait Analysis May Not Approximate Outdoor Running Gait Based on Novel Drone Technology.

Sports health·2021
Same author

Assessing the impact of COVID-19 on registered interventional clinical trials.

Clinical and translational science·2021
Same journal

Covid and data science: Understanding <i>R</i><sub>0</sub> could change your life.

Teaching statistics·2024
Same journal

Satisfaction with online teaching of medical statistics during the COVID-19 pandemic: A survey by the Education Committee of the Italian Society of Medical Statistics and Clinical Epidemiology.

Teaching statistics·2021
Same journal

Investigating populations via penguins and their poo!

Teaching statistics·2019
Same journal

May the odds be ever in your favour.

Teaching statistics·2019
Same journal

Biased sampling activity: an investigation to promote discussion.

Teaching statistics·2019
Same journal

Randomization-Based Statistical Inference: A Resampling and Simulation Infrastructure.

Teaching statistics·2018
See all related articles

Related Experiment Video

Updated: Jun 28, 2025

Global and Current Research Trends of Single-Cell Sequencing in Cancer: A Bibliometric and Visualization Study
07:49

Global and Current Research Trends of Single-Cell Sequencing in Cancer: A Bibliometric and Visualization Study

Published on: April 18, 2025

157

Some teaching resources using R with illustrative examples exploring COVID-19 data.

Arthur Berg1, Nour Hawila1

  • 1Division of Biostatistics Penn State University Hershey Pennsylvania USA.

Teaching Statistics
|April 12, 2024
PubMed
Summary
This summary is machine-generated.

This article explores using R for data science education and analyzes COVID-19 death rates by ethnicity. It provides reproducible R code for exploring sensitive public health data.

Keywords:
IDSSPdata sciencemapsteaching

More Related Videos

Swabbing the Urban Environment - A Pipeline for Sampling and Detection of SARS-CoV-2 From Environmental Reservoirs
07:13

Swabbing the Urban Environment - A Pipeline for Sampling and Detection of SARS-CoV-2 From Environmental Reservoirs

Published on: April 9, 2021

4.2K
Heuristic Mining of Hierarchical Genotypes and Accessory Genome Loci in Bacterial Populations
08:03

Heuristic Mining of Hierarchical Genotypes and Accessory Genome Loci in Bacterial Populations

Published on: December 7, 2021

2.1K

Related Experiment Videos

Last Updated: Jun 28, 2025

Global and Current Research Trends of Single-Cell Sequencing in Cancer: A Bibliometric and Visualization Study
07:49

Global and Current Research Trends of Single-Cell Sequencing in Cancer: A Bibliometric and Visualization Study

Published on: April 18, 2025

157
Swabbing the Urban Environment - A Pipeline for Sampling and Detection of SARS-CoV-2 From Environmental Reservoirs
07:13

Swabbing the Urban Environment - A Pipeline for Sampling and Detection of SARS-CoV-2 From Environmental Reservoirs

Published on: April 9, 2021

4.2K
Heuristic Mining of Hierarchical Genotypes and Accessory Genome Loci in Bacterial Populations
08:03

Heuristic Mining of Hierarchical Genotypes and Accessory Genome Loci in Bacterial Populations

Published on: December 7, 2021

2.1K

Area of Science:

  • Data Science Education
  • Public Health Research
  • Biostatistics

Background:

  • Implementing data science curricula requires accessible tools and relevant datasets.
  • COVID-19 pandemic highlighted health disparities, necessitating data-driven exploration.
  • R is a powerful, versatile tool for statistical analysis and data visualization.

Purpose of the Study:

  • To guide educators in using R for data science instruction.
  • To demonstrate R's application in analyzing ethnic/racial disparities in COVID-19 mortality.
  • To provide reproducible code for educational and research purposes.

Main Methods:

  • Utilizing R and R-related tools for curriculum implementation.
  • Applying R for exploratory data analysis of COVID-19 mortality data.
  • Leveraging R markdown for reproducible graphics and analysis.

Main Results:

  • R facilitates effective data science education with accessible resources.
  • Analysis revealed ethnic/racial distributions associated with COVID-19 death rates.
  • Supplementary R markdown files enable direct replication of findings.

Conclusions:

  • R is a valuable tool for both data science education and public health research.
  • Data exploration of COVID-19 disparities using R can inform targeted interventions.
  • Educational materials should be presented with sensitivity to the pandemic's impact.