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Related Concept Videos

Introduction to R01:11

Introduction to R

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 functionality,...
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Geographic Information Systems (GIS) operate across three levels of application, each representing an increasing degree of complexity: data management, analysis, and prediction. These levels reflect the expanding functionality and versatility of GIS technology in handling spatial data for diverse purposes.Data ManagementAt its foundational level, GIS serves as a tool for data management, enabling the input, storage, retrieval, and organization of spatial data. This level is often employed in...
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Statistical Methods for Analyzing Epidemiological Data01:25

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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:
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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.
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Constructing and Visualizing Models using Mime-based Machine-learning Framework
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Creating web applications for spatial epidemiological analysis and mapping in R using Rwui.

Richard Newton1, Andrew Deonarine, Lorenz Wernisch

  • 1MRC Biostatistics Unit, Robinson Way, Cambridge, CB2 0SR, UK. richard.newton@mrc-bsu.cam.ac.uk.

Source Code for Biology and Medicine
|April 5, 2011
PubMed
Summary

Rwui is a web application that simplifies running R scripts for statistical analysis. Recent updates allow it to generate geographic maps, enhancing spatial epidemiological analysis for users unfamiliar with R or web programming.

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Area of Science:

  • Epidemiology
  • Biostatistics
  • Computer Science

Background:

  • Web-based applications can make powerful statistical analyses accessible to non-programmers.
  • Geographic mapping is crucial for spatial epidemiology, but R's visualization tools are limited.
  • Existing R-based epidemiological analysis tools often lack user-friendly interfaces for data visualization.

Purpose of the Study:

  • To update the Rwui web application to support the creation of R-based web applications that visualize results as geographic maps.
  • To enhance the utility of Rwui for spatial epidemiological analysis.

Main Methods:

  • Rwui, a web application framework for R scripts, was updated.
  • The updates enable the creation of web applications that display R script outputs as geographic maps.

Main Results:

  • Rwui can now generate web applications that return analysis results as geographic maps.
  • This facilitates the use of R for spatial epidemiological analysis through a user-friendly web interface.

Conclusions:

  • Rwui empowers statisticians to build web applications for R scripts without web development expertise.
  • Users can access R-based analyses and visualizations, including geographic maps, without learning R.
  • Recent Rwui enhancements improve its suitability for spatial epidemiological research.