Data Visualization in R

2019 ◽  
pp. 205-222
Author(s):  
S. R. Mani Sekhar ◽  
Siddesh G. M. ◽  
Sunilkumar S. Manvi
Keyword(s):  
Decision Making ◽  
Case Studies ◽  
Data Visualization ◽  
Information Visualization ◽  
Real World ◽  
Valuable Insight ◽  
Visualization Method ◽  
Use Of Data ◽  
R Programming ◽  
Visualization Techniques

Data visualization helps the users to understand the relationships and associations between information. Visualization helps in minimizing the errors generated during decision making. Different visualization methods have been developed to unlock the valuable insight. These methods have been developed on the supposition that the information to be present is free from ambiguity. This chapter provides an overview of data visualization techniques in R programming. Various methods have been discussed with supported explanation and examples which in turn helps the reader to create their own visualization method. Later, four different case studies are presented to understand the importance and use of data visualization in real-world problems.

Interactive Data Visualization Techniques Applied to Healthcare Decision Making

2017 ◽  
pp. 1157-1171 ◽  
Author(s):  
Zhecheng Zhu ◽  
Heng Bee Hoon ◽  
Kiok-Liang Teow
Keyword(s):  
Decision Making ◽  
Case Studies ◽  
Data Visualization ◽  
Capacity Planning ◽  
Healthcare Systems ◽  
Healthcare Decision ◽  
Healthcare Decision Making ◽  
Patient Profiling ◽  
Interactive Data ◽  
Visualization Techniques

Data visualization techniques are widely applied in all kinds of organizations, turning tables of numbers into visualizations for discovery, information communication, and knowledge sharing. Data visualization solutions can be found everywhere in healthcare systems from hospital operations monitoring and patient profiling to demand projection and capacity planning. In this chapter, interactive data visualization techniques are discussed and their applications to various aspects of healthcare systems are explored. Compared to static data visualization techniques, interactive ones allow users to explore the data and find the insights themselves. Four case studies are given to illustrate how interactive data visualization techniques are applied in healthcare: summary and overview, information selection and filtering, patient flow visualization, and geographical and longitudinal analyses. These case studies show that interactive data visualization techniques expand the boundary of data visualization as a pure presentation tool and bring certain analytical capability to support better healthcare decision making.

Interactive Data Visualization Techniques Applied to Healthcare Decision Making

2015 ◽  
pp. 46-59 ◽  
Author(s):  
Zhecheng Zhu ◽  
Heng Bee Hoon ◽  
Kiok-Liang Teow
Keyword(s):  
Decision Making ◽  
Case Studies ◽  
Data Visualization ◽  
Capacity Planning ◽  
Healthcare Systems ◽  
Healthcare Decision ◽  
Healthcare Decision Making ◽  
Patient Profiling ◽  
Interactive Data ◽  
Visualization Techniques

Data visualization techniques are widely applied in all kinds of organizations, turning tables of numbers into visualizations for discovery, information communication, and knowledge sharing. Data visualization solutions can be found everywhere in healthcare systems from hospital operations monitoring and patient profiling to demand projection and capacity planning. In this chapter, interactive data visualization techniques are discussed and their applications to various aspects of healthcare systems are explored. Compared to static data visualization techniques, interactive ones allow users to explore the data and find the insights themselves. Four case studies are given to illustrate how interactive data visualization techniques are applied in healthcare: summary and overview, information selection and filtering, patient flow visualization, and geographical and longitudinal analyses. These case studies show that interactive data visualization techniques expand the boundary of data visualization as a pure presentation tool and bring certain analytical capability to support better healthcare decision making.

Data Visualization

2014 ◽  
pp. 322-351 ◽  
Author(s):  
Ravishankar Palaniappan
Keyword(s):  
Data Structure ◽  
Data Visualization ◽  
Real World ◽  
Research Field ◽  
Use Case ◽  
Visualization Method ◽  
Visualization Process ◽  
Active Research ◽  
Real World Datasets ◽  
Qualitative Exploration

Data visualization has the potential to aid humanity not only in exploring and analyzing large volume datasets but also in identifying and predicting trends and anomalies/outliers in a “simple and consumable” approach. These are vital to good and timely decisions for business advantage. Data Visualization is an active research field, focusing on the different techniques and tools for qualitative exploration in conjunction with quantitative analysis of data. However, an increase in volume, multivariate, frequency, and interrelationships of data will make the data visualization process notoriously difficult. This necessitates “innovative and iterative” display techniques. Either overlooking any dimensions/relationships of data structure or choosing an unfitting visualization method will quickly lead to a humanitarian uninterpretable “junk chart,” which leads to incorrect inferences or conclusions. The purpose of this chapter is to introduce the different phases of data visualization and various techniques which help to connect and empower data to mine insights. It exemplifies on how “data visualization” helps to unravel the important, meaningful, and useful insights including trends and outliers from real world datasets, which might otherwise be unnoticed. The use case in this chapter uses both simulated and real-world datasets to illustrate the effectiveness of data visualization.

Dust & Magnet: Multivariate Information Visualization Using a Magnet Metaphor

2005 ◽  
Vol 4 (4) ◽  
pp. 239-256 ◽  
Author(s):  
Ji Soo Yi ◽  
Rachel Melton ◽  
John Stasko ◽  
Julie A. Jacko
Keyword(s):  
General Population ◽  
Information Visualization ◽  
Real World ◽  
Daily Life ◽  
Information Age ◽  
Visualization Tool ◽  
Visualization Technique ◽  
Visualization Techniques

The use of multivariate information visualization techniques is intrinsically difficult because the multidimensional nature of data cannot be effectively presented and understood on real-world displays, which have limited dimensionalities. However, the necessity to use these techniques in daily life is increasing as the amount and complexity of data grows explosively in the information age. Thus, multivariate information visualization techniques that are easier to understand and more accessible are needed for the general population. In order to meet this need, the present paper proposes Dust & Magnet, a multivariate information visualization technique using a magnet metaphor and various interactive techniques. The intuitive magnet metaphor and subsequent interactions facilitate the ease of learning this multivariate information visualization technique. A visualization tool such as Dust & Magnet has the potential to increase the acceptance of and utility for multivariate information by a broader population of users who are not necessarily knowledgeable about multivariate information visualization techniques.

scholarly journals An Interactive Data Visualization Framework for Exploring Geospatial Environmental Datasets and Model Predictions

Water ◽  
2020 ◽  
Vol 12 (10) ◽  
pp. 2928
Author(s):  
Jeffrey D. Walker ◽  
Benjamin H. Letcher ◽  
Kirk D. Rodgers ◽  
Clint C. Muhlfeld ◽  
Vincent S. D’Angelo
Keyword(s):  
Decision Making ◽  
Case Studies ◽  
Data Visualization ◽  
Large Scale ◽  
Research Management ◽  
Web Based ◽  
Environmental Models ◽  
Potential Benefits ◽  
Interactive Data ◽  
Data Visualizations

With the rise of large-scale environmental models comes new challenges for how we best utilize this information in research, management and decision making. Interactive data visualizations can make large and complex datasets easier to access and explore, which can lead to knowledge discovery, hypothesis formation and improved understanding. Here, we present a web-based interactive data visualization framework, the Interactive Catchment Explorer (ICE), for exploring environmental datasets and model outputs. Using a client-based architecture, the ICE framework provides a highly interactive user experience for discovering spatial patterns, evaluating relationships between variables and identifying specific locations using multivariate criteria. Through a series of case studies, we demonstrate the application of the ICE framework to datasets and models associated with three separate research projects covering different regions in North America. From these case studies, we provide specific examples of the broader impacts that tools like these can have, including fostering discussion and collaboration among stakeholders and playing a central role in the iterative process of data collection, analysis and decision making. Overall, the ICE framework demonstrates the potential benefits and impacts of using web-based interactive data visualization tools to place environmental datasets and model outputs directly into the hands of stakeholders, managers, decision makers and other researchers.

Data visualization and data fusion on the visual performance of illustration

2020 ◽  
Vol 39 (6) ◽  
pp. 8795-8803
Author(s):  
Gongxiang Huang ◽  
Huimin Qu
Keyword(s):  
Flow Field ◽  
Heat Source ◽  
Data Fusion ◽  
Data Visualization ◽  
Information Visualization ◽  
Stratified Flow ◽  
Visual Performance ◽  
Visualization Method ◽  
Background Theory ◽  
Flow Field Design

The influence of COVID-19 causes a certain impact on data visualization and data fusion on the visual performance of illustration. Based on the development of illustration, this paper discusses the relationship between illustration, text information and media. This paper studies the feasibility of the combination of illustration and information visualization. In this paper, the interactive image segmentation and gridding methods are proposed. Then, the background theory and significance of flow field design are described, and the flow field generation method based on heat source diffusion is proposed. In this paper, the shadow of the topology of the convective field through the interaction input of the flow field design is analyzed, and then compared with the related work. In the visualization of flow field, based on the weighted distance field formed by the diffusion of heat source, a visualization method of stratified flow field line is proposed. Finally, the visualization method of stratified flow field is explained and its effect is demonstrated. Experimental data show that the information visualization method proposed in this paper can improve the efficiency and accuracy of illustration information extraction.

Application of Geographical Information System and Interactive Data Visualization in Healthcare Decision Making

2017 ◽  
pp. 1244-1254
Author(s):  
Zhecheng Zhu
Keyword(s):  
Decision Making ◽  
Information System ◽  
Data Visualization ◽  
Healthcare Services ◽  
Geographical Information ◽  
Multiple Data ◽  
Interactive Data ◽  
Visual Impact ◽  
Site Planning ◽  
Visualization Techniques

This paper focuses on two techniques and their applications in healthcare systems: geographic information system (GIS) and interactive data visualization. GIS is a type of technique applied to manipulate, analyze and display spatial information. It is a useful tool tackling location related problems. GIS applications in healthcare include evaluation of accessibility to healthcare facilities, site planning of new healthcare services and analysis of risks and spreads of infectious diseases. Interactive data visualization is a collection of techniques translating data from its numeric format to graphic presentation dynamically for easy understanding and visual impact. Compared to conventional static data visualization techniques, interactive data visualization techniques allow user to self-explore the entire data set by instant slice and dice, quick switching among multiple data sources. Adjustable granularity of interactive data visualization allows for both detailed micro information and aggregated macro information displayed in a single chart. Animated transition adds extra visual impact that describes how system transits from one state to another. When applied to healthcare system, interactive visualization techniques are useful in areas such as information integration, flow or trajectory presentation and location related visualization, etc. One area both techniques intersect is location analysis. In this paper, real life case studies will be given to illustrate how these two techniques, when combined together, help in solving quantitative or qualitative location related problem, visualizing geographical information and accelerating decision making procedures.

Interactive Data Visualization to Understand Data Better

2017 ◽  
pp. 27-36
Author(s):  
Zhecheng Zhu ◽  
Bee Hoon Heng ◽  
Kiok-Liang Teow
Keyword(s):  
Case Studies ◽  
Healthcare System ◽  
Data Visualization ◽  
Healthcare Systems ◽  
Multiple Data ◽  
Interactive Data ◽  
Interactive Map ◽  
Visual Impact ◽  
Visualization Techniques

This paper focuses on interactive data visualization techniques and their applications in healthcare systems. Interactive data visualization is a collection of techniques translating data from its numeric format to graphic presentation dynamically for easy understanding and visual impact. Compared to conventional static data visualization techniques, interactive data visualization techniques allow users to self-explore the entire data set by instant slice and dice, quick switching among multiple data sources. Adjustable granularity of interactive data visualization allows for both detailed micro information and aggregated macro information displayed in a single chart. Animated transition adds extra visual impact that describes how system transits from one state to another. When applied to healthcare system, interactive visualization techniques are useful in areas such as information integration, flow or trajectory presentation and location related visualization, etc. In this paper, three case studies are shared to illustrate how interactive data visualization techniques are applied to various aspects of healthcare systems. The first case study shows a pathway visualization representing longitudinal disease progression of a patient cohort. The second case study shows a dashboard profiling different patient cohorts from multiple perspectives. The third case study shows an interactive map illustrating patient geographical distribution at adjustable granularity. All three case studies illustrate that interactive data visualization techniques help quick information access, fast knowledge sharing and better decision making in healthcare system.

scholarly journals DATA VISUALIZATION TECHNIQUES: A REVIEW

2020 ◽  
Vol 7 (8) ◽  
pp. 18-26
Keyword(s):  
Decision Making ◽  
Big Data ◽  
Data Visualization ◽  
Fast Rate ◽  
Time Data ◽  
Big Data Visualization ◽  
Basic Concepts ◽  
Visualization Techniques ◽  
Pictorial Form

Data visualization involves representing data and information in a graphical or pictorial form so that it can be easily understandable. At Present time, data is increasing at a very fast rate so, it is important to visualize and analyze the massive amount of data by using various visualization techniques. Data Visualization techniques are very helpful to visualize and understand outliers, trends, and patterns in data and thus helpful in decision making. This paper presents a review of the basic concepts of data visualization and various techniques and tools used for visualizing data. Some big data visualization techniques, which are the need of the hour, are also being discussed.

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