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Data are individual items of information obtained from a population or sample. Data may be classified as qualitative (categorical), quantitative continuous, or quantitative discrete. Because it is not practical to measure the entire population in a study, researchers use samples to represent the population. A random sample is a representative group from the population chosen by using a method that gives each individual in the population an equal chance of being included in the sample. Random...
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In statistics, several tools are used to interpret the data. Measures of central tendency represent the characteristics of the data, such as mean, median, and mode. Additionally, measures of variance like standard deviation and range are used to find the spread of data from the mean. Relative standing measures the distance between data locations. Commonly used measures of relative standings are percentile, z score, and quartiles.
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A pie chart (or a pie graph) is a circular graphical chart or a pictorial representation of categorical data. It is divided into slices of pie each indicating numerical proportions. It is also used to show the relative sizes of data in a single chart.
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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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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...
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Geographic Information Systems (GIS) are tools for storing, analyzing, and displaying spatial data alongside related attributes. Unlike traditional information systems that address general queries, GIS incorporates spatial components, enabling users to answer "where" and "how far." For example, GIS can process housing data linked to geographic locations like zip codes, allowing insights into population density or housing distribution through thematic maps.GIS integrates technologies such as...
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Feast for the Eyes: An Introduction to Data Visualization.

Tara J Brigham1

  • 1a Winn-Dixie Foundation Medical Library, Mayo Clinic , Jacksonville , Florida , USA.

Medical Reference Services Quarterly
|April 8, 2016
PubMed
Summary

Data visualization uses charts and images to present data, making it more accessible. Its popularity is growing due to easy creation tools and social media trends.

Area of Science:

  • Information Science
  • Data Science
  • Library Science

Background:

  • Data visualization is the graphical representation of data.
  • Its popularity has surged due to accessible tools and social media.
  • Free data sources further fuel its widespread adoption.

Purpose of the Study:

  • To define data visualization and explore its current applications.
  • To discuss the advantages and potential drawbacks of data visualization.
  • To examine the specific uses and implications of data visualization within library settings.

Main Methods:

  • Exploratory analysis of current data visualization trends.
  • Review of existing literature on data visualization benefits and challenges.
  • Case study approach to library applications (implied).
Keywords:
Data visualizationdatavizinfographicinformation visualization

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Main Results:

  • Data visualization is increasingly accessible to a wider audience.
  • Social media and readily available data contribute to its rise.
  • Libraries can benefit from data visualization, but potential issues exist.

Conclusions:

  • Data visualization is a growing field with significant potential.
  • Understanding its benefits and challenges is crucial for effective implementation.
  • Further exploration of visualization guides is recommended for practitioners.