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Digital Scholarship & Digital Humanities

Description

Data visualizations are a graphical representation of data that tell a story and can be used to identify trends, patterns, and relationships. Visualizations can be static, animated, or interactive. 

  • Static visualizations provide users with a single view of data
  • Animated visualizations incorporate motion to convey information to users
  • Interactive visualizations allow users to interact with and customize visualizations.

Common types of visualizations include:

Bar Charts Pie Charts Line Charts
Compare data across categories Show proportions of a whole Show trends over time

Bar chart comparing body mass of penguins by species (Adelie, Chinstrap, and Gentoo) and gender (male and female).

Source

Pie chart showing how different product categories such as accessories, appliances, binders, chairs, and phones contribute to total sales.

Source

Line chart plotting the coherence of two signals over time using.

Source
Histograms Scatter Plots Boxplots
Display the frequency distribution of continuous data Show the relationships between numerical values Show the distribution of values along an axis

Stacked histogram on a log scale

Source

Scatterplot with continuous hues based on year and sizes based on mass.

Source

Box Plot built in R with ggplot2 depicting the spread of life expectancy for each continent.

Built in R with ggplot2
GIS Maps Network Graphs Heat Maps
Represent data spatially Plot relationships between data points Use color to represent density

Map showing African American population density in 1960 United States at county level with circles colored based on sentiment and weighted based on size.

Built in ArGIS Pro

Network graph depicting similar papers in a field. The size of the node is based on the number of citations and the color is based on publication year.

Source

Heat map shows number of cases of measles per 100,000 people across all 50 US states from 1928 to 2012. The map reveals a significant decline of Measles cases after the vaccine was introduced in 1963.

Source

Tools

In order to visualize data, you will need access to visualization software. There are two types of visualization software: Graphical User Interface (GUI) software and code-based software.

Graphical User Interface (GUI)

Excel is a popular spreadsheet editor with a variety of graph and chart options for visualizing data. Excel is FREE for all University of Mississippi faculty, staff, and students as part of the campus-wide Microsoft Office 365 license.

Tableau is a tool with a simple to use "drag-and-drop" interface that allows users to create interactive visualizations and dashboards from a variety of data sources.  Tableau Public is FREE to use.

Power BI (Business Intelligence) is a collection of software services, apps, and connectors that work together to turn raw data into static or interactive visualizations.  Power BI is s FREE for all University of Mississippi faculty, staff, and students as part of the campus-wide Microsoft Office 365 license.

Code Based

R is a FREE software environment for statistical computing and graphics. R packages such as ggplot2 are used for data visualization.

Python is a free general purpose programming language. Python libraries such as MatPlotLib and Seaborn are used for creating static, animated, and interactive visualizations.

Shiny is a free package that can be used to create interactive data visualizations in either R or Python. 

SAS is a computer programming language that can be used to create data visualizations. SAS OnDemand is free for academics and offers both programming and point-and-click interfaces.

Data Visualization Resources

The following are curated lists of datasets for visualization. Since any dataset can be visualized, be sure to also check the Datasets section of this guide's "Resources" page.

Sample Projects