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In digital humanities and computational social sciences, visualizations refer to the graphical representation of data to help researchers and scholars gain insights, identify patterns, and communicate findings effectively. Visualizations can range from simple charts and graphs to complex interactive dashboards and three-dimensional models.

Commonly Used Tools, Software, and Programs for Visualizations:

  Tableau: Tableau is a powerful data visualization tool that allows users to create interactive and shareable dashboards. It supports a wide range of data sources.

  Power BI: Microsoft Power BI is a business analytics tool that provides a suite of visualization tools for data exploration, analysis, and sharing.

  D3.js: D3.js (Data-Driven Documents) is a JavaScript library for creating interactive and dynamic visualizations in web browsers. It's highly customizable.

  ggplot2 (R): ggplot2 is a widely used data visualization package in R. It provides a high-level grammar for creating a wide range of statistical graphics.

  matplotlib/seaborn (Python): These Python libraries are used for creating static, animated, and interactive visualizations. Matplotlib is particularly popular for basic plotting, while seaborn provides more high-level, aesthetically pleasing visualizations.

  Adobe Illustrator: For creating custom and highly polished visualizations, Adobe Illustrator is a widely used graphic design tool.

  Gephi: Gephi, which was mentioned earlier for network analysis, also provides powerful tools for visualizing and exploring networks.

  Google Charts and Google Data Studio: These web-based tools are useful for creating interactive and shareable visualizations, and they integrate well with other Google services.

  Plotly: Plotly is a versatile Python library for creating interactive plots and dashboards. It supports a wide range of chart types.

  Sigma.js: This JavaScript library is specifically designed for visualizing graphs and networks in web browsers.

Useful Links
Spatial Analysis with R

Applied Spatial Data Analysis with R

Data Visualization in R: Introduction to ggplot

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