Visualizations provide an accesible way to analyze our data. By generating a visual representation of data, they can help researchers find patterns, and outliers between concepts, themes, and more.  Examples of visualizations include word clouds, graphs, charts, and maps.

Out-of-the-Box

• Tableau
  For creating interactive data visualizations for the web

• RAWGraphs
  For creating custom visualizations as vector-based (SVG) and raster (PNG) images

• Piktochart
  For creating professional-looking, data-driven infographics, presentations, and reports

• D3.sj
  For producing dynamic, interactive data visualizations in web browsers

• Gephi
  For network analysis and visualization

• Google Charts
  For creating interactive charts and data tools

Programmatic

Python

• Matplotlib
  For creating static, animated, and interactive visualizations

• Seaborn
  For drawing attractive and informative statistical graphics

• ggplot
  For creating plot-based data visualizations incrementally and simply;  based on the 'Grammar of Graphics

• Bokeh
  For creating interactive visualizations for modern web browsers (powered by JavaScript), from simple plots to complex dashboards with streaming datasets

• Pygal
  For creating interactive and highly customizable graphs and charts that can be output as an SVG (Scalable Vector Graphics)

• Plotly
  For creating interactive, publication-quality graphs and charts

• geoplotlib
  For visualizing geographical data and making maps

• Gleam
  For building interactive web visualizations of data

• missingno
  For creating missing data visualizations and utilities to get quick visual summaries of the completeness (or lack thereof) of a dataset

• Leather
  For creating basic charts as SVGs

R

• ggplot2
  For 'declaratively' creating graphics, based on "The Grammar of Graphics"; you provide the data, tell 'ggplot2' how to map variables to aesthetics, what graphical primitives to use, and it takes care of the details

• Lattice
  For visualizing multi-variate data; inspired by Trellis graphics

• highcharter
  For creating dynamic charts and schematic maps with built-in and easy-to-customize themes 

• Leaflet
  For building interactive maps

• RColorBrewer
  For manipulating colors in plots, graphs, and maps color schemes

• Plotly
  For making interactive plots

• RGL
  For producing interactive 3-D plots

• dygraphs
  For charting time-series data

• plotrix
  For plotting data; includes various labeling, axis and color scaling functions

• vcd
  For plotting categorical data

Java

• Weka
  For data preprocessing (e.g., stemming, data resampling, transformation), classification, regression, clustering, latent semantic analysis (LSA, LSI), association rules, visualization, filtering, and anonymization

• Data Visualization Catalogue
  A handy guide and library of different data visualization techniques, tools, and a learning resource for data visualization

• Python Graph Gallery

• R Graph Gallery

• Drucker, Johanna. “Humanities Approaches to Graphical Display.” Digital Humanities Quarterly 5, no. 1 (2011).

• Harvard Innovation Labs. "Harvard I-Lab | Data Visualization for Non-Programmers." YouTube, Apr 10, 2015.

• Phew, Stephen. "Data Visualization for Human Perception," in The Encyclopedia of Human-Computer Interaction,, edited by Interaction Design Foundation, Second edition. n.d.

• Sinclair, Stéfan, Stan Ruecker, Milena Radzikowska, and Implementing New Knowledge Environments (INKE). “Information Visualization for Humanities Scholars," in Literary Studies in the Digital Age: An Evolving Anthology, edited by Kenneth M. Price, Ray Siemens, Dene Grigar, and Elizabeth M. Lorang. 2019.

• Kilpatrick, Garin. “10 Awesome Twitter Analytics and Visualization Tools.” Twitter Tools and Twitter Tips Blog, January 2, 2019.

• Hepworth, Katherine, and Christopher Church. 2019. “Racism in the Machine: Visualization Ethics in Digital Humanities Projects.” Digital Humanities Quarterly 12, no. 4 (2018).

• Cairo, Alberto. How Charts Lie: Getting Smarter about Visual Information. First edition. New York, N.Y: WWNorton & Company, 2019.

• Drouin, Jeffrey. Network Analysis, Text Mining, and Teaching the Little Review: Network Analysis, Text Mining, and Teaching the Little Review.” The Journal of Modern Periodical Studies 5, no. (2014): 110–35.

• Jänicke, S., G. Franzini, M. F. Cheema, and G. Scheuermann. “Visual Text Analysis in Digital Humanities.” Computer Graphics Forum 36, no. 6 (2017): 226–50.

• Metoyer, Ronald, Qiyu Zhi, Bart Janczuk, and Walter Scheirer. “Coupling Story to Visualization: Using Textual Analysis as a Bridge Between Data and Interpretation.” In 23rd International Conference on Intelligent User Interfaces, 503–507. IUI ’18. Tokyo, Japan: Association for Computing Machinery, 2018.

• Burgess, Julian, and Gabriel Dance. 2013. “The State of Our Union Is … Dumber: How Linguistic Standards Have Declined in the President’s Address.” The Guardian. February 12, 2013.

• “The COVID Racial Data Tracker.” n.d. The COVID Tracking Project.  

• Egan, Jim, and Jean Bauer. “Mapping Colonial Americas Publishing Project.” n.d.

• Wilhelm, Thomas, Manuel Burghardt, and Christian Wolff. n.d. “Visualising Hamlet - To See or Not to See."

• Kaufman, Michi. “‘Everything on Paper Will Be Used Against Me:’ Quantifying Kissinger. Text Analysis, Visualization and Historical Interpretation of the National Security Archive’s Kissinger Correspondence.” n.d.

• Weber, Jasmine. 2019. “How W.E.B. Du Bois Meticulously Visualized 20th-Century Black America.” Hyperallergic. February 5, 2019.