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.
For creating interactive data visualizations for the web
For creating custom visualizations as vector-based (SVG) and raster (PNG) images
For creating professional-looking, data-driven infographics, presentations, and reports
For producing dynamic, interactive data visualizations in web browsers
For network analysis and visualization
For creating interactive charts and data tools
For creating static, animated, and interactive visualizations
For drawing attractive and informative statistical graphics
For creating plot-based data visualizations incrementally and simply; based on the 'Grammar of Graphics
For creating interactive and highly customizable graphs and charts that can be output as an SVG (Scalable Vector Graphics)
For creating interactive, publication-quality graphs and charts
For visualizing geographical data and making maps
For building interactive web visualizations of data
For creating missing data visualizations and utilities to get quick visual summaries of the completeness (or lack thereof) of a dataset
For creating basic charts as SVGs
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
For visualizing multi-variate data; inspired by Trellis graphics
For creating dynamic charts and schematic maps with built-in and easy-to-customize themes
For building interactive maps
For manipulating colors in plots, graphs, and maps color schemes
For making interactive plots
For producing interactive 3-D plots
For charting time-series data
For plotting data; includes various labeling, axis and color scaling functions
For plotting categorical data
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
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.
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.
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.
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.