Data visualization provides a means of graphically displaying information to effectively communicate ideas, illustrate emergent patterns, or tell a story. Humans have used graphical representations of data for hundreds of years and the Data Cooperative is here to support your data story.
- In The Human Side of Data, designer Giorgia Lupi presents stunning graphical displays of data and illustrates several ways of conveying meaning through images. The talk includes an exploration of the work of Ada Lovelace, Rachel Carson, and Mae Jemison through data visualization (at 36:20 in the talk).
- When creating data visualizations, accessibility should be front and center. The ColorBrewer tool provides a place to test different color schemes for audiences with differing visual abilities.
- Data Visualization: A Practical Introduction by Kieran Healy provides an excellent introduction into the field of data visualization and is full of examples with the popular ggplot2 package for R. It also includes all the R code and data necessary to recreate the visualizations yourself.
- For even more examples with R, Data Visualisation with R: 111 Examples by Thomas Rahlf uses base R graphics for a variety of plotting solutions.
- If you prefer working in Python, Ashwin Pajankar's Practical Python Data Visualization: A Fast Track Approach to Learning Data Visualization with Python covers the basics of the matplotlib package for plotting data.
- The Voyager site provides an interactive way to explore different data visualization approaches. You can use a variety or datasets or upload your own!
- The Data Visualization Roadshow provides an overview of best practices and campus resources for telling your data story.
- Data & Viz Drop-In is a weekly opportunity to get help on your project or visualization. There are folks on hand who can help out with R, python, GIS and visualization.
- R: the Tuesday morning R sessions provide a place where you can learn data visualization skills or troubleshoot challenges you might face with the R programming lesson.
- Geospatial data: are you interested in visualizing data in a geographic context? Check out the Libraries' geospatial data and GIS resources for upcoming opportunities and resources.
- The Data Cooperative at the University of Arizona Libraries holds an annual Data Visualization Challenge to highlight how UArizona students use visual displays of data to tell stories.
Data visualization software provides a means of transforming raw data to meaningful images.
- Tableau is a full-feature program useful for creating dashboards and crisp visualizations. It provides a graphical user interface to interact with several powerful features. Computers with Tableau installed are available in the Main Library and the Weaver Science-Engineering Library.
- The R programming language is a great means of creating reproducible data visualizations.
- The ggplot2 package allows you to create high-quality data visualization in R; the Software Carpentry lesson R for Reproducible Scientific Analysis provides a nice introduction to the ggplot2 package.
- If you are interested in seeing examples of data visualizations in R, check out the #TidyTuesday hashtag on Twitter or join in the fun yourself at https://github.com/rfordatascience/tidytuesday.
- Python, a scripting language similar to R, also has a variety of data visualization packages
- The matplotlib library provides a suite of tools for creating static, animated, and interactive data visualizations.
- seaborn uses the matplotlib library, but provides an enhanced interface for rich data visualization.
- If you want to create interactive web visualizations in Python, check out the plot.ly package.
Post-processing software allows enhancements and makes graphic design easier.
- Inkscape is free, open-source vector graphics program akin to Adobe Illustrator.
- GIMP (GNU Image Manipulation Program) is also free, open-source raster graphics program like Adobe Photoshop.
- Adobe Creative Cloud includes multiple programs for creating and modifying images.
In search of datasets to practice creating visualizations? Here are some places to find cool datasets.
- Data Management: Finding Data page
- awesome public datasets (GitHub)
- data is plural (check out the newsletter archive)
- awesome geospatial data