Data Visualization

Selecting the right tool for data visualization often depends on the data type in question and the user's comfort level with coding. On the other hand, most tools can create almost any core visualizations once you learn how to use them. Fortunately, there are many options! While this list isn't an exhaustive overview of data visualization tools/software available (see University of Houston's Data Visualization list and Awesome dataviz on GitHub for more extensive coverage of visualization software/libraries), it can provide a starting point. 

There are a variety of reasons you may prefer a point-and-click interface for making data visualizations. The main issue to consider is what type of data you are working with, as many tools are specialized to work with certain data formats.

Check out the comparison of data visualization tools from UMN DASH where the strengths, weaknesses and prerequisite knowledge needed are discussed.

If your data is: mostly or all numeric (e.g., GDP over time, species counts, coded survey data, etc.)

• Excel 
  Excel remains a frequently used platform for exploratory (and explanatory) data visualization, especially for those in business, marketing, economics, and finance. This guide is adapted from Duke University Libraries and provides an introduction to the visualization capabilities of Excel.

• Tableau 
  Tableau lets you import many kinds of numeric or categorical data and produce a range of graphics with great interactivity. Combining multiple data sources in one visualization is also possible. If you have access to Tableau server, you can automate refreshes of your data; Tableau Public allows you to publish for free if user/data privacy is not a concern (it also has a gallery that can inspire you). University instructors and students can use the authoring client for free!

• RAW Graphs 
  RAW Graphs is an online platform to make data visualizations.The interface allows users to select graph type (i.e., scatterplot, bar chart, dendrogram, etc.) based on type of input data (i.e., numeric, categorical).

• plotly 
  Plotly is an entirely web-based interface for making graphics. It does not require any coding knowledge, but can interface with both R and Python. The community version of plotly is free to use.

• Gephi 
  Gephi is a free software for visualizing networks, comprised of "nodes" and "edges". The main website hosts official tutorials and also links to popular community-developed tutorials.

• Platform-specific tools 
  Some websites/organizations that host data available for analysis also include visualization tools specifically for that data. This guide from George Mason University covers selected platform-specific visualization tools (i.e., Data-Planet, Social Explorer, SimplyAnalytics).

If your data is: raw text (e.g., newspaper articles, journal articles, any literature)

• Voyant 
  Voyant is an online point-and-click tool for text analysis. While the default graphics are impressive, it allows limited customizing of analysis and graphs and may be most useful for exploratory visualization.

• Corpus-specific tools 
  Certain corpora have built-in visualization tools, such as Google Books ngram viewer, HathiTrust Bookworm, or JSTOR for Research.

For more information about text mining tools, especially in the context of Digital Humanities, reach out to the Digital Arts Sciences and Humanities.

If you are working with a scripting language for other aspects of data analysis, you're in luck! You can often use the same software for everything from data cleaning to data visualization.

• R 
  R is not only a standard statistical analysis tool, but also a powerful visualization platform. The ggplot2 package is the primary graphic-making package. There are also numerous packages meant to extend the functionality of ggplot2. From animations to maps to other advanced graphic options (check out shiny to make interactive plots!), these extension packages help make publication-worthy graphs. For those working with text data, the tidytext and tm packages are good options for cleaning, analyzing, and visualizing text data.
   
• Python 
  Like R, Python has libraries to make impressive visualizations. While matplotlib is the main graphics library, there are additional Python libraries focused on visualization, including making interactive plots/charts, 3D images, maps, and more. (Read here for a more in-depth discussion of how the Python visualization libraries fit together.) When working with text data, the nltk and TextBlob libraries are useful for analysis and visualization.

If your data is geospatial (vector, raster)

The Map Librarian is here to help you with your geospatial data questions! Additionally, the computers in the library are equipped with ArcGIS and available for use.