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Selecting the right tool for data visualization often depends on the data type in question and the user's comfort level with coding. Fortunately, there are many options! While this list isn't an exhaustive overview of data visualization tools/software available (see hereand here for more extensive coverage of visualization software/libraries), it can provide a starting point. Email the Data Science Librarian if you have any questions.
Point and click options
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.
If your data is: mostly or all numeric (e.g., gross domestic product over time, species counts, coded survey data, etc.)
Excel remains a frequently used platform for exploratory (and explanatory) data visualization, especially for those in business, marketing, economics, and finance. This guide from Duke University Libraries provides an introduction to the visualization capabilities of Excel.
Tableau works with numeric and categorical data to produce advanced graphics. Browse the Tableau public gallery to see examples of visuals and dashboards. The Datacruncher machine in the Data & GIS Lab has a Tableau license and a trial version of Tableau is available for download from the website.
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).
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 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.
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 Scholarship Librarian.
Canva is a an online graphic design platform. Users can start from a variety of templates, including for infographics.
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 for both numeric and text data.
R is not only a standard statistical analysis tool, but also a powerful visualization platform. The ggplot2package 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.
Like R, Python has libraries to make impressive visualizations. While matplotlibis the main graphics library, there are additional Python libraries focused on visualization, including making interactive plots/charts, 3D images, maps, and more. (Read herefor 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.