A growing area of large-scale data analysis is the visualization and sharing of results of analyses. Data scientists need to communicate complex data and results in concise and clear ways, leading to an explosion of platforms, tools, software and approaches for data visualization.
On this page, we provide an overview of resources for learning how to visualize data, software for data visualization, and tools developed at Fred Hutch. While this is not an exhaustive list, we have highlighted what tends to be the most commonly employed or easiest to access resources.
While it is possible to plot using base R, there are many packages available to make plotting easier and more visually appealing. Data visualization in R has been dominated by the {ggplot} package and a wealth of add-on packages that allow for further customization (such as {RColorBrewer} for color palettes and themes, etc). Meanwhile, the communication of data visualizations via interactive webapps like Shiny apps, are also R based and lend themselves well to displaying {ggplot} and {plotly} type visualizations.
Historically, the Matplotlib had been the go-to library for scientific data visualization in Python. Matplotlib is still a powerful plotting tool, but its syntax is complex and the graphics can look outdated when compared to R’s {ggplot2}. Matplotlib is still often used over other Python data visualization libraries (particularly for machine learning workflows), but that this is due more to tradition in the software development community than better features.
The seaborn library was developed as an easier to use and updated version of Matplotlib and the plotnine library was developed to mimic {ggplot2}’s grammar of graphics style plotting syntax. Still, some Python users choose to do their data processing in Python and switch to R for visualization.
The plotly and Altair libraries in Python are two options for interactive visualizations.
Fred Hutch’s Center IT (CIT) supports a wide range of commonly used software at little to no cost to you! We’ve pulled out a shortlist of software relevant to data visualization, but you can view the entire software catalog here. Tableau, MATLAB, and Microsoft Excel all are great options for users who prefer a point and click data visualization tools.
The FH-Data Slack, and more specifically the #data-viz channel, is always available as a space for researchers to ask questions and share resources about data visualization.
Books can be a great way to dive deeper into a specific coding subject and fortunately many of these books are available online for free! The Fundamentals of Data Visualization by Claus Wilke is a great reference for code agnostic data visualization concepts. For language specific data visualization references, books and documentation that cover a specific language (like Python or R) will often also cover the basics of plotting in that language.
Data visualization focused blogs and screencasts can be a great way to find inspiration and think outside the box.