R & Python 101: Additional Charting Libraries
At Periscope Data, the most common feature request we receive from current customers is new chart options. Rather than handle these as a series of one-off feature builds, we decided to give more power to our customers by enabling them to use R and Python to create their own visualizations that could then be displayed in dashboards.
With these advanced languages, Periscope Data customers are no longer limited to a predetermined list of visualization options. Instead, those tables generated in SQL can now be passed into R or Python, where the information can be analyzed deeply and displayed in an unlimited variety of ways. Data analysts are creative problem solvers and with the right tools, they can creatively illustrate their findings in any way that helps them convey their insights effectively.
Using R and Python to supplement charting libraries
Periscope data was designed to give analysts a toolkit of charts that are easy to read and can effectively analyze a specific phenomenon — lines, bars, pies, tables, scatter plots, etc. These charts are widely accessible, but a simple charting toolkit also means a simple range of insights that can be communicated. As insights expand to increasingly complex research, more complex charting tools will be needed to illustrate those findings. Using R and Python to expand to the charting possibilities means an expansion of the types of information that can be studied and conveyed with those visualizations.
Although R and Python were only recently made available to Periscope Data customers, we’ve seen data teams creating a range of new visualizations: box and whisker charts, treemaps, formatted tables, customized number overlays, calendar-like charts, heat maps, log scales, quadrant charts and more. These charts all come with the ease of collaboration and sharing that customers expect from Periscope Data. There really is no limit to what data teams can create with R and Python.
Supplementing the Periscope Data charting libraries
Using R and Python to create new types of charts in Periscope Data is simple. Just run a standard SQL query, and select the desired language form the dropdown below the SQL editor. From there, you can use 25+ supported charting libraries to customize your visualization. When R and Python evolve and add new libraries, your charting options in Periscope data will grow even more robust. As the visualization options expand, data teams can perform more advanced analyses, find previously hidden insights and add even more value to their companies.
If you’re interested in exploring seeing creative new charting options, visit the data visualization catalogue to get inspiration. To learn more about how your data team can use R and Python to visualize your data in new ways, download our guide.