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What's New?

February 21, 2022

Plot Special Release

🎁 New Features

Inside Plot - our new analysis panel - we’ve added more charts to visualise the relationships between one or two variables. With Plot, you can create bar charts, heat maps, box plots and all of the time-series visualisations previously found inside Trends!

Yep ... Graphext’s exploratory features just got a lot more powerful. Here’s an overview of what we’ve added but check the full Plot documentation to learn more.

"There is magic in graphs. The profile of a curve reveals in a flash a whole situation - the life history of an epidemic, a panic, or an era of prosperity."

- Henry D. Hubbard

What Can I Do With Charts in Plot?

Plot charts are designed to help you quickly measure value distribution between one or two variables in your data. The different types of charts you will find require different data types - for instance Overview, Compared Segments and Segmented Overview charts require date values. 

You can use your sidebar filters to restrict the data presented inside charts in Plot as well as changing the way that data is aggregated or summarized using the dropdowns at the top left of your chart.

Note: Plot is an evolution of Graphext’s Trends panel. You can still use our Trends charts inside Plot but we’ve added more variety to the types of visualizations you can create. 

New Charts in Plot

Bar Charts

Bar charts are a simple representation of two variables. The variable represented on the Y axis must be quantitative. The variable on the X axis can either be quantitative or categorical.

Best used for ... Understanding how values from one or two variables are distributed.

Box Plots

Box plots are great for showing the value distribution belonging to a quantitative variable over a number of different categorical data segments. They represent quartile ranges and median values associated with these categories.

Best used for ... Measuring correlation between pairs of variables.

Heat Maps

Heat Maps are great at spotting correlation patterns between pairs of variables. They use a color spectrum to represent density of your dataset at points where values from two variables meet.

Best used for ... Measuring correlation between pairs of variables. 


Read more about Plot in our documentation here.