6.   Data Visualization

Previously, we briefly mentioned some ways to visualize your data (section 4.2). This current section provides further details on visualizing data both using Biomet.net functions, and we also provide some standalone (non-pipeline) functions to help plot data from your database using Matlab, Python, and R.

Biomet.net functions

These are our recommended methods for viewing and analyzing your data, both during cleaning, and once you have real-time data flowing.

Matlab:

There are several Matlab functions for visualizing and analyzing data:

• plotApp: a Matlab graphical user interface (GUI) which we recommend using during database set up and cleaning of your data (see section 6.1 for details).
  
• gui_Browse_Folder: given a filepath, this function will open a new figure window and provide a dropdown containing all traces located at that filepath location. You can pick one or scroll through using the arrow buttons (see section 6.3).
  
• guiPlotTraces: this is an older function with less utility than the plotApp GUI, but it still displays your data for quick viewing (see section 6.3).

RShiny:

There is also an R Shiny App which is useful for viewing your flux data in real-time (see section 6.2 for details). You can host this on an RShiny server to make your data publicly viewable.

Standalone non-Biomet.net functions

Section 6.4 provides functions in Matlab, Python, and R, to help you load data for your general data analysis.