The high resolution of bio-logging sensor data relative to the duration of deployments limits the utility of static figures. sb_plot_data and sb_plot_predictions use plotly to create interactive figures for looking in-depth at bio-logging data.

sb_plot_data(deployid, sensors, events)

sb_plot_predictions(deployid, sensors, predictions, outcomes = NULL)



[character(1)] ID of the deployment to visualize. Must be a deployment in both sensors and events.


[Sensors] Bio-logging sensor data (see Sensors).


[Events] Labeled behavioral events (see Events).


[Predictions] Predicted behavioral events (see sb_predict).


[Outcomes] Outcomes of predicted behavioral events (see sb_assess) (optional).


An interactive figure with time on the x-axis and a separate y-axis for each bio-logging sensor variable, arranged vertically. Interaction options include: click-and-drag to zoom in, double-click to zoom out, hover to see details (exact date, time, and value).


Points indicate known behavioral events.


Solid points indicate predicted behavioral events. If actual events are known (i.e, outcomes is not NULL), they are plotted as hollow circles and color-coded as follows: true positive as blue solid point in blue hollow point, false positive as red solid point, false negative as red hollow point. In addition to the bio-logging sensor variables, there's an additional time series for the "local probability" of an event (see vignette(TODO)).


# Load sample data and split test/train
c(lunge_sensors, lunge_events) %<-% load_lunges()
test_deployids <- deployments(lunge_sensors)[1:3]
deployid <- test_deployids[1]
c(sensors_test, sensors_train) %<-% divide(lunge_sensors, test_deployids)
c(events_test, events_train) %<-% divide(lunge_events, test_deployids)

# Visualize sensor and event data for one deployment
sb_plot_data(deployid, lunge_sensors, lunge_events)