Basic Visualization and Plotting
Overview
Teaching: 30 min
Exercises: 0 minQuestions
How can I use GLATOS to plot my data?
What kinds of plots can I make with my data?
Objectives
We can use GLATOS to quickly and effectively visualize our data, now that we’ve cleaned it up.
One of the simplest ways is to use an abacus plot to display animal detections against the appropriate stations.
# Visualizing Data - Abacus Plots ####
# ?glatos::abacus_plot
# customizable version of the standard VUE-derived abacus plots
abacus_plot(detections_w_events,
location_col='station',
main='NSBS Detections By Station') # can use plot() variables here, they get passed thru to plot()
This is good, but cluttered. We can also filter out a single animal ID and plot only the abacus plot for that.
# pick a single fish to plot
abacus_plot(detections_filtered[detections_filtered$animal_id== "NSBS-Alison",],
location_col='station',
main="NSBS-Alison Detections By Station")
Additionally, if we only wanted to plot for a subset of receivers, we could do That as well, using the following code to isolate a particular subset. This is less useful for our Blue Shark dataset, and more applicable to a dataset structured like GLATOS, where the glatos_array column would link multiple receivers together into an array, which we could then select on. In this case, we’re only using our stations, so it’s less important.
# subset of receivers?
receivers
receivers_subset <- receivers[receivers$station %in% c('HFX005', 'HFX031', 'HFX040'),]
receivers_subset
det_first <- min(detections$detection_timestamp_utc)
det_last <- max(detections$detection_timestamp_utc)
receivers_subset <- receivers_subset[
receivers_subset$deploy_date_time < det_last &
receivers_subset$recover_date_time > det_first &
!is.na(receivers_subset$recover_date_time),] #removes deployments without recoveries
locs <- unique(receivers_subset$station)
locs
In keeping with earlier lessons, we could also do the same selection using dplyr’s filter() method.
receivers
receivers_subset <- receivers[receivers$station %in% c('HFX005', 'HFX031', 'HFX040'),]
receivers_subset
det_first <- min(detections$detection_timestamp_utc)
det_last <- max(detections$detection_timestamp_utc)
receivers_subset <- filter(receivers_subset, deploy_date_time < det_last &
recover_date_time > det_first &
!is.na(receivers_subset$recover_date_time))
receivers_subset
Note that the row indices are different- filter automatically resets them to 1-3, whereas selecting them with base R preserves the original numbering. Use whichever best suits your needs.
We can also plot abacus plots with receiver histories, which can give us a better idea of in which order our tracked fish travelled through our receivers. We just have to pass a few extra parameters to the abacus plot function, including our receivers array so that it builds out the history.
Note that we must add a glatos_array column to receivers before we can plot it in this way- the function still expects GLATOS data. For our purposes, it is enough to use the station column, but different variables may suit your data differently.
# Abacus Plots w/ Receiver History ####
# Using the receiver data frame from the start:
# See the receiver history behind the detections to know what you could see.
receivers$glatos_array = receivers$station
abacus_plot(detections_filtered[detections_filtered$animal_id == 'NSBS-Hooker',],
pch = 16,
type='b',
receiver_history=receivers,
location_col = 'station')
If we want to see actual physical distribution, a bubble plot will serve us better.
Before we can plot this data properly, we need to download a shapefile of Nova Scotia. This will give us a map on which we can plot our data. We can get a suitable Shapefile for Nova Scotia from GADM, the Global Administrative boundaries reference. The following code will retrieve first the country, then the province:
Canada <- getData('GADM', country="CAN", level=1)
NS <- Canada[Canada$NAME_1=="Nova Scotia",]
With the map generated, we can pass it to the bubble plot and see the results.
# Bubble Plots for Spatial Distribution of Fish ####
# bubble variable gets the summary data that was created to make the plot
detections_filtered
bubble <- detection_bubble_plot(detections_filtered,
location_col = 'station',
map = NS,
col_grad=c('white', 'green'),
background_xlim = c(-66, -62),
background_ylim = c(42, 46))
There are additional customisations we can perform, which let us tune the output of the bubble plot function to better suit our needs. A few of the parameters are demonstrated below, but we encourage you to investigate the documentation and see what suits Your needs.
# more complex example including zeroes by adding a specific
# receiver locations dataset, in this case the receivers dataset above.
bubble_custom <- detection_bubble_plot(detections_filtered,
location_col='station',
map = NS,
background_xlim = c(-63.75, -63.25),
background_ylim = c(44.25, 44.5),
symbol_radius = 0.7,
receiver_locs = receivers,
col_grad=c('white', 'green'))
Key Points