More Features of glatos

Overview

Teaching: 15 min
Exercises: 0 min

Questions

• What other features does glatos offer?

Objectives

glatos has more advanced analytic tools that let you manipulate your data further. We’ll cover a few of these features now, to show you how to take your data beyond just filtering and event creation. We’ll also show you how to move your data from glatos to VTrack, another powerful suite of data manipulation tools. By combining the glatos package’s powerful built-in functions with its interoperability across scientific R packages, we’ll show you how to derive powerful insights from your data, and format it in a way that lets you demonstrate them.

glatos can be used to get the residence index of your animals at all the different stations. In fact, glatos offers five different methods for calculating Residence Index. For this lesson, we will showcase two of them, but more information on the others can be found in the glatos documentation.

The residence_index() function requires an events object to create a residence index. We will start by creating a subset like we did in the last lesson. This will save us some time, since running the residence index on the full set is prohibitively long for the scope of this workshop.

First we will decide which animals to base our subset on. To help us with this, we can use group_by on the events object to make it easier to identify good candidates.

#Using all the events data will take too long, so we will subset to just use a couple animals
events %>% group_by(animal_id) %>% summarise(count=n()) %>% arrange(desc(count))

#In this case, we have already decided to use these three animal IDs as the basis for our subset.
subset_animals <- c('TQCS-1049274-2008-02-28', 'TQCS-1049271-2008-02-28', 'TQCS-1049258-2008-02-14')
events_subset <- events %>% filter(animal_id %in% subset_animals)

events_subset

Now that we have a subset of our events object, we can apply the residence_index functions.

# Calc residence index using the Kessel method

rik_data <- residence_index(events_subset,
                            calculation_method = 'kessel')
# "Kessel" method is a special case of "time_interval" where time_interval_size = "1 day"
rik_data

# Calc residence index using the time interval method, interval set to 6 hours
rit_data <- residence_index(events_subset,
                            calculation_method = 'time_interval',
                            time_interval_size = "6 hours")

rit_data

Although the code we’ve written for each method of calculating the residence index is similar, the different parameters and calculation methods mean that these will return different results. It is up to you to investigate which of the methods within glatos best suits your data and its intended application.

One of the development goals of the glatos package is interoperability with other scientific R packages. Currently, we can ‘crosswalk’ data from glatos data to the package VTrack. By ‘crosswalk’, we mean that we can take data that has been manipulated and formatted by glatos, and make it usable by another package, in this case, VTrack. We’ll use the same dataset as before, but we’ll also add some related metadata from a different file.

First, let’s get the tagging metadata.

?prepare_deploy_sheet

tags <- prepare_tag_sheet('TQCS_metadata_tagging.xlsx', sheet_name=1, header_line = 1)
receivers <- prepare_deploy_sheet('TEQ_Deployments_201001_201201.xlsx', sheet_name = 1, header_line = 0, combine_arr_stn = FALSE)

We’ll need this TEQ metadata because the format that VTrack is expecting requires deployment information. The convert_otn_to_att function in glatos will handle assembling the data as long as you pass it the correct deployment metadata. As always, if you have any further questions about the function, the documentation is always available.

Because of differences with how the FACT data is formatted, we need to use prepare_deploy_sheet rather than read_otn_deployments. Depending on how much data you import and from how many sources, you may see both of these functions. More information on how they work and what they do can, as always, be found in the glatos documentation and help files. For now, it is enough to know that they are two different ways of doing the same thing- preparing receiver metadata for an OTN-format to ATT-format conversion- and which one to use depends on the format of your data. For FACT data, we use prepare_deploy_sheet.

We also need to quickly add some columns that FACT extracts do not have, since VTrack will look for them. Remember, the syntax for creating and filling a blank column in your dataframe is the same square-bracket syntax we covered in the earliest parts of this workshop. We also have to rename the station names in our receivers object so that they match the format of the station names in our detections object.

#Add columns missing from FACT extracts.
detections_filtered['sensorvalue'] = NA
detections_filtered['sensorunit'] = NA

# Rename the station names in receivers to match station names in detections (No longer needed with `combine_arr_stn`)
# receivers <- receivers %>% mutate(station=substring(station, 4))

Now that we have all the pieces- detections, tags, and deployment metadata- we can run the convert_otn_to_att function, which will take the glatos data, in OTN format, and convert it into the ATT format, for VTrack.

?convert_otn_to_att

ATTdata <- convert_otn_to_att(detections_filtered, tags, deploymentSheet = receivers)

# ATT is split into 3 objects, we can view them like this
ATTdata$Tag.Detections
ATTdata$Tag.Metadata
ATTdata$Station.Information

With this done, you can use your data (now in the ATTdata object) with the VTrack package. You may notice that not all the detections made it into the ATT object. That’s because the conversion function only keeps detections which occur on receivers for which we have deployment metadata. Detections with no deployment metadata are excluded. This is to prevent issues with VTrack.

Now that our data is in a format that VTrack can understand, we can apply VTrack’s functions to it. For example, we can call VTrack’s abacusPlot function to generate an abacus plot of our data:

# Now that we have an ATT dataframe, we can use it in VTrack functions:

# Abacus plot:
VTrack::abacusPlot(ATTdata)

This may not be especially exciting. However, VTrack has its own set of unique features, just like glatos. To use the spacial features of VTrack, however, we have to give the ATT object a coordinate system to use.

# If you're going to do spatial things in ATT:
library(rgdal)
# Tell the ATT dataframe its coordinates are in decimal lat/lon
proj <- CRS("+init=epsg:4326")
attr(ATTdata, "CRS") <-proj

Once that’s done, we can use VTrack’s functions on our dataset. For example, the COA function, which calculates your dataset’s Centers of Activity, can be used like this:

#Calculate centers of activity.
?COA
coa <- VTrack::COA(ATTdata)

coa %>% group_by(Tag.ID) %>% summarize(n())
coa

To see what this brings us, let’s take a look at a plot of the COAs from VTrack. We’ll use animal ‘TQCS-1049273-2008-02-28’ for this.

We also need to get a shapefile for the Florida coastline so that we have something onto which we plot our data. At the time of this workshop, we’ve had some problems connecting to the GADM service from which we get this shapefile. We encourage you to try it as presented in the code. If that doesn’t work, however, you can still get the shapefile this way:

• Go to http://biogeo.ucdavis.edu/data/gadm3.6/Rsp/gadm36_USA_1_sp.rds. Your browser should download a file called gadm36_USA_1_sp.rds.
• Navigate to the file in your file explorer and get the path.
• In RStudio, navigate to the console and run the command USA <- readRDS('/file/path/to/gadm36_USA_1_sp.rds')

You should now be able to continue the code from the line FL <- USA[USA$NAME_1=="Florida",].

# Plot a COA
coa_single <- coa %>% filter(Tag.ID == 'TQCS-1049273-2008-02-28')

# We'll use raster to get the polygon
library(raster)

#The line below might fail. If it does, use one of the alternate methods, or refer to the steps above to get the shapefile
USA <- getData('GADM', country="USA", level=1)

#Alternative method of getting the polygon. 
# f <-  'http://biogeo.ucdavis.edu/data/gadm3.6/Rsp/gadm36_USA_1_sp.rds'
# b <- basename(f)
# download.file(f, b, mode="wb", method="curl")
# if the above doesn't return a file:
# download.file(f,b, method='wget', extra=c('--no-check-certificate'))
# USA <- readRDS('gadm36_USA_1_sp.rds')

FL <- USA[USA$NAME_1=="Florida",]

# plot the object and zoom in to the St. Lucie River and Jupiter Inlet. Set colour of ground to green Add labels to the axises
plot(FL, xlim=c(-80.75, -80), ylim=c(27, 27.5), col='green', xlab="Longitude", ylab="Latitude")

# For much more zoomed in plot
# plot(FL, xlim=c(-80.4, -80.0), ylim=c(27, 27.3), col='green', xlab="Longitude", ylab="Latitude")

# Create a palette
color <- c(colorRampPalette(c('pink', 'red'))(max(coa_single$Number.of.Detections)))

#add the points

points(coa_single$Longitude.coa, coa_single$Latitude.coa, pch=19, col=color[coa_single$Number.of.Detections],
    cex=log(coa_single$Number.of.Stations) + 0.5) # cex is for point size. natural log is for scaling purposes


# add axes and title
axis(1)
axis(2)
title("Centers of Activities for TQCS-1049273-2008-02-28")

For even more data processing functions, here’s an example of dispersalSummary, which calculates your dataset’s metrics of dispersion.

# Dispersal information
# ?dispersalSummary
dispSum<-dispersalSummary(ATTdata)

View(dispSum)

# Get only the detections when the animal just arrives at a station
dispSum %>% filter(Consecutive.Dispersal > 0) %>%  View

This is only the beginning of what you can do with VTrack and its powerful suite of analysis functions, but a full lesson on VTrack is outside the scope of this workshop. We encourage you to look at the VTrack documentation to see what potential applications it might have to your data.

We will, however, continue with glatos for one more lesson, in which we will cover some basic, but very versatile visualization functions provided by the package.

Key Points