More Features of glatos

Overview

Teaching: 15 min
Exercises: 0 min

Questions

• What other features does glatos offer?

Objectives

Note to instructors: please choose the relevant Network below when teaching

ACT Node

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. 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('PROJ59-1191631-2014-07-09', 'PROJ59-1191628-2014-07-07', 'PROJ64-1218527-2016-06-07')
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.

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

FACT Node

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. 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.

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

GLATOS Network

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. 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. With a dataset of this size, it is not strictly necessary, but it is useful to know how to do. On larger datasets, the residence_index() function can take a prohibitively long time to run, and as such there are instances in which you will not want to use the full dataset. Another example of subsetting is therefore helpful.

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 two animal IDs as the basis for our subset.

subset_animals <- c('22', '153')
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.

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

MIGRAMAR Node

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. 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('GMR-25724-2014-01-22', 'GMR-25718-2014-01-17', 'GMR-25720-2014-01-18')
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.

We will 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