Package 'SqueakR'

Description

Loads in a specific Excel File, and (optional) selects a time subset of data.

Usage

add_timepoint_data(data_path, t1 = "", t2 = "")

Arguments

Value

An object containing the call data (under time constraints if specified)

Examples

## Not run: add_timepoint_data(
  data_path = "~/inst/extdata/Example_Mouse_Data.xlsx",
  t1 = 3, t2 = 12
)
## End(Not run)

Description

Adds summarized timepoint data (acquired by running 'add_timepoint_data()', followed by 'score_timepoint_data()') to the created experiment object.

Usage

add_to_experiment(experiment, added_data)

Arguments

Value

A list object containing statistics and metadata for the entire experiment. The 'groups' and 'experimenters' field will auto-populate based on added data using the 'update_experiment()' function.

Examples

## Not run: add_to_experiment(experiment = experiment, added_data = my_data)

Description

Compares data across experimental groups, plotting the data as a bar graph with error bars.

Usage

analyze_factor(experiment, analysis_factor)

Arguments

Value

A bar graph comparing the analysis_factor between groups

Examples

## Not run: analyze_factor(experiment = experiment, analysis_factor = "tonality")

Description

A pipeline function which creates a new experiment object, and adds data and metadata referenced from an external Google Sheets document.

Usage

autosqueakRpipeline()

Value

An object containing the full experiment with all data added.

Examples

## Not run: expt <- autosqueakRpipeline()

Description

Creates an experiment object which will contain all data and metadata. This object will be saved and timestamped by date, so if working with this file over different dates, the object will not be overwritten as a new object will be created (to preserve backups).

Usage

create_experiment(experiment_name)

Arguments

Value

A list object containing statistics and metadata for the entire experiment. The 'groups' and 'experimenters' field will auto-populate based on added data using the 'update_experiment()' function.

Examples

create_experiment(experiment_name = "My-Project")

Description

Data exported from DeepSqueak after processing 'Example Mouse Recording.flac'

Usage

data(deepsqueak_data)

Format

A "data.frame" with 714 rows and 17 columns. The definitions of these parameters are sourced from the link in the references section.

ID

The number of the call

Label

The label extracted from DeepSqueak

Accepted

Whether the call was accepted (as a call)

Score

The call score, from DeepSqueak

Begin Time (s)

The time when the call started

End Time (s)

The time when the call ended

Call Length (s)

The length of the call

Principal Frequency (kHz)

The median frequency of the call

Low Freq (kHz)

The minimum frequency of the call

High Freq (kHz)

The maximum frequency of the call

Delta Freq (kHz)

The difference between the maximum and minimum call frequencies

Frequency Standard Deviation (kHz)

The standard deviation of the call

Slope (kHz/s)

The slope of the call

Sinuosity

The call contour path length divided by the distance between start and end of the call

Mean Power (dB/Hz)

Tonality

The signal/noise ratio

Peak Freq

The frequency at the highest power

Source

(DeepSqueak GitHub)

References

(DeepSqueak Wiki: Export to Excel Page)

Examples

data(deepsqueak_data)

Description

Lists a condensed summary of data stored in the experiment object.

Usage

describe_experiment(experiment)

Arguments

Value

A list of information about the experiment

Examples

## Not run: describe_experiment(experiment = experiment_object)

Description

Plots call clusters within a data-point in a 3D Plotly graph, with principal frequency on the x-axis, mean power on the y-axis, and call length on the z-axis.

Usage

plotClusters(data_path)

Arguments

Value

3D scatterplot

Examples

## Not run: plotClusters(data_path = "path")

Description

Plots call density against principal frequency and call length as a contoured heatmap. Histograms for these variables are displayed across their respective axes.

Usage

plotContours(data_path)

Arguments

Value

2D contour plot

Examples

## Not run: plotContours(data_path = "path")

Description

Plots a box-plot based on principal frequency, grouped by custom labels designated in DeepSqueak.

Usage

plotCorrelations(
  data_path,
  graph_title = "Correlation Matrix",
  graph_subtitle = "Correlation coefficients labeled."
)

Arguments

Value

A ggplot2 visualization of the box-plot shown in the viewer window, which can be manually exported.

Examples

## Not run: plotCorrelations(
  data_path = "inst/extdata/Example_Mouse_Data.xlsx",
  graph_title = "myTitle", graph_subtitle = "myDescription"
)
## End(Not run)

Description

Plots a distribution of delta frequencies as a histogram.

Usage

plotDeltaHistogram(
  data_path,
  graph_title = "Delta Frequency-Labeled Histogram",
  graph_subtitle = "Delta Frequency measures the kHz range of each detected call."
)

Arguments

Value

A ggplot2 visualization of the histogram shown in the viewer window, which can be manually exported.

Examples

## Not run: plotDeltaHistogram(
  data_path = "inst/extdata/Example_Mouse_Data.xlsx",
  graph_title = "myTitle", graph_subtitle = "myDescription"
)
## End(Not run)

Description

Plots a density graph, separating each custom label group into a separate plot in the figure.

Usage

plotDensitySplitByCustom(
  data_path,
  graph_title = "Call Distribution, Split by Custom Category Labels",
  graph_subtitle = "Calls are split by custom labels designated in DeepSqueak.",
  chosen_group = c()
)

Arguments

Value

A ggplot2 visualization of the density graph shown in the viewer window, which can be manually exported.

Examples

## Not run: plotDensitySplitByCustom(
  data_path = "inst/extdata/Example_Mouse_Data.xlsx",
  graph_title = "myTitle", graph_subtitle = "myDescription"
)
## End(Not run)

Description

Plots a density graph, separating each duration group into a separate plot in the figure.

Usage

plotDensitySplitByDuration(
  data_path,
  graph_title = "Call Distribution Grouped by Duration (s)",
  graph_subtitle = "Duration groups are rounded to the nearest 0.01 second.",
  chosen_group = c()
)

Arguments

Value

A ggplot2 visualization of the density graph shown in the viewer window, which can be manually exported.

Examples

## Not run: plotDensitySplitByDuration(
  data_path = "inst/extdata/Example_Mouse_Data.xlsx",
  graph_title = "myTitle", graph_subtitle = "myDescription"
)
## End(Not run)

Description

Plots a density graph, separating each frequency group into a separate plot in the figure.

Usage

plotDensitySplitByFrequency(
  data_path,
  graph_title = "Call Distribution, Split by Frequency Range (kHz)",
  graph_subtitle = "Calls are split by frequency ranges of 10 kHz.",
  chosen_group = c()
)

Arguments

Value

A ggplot2 visualization of the density graph shown in the viewer window, which can be manually exported.

Examples

## Not run: plotDensitySplitByFrequency(
  data_path = "inst/extdata/Example_Mouse_Data.xlsx",
  graph_title = "myTitle", graph_subtitle = "myDescription"
)
## End(Not run)

Description

Plots a density graph, grouped by custom labels set in DeepSqueak, collapsed into a single graph.

Usage

plotDensityStackedByCustom(
  data_path,
  graph_title = "Call Distribution Grouped by Custom Category Labels",
  graph_subtitle = "Calls are grouped by custom categories designated in DeepSqueak.",
  chosen_group = c()
)

Arguments

Value

A ggplot2 visualization of the density graph shown in the viewer window, which can be manually exported.

Examples

## Not run: plotDensityStackedByCustom(
  data_path = "inst/extdata/Example_Mouse_Data.xlsx",
  graph_title = "myTitle", graph_subtitle = "myDescription"
)
## End(Not run)

Description

Plots a density graph, grouped by duration of each call (rounded to the nearest 0.01 second), collapsed into a single graph.

Usage

plotDensityStackedByDuration(
  data_path,
  graph_title = "Call Distribution Grouped by Duration (s)",
  graph_subtitle = "Duration groups are rounded to the nearest 0.01 second.",
  chosen_group = c()
)

Arguments

Value

A ggplot2 visualization of the density graph shown in the viewer window, which can be manually exported.

Examples

## Not run: plotDensityStackedByDuration(
  data_path = "inst/extdata/Example_Mouse_Data.xlsx",
  graph_title = "myTitle", graph_subtitle = "myDescription"
)
## End(Not run)

Description

Plots a density graph, grouped by frequency group collapsed into a single graph.

Usage

plotDensityStackedByFrequency(
  data_path,
  graph_title = "Call Distribution Grouped by Frequency Range (kHz)",
  graph_subtitle = "Calls are grouped by frequency ranges of 10 kHz.",
  chosen_group = c()
)

Arguments

Value

A ggplot2 visualization of the density graph shown in the viewer window, which can be manually exported.

Examples

## Not run: plotDensityStackedByFrequency(
  data_path = "inst/extdata/Example_Mouse_Data.xlsx",
  graph_title = "myTitle", graph_subtitle = "myDescription"
)
## End(Not run)

Description

Plots an ethnogram, representing when detected calls occur through the length of the recording.

Usage

plotEthnogram(
  data_path,
  graph_title = "Call Ethnogram",
  graph_subtitle = "Calls are indicated by a vertical line."
)

Arguments

Value

A ggplot2 visualization of the ethnogram shown in the viewer window, which can be manually exported.

Examples

## Not run: plotEthnogram(
  data_path = "inst/extdata/Example_Mouse_Data.xlsx",
  graph_title = "myTitle", graph_subtitle = "myDescription"
)
## End(Not run)

Description

Plots a tonality ethnogram, representing tonality (clarity) of calls throughout the recording.

Usage

plotEthnogramSplitByTonality(
  data_path,
  graph_title = "Ethnogram Split By Tonality",
  graph_subtitle = "Tonality: Signal/noise"
)

Arguments

Value

A ggplot2 visualization of the ethnogram shown in the viewer window, which can be manually exported.

Examples

## Not run: plotEthnogramSplitByTonality(
  data_path = "inst/extdata/Example_Mouse_Data.xlsx",
  graph_title = "myTitle", graph_subtitle = "myDescription"
)
## End(Not run)

Description

Plots a box-plot based on principal frequency, grouped by custom labels designated in DeepSqueak.

Usage

plotPrincipalBoxplot(
  data_path,
  graph_title = "Principal Frequency-Labeled Box-Plot",
  graph_subtitle = "Main frequencies where calls labeled in DeepSqueak predominate."
)

Arguments

Value

A ggplot2 visualization of the box-plot shown in the viewer window, which can be manually exported.

Examples

## Not run: plotPrincipalBoxplot(
  data_path = "inst/extdata/Example_Mouse_Data.xlsx",
  graph_title = "myTitle", graph_subtitle = "myDescription"
)
## End(Not run)

Description

Plots a density graph, separating each frequency group into a separate plot in the figure.

Usage

plotSummaryPDF(data_path, save_path = tempdir(), experimenter = "Anon")

Arguments

Value

A PDF containing a series of relevant ethnograms and density graphs assessing the recording.

Examples

## Not run: plotSummaryPDF(
  data_path = "inst/extdata/Example_Mouse_Data.xlsx",
  save_path = tempdir(), experimenter = "experimenter_name"
)
## End(Not run)

Description

Plots interactive sunburst plot for animal distribution across experimental groups. Currently only valid for between-groups studies.

Usage

plotSunburstAnimals(experiment)

Arguments

Value

Sunburst distribution plotting animal distribution

Examples

## Not run: plotSunburstAnimals(experiment = experiment)

Description

Plots interactive sunburst plot for experimenter distribution across experimental groups. Currently only valid for between-groups studies.

Usage

plotSunburstExperimenters(experiment)

Arguments

Value

Sunburst distribution plotting experimenter contribution

Examples

## Not run: plotSunburstExperimenters(experiment = experiment)

Description

Plots interactive 3D plot for density of detected calls against call length (s) and principal frequency (kHz), using bivariate kernel density estimation provided via the MASS package

Usage

plotSurface(data_path, res = 60)

Arguments

Value

3D surface plot for the selected call dataset

Examples

## Not run: plotSurface(data_path = "path")

Description

Removes the data object of interest from the experiment according to a data_id. Removing the data with data_id = 12 removes the 12th dataset added to the experiment.

Usage

remove_experiment_data(experiment, data_id)

Arguments

Value

The experiment file, with the rejected call data removed.

Examples

## Not run: experiment <- remove_experiment_data(experiment = experiment_object, data_id = 12)

Description

Saves the experiment as an .RData file in a specified location.

Usage

save_experiment(experiment, save_path)

Arguments

Value

The experiment object, saved as "[NAME_OF_EXPERIMENT] ([SAVE_DATE]).RData" to the specified location.

Examples

## Not run: save_experiment(experiment = experiment_object, save_path = tempdir())

Description

Transforms data into a list of mean, standard deviation, and range of several call metrics.

Usage

score_timepoint_data(data_subset, group, animal, id, experimenter)

Arguments

Value

A list object containing statistics and metadata for each file.

Examples

## Not run: score_timepoint_data(
  data_subset = my_subsetted_data, group = "Control",
  experimenter = "Experimenter 1", animal = "4207", id = "Data_sheet.xlsx"
)
## End(Not run)

Description

A pipeline function which creates a new experiment object and adds data by prompting the user for metadata (for every file that is added).

Usage

semisqueakRpipeline()

Value

An object containing the full experiment with all data added.

Examples

## Not run: expt <- semisqueakRpipeline()

Description

Aggregates raw data tables for all data added to experiment object, and calculates and displays ANOVA statistics.

Usage

squeakrANOVA(experiment, analysis_factor)

Arguments

Value

A table comparing the analysis_factor between groups

Examples

## Not run: squeakrANOVA(experiment = experiment, analysis_factor = "Delta_Freq")

Description

Run the SqueakR Shiny Dashboard locally to interface with experimental data.

Usage

squeakRDashboard()

Value

A Shiny Dashboard

Examples

## Not run: squeakRDashboard()

Description

Aggregates raw data for experiment and summarizes descriptive statistics of the current results, grouped by experimental groups.

Usage

squeakrSummary(experiment)

Arguments

Value

Text which summarizes experiment data split by group

Examples

## Not run: squeakrSTATS(experiment = experiment)

Description

Creates a vector of the original call file names, indexed by the order they are listed in the experiment. This allows experimenters to unblind themselves to the data they collect

Usage

unblind_all_ids(experiment)

Arguments

Value

A vector representing the original call file names

Examples

## Not run: decode_experiment_ids(experiment)

Description

Finds the index of a dataset matching a supplied file name in the experiment.

Usage

unblind_data_id(experiment, filename)

Arguments

Value

A number or numbers representing index or indices where that file appears in the experiment

Examples

## Not run: unblind_data_id(experiment, "my_data1.xlsx")

Description

Finds the name of a dataset matching a supplied index in the set of data

Usage

unblind_data_name(experiment, id)

Arguments

Value

The name of the original file, corresponding to the data at the requested index

Examples

## Not run: unblind_data_name(experiment, 2)

Description

Auto-populates 'groups', 'experimenters', and 'animals' fields in experiment object by checking experimental data (within the experiment object) for new data.

Usage

update_experiment(experiment)

Arguments

Value

A list object containing statistics and metadata for the entire experiment. The 'groups', 'experimenters', and 'animals' fields will auto-populate based on added data using the 'update_experiment()' function.

Examples

## Not run: update_experiment(experiment)