Package 'forceR' reference manual

Title:	Force Measurement Analyses
Description:	For cleaning and analysis of graphs, such as animal closing force measurements. 'forceR' was initially written and optimized to deal with insect bite force measurements, but can be used for any time series. Includes a full workflow to load, plot and crop data, correct amplifier and baseline drifts, identify individual peak shapes (bites), rescale (normalize) peak curves, and find best polynomial fits to describe and analyze force curve shapes.
Authors:	Peter T. Rühr [aut, cre] , Alexander Blanke [ctb]
Maintainer:	Peter T. Rühr <[email protected]>
License:	MIT + file LICENSE
Version:	1.0.21
Built:	2025-01-29 06:10:37 UTC
Source:	https://github.com/peter-t-ruehr/forcer

Charge Amplifier Drift Correction

Description

Removes the systemic, asymptotical drift of charge amplifiers with resistor-capacitor (RC) circuits.

Usage

amp_drift_corr(
  filename,
  tau = 9400,
  res.reduction = 10,
  plot.to.screen = FALSE,
  write.data = FALSE,
  write.PDFs = FALSE,
  write.logs = FALSE,
  output.folder = NULL,
  show.progress = FALSE
)
amp_drift_corr(
  filename,
  tau = 9400,
  res.reduction = 10,
  plot.to.screen = FALSE,
  write.data = FALSE,
  write.PDFs = FALSE,
  write.logs = FALSE,
  output.folder = NULL,
  show.progress = FALSE
)

Arguments

`filename`	Path to file on which amplifier drift correction should be performed.
`tau`	Numeric time constant of charge amplifier in the same time unit as the measurement data. Default: `9400`
`res.reduction`	A numeric value to reduce the number of time steps by during plotting. Speeds up the plotting process and reduces PDF size. Has no effect on the results, only on the plots. Default: `10`.
`plot.to.screen`	A logical value indicating if results should be plotted in the current R plot device. Default: `FALSE`.
`write.data`	A logical value indicating if drift-corrected file should be saved. If yes, it will be saved in `output.folder`. Default: `FALSE`.
`write.PDFs`	A logical value indicating whether results should be saved as PDFs. Does not slow down the process as much as printing to the R plot device and is considered necessary to quality check the results. If yes, it will be saved in `output.folder/PDFs`. Default: `FALSE`.
`write.logs`	A logical value indicating whether a log file with information on the method and values used to correct the amplifier drift should be saved. Is considered necessary for reproducibility. If yes, it will be saved in `output.folder/logs`. Default: `FALSE`.
`output.folder`	Path to folder where data, PDF and log files should be stored.
`show.progress`	A logical value indicating if progress should be printed to the console. Slows down the process. Default: `FALSE`.

Details

forceR generally expects file names to start with a leading number specifying the measurement number (E.g. "0001_G_maculatus.csv"). The number ("0001") is used to keep data files, log files, and PDF files of the same measurement associated with each other.

The input file should be in the following format:

`t`		`y`
`t.1`		`y.2`
`...`		`...`
`t.n`		`y.n`

Value

Returns a tibble containing the amplifier drift-corrected data in the following format

`t`		`y`
`t.1`		`y.2`
`...`		`...`
`t.n`		`y.n`

Examples

# define file for amplifier drift correction
filename <- forceR_example(type = "raw")

# Run amplifier drift correction without saving files or printing to screen:
file.ampdriftcorr <- amp_drift_corr(filename = filename,
                                     tau = 9400,
                                     res.reduction = 10,
                                     plot.to.screen = FALSE,
                                     write.data = FALSE,
                                     write.PDFs = FALSE,
                                     write.logs = FALSE,
                                     output.folder,
                                     show.progress = FALSE)
# file.ampdriftcorr

# Run amplifier drift correction with saving files and printing to screen:
#    - commented out to pass package tests
# file.ampdriftcorr <- amp_drift_corr(filename = filename,
#                                       tau = 9400,
#                                       res.reduction = 10,
#                                       plot.to.screen = TRUE,
#                                       write.data = TRUE,
#                                       write.PDFs = TRUE,
#                                       write.logs = TRUE,
#                                       output.folder = "./ampdriftcorr",
#                                       show.progress = TRUE)
#
# file.ampdriftcorr

# define file for amplifier drift correction
filename <- forceR_example(type = "raw")

# Run amplifier drift correction without saving files or printing to screen:
file.ampdriftcorr <- amp_drift_corr(filename = filename,
                                     tau = 9400,
                                     res.reduction = 10,
                                     plot.to.screen = FALSE,
                                     write.data = FALSE,
                                     write.PDFs = FALSE,
                                     write.logs = FALSE,
                                     output.folder,
                                     show.progress = FALSE)
# file.ampdriftcorr

# Run amplifier drift correction with saving files and printing to screen:
#    - commented out to pass package tests
# file.ampdriftcorr <- amp_drift_corr(filename = filename,
#                                       tau = 9400,
#                                       res.reduction = 10,
#                                       plot.to.screen = TRUE,
#                                       write.data = TRUE,
#                                       write.PDFs = TRUE,
#                                       write.logs = TRUE,
#                                       output.folder = "./ampdriftcorr",
#                                       show.progress = TRUE)
#
# file.ampdriftcorr

Average Curves per Group

Description

Calculates mean curve shape per group (here: species) and rescales result on the y axis to range from 0 to 1.

Usage

avg_peaks(df, path.data = NULL)
avg_peaks(df, path.data = NULL)

Arguments

`df`	The resulting tibble of the function `red_peaks_100()`. See `?red_peaks_100` for more details.
`path.data`	A string character defining where to save the results. If `NULL`, data will not be saved to disk. Default: `NULL`.

Value

This function returns a tibble made of three columns: species containing the species names, index ranging from 1 to 100 for each species, and force.norm.100 containing the averaged and rescaled curve of each species.

Examples

# Using the forceR::df.all.200.tax dataset:

# calculate mean curves per species
peaks.df.100.avg <- avg_peaks(df = forceR::peaks.df.norm.100,
                               path.data = NULL)

# plot averaged normalized curves per species
require(ggplot2)
ggplot(peaks.df.100.avg,
          aes(x = index ,
               y = force.norm.100.avg,
               colour=species)) +
  geom_line()

# Using the forceR::df.all.200.tax dataset:

# calculate mean curves per species
peaks.df.100.avg <- avg_peaks(df = forceR::peaks.df.norm.100,
                               path.data = NULL)

# plot averaged normalized curves per species
require(ggplot2)
ggplot(peaks.df.100.avg,
          aes(x = index ,
               y = force.norm.100.avg,
               colour=species)) +
  geom_line()

Automatic or Manual Baseline Correction of Time Series

Description

If baseline (zero-line) of measurement is unstable (e.g. due to temperature fluctuations, wind, ...), the baseline needs to be continually adjusted throughout the measurement. This script allows an automatic adjustment of the baseline. The automatic approach invokes a sliding window, during which the 'minimum' within each sliding window is stored. A 'minimum' is defined by the quantile.size: if set to 0.05, the value below which only 5% of the measurement data within the sliding window lies, is treated as the current window's minimum. This prevents the treatment of potential artifacts as minima. In a second iteration, another sliding window calculates the average of these 'minima'. The resulting values are subtracted from the original time series. This approach works well for time series with relatively short peaks. If the automatic approach does not yield acceptable results, an interactive manual approach to correct the baseline can be performed instead.

Usage

baseline_corr(
  filename,
  corr.type = "auto",
  window.size.mins = 1000,
  window.size.means = NULL,
  quantile.size = 0.05,
  y.scale = 0.5,
  res.reduction = 10,
  Hz = 100,
  plot.to.screen = FALSE,
  write.data = FALSE,
  write.PDFs = FALSE,
  write.logs = FALSE,
  output.folder = NULL,
  show.progress = FALSE
)
baseline_corr(
  filename,
  corr.type = "auto",
  window.size.mins = 1000,
  window.size.means = NULL,
  quantile.size = 0.05,
  y.scale = 0.5,
  res.reduction = 10,
  Hz = 100,
  plot.to.screen = FALSE,
  write.data = FALSE,
  write.PDFs = FALSE,
  write.logs = FALSE,
  output.folder = NULL,
  show.progress = FALSE
)

Arguments

`filename`	A character string containing the full path to the measurement file that needs correction. See Details for info on what the file should look like.
`corr.type`	Character string defining the desired mode of baseline correction. One of `"auto"` or `"manual"`. Default: `"auto"`
`window.size.mins`	A numeric value for the size of the search window to find minima in. Should be in the same time unit as the measurement. Longer peaks require higher values, shorter peaks require smaller values. Default: `1000`.
`window.size.means`	A numeric value for the size of the window to average the minima in. Should be in the same time unit as the measurement. By default (`NULL`), the same value as specified for `window.size.mins` is used.
`quantile.size`	A numerical value between `0` and `1` to define the quantile which is treated as the 'minimum' of a sliding window. Default: `0.05`.
`y.scale`	A numeric value to reduce the y-axis range during plotting. This simplifies the manual placement of the points during the manual correction procedure.
`res.reduction`	A numeric value to reduce the number of time steps by during plotting. Speeds up the plotting process and reduces PDF size. Has no effect on the results, only on the plots. Default: `10`.
`Hz`	A numeric value to reduce sampling frequency for temporary analyses. This works as a smoothing filter during temporary analyses and does not reduce the actual sampling frequency of the data. Default: `100`.
`plot.to.screen`	A logical value indicating if results should be plotted in the current R plot device. Default: `FALSE`.
`write.data`	A logical value indicating if drift-corrected file should be saved. If yes, it will be saved in `output.folder`. Default: `FALSE`.
`write.PDFs`	A logical value indicating whether results should be saved as PDFs. Does not slow down the process as much as printing to the R plot device and is considered necessary to quality check the results. If yes, it will be saved in `output.folder/PDFs`. Default: `FALSE`.
`write.logs`	A logical value indicating whether a log file with information on the method and values used to correct the baseline drift should be saved. Is considered necessary for reproducibility. If yes, it will be saved in `output.folder/logs`. Default: `FALSE`.
`output.folder`	Path to folder where data, PDF and log files should be stored. Default: `NULL.`
`show.progress`	A logical value indicating if progress should be printed to the console. Default: `FALSE`.

Details

forceR generally expects file names to start with a leading number specifying the measurement number (E.g. "0001_G_maculatus.csv"). The number ("0001") is used to keep data files, log files, and PDF files of the same measurement associated with each other.

The input files should to be in the following format:

`t`		`y`
`t.1`		`y.2`
`...`		`...`
`t.n`		`y.n`

In case there are more than two columns, only the first two columns will be used. If the first two columns are not named 't' and 'y', they will be renamed.

Value

Returns a tibble containing the amplifier baseline drift-corrected data in the following format

`t`		`y`
`t.1`		`y.2`
`...`		`...`
`t.n`		`y.n`

Examples

#'########### AUTOMATIC MODE
# define file to apply the baseline drift correction to
filename <- forceR_example(type = "ampdriftcorr")

# run automatic baseline drift corrections without saving files or
#   printing to screen:
file.baseline_corr <- baseline_corr(filename = filename,
                                     corr.type = "auto",
                                     window.size.mins = 1000,
                                     window.size.means = NULL,
                                     quantile.size = 0.05,
                                     y.scale = 0.5,
                                     res.reduction = 10,
                                     Hz = 100,
                                     plot.to.screen = FALSE,
                                     write.data = FALSE,
                                     write.PDFs = FALSE,
                                     write.logs = FALSE,
                                     show.progress = FALSE)

file.baseline_corr

#'########### MANUAL MODE

# define file to apply the baseline drift correction to
filename <- forceR_example(type = "ampdriftcorr")

# run interactive baseline drift corrections with saving files and
#   printing to screen:  - out-commented to pass package tests

# file.baseline_corr <- baseline_corr(filename = filename,
#                                       corr.type = "manual",
#                                       window.size.mins = 1000,
#                                       window.size.means = NULL,
#                                       quantile.size = 0.05,
#                                       y.scale = 0.5,
#                                       res.reduction = 10,
#                                       Hz = 100,
#                                       plot.to.screen = TRUE,
#                                       output.folder = "./baselinecorr",
#                                       show.progress = TRUE)

# file.baseline_corr
#'########### AUTOMATIC MODE
# define file to apply the baseline drift correction to
filename <- forceR_example(type = "ampdriftcorr")

# run automatic baseline drift corrections without saving files or
#   printing to screen:
file.baseline_corr <- baseline_corr(filename = filename,
                                     corr.type = "auto",
                                     window.size.mins = 1000,
                                     window.size.means = NULL,
                                     quantile.size = 0.05,
                                     y.scale = 0.5,
                                     res.reduction = 10,
                                     Hz = 100,
                                     plot.to.screen = FALSE,
                                     write.data = FALSE,
                                     write.PDFs = FALSE,
                                     write.logs = FALSE,
                                     show.progress = FALSE)

file.baseline_corr

#'########### MANUAL MODE

# define file to apply the baseline drift correction to
filename <- forceR_example(type = "ampdriftcorr")

# run interactive baseline drift corrections with saving files and
#   printing to screen:  - out-commented to pass package tests

# file.baseline_corr <- baseline_corr(filename = filename,
#                                       corr.type = "manual",
#                                       window.size.mins = 1000,
#                                       window.size.means = NULL,
#                                       quantile.size = 0.05,
#                                       y.scale = 0.5,
#                                       res.reduction = 10,
#                                       Hz = 100,
#                                       plot.to.screen = TRUE,
#                                       output.folder = "./baselinecorr",
#                                       show.progress = TRUE)

# file.baseline_corr

Classifier

Description

Start and end time values of the 5 strongest peaks per species of df.all with the names of the measurements in which they occur.

Usage

classifier
classifier

Format

A data frame with 24 rows and 5 columns:

species: species names
specimen: specimen names
measurement: measurement names
amp: amplifier values, in V/N
lever.ratio: ration of on-lever to out-lever of mesaurement setup

Details

Result of rescale_peaks().

Converts LJStream *.dat file to standard time series.

Description

Converts LJStream *.dat file to standard time series.

Usage

convert_measurement(file, path.data = NULL, collect_garbage = FALSE)
convert_measurement(file, path.data = NULL, collect_garbage = FALSE)

Arguments

`file`	File path to raw measurement (*.dat file).
`path.data`	A string character defining where to save the results. If `NULL`, data is not stored in a file. Default: `NULL`.
`collect_garbage`	Logical. If `TRUE`, then the `gc()` command will be run silently to try to clean up memory. This may help when running `convert_measurement` in a loop, even though memory cluttering cannot be fully prevented. If such a loop crashes, the loop should be split into several separate loops to convert all files. Default: `FALSE`.

Value

Returns and, if ⁠path.data is not NULL⁠, saves data in csv-format in path.data.

The output tibble has the following format:

`t`		`y`
`t.1`		`y.1`
`...`		`...`
`t.n`		`y.n`

Examples

# get file path of forceR example file
filename <- forceR_example(type = "LJStream")
file.converted <- convert_measurement (file = filename,
                       path.data = NULL)
file.converted

# get file path of forceR example file
filename <- forceR_example(type = "LJStream")
file.converted <- convert_measurement (file = filename,
                       path.data = NULL)
file.converted

Manually Correct Single Peak

Description

Interactive correction of a single peak.

Usage

correct_peak(
  df.peaks,
  df.data,
  measurement,
  peak,
  additional.msecs = 500,
  path.data = NULL
)
correct_peak(
  df.peaks,
  df.data,
  measurement,
  peak,
  additional.msecs = 500,
  path.data = NULL
)

Arguments

`df.peaks`	The resulting tibble of the function `find_peaks()`. See `?find_peaks` for more details.
`df.data`	A data frame or tibble in the below format. The columns `t` (time), `force`, `measurement`, and `specimen`. (measurement ID) must be present. This will usually be the same table that was used before in `find_peaks()`.
`measurement`	A character string defining the measurement ID (e.g. measurement number) of the peak to be corrected. Must be congruent with the respective measurement ID within `df.peaks` and `df`.
`peak`	A numerical value defining the peak to be corrected.
`additional.msecs`	A numerical value defining the time before and after the originally defined window of the peak to be corrected. Higher values allow defining start and end points further away from the original start and end points. Default: `500`. @param path.data A string character defining where to save the result and log file. If `NULL` (default), data is not stored in files. Log files cannot be retrieved in this case.
`path.data`	A string character defining where to save the results. If `NULL` (default), data is not stored in a file.

Details

When running the function, you will be prompted to select the actual start and end of the current peak of the current measurement. If more or less than exactly 2 points are defined, the process is terminated.

Value

Changes values within df.peaks and returns the changed tibble.

`df.data` needs to contain the following columns:

`t`	`force`	`measurement`
`t.1`	`force.1`	`measurement.1`
`...`	`...`	`...`
`t.n`	`force.n`	`measurement.m`

Examples

# Using the forceR::df.all.200.tax dataset:

# This function needs user input.
peaks.df <- correct_peak(df.peaks = forceR::peaks.df,
                        df.data = forceR::df.all.200.tax,
                        measurement = "m_01",
                        peak = 1,
                        additional.msecs = 5)

# Using the forceR::df.all.200.tax dataset:

# This function needs user input.
peaks.df <- correct_peak(df.peaks = forceR::peaks.df,
                        df.data = forceR::df.all.200.tax,
                        measurement = "m_01",
                        peak = 1,
                        additional.msecs = 5)

Crop Time Series

Description

Interactive function to crop a time series.

Usage

crop_measurement(file, path.data = NULL)
crop_measurement(file, path.data = NULL)

Arguments

`file`	File path to measurement.
`path.data`	A string character defining where to save the results. If `NULL`, data is not stored in a file. Default: `NULL`.

Details

Select points at start and end of desired part of measurements. Only the last two points will be taken into account to allow the user to correct erroneous clicks.

If a measurement contains two distinct regions of bites with a lot of unnecessary data and/or measurement artefacts in-between (such as user-made peaks), I recommend to manually copy the RAW data files, give the copy a new measurement number (as if it was actually a separate measurement), and crop the distinct parts containing actual bites separately from the two copies of the file. For more distinct regions, create more copies.

I recommend to not crop the files too much in case baseline corrections are needed later, because then the baseline_corr() function will not be able to figure out where the actual baseline might be. Leaving several seconds before and after the first and last bite of a series will prevent such problems.

Value

Returns and, if ⁠path.data is not NULL⁠, saves data in csv-format in path.data.

The tibble has the following format:

`t`		`y`
`t.1`		`y.1`
`...`		`...`
`t.n`		`y.n`

Examples

# get file path of forceR example file
filename <- forceR_example(type = "raw")

# # crop file without storing result as file - out-commented to pass package tests
# file.cropped <- crop_measurement(file = filename,
#                    path.data = NULL)

# file.cropped

# get file path of forceR example file
filename <- forceR_example(type = "raw")

# # crop file without storing result as file - out-commented to pass package tests
# file.cropped <- crop_measurement(file = filename,
#                    path.data = NULL)

# file.cropped

Simulated Force Measurements with Taxonomic Info.

Description

A series of 24 measurements with six simulated peaks each. Simulated using the simulate_bites() function . Fits the classifier sheet that is also part of the forceR package.

Usage

df.all
df.all

Format

A data frame with 9,600 rows and 3 columns:

t: time, in ms
y: measured values, in V
measurement: measurement names

Simulated Time Series - e.g. Bite Force Measurements

Description

A series of 24 measurements with six simulated peaks each. Simulated using the simulate_bites() function. Reduced to a sampling frequency of 200 Hz with reduce_frq(). Fits the classifier sheet that is also part of the forceR package.

Usage

df.all.200
df.all.200

Format

A data frame with 1,944 rows and 3 columns:

t: time, in ms
y: measured values, in V
measurement: measurement names

Simulated Force Measurements with Taxonomic Info.

Description

A series of 24 measurements with six simulated peaks each. Simulated using the simulate_bites() function and translated into force values with y_to_force(). Fits the classifier sheet that is also part of the forceR package.

Usage

df.all.200.tax
df.all.200.tax

Format

A data frame with 1,944 rows and 5 columns:

species: species names
specimen: specimen names
measurement: measurement names
t: time, in ms
force: measured values, in N

Find Best Polynomial Fits for Curves

Description

Calculates best model fits for all curves based on AIC criterion. The function fits polynomial functions with 1 to 20 coefficients and uses the Akaike Information Criterion (AIC) to evaluate the goodness of the fits. A model is considered a good fit, when the percentage of change from one model to the next (e.g. a model with 6 coefficients to a model with 7 coefficients) is, e.g. ⁠< 5%⁠ when threshold = 5. The first for models meeting this criterion are plotted as colored graphs and the AICs of these models are visualized in a second plot for each curve. All first four coefficients per curve that fulfill the criterion are stored and in the end, a histogram of how often which coefficients were good fits is plotted as well. The function returns the numerical value of the coefficient that fulfilled the criterion of a good fit in most curves.

Usage

find_best_fits(
  df,
  degrees = 1:20,
  threshold = 5,
  zero_threshold = NULL,
  plot.to.screen = FALSE,
  path.data = NULL,
  path.plots = NULL,
  show.progress = FALSE
)
find_best_fits(
  df,
  degrees = 1:20,
  threshold = 5,
  zero_threshold = NULL,
  plot.to.screen = FALSE,
  path.data = NULL,
  path.plots = NULL,
  show.progress = FALSE
)

Arguments

`df`	The resulting tibble of the function `avg_peaks()`. See below for more details.
`degrees`	Numerical vector of polynomial degrees to test. Cannot be infinitely high - if two high, throws error: `⁠'degree' must be less than number of unique points⁠`. Default: `1:20`.
`threshold`	Percentage of AIC change compared to previous degree to fit the good-fit-criteria (s.a.). Default: `5`.
`zero_threshold`	Either numerical or `NULL`: If numerical, the function checks if the graph of the current model starts and ends near zero, e.g. below 0.2 if `zero_threshold = 0.2`. Default: `NULL`.
`plot.to.screen`	A logical value indicating if results should be plotted in the current R plot device. Default: `FALSE`.
`path.data`	A string character defining where to save the results. If `NULL`, data is not stored in a file. Default: `NULL`.
`path.plots`	A string character defining where to save the plots. If `NULL`, plots will not be saved to PDF files. Default: `NULL`.
`show.progress`	A logical value indicating if progress should be printed to the console. Default: `FALSE`.

Details

#' This function expects a tibble made of three columns as df: species containing the species names, index numerical column, e.g. time (but can be arbitrary continuous unit), for each species, and force.norm.100 containing the averaged and rescaled curve of each species.

Value

Returns the a numerical value representing the number of coefficient that was most often under the first 4 models that were followed by an AIC-change ⁠<= 5%⁠ by the next model. Additionally, plots showing the model fits and a histogram of the coefficients that met the 5%-criterion can be plotted to the plot device or saved as PDFs in path.plots.

Examples

# Using the forceR::peaks.df.100.avg dataset:

# find smallest polynomial degree that best describes all curves
best.fit.poly <- find_best_fits(df = forceR::peaks.df.100.avg)

best.fit.poly

# Using the forceR::peaks.df.100.avg dataset:

# find smallest polynomial degree that best describes all curves
best.fit.poly <- find_best_fits(df = forceR::peaks.df.100.avg)

best.fit.poly

Find Peaks

Description

Identifies peaks in a first iteration and optimizes the starts and ends of the strongest peaks per species in a second iteration.

Usage

find_strongest_peaks(
  df,
  no.of.peaks = 5,
  initial.threshold = 0.05,
  slope.length.start = 5,
  slope.length.end = 5,
  slope.thresh.start = 0.02,
  slope.thresh.end = 0.02,
  path.data = NULL,
  path.plots = NULL,
  show.progress = FALSE
)
find_strongest_peaks(
  df,
  no.of.peaks = 5,
  initial.threshold = 0.05,
  slope.length.start = 5,
  slope.length.end = 5,
  slope.thresh.start = 0.02,
  slope.thresh.end = 0.02,
  path.data = NULL,
  path.plots = NULL,
  show.progress = FALSE
)

Arguments

`df`	A data frame or tibble in the below format. The columns `t` (= time), `force` and `measurement` (= measurement ID) must be present.
`no.of.peaks`	A numeric value defining how many peaks per `species` (not per `measurement`") should be identified. The function will always return the strongest peaks. Default: `5`
`initial.threshold`	A numeric value defining the threshold (in % of the maximum force of the measurement) that is used during the first iteration. Default: `0.05`
`slope.length.start`	A numeric value defining the window size (in time steps) of slope calculation for peak starts during the second iteration. Default: `5`
`slope.length.end`	A numeric value defining the window size (in time steps) of slope calculation for peak ends during the second iteration. Default: `5`
`slope.thresh.start`	A numeric value defining the threshold at which to stop the sliding window and save the current time point as the actual start of the current peak. Default: `0.04`
`slope.thresh.end`	A numeric value defining the threshold at which to stop the sliding window and save the current time point as the actual end of the current peak. Default: `0.04`
`path.data`	A string character defining where to save the results. If `NULL` (default), data is not stored in a file. Default: `NULL`.
`path.plots`	A string character defining where to save the plots. Default: `NULL`.
`show.progress`	A logical value indicating if progress should be printed to the console. Default: `FALSE`.

Details

The input data frame df needs to contain the following columns:

`t`	`force`	`measurement`
`t.1`	`force.1`	`measurement.1`
`...`	`...`	`...`
`t.n`	`force.n`	`measurement.m`

Value

Creates a tibble in the following format and saves it as a CSV-file: The column species contains one species per row
(⁠species.1 ... species.n⁠).

The column measurements contains as many measurements as no.of.peaks, separated by '⁠;⁠':
(⁠measurement.1; ...; measurements.no.of.peaks⁠).

The column starts contains as many peak starts as no.of.peaks, separated by '⁠;⁠':
(⁠start.1; ...; start.no.of.peaks⁠).

The column ends contains as many peak ends as no.of.peaks, separated by '⁠;⁠':
(⁠end.1; ...; end.no.of.peaks⁠).

Examples

require(dplyr)
# Using the forceR::df.all.200.tax dataset:

# reduce dataset (only rows 40 to 95 of species A (containing data of
  # measurement 1 and 2 (m_01 and m_02)))
df.all.200.tax_filtered <- forceR::df.all.200.tax[40:95, ] %>%
  filter(species == "species_A")

# find the 4 strongest peaks
peaks.df <- find_strongest_peaks(df = df.all.200.tax_filtered,
                                 no.of.peaks = 4)

# plot results (three peaks in measurement 1, 1 peak in measurement 2):
# plot_peaks(df.peaks = peaks.df,
#            df.data = df.all.200.tax_filtered,
#            additional.msecs = 20)

require(dplyr)
# Using the forceR::df.all.200.tax dataset:

# reduce dataset (only rows 40 to 95 of species A (containing data of
  # measurement 1 and 2 (m_01 and m_02)))
df.all.200.tax_filtered <- forceR::df.all.200.tax[40:95, ] %>%
  filter(species == "species_A")

# find the 4 strongest peaks
peaks.df <- find_strongest_peaks(df = df.all.200.tax_filtered,
                                 no.of.peaks = 4)

# plot results (three peaks in measurement 1, 1 peak in measurement 2):
# plot_peaks(df.peaks = peaks.df,
#            df.data = df.all.200.tax_filtered,
#            additional.msecs = 20)

Get path to forceR example

Description

forceR comes with example files of short bite force measurements. The files are stored in ⁠forceR's⁠ inst/extdata folder, and this function returns the path to that folder or one of the files so they can be used in examples.

Usage

forceR_example(type = "folder")
forceR_example(type = "folder")

Arguments

type

A character string (either "folder", "raw", or "ampdriftcorr") defining if the path returned be the function should point to one of the the example files or the folder containing them. Default: "folder".

Value

If type = "folder": returns the file path to the folder containing BF_raw.csv and BF_ampdriftcorr.csv.

If type = "LJStream": returns the file path to BF_raw.csv, which contains a short bite force raw measurement.

If type = "raw": returns the file path to BF_raw.csv, which contains a short bite force raw measurement.

If type = "ampdriftcorr": returns the file path to BF_ampdriftcorr.csv, which contains a short bite force raw measurement where the amplifier drift as been corrected for by the amp_drift_corr() funcion.

Load Multiple Measurements

Description

Loads multiple measurements.

Usage

load_mult(folder, columns = c(1:2), show.progress = FALSE)
load_mult(folder, columns = c(1:2), show.progress = FALSE)

Arguments

`folder`	Character string containing the path to the measurements.
`columns`	A vector of column numbers. The first entry will be used as the x-axis values, the second entry as y-axis values. All other columns will be ignored. Default: `c(1,2)`.
`show.progress`	A logical value indicating if progress should be printed to the console. Default: `FALSE`.

Details

The input files need to be in the following format (even though column names do not matter):

`t`	`y`
`t.1`	`y.1`
`...`	`...`
`t.n`	`y.n`

All columns except the first two are removed.

Value

Returns a tibble in the format

`t`	`y`	`filename`
`t.1`	`y.1`	`...`
`...`	`...`	`...`
`t.n`	`y.n`	`...`

Examples

# store name of folder that contains files
input.folder <- forceR_example(type = "folder")

# load a mutiple files
df.all <- load_mult(folder = input.folder,
                    columns = c(1:2),
                    show.progress = FALSE)
# store name of folder that contains files
input.folder <- forceR_example(type = "folder")

# load a mutiple files
df.all <- load_mult(folder = input.folder,
                    columns = c(1:2),
                    show.progress = FALSE)

Load single measurement

Description

Loads a single measurement.

Usage

load_single(filename, columns = c(1:2))
load_single(filename, columns = c(1:2))

Arguments

`filename`	Character string containing the path to measurement file.
`columns`	A vector of column numbers. The first entry will be used as the x-axis values, the second entry as y-axis values. All other columns will be ignored. Default: `c(1,2)`.

Details

#' The input files need to be in the following format (even though column names do not matter):

`t`		`y`
`t.1`		`y.1`
`...`		`...`
`t.n`		`y.n`

All columns except the first two are removed.

Value

A tibble with two columns named "t" and "y".

Examples

# Store filename
filename <- forceR_example(type="raw")

df.1 <- load_single(filename,
                    columns = c(1:2))

# Store filename
filename <- forceR_example(type="raw")

df.1 <- load_single(filename,
                    columns = c(1:2))

Polynomial Models Describing Average Peak Shapes.

Description

Extracted from df.all.

Usage

models
models

Format

A list with 4 named entries, each containing the 4-degree polynomial model that describes the average peak shape of the given species.

Peak Duration and Maximum Force

Description

Calculate duration and maximum force for each individual peak.

Usage

peak_duration_max_force(
  df.peaks,
  df.data,
  path.data = NULL,
  show.progress = FALSE
)
peak_duration_max_force(
  df.peaks,
  df.data,
  path.data = NULL,
  show.progress = FALSE
)

Arguments

`df.peaks`	The resulting tibble of the function `find_peaks()`. See `?find_peaks` for more details.
`df.data`	A data frame or tibble in the below format. The columns `t` (time), `force`, `measurement`, and `specimen`. (measurement ID) must be present. This will usually be the same table that was used before in `find_peaks()`.
`path.data`	A string character defining where to save the results. If `NULL` (default), data is not stored in a file.
`show.progress`	A logical value indicating if progress should be printed to the console. Default: `FALSE`.

Value

Changes values within df.peaks and returns the changed tibble.

`df.data` needs to contain the following columns:

`t`	`force`	`measurement`
`t.1`	`force.1`	`measurement.1`
`...`	`...`	`...`
`t.n`	`force.n`	`measurement.m`

Examples

# Using the forceR::df.all.200.tax dataset:

# This function needs user input.
peaks.df <- correct_peak(df.peaks = forceR::peaks.df,
                        df.data = forceR::df.all.200.tax,
                        measurement = "m_01",
                        peak = 1,
                        additional.msecs = 5)

# Using the forceR::df.all.200.tax dataset:

# This function needs user input.
peaks.df <- correct_peak(df.peaks = forceR::peaks.df,
                        df.data = forceR::df.all.200.tax,
                        measurement = "m_01",
                        peak = 1,
                        additional.msecs = 5)

Convert Time Series to Polynomial

Description

Convert Time Series to Polynomial

Usage

peak_to_poly(df, coeff, path.data = NULL, show.progress = FALSE)
peak_to_poly(df, coeff, path.data = NULL, show.progress = FALSE)

Arguments

`df`	The resulting tibble of the function `avg_peaks()`. See `?avg_peaks` for more details.
`coeff`	A numerical value indicating the number of coefficients the model used to fit on the time series data should have.
`path.data`	A string character defining where to save the results as `⁠.csv⁠` and `⁠.R⁠`. If `NULL`, data is not stored in files. Default: `NULL`.
`show.progress`	A logical value indicating if progress should be printed to the console. Default: `FALSE`.

Value

A list with the length equal to the number of unique species within df containing the fitted models.

Examples

# Using the forceR::peaks.df.100.avg dataset:

# define the number of coefficients the polynomial models should have
number_of_coeffs = 4

# convert curves to polynomial models
models <- peak_to_poly(df = forceR::peaks.df.100.avg,
                        coeff = number_of_coeffs)

models
# Using the forceR::peaks.df.100.avg dataset:

# define the number of coefficients the polynomial models should have
number_of_coeffs = 4

# convert curves to polynomial models
models <- peak_to_poly(df = forceR::peaks.df.100.avg,
                        coeff = number_of_coeffs)

models

Starts and Ends of the 5 Strongest Peaks

Description

Start and end time values of the 5 strongest peaks per species of df.all with the names of the measurements in which they occur.

Usage

peaks.df
peaks.df

Format

A data frame with 4 rows and 4 columns:

species: species names
measurements: measurement names
starts: start values, in ms
ends: end values, in ms

Details

Result of rescale_peaks().

Average Peak Shapes per Species

Description

Normalized force values describing the average shape of the 5 strongest peaks per species of df.all

Usage

peaks.df.100.avg
peaks.df.100.avg

Format

A data frame with 400 rows and 3 columns:

species: species names
index: values from 1:100 in each species
force.norm.100.avg: normalized values reduced to 100 observations per species, in N

Normalized Peak Shapes

Description

Normalized force values describing the shapes of all 5 strongest peaks per species of df.all

Usage

peaks.df.norm
peaks.df.norm

Format

A data frame with 223 rows and 6 columns:

measurement: measurement names
peak: peak numbers
t.norm: time values from 0:1 in each measurement, in ms
force.norm: force values from 0:1 in each measurement, in N
species: species names
specimen: specimen names

Details

Result of rescale_peaks().

Normalized Peak Shapes with 100 Observations

Description

Normalized force values describing the shapes of all 5 strongest peaks per species of df.all, reduced to 100 observations per measurement.

Usage

peaks.df.norm.100
peaks.df.norm.100

Format

A data frame with 2000 rows and 6 columns:

species: species names
measurement: measurement names
specimen: specimen names
peak: peak numbers
index: values from 1:100 in each measurement
force.norm.100: normalized values reduced to 100 observations per measurement, in N

Details

Result of rescale_peaks().

Plot raw measurement

Description

Plots a time series.

Usage

plot_measurement(file, columns = c(1:2))
plot_measurement(file, columns = c(1:2))

Arguments

`file`	File path to measurement.
`columns`	A vector of column numbers. The first entry will be used as the x-axis values, the second entry as y-axis values. All other columns will be ignored. Default: `c(1,2)`.

Details

#' The input files need to be in the following format (even though column names do not matter):

`t`		`y`
`t.1`		`y.1`
`...`		`...`
`t.n`		`y.n`

Value

Creates a plot in the current plot device.

Examples

filename = forceR_example(type = "raw")
plot_measurement(filename)

filename = forceR_example(type = "raw")
plot_measurement(filename)

Plot Peaks

Description

Plots the peaks identified by the function find_peaks().

Usage

plot_peaks(
  df.peaks,
  df.data,
  additional.msecs = 2000,
  plot.to.screen = TRUE,
  path.plots = NULL,
  show.progress = FALSE
)
plot_peaks(
  df.peaks,
  df.data,
  additional.msecs = 2000,
  plot.to.screen = TRUE,
  path.plots = NULL,
  show.progress = FALSE
)

Arguments

`df.peaks`	df.peaks The resulting tibble of the function `find_peaks()`. See `?find_peaks` for more details.
`df.data`	A data frame or tibble in the below format. The columns `t` (time), `force` and `measurement` (measurement ID) must be present. This will usually be the same table that was used before in `find_peaks()`.
`additional.msecs`	A numeric value indicating how many m.secs before and after the actual peak curve should be plotted. Default: `2000`
`plot.to.screen`	A logical value indicating if results should be plotted in the current R plot device. Default: `TRUE`.
`path.plots`	A string character defining where to save the plots. If `NULL`, plots will not be saved to PDF files. Default: `NULL`
`show.progress`	A logical value indicating if progress should be printed to the console. Default: `FALSE`.

Details

df.peaks at least needs to contain the following columns:

measurements | starts | ends | | :—-: | :—-: |:—-: |:—-: | | measurements.1 | starts.1 | ends.1 | | ... | ... | ... | | measurements.n | starts.m | ends.m |

Check forceR::peaks.df to see an example tibble.

df.data at least needs to contain the following columns:

`t`	`force`	`measurement`
`t.1`	`force.1`	`measurement.1`
`...`	`...`	`...`
`t.n`	`force.n`	`measurement.m`

Check forceR::df.all.200.tax to see an example tibble.

Value

Plots one graph per peak curve and, if plot.to.pdf == TURE, saves all peak curves as one PDF at path.plots.

Examples

# Using the first row of forceR::peaks.df and the forceR::df.all.200.tax dataset:

# plot peaks
plot_peaks(df.peaks = forceR::peaks.df[1, ],
           df.data = forceR::df.all.200.tax,
           additional.msecs = 20) # instead of the default (2000) because of
                                  # the highly downsampled example dataset.

# Using the first row of forceR::peaks.df and the forceR::df.all.200.tax dataset:

# plot peaks
plot_peaks(df.peaks = forceR::peaks.df[1, ],
           df.data = forceR::df.all.200.tax,
           additional.msecs = 20) # instead of the default (2000) because of
                                  # the highly downsampled example dataset.

Print progress

Description

Prints loop progress in ⁠[%]⁠ to console.

Usage

print_progress(current, end)
print_progress(current, end)

Arguments

`current`	Numeric value of current loop iteration.
`end`	Numeric value number last loop iteration.

Value

Prints percentage of loop progress to console.

Reduce Peaks

Description

Reduces curves to 100 observations per peak.

Usage

red_peaks_100(
  df,
  plot.to.screen = FALSE,
  path.data = NULL,
  path.plots = NULL,
  show.progress = FALSE
)
red_peaks_100(
  df,
  plot.to.screen = FALSE,
  path.data = NULL,
  path.plots = NULL,
  show.progress = FALSE
)

Arguments

`df`	The resulting tibble of the function `rescale_peaks()`. The columns `species`, `specimen`, `measurement`, `peak`, and `force.norm` must be present. See `?rescale_peaks` for more details.
`plot.to.screen`	A logical value indicating if results should be plotted in the current R plot device. Default: `FALSE`.
`path.data`	A string character defining where to save the results. If `NULL`, data will not be saved to disk. Default: `NULL`.
`path.plots`	A string character defining where to save result plots. If `NULL`, plots will not be saved to disk. Default: `NULL`.
`show.progress`	A logical value indicating if progress should be printed to the console. Default: `FALSE`.

Value

This function returns a tibble with a similar format as df, but the columns t, force, t.norm and force.norm are replaced by the columns index, ranging from 1 to 100, and force.norm.100, containing the rescaled force data ranging from 0 to 1. Since the time series has been reduced to 100 observations, this tibble will always contain 100 rows per peak.

`df` needs to contain the following columns:

`force`	`measurement`	`peak`
`force.norm.norm.1`	`measurement.1`	`peak.1`
`...`	`...`	`...`
`force.norm.n`	`measurement.m`	`peak.n`

Examples

# Using the forceR::df.all.200.tax dataset:
peaks.df.norm.100 <- red_peaks_100(df = forceR::peaks.df.norm,
                                   path.data = NULL,
                                   path.plots = NULL,
                                   show.progress = FALSE)

peaks.df.norm.100

# Using the forceR::df.all.200.tax dataset:
peaks.df.norm.100 <- red_peaks_100(df = forceR::peaks.df.norm,
                                   path.data = NULL,
                                   path.plots = NULL,
                                   show.progress = FALSE)

peaks.df.norm.100

Reduce Sampling Frequency

Description

Reduces the sampling frequency to a certain Hz value. If the desired frequency is smaller than the original frequency, the data remains unchanged.

Usage

reduce_frq(df, Hz = 200, measurement.col = NULL)
reduce_frq(df, Hz = 200, measurement.col = NULL)

Arguments

`df`	Data frame or tibble in the below mentioned format.
`Hz`	Numeric value of desired frequency. Default `200`
`measurement.col`	Character string. If `measurement.col` is not defined, the whole input data frames will be treated as if it was just one single time series. This is okay for data frames like that indeed only contain one time series, but for data frames with multiple time series, a grouping column needs to be defined. Default: `NULL`

Details

The input data frame or tibble should have the following format:

`t`	`y`
`t.1`	`y.1`
`...`	`...`
`t.n`	`y.n`

or, if measurement.col is not NULL, then

`t`	`y`	`measurement.col`
`t.1`	`y.1`	`...`
`...`	`...`	`...`
`t.n`	`y.n`	`...`

Since, when not NULL, the measurement.col is called by its character string, the position of the column does not matter, except it must not be among the first two columns which are reserved for t and y.

All columns except the first two are removed. Values in t are expected to be in m.secs.

Value

Returns a tibble reduced to the desired frequency in the following format:

`t`	`y`
`t.1`	`y.1`
`...`	`...`
`t.n`	`y.n`

or, if measurement.col is not NULL, then

`t`	`y`	`measurement.col`
`t.1`	`y.1`	`...`
`...`	`...`	`...`
`t.n`	`y.n`	`...`

Examples

require(dplyr)
# Using the forceR::df.all dataset that was
# simulated with forceR::simulate_bites()

# reduce sampling frequency to 200 Hz
df.all.200 <- reduce_frq(df = df.all,
                         Hz = 200,
                         measurement.col = "measurement")

plot(df.all.200 %>%
       filter(measurement == "m_02") %>%
       select(t, y),
     type = "l", col = "black")
lines(df.all.200 %>%
       filter(measurement == "m_01") %>%
       select(t, y),
     type = "l", col = "blue")
require(dplyr)
# Using the forceR::df.all dataset that was
# simulated with forceR::simulate_bites()

# reduce sampling frequency to 200 Hz
df.all.200 <- reduce_frq(df = df.all,
                         Hz = 200,
                         measurement.col = "measurement")

plot(df.all.200 %>%
       filter(measurement == "m_02") %>%
       select(t, y),
     type = "l", col = "black")
lines(df.all.200 %>%
       filter(measurement == "m_01") %>%
       select(t, y),
     type = "l", col = "blue")

Rescale Peaks

Description

Rescales time series in x and y to values ranging from 0 to 1.

Usage

rescale_peaks(
  df.peaks,
  df.data,
  plot.to.screen = FALSE,
  path.data = NULL,
  show.progress = FALSE
)
rescale_peaks(
  df.peaks,
  df.data,
  plot.to.screen = FALSE,
  path.data = NULL,
  show.progress = FALSE
)

Arguments

`df.peaks`	The resulting tibble of the function `find_strongest_peaks()`. See `?find_strongest_peaks` for more details.
`df.data`	A data frame or tibble in the below format. The columns `t` (time), `force` and `measurement` (measurement ID) must be present. This will usually be the same table that was used before in `find_strongest_peaks()`.
`plot.to.screen`	A logical value indicating if results should be plotted in the current R plot device. Default: `FALSE`.
`path.data`	A string character defining where to save the results. If `NULL`, data will not be saved to disk. Default: `NULL`.
`show.progress`	A logical value indicating if progress should be printed to the console. Default: `FALSE`.

Details

df.peaks at least needs to contain the following columns:

`species`	`measurements`	`starts`	`ends`
`species.1`	`measurements.1`	`starts.1`	`ends.1`
`...`	`...`	`...`	`...`
`species.n`	`measurements.n`	`starts.m`	`ends.m`

Check forceR::peaks.df to see an example tibble.

df.data at least needs to contain the following columns:

`t`	`force`	`measurement`
`t.1`	`force.1`	`measurement.1`
`...`	`...`	`...`
`t.n`	`force.n`	`measurement.m`

Check forceR::df.all.200.tax to see an example tibble.

Value

This function returns a tibble in the same format as df, but with the additional columns t.norm and force.norm which will contain the rescaled time and force data both ranging from 0 to 1.

Examples

# Using the forceR::df.all.200.tax and forceR::df.all.200.tax datasets:

# rescale bites
peaks.df.norm <- rescale_peaks(df.peaks = forceR::peaks.df,
                               df.data = forceR::df.all.200.tax,
                               plot.to.screen = FALSE,
                               path.data = NULL,
                               show.progress = FALSE)


# maximum values of time and force both range from 0 - 1:
range(peaks.df.norm$t.norm)
range(peaks.df.norm$force.norm)

# Using the forceR::df.all.200.tax and forceR::df.all.200.tax datasets:

# rescale bites
peaks.df.norm <- rescale_peaks(df.peaks = forceR::peaks.df,
                               df.data = forceR::df.all.200.tax,
                               plot.to.screen = FALSE,
                               path.data = NULL,
                               show.progress = FALSE)


# maximum values of time and force both range from 0 - 1:
range(peaks.df.norm$t.norm)
range(peaks.df.norm$force.norm)

Scale data series to new minimum and maximum

Description

Maps a series of numeric values to a new range defined by minimum (from) and maximum (to).

Usage

rescale_to_range(data, from, to)
rescale_to_range(data, from, to)

Arguments

`data`	numeric vector containing the data to be scaled
`from`	minimum of new range
`to`	maximum of new range

Value

numeric vector with scaled data

Examples

rescale_to_range(data = 1:10,
                  from = 1,
                  to = 100)
rescale_to_range(data = 1:10,
                  from = 1,
                  to = 100)

Simulate bites

Description

Simulates either sinusoidal or plateau-like bites.

Usage

simulate_bites(
  no.of.bites = 5,
  length.of.bite = 1000,
  length.of.series = 10000,
  max.y = 1,
  max.y.jit = NULL,
  peak.pos = 50,
  slope.perc.start = 10,
  slope.perc.end = slope.perc.start,
  jit = NULL,
  bite.type = "sin",
  plot = TRUE
)
simulate_bites(
  no.of.bites = 5,
  length.of.bite = 1000,
  length.of.series = 10000,
  max.y = 1,
  max.y.jit = NULL,
  peak.pos = 50,
  slope.perc.start = 10,
  slope.perc.end = slope.perc.start,
  jit = NULL,
  bite.type = "sin",
  plot = TRUE
)

Arguments

`no.of.bites`	Number of bites in time series. Default: `5`.
`length.of.bite`	Length of each bite. Default: `1000`.
`length.of.series`	Length of the whole time series. Default: `10000`.
`max.y`	Maximum y value. Default: `1`.
`max.y.jit`	Jitter above and below maximum y value in `⁠[%]⁠` of maximum y value. Default: `NULL`.
`peak.pos`	Position (in percent) of peak within peak curve. Only applies to `⁠bite.type = "sin⁠` (sinusoidal bites.) Default: same as `50`.
`slope.perc.start`	Percentage of how much of the whole bite is defined by the ascending slope. Only applies to `⁠bite.type = "plat⁠` (plateau-like bites.) Default: `10`.
`slope.perc.end`	Percentage of how much of the whole bite is defined by the descending slope. Only applies to `⁠bite.type = "plat⁠` (plateau-like bites.) Default: same as `slope.perc.start`.
`jit`	Jitter along the whole time series. Default: `NULL`.
`bite.type`	String: either "sin" or "plat" for sinusoidal or plateau-like bites, respectively. Default: `"sin"`.
`plot`	Logical. If `TRUE`, the simulated time series will be plotted to the active plot device. Default: `TRUE`.

Value

Returns a tibble with the columns t and y containing simulated bites.

Examples

# simulate a time series with sinusoidal bites
# where the peaks are located in the centers of the bites.
simulate_bites(no.of.bites = 5,
                length.of.bite = 1000,
                length.of.series = 10000,
                max.y = 5,
                max.y.jit = 15,
                jit = 0.5,
                peak.pos = 0.5,
                bite.type = "sin",
                plot = TRUE)

# simulate a time series with sinusoidal bites
# where the peaks are located towards the ends of the bites.
simulate_bites(no.of.bites = 5,
                length.of.bite = 1000,
                length.of.series = 10000,
                max.y = 5,
                max.y.jit = 15,
                jit = 0.5,
                peak.pos = 0.8,
                bite.type = "sin",
                plot = TRUE)

# simulate a time series with plateau-like bites
simulate_bites(no.of.bites = 5,
                length.of.bite = 1000,
                length.of.series = 10000,
                max.y = 5,
                max.y.jit = 15,
                jit = 1,
                bite.type = "plat",
                plot = TRUE)

# simulate a time series with plateau-like bites
# with slowly ascending bite start and abprupt bite end.
simulate_bites(no.of.bites = 5,
                length.of.bite = 1000,
                length.of.series = 10000,
                max.y = 5,
                max.y.jit = 15,
                slope.perc.start = 60,
                slope.perc.end = 10,
                jit = 1,
                bite.type = "plat",
                plot = TRUE)
# simulate a time series with sinusoidal bites
# where the peaks are located in the centers of the bites.
simulate_bites(no.of.bites = 5,
                length.of.bite = 1000,
                length.of.series = 10000,
                max.y = 5,
                max.y.jit = 15,
                jit = 0.5,
                peak.pos = 0.5,
                bite.type = "sin",
                plot = TRUE)

# simulate a time series with sinusoidal bites
# where the peaks are located towards the ends of the bites.
simulate_bites(no.of.bites = 5,
                length.of.bite = 1000,
                length.of.series = 10000,
                max.y = 5,
                max.y.jit = 15,
                jit = 0.5,
                peak.pos = 0.8,
                bite.type = "sin",
                plot = TRUE)

# simulate a time series with plateau-like bites
simulate_bites(no.of.bites = 5,
                length.of.bite = 1000,
                length.of.series = 10000,
                max.y = 5,
                max.y.jit = 15,
                jit = 1,
                bite.type = "plat",
                plot = TRUE)

# simulate a time series with plateau-like bites
# with slowly ascending bite start and abprupt bite end.
simulate_bites(no.of.bites = 5,
                length.of.bite = 1000,
                length.of.series = 10000,
                max.y = 5,
                max.y.jit = 15,
                slope.perc.start = 60,
                slope.perc.end = 10,
                jit = 1,
                bite.type = "plat",
                plot = TRUE)

Sorts files after corrections

Description

The files of each of the various possible correction steps (cropping, amplififer correction, drift correction) are all located in their own folders. This function gets all files that represent the last correction step of a given measurement out of all those folders and saves them in the results.folder.

Usage

sort_files(data.folders, results.folder, move = FALSE)
sort_files(data.folders, results.folder, move = FALSE)

Arguments

`data.folders`	Character vector containing full folder paths of folders to check. This list must be sorted according to the chronology of previous file editing. If a measurement exists in the last folder, this is copied or moved into the `results.folder`, and files of the same measurement located in the other folders will be ignored. Hence, the one file per measurement that underwent most correction steps will be stored in the `results.folder`, while the rest of the files of the same measurement remain in place.
`results.folder`	Character string defining the full path to the folder where the desired files will be stored.
`move`	A logical value specifying if files should be moved (`move = TRUE`) or copied (`move = FALSE`). Default: `FALSE`.

Details

The function will look for leading numbers in the file names specifying the measurement number to find corresponding files in the different folders. E.g., it will identify "0001_ABCD.csv", "0001_ABCD_ampdriftcorr.csv", and "0001_ABCD_ampdriftcorr_baselincorr.csv" as stemming from the same measurement and sort them accordingly.

Value

This functions does not create new files but sorts existing files. It does, however, create the results.folder in case it did not exist before.

Examples

# define data.folders
data.folders <- c("./raw",
                   "./cropped",
                   "./ampdriftcorr",
                   "./baselinecorr")

# define the folder in which one corrected file per original raw measurement
#   should be stored.
results.folder <- "./corrected"

# run the file sorting - commented out to pass package tests
# sort_files(data.folders = data.folders,
#               results.folder = results.folder,
#               move = FALSE)
# define data.folders
data.folders <- c("./raw",
                   "./cropped",
                   "./ampdriftcorr",
                   "./baselinecorr")

# define the folder in which one corrected file per original raw measurement
#   should be stored.
results.folder <- "./corrected"

# run the file sorting - commented out to pass package tests
# sort_files(data.folders = data.folders,
#               results.folder = results.folder,
#               move = FALSE)

Summarize Table

Description

Finds minimum, maximum and standard deviation of force per measurement and taxon and creates summary tibble.

Usage

summarize_measurements(df, var1, var2)
summarize_measurements(df, var1, var2)

Arguments

`df`	Data frame or tibble containing at least three columns. The column names must contain the grouping variables defined in `var1` and `var2` and the column `force` (time series of force measurements).
`var1`	A character string defining the column to calculate minimal and maximal force values per measurement. This must be the column that contains the unique measurement ID, e.g. measurement number.
`var2`	A character string defining the column for which the summary should be calculated.

Value

A tibble summarizing the input data frame df. The resulting tibble will contain the columns t, force, measurement, species, specimen, amp, lever.ratio, max.F.measurement, mean.F.specimen, max.F.specimen, sdv.max.F.specimen, n.measurements.in.specimen.

Examples

# Using the forceR::df.all.200.tax dataset:

# sumarize by measurement and specimen
df.summary.specimen <- summarize_measurements(df = df.all.200.tax,
                                              var1 = "measurement",
                                              var2 = "specimen")


#  plot results
## Not run: 
require(ggplot2)
ggplot(data = df.summary.specimen, mapping = aes(x=specimen,y=max.F.measurement)) +
  geom_jitter(aes(color='blue'),alpha=0.7) +
  geom_boxplot(fill="bisque",color="black",alpha=0.3) +
  # scale_y_log10() +
  labs(y="max(F)/specimen") +
  guides(color="none") +
  theme_minimal()

## End(Not run)
# Using the forceR::df.all.200.tax dataset:

# sumarize by measurement and specimen
df.summary.specimen <- summarize_measurements(df = df.all.200.tax,
                                              var1 = "measurement",
                                              var2 = "specimen")


#  plot results
## Not run: 
require(ggplot2)
ggplot(data = df.summary.specimen, mapping = aes(x=specimen,y=max.F.measurement)) +
  geom_jitter(aes(color='blue'),alpha=0.7) +
  geom_boxplot(fill="bisque",color="black",alpha=0.3) +
  # scale_y_log10() +
  labs(y="max(F)/specimen") +
  guides(color="none") +
  theme_minimal()

## End(Not run)

Get Today's Date as String

Description

Creates a character string containing today's date in the format "yyyy_mm_dd" which can be used in file names.

Usage

today()
today()

Value


`date.string`	a character string of today's date in the format "yyyy_mm_dd"

Convert Time Series to Force

Description

Converts a time series, e.g. a continuous voltage measurement from a sensor to force data according to an amplification value and, depending on the measurement setup, the lever ratio of the rocker forwarding the force from the point the force acts on to the sensor.

Usage

y_to_force(df, classifier, measurement.col)
y_to_force(df, classifier, measurement.col)

Arguments

`df`	Data frame or tibble in the below mentioned format. This should contain the time series, with one line per time step and measurement.
`classifier`	Classifier in the below mentioned format.
`measurement.col`	Character string. If `measurement.col` is not defined, the whole input data frames will be treated as if it was just one single time series. This is okay for data frames like that indeed only contain one time series, but for data frames with multiple time series, a grouping column needs to be defined. Default: `NULL`

Details

These values should be stored in a classifier (s. below).

The classifier should have at leaste the following columns:

`measurement`	`amp`	`lever.ratio`
`measurement.1`	`amp.1`	`lever.ratio.1`
`...`	`...`	`...`
`measurement.n`	`amp.n`	`lever.ratio.n`

It must contain one row per unique measurement number that is present in the df.

The force (F) in Newton is calculated via the following formula:

F = y * lever.ratio * (1 / amp)

where y is the measurement series, e.g. in V,
amp is the amplification value, e.g. in V/N,
and lever.ratio is the mechanical lever ratio of the measurement setup.

df should have the following format:

`t`	`y`	`measurement`
`t.1`	`y.1`	`measurement.1`
`t.2`	`y.2`	`measurement.1`
`...`	`...`	`...`
`t.n`	`y.n`	`measurement.1`
`t.1`	`y.1`	`measurement.2`
`t.2`	`y.2`	`measurement.2`
`...`	`...`	`...`
`t.m`	`y.m`	`measurement.2`
`...`	`...`	`...`
`t.o`	`y.o`	`measurement.o`

Value

Returns a tibble in the same format as the input tibble with an additional column called '"'force'.

Examples

# convert y column of df.all to force column and add taxonomic data
# using info from classifier
df.all.tax <- y_to_force(df = forceR::df.all.200,
                      classifier = forceR::classifier,
                      measurement.col = "measurement")

df.all.tax

# convert y column of df.all to force column and add taxonomic data
# using info from classifier
df.all.tax <- y_to_force(df = forceR::df.all.200,
                      classifier = forceR::classifier,
                      measurement.col = "measurement")

df.all.tax

Package 'forceR'

Help Index

Charge Amplifier Drift Correction

Description

Usage

Arguments

Details

Value

Examples

Average Curves per Group

Description

Usage

Arguments

Value

Examples

Automatic or Manual Baseline Correction of Time Series

Description

Usage

Arguments

Details

Value

Examples

Classifier

Description

Usage

Format

Details

Converts LJStream *.dat file to standard time series.

Description

Usage

Arguments

Value

Examples

Manually Correct Single Peak

Description

Usage

Arguments

Details

Value

df.data needs to contain the following columns:

Examples

Crop Time Series

Description

Usage

Arguments

Details

Value

Examples

Simulated Force Measurements with Taxonomic Info.

Description

Usage

Format

Simulated Time Series - e.g. Bite Force Measurements

Description

Usage

Format

Simulated Force Measurements with Taxonomic Info.

Description

Usage

Format

Find Best Polynomial Fits for Curves

Description

Usage

Arguments

Details

Value

Examples

Find Peaks

Description

Usage

Arguments

Details

Value

Examples

Get path to forceR example

Description

Usage

Arguments

Value

Load Multiple Measurements

`df.data` needs to contain the following columns:

`df.data` needs to contain the following columns: