Title:	Tools for Manipulating Gas Exchange Data
Version:	0.1.4
Description:	Set of tools for manipulating gas exchange data from cardiopulmonary exercise testing.
License:	MIT + file LICENSE
URL:	https://fmmattioni.github.io/whippr/, https://github.com/fmmattioni/whippr
BugReports:	https://github.com/fmmattioni/whippr/issues
Encoding:	UTF-8
Imports:	readxl (≥ 1.3.1), dplyr (≥ 1.0.1), stringr (≥ 1.4.0), lubridate (≥ 1.7.9), magrittr, tibble, zoo, purrr, tidyr (≥ 1.1.1), broom (≥ 0.7.0), cli, ggplot2 (≥ 3.4.0), glue, minpack.lm, patchwork (≥ 1.0.1), rlang, nlstools, pillar
RoxygenNote:	7.3.2
Suggests:	knitr, rmarkdown, fansi, collapsibleTree, testthat, shiny, miniUI, datapasta, rstudioapi, htmltools, readr, anomalize, ggforce, ggtext, forcats
NeedsCompilation:	no
Packaged:	2025-07-11 14:28:39 UTC; fmattioni
Author:	Felipe Mattioni Maturana [aut, cre]
Maintainer:	Felipe Mattioni Maturana <felipe.mattioni@med.uni-tuebingen.de>
Repository:	CRAN
Date/Publication:	2025-07-13 23:10:02 UTC

whippr: Tools for Manipulating Gas Exchange Data

Description

Set of tools for manipulating gas exchange data from cardiopulmonary exercise testing.

Author(s)

Maintainer: Felipe Mattioni Maturana felipe.mattioni@med.uni-tuebingen.de (ORCID)

See Also

Useful links:

• https://fmmattioni.github.io/whippr/

• https://github.com/fmmattioni/whippr

• Report bugs at https://github.com/fmmattioni/whippr/issues

Pipe operator

Description

See magrittr::%>% for details.

Usage

lhs %>% rhs

Detect outliers

Description

It detects outliers based on prediction bands for the given level of confidence provided.

Usage

detect_outliers(
  .data,
  test_type = c("incremental", "kinetics"),
  vo2_column = "VO2",
  cleaning_level = 0.95,
  cleaning_baseline_fit,
  protocol_n_transitions,
  protocol_baseline_length,
  protocol_transition_length,
  method_incremental = c("linear", "anomaly"),
  verbose = TRUE,
  ...
)

Arguments

Details

TODO

Value

a tibble

Examples

## Not run: 
## get file path from example data
path_example <- system.file("example_cosmed.xlsx", package = "whippr")

## read data
df <- read_data(path = path_example, metabolic_cart = "cosmed")

## detect outliers
data_outliers <- detect_outliers(
  .data = df,
  test_type = "kinetics",
  vo2_column = "VO2",
  cleaning_level = 0.95,
  cleaning_baseline_fit = c("linear", "exponential", "exponential"),
  protocol_n_transitions = 3,
  protocol_baseline_length = 360,
  protocol_transition_length = 360,
  verbose = TRUE
 )

## get file path from example data
path_example_ramp <- system.file("ramp_cosmed.xlsx", package = "whippr")

## read data from ramp test
df_ramp <- read_data(path = path_example_ramp, metabolic_cart = "cosmed")

## normalize incremental test data
ramp_normalized <- df_ramp %>%
 incremental_normalize(
   .data = .,
   incremental_type = "ramp",
   has_baseline = TRUE,
   baseline_length = 240,
   work_rate_magic = TRUE,
   baseline_intensity = 20,
   ramp_increase = 25
 )

## detect ramp outliers
data_ramp_outliers <- detect_outliers(
  .data = ramp_normalized,
  test_type = "incremental",
  vo2_column = "VO2",
  cleaning_level = 0.95,
  method_incremental = "linear",
  verbose = TRUE
 )

## End(Not run)

Get residuals

Description

Computes residuals from the VO2 kinetics model.

Usage

get_residuals(.model)

Arguments

Value

a tibble containing the data passed to augment, and additional columns:

Normalize incremental test data

Description

Detect protocol phases (baseline, ramp, steps), normalize work rate, and time-align baseline phase (baseline time becomes negative).

Usage

incremental_normalize(
  .data,
  incremental_type = c("ramp", "step"),
  has_baseline = TRUE,
  baseline_length = NULL,
  work_rate_magic = FALSE,
  baseline_intensity = NULL,
  ramp_increase = NULL,
  step_start = NULL,
  step_increase = NULL,
  step_length = NULL,
  ...
)

Arguments

Value

a tibble

Examples

## Not run: 
## get file path from example data
path_example <- system.file("ramp_cosmed.xlsx", package = "whippr")

## read data from ramp test
df <- read_data(path = path_example, metabolic_cart = "cosmed")

## normalize incremental test data
ramp_normalized <- df %>%
 incremental_normalize(
   .data = .,
   incremental_type = "ramp",
   has_baseline = TRUE,
   baseline_length = 240,
   work_rate_magic = TRUE,
   baseline_intensity = 20,
   ramp_increase = 25
 )

## get file path from example data
path_example_step <- system.file("step_cortex.xlsx", package = "whippr")

## read data from step test
df_step <- read_data(path = path_example_step, metabolic_cart = "cortex")

## normalize incremental test data
step_normalized <- df_step %>%
 incremental_normalize(
   .data = .,
   incremental_type = "step",
   has_baseline = TRUE,
   baseline_length = 120,
   work_rate_magic = TRUE,
   baseline_intensity = 0,
   step_start = 50,
   step_increase = 25,
   step_length = 180
 )

## End(Not run)

Interpolate data from breath-by-breath into second-by-second

Description

This function interpolates the data based on the time column. It takes the breath-by-breath data and transforms it into second-by-second.

Usage

interpolate(.data)

Arguments

Value

a tibble

Examples

## Not run: 
## get file path from example data
path_example <- system.file("example_cosmed.xlsx", package = "whippr")

## read data
df <- read_data(path = path_example, metabolic_cart = "cosmed")

df %>%
 interpolate()

## End(Not run)

Model diagnostics

Description

Plots different model diagnostics for checking the model performance.

Usage

model_diagnostics(.residuals_tbl)

Arguments

Value

a patchwork object

Construct a new tibble with metadata

Description

Construct a new tibble with metadata

Usage

new_whippr_tibble(.data, metadata)

Arguments

Value

a tibble

Normalize first breath

Description

This is needed specially when the data gets filtered. For example, if the data file does not only contain the baseline and transitions performed, we will have to normalize the time column. This function will make sure that in case the first breath does not start at zero, it will create a zero data point, duplicating the first breath. This will make sure the data does not get shifted (misalignment).

Usage

normalize_first_breath(.data)

Arguments

Value

a tibble

Normalize time column

Description

Normalizes the the time column such that the baseline phase has negative time values. Point zero will then represent the start of the transition phase.

Usage

normalize_time(.data, protocol_baseline_length)

Arguments

Value

a tibble

Normalize transitions

Description

Recognizes and normalizes the time column of each transition. It will also label the transitions into: 'baseline' or 'transition'.

Usage

normalize_transitions(
  .data,
  protocol_n_transitions,
  protocol_baseline_length,
  protocol_transition_length
)

Arguments

Value

a tibble

Anomaly method for detecting outliers from an incremental test

Description

Function for internal use only. It will not be exported.

Usage

outliers_anomaly(.data, time_column, vo2_column, cleaning_level)

Arguments

Value

a tibble

Linear method for detecting outliers from an incremental test

Description

Function for internal use only. It will not be exported.

Usage

outliers_linear(.data, time_column, vo2_column, cleaning_level)

Arguments

Value

a tibble

Perform average on second-by-second data

Description

This function performs either a bin- or a rolling-average on the interpolated data. You must specify the type of the average before continuing.

Usage

perform_average(
  .data,
  type = c("bin", "rolling", "ensemble"),
  bins = 30,
  bin_method = c("ceiling", "round", "floor"),
  rolling_window = 30
)

Arguments

Details

Ensemble average is used in VO2 kinetics analysis, where a series of transitions from baseline to the moderate/heavy/severe intensity-domain is ensembled averaged into a single 'bout' for further data processing.

When using bin averaging, the bin_method parameter controls how time points are assigned to bins:

• "ceiling": Rounds up to the next bin boundary (recommended)

• "round": Rounds to the nearest bin boundary

• "floor": Rounds down to the previous bin boundary

Value

a tibble

Examples

## Not run: 
## get file path from example data
path_example <- system.file("example_cosmed.xlsx", package = "whippr")

## read data
df <- read_data(path = path_example, metabolic_cart = "cosmed")

## interpolate and perform 30-s bin-average
df %>%
 interpolate() %>%
 perform_average(type = "bin", bins = 30)

## interpolate and perform 30-s rolling-average
df %>%
 interpolate() %>%
 perform_average(type = "rolling", rolling_window = 30)

## End(Not run)

Perform VO2 kinetics fitting

Description

Performs the fitting process for the VO2 kinetics analysis. At this point, the data should already have been cleaned (outliers removed) and processed (interpolated, time-aligned, ensembled-averaged, and bin-averaged).

Usage

perform_kinetics(
  .data_processed,
  intensity_domain = c("moderate", "heavy", "severe"),
  fit_level = 0.95,
  fit_phase_1_length,
  fit_baseline_length,
  fit_transition_length,
  verbose = TRUE,
  ...
)

Arguments

Details

See ?vo2_kinetics for details.

Value

a tibble containing one row and the nested columns:

Perform VO2max calculation

Description

It performs the calculation of VO2max, HRmax, and maximal RER. Additionally, it detects whether a plateau can be identified from your data.

Usage

perform_max(
  .data,
  vo2_column = "VO2",
  vo2_relative_column = NULL,
  heart_rate_column = NULL,
  rer_column = NULL,
  average_method = c("bin", "rolling"),
  average_length = 30,
  plot = TRUE,
  verbose = TRUE
)

Arguments

Value

a tibble

Examples

## Not run: 
## get file path from example data
path_example <- system.file("ramp_cosmed.xlsx", package = "whippr")

## read data from ramp test
df <- read_data(path = path_example, metabolic_cart = "cosmed")

## normalize incremental test data
ramp_normalized <- df %>%
 incremental_normalize(
   .data = .,
   incremental_type = "ramp",
   has_baseline = TRUE,
   baseline_length = 240,
   work_rate_magic = TRUE,
   baseline_intensity = 20,
   ramp_increase = 25
 )

## detect outliers
data_ramp_outliers <- detect_outliers(
 .data = ramp_normalized,
 test_type = "incremental",
 vo2_column = "VO2",
 cleaning_level = 0.95,
 method_incremental = "linear",
 verbose = TRUE
)

## analyze VO2max
perform_max(
 .data = data_ramp_outliers,
 vo2_column = "VO2",
 vo2_relative_column = "VO2/Kg",
 heart_rate_column = "HR",
 rer_column = "R",
 average_method = "bin",
 average_length = 30,
 plot = TRUE,
 verbose = FALSE
)

## End(Not run)

Plot incremental test work rate

Description

Visualize what was done during the process of deriving the work rate from the incremental test protocol

Usage

plot_incremental(.data)

Arguments

Value

a ggplot object

Plot outliers

Description

Plot outliers

Usage

plot_outliers(.data)

Arguments

Value

a patchwork object

Extract confidence and prediction bands

Description

It extracts confidence and prediction bands from the nls model. It is used only for data cleaning.

Usage

predict_bands(
  .data,
  time_column = "t",
  vo2_column = "VO2",
  cleaning_level = 0.95,
  cleaning_baseline_fit = c("linear", "exponential")
)

Arguments

Value

a tibble containing the following columns:

Extract confidence and prediction bands for the baseline phase

Description

Extract confidence and prediction bands for the baseline phase

Usage

predict_bands_baseline(
  .data,
  time_column,
  vo2_column,
  cleaning_level,
  cleaning_baseline_fit
)

Arguments

Value

a tibble containing the following columns:

Extract confidence and prediction bands for the transition phase

Description

Extract confidence and prediction bands for the transition phase

Usage

predict_bands_transition(
  .data,
  time_column,
  vo2_column,
  cleaning_level,
  cleaning_model
)

Arguments

Value

a tibble containing the following columns:

Whippr print method

Description

Whippr print method

Usage

## S3 method for class 'whippr'
print(x, ...)

Arguments

Process data for VO2 kinetics fitting

Description

It removes the outliers detected through detect_outliers(), interpolates each transition, ensemble-averages all the transitions into one, performs a bin-average, and normalizes the time column (time zero will indicate the end of baseline and the start of the transition phase).

Usage

process_data(.data_outliers, protocol_baseline_length, fit_bin_average)

Arguments

Details

TODO

Value

a tibble with the time-aligned, ensembled-averaged, and bin-averaged data.

Read data from metabolic cart

Description

It reads the raw data exported from the metabolic cart.

Usage

read_data(
  path,
  metabolic_cart = c("cosmed", "cortex", "nspire", "parvo", "geratherm", "cardiocoach",
    "custom"),
  time_column = "t",
  work_rate_column = NULL
)

Arguments

Value

a tibble

Remove empty rows and/or columns from a data.frame or matrix.

Description

Removes all rows and/or columns from a data.frame or matrix that are composed entirely of NA values.

Usage

remove_empty(dat, which = c("rows", "cols"), cutoff = 1)

Arguments

Value

Returns the object without its missing rows or columns.

Manual data cleaner

Description

Usually manual data cleaning should be avoided. However, sometimes in gas exchange data there is the need to delete a few clear "bad breaths" (noise). In these situations you may use this function. Although it is encouraged that you use the detect_outliers() function, you may use this function at your own risk. This function can also be used to clean other kind of data, like heart rate data.

Usage

run_manual_cleaner(.data, width = 1200, height = 900)

Arguments

Value

The code to reproduce the manual data cleaning.

Whippr ggplot2 theme

Description

This theme was inspired by the plots from the Acta Physiologica Journal

Usage

theme_whippr(base_size = 14, base_family = "sans")

Arguments

Value

a ggplot2 object

Undo/Redo History Buttons

Description

This is a simple Shiny module for undo/redo history. The Shiny module accepts an arbitrary reactive data value. Changes in the state of this reactive value are tracked and added to the user's history. The user can then repeatedly undo and redo to walk through this stack. The module returns the current selected value of the reactive from this historical stack, or NULL when the app state was changed by the user. Because this reactive can hold arbitrary data about the state of the Shiny app, it is up to the app developer to use the returned current value to update the Shiny apps' inputs and UI elements.

Usage

undoHistory(id, value, value_debounce_rate = 500)

Arguments

Value

The undoHistory() module returns the currently selected history item as the user moves through the stack, or NULL if the last update was the result of user input. The returned value has the same structure as the reactive value passed to undoHistory().

VO2 kinetics

Description

It performs the whole process of the VO2 kinetics data analysis, which includes: data cleaning (detect_outliers()); outliers removal, interpolation, ensemble-averaging transitions and bin-avering final dataset (process_data()), and modelling VO2 kinetics (perform_kinetics()). This function is a general function that will call these separate functions. You can also call each one of them separately if you want.

Usage

vo2_kinetics(
  .data,
  intensity_domain = c("moderate", "heavy", "severe"),
  vo2_column = "VO2",
  protocol_n_transitions,
  protocol_baseline_length,
  protocol_transition_length,
  cleaning_level = 0.95,
  cleaning_baseline_fit,
  fit_level = 0.95,
  fit_bin_average,
  fit_phase_1_length,
  fit_baseline_length,
  fit_transition_length,
  verbose = TRUE,
  ...
)

Arguments

Details

The function is a wrapper of smaller functions and has important arguments:

• protocol_ = sets arguments related to the protocol used.

• cleaning_ = sets arguments related to data cleaning.

• fit_ = sets arguments related to VO2 kinetics fitting.

The function works like the following sequence:

vo2_kinetics( ):

• detect_outliers( ) = separates the data into the number of transitions indicated, and fits each baseline and transition phase indiviudally, retrieving the predictions bands for the level indicated. Then it recognizes breaths lying outside the prediciton bands and flag them as outliers.

• plot_outliers( ) = plots each transition identifying outliers.

• process_data( ) = It removes the outliers detected through detect_outliers(), interpolates each transition, ensemble-averages all the transitions into one, performs a bin-average, and normalizes the time column (time zero will indicate the end of baseline and the start of the transition phase).

• perform_kinetics( ) = performs the VO2 kinetics fitting based on the fit_ parameters given. It also calculates the residuals, and plots the final fit as well as residuals for model diagnostics.

Value

a tibble containing one row and the nested columns:

VO2 kinetics

VO2 kinetics, described as the rate of adjustment of the oxidative energy system to an instantaneous increase in the energy demand, is exponential in nature, and it is described by the oxygen uptake (VO2) time-constant (\tauVO2) (Murias, Spencer and Paterson (2014); Poole and Jones (2011)).

VO2 kinetics analysis provides understanding of the mechanisms that regulate the rate at which oxidative phosphorylation adapts to step changes in exercise intensities and ATP requirement. This is usually accomplished by performing step transitions from a baseline intensity to a higher work rate in either the moderate-, heavy-, or severe-intensity domain (Murias et al., 2011).

Three distinct phases may be observed in the VO2 response during on-transient exercise:

Phase I: also termed as the cardiodynamic phase, it represents the circulatory transit delay on the VO2 response as a result of the increase in the pulmonary blood flow that does not reflect the increase in oxygen extraction in the active muscles. The time-window of the Phase I is determined in the fit_phase_1_length argument, which will be internally passed into the perform_kinetics() function.

Phase II: also termed as the primary component, represents the exponential increase in VO2 related to the continued increase in pulmonary and muscle blood flow. The Phase II is described by the time-constant parameter (\tau) in the mono-exponential model (see below), and it is defined as the duration of time (in seconds) for the VO2 response to increase to 63% of the required steady-state.

Phase III: represents the steady-state phase of the VO2 response during moderate-intensity exercise.

Moderate-intensity domain

The on-transient response from baseline to a transition within the moderate-intensity domain is analyzed using a mono-exponential model:

VO_{2\left(t\right)}=baseline+amplitude\cdot\left(1-e^{^{-\frac{\left(t-TD\right)}{tau}}}\right)

where:

• VO2(t) = the oxygen uptake at any given time.

• baseline = the oxygen uptake associated with the baseline phase.

• amplitude = the steady-state increase increase in oxygen uptake above baseline.

• TD = the time delay.

• \tau = the time constant defined as the duration of time for the oxygen uptake to increase to 63% of the steady-state increase.

The baseline value in the mono-exponential model is a fixed value and pre-determined as the mean of the VO2 response (i.e., linear model with the slope set as zero) during the baseline phase. The time window of the baseline period is determined in the fit_baseline_length argument, which will be internally passed into the perform_kinetics() function.

Diverse exercise protocols exist to determine VO2 kinetics in the moderate-intensity domain. Usually, the protocol consists of multiple transitions (typically 3 or 4) from a baseline exercise-intensity to an exercise-intensity below the gas exchange threshold (typically the power output associated with 90% of the gas exchange threshold). Bbaseline and transition phases are usually performed for 6 minutes each. The reason that 6 minutes is done for each phase is to give enough time for both to reach a steady-state response:

For example, for each multiple of the time-constant (\tau), VO2 increases by 63% of the difference between the previous \tau and the required steady-state. This means:

• 1 \tau ⁠= 63%⁠ \Delta.

• 2 \tau ⁠= 86%⁠ \Delta ⁠[100% - 63% = 37%; (37% x 63%) + 63% = 86%]⁠.

• 3 \tau ⁠= 95%⁠ \Delta ⁠[100% - 86% = 14%; (14% x 63%) + 86% = 95%]⁠.

• 4 \tau ⁠= 98%⁠ \Delta ⁠[100% - 95% = 5%; (5% x 63%) + 95% = 98%]⁠.

In practical terms, let's imagine that a given participant has a \tau = 60 seconds. This means that this person would need 240 seconds (⁠4 x 60⁠) to reach steady-state (98% of the response) in the moderate-intensity domain. This would leave other 120 seconds (2 minutes) of transition, so the protocol of performing 6-min transitions makes sure enough time is given.

Now let's imagine that another person has a \tau = 20 seconds. This means that this person would need 80 seconds (⁠4 x 20⁠) to reach steady-state (98% of the response) in the moderate-intensity domain.

Given that there is enough time to reach a VO2 steady-state response with 6 minutes of transition, that means that for the final fit (when the transitions were cleaned, ensembled-averaged, and bin-averaged) there is no need to include the whole 6 minutes of the transition. This strategy avoids superfluous sections of the steady‐state data, thus maximizing the quality of the fit during the exercise on‐transient (Bell et al., 2001). This may be specified through the fit_transition_length argument, which will be internally passed into the perform_kinetics() function.

As for bin-averages in the final fit, usually the data are averaged into 5-s or 10-s bins, 5-s being the most common (Keir et al., 2014). This may be specified through the fit_bin_average argument, which will be internally passed into the process_data() function.

Heavy- and severe-intensity domains

TODO

References

Bell, C., Paterson, D. H., Kowalchuk, J. M., Padilla, J., & Cunningham, D. A. (2001). A comparison of modelling techniques used to characterise oxygen uptake kinetics during the on-transient of exercise. Experimental Physiology, 86(5), 667-676.

Keir, D. A., Murias, J. M., Paterson, D. H., & Kowalchuk, J. M. (2014). Breath‐by‐breath pulmonary O2 uptake kinetics: effect of data processing on confidence in estimating model parameters. Experimental physiology, 99(11), 1511-1522.

Murias, J. M., Spencer, M. D., & Paterson, D. H. (2014). The critical role of O2 provision in the dynamic adjustment of oxidative phosphorylation. Exercise and sport sciences reviews, 42(1), 4-11.

Murias, J. M., Spencer, M. D., Kowalchuk, J. M., & Paterson, D. H. (2011). Influence of phase I duration on phase II VO2 kinetics parameter estimates in older and young adults. American Journal of Physiology-regulatory, integrative and comparative physiology, 301(1), R218-R224.

Poole, D. C., & Jones, A. M. (2011). Oxygen uptake kinetics. Comprehensive Physiology, 2(2), 933-996.

Examples

## Not run: 
## get file path from example data
path_example <- system.file("example_cosmed.xlsx", package = "whippr")

## read data
df <- read_data(path = path_example, metabolic_cart = "cosmed", time_column = "t")

## VO2 kinetics analysis
results_kinetics <- vo2_kinetics(
  .data = df,
  intensity_domain = "moderate",
  vo2_column = "VO2",
  protocol_n_transitions = 3,
  protocol_baseline_length = 360,
  protocol_transition_length = 360,
  cleaning_level = 0.95,
  cleaning_baseline_fit = c("linear", "exponential", "exponential"),
  fit_level = 0.95,
  fit_bin_average = 5,
  fit_phase_1_length = 20,
  fit_baseline_length = 120,
  fit_transition_length = 240,
  verbose = TRUE
)

## End(Not run)

VO2max

Description

It performs the whole process of the VO2max data analysis, which includes: data standardization and normalization according to incremental protocol (incremental_normalize()), 'bad breaths' detection (detect_outliers()), mean response time calculation (incremental_mrt()) (currently ignored), and maximal values calculation (VO2, PO, HR, RER) (perform_max()).

Usage

vo2_max(
  .data,
  vo2_column = "VO2",
  vo2_relative_column = NULL,
  heart_rate_column = NULL,
  rer_column = NULL,
  detect_outliers = TRUE,
  average_method = c("bin", "rolling"),
  average_length = 30,
  mrt,
  plot = TRUE,
  verbose = TRUE,
  ...
)

Arguments

Details

TODO

Value

a tibble containing one row and the following columns:

Examples

## Not run: 
## get file path from example data
path_example <- system.file("ramp_cosmed.xlsx", package = "whippr")

## read data from ramp test
df <- read_data(path = path_example, metabolic_cart = "cosmed")

## normalize incremental test data
ramp_normalized <- df %>%
 incremental_normalize(
   .data = .,
   incremental_type = "ramp",
   has_baseline = TRUE,
   baseline_length = 240,
   work_rate_magic = TRUE,
   baseline_intensity = 20,
   ramp_increase = 25
 )

## detect outliers
data_ramp_outliers <- detect_outliers(
 .data = ramp_normalized,
 test_type = "incremental",
 vo2_column = "VO2",
 cleaning_level = 0.95,
 method_incremental = "linear",
 verbose = TRUE
)

## analyze VO2max
perform_max(
 .data = data_ramp_outliers,
 vo2_column = "VO2",
 vo2_relative_column = "VO2/Kg",
 heart_rate_column = "HR",
 rer_column = "R",
 average_method = "bin",
 average_length = 30,
 plot = TRUE,
 verbose = FALSE
)

## End(Not run)

Work rate for a ramp-incremental test

Description

This function produces the work rate throughout a ramp-incremental test given the procotol

Usage

work_rate_ramp(.data, baseline_intensity, ramp_increase)

Arguments

Value

a tibble

Work rate for a step-incremental test

Description

This function produces the work rate throughout a step-incremental test given the protocol This will retrieve both the 'original' work rates, and also will perform a 'linearization' of the steps.

Usage

work_rate_step(
  .data,
  baseline_intensity,
  step_start,
  step_increase,
  step_length
)

Arguments

Value

a tibble