| Title: | Manage Data from Cardiopulmonary Exercise Testing |
|---|---|
| Description: | Import, process, summarize and visualize raw data from metabolic carts. See Robergs, Dwyer, and Astorino (2010) <doi:10.2165/11319670-000000000-00000> for more details on data processing. |
| Authors: | Simon Nolte [aut, cre]  ,
Manuel Ramon [rev] (reviewed the package (v. 0.0.5) for rOpenSci, see
<https://github.com/ropensci/software-review/issues/541>),
James Hunter [rev] (reviewed the package (v. 0.0.5) for rOpenSci, see
<https://github.com/ropensci/software-review/issues/541>) |
| Maintainer: | Simon Nolte <[email protected]> |
| License: | MIT + file LICENSE |
| Version: | 0.2.1.9000 |
| Built: | 2024-09-26 20:15:39 UTC |
| Source: | https://github.com/ropensci/spiro |
add_bodymass() adds body mass-related variables to processed gas
exchange data.
add_bodymass(data, bodymass = NULL)add_bodymass(data, bodymass = NULL)
data |
A |
bodymass |
A numeric value to manually set the participant's body mass. Defaults to NULL to use body mass data from the file's meta data. Set to NA to ignore the meta data without setting a new body mass. |
Based on an individual's body mass, relative oxygen uptake (VO2_rel) and carbon dioxide output (VCO2_rel) are calculated. For running protocols, running economy (RE) is calculated.
A data.frame of the class spiro containing the
cardiopulmonary exercise testing data including variables related to body
mass.
# get example file file <- spiro_example("zan_gxt") s <- spiro(file) out <- add_bodymass(s, bodymass = 65.3) head(out)# get example file file <- spiro_example("zan_gxt") s <- spiro(file) out <- add_bodymass(s, bodymass = 65.3) head(out)
add_hr() imports an external file containing heart rate data and adds
it to an existing gas exchange data file.
add_hr(data, hr_file, hr_offset = 0)add_hr(data, hr_file, hr_offset = 0)
data |
A |
hr_file |
The absolute or relative path of a |
hr_offset |
An integer, corresponding to the temporal offset of the heart-rate file. By default the start of the heart rate measurement is linked to the start of the gas exchange measurement. A positive value means, that the heart rate measurement started after the begin of the gas exchange measurements; a negative value means it started before. |
Heart rate data will be imported from a .tcx file. After interpolating
the data to full seconds, it is then matched to the imported gas exchange
data.
A data.frame of the class spiro containing the
cardiopulmonary exercise testing data including heart rate data.
# Get example data oxy_file <- spiro_example("zan_ramp") hr_file <- spiro_example("hr_ramp.tcx") # Import and process spiro data oxy_data <- spiro(oxy_file) # Add heart rate data out <- add_hr(oxy_data, hr_file) head(out)# Get example data oxy_file <- spiro_example("zan_ramp") hr_file <- spiro_example("hr_ramp.tcx") # Import and process spiro data oxy_data <- spiro(oxy_file) # Add heart rate data out <- add_hr(oxy_data, hr_file) head(out)
add_protocol() adds a predefined test protocol to an existing set of
data from an exercise test.
add_protocol(data, protocol)add_protocol(data, protocol)
data |
A spiro |
protocol |
A |
A data.frame of the class spiro with cardiopulmonary
parameters and the corresponding load data.
set_protocol for protocol setting with helper functions.
set_protocol_manual for manual protocol design.
get_protocol For automated extraction of protocols from raw data.
# Get example data file <- spiro_example("zan_gxt") s <- spiro(file) out <- add_protocol( s, set_protocol(pt_pre(60), pt_steps(300, 50, 50, 7, 30)) ) head(out)# Get example data file <- spiro_example("zan_gxt") s <- spiro(file) out <- add_protocol( s, set_protocol(pt_pre(60), pt_steps(300, 50, 50, 7, 30)) ) head(out)
Internal function for spiro_smooth.
bw_filter(x, n = 3, W = 0.04, zero_lag = TRUE)bw_filter(x, n = 3, W = 0.04, zero_lag = TRUE)
x |
A numeric vector on which the digital filter should be applied |
n |
Order of the Butterworth filter, defaults to 3 |
W |
Low-pass cut-off frequency of the filter, defaults to 0.04 |
zero_lag |
Whether a zero phase (forwards-backwards) filter should be applied. |
Digital filtering might be a preferable processing strategy for smoothing data from gas exchange measures when compared to moving averages. Robergs et al. (2010) proposes a third order Butterworth filter with a low-pass cut-off frequency of 0.04 for filtering VO2 data.
It should be noted that Butterworth filter comprise a time lag. A method to create a time series with zero lag is to subsequently apply two Butterworth filters in forward and reverse direction (forwards-backwards filtering). While this procedure removes any time lag it changes the magnitude of the filtering response, i.e. the resulting filter has not the same properties (order and cut-off frequency) as a single filter.
A numeric vector of the same length as x.
# Get VO2 data from example file vo2_vector <- spiro(spiro_example("zan_gxt"))$VO2 out <- bw_filter(vo2_vector) head(out, n = 20)# Get VO2 data from example file vo2_vector <- spiro(spiro_example("zan_gxt"))$VO2 out <- bw_filter(vo2_vector) head(out, n = 20)
get_anonid() returns the anonymization id corresponding to given
personal data.
get_anonid(name, surname, birthday = NULL)get_anonid(name, surname, birthday = NULL)
name |
A character string, containing the participant's name as present in the raw data file. |
surname |
A character string, containing the participant's surname as present in the raw data file. |
birthday |
A character string, containing the participant's birthday as present in the raw data file. If no birthday data is available in the raw data, this is ignored. |
By default, the spiro package anonymizes personal information obtained from file meta data. The data are saved to the "info" attribute of a spiro() call. The default anonymization ensures that no personal information is accidentally revealed, e.g. by sharing spiro outputs as .Rda files.
While there is no way to directly deanonymize the data, get_anonid() allows you to recreate the ids, when meta data (name, surname and birthday) are known. Birthday is only used within the id generation if available in the original raw data.
To disable the anonymization process during import use
spiro(anonymize = FALSE)
A character string, containing the anonymized id.
get_anonid("Jesse", "Owens", "12.09.1913")get_anonid("Jesse", "Owens", "12.09.1913")
get_protocol() gets the underlying test protocol based on given load
data.
get_protocol(data)get_protocol(data)
data |
A |
A data.frame with the duration and load of each protocol step.
# Import example data raw_data <- spiro_raw(data = spiro_example("zan_gxt")) get_protocol(raw_data)# Import example data raw_data <- spiro_raw(data = spiro_example("zan_gxt")) get_protocol(raw_data)
knit_print.spiro() provides a method for printing data.frames
from spiro within knitr.
## S3 method for class 'spiro' knit_print(x, min = 10, max = 20, digits = 2, ...)## S3 method for class 'spiro' knit_print(x, min = 10, max = 20, digits = 2, ...)
x |
A |
min |
An integer, which sets the number of rows to which |
max |
An integer, setting the maximal number of rows to be not cut to
|
digits |
An integer giving the number of decimals to be rounded to. |
... |
Passing of additional arguments to |
Cardiopulmonary exercise testing data imported by spiro will
often come in large data.frames. When knitting R Markdown documents
these will normally be printed in full size.
This function provides a method for data.frames of the class
spiro to limit the number of rows displayed to min if it
exceeds max. The number of hidden data rows will be printed below the
data.frame.
The function prints its argument and returns it invisibly.
# Get example data s <- spiro(spiro_example("zan_gxt")) knitr::knit_print(s)# Get example data s <- spiro(spiro_example("zan_gxt")) knitr::knit_print(s)
Printing method for spiro objects that rounds output to two decimals.
## S3 method for class 'spiro' print(x, round = 2, ...)## S3 method for class 'spiro' print(x, round = 2, ...)
x |
A |
round |
An integer giving the number of decimals to be rounded to. |
... |
Passing of additional arguments to |
The function prints its argument and returns it invisibly.
# Get example data s <- spiro(spiro_example("zan_gxt")) out <- print(s) head(out)# Get example data s <- spiro(spiro_example("zan_gxt")) out <- print(s) head(out)
set_protocol() allows to set a load profile for an exercise test
based on profile sections.
set_protocol(...) pt_pre(duration) pt_wu(duration, load, rest.duration = 0) pt_steps( duration, load, increment, count, rest.duration = 0, last.duration = NULL ) pt_const(duration, load, count, rest.duration = 0, last.duration = NULL)set_protocol(...) pt_pre(duration) pt_wu(duration, load, rest.duration = 0) pt_steps( duration, load, increment, count, rest.duration = 0, last.duration = NULL ) pt_const(duration, load, count, rest.duration = 0, last.duration = NULL)
... |
Functions related to sections of the load profile, such as
|
duration |
A number, giving the duration of the test section or a single load within the test section (in seconds). |
load |
A number, giving the (initial) load of a section. |
rest.duration |
A number, specifying the duration of (each) rest (in seconds). |
increment |
A number, giving the difference in load between the current and the following load step. |
count |
An integer for the number of load sections. |
last.duration |
A number, giving the duration of the last load step (in seconds). |
A data.frame with the duration and load of each protocol step.
pt_pre(): Add pre-measures to a load protocol
pt_wu(): Add a warm up to a load protocol
pt_steps(): Add a stepwise load protocol
pt_const(): Add a constant load protocol
set_protocol_manual for manual protocol design.
get_protocol for automated extracting of protocols from raw data.
set_protocol(pt_pre(60), pt_wu(300, 100), pt_steps(180, 150, 25, 8, 30))set_protocol(pt_pre(60), pt_wu(300, 100), pt_steps(180, 150, 25, 8, 30))
set_protocol_manual() allows to set any user-defined load profile
for an exercise test.
set_protocol_manual(duration, load = NULL) ## Default S3 method: set_protocol_manual(duration, load) ## S3 method for class 'data.frame' set_protocol_manual(duration, load = NULL)set_protocol_manual(duration, load = NULL) ## Default S3 method: set_protocol_manual(duration, load) ## S3 method for class 'data.frame' set_protocol_manual(duration, load = NULL)
duration |
Either a numeric vector containing the duration (in seconds)
of each load step, or a |
load |
A numeric vector of the same length as |
A data.frame with the duration and load of each protocol step.
set_protocol_manual(default): Default method when duration and load are
given separately
set_protocol_manual(data.frame): Method for data frames with a duration and a
load column
set_protocol for protocol setting with helper functions.
get_protocol For automated extracting of protocols from raw data.
set_protocol_manual( duration = c(300, 120, 300, 60, 300), load = c(3, 5, 3, 6, 3) ) # using a data.frame as input pt_data <- data.frame( duration = c(180, 150, 120, 90, 60, 30), load = c(200, 250, 300, 350, 400, 450) ) set_protocol_manual(pt_data)set_protocol_manual( duration = c(300, 120, 300, 60, 300), load = c(3, 5, 3, 6, 3) ) # using a data.frame as input pt_data <- data.frame( duration = c(180, 150, 120, 90, 60, 30), load = c(200, 250, 300, 350, 400, 450) ) set_protocol_manual(pt_data)
spiro() wraps multiple functions to import and process raw data from
metabolic carts into a data.frame.
spiro( file, device = NULL, bodymass = NULL, hr_file = NULL, hr_offset = 0, protocol = NULL, anonymize = TRUE )spiro( file, device = NULL, bodymass = NULL, hr_file = NULL, hr_offset = 0, protocol = NULL, anonymize = TRUE )
file |
The absolute or relative path of the file that contains the gas exchange data. |
device |
A character string, specifying the device for measurement. By
default the device type is guessed by the characteristics of the
|
bodymass |
Numeric value for the individual's body mass, if the default
value saved in the |
hr_file |
The absolute or relative path of a |
hr_offset |
An integer, corresponding to the temporal offset of the heart-rate file. By default the start of the heart rate measurement is linked to the start of the gas exchange measurement. A positive value means, that the heart rate measurement started after the begin of the gas exchange measurements; a negative value means it started before. |
protocol |
A |
anonymize |
Whether meta data should be anonymized during import.
Defaults to TRUE. See |
This function performs multiple operations on raw data from metabolic carts.
It imports the raw data from a file, which might be complemented by an
additional .tcx file with heart rate data.
After using this function, you may summarize the resulting data frame with
spiro_summary and spiro_max, or plot it with
spiro_plot.
A data.frame of the class spiro with cardiopulmonary
parameters interpolated to seconds and the corresponding load data.
The attribute "protocol" provides additional information on the
underlying testing protocol. The attribute "info" contains
additional meta data from the original raw data file. The attribute
"raw" gives the imported raw data (without interpolation, similar to
calling spiro_raw).
Different metabolic carts yield different output formats for their data. By
default, this function will guess the used device based on the
characteristics of the given file. This behavior can be overridden by
explicitly stating the device argument.
The currently supported metabolic carts are:
CORTEX (.xlsx, .xls or files .xml in
English or German language)
COSMED (.xlsx or .xls files, in English or
German language)
Vyntus (.txt files in French, German or Norwegian
language)
ZAN (.dat files in German language, usually with
names in the form of "EXEDxxx")
The spiro function can import personal meta data (name, sex, birthday, ...).
By default this data is anonymized with anonymize = TRUE, see
get_anonid for more information.
Breath-by-breath data is linearly interpolated to get data points for every
full second. Based on the given load data, the underlying exercise protocol
is guessed and applied to the data. If no load data is available or the
protocol guess turns wrong, you can manually specify the exercise
protocol by using set_protocol or
set_protocol_manual. If you want to skip the automated protocol
guessing without providing an alternative, set protocol = NA. Note
that in this case, some functions relying on load data (such as
spiro_summary) will not work.
Additional variables of gas exchange are calculated for further analysis. Per
default the body mass saved in the file's metadata is used for calculating
relative measures. It is possible to specify bodymass manually to the
function, overriding that value.
Protocols, heart rate data and body mass information can also be given in a
piping coding style using the functions add_protocol,
add_hr and add_bodymass (see examples).
# get example file file <- spiro_example("zan_gxt") out <- spiro(file) head(out) # import with user-defined test profile p <- set_protocol(pt_pre(60), pt_steps(300, 2, 0.4, 9, 30)) out2 <- spiro(file, protocol = p) head(out2) # import with additional heart rate data oxy_file <- spiro_example("zan_ramp") hr_file <- spiro_example("hr_ramp.tcx") out3 <- spiro(oxy_file, hr_file = hr_file) head(out3) # use the add_* functions in a pipe # Note: base R pipe requires R version 4.1 or greater) ## Not run: spiro(file) |> add_hr(hr_file = hr_file, hr_offset = 0) |> add_bodymass(68.2) ## End(Not run)# get example file file <- spiro_example("zan_gxt") out <- spiro(file) head(out) # import with user-defined test profile p <- set_protocol(pt_pre(60), pt_steps(300, 2, 0.4, 9, 30)) out2 <- spiro(file, protocol = p) head(out2) # import with additional heart rate data oxy_file <- spiro_example("zan_ramp") hr_file <- spiro_example("hr_ramp.tcx") out3 <- spiro(oxy_file, hr_file = hr_file) head(out3) # use the add_* functions in a pipe # Note: base R pipe requires R version 4.1 or greater) ## Not run: spiro(file) |> add_hr(hr_file = hr_file, hr_offset = 0) |> add_bodymass(68.2) ## End(Not run)
spiro_example returns the file path for example data files within the
spiro package.
spiro_example(file = NULL)spiro_example(file = NULL)
file |
Name of the file, either "zan_gxt", "zan_ramp" or "hr_ramp.tcx". Leave the argument empty to get a vector with the paths of all three example files. |
A character vector with the absolute file path of the example file(s).
# get path of a specific example data file spiro_example("zan_gxt") # get all paths of example data files spiro_example()# get path of a specific example data file spiro_example("zan_gxt") # get all paths of example data files spiro_example()
This function has been deprecated as of package version 0.2.0. It will
be removed in the next version release. Please use spiro for
automated import and processing or spiro_raw to import only raw
data.
spiro_import(file, device = NULL, anonymize = TRUE)spiro_import(file, device = NULL, anonymize = TRUE)
file |
The absolute or relative path of the file that contains the gas exchange data. |
device |
A character string, specifying the device for measurement. By
default the device type is guessed by the characteristics of the
|
anonymize |
Whether meta data should be anonymized during import.
Defaults to TRUE. See |
spiro_max() returns a data.frame with the maximum gas exchange
parameters of an exercise test.
spiro_max(data, smooth = 30, hr_smooth = FALSE)spiro_max(data, smooth = 30, hr_smooth = FALSE)
data |
A |
smooth |
Parameter giving the filter methods for smoothing the data.
Default is |
hr_smooth |
A logical, whether smoothing should also apply to heart rate
data. Default is |
Before calculating the maximum values, the raw data is smoothed. Default
smoothing method is a 30-second rolling average. See the smooth
argument in spiro_smooth for more options, such as breath-based
averages or digital filtering.
Parameters calculated are the maxima of oxygen uptake (absolute and relative), carbon dioxide output, minute ventilation, respiratory exchange ratio (RER), and heart rate. The maximum values are defined as the highest single data values after the smoothing.
For the maximum RER a different algorithm is used, as the RER during and after rest may exceed the peak value during exercise. Therefore only values during the last ten percent of the exercise time are considered for the RERmax determination. The RERmax calculation works best for data from tests without rest intervals (e.g., ramp tests) and with attached load protocol data.
A data.frame with the maximum parameter values of the data.
# Import and process example data sets gxt_data <- spiro(file = spiro_example("zan_gxt")) spiro_max(gxt_data) # Use an averaging over a time interval of 15 seconds spiro_max(gxt_data, smooth = 15) # Use an averaging over an interval of 15 breaths spiro_max(gxt_data, smooth = "15b")# Import and process example data sets gxt_data <- spiro(file = spiro_example("zan_gxt")) spiro_max(gxt_data) # Use an averaging over a time interval of 15 seconds spiro_max(gxt_data, smooth = 15) # Use an averaging over an interval of 15 breaths spiro_max(gxt_data, smooth = "15b")
spiro_plot() returns a ggplot2 graph visualizing data from
cardiopulmonary exercise testing.
spiro_plot( data, which = 1:9, smooth = "fz", base_size = 13, style_args = list(), grid_args = list(), vert_lines = FALSE )spiro_plot( data, which = 1:9, smooth = "fz", base_size = 13, style_args = list(), grid_args = list(), vert_lines = FALSE )
data |
A |
which |
A numeric integer setting the plot panels to be displayed. The panels are numbered in the order of the traditional Wasserman 9-Panel Plot:
|
smooth |
Parameter giving the filter methods for smoothing the data.
Default is |
base_size |
An integer controlling the base size of the plots (in pts). |
style_args |
A list of arguments controlling the color and size of lines and points. See the section 'Customization' for possible arguments. Additional arguments are passed to ggplot2::theme() to modify the appearance of the plots. |
grid_args |
A list of arguments passed to |
vert_lines |
Whether vertical lines should be displayed at the time points of the first warm-up load, first load, and last load. Defaults to FALSE. |
This function provides a shortcut for visualizing data from metabolic carts
processed by the spiro function.
There are three ways to customize the appearance of plots in
spiro_plot. First, you can control the color and size of points and
lines with the style_args argument. For a list of available arguments
that should be passed in form of a list, see below. Second, you can change
the appearance of axis and plot elements (e.g, axis titles, panel lines) by
passing arguments over to ggplot2::theme() via the style_args
argument. Third, you can modify the arrangement of plots by the which
argument and customize the arrangement by passing arguments to
cowplot::plot_grid() via the grid_args argument.
size = 2Defines the size of all points
linewidth = 1Defines the width of all lines
color_VO2 = "#c00000", color_VCO2 = "#0053a4",
color_VE = "#003300", color_VT = "grey30",
color_RER = "#003300", color_HR = "red",
color_pulse = "pink"
Define the color of lines and points in the following plot panels: VO2 (panel 3,6,9), VCO2 (3,4,5,6,9), VE (1), VT (7), RER (8), HR (2,5), pulse (2)
Are passed to ggplot2::theme()
A ggplot object.
# Import and process example data ramp_data <- spiro( file = spiro_example("zan_ramp"), hr_file = spiro_example("hr_ramp.tcx") ) # Display the traditional Wasserman 9-Panel Plot spiro_plot(ramp_data) # Display selected panels, here V-Slope spiro_plot(ramp_data, which = 5) # Modify the arrangement of plots by passing arguments to # cowplot::plot_grid() via the grid_args argument spiro_plot(ramp_data, which = c(4, 5, 6, 8), grid_args = list(nrow = 1)) # Modify the appearance of plots using the style_args argument spiro_plot(ramp_data, style_args = list(size = 0.3, color_VCO2 = "black")) # Modify the appearance of plots by passing arguments to ggplot2::theme() via # the style_args argument spiro_plot(ramp_data, style_args = list(axis.title.x = ggplot2::element_text(colour = "green")) )# Import and process example data ramp_data <- spiro( file = spiro_example("zan_ramp"), hr_file = spiro_example("hr_ramp.tcx") ) # Display the traditional Wasserman 9-Panel Plot spiro_plot(ramp_data) # Display selected panels, here V-Slope spiro_plot(ramp_data, which = 5) # Modify the arrangement of plots by passing arguments to # cowplot::plot_grid() via the grid_args argument spiro_plot(ramp_data, which = c(4, 5, 6, 8), grid_args = list(nrow = 1)) # Modify the appearance of plots using the style_args argument spiro_plot(ramp_data, style_args = list(size = 0.3, color_VCO2 = "black")) # Modify the appearance of plots by passing arguments to ggplot2::theme() via # the style_args argument spiro_plot(ramp_data, style_args = list(axis.title.x = ggplot2::element_text(colour = "green")) )
spiro_raw() retrieves cardiopulmonary raw data from various types of
metabolic cart files, or from objects previously imported and processed with
spiro.
spiro_raw(data, device = NULL, anonymize = TRUE) ## Default S3 method: spiro_raw(data, device = NULL, anonymize = TRUE) ## S3 method for class 'spiro' spiro_raw(data, device = NULL, anonymize = TRUE)spiro_raw(data, device = NULL, anonymize = TRUE) ## Default S3 method: spiro_raw(data, device = NULL, anonymize = TRUE) ## S3 method for class 'spiro' spiro_raw(data, device = NULL, anonymize = TRUE)
data |
Either the absolute or relative path of the file that contains
the gas exchange data, or a data frame of the class |
device |
A character string, specifying the device for measurement. By
default the device type is guessed by the characteristics of the
|
anonymize |
Whether meta data should be anonymized during import.
Defaults to TRUE. See |
The default way of importing data into the spiro package is using the
spiro function. Besides importing this will perform further
processing steps such as the interpolation of data or the calculation of
additional variables. But in some cases the original raw data may be
preferable compared to the processed raw data. spiro_raw can either
retrieve the raw data from an already imported data set or from a new raw
data file.
A data.frame with data. The attribute info contains
addition meta-data retrieved from the original file.
spiro_raw(default): Method for direct import from metabolic cart raw data
file
spiro_raw(spiro): Method for objects of class spiro, usually files
previously imported and processed with spiro
# Get example data file <- spiro_example("zan_gxt") # direct import of raw data out <- spiro_raw(file) head(out) # retrieval of raw data from previously processed object s <- spiro(file) out2 <- spiro_raw(s) head(out2)# Get example data file <- spiro_example("zan_gxt") # direct import of raw data out <- spiro_raw(file) head(out) # retrieval of raw data from previously processed object s <- spiro(file) out2 <- spiro_raw(s) head(out2)
Filter vectors and data frames with moving averages and digital filters.
Provides the data filtering for spiro_max and
spiro_plot.
spiro_smooth(data, smooth = 30, columns = NULL, quiet = FALSE)spiro_smooth(data, smooth = 30, columns = NULL, quiet = FALSE)
data |
A data frame of the class |
smooth |
An integer or character string specifying the smoothing
strategy and parameters. Default is |
columns |
A character vector of the data columns that should be
filtered. By default the filtering applies to all data column of
|
quiet |
Whether warning message should be suppressed. Default is FALSE. |
Raw data from cardiopulmonary is usually noisy due to measurement error and biological breath-to-breath variability. When processing or visualizing the gas exchange data, it is often helpful to filter the raw data. This function provides different filtering methods (time average, breath average, digital filters).
Breath-based and digital filters will be applied on the raw breath-by-breath data. Time-based averages will be used on the interpolated data.
A data frame
smooth = 30)A (centered) moving
average over a defined time span. The number can be given as an integer
or as a character (e.g. smooth = "30") and defines the length of
the calculation interval in seconds.
smooth = "15b")A (centered) moving average over a defined number of breaths. The integer before the letter 'b' defines the number of breaths for the calculation interval.
smooth = "0.04f3")A digital
Butterworth filter (with lag). The number before the letter 'f' defines
the low-pass cut-off frequency, the number after the letter 'f' gives the
order of the filter. See bw_filter for more details.
smooth = "0.04fz3")A
digital forwards-backwards Butterworth filter (without lag). The number
before the letter 'f' defines the low-pass cut-off frequency, the number
after gives the order of the filter. See bw_filter for more
details.
# Get example data file <- spiro_example("zan_gxt") d <- spiro(file) out <- spiro_smooth(d, 30) head(out) # filter only the VO2 column with a zero-phase Butterworth filter out2 <- spiro_smooth(d, "0.04fz3", columns = "VO2") head(out2)# Get example data file <- spiro_example("zan_gxt") d <- spiro(file) out <- spiro_smooth(d, 30) head(out) # filter only the VO2 column with a zero-phase Butterworth filter out2 <- spiro_smooth(d, "0.04fz3", columns = "VO2") head(out2)
spiro_summary() returns a data.frame summarizing the main
parameters for each step of a cardiopulmonary exercise test.
spiro_summary(data, interval = 120, quiet = FALSE, exclude = FALSE)spiro_summary(data, interval = 120, quiet = FALSE, exclude = FALSE)
data |
A |
interval |
An integer giving the length of the computational interval in seconds. |
quiet |
A logical value, whether or not messages should be displayed, for example when intervals are shortened for specific steps. |
exclude |
A logical value, whether the last step should be excluded from the summary if it was not completely performed. |
This function generates mean values of gas exchange and cardiac parameters
for all steps of an exercise test. The calculation returns the mean of a
given interval before the end of each step.
If the interval exceeds the duration of any step, a message will be
displayed. If the interval exceeds the duration of all steps, it will be
reset to the duration of the longest step. You can silence all messages by
setting quiet = TRUE.
When setting exclude = TRUE the function will check whether the last
load step was terminated early. If this was the case, the step will not be
displayed in the summary.
A data.frame with the mean parameters for each step of the
exercise protocol.
# Import and process example data gxt_data <- spiro(file = spiro_example("zan_gxt")) spiro_summary(gxt_data)# Import and process example data gxt_data <- spiro(file = spiro_example("zan_gxt")) spiro_summary(gxt_data)