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Plot data from cardiopulmonary exercise data files

Source: R/spiro_plot.R
spiro_plot.Rd

spiro_plot() returns a ggplot2 graph visualizing data from cardiopulmonary exercise testing.

Usage

spiro_plot(
  data,
  which = 1:9,
  smooth = "fz",
  base_size = 13,
  style_args = list(),
  grid_args = list(),
  vert_lines = FALSE
)

Arguments

data

A data.frame of the class spiro containing the gas exchange data. Usually the output of a spiro call.

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:

  • 1: VE over time

  • 2: HR and oxygen pulse over time

  • 3: VO2, VCO2 and load over time

  • 4: VE over VCO2

  • 5: V-Slope: HR and VCO2 over VO2

  • 6: EQVO2 and EQVCO2 over time

  • 7: VT over VE

  • 8: RER over time

  • 9: PetO2 and PetCO2 over time

smooth

Parameter giving the filter methods for smoothing the data. Default is fz for a zero phase Butterworth filter. See spiro_smooth for more details and other filter methods (e.g. time based averages)

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 cowplot::plot_grid() to modify the arrangement of the plots.

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.

Value

A ggplot object.

Details

This function provides a shortcut for visualizing data from metabolic carts processed by the spiro function.

Customization

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.

Style arguments

size = 2

Defines the size of all points

linewidth = 1

Defines 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)

Additional arguments

Are passed to ggplot2::theme()

Examples

# \donttest{
# 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)
#> For heart rate data, smoothing was based on interpolated values.


# 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"))
#> For heart rate data, smoothing was based on interpolated values.


# 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"))
)
#> For heart rate data, smoothing was based on interpolated values.

# }