Visualize
We’ll now import the workloop.ddf file included with workloopR. Because this experiment involved using a gear ratio of 2, we’ll use fix_GR() to also implement this correction.
Ultimately, an object of classes workloop, muscle_stim, and data.frame is produced. muscle_stim objects are used throughout workloopR to help with data formatting and error checking across functions. Additionally setting the class to workloop allows our functions to understand that the data have properties that other experiment types (twitch, tetanus) do not.
## The file workloop.ddf is included and therefore can be accessed via
## system.file("subdirectory","file_name","package") . We'll then use
## read_ddf() to import it, creating an object of class "muscle_stim".
## fix_GR() multiplies Force by 2 and divides Position by 2
workloop_dat <-
system.file(
"extdata",
"workloop.ddf",
package = 'workloopR') %>%
read_ddf(phase_from_peak = TRUE) %>%
fix_GR(GR = 2)
summary(workloop_dat)
#> # Workloop Data: 3 channels recorded over 0.3244s
#>
#> File ID: workloop.ddf
#> Mod Time (mtime): 2021-05-04 18:12:35
#> Sample Frequency: 10000Hz
#>
#> data.frame Columns:
#> Position (mm)
#> Force (mN)
#> Stim (TTL)
#>
#> Stimulus Offset: 0.012s
#> Stimulus Frequency: 300Hz
#> Stimulus Width: 0.2ms
#> Stimulus Pulses: 4
#> Gear Ratio: 2
#>
#> Cycle Frequency: 28Hz
#> Total Cycles (L0-to-L0): 6
#> Amplitude: 1.575mmRunning summary() on a `muscle_stim shows a handy summary of file properties, data, and experimental parameters.
Let’s plot Time against Force, Position, and Stimulus (Stim) to visualize the time course of the work loop experiment.
To get them all plotted in the same figure, we’ll transform the data as they are being plotted. Please note that this is for aesthetic purposes only - the underlying data will not be changed after the plotting is complete.
scale_position_to_force <- 3000
workloop_dat %>%
# Set the x axis for the whole plot
ggplot(aes(x = Time)) +
# Add a line for force
geom_line(aes(y = Force, color = "Force"),
lwd = 1) +
# Add a line for Position, scaled to approximately the same range as Force
geom_line(aes(y = Position * scale_position_to_force, color = "Position")) +
# For stim, we only want to plot where stimulation happens, so we filter the data
geom_point(aes(y = 0, color = "Stim"), size = 1,
data = filter(workloop_dat, Stim == 1)) +
# Next we add the second y-axis with the corrected units
scale_y_continuous(sec.axis = sec_axis(~ . / scale_position_to_force, name = "Position (mm)")) +
# Finally set colours, labels, and themes
scale_color_manual(values = c("#FC4E2A", "#4292C6", "#373737")) +
labs(y = "Force (mN)", x = "Time (secs)", color = "Parameter:") +
ggtitle("Time course of \n work loop experiment") +
theme_bw() +
theme(legend.position = "bottom", legend.direction = "horizontal")
There’s a lot to digest here. The blue trace shows the change in length of the muscle via cyclical, sinusoidal changes to Position. The dark gray Stim dots show stimulation on a off vs. on basis. Stimulus onset is close to when the muscle is at L0 and the stimulator zapped the muscle four times in pulses of 0.2 ms width at 300 Hz. The resulting force development is shown in red. These cycles of length change and stimulation occurred a total of 6 times (measuring L0-to-L0).
