Plotting workloop objects
Working with single files
Let’s start by visualizing the raw traces in our data files, specifically position, force, and stimulation over time.
workloop_dat<-
system.file(
"extdata",
"workloop.ddf",
package = 'workloopR') %>%
read_ddf(phase_from_peak = TRUE) %>%
fix_GR(2)# To overlay position and force, we need them to be on comparable scales
# We will then use two y-axis to make the units clear
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")
Next, we would select cycles from the workloop in preparation for analysis. Before we do this, let’s keep all the cycles and visualize how select_cycles() splits the data. Note that you can include 0 in the keep_cycles argument to include data that is categorized as being outside of a complete cycle. This assigns a single cycle label (a) to the data before and after the complete cycles.
# Let's calculate x and y positions to add labels for each cycle
workloop_dat<-
workloop_dat %>%
select_cycles('lo', 0:6)
label_dat<-
workloop_dat %>%
group_by(Cycle) %>%
summarize(
x = mean(Time)
) %>%
# And add another row for the incomplete cycles at the beginning
bind_rows(data.frame(
Cycle = 'a',
x = 0))
workloop_dat %>%
ggplot(aes(x = Time, y = Position, colour = Cycle)) +
geom_point(size=1) +
geom_text(aes(x, y=2.1, colour = Cycle, label = Cycle), data = label_dat) +
labs(y = "Position (mm)", x = "Time (secs)") +
ggtitle("Division of position\nby `select_cycles()`") +
theme_bw() +
theme(legend.position = "none")
Visualizing the cycles is a highly recommended in case noise before or after the experimental procedure is interpreted as a cycle.
Let’s go ahead and use cycles 2 to 5 (labeled c-f in the previous plot). Note however that the cycle labels will be reassigned from a-d when we subset the data.
workloop_dat<-
workloop_dat %>%
select_cycles('p2p', 2:5)Now let’s plot some work loops!
# Let's start with a single cycle using colour to indicate time
workloop_dat %>%
filter(Cycle == 'a') %>%
ggplot(aes(x = Position, y = Force)) +
geom_path(aes(colour = Time)) +
labs(y = "Force (mN)", x = "Position (mm)", colour = "Time (sec)") +
ggtitle("Single work loop") +
theme_bw() +
theme(legend.position = "bottom", legend.direction = "horizontal")
# Now let's see how the work loop changes across cycles
# We can use arrows to indicate direction through time
workloop_dat %>%
ggplot(aes(x = Position, y = Force)) +
geom_path(aes(colour = Cycle), arrow=arrow()) +
labs(y = "Force (mN)", x = "Position (mm)", colour = "Cycle index") +
ggtitle("Work loops by cycle index") +
theme_bw() +
theme(legend.position = "bottom", legend.direction = "horizontal")
Working with single files
Working with multiple files is a little trickier as multiple the data are stored in separate data.frames organized into a list. The easiest way to deal with this issue is to add a column specifying the file id and concatenating the data together. Refer to the “Batch processing” vignette for more information on working with multiple files.
multi_workloop_dat<-
system.file(
"extdata/wl_duration_trials",
package = 'workloopR') %>%
read_ddf_dir(phase_from_peak = TRUE) %>%
map(fix_GR, 2) %>%
map(select_cycles,'p2p', 4) %>%
map(analyze_workloop)
# Summarize provides a quick way to pull out most experimental parameters, etc
multi_workloop_dat %>%
summarize_wl_trials %>%
ggplot(aes(Stimulus_Pulses, Mean_Power)) +
geom_point() +
labs(y = "Mean Power (W)", x = "Stim Duration (pulses)") +
ggtitle("Mean power over trial\nby stimulus duration") +
theme_bw() +
theme(legend.position = "bottom", legend.direction = "horizontal")
# Accessing the time course data requires more manipulation
multi_workloop_dat %>%
map(~ mutate(.x$cycle_a, stim_pulses = attr(.x, "stimulus_pulses"))) %>%
bind_rows %>%
ggplot(aes(Percent_of_Cycle, Inst_Power)) +
geom_path(aes(colour = as.factor(stim_pulses)))+
labs(y = "Power (W)", x = "Percent of Cycle", colour = "Stim Duration") +
ggtitle("Time course of instantaneous\npower by stimulus duration") +
theme_bw() +
theme(legend.position = "bottom", legend.direction = "horizontal")
#> Warning: Removed 3 row(s) containing missing values (geom_path).
