inst/doc/Plotting-workloopR.R

In workloopR: Analysis of Work Loops and Other Data from Muscle Physiology Experiments

## ---- include = FALSE---------------------------------------------------------
knitr::opts_chunk$set(
  collapse = TRUE,
  comment = "#>"
)

## ----package_loading, message=FALSE, warning=FALSE----------------------------
library(workloopR)
library(magrittr)
library(ggplot2)
library(purrr)
library(tidyr)
library(dplyr)

## ----data_import--------------------------------------------------------------
workloop_dat<-
  system.file(
    "extdata",
    "workloop.ddf",
    package = 'workloopR') %>%
  read_ddf(phase_from_peak = TRUE) %>%
  fix_GR(2)

## ----raw_trace----------------------------------------------------------------
# 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")

## ----annotate_cycles, warning=FALSE-------------------------------------------
# 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")

## ----select_cycles------------------------------------------------------------
workloop_dat<-
  workloop_dat %>%
  select_cycles('p2p', 2:5)

## ----analyze_workloop---------------------------------------------------------
# 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")

## ----multifile----------------------------------------------------------------
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")

## ----isometric_annotation-----------------------------------------------------
twitch_dat<-
  system.file(
    "extdata",
    "twitch.ddf",
    package = 'workloopR') %>%
  read_ddf() %>%
  fix_GR(2)

# We now need to reshape the single row into three columns, a label for the point, an x value for the label (time), and a y value (force). See the `tidyr` package and associated vignettes on reshaping tips
label_dat<-
  twitch_dat %>%
  isometric_timing(c(10,90),50) %>%
  gather(label, value) %>%
  filter(label != 'file_id') %>%
  separate(label, c("type", "identifier"), "_", extra="merge") %>%
  spread(type,value)
label_dat$time<-as.numeric(label_dat$time)
label_dat$force<-as.numeric(label_dat$force)

ggplot() +
  geom_line(aes(Time, Force), data = twitch_dat) +
  geom_point(aes(time, force), data = label_dat) +
  geom_text(aes(time, force, label = identifier), hjust=-0.15, data = label_dat) +
  labs(y = "Force (mN)", x = "Time (sec)") +
  ggtitle("Force development in a twitch trial") +
  theme_bw() +
  theme(legend.position = "bottom", legend.direction = "horizontal")

## ----iso_multi----------------------------------------------------------------
multi_twitch_dat<-
  system.file(
    "extdata/twitch_csv",
    package = 'workloopR') %>%
  list.files(full.names = T) %>%
  map(read.csv) %>%
  map2(c("2mA","3mA","4mA","5mA"), ~as_muscle_stim(.x, type = 'twitch', file_id = .y))

# Next we want another data.frame of label data
multi_label_dat<-
  multi_twitch_dat %>%
  map_dfr(isometric_timing) %>%
  select(file_id, ends_with("peak")) %>%
  mutate(label = paste0(round(force_peak),"mV"))

# Once again we want the data in a single data.frame with a column for which trial it came from
multi_twitch_dat %>%
  map_dfr(~mutate(.x, file_id = attr(.x, "file_id"))) %>%
  ggplot(aes(x = Time, y = Force, colour = file_id)) +
  geom_line() +
  geom_text(aes(time_peak, force_peak, label = label), hjust=-0.7, data = multi_label_dat) +
  labs(y = "Force (mN)", x = "Time (sec)", colour = "Stimulation Current") +
  ggtitle("Force development across twitch trials") +
  theme_bw() +
  theme(legend.position = "bottom", legend.direction = "horizontal")