Introduction to workloopR
Vikram B. Baliga
In this vignette, we’ll provide an overview of core functions in
workloopR. Other vignettes within the package give more details with respect to specific use-cases. Examples with code can also be found within each function’s Help doc.
workloopR (pronounced “work looper”) provides functions for the import, transformation, and analysis of muscle physiology experiments. As you may have guessed, the initial motivation was to provide functions to analyze work loop experiments in R, but we have expanded this goal to cover additional types of experiments that are often involved in work loop procedures. There are three currently supported experiment types: work loop, simple twitch, and tetanus.
To cut to the chase,
workloopR offers the ability to import, transform, and then analyze a data file. For example, with a work loop file:
library(workloopR) ## import the workloop.ddf file included in workloopR wl_dat <-read_ddf(system.file("extdata", "workloop.ddf", package = 'workloopR'), phase_from_peak = TRUE) ## select cycles 3 through 5 using a peak-to-peak definition wl_selected <- select_cycles(wl_dat, cycle_def = "p2p", keep_cycles = 3:5) ## run the analysis function and get the full object wl_analyzed <- analyze_workloop(wl_selected, GR = 2) ## for brevity, the print() method for this object produces a simple output wl_analyzed #> File ID: workloop.ddf #> Cycles: 3 cycles kept out of 6 #> Mean Work: 0.00308 J #> Mean Power: 0.08474 W ## but see the structure for the full output, e.g. #str(wl_analyzed) ## or run the analysis but get the simplified version wl_analyzed_simple <- analyze_workloop(wl_selected, simplify = TRUE, GR = 2) wl_analyzed_simple #> Cycle Work Net_Power #> a A 0.002785397 0.07639783 #> b B 0.003147250 0.08661014 #> c C 0.003305744 0.09122522
Batch processing of files within a directory (e.g. successive trials of an experiment) is also readily achieved:
## batch read and analyze files included with workloopR analyzed_wls <- read_analyze_wl_dir(system.file("extdata/wl_duration_trials", package = 'workloopR'), cycle_def = "p2p", keep_cycles = 2:4, phase_from_peak = TRUE ) ## now summarize summarized_wls <- summarize_wl_trials(analyzed_wls) summarized_wls #> File_ID Cycle_Frequency Amplitude Phase Stimulus_Pulses #> 1 01_4pulse.ddf 28 3.15 -24.36 4 #> 2 02_2pulse.ddf 28 3.15 -24.64 2 #> 3 03_6pulse.ddf 28 3.15 -24.92 6 #> 4 04_4pulse.ddf 28 3.15 -24.64 4 #> Stimulus_Frequency mtime Mean_Work Mean_Power #> 1 300 1701714100 0.0028362363 0.078967198 #> 2 300 1701714100 0.0009686570 0.026247519 #> 3 300 1701714100 -0.0001310863 -0.004017894 #> 4 300 1701714100 0.0024082708 0.066959552
Sections below will give more specific overviews.
Data that are stored in .ddf format (e.g. generated by Aurora Scientific’s Dynamic Muscle Control and Analysis Software) are easily imported via the function
read_ddf(). Two additional all-in-one functions (
read_analyze_wl_dir()) also import data and subsequently transform and analyze them. More on those functions later!
Importing via these functions generates objects of class
muscle_stim, which are formatted to work nicely with
workloopR’s core functions and help with error checking procedures throughout the package.
muscle_stim objects are organized to store time-series data for Time, Position, Force, and Stimulation in a
data.frame and also store core metadata and experimental parameters as Attributes.
We’ll provide a quick example using data that are included within the package.
library(workloopR) ## import the workloop.ddf file included in workloopR wl_dat <-read_ddf(system.file("extdata", "workloop.ddf", package = 'workloopR'), phase_from_peak = TRUE) ## muscle_stim objects have their own print() and summary() S3 methods ## for example: summary(wl_dat) # some handy info about the imported file #> # Workloop Data: 3 channels recorded over 0.3244s #> #> File ID: workloop.ddf #> Mod Time (mtime): 2023-12-04 18:21:39.730959 #> 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: 1 #> #> Cycle Frequency: 28Hz #> Total Cycles (L0-to-L0): 6 #> Amplitude: 3.15mm ## see the first few rows of data stored within head(wl_dat) #> # Workloop Data: 3 channels recorded over 6e-04s #> File ID: workloop.ddf #> #> Time Position Force Stim #> 1 1e-04 0.503939 304.8715 0 #> 2 2e-04 0.506197 305.5165 0 #> 3 3e-04 0.505552 304.8715 0 #> 4 4e-04 0.506197 304.3875 0 #> 5 5e-04 0.505229 305.1940 0 #> 6 6e-04 0.506842 305.0330 0
Again, important object metadata and experimental parameters are stored as attributes. We make extensive use of attributes throughout the package and most functions will update at least one attribute after completion. So please see this feature of your
muscle_stim objects for important info!
You can use
attributes on an object itself (e.g.
attributes(wl_dat)), but we’ll avoid doing so because the printout can be pretty lengthy.
Instead, let’s just look at a couple interesting ones.
## names(attributes(x) gives a list of all the attributes' names names(attributes(wl_dat)) #>  "names" "class" "row.names" #>  "stimulus_frequency" "cycle_frequency" "total_cycles" #>  "cycle_def" "amplitude" "phase" #>  "position_inverted" "units" "sample_frequency" #>  "header" "units_table" "protocol_table" #>  "stim_table" "stimulus_pulses" "stimulus_offset" #>  "stimulus_width" "gear_ratio" "file_id" #>  "mtime" ## take a look at the stimulation protocol attr(wl_dat, "protocol_table") #> Wait.s Then.action On.port Units Parameters #> 1 0.00 Stimulus-Train Stimulator .012, 300, 0.2, 4, 28 NA #> 2 0.01 Sine Wave Length Out 28,3.15,6 NA #> 3 0.00 Stimulus-Train Stimulator 0,0,0,0,0 NA #> 4 0.10 Stop NA ## at what frequency were cyclic changes to Position performed? attr(wl_dat, "cycle_frequency") #>  28 ## at what frequency were data recorded? attr(wl_dat, "sample_frequency") #>  10000
Data that are read from other file formats can be constructed into
muscle_stim objects via
as_muscle_stim(). Should you need to do this, please refer to our vignette “Importing data from non .ddf sources” for an overview.
Prior to analyses, data can be transformed or corrected. Transformational functions include gear ratio correction (
fix_GR()) and position inversion (
invert_position()). The core idea behind these two functions is to correct issues related to data acquisition.
For example, to apply a gear ratio correction of 2:
Core analytical functions include
analyze_workloop() for work loop files and
isometric_timing() for twitches.
analyze_workloop() computes instantaneous velocity, net work, instantaneous power, and net power for work loop experiments on a per-cycle basis.
isometric_timing() provides summarization of twitch kinetics.
To see more details about these functions, please refer to “Analyzing work loop experiments in workloopR” for work loop analyses and “Working with twitch files in workloopR” for twitches.
Some functions are readily available for batch processing of files. The
read_analyze_wl_dir() function allows for the batch import, cycle selection, gear ratio correction, and ultimately work & power computation for all work loop experiment files within a specified directory. The
summarize_wl_trials() functions organize scanned files by recency (according to their time of last modification: ‘mtime’) and then report work and power output in the order that trials were run.
This ultimately allows for the
time_correct() function to correct for degradation of the muscle (according to power & work) over time, assuming that the first and final trials are identical in experimental parameters. If these parameters are not identical, we advise against using this function.