Many of the functions in the workloopR package are built to facilitate batch processing of workloop and related data files. This vignette will start with an overview of how the functions were intended to be used for batch processing and then provide specific examples.
We generally expect a single file to store data from a single experimental trial, whereas directories hold data from all the trials of a single experiment. Accordingly, the muscle_stim objects created and used by most of the workloopR functions are intended to hold data from a single trial of a workloop or related experiment. Lists are then used to package together trials from a single experiment. This also lends itself to using recursion to transform and analyze all data from a single experiment.
In broad strokes, there are three ways that batch processing has been worked into workloopR functions. First, some functions like the *_dir() family of import functions and summarize_wl_trials() specifically generate or require lists of muscle_stim objects. Second, the first argument of all other functions are the objects being manipulated, which can help clean up recursion using the purrr::map() family of functions. Finally, some functions return summarized data as single rows of a data.frame that can easily be bound together to generate a summary table.
This vignette will rely heavily on the purrr::map() family of functions for recursion, though it should be mentioned that the base::apply() family of functions would work as well.
library(workloopR)
library(magrittr)
library(purrr)*_dir() functionsBoth read_ddf() and read_analyze_wl() have alternatives suffixed by _dir() to read in multiple files from a directory. Both take a path to the directory and an optional regular expression to filter files by and return a list of muscle_stim objects or analyzed_workloop objects, respectively.
workloop_trials_list<-
system.file(
"extdata/wl_duration_trials",
package = 'workloopR') %>%
read_ddf_dir(phase_from_peak = TRUE)
workloop_trials_list[1:2]
#> [[1]]
#> # Workloop Data: 3 channels recorded over 0.3244s
#> File ID: 01_4pulse.ddf
#>
#> Time Position Force Stim
#> 1 1e-04 0.698128 57.2970 0
#> 2 2e-04 0.699741 57.7805 0
#> 3 3e-04 0.699418 58.9095 0
#> 4 4e-04 0.697160 55.8450 0
#> 5 5e-04 0.698773 58.2645 0
#> 6 6e-04 0.698451 57.4580 0
#> # … with 3238 more rows
#>
#> [[2]]
#> # Workloop Data: 3 channels recorded over 0.3244s
#> File ID: 02_2pulse.ddf
#>
#> Time Position Force Stim
#> 1 1e-04 0.698773 47.9420 0
#> 2 2e-04 0.698451 48.1035 0
#> 3 3e-04 0.700064 48.2645 0
#> 4 4e-04 0.698451 48.5875 0
#> 5 5e-04 0.698773 48.7485 0
#> 6 6e-04 0.699418 49.0710 0
#> # … with 3238 more rowsThe sort_by argument can be used to rearrange this list by any attribute of the read-in objects. By default, the objects are sorted by their modification time. Other arguments of read_ddf() and read_analyze_wl() can also be passed to their *_dir() alternatives as named arguments.
analyzed_wl_list<-
system.file(
"extdata/wl_duration_trials",
package = 'workloopR') %>%
read_analyze_wl_dir(sort_by = 'file_id',
phase_from_peak = TRUE,
cycle_def = 'lo',
keep_cycles = 3)
analyzed_wl_list[1:2]
#> [[1]]
#> File ID: 01_4pulse.ddf
#> Cycles: 1 cycles kept out of 5
#> Mean Work: 0.00274 J
#> Mean Power: 0.07843 W
#>
#>
#> [[2]]
#> File ID: 02_2pulse.ddf
#> Cycles: 1 cycles kept out of 5
#> Mean Work: 0.00098 J
#> Mean Power: 0.02783 WIn a series of workloop trials, it can useful to see how mean power and work change as you vary different experimental parameters. To facilitate this, summarize_wl_trials() specifically takes a list of analyzed_workloop objects and returns a data.frame of this information. We will explore ways of generating lists of analyzed workloops without using read_analyze_wl_dir() in the following section.
analyzed_wl_list %>%
summarize_wl_trials
#> 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 1620177156 0.0027387056 0.078427135
#> 2 300 1620177156 0.0009849216 0.027832717
#> 3 300 1620177156 -0.0002192395 0.004323004
#> 4 300 1620177156 0.0022793831 0.065468837One of the more realistic use cases for manual recursion is for importing data from multiple related trials that are not stored in ddf format. As with importing individual non-ddf data sources, we start by reading the data into a data.frame, only now we want a list of data.frames. In this example, we will read in csv files and stitch them into a list using purrr::map()
non_ddf_list<-
# Generate a vector of file names
system.file(
"extdata/twitch_csv",
package = 'workloopR') %>%
list.files(full.names = T) %>%
# Read into a list of data.frames
map(read.csv) %>%
# Coerce into a workloop object
map(as_muscle_stim, type = "twitch")Applying a constant transformation to a list of muscle_stim objects is fairly straightforward using purrr::map().
non_ddf_list<-
non_ddf_list %>%
map(~{
attr(.x,"stimulus_width")<-0.2
attr(.x,"stimulus_offset")<-0.1
return(.x)
}) %>%
map(fix_GR,2)Applying a non-constant transformation like setting a unique file ID can be done using purrr::map2().
file_ids<-paste0("0",1:4,"-",2:5,"mA-twitch.csv")
non_ddf_list<-
non_ddf_list %>%
map2(file_ids, ~{
attr(.x,"file_id")<-.y
return(.x)
})
non_ddf_list
#> [[1]]
#> # Twitch Data: 3 channels recorded over 0.4001s
#> File ID: 01-2mA-twitch.csv
#>
#> Time Position Force Stim
#> 1 1e-04 -3.002651 474.262 0
#> 2 2e-04 -3.001682 471.682 0
#> 3 3e-04 -3.001360 472.650 0
#> 4 4e-04 -3.000554 471.037 0
#> 5 5e-04 -3.001199 472.004 0
#> 6 6e-04 -3.001360 472.327 0
#> # … with 3995 more rows
#>
#> [[2]]
#> # Twitch Data: 3 channels recorded over 0.4001s
#> File ID: 02-3mA-twitch.csv
#>
#> Time Position Force Stim
#> 1 1e-04 -3.002489 476.520 0
#> 2 2e-04 -3.001199 475.553 0
#> 3 3e-04 -3.000876 474.585 0
#> 4 4e-04 -3.001199 473.940 0
#> 5 5e-04 -3.001199 474.262 0
#> 6 6e-04 -3.001360 474.262 0
#> # … with 3995 more rows
#>
#> [[3]]
#> # Twitch Data: 3 channels recorded over 0.4001s
#> File ID: 03-4mA-twitch.csv
#>
#> Time Position Force Stim
#> 1 1e-04 -3.002651 451.037 0
#> 2 2e-04 -3.001360 449.747 0
#> 3 3e-04 -3.000715 449.747 0
#> 4 4e-04 -3.001037 449.424 0
#> 5 5e-04 -3.000876 449.424 0
#> 6 6e-04 -3.001521 450.069 0
#> # … with 3995 more rows
#>
#> [[4]]
#> # Twitch Data: 3 channels recorded over 0.4001s
#> File ID: 04-5mA-twitch.csv
#>
#> Time Position Force Stim
#> 1 1e-04 -3.002327 446.521 0
#> 2 2e-04 -3.001521 445.876 0
#> 3 3e-04 -3.001199 445.876 0
#> 4 4e-04 -3.001199 445.876 0
#> 5 5e-04 -3.001360 445.553 0
#> 6 6e-04 -3.001037 446.199 0
#> # … with 3995 more rowsAnalysis can similarly be run recursively. isometric_timing() in particular returns a single row of a data.frame with timings and forces for key points in an isometric dataset. Here we can use purrr::map_dfr() to bind the rows together for neatness.
non_ddf_list %>%
map_dfr(isometric_timing)
#> file_id time_stim force_stim time_peak force_peak time_rising_10
#> 1 01-2mA-twitch.csv 0.1002 480.391 0.1153 654.258 0.1049
#> 2 02-3mA-twitch.csv 0.1002 461.682 0.1149 748.772 0.1050
#> 3 03-4mA-twitch.csv 0.1002 450.069 0.1145 799.416 0.1049
#> 4 04-5mA-twitch.csv 0.1002 448.134 0.1141 824.899 0.1048
#> force_rising_10 time_rising_90 force_rising_90 time_relaxing_90
#> 1 497.810 0.1118 637.484 0.1216
#> 2 492.326 0.1109 720.708 0.1207
#> 3 488.778 0.1106 764.901 0.1201
#> 4 488.778 0.1107 787.803 0.1198
#> force_relaxing_90 time_relaxing_50 force_relaxing_50
#> 1 637.807 0.1348 567.486
#> 2 721.031 0.1325 605.872
#> 3 764.901 0.1314 624.904
#> 4 788.126 0.1311 636.517