For a list of viewr objects, run through the pipeline (from relabel axes up through get full trajectories, as desired) via clean_viewr()

clean_viewr_batch(obj_list, file_announce = FALSE, ...)

Arguments

obj_list

A list of viewr objects (i.e. a list of tibbles that each have attribute pathviewR_steps that includes "viewr")

file_announce

Should the function report each time a file is processed? Default FALSE; if TRUE, a message will appear in the console each time a file has been cleaned successfully.

...

Arguments to be passed in that specify how this function should clean files.

Value

A list of viewr objects (tibble or data.frame with attribute pathviewR_steps that includes "viewr") that have been passed through the corresponding cleaning functions.

Details

viewr objects should be in a list, e.g. the object generated by import_batch().

See clean_viewr() for details of how cleaning steps are handled and/or refer to the corresponding cleaning functions themselves.

See also

Author

Vikram B. Baliga

Examples

## Since we only have one example file of each type provided ## in pathviewR, we will simply import the same example multiple ## times to simulate batch importing. Replace the contents of ## the following list with your own list of files to be imported. ## Make a list of the same example file 3x import_list <- c(rep( system.file("extdata", "pathviewR_motive_example_data.csv", package = 'pathviewR'), 3 )) ## Batch import motive_batch_imports <- import_batch(import_list, import_method = "motive", import_messaging = TRUE)
#> File 1 imported.
#> File 2 imported.
#> File 3 imported.
## Batch cleaning of these imported files ## via clean_viewr_batch() motive_batch_cleaned <- clean_viewr_batch( file_announce = TRUE, motive_batch_imports, desired_percent = 50, max_frame_gap = "autodetect", span = 0.95 )
#> autodetect is an experimental feature -- please report issues.
#> File 1 has been cleaned successfully.
#> autodetect is an experimental feature -- please report issues.
#> File 2 has been cleaned successfully.
#> autodetect is an experimental feature -- please report issues.
#> File 3 has been cleaned successfully.
## Alternatively, use import_and_clean_batch() to ## combine import with cleaning on a batch of files motive_batch_import_and_clean <- import_and_clean_batch( import_list, import_method = "motive", import_messaging = TRUE, motive_batch_imports, desired_percent = 50, max_frame_gap = "autodetect", span = 0.95 )
#> File 1 imported.
#> File 2 imported.
#> File 3 imported.
#> autodetect is an experimental feature -- please report issues.
#> autodetect is an experimental feature -- please report issues.
#> autodetect is an experimental feature -- please report issues.
## Each of these lists of objects can be bound into ## one viewr object (i.e. one tibble) via ## bind_viewr_objects() motive_bound_one <- bind_viewr_objects(motive_batch_cleaned) motive_bound_two <- bind_viewr_objects(motive_batch_import_and_clean) ## Either route results in the same object ultimately: identical(motive_bound_one, motive_bound_two)
#> [1] TRUE