View source: R/batch_functions.R
clean_viewr_batch | R Documentation |
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, ...)
obj_list |
A list of viewr objects (i.e. a list of tibbles that each
have attribute |
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. |
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.
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.
Vikram B. Baliga
Other batch functions:
bind_viewr_objects()
,
import_and_clean_batch()
,
import_batch()
## 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)
## 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
)
## 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
)
## 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)
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