Like clean_viewr_batch()
, but with import as the first step too
import_and_clean_batch( file_path_list, import_method = c("flydra", "motive"), file_id = NA, subject_name = NULL, frame_rate = NULL, simplify_marker_naming = TRUE, import_messaging = FALSE, ... )
file_path_list | A list of file paths leading to files to be imported. |
---|---|
import_method | Either "flydra" or "motive" |
file_id | (Optional) identifier for this file. If not supplied, this
defaults to |
subject_name | For Flydra, the subject name applied to all files |
frame_rate | For Flydra, the frame rate applied to all files |
simplify_marker_naming | For Motive, if Markers are encountered, should they be renamed from "Subject:marker" to "marker"? Defaults to TRUE |
import_messaging | Should this function report each time a file has been processed? |
... | Additional arguments to specify how data should be cleaned. |
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.
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.
Other data import functions:
as_viewr()
,
import_batch()
,
read_flydra_mat()
,
read_motive_csv()
Other batch functions:
bind_viewr_objects()
,
clean_viewr_batch()
,
import_batch()
Vikram B. Baliga
## 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)#> [1] TRUE