pathviewr (version 1.1.8)

read_motive_csv: Import data from a CSV exported from Optitrack's Motive software

Description

read_motive_csv() is designed to import data from a CSV that has been exported from Optitrack's Motive software. The resultant object is a tibble that additionally has important metadata stored as attributes (see Details).

Usage

read_motive_csv(file_name, file_id = NA, simplify_marker_naming = TRUE, ...)

Value

A tibble with numerical data in columns. The first two columns will have frame numbers and time (assumed to be in secs), respectively. Columns 3 and beyond will contain the numerical data on the position or rotation of rigid bodies and/or markers that appear in the Motive CSV file. Each row corresponds to the position or rotation of all objects at a given time (frame).

Arguments

file_name

A file (or path to file) in CSV format

file_id

(Optional) identifier for this file. If not supplied, this defaults to basename(file_name).

simplify_marker_naming

If Markers are encountered, should they be renamed from "Subject:marker" to "marker"? Defaults to TRUE

...

Additional arguments passed from other pathviewr functions

Warning

This function was written to read CSVs exported using Motive's Format Version 1.23 and is not guaranteed to work with those from other versions. Please file an Issue on our Github page if you encounter any problems.

Author

Vikram B. Baliga

Details

Uses data.table::fread() to import data from a CSV file and ultimately store it in a tibble. This object is also labeled with the attribute pathviewr_steps with value viewr to indicate that it has been imported by pathviewr and should be friendly towards use with other functions in our package. Additionally, the following metadata are stored in the tibble's attributes: header information from the Motive CSV file (header), original IDs for each object (Motive_IDs), the name of each subject in each data column (subject_names_full) and unique values of subject names (subject_names_simple), the type of data (rigid body or marker) that appears in each column (data_types_full) and overall (data_types_simple), and original data column names in the CSV (d1, d2). See Example below for example code to inspect attributes.

See Also

read_flydra_mat for importing Flydra data

Other data import functions: as_viewr(), import_and_clean_batch(), import_batch(), read_flydra_mat()

Examples

library(pathviewr)

## Import the example Motive data included in the package
motive_data <-
  read_motive_csv(system.file("extdata", "pathviewr_motive_example_data.csv",
                             package = 'pathviewr'))

## Names of variables in the resulting tibble
names(motive_data)

## A variety of metadata are stored as attributes. Of particular interest:
attr(motive_data, "pathviewr_steps")
attr(motive_data, "file_id")
attr(motive_data, "header")
attr(motive_data, "Motive_IDs")
attr(motive_data, "subject_names_full")
attr(motive_data, "subject_names_simple")
attr(motive_data, "motive_data_names")
attr(motive_data, "motive_data_types_full")
attr(motive_data, "motive_data_types_simple")

## Of course, all attributes can be viewed as a (long) list via:
attributes(motive_data)