mousetrap (version 1.2.0)

read_mousetracker: Read data exported from MouseTracker.

Description

read_mousetracker imports data that was collected using MouseTacker (Freeman & Ambady, 2010) and exported to a .csv file through MouseTracker Analyzer. It focuses on the raw data, and provides several options to remove pre-processed aggregate data (see Details).

Usage

read_mousetracker(file, remove_mean_data = TRUE, last_lines_to_rm = 0, remove_empty_columns = TRUE)

Arguments

file

a character string. The name of the file which the data are to be read from. If it does not contain an absolute path, the file name is relative to the current working directory.

remove_mean_data

logical indicating if mean subject data (i.e., the aggregated trajectories) should be automatically detected and removed. This only works for data from "time normalized analysis" in MouseTracker (see Details).

last_lines_to_rm

integer indicating the number of last lines to omit from the imported data set. Typically used for data from "raw time analysis" in MouseTracker (see Details). This option is independent of remove_mean_data. The removal of the last lines is performed after the optional removal of the auto-detected mean data.

remove_empty_columns

logical indicating whether columns containing NAs only should be removed.

Value

A data.frame.

Details

MouseTracker by default includes the (per subject) aggregated trajectories in the exported data set. As these are typically not desired in the trial-based dataset, read_mousetracker provides several ways of removing them.

Data that stem from a "time normalized analysis" in MouseTracker Analyzer, can be read into R using read_mousetracker with default arguments (the aggregated trajectory data are automatically detected and removed, as they are marked by a row labeled "MEAN SUBJECT-BY-SUBJECT DATA" in the .csv file).

Data that stem from a "raw time analysis" in MouseTracker Analyzer, also usually contain the aggregated trajectory data. However, as they are not marked by a specific row label in the .csv file, the number of last lines that should be omitted has to be specified explicitly using the argument last_lines_to_rm. Typically, this number is two times the number of data files.

After reading the data into R, mt_import_wide can be used to prepare the trajectory data for analyses using the mousetrap library.

The current version of read_mousetracker has been tested with data exported from MouseTracker Version 2.82.

References

Freeman, J. B., & Ambady, N. (2010). MouseTracker: Software for studying real-time mental processing using a computer mouse-tracking method. Behavior Research Methods, 42(1), 226-241.

See Also

mt_import_wide to prepare the trajectory data for analyses in mousetrap.

Examples

## Not run: 
# # Data from "time normalized analysis" in MouseTracker
# raw_data <- read_mousetracker("tn_data_exported.csv")
# 
# # Data from "raw time analysis" in MouseTracker
# raw_data <- read_mousetracker("raw_data_exported.csv",last_lines_to_rm=2*8)
# ## End(Not run)