AOI_time_binned {eyetools} | R Documentation |
Binned time analysis of area of interest entries
Description
Analyses total time on defined AOI regions across trials separated into bins. Works with raw data as the input. Data can be separated into bins of a given length of time and the number of bins per trial is calculated automatically, keeping the bin length consistent across varying lengths of trial. Any r=data that cannot fill a bin (tpyically the last few milliseconds of the trial) are dropped to ensure that bins are of a consistent length
Usage
AOI_time_binned(
data,
AOIs,
AOI_names = NULL,
sample_rate = NULL,
bin_length = NULL,
max_time = NULL,
as_prop = FALSE,
participant_ID = "participant_ID"
)
Arguments
data |
A dataframe of raw data |
AOIs |
A dataframe of areas of interest (AOIs), with one row per AOI (x, y, width_radius, height). |
AOI_names |
An optional vector of AOI names to replace the default "AOI_1", "AOI_2", etc. |
sample_rate |
Optional sample rate of the eye-tracker (Hz) for use with data. If not supplied, the sample rate will be estimated from the time column and the number of samples. |
bin_length |
the time duration to be used for each bin. |
max_time |
maximum length of time to use, default is total trial length |
as_prop |
whether to return time in AOI as a proportion of the total time of trial |
participant_ID |
the variable that determines the participant identifier. If no column present, assumes a single participant |
Details
AOI_time_binned can take either single participant data or multiple participants where there is a variable for unique participant identification.
The function looks for an identifier named participant_ID
by default and will treat this as multiple-participant data as default,
if not it is handled as single participant data, or the participant_ID needs to be specified
Value
a dataframe containing the time on the passed AOIs for each trial. One column for each AOI separated by trial.
Examples
data <- combine_eyes(HCL)
#with bins of 100ms each and only for the first 2000ms
AOI_time_binned(data = data, AOIs = HCL_AOIs, participant_ID = "pNum",
bin_length = 100, max_time = 2000)