This should be the first function you use when using eyetrackingR for a project (potentially with the exception of add_aoi, if you need to add AOIs). This function takes your raw dataframe, as well as information about your dataframe. It confirms that all the columns are the right format, based on this information. Further if treat_non_aoi_looks_as_missing is set to TRUE, it converts non-AOI looks to missing data (see the "Preparing your data" vignette for more information).

make_eyetrackingr_data(
data,
participant_column,
trackloss_column,
time_column,
trial_column,
aoi_columns,
treat_non_aoi_looks_as_missing,
item_columns = NULL
)

## Arguments

data

Your original data. See details section below.

participant_column

Column name for participant identifier

trackloss_column

Column name indicating trackloss

time_column

Column name indicating time

trial_column

Column name indicating trial identifier

aoi_columns

Names of AOIs

treat_non_aoi_looks_as_missing

This is a logical indicating how you would like to perform "proportion-looking" calculations, which are central to eyetrackingR's eyetracking analyses. If set to TRUE, any samples that are not in any of the AOIs (defined with the aoi_columns argument) are treated as missing data; when it comes time for eyetrackingR to calculate proportion looking to an AOI, this will be calculated as "time looking to that AOI divided by time looking to all other AOIs." In contrast, if this parameter is set to FALSE, proportion looking to an AOI will be calculated as "time looking to that AOI divided by total time looking."

item_columns

Column names indicating items (optional)

## Value

Dataframe ready for use in eyetrackingR.

## Details

eyetrackingR is designed to deal with data in a (relatively) raw form, where each row specifies a sample. Each row should represent an equally spaced unit of time (e.g., if your eye-tracker's sample rate is 100hz, then each row corresponds to the eye-position every 10ms). This is in contrast to the more parsed data that the software bundled with eye-trackers can sometimes output (e.g., already parsed into saccades or fixations). For eyetrackingR, the simplest data is the best. This also maximizes compatibility: eyetrackingR will work with any eye-tracker's data (e.g., Eyelink, Tobii, etc.), since it requires the most basic format.

## Examples

data(word_recognition)
data <- make_eyetrackingr_data(word_recognition,
participant_column = "ParticipantName",
trial_column = "Trial",
time_column = "TimeFromTrialOnset",
trackloss_column = "TrackLoss",
aoi_columns = c('Animate','Inanimate'),
treat_non_aoi_looks_as_missing = TRUE
)