# Event variable created from Subject and TRIAL_INDEX # Working on optional columns. # RIGHT_ACCELERATION_Y converted to numeric. # RIGHT_ACCELERATION_X converted to numeric. # LEFT_ACCELERATION_Y converted to numeric. # LEFT_ACCELERATION_X converted to numeric. # RECORDING_SESSION_LABEL renamed to Subject. ![]() # All optional columns are present in the data. ![]() # All required columns are present in the data. Upon completion, the function prints a summary indicating the results.ĭat0 <- ppl_prep_data( data = Pupildat, Subject = "RECORDING_SESSION_LABEL", Item = "item") # Checking required columns. Should you choose to define the Event variable differently, you can override the default however, do so cautiously as this may impact the performance of subsequent operations because it must index each time sequence in the data uniquely. This Event variable is required internally for subsequent operations. Lastly, a new column called Event will be created which indexes each unique recording sequence and corresponds to the combination of Subject and TRIAL_INDEX. If you don’t have an item identifier column, by default the value of this parameter is NA. In doing so, the function will standardize the name of the column to Item and will ensure it is encoded as a factor. If your data contain a column corresponding to an item identifier please specify it in the Item parameter. The function will rename it Subject and will ensure it is encoded as a factor. Typical Data Viewer output contains a column called RECORDING_SESSION_LABEL which is the name of the column containing the subject identifier. It also checks for columns which are not required for basic preporcessing (e.g., SAMPLE_MESSAGE), but are needed to use the extra functions such as align_msg.Īdditionally, the Subject parameter is used to specify the column corresponding to the subject identifier. The ppl_prep_data function examines the presence and class of specific columns (e.g., LEFT_PUPIL_SIZE, RIGHT_PUPIL_SIZE, LEFT_IN_BLINK, RIGHT_IN_BLINK, TIMESTAMP, and TRIAL_INDEX) to ensure they are present in the data and appropriately assigned (e.g., categorical variables are encoded as factors). ![]() In order for the functions in the package to work appropriately, the data must be in a specific format, i.e., SR sample report. For more information about dplyr, please refer to its reference manual and extensive collection of vignettes. Lastly, the functions included here, internally make use of dplyr for manipulating and restructuring data. Additionally, it is preferable to export to a. The presence of necessary columns will also be checked internally when processing the data. This will ensure that you have all of the necessary columns for the functions contained in this package to work. The Sample Report should be exported along with all available columns. If you have not aligned your data to a particular message in Data Viewer, please refer to the Message Alignment vignette for functions related to this. ![]() However, this package is also able to preprocess data without a specified relative interest period. For this basic example, it is assumed that you have specified an interest period relative to the onset of the critical stimulus in Data Viewer (i.e., aligned to a specific sample message). Second, your data must have been exported using SR Research Data Viewer software, i.e., a sample report. Before using this package a number of steps are required: First, your pupil size data must have been collected using an SR Research Eyelink eye tracker.
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