| Title: | Analyse Eye Data |
|---|---|
| Description: | Enables the automation of actions across the pipeline, including initial steps of transforming binocular data and gap repair to event-based processing such as fixations, saccades, and entry/duration in Areas of Interest (AOIs). It also offers visualisation of eye movement and AOI entries. These tools take relatively raw (trial, time, x, and y form) data and can be used to return fixations, saccades, and AOI entries and time spent in AOIs. As the tools rely on this basic data format, the functions can work with data from any eye tracking device. Implements fixation and saccade detection using methods proposed by Salvucci and Goldberg (2000) <doi:10.1145/355017.355028>. |
| Authors: | Tom Beesley [aut, cre], Matthew Ivory [aut] |
| Maintainer: | Tom Beesley <[email protected]> |
| License: | GPL-3 |
| Version: | 0.8.1 |
| Built: | 2025-02-17 05:47:33 UTC |
| Source: | https://github.com/tombeesley/eyetools |
Analyses the sequence of entries into defined AOI regions across trials. Can only be used with fixation data with a "fix_n" column denoting fixation events.
AOI_seq( data, AOIs, AOI_names = NULL, participant_ID = "participant_ID", progress = TRUE )AOI_seq( data, AOIs, AOI_names = NULL, participant_ID = "participant_ID", progress = TRUE )
data |
A dataframe with fixation data (from fixation_dispersion). Either single or multi participant 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. |
participant_ID |
the variable that determines the participant identifier. If no column present, assumes a single participant |
progress |
Display a progress bar |
a dataframe containing the sequence of entries into AOIs on each trial, entry/exit/duration time into AOI
data <- combine_eyes(HCL) fix_d <- fixation_dispersion(data, participant_ID = "pNum") AOI_seq(fix_d, AOIs = HCL_AOIs, participant_ID = "pNum")data <- combine_eyes(HCL) fix_d <- fixation_dispersion(data, participant_ID = "pNum") AOI_seq(fix_d, AOIs = HCL_AOIs, participant_ID = "pNum")
Analyses total time on defined AOI regions across trials. Works with fixation and raw data as the input (must use one or the other, not both).
AOI_time( data, data_type = NULL, AOIs, AOI_names = NULL, sample_rate = NULL, as_prop = FALSE, trial_time = NULL, participant_ID = "participant_ID" )AOI_time( data, data_type = NULL, AOIs, AOI_names = NULL, sample_rate = NULL, as_prop = FALSE, trial_time = NULL, participant_ID = "participant_ID" )
data |
A dataframe of either fixation data (from fix_dispersion) or raw data |
data_type |
Whether data is a fixation ("fix") or raw data ("raw") |
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. |
as_prop |
whether to return time in AOI as a proportion of the total time of trial |
trial_time |
needed if as_prop is set to TRUE. a vector of the time taken in each trial. Equal to the length of x trials by y participants in the dataset |
participant_ID |
the variable that determines the participant identifier. If no column present, assumes a single participant |
AOI_time 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
a dataframe containing the time on the passed AOIs for each trial. One column for each AOI separated by trial.
data <- combine_eyes(HCL) fix_d <- fixation_dispersion(data, participant_ID = "pNum") # fixation data AOI_time(data = fix_d, data_type = "fix", AOIs = HCL_AOIs, participant_ID = "pNum") #raw data AOI_time(data = data, data_type = "raw", AOIs = HCL_AOIs, participant_ID = "pNum") #as proportional data AOI_time(data = fix_d, data_type = "fix", AOIs = HCL_AOIs, participant_ID = "pNum", as_prop = TRUE, trial_time = HCL_behavioural$RT)data <- combine_eyes(HCL) fix_d <- fixation_dispersion(data, participant_ID = "pNum") # fixation data AOI_time(data = fix_d, data_type = "fix", AOIs = HCL_AOIs, participant_ID = "pNum") #raw data AOI_time(data = data, data_type = "raw", AOIs = HCL_AOIs, participant_ID = "pNum") #as proportional data AOI_time(data = fix_d, data_type = "fix", AOIs = HCL_AOIs, participant_ID = "pNum", as_prop = TRUE, trial_time = HCL_behavioural$RT)
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
AOI_time_binned( data, AOIs, AOI_names = NULL, sample_rate = NULL, bin_length = NULL, max_time = NULL, as_prop = FALSE, participant_ID = "participant_ID" )AOI_time_binned( data, AOIs, AOI_names = NULL, sample_rate = NULL, bin_length = NULL, max_time = NULL, as_prop = FALSE, participant_ID = "participant_ID" )
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 |
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
a dataframe containing the time on the passed AOIs for each trial. One column for each AOI separated by trial.
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)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)
Combines the data from binocular samples into X/Y coordinate pairs. Two methods can be used: "average" or "best_eye". For "average", the result is based on the average of the two eyes for each sample, or for samples where there is data from only a single eye, that eye is used. For "best_eye", a summary of the proportion of missing samples is computed, and the eye with the fewest missing samples is used.
combine_eyes(data, method = "average")combine_eyes(data, method = "average")
data |
raw data with columns time, left_x, left_y, right_x, right_y, and trial |
method |
either "average" or "best_eye" - see description. |
a dataframe of x-2 variables (with left_x and right_x condensed to x, and left_y and right_y condensed to y) and the same number of observations as the input data
combine_eyes(HCL, method = "average")combine_eyes(HCL, method = "average")
A tool for comparing the two different algorithms present in this package. This function is useful for assessing the data as well as exploring which algorithm is likely to fit data more appropriately. The raw data is run through both algorithms (using the same specified dispersion tolerances, etc.) before making comparisons of the underlying data. Can only be used for single participant data.
compare_algorithms( data, plot_fixations = TRUE, print_summary = TRUE, sample_rate = NULL, threshold = 100, min_dur = 150, min_dur_sac = 20, disp_tol = 100, NA_tol = 0.25, smooth = FALSE )compare_algorithms( data, plot_fixations = TRUE, print_summary = TRUE, sample_rate = NULL, threshold = 100, min_dur = 150, min_dur_sac = 20, disp_tol = 100, NA_tol = 0.25, smooth = FALSE )
data |
A dataframe with raw data (time, x, y, trial) for one participant |
plot_fixations |
Whether to plot the detected fixations. default as TRUE |
print_summary |
Whether to print the summary table. default as TRUE |
sample_rate |
sample rate of the eye-tracker. If default of NULL, then it will be computed from the timestamp data and the number of samples. Supplied to the VTI algorithm |
threshold |
velocity threshold (degrees of VA / sec) to be used for identifying saccades. Supplied to the VTI algorithm |
min_dur |
Minimum duration (in milliseconds) of period over which fixations are assessed. Supplied to both algorithms. |
min_dur_sac |
Minimum duration (in milliseconds) for saccades to be determined. Supplied to the VTI algorithm |
disp_tol |
Maximum tolerance (in pixels) for the dispersion of values allowed over fixation period. Supplied to both algorithms |
NA_tol |
the proportion of NAs tolerated within any window of samples that is evaluated as a fixation. Supplied to the dispersion algorithm |
smooth |
include a call to eyetools::smoother on each trial. Supplied to the VTI algorithm |
a list of the fixation data, correlation output, and data used for plotting
data <- combine_eyes(HCL) data <- interpolate(data, participant_ID = "pNum") compare_algorithms(data[data$pNum == 119,])data <- combine_eyes(HCL) data <- interpolate(data, participant_ID = "pNum") compare_algorithms(data[data$pNum == 119,])
A function to perform conditional transformations of the x/y raw data. The function takes the dataframe and performs a single axis flip based on the values specified in the cond_column. The primary use of this function is to correct or normalise the data when counterbalancing stimulus placement within experiments (e.g., having a target stimulus appear on the left and right equally often)
conditional_transform( data, flip = c("x", "y"), cond_column, cond_values, resolution_x = 1920, resolution_y = 1080, message = TRUE )conditional_transform( data, flip = c("x", "y"), cond_column, cond_values, resolution_x = 1920, resolution_y = 1080, message = TRUE )
data |
a dataframe that includes columns x and y and the column specified in cond_column. Can be raw, fixation, or saccade data. |
flip |
either "x", to flip across vertical midline, or "y" to flip across horizontal midline |
cond_column |
a column name, on which the flips are conditional |
cond_values |
a single value or vector stating which values in con_column result in a flip |
resolution_x |
screen size in pixels for the x axis |
resolution_y |
screen size in pixels for the y axis |
message |
whether to output messages during function. Useful to turn off when using in a vectorised fashion where it is running multiple times |
a dataframe of the equivalent format as the input data
data <- combine_eyes(HCL) data <- merge(data, HCL_behavioural) conditional_transform(data, flip = "x", cond_column = "cue_order", cond_values = 2)data <- combine_eyes(HCL) data <- merge(data, HCL_behavioural) conditional_transform(data, flip = "x", cond_column = "cue_order", cond_values = 2)
Create a blank data frame for populating with AOIs
create_AOI_df(num_AOIs = 3, shape = "rect", AOI_data = NULL)create_AOI_df(num_AOIs = 3, shape = "rect", AOI_data = NULL)
num_AOIs |
number of AOIs, setting the number of rows |
shape |
whether the AOI is rectangular ("rect") or circular ("circ") |
AOI_data |
a list of data for each AOI, ordered by x, y, width_radius, and height |
a dataframe in the standard format required for eyetools
# create an empty data frame with 3 rectangular shaped AOIs create_AOI_df(3, shape = "rect") # create an AOI dataframe with data create_AOI_df(3, shape = "rect", AOI_data = list(c(460,840,400,300), c(1460,840,400,300), c(960,270,300,500))) # creating data for circular AOIs create_AOI_df(3, shape = "circ", AOI_data = list(c(460,840,400), c(1460,840,400), c(960,270,300)))# create an empty data frame with 3 rectangular shaped AOIs create_AOI_df(3, shape = "rect") # create an AOI dataframe with data create_AOI_df(3, shape = "rect", AOI_data = list(c(460,840,400,300), c(1460,840,400,300), c(960,270,300,500))) # creating data for circular AOIs create_AOI_df(3, shape = "circ", AOI_data = list(c(460,840,400), c(1460,840,400), c(960,270,300)))
Takes a single value or vector of distances and returns the visual angle equivalent.
dist_to_visual_angle( vector, dist_type = "cm", view_dist_cm = 60, screen_width_cm = 51, screen_width_pixels = 1920 )dist_to_visual_angle( vector, dist_type = "cm", view_dist_cm = 60, screen_width_cm = 51, screen_width_pixels = 1920 )
vector |
vector of distances (or single distance) |
dist_type |
default is "cm". Specify "pixel" for conversion from pixel values. |
view_dist_cm |
viewing distance in cm. Default of 60cm. |
screen_width_cm |
used in conversion of pixel values. Default is 51 cm (24" monitor). |
screen_width_pixels |
used in conversion of pixel values. Default is 1920 pixels. |
an equivalent-sized object to the input
# calculate visual angle for stimulus of 5cm dist_to_visual_angle(5) # calculate visual angle of stimuli 2 and 10cm width at 50 cm viewing angle dist_to_visual_angle(c(2,10), view_dist_cm = 50) # calculate visual angle of 150 pixel wide dist_to_visual_angle(150, dist_type = "pixels")# calculate visual angle for stimulus of 5cm dist_to_visual_angle(5) # calculate visual angle of stimuli 2 and 10cm width at 50 cm viewing angle dist_to_visual_angle(c(2,10), view_dist_cm = 50) # calculate visual angle of 150 pixel wide dist_to_visual_angle(150, dist_type = "pixels")
Detects fixations by assessing dispersion of the eye position, using a method that is similar to that proposed by Salvucci & Goldberg (1996). Evaluates the maximum dispersion (distance) between x/y coordinates across a window of data. Looks for sufficient periods in which this maximum dispersion is below the specified dispersion tolerance. NAs are considered breaks in the data and are not permitted within a valid fixation period.
fixation_dispersion( data, min_dur = 150, disp_tol = 100, NA_tol = 0.25, progress = TRUE, participant_ID = "participant_ID" )fixation_dispersion( data, min_dur = 150, disp_tol = 100, NA_tol = 0.25, progress = TRUE, participant_ID = "participant_ID" )
data |
A dataframe with raw data (time, x, y, trial) for one participant (the standardised raw data form for eyetools) |
min_dur |
Minimum duration (in milliseconds) of period over which fixations are assessed |
disp_tol |
Maximum tolerance (in pixels) for the dispersion of values allowed over fixation period |
NA_tol |
the proportion of NAs tolerated within any window of samples that is evaluated as a fixation |
progress |
Display a progress bar |
participant_ID |
the variable that determines the participant identifier. If no column present, assumes a single participant |
It 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
a dataframe containing each detected fixation by trial, with mean x/y position in pixel, start and end times, and duration.
Salvucci, D. D., & Goldberg, J. H. (2000). Identifying fixations and saccades in eye-tracking protocols. Proceedings of the Symposium on Eye Tracking Research & Applications - ETRA '00, 71–78.
data <- combine_eyes(HCL) fixation_dispersion(data, participant_ID = "pNum")data <- combine_eyes(HCL) fixation_dispersion(data, participant_ID = "pNum")
Determine fixations by assessing the velocity of eye-movements, using a method that is similar to that proposed by Salvucci & Goldberg (1996). Applies the algorithm used in VTI_saccade and removes the identified saccades before assessing whether separated fixations are outside of the dispersion tolerance. If they are outside of this tolerance, the fixation is treated as a new fixation regardless of the length of saccade separating them. Compared to fixation_dispersion(), fixation_VTI() is more conservative in determining a fixation as smaller saccades are discounted and the resulting data is treated as a continued fixation (assuming it is within the pixel tolerance set by disp_tol). Returns a summary of the fixations found per trial, including start and end coordinates, timing, duration, mean velocity, and peak velocity.
fixation_VTI( data, sample_rate = NULL, threshold = 100, min_dur = 150, min_dur_sac = 20, disp_tol = 100, smooth = FALSE, progress = TRUE, participant_ID = "participant_ID" )fixation_VTI( data, sample_rate = NULL, threshold = 100, min_dur = 150, min_dur_sac = 20, disp_tol = 100, smooth = FALSE, progress = TRUE, participant_ID = "participant_ID" )
data |
A dataframe with raw data (time, x, y, trial) for one participant |
sample_rate |
sample rate of the eye-tracker. If default of NULL, then it will be computed from the timestamp data and the number of samples |
threshold |
velocity threshold (degrees of VA / sec) to be used for identifying saccades. |
min_dur |
Minimum duration (in milliseconds) of period over which fixations are assessed |
min_dur_sac |
Minimum duration (in milliseconds) for saccades to be determined |
disp_tol |
Maximum tolerance (in pixels) for the dispersion of values allowed over fixation period |
smooth |
include a call to eyetools::smoother on each trial |
progress |
Display a progress bar |
participant_ID |
the variable that determines the participant identifier. If no column present, assumes a single participant |
It 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
a dataframe containing each detected fixation by trial, with mean x/y position in pixel, start and end times, and duration.
Salvucci, D. D., & Goldberg, J. H. (2000). Identifying fixations and saccades in eye-tracking protocols. Proceedings of the Symposium on Eye Tracking Research & Applications - ETRA '00, 71–78.
data <- combine_eyes(HCL) data <- interpolate(data, participant_ID = "pNum") fixation_VTI(data[data$pNum == 119,], participant_ID = "pNum")data <- combine_eyes(HCL) data <- interpolate(data, participant_ID = "pNum") fixation_VTI(data[data$pNum == 119,], participant_ID = "pNum")
The dataset contains data from two participants and the first six trials of the study.
HCLHCL
A dataframe of 31,041 observations and seven variables
participant number
timestamp of the sample (milliseconds)
x coordinate of the left eye
y coordinate of the left eye
x coordinate of the right eye
y coordinate of the right eye
trial number
...
This dataframe contains three rectangular areas of interest (AOIs), set out for use with the HCL dataset. Values are in pixels.
HCL_AOIsHCL_AOIs
A data frame with 3 rows and 4 variables:
centred x coordinate of the AOI
centred y coordinate of the AOI
either the width of the AOI, or the radius for circular AOIs
the height of the AOI; should be NA for circular AOIs
...
This contains information on stimuli (such as the side the predictive cue was presented on) as well as response data, including accuracy and response times
HCL_behaviouralHCL_behavioural
A dataframe of 12 observations and eight variables
participant number
trial number
Are these necessary columns?
Are these necessary columns?
whether the predictive cue os presented on the left (1) or the right (2)
NAre these necessary columns?
response accuracy
response time in milliseconds
...
A function to get the message event files from a TOBII-generated hdf5 file to dataframe. Used when a Psychopy experiment uses the io.sendMessageEvent() to record events
hdf5_get_event(filename)hdf5_get_event(filename)
filename |
the hdf5 file generated from TOBII |
A dataframe of message events as recorded by TOBII eye trackers
## Not run: raw_data <- hdf5_get_event("example_TOBII.hdf5") ## End(Not run)## Not run: raw_data <- hdf5_get_event("example_TOBII.hdf5") ## End(Not run)
A function to convert TOBII-generated hdf5 files to a dataframe
hdf5_to_df(filename)hdf5_to_df(filename)
filename |
the hdf5 file generated from TOBII |
A list of dataframes collected from the eyetracker content, if only one eyetracking event is present, return this as a single dataframe
## Not run: raw_data <- hdf5_to_df("example_TOBII.hdf5") ## End(Not run)## Not run: raw_data <- hdf5_to_df("example_TOBII.hdf5") ## End(Not run)
Extends the zoo::na.approx and zoo::na.spline functions to include a report which provides the proportion of missing data before and after the interpolation process. This is handy for evaluating the effectiveness of the repair.
interpolate( data, maxgap = 150, method = "approx", sample_rate = NULL, report = FALSE, participant_ID = "participant_ID" )interpolate( data, maxgap = 150, method = "approx", sample_rate = NULL, report = FALSE, participant_ID = "participant_ID" )
data |
dataframe with columns time, x, y, trial (the standardised raw data form for eyeproc) |
maxgap |
maximum time gap of consecutive trackloss to fill (in ms). Any longer gaps will be left unchanged (see zoo package) |
method |
"approx" for linear interpolation or "spline" for cubic spline interpolation |
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. |
report |
default is FALSE. If TRUE, then the return value is a list containing the returned data frame and the report. |
participant_ID |
the variable that determines the participant identifier. If no column present, assumes a single participant |
It 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
a dataframe of the same shape of the input data
data <- combine_eyes(HCL) interpolate(data, maxgap = 150, participant_ID = "pNum")data <- combine_eyes(HCL) interpolate(data, maxgap = 150, participant_ID = "pNum")
A visualisation tool for plotting the changes in defined AOI regions across a single trial time.
plot_AOI_growth( data = NULL, AOIs = NULL, AOI_names = NULL, type = NULL, trial_number = NULL, plot_time_not_in_AOI = FALSE )plot_AOI_growth( data = NULL, AOIs = NULL, AOI_names = NULL, type = NULL, trial_number = NULL, plot_time_not_in_AOI = FALSE )
data |
raw data in standard raw data form (time, x, y, trial) |
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. To omit AOIs from the plot, use NA in relevant vector position |
type |
either "abs" (absolute) or "prop" (proportion) |
trial_number |
can be used to select particular trials within the data |
plot_time_not_in_AOI |
boolean as to whether to include proportion of time spent outside AOIs |
a plot of the raw data
data <- combine_eyes(HCL) data <- data[data$pNum == 118 & data$trial == 1,] data <- interpolate(data) # plot absolute and then proportional plot_AOI_growth(data = data, AOIs = HCL_AOIs, type = "abs") plot_AOI_growth(data = data, AOIs = HCL_AOIs, type = "prop")data <- combine_eyes(HCL) data <- data[data$pNum == 118 & data$trial == 1,] data <- interpolate(data) # plot absolute and then proportional plot_AOI_growth(data = data, AOIs = HCL_AOIs, type = "abs") plot_AOI_growth(data = data, AOIs = HCL_AOIs, type = "prop")
Plots a heatmap of raw data.
plot_heatmap( data = NULL, trial_number = NULL, bg_image = NULL, res = c(0, 1920, 0, 1080), flip_y = FALSE, alpha_control = 0.1, plot_header = FALSE )plot_heatmap( data = NULL, trial_number = NULL, bg_image = NULL, res = c(0, 1920, 0, 1080), flip_y = FALSE, alpha_control = 0.1, plot_header = FALSE )
data |
data in standard raw data form (time, x, y, trial) |
trial_number |
can be used to select particular trials within the data |
bg_image |
The filepath of an image to be added to the plot, for example to show a screenshot of the task. |
res |
resolution of the display to be shown, as a vector (xmin, xmax, ymin, ymax) |
flip_y |
reverse the y axis coordinates (useful if origin is top of the screen) |
alpha_control |
a single value to determine how much of the heatmap to obscure. Between 0 and 1. Lower values include more data in the heatmap |
plot_header |
display the header title text which explains graphical features of the plot. |
a plot of the raw data
data <- combine_eyes(HCL) data <- data[data$pNum == 118,] # plot all trials data plot_heatmap(data, alpha_control = .01) #plot one trial plot_heatmap(data, trial_number = 1)data <- combine_eyes(HCL) data <- data[data$pNum == 118,] # plot all trials data plot_heatmap(data, alpha_control = .01) #plot one trial plot_heatmap(data, trial_number = 1)
A tool for visualising the timecourse of raw data over a single trial. If data from multiple trials are present, then a single trial will be sampled at random. Alternatively, the trial_number can be specified. Data can be plotted across the whole trial, or can be split into bins to present distinct plots for each time window.
plot_seq( data = NULL, trial_number = NULL, AOIs = NULL, bg_image = NULL, res = c(0, 1920, 0, 1080), flip_y = FALSE, plot_header = FALSE, bin_time = NULL, bin_range = NULL )plot_seq( data = NULL, trial_number = NULL, AOIs = NULL, bg_image = NULL, res = c(0, 1920, 0, 1080), flip_y = FALSE, plot_header = FALSE, bin_time = NULL, bin_range = NULL )
data |
A dataframe with raw data. If multiple trials are used, then one trial is sampled at random. |
trial_number |
can be used to select a particular trial within the data |
AOIs |
A dataframe of areas of interest (AOIs), with one row per AOI (x, y, width_radius, height). |
bg_image |
The filepath of an image to be added to the plot, for example to show a screenshot of the task. |
res |
resolution of the display to be shown, as a vector (xmin, xmax, ymin, ymax) |
flip_y |
reverse the y axis coordinates (useful if origin is top of the screen) |
plot_header |
display the header title text which explains graphical features of the plot. |
bin_time |
if wanting to split data into bins, the time (in ms) for each bin of data to be displayed |
bin_range |
if wanting to split data into bins, the first and last bin to be display, e.g., c(1,5) |
a plot of the raw data representing changes over time
data <- combine_eyes(HCL) # plot the raw data plot_seq(data = data[data$pNum == 118,]) # with AOIs plot_seq(data = data[data$pNum == 118,], AOIs = HCL_AOIs) # plot raw data with bins plot_seq(data = data[data$pNum == 118,], bin_time = 500)data <- combine_eyes(HCL) # plot the raw data plot_seq(data = data[data$pNum == 118,]) # with AOIs plot_seq(data = data[data$pNum == 118,], AOIs = HCL_AOIs) # plot raw data with bins plot_seq(data = data[data$pNum == 118,], bin_time = 500)
A tool for visualising raw eye-data, processed fixations, and saccades. Can use all three data types together and independently. Fixations can be labeled in the order they were made. Can overlay areas of interest (AOIs) and customise the resolution.
plot_spatial( raw_data = NULL, fix_data = NULL, sac_data = NULL, AOIs = NULL, trial_number = NULL, bg_image = NULL, res = c(0, 1920, 0, 1080), flip_y = FALSE, show_fix_order = TRUE, plot_header = FALSE )plot_spatial( raw_data = NULL, fix_data = NULL, sac_data = NULL, AOIs = NULL, trial_number = NULL, bg_image = NULL, res = c(0, 1920, 0, 1080), flip_y = FALSE, show_fix_order = TRUE, plot_header = FALSE )
raw_data |
data in standard raw data form (time, x, y, trial) |
fix_data |
data output from fixation function |
sac_data |
data output from saccade function |
AOIs |
A dataframe of areas of interest (AOIs), with one row per AOI (x, y, width_radius, height). If using circular AOIs, then the 3rd column is used for the radius and the height should be set to NA. |
trial_number |
can be used to select particular trials within the data |
bg_image |
The filepath of an image to be added to the plot, for example to show a screenshot of the task. |
res |
resolution of the display to be shown, as a vector (xmin, xmax, ymin, ymax) |
flip_y |
reverse the y axis coordinates (useful if origin is top of the screen) |
show_fix_order |
label the fixations in the order they were made |
plot_header |
display the header title text which explains graphical features of the plot. |
a plot of the raw data
data <- combine_eyes(HCL) data <- data[data$pNum == 118,] # plot the raw data plot_spatial(raw_data = data) # plot both raw and fixation data together plot_spatial(raw_data = data, fix_data = fixation_dispersion(data)) #plot one trial plot_spatial(raw_data = data, fix_data = fixation_dispersion(data), trial_number = 1)data <- combine_eyes(HCL) data <- data[data$pNum == 118,] # plot the raw data plot_spatial(raw_data = data) # plot both raw and fixation data together plot_spatial(raw_data = data, fix_data = fixation_dispersion(data)) #plot one trial plot_spatial(raw_data = data, fix_data = fixation_dispersion(data), trial_number = 1)
Use the velocity threshold algorithm from Salvucci & Goldberg (1996) to determine saccadic eye movements. Returns a summary of the saccades found per trial, including start and end coordinates, timing, duration, mean velocity, and peak velocity.
saccade_VTI( data, sample_rate = NULL, threshold = 150, min_dur = 20, participant_ID = "participant_ID" )saccade_VTI( data, sample_rate = NULL, threshold = 150, min_dur = 20, participant_ID = "participant_ID" )
data |
A dataframe with raw data (time, x, y, trial) for one participant |
sample_rate |
sample rate of the eye-tracker. If default of NULL, then it will be computed from the timestamp data and the number of samples |
threshold |
velocity threshold (degrees of VA / sec) to be used for identifying saccades |
min_dur |
minimum duration (ms) expected for saccades. This helps to avoid identification of very short saccades occurring at the boundary of velocity threshold |
participant_ID |
the variable that determines the participant identifier. If no column present, assumes a single participant |
It 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
a data frame giving the saccades found by trial
data <- combine_eyes(HCL) saccade_VTI(data, participant_ID = "pNum")data <- combine_eyes(HCL) saccade_VTI(data, participant_ID = "pNum")
A wrapper for the stats::loess function, with default parameters suitable for smoothing raw eye data
smoother(data, span = 0.1, plot = FALSE, participant_ID = "participant_ID")smoother(data, span = 0.1, plot = FALSE, participant_ID = "participant_ID")
data |
A dataframe with raw data (time, x, y, trial) for one participant |
span |
From stats::loess. The parameter alpha which controls the degree of smoothing. |
plot |
whether to plot the raw and smoothed plot for inspection |
participant_ID |
the variable that determines the participant identifier. If no column present, assumes a single participant |
It 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
a dataframe of the same shape as the input data
data <- combine_eyes(HCL) smoother(data, participant_ID = "pNum") #with an inspection plot smoother(data, span = .02, participant_ID = "pNum", plot = TRUE)data <- combine_eyes(HCL) smoother(data, participant_ID = "pNum") #with an inspection plot smoother(data, span = .02, participant_ID = "pNum", plot = TRUE)