README.md
In jashubbard/itrackR: itrackR - eyetracking analysis
itrackR 
itrackR Basics
Jason Hubbard
itrackR is an R package for high-level analyses of eyetracking data. For now it is only compatible with EDF files from SR-Research Eyelink eyetrackers. This is currently a work-in-progress. Some functions are missing documentation, but you can see an example of a full analysis with vignette('itrackR'). You will first need to install SR Research's API, found here: (https://www.sr-support.com/forumdisplay.php?17-EyeLink-Display-Software) and the edfR package in order to import the edf files. These aren't on CRAN yet, so everything should be installed via devtools:
install.packages('devtools')
#build_vignettes takes a long time. Set to FALSE if you don't need them.
devtools::install_github('jashubbard/itrackR', build_vignettes=TRUE)
NEWS (10-12-2016)
- Version 0.9
- Some documentation now included.
- Totally revamped code for handling pupil data (using a sqlite backend)
- Total rewrite of epoching, aggregating, and plotting fixations
This will acquaint you with some of the basics of itrackR. There are a number of function for doing high-level analyses of eyetracking data.
Example Data
Some sample data is included with itrackR. This includes 2 EDF files from an SR-Research Eyetracker, and a behavioral file (saved as a .rds). You can load them using itrackR.data:
library(itrackR)
datapath <- itrackr.data('path')
edf_files <- itrackr.data('edfs')
beh <- itrackr.data('beh')
datapath will point to the data folder wherever itrackR is installed:
datapath
## [1] "/Library/Frameworks/R.framework/Versions/3.2/Resources/library/itrackR/extdata/"
edfs is a list of the 2 edfs found in that folder:
edf_files
## [1] "/Library/Frameworks/R.framework/Versions/3.2/Resources/library/itrackR/extdata//104_exp.edf"
## [2] "/Library/Frameworks/R.framework/Versions/3.2/Resources/library/itrackR/extdata//105_exp.edf"
beh is a data frame of the behavioral data for the same 2 subjects.
| ID| Block| Trial| Task| Conflict| Targetpos| Distractorpos|
|----:|------:|------:|-----:|---------:|----------:|--------------:|
| 104| 1| 1| 1| 1| 2| 11|
| 104| 1| 2| 1| 0| 4| NA|
| 104| 1| 3| 1| 0| 2| NA|
| 104| 1| 4| 1| 0| 6| NA|
| 104| 1| 5| 1| 0| 4| NA|
| 104| 1| 6| 1| 0| 2| NA|
| 104| 1| 7| 1| 1| 3| 8|
| 104| 1| 8| 1| 1| 6| 9|
| 104| 1| 9| 1| 1| 6| 9|
| 104| 1| 10| 1| 1| 3| 7|
Loading Data
We start by initializing the itrackR object and loading the data. This can be done in a couple different ways. If we have a list of all edf files (in the current working directory, or with full path names) we can do it like this.
z <- itrackr(edfs=edf_files)
## Loading file /Library/Frameworks/R.framework/Versions/3.2/Resources/library/itrackR/extdata//104_exp.edf
## Loading Events (59201 events across 1325 trials)....
## Done with 104_exp
##
## Loading file /Library/Frameworks/R.framework/Versions/3.2/Resources/library/itrackR/extdata//105_exp.edf
## Loading Events (61362 events across 1325 trials)....
## Done with 105_exp
##
##
## Done processing all files!
#Alternatively, we can provide the path and a search pattern to find all edfs in a certain folder:
#z <- itrackr(path=datapath, pattern='*.edf')
Object Structure
The itrackR object consists of fields for each relevant event. Each one is a data frame. The ID fields specifies each subject. The subject ID is formed from extracting only the numeric data from the EDF file.
z$fixations
| | eyetrial| sttime| entime| gavx| gavy| ID| fixation_key|
|-----|---------:|-------:|-------:|------:|------:|----:|--------------:|
| 6 | 1| 8| 159| 493.9| 433.5| 104| 1|
| 10 | 1| 172| 2817| 528.0| 409.5| 104| 2|
| 16 | 1| 3036| 3106| 520.4| 396.8| 104| 3|
| 20 | 1| 3112| 4423| 527.8| 399.7| 104| 4|
| 26 | 1| 4713| 8625| 525.2| 401.3| 104| 5|
| 32 | 1| 8956| 10101| 534.7| 406.3| 104| 6|
| 38 | 1| 11171| 11327| 500.1| 437.3| 104| 7|
| 42 | 1| 11349| 11813| 529.6| 406.7| 104| 8|
| 48 | 1| 12732| 12788| 527.7| 416.9| 104| 9|
| 52 | 1| 12799| 13577| 532.1| 411.5| 104| 10|
z$saccades
| | eyetrial| sttime| entime| gstx| gsty| genx| geny| avel| pvel| ID| saccade_key|
|-----|---------:|-------:|-------:|------:|------:|------:|------:|------:|-------:|----:|-------------:|
| 8 | 1| 160| 171| 497.2| 430.9| 512.7| 417.5| 50.7| 64.9| 104| 1|
| 14 | 1| 2818| 3035| 532.9| 403.6| 522.5| 360.7| 295.0| 1024.5| 104| 2|
| 18 | 1| 3107| 3111| 522.3| 404.2| 527.7| 400.7| 36.7| 39.0| 104| 3|
| 24 | 1| 4424| 4712| 522.2| 391.9| 509.8| 375.7| 273.1| 1001.0| 104| 4|
| 30 | 1| 8626| 8955| 523.4| 400.6| 520.7| 384.3| 236.9| 928.0| 104| 5|
| 36 | 1| 10102| 11170| 536.3| 399.6| 574.7| 542.5| 242.9| 775.6| 104| 6|
| 40 | 1| 11328| 11348| 496.9| 444.5| 514.9| 408.7| 57.6| 96.3| 104| 7|
| 46 | 1| 11814| 12731| 534.1| 401.9| 507.9| 730.0| 264.4| 830.8| 104| 8|
| 50 | 1| 12789| 12798| 528.1| 415.5| 521.0| 399.4| 52.0| 61.4| 104| 9|
| 56 | 1| 13578| 14196| 535.1| 409.2| 543.4| 391.1| 202.2| 965.2| 104| 10|
z$messages shows the messages that were sent to Eyelink during the experiment
| | eyetrial| sttime| message | ID| message_key|
|-----|---------:|-------:|:---------------------------|----:|-------------:|
| 3 | 1| 1| !MODE RECORD CR 1000 2 1 L | 104| 1|
| 5 | 1| 55| BASELINE_START BLOCK 1 | 104| 2|
| 166 | 1| 60053| BASELINE_END BLOCK 1 | 104| 3|
| 172 | 2| 85998| !MODE RECORD CR 1000 2 1 L | 104| 4|
| 174 | 2| 86036| TRIALSTART | 104| 5|
| 175 | 2| 86036| BLOCK 1 | 104| 6|
| 176 | 2| 86036| TRIAL 1 | 104| 7|
| 181 | 2| 87098| STIMONSET | 104| 8|
| 198 | 2| 89710| RESPONSE | 104| 9|
| 204 | 3| 89755| !MODE RECORD CR 1000 2 1 L | 104| 10|
ROIs
Much of the analysis depends on specifying regions of interest (ROIs). We can then determine if fixations lie within these ROIs. First we specify all possible ROIs that may occur in an experiment. The function radialCoords makes it easy to specify a set of evenly-spaced coordinates arranged in a ring. We will create elliptical ROIs in this example. roiFlower makes it easy to rotate the ellipses to make a flower-like pattern.
#generate coordinates for our ROIs
innercoords <- radialCoords(x=512,y=384,numpoints=6,radius=240);
outercoords <- radialCoords(512, 384,6, 280,starting_angle=30); #larger radius, starting w/ 30 degree offset
#specify rotations of ellipses
angles <- roiFlower(12)
We use makeROIs to specify them. We can make elliptical or circular ROIs. First we make the inner ones. We can check our progress using plot_rois. Note the plots have the origin at the upper-left. This means that ROI #1 is actually at the 12 o'clock position if we viewed the plot in the regular orientation.
#make elliptical ROIs and plot them
z <- makeROIs(z,innercoords,shape='ellipse',xradius=60, yradius=120, angles=angles[c(1,3,5,7,9,11)])
plot_rois(z)
Now we add the outer ROIs. We make sure and specify the append option, and also provide names. For now, names are limited to numbers. If you don't specify them, it will just use 1...n.
#make elliptical ROIs and plot them
z <- makeROIs(z,outercoords,shape='ellipse',xradius=60, yradius=120, angles=angles[c(2,4,6,8,10,12)], names=7:12, append=T)
plot_rois(z)
Finally let's include a central, circular ROI.
#coordinates have to be a matrix:
centercoords <- matrix(c(512,384),nrow=1)
z <- makeROIs(z,centercoords,shapes='circle',radius=65, names=13, append=T)
plot_rois(z)
Plotting
Once the ROIs are added, we can easily make scatterplots of fixations for each subject. This allows us to find calibration issues.
plot(z, zoom=TRUE)
You can also plot everyone's data on one plot using the oneplot argument.
plot(z,zoom=TRUE,oneplot=TRUE)
Merging with behavioral data
Ideally, we send a message to Eyelink on every trial in order to identify it in the EDF file. Every time the eyetracker is started and stopped, we call this a separate trial. In this example, every trial the message "BLOCK X" and "TRIAL X" was sent to eyelink. We can see the trial-wise information in the header of our object:
z$header
knitr::kable(head(z$header, 10))
| eyetrial| starttime| endtime| duration| ID| first_sample|
|---------:|----------:|--------:|---------:|----:|--------------:|
| 1| 1| 60056| 60055| 104| 220490|
| 2| 85998| 89719| 3721| 104| 220490|
| 3| 89755| 92002| 2247| 104| 220490|
| 4| 92041| 93849| 1808| 104| 220490|
| 5| 93886| 95800| 1914| 104| 220490|
| 6| 95834| 98145| 2311| 104| 220490|
| 7| 98185| 100203| 2018| 104| 220490|
| 8| 100234| 103344| 3110| 104| 220490|
| 9| 103374| 105712| 2338| 104| 220490|
| 10| 105738| 107800| 2062| 104| 220490|
Next we specify index variables that uniquely identify trials. This should be present in both the edf and behavioral file. set_index searches through the messages, finds the relevant ones, and extracts the numeric data. set_index can take a regular expression to find anything that matches this pattern, and numeric.only tells it to ignore any text (e.g., "BLOCK "). The variable names are stored in z$indexvars and the information is added to z$header.
find_messages is for pulling other message information from the EDF file. Here we want to have the timestamps of the stimulus onset and the response so we can refer to them later.
add_behdata merges the behavioral file with the eye data, based on the index variables. This only works if we run set_index first (so we have Block and Trial variables in the behavioral data frame, and in our itrackR object).
#find messages to use as our index variables (to merge with our behavioral data)
z<- set_index(z,varnames=c('Block','Trial'), patterns=c('^BLOCK [0-9]*','^TRIAL [0-9]*'), numeric.only=T)
#find messages that specify the onset of events, extract the timestamps
z <- find_messages(z,varnames=c('STIMONSET','RESPONSE'), patterns=c('STIMONSET','RESPONSE'), timestamp=T)
#merge with behavioral data
z <- add_behdata(z,beh,append=F)
Now z$beh contains the behavioral data that matches with the eyetracking data, based on the index variables you specified (Block and Trial). It adds the variable eyetrial, which is also found in the header,fixations, saccades, blinks, and messages data frames in the itrackR object. It also adds the timestamps that you requested using find_messages:
| ID| Block| Trial| Task| Conflict| Targetpos| Distractorpos| eyetrial| starttime|
|----:|------:|------:|-----:|---------:|----------:|--------------:|---------:|----------:|
| 104| 1| 1| 1| 1| 2| 11| 2| 85998|
| 104| 1| 10| 1| 1| 3| 7| 11| 107836|
| 104| 1| 11| 1| 0| 2| NA| 12| 110025|
| 104| 1| 12| 1| 1| 4| 11| 13| 111913|
| 104| 1| 13| 1| 1| 6| 12| 14| 114633|
| 104| 1| 14| 1| 1| 4| 9| 15| 117898|
| 104| 1| 15| 1| 1| 6| 8| 16| 120763|
| 104| 1| 16| 1| 1| 5| 11| 17| 123646|
| 104| 1| 17| 1| 0| 2| NA| 18| 126331|
| 104| 1| 18| 1| 0| 4| NA| 19| 128505|
We can also see that the header has been updated
z$header
knitr::kable(head(z$header, 10))
| eyetrial| starttime| endtime| duration| ID| first_sample| Block| Trial| STIMONSET| RESPONSE|
|---------:|----------:|--------:|---------:|----:|--------------:|------:|------:|----------:|---------:|
| 1| 1| 60056| 60055| 104| 220490| NA| NA| NA| NA|
| 2| 85998| 89719| 3721| 104| 220490| 1| 1| 87098| 89710|
| 3| 89755| 92002| 2247| 104| 220490| 1| 2| 90825| 91998|
| 4| 92041| 93849| 1808| 104| 220490| 1| 3| 93110| 93846|
| 5| 93886| 95800| 1914| 104| 220490| 1| 4| 94966| 95798|
| 6| 95834| 98145| 2311| 104| 220490| 1| 5| 96903| 98142|
| 7| 98185| 100203| 2018| 104| 220490| 1| 6| 99255| 100197|
| 8| 100234| 103344| 3110| 104| 220490| 1| 7| 101299| 103341|
| 9| 103374| 105712| 2338| 104| 220490| 1| 8| 104438| 105709|
| 10| 105738| 107800| 2062| 104| 220490| 1| 9| 106802| 107797|
Now we can plot a subset of our data based on the behaivoral data. Just use the condition argument when plotting. Note it uses a similar syntax as subset. Here we're plotting data only from the first 5 blocks:
plot(z,zoom=TRUE,oneplot=TRUE, condition = Block<=5)
## Warning: Removed 303 rows containing missing values (geom_point).
Drift Correction
It looks like subject 104 is off-center, due to poor calibration. We can correct for this using drift_correct. We can optionally specify a grouping variable (from the behavioral data) so that correction is done separately for each level of that variable. In this case, let's perform correction for each subject and block. The threshold specifies the minimum amount of movement detected before we actually do any correction.
z <- drift_correct(z,vars='Block',threshold=15)
plot(z,zoom=T)
Much better!
Determining Fixation/Saccade "Hits"
Next we code whether each fixation and saccade "hit" any of the ROIs using calcHits
z <- calcHits(z)
Note that the fixations data frame now has binary vectors for each ROI specifying whether the fixation hit that item or not:
knitr::kable(head(z$fixations, 10))
| fixation_key| ID| eyetrial| sttime| entime| gavx| gavy| roi_1| roi_2| roi_3| roi_4| roi_5| roi_6| roi_7| roi_8| roi_9| roi_10| roi_11| roi_12| roi_13|
|--------------:|----:|---------:|-------:|-------:|------:|------:|-------:|-------:|-------:|-------:|-------:|-------:|-------:|-------:|-------:|--------:|--------:|--------:|--------:|
| 1| 104| 1| 8| 159| 493.9| 410.7| 0| 0| 0| 0| 0| 0| 0| 0| 0| 0| 0| 0| 1|
| 2| 104| 1| 172| 2817| 528.0| 386.7| 0| 0| 0| 0| 0| 0| 0| 0| 0| 0| 0| 0| 1|
| 3| 104| 1| 3036| 3106| 520.4| 374.0| 0| 0| 0| 0| 0| 0| 0| 0| 0| 0| 0| 0| 1|
| 4| 104| 1| 3112| 4423| 527.8| 376.9| 0| 0| 0| 0| 0| 0| 0| 0| 0| 0| 0| 0| 1|
| 5| 104| 1| 4713| 8625| 525.2| 378.5| 0| 0| 0| 0| 0| 0| 0| 0| 0| 0| 0| 0| 1|
| 6| 104| 1| 8956| 10101| 534.7| 383.5| 0| 0| 0| 0| 0| 0| 0| 0| 0| 0| 0| 0| 1|
| 7| 104| 1| 11171| 11327| 500.1| 414.5| 0| 0| 0| 0| 0| 0| 0| 0| 0| 0| 0| 0| 1|
| 8| 104| 1| 11349| 11813| 529.6| 383.9| 0| 0| 0| 0| 0| 0| 0| 0| 0| 0| 0| 0| 1|
| 9| 104| 1| 12732| 12788| 527.7| 394.1| 0| 0| 0| 0| 0| 0| 0| 0| 0| 0| 0| 0| 1|
| 10| 104| 1| 12799| 13577| 532.1| 388.7| 0| 0| 0| 0| 0| 0| 0| 0| 0| 0| 0| 0| 1|
This is not terribly useful if your task-relevant ROI changes positions on each trial. You can use mapROIs map your experiment-wide ROIs (1,2,3...13) to trial-specific ROIs ('target','distractor'). You just need a variable in your behavioral data that specifies the number of the relevant ROI. Here, Targetpos specifies the target location, and Distractorpos specifies the distractor location:
z <- mapROIs(z,names=c('target','distractor'),indicators=c('Targetpos','Distractorpos'))
Now we can see a target_hit and distractor_hit variable in our fixation data frame
knitr::kable(head(z$fixations, 10))
| fixation_key| ID| eyetrial| sttime| entime| gavx| gavy| roi_1| roi_2| roi_3| roi_4| roi_5| roi_6| roi_7| roi_8| roi_9| roi_10| roi_11| roi_12| roi_13| Targetpos| Distractorpos| target_hit| distractor_hit|
|--------------:|----:|---------:|-------:|-------:|------:|------:|-------:|-------:|-------:|-------:|-------:|-------:|-------:|-------:|-------:|--------:|--------:|--------:|--------:|----------:|--------------:|------------:|----------------:|
| 1| 104| 1| 8| 159| 493.9| 410.7| 0| 0| 0| 0| 0| 0| 0| 0| 0| 0| 0| 0| 1| NA| NA| NA| NA|
| 2| 104| 1| 172| 2817| 528.0| 386.7| 0| 0| 0| 0| 0| 0| 0| 0| 0| 0| 0| 0| 1| NA| NA| NA| NA|
| 3| 104| 1| 3036| 3106| 520.4| 374.0| 0| 0| 0| 0| 0| 0| 0| 0| 0| 0| 0| 0| 1| NA| NA| NA| NA|
| 4| 104| 1| 3112| 4423| 527.8| 376.9| 0| 0| 0| 0| 0| 0| 0| 0| 0| 0| 0| 0| 1| NA| NA| NA| NA|
| 5| 104| 1| 4713| 8625| 525.2| 378.5| 0| 0| 0| 0| 0| 0| 0| 0| 0| 0| 0| 0| 1| NA| NA| NA| NA|
| 6| 104| 1| 8956| 10101| 534.7| 383.5| 0| 0| 0| 0| 0| 0| 0| 0| 0| 0| 0| 0| 1| NA| NA| NA| NA|
| 7| 104| 1| 11171| 11327| 500.1| 414.5| 0| 0| 0| 0| 0| 0| 0| 0| 0| 0| 0| 0| 1| NA| NA| NA| NA|
| 8| 104| 1| 11349| 11813| 529.6| 383.9| 0| 0| 0| 0| 0| 0| 0| 0| 0| 0| 0| 0| 1| NA| NA| NA| NA|
| 9| 104| 1| 12732| 12788| 527.7| 394.1| 0| 0| 0| 0| 0| 0| 0| 0| 0| 0| 0| 0| 1| NA| NA| NA| NA|
| 10| 104| 1| 12799| 13577| 532.1| 388.7| 0| 0| 0| 0| 0| 0| 0| 0| 0| 0| 0| 0| 1| NA| NA| NA| NA|
Saving data
Next we probably want to do statistics on our eyetracking data. We want to have our behavioral data merged with the eye data, including our ROI "hits". Just use eyemerge to pull out the relevant information. Any eyetracking data that does not match behavioral data will still be included (all behavioral variables will just be NA).
fixes <- eyemerge(z,'fixations')
#including only some behavioral variables. ID and indexvars are always included
saccs <- eyemerge(z,'saccades',behdata=c('Task'))
#by default only mapped ROIs are included. Here we can include all 13 rois, plus the mapped ones
fixes_all <- eyemerge(z,'fixations',all.rois = T)
We can also subset based on our behavioral variables using the condition argument:
fixes_first5 <- eyemerge(z,'fixations',condition = Block <= 5)
max(fixes_first5$Block,na.rm=T)
## [1] 5
Timeseries plots
Sometimes you want to see the tendency of the eyes to look at a particular ROI over time, relative to some event. To look at this, we first determine epochs around our event of interest, in this case, STIMONSET. We first run epoch_fixations for each ROI that we're interested in.
#start at stimulus onset, going 700ms after that point. Bin the data into 25ms time bins.
#repeate for the target and the distractor ROIs we created
z <- epoch_fixations(z,c('target','distractor'),event='STIMONSET',start = 0, end = 700, binwidth = 25)
Next we want to visualize these timeseries using plot_fixation_epochs. We can generate separate lines for each level of a factor (specified in the behavioral data) or for different ROIs. You can specify variables that define the different lines, as well as the rows and columns in separate panels. Behind the scenes, the function first aggregates based on ID and the factors your specify, then across subjects.
#plot the timeseries data for fixations to target, separately for the Conflict and Task conditions
plot_fixation_epochs(z,event='STIMONSET',rois=c('target'),group=c('Conflict'),cols='Task')
## $plot
## Warning: Removed 1 rows containing missing values (geom_point).
##
## $data
## Source: local data frame [116 x 7]
## Groups: Conflict, Task, timepoint [116]
##
## Conflict Task timepoint roi val grouping color
## <int> <int> <dbl> <chr> <dbl> <fctr> <fctr>
## 1 0 1 0 target 0 0 0
## 2 0 1 25 target 0 0 0
## 3 0 1 50 target 0 0 0
## 4 0 1 75 target 0 0 0
## 5 0 1 100 target 0 0 0
## 6 0 1 125 target 0 0 0
## 7 0 1 150 target 0 0 0
## 8 0 1 175 target 0 0 0
## 9 0 1 200 target 0 0 0
## 10 0 1 225 target 0 0 0
## # ... with 106 more rows
#plot fixations to target and disctractor for the same conditions
#you must specify 'roi' as one of the plotting variables for it to work.
plot_fixation_epochs(z,event='STIMONSET',rois=c('target','distractor'),group=c('roi','Conflict'),color='Conflict',cols='Task')
## $plot
## Warning: Removed 60 rows containing missing values (geom_point).
##
## $data
## Source: local data frame [232 x 7]
## Groups: roi, Conflict, Task [8]
##
## roi Conflict Task timepoint val grouping color
## <chr> <int> <int> <dbl> <dbl> <fctr> <fctr>
## 1 distractor 0 1 0 NaN distractor.0 0
## 2 distractor 0 1 25 NaN distractor.0 0
## 3 distractor 0 1 50 NaN distractor.0 0
## 4 distractor 0 1 75 NaN distractor.0 0
## 5 distractor 0 1 100 NaN distractor.0 0
## 6 distractor 0 1 125 NaN distractor.0 0
## 7 distractor 0 1 150 NaN distractor.0 0
## 8 distractor 0 1 175 NaN distractor.0 0
## 9 distractor 0 1 200 NaN distractor.0 0
## 10 distractor 0 1 225 NaN distractor.0 0
## # ... with 222 more rows
#plot difference waves (target - distractor fixations). Plot on separate rows insetead of columns.
#This example doesn't make much sense because distractors aren't present on no-conflict trials
plot_fixation_epochs(z,event='STIMONSET',rois=c('target','distractor'),group=c('roi','Conflict'),rows='Task',type='difference')
## $plot
## Warning: Removed 59 rows containing missing values (geom_point).
##
## $data
## Source: local data frame [116 x 7]
## Groups: roi, Conflict, Task [4]
##
## roi Conflict Task timepoint val grouping color
## <chr> <int> <int> <dbl> <dbl> <fctr> <fctr>
## 1 difference 0 1 0 NaN difference.0 difference.0
## 2 difference 0 1 25 NaN difference.0 difference.0
## 3 difference 0 1 50 NaN difference.0 difference.0
## 4 difference 0 1 75 NaN difference.0 difference.0
## 5 difference 0 1 100 NaN difference.0 difference.0
## 6 difference 0 1 125 NaN difference.0 difference.0
## 7 difference 0 1 150 NaN difference.0 difference.0
## 8 difference 0 1 175 NaN difference.0 difference.0
## 9 difference 0 1 200 NaN difference.0 difference.0
## 10 difference 0 1 225 NaN difference.0 difference.0
## # ... with 106 more rows
#Repeat, but on a subset of the data. Say only when Conflict==1
plot_fixation_epochs(z,event='STIMONSET',rois=c('target','distractor'),group=c('roi','Conflict'),rows='Task',type='difference',condition = Conflict==1)
## $plot
## Warning: Removed 1 rows containing missing values (geom_point).
##
## $data
## Source: local data frame [58 x 7]
## Groups: roi, Conflict, Task [2]
##
## roi Conflict Task timepoint val grouping
## <chr> <int> <int> <dbl> <dbl> <fctr>
## 1 difference 1 1 0 0.25000000 difference.1
## 2 difference 1 1 25 0.05555556 difference.1
## 3 difference 1 1 50 0.03846154 difference.1
## 4 difference 1 1 75 0.02777778 difference.1
## 5 difference 1 1 100 0.02500000 difference.1
## 6 difference 1 1 125 0.02380952 difference.1
## 7 difference 1 1 150 0.02380952 difference.1
## 8 difference 1 1 175 -0.04545455 difference.1
## 9 difference 1 1 200 -0.36708861 difference.1
## 10 difference 1 1 225 -0.46642857 difference.1
## # ... with 48 more rows, and 1 more variables: color <fctr>
jashubbard/itrackR documentation built on May 18, 2019, 4:53 p.m.
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itrackR
itrackR Basics
Jason Hubbard
itrackR is an R package for high-level analyses of eyetracking data. For now it is only compatible with EDF files from SR-Research Eyelink eyetrackers. This is currently a work-in-progress. Some functions are missing documentation, but you can see an example of a full analysis with vignette('itrackR'). You will first need to install SR Research's API, found here: (https://www.sr-support.com/forumdisplay.php?17-EyeLink-Display-Software) and the edfR package in order to import the edf files. These aren't on CRAN yet, so everything should be installed via devtools:
install.packages('devtools')
#build_vignettes takes a long time. Set to FALSE if you don't need them.
devtools::install_github('jashubbard/itrackR', build_vignettes=TRUE)
NEWS (10-12-2016)
- Version 0.9
- Some documentation now included.
- Totally revamped code for handling pupil data (using a sqlite backend)
- Total rewrite of epoching, aggregating, and plotting fixations
This will acquaint you with some of the basics of itrackR. There are a number of function for doing high-level analyses of eyetracking data.
Example Data
Some sample data is included with itrackR. This includes 2 EDF files from an SR-Research Eyetracker, and a behavioral file (saved as a .rds). You can load them using itrackR.data:
library(itrackR)
datapath <- itrackr.data('path')
edf_files <- itrackr.data('edfs')
beh <- itrackr.data('beh')
datapath will point to the data folder wherever itrackR is installed:
datapath
## [1] "/Library/Frameworks/R.framework/Versions/3.2/Resources/library/itrackR/extdata/"
edfs is a list of the 2 edfs found in that folder:
edf_files
## [1] "/Library/Frameworks/R.framework/Versions/3.2/Resources/library/itrackR/extdata//104_exp.edf"
## [2] "/Library/Frameworks/R.framework/Versions/3.2/Resources/library/itrackR/extdata//105_exp.edf"
beh is a data frame of the behavioral data for the same 2 subjects.
| ID| Block| Trial| Task| Conflict| Targetpos| Distractorpos| |----:|------:|------:|-----:|---------:|----------:|--------------:| | 104| 1| 1| 1| 1| 2| 11| | 104| 1| 2| 1| 0| 4| NA| | 104| 1| 3| 1| 0| 2| NA| | 104| 1| 4| 1| 0| 6| NA| | 104| 1| 5| 1| 0| 4| NA| | 104| 1| 6| 1| 0| 2| NA| | 104| 1| 7| 1| 1| 3| 8| | 104| 1| 8| 1| 1| 6| 9| | 104| 1| 9| 1| 1| 6| 9| | 104| 1| 10| 1| 1| 3| 7|
Loading Data
We start by initializing the itrackR object and loading the data. This can be done in a couple different ways. If we have a list of all edf files (in the current working directory, or with full path names) we can do it like this.
z <- itrackr(edfs=edf_files)
## Loading file /Library/Frameworks/R.framework/Versions/3.2/Resources/library/itrackR/extdata//104_exp.edf
## Loading Events (59201 events across 1325 trials)....
## Done with 104_exp
##
## Loading file /Library/Frameworks/R.framework/Versions/3.2/Resources/library/itrackR/extdata//105_exp.edf
## Loading Events (61362 events across 1325 trials)....
## Done with 105_exp
##
##
## Done processing all files!
#Alternatively, we can provide the path and a search pattern to find all edfs in a certain folder:
#z <- itrackr(path=datapath, pattern='*.edf')
Object Structure
The itrackR object consists of fields for each relevant event. Each one is a data frame. The ID fields specifies each subject. The subject ID is formed from extracting only the numeric data from the EDF file.
z$fixations
| | eyetrial| sttime| entime| gavx| gavy| ID| fixation_key| |-----|---------:|-------:|-------:|------:|------:|----:|--------------:| | 6 | 1| 8| 159| 493.9| 433.5| 104| 1| | 10 | 1| 172| 2817| 528.0| 409.5| 104| 2| | 16 | 1| 3036| 3106| 520.4| 396.8| 104| 3| | 20 | 1| 3112| 4423| 527.8| 399.7| 104| 4| | 26 | 1| 4713| 8625| 525.2| 401.3| 104| 5| | 32 | 1| 8956| 10101| 534.7| 406.3| 104| 6| | 38 | 1| 11171| 11327| 500.1| 437.3| 104| 7| | 42 | 1| 11349| 11813| 529.6| 406.7| 104| 8| | 48 | 1| 12732| 12788| 527.7| 416.9| 104| 9| | 52 | 1| 12799| 13577| 532.1| 411.5| 104| 10|
z$saccades
| | eyetrial| sttime| entime| gstx| gsty| genx| geny| avel| pvel| ID| saccade_key| |-----|---------:|-------:|-------:|------:|------:|------:|------:|------:|-------:|----:|-------------:| | 8 | 1| 160| 171| 497.2| 430.9| 512.7| 417.5| 50.7| 64.9| 104| 1| | 14 | 1| 2818| 3035| 532.9| 403.6| 522.5| 360.7| 295.0| 1024.5| 104| 2| | 18 | 1| 3107| 3111| 522.3| 404.2| 527.7| 400.7| 36.7| 39.0| 104| 3| | 24 | 1| 4424| 4712| 522.2| 391.9| 509.8| 375.7| 273.1| 1001.0| 104| 4| | 30 | 1| 8626| 8955| 523.4| 400.6| 520.7| 384.3| 236.9| 928.0| 104| 5| | 36 | 1| 10102| 11170| 536.3| 399.6| 574.7| 542.5| 242.9| 775.6| 104| 6| | 40 | 1| 11328| 11348| 496.9| 444.5| 514.9| 408.7| 57.6| 96.3| 104| 7| | 46 | 1| 11814| 12731| 534.1| 401.9| 507.9| 730.0| 264.4| 830.8| 104| 8| | 50 | 1| 12789| 12798| 528.1| 415.5| 521.0| 399.4| 52.0| 61.4| 104| 9| | 56 | 1| 13578| 14196| 535.1| 409.2| 543.4| 391.1| 202.2| 965.2| 104| 10|
z$messages shows the messages that were sent to Eyelink during the experiment
| | eyetrial| sttime| message | ID| message_key| |-----|---------:|-------:|:---------------------------|----:|-------------:| | 3 | 1| 1| !MODE RECORD CR 1000 2 1 L | 104| 1| | 5 | 1| 55| BASELINE_START BLOCK 1 | 104| 2| | 166 | 1| 60053| BASELINE_END BLOCK 1 | 104| 3| | 172 | 2| 85998| !MODE RECORD CR 1000 2 1 L | 104| 4| | 174 | 2| 86036| TRIALSTART | 104| 5| | 175 | 2| 86036| BLOCK 1 | 104| 6| | 176 | 2| 86036| TRIAL 1 | 104| 7| | 181 | 2| 87098| STIMONSET | 104| 8| | 198 | 2| 89710| RESPONSE | 104| 9| | 204 | 3| 89755| !MODE RECORD CR 1000 2 1 L | 104| 10|
ROIs
Much of the analysis depends on specifying regions of interest (ROIs). We can then determine if fixations lie within these ROIs. First we specify all possible ROIs that may occur in an experiment. The function radialCoords makes it easy to specify a set of evenly-spaced coordinates arranged in a ring. We will create elliptical ROIs in this example. roiFlower makes it easy to rotate the ellipses to make a flower-like pattern.
#generate coordinates for our ROIs
innercoords <- radialCoords(x=512,y=384,numpoints=6,radius=240);
outercoords <- radialCoords(512, 384,6, 280,starting_angle=30); #larger radius, starting w/ 30 degree offset
#specify rotations of ellipses
angles <- roiFlower(12)
We use makeROIs to specify them. We can make elliptical or circular ROIs. First we make the inner ones. We can check our progress using plot_rois. Note the plots have the origin at the upper-left. This means that ROI #1 is actually at the 12 o'clock position if we viewed the plot in the regular orientation.
#make elliptical ROIs and plot them
z <- makeROIs(z,innercoords,shape='ellipse',xradius=60, yradius=120, angles=angles[c(1,3,5,7,9,11)])
plot_rois(z)
Now we add the outer ROIs. We make sure and specify the append option, and also provide names. For now, names are limited to numbers. If you don't specify them, it will just use 1...n.
#make elliptical ROIs and plot them
z <- makeROIs(z,outercoords,shape='ellipse',xradius=60, yradius=120, angles=angles[c(2,4,6,8,10,12)], names=7:12, append=T)
plot_rois(z)
Finally let's include a central, circular ROI.
#coordinates have to be a matrix:
centercoords <- matrix(c(512,384),nrow=1)
z <- makeROIs(z,centercoords,shapes='circle',radius=65, names=13, append=T)
plot_rois(z)
Plotting
Once the ROIs are added, we can easily make scatterplots of fixations for each subject. This allows us to find calibration issues.
plot(z, zoom=TRUE)
You can also plot everyone's data on one plot using the oneplot argument.
plot(z,zoom=TRUE,oneplot=TRUE)
Merging with behavioral data
Ideally, we send a message to Eyelink on every trial in order to identify it in the EDF file. Every time the eyetracker is started and stopped, we call this a separate trial. In this example, every trial the message "BLOCK X" and "TRIAL X" was sent to eyelink. We can see the trial-wise information in the header of our object:
z$header
knitr::kable(head(z$header, 10))
| eyetrial| starttime| endtime| duration| ID| first_sample| |---------:|----------:|--------:|---------:|----:|--------------:| | 1| 1| 60056| 60055| 104| 220490| | 2| 85998| 89719| 3721| 104| 220490| | 3| 89755| 92002| 2247| 104| 220490| | 4| 92041| 93849| 1808| 104| 220490| | 5| 93886| 95800| 1914| 104| 220490| | 6| 95834| 98145| 2311| 104| 220490| | 7| 98185| 100203| 2018| 104| 220490| | 8| 100234| 103344| 3110| 104| 220490| | 9| 103374| 105712| 2338| 104| 220490| | 10| 105738| 107800| 2062| 104| 220490|
Next we specify index variables that uniquely identify trials. This should be present in both the edf and behavioral file. set_index searches through the messages, finds the relevant ones, and extracts the numeric data. set_index can take a regular expression to find anything that matches this pattern, and numeric.only tells it to ignore any text (e.g., "BLOCK "). The variable names are stored in z$indexvars and the information is added to z$header.
find_messages is for pulling other message information from the EDF file. Here we want to have the timestamps of the stimulus onset and the response so we can refer to them later.
add_behdata merges the behavioral file with the eye data, based on the index variables. This only works if we run set_index first (so we have Block and Trial variables in the behavioral data frame, and in our itrackR object).
#find messages to use as our index variables (to merge with our behavioral data)
z<- set_index(z,varnames=c('Block','Trial'), patterns=c('^BLOCK [0-9]*','^TRIAL [0-9]*'), numeric.only=T)
#find messages that specify the onset of events, extract the timestamps
z <- find_messages(z,varnames=c('STIMONSET','RESPONSE'), patterns=c('STIMONSET','RESPONSE'), timestamp=T)
#merge with behavioral data
z <- add_behdata(z,beh,append=F)
Now z$beh contains the behavioral data that matches with the eyetracking data, based on the index variables you specified (Block and Trial). It adds the variable eyetrial, which is also found in the header,fixations, saccades, blinks, and messages data frames in the itrackR object. It also adds the timestamps that you requested using find_messages:
| ID| Block| Trial| Task| Conflict| Targetpos| Distractorpos| eyetrial| starttime| |----:|------:|------:|-----:|---------:|----------:|--------------:|---------:|----------:| | 104| 1| 1| 1| 1| 2| 11| 2| 85998| | 104| 1| 10| 1| 1| 3| 7| 11| 107836| | 104| 1| 11| 1| 0| 2| NA| 12| 110025| | 104| 1| 12| 1| 1| 4| 11| 13| 111913| | 104| 1| 13| 1| 1| 6| 12| 14| 114633| | 104| 1| 14| 1| 1| 4| 9| 15| 117898| | 104| 1| 15| 1| 1| 6| 8| 16| 120763| | 104| 1| 16| 1| 1| 5| 11| 17| 123646| | 104| 1| 17| 1| 0| 2| NA| 18| 126331| | 104| 1| 18| 1| 0| 4| NA| 19| 128505|
We can also see that the header has been updated
z$header
knitr::kable(head(z$header, 10))
| eyetrial| starttime| endtime| duration| ID| first_sample| Block| Trial| STIMONSET| RESPONSE| |---------:|----------:|--------:|---------:|----:|--------------:|------:|------:|----------:|---------:| | 1| 1| 60056| 60055| 104| 220490| NA| NA| NA| NA| | 2| 85998| 89719| 3721| 104| 220490| 1| 1| 87098| 89710| | 3| 89755| 92002| 2247| 104| 220490| 1| 2| 90825| 91998| | 4| 92041| 93849| 1808| 104| 220490| 1| 3| 93110| 93846| | 5| 93886| 95800| 1914| 104| 220490| 1| 4| 94966| 95798| | 6| 95834| 98145| 2311| 104| 220490| 1| 5| 96903| 98142| | 7| 98185| 100203| 2018| 104| 220490| 1| 6| 99255| 100197| | 8| 100234| 103344| 3110| 104| 220490| 1| 7| 101299| 103341| | 9| 103374| 105712| 2338| 104| 220490| 1| 8| 104438| 105709| | 10| 105738| 107800| 2062| 104| 220490| 1| 9| 106802| 107797|
Now we can plot a subset of our data based on the behaivoral data. Just use the condition argument when plotting. Note it uses a similar syntax as subset. Here we're plotting data only from the first 5 blocks:
plot(z,zoom=TRUE,oneplot=TRUE, condition = Block<=5)
## Warning: Removed 303 rows containing missing values (geom_point).
Drift Correction
It looks like subject 104 is off-center, due to poor calibration. We can correct for this using drift_correct. We can optionally specify a grouping variable (from the behavioral data) so that correction is done separately for each level of that variable. In this case, let's perform correction for each subject and block. The threshold specifies the minimum amount of movement detected before we actually do any correction.
z <- drift_correct(z,vars='Block',threshold=15)
plot(z,zoom=T)
Much better!
Determining Fixation/Saccade "Hits"
Next we code whether each fixation and saccade "hit" any of the ROIs using calcHits
z <- calcHits(z)
Note that the fixations data frame now has binary vectors for each ROI specifying whether the fixation hit that item or not:
knitr::kable(head(z$fixations, 10))
| fixation_key| ID| eyetrial| sttime| entime| gavx| gavy| roi_1| roi_2| roi_3| roi_4| roi_5| roi_6| roi_7| roi_8| roi_9| roi_10| roi_11| roi_12| roi_13| |--------------:|----:|---------:|-------:|-------:|------:|------:|-------:|-------:|-------:|-------:|-------:|-------:|-------:|-------:|-------:|--------:|--------:|--------:|--------:| | 1| 104| 1| 8| 159| 493.9| 410.7| 0| 0| 0| 0| 0| 0| 0| 0| 0| 0| 0| 0| 1| | 2| 104| 1| 172| 2817| 528.0| 386.7| 0| 0| 0| 0| 0| 0| 0| 0| 0| 0| 0| 0| 1| | 3| 104| 1| 3036| 3106| 520.4| 374.0| 0| 0| 0| 0| 0| 0| 0| 0| 0| 0| 0| 0| 1| | 4| 104| 1| 3112| 4423| 527.8| 376.9| 0| 0| 0| 0| 0| 0| 0| 0| 0| 0| 0| 0| 1| | 5| 104| 1| 4713| 8625| 525.2| 378.5| 0| 0| 0| 0| 0| 0| 0| 0| 0| 0| 0| 0| 1| | 6| 104| 1| 8956| 10101| 534.7| 383.5| 0| 0| 0| 0| 0| 0| 0| 0| 0| 0| 0| 0| 1| | 7| 104| 1| 11171| 11327| 500.1| 414.5| 0| 0| 0| 0| 0| 0| 0| 0| 0| 0| 0| 0| 1| | 8| 104| 1| 11349| 11813| 529.6| 383.9| 0| 0| 0| 0| 0| 0| 0| 0| 0| 0| 0| 0| 1| | 9| 104| 1| 12732| 12788| 527.7| 394.1| 0| 0| 0| 0| 0| 0| 0| 0| 0| 0| 0| 0| 1| | 10| 104| 1| 12799| 13577| 532.1| 388.7| 0| 0| 0| 0| 0| 0| 0| 0| 0| 0| 0| 0| 1|
This is not terribly useful if your task-relevant ROI changes positions on each trial. You can use mapROIs map your experiment-wide ROIs (1,2,3...13) to trial-specific ROIs ('target','distractor'). You just need a variable in your behavioral data that specifies the number of the relevant ROI. Here, Targetpos specifies the target location, and Distractorpos specifies the distractor location:
z <- mapROIs(z,names=c('target','distractor'),indicators=c('Targetpos','Distractorpos'))
Now we can see a target_hit and distractor_hit variable in our fixation data frame
knitr::kable(head(z$fixations, 10))
| fixation_key| ID| eyetrial| sttime| entime| gavx| gavy| roi_1| roi_2| roi_3| roi_4| roi_5| roi_6| roi_7| roi_8| roi_9| roi_10| roi_11| roi_12| roi_13| Targetpos| Distractorpos| target_hit| distractor_hit| |--------------:|----:|---------:|-------:|-------:|------:|------:|-------:|-------:|-------:|-------:|-------:|-------:|-------:|-------:|-------:|--------:|--------:|--------:|--------:|----------:|--------------:|------------:|----------------:| | 1| 104| 1| 8| 159| 493.9| 410.7| 0| 0| 0| 0| 0| 0| 0| 0| 0| 0| 0| 0| 1| NA| NA| NA| NA| | 2| 104| 1| 172| 2817| 528.0| 386.7| 0| 0| 0| 0| 0| 0| 0| 0| 0| 0| 0| 0| 1| NA| NA| NA| NA| | 3| 104| 1| 3036| 3106| 520.4| 374.0| 0| 0| 0| 0| 0| 0| 0| 0| 0| 0| 0| 0| 1| NA| NA| NA| NA| | 4| 104| 1| 3112| 4423| 527.8| 376.9| 0| 0| 0| 0| 0| 0| 0| 0| 0| 0| 0| 0| 1| NA| NA| NA| NA| | 5| 104| 1| 4713| 8625| 525.2| 378.5| 0| 0| 0| 0| 0| 0| 0| 0| 0| 0| 0| 0| 1| NA| NA| NA| NA| | 6| 104| 1| 8956| 10101| 534.7| 383.5| 0| 0| 0| 0| 0| 0| 0| 0| 0| 0| 0| 0| 1| NA| NA| NA| NA| | 7| 104| 1| 11171| 11327| 500.1| 414.5| 0| 0| 0| 0| 0| 0| 0| 0| 0| 0| 0| 0| 1| NA| NA| NA| NA| | 8| 104| 1| 11349| 11813| 529.6| 383.9| 0| 0| 0| 0| 0| 0| 0| 0| 0| 0| 0| 0| 1| NA| NA| NA| NA| | 9| 104| 1| 12732| 12788| 527.7| 394.1| 0| 0| 0| 0| 0| 0| 0| 0| 0| 0| 0| 0| 1| NA| NA| NA| NA| | 10| 104| 1| 12799| 13577| 532.1| 388.7| 0| 0| 0| 0| 0| 0| 0| 0| 0| 0| 0| 0| 1| NA| NA| NA| NA|
Saving data
Next we probably want to do statistics on our eyetracking data. We want to have our behavioral data merged with the eye data, including our ROI "hits". Just use eyemerge to pull out the relevant information. Any eyetracking data that does not match behavioral data will still be included (all behavioral variables will just be NA).
fixes <- eyemerge(z,'fixations')
#including only some behavioral variables. ID and indexvars are always included
saccs <- eyemerge(z,'saccades',behdata=c('Task'))
#by default only mapped ROIs are included. Here we can include all 13 rois, plus the mapped ones
fixes_all <- eyemerge(z,'fixations',all.rois = T)
We can also subset based on our behavioral variables using the condition argument:
fixes_first5 <- eyemerge(z,'fixations',condition = Block <= 5)
max(fixes_first5$Block,na.rm=T)
## [1] 5
Timeseries plots
Sometimes you want to see the tendency of the eyes to look at a particular ROI over time, relative to some event. To look at this, we first determine epochs around our event of interest, in this case, STIMONSET. We first run epoch_fixations for each ROI that we're interested in.
#start at stimulus onset, going 700ms after that point. Bin the data into 25ms time bins.
#repeate for the target and the distractor ROIs we created
z <- epoch_fixations(z,c('target','distractor'),event='STIMONSET',start = 0, end = 700, binwidth = 25)
Next we want to visualize these timeseries using plot_fixation_epochs. We can generate separate lines for each level of a factor (specified in the behavioral data) or for different ROIs. You can specify variables that define the different lines, as well as the rows and columns in separate panels. Behind the scenes, the function first aggregates based on ID and the factors your specify, then across subjects.
#plot the timeseries data for fixations to target, separately for the Conflict and Task conditions
plot_fixation_epochs(z,event='STIMONSET',rois=c('target'),group=c('Conflict'),cols='Task')
## $plot
## Warning: Removed 1 rows containing missing values (geom_point).
##
## $data
## Source: local data frame [116 x 7]
## Groups: Conflict, Task, timepoint [116]
##
## Conflict Task timepoint roi val grouping color
## <int> <int> <dbl> <chr> <dbl> <fctr> <fctr>
## 1 0 1 0 target 0 0 0
## 2 0 1 25 target 0 0 0
## 3 0 1 50 target 0 0 0
## 4 0 1 75 target 0 0 0
## 5 0 1 100 target 0 0 0
## 6 0 1 125 target 0 0 0
## 7 0 1 150 target 0 0 0
## 8 0 1 175 target 0 0 0
## 9 0 1 200 target 0 0 0
## 10 0 1 225 target 0 0 0
## # ... with 106 more rows
#plot fixations to target and disctractor for the same conditions
#you must specify 'roi' as one of the plotting variables for it to work.
plot_fixation_epochs(z,event='STIMONSET',rois=c('target','distractor'),group=c('roi','Conflict'),color='Conflict',cols='Task')
## $plot
## Warning: Removed 60 rows containing missing values (geom_point).
##
## $data
## Source: local data frame [232 x 7]
## Groups: roi, Conflict, Task [8]
##
## roi Conflict Task timepoint val grouping color
## <chr> <int> <int> <dbl> <dbl> <fctr> <fctr>
## 1 distractor 0 1 0 NaN distractor.0 0
## 2 distractor 0 1 25 NaN distractor.0 0
## 3 distractor 0 1 50 NaN distractor.0 0
## 4 distractor 0 1 75 NaN distractor.0 0
## 5 distractor 0 1 100 NaN distractor.0 0
## 6 distractor 0 1 125 NaN distractor.0 0
## 7 distractor 0 1 150 NaN distractor.0 0
## 8 distractor 0 1 175 NaN distractor.0 0
## 9 distractor 0 1 200 NaN distractor.0 0
## 10 distractor 0 1 225 NaN distractor.0 0
## # ... with 222 more rows
#plot difference waves (target - distractor fixations). Plot on separate rows insetead of columns.
#This example doesn't make much sense because distractors aren't present on no-conflict trials
plot_fixation_epochs(z,event='STIMONSET',rois=c('target','distractor'),group=c('roi','Conflict'),rows='Task',type='difference')
## $plot
## Warning: Removed 59 rows containing missing values (geom_point).
##
## $data
## Source: local data frame [116 x 7]
## Groups: roi, Conflict, Task [4]
##
## roi Conflict Task timepoint val grouping color
## <chr> <int> <int> <dbl> <dbl> <fctr> <fctr>
## 1 difference 0 1 0 NaN difference.0 difference.0
## 2 difference 0 1 25 NaN difference.0 difference.0
## 3 difference 0 1 50 NaN difference.0 difference.0
## 4 difference 0 1 75 NaN difference.0 difference.0
## 5 difference 0 1 100 NaN difference.0 difference.0
## 6 difference 0 1 125 NaN difference.0 difference.0
## 7 difference 0 1 150 NaN difference.0 difference.0
## 8 difference 0 1 175 NaN difference.0 difference.0
## 9 difference 0 1 200 NaN difference.0 difference.0
## 10 difference 0 1 225 NaN difference.0 difference.0
## # ... with 106 more rows
#Repeat, but on a subset of the data. Say only when Conflict==1
plot_fixation_epochs(z,event='STIMONSET',rois=c('target','distractor'),group=c('roi','Conflict'),rows='Task',type='difference',condition = Conflict==1)
## $plot
## Warning: Removed 1 rows containing missing values (geom_point).
##
## $data
## Source: local data frame [58 x 7]
## Groups: roi, Conflict, Task [2]
##
## roi Conflict Task timepoint val grouping
## <chr> <int> <int> <dbl> <dbl> <fctr>
## 1 difference 1 1 0 0.25000000 difference.1
## 2 difference 1 1 25 0.05555556 difference.1
## 3 difference 1 1 50 0.03846154 difference.1
## 4 difference 1 1 75 0.02777778 difference.1
## 5 difference 1 1 100 0.02500000 difference.1
## 6 difference 1 1 125 0.02380952 difference.1
## 7 difference 1 1 150 0.02380952 difference.1
## 8 difference 1 1 175 -0.04545455 difference.1
## 9 difference 1 1 200 -0.36708861 difference.1
## 10 difference 1 1 225 -0.46642857 difference.1
## # ... with 48 more rows, and 1 more variables: color <fctr>
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