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).
Arguments
- 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 (name, x, y, width_radius, height).
- 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
Value
a dataframe containing the time on the passed AOIs for each trial. One column for each AOI separated by trial.
Details
Analyses data separately for each unique combination of values in pID and trial. Returned values can be absolute time or proportion of time over the period.
Examples
# \donttest{
data <- combine_eyes(HCL)
fix_d <- fixation_dispersion(data)
# fixation data
AOI_time(data = fix_d, data_type = "fix", AOIs = HCL_AOIs)
#> pID trial AOI time
#> 1 118 1 left 3500
#> 2 118 2 left 1400
#> 3 118 3 left 576
#> 4 118 4 left 2563
#> 5 118 5 left 253
#> 6 118 6 left 789
#> 7 118 1 right 1598
#> 8 118 2 right 1292
#> 9 118 3 right 743
#> 10 118 4 right 3574
#> 11 118 5 right 1101
#> 12 118 6 right 909
#> 13 118 1 centre 6056
#> 14 118 2 centre 3924
#> 15 118 3 centre 2932
#> 16 118 4 centre 2609
#> 17 118 5 centre 2136
#> 18 118 6 centre 2564
#> 19 119 1 left 3656
#> 20 119 2 left 912
#> 21 119 3 left 1959
#> 22 119 4 left 4288
#> 23 119 5 left 4805
#> 24 119 6 left 3447
#> 25 119 1 right 1743
#> 26 119 2 right 880
#> 27 119 3 right 2152
#> 28 119 4 right 2043
#> 29 119 5 right 3270
#> 30 119 6 right 2920
#> 31 119 1 centre 2740
#> 32 119 2 centre 1944
#> 33 119 3 centre 2106
#> 34 119 4 centre 1686
#> 35 119 5 centre 2983
#> 36 119 6 centre 4962
#raw data
AOI_time(data = data, data_type = "raw", AOIs = HCL_AOIs)
#> Eye tracker frequency estimated at:300.052002080083Hz
#> pID trial AOI time
#> 1 118 1 left 3663
#> 2 118 2 left 1420
#> 3 118 3 left 593
#> 4 118 4 left 2580
#> 5 118 5 left 370
#> 6 118 6 left 817
#> 7 118 1 right 1723
#> 8 118 2 right 1373
#> 9 118 3 right 797
#> 10 118 4 right 3783
#> 11 118 5 right 1160
#> 12 118 6 right 927
#> 13 118 1 centre 6199
#> 14 118 2 centre 4179
#> 15 118 3 centre 3089
#> 16 118 4 centre 2903
#> 17 118 5 centre 2350
#> 18 118 6 centre 2916
#> 19 119 1 left 3926
#> 20 119 2 left 953
#> 21 119 3 left 2020
#> 22 119 4 left 4376
#> 23 119 5 left 5066
#> 24 119 6 left 3513
#> 25 119 1 right 1813
#> 26 119 2 right 1046
#> 27 119 3 right 2333
#> 28 119 4 right 2120
#> 29 119 5 right 3723
#> 30 119 6 right 3153
#> 31 119 1 centre 2816
#> 32 119 2 centre 2166
#> 33 119 3 centre 2410
#> 34 119 4 centre 1770
#> 35 119 5 centre 3183
#> 36 119 6 centre 5056
#as proportional data
AOI_time(data = fix_d, data_type = "fix", AOIs = HCL_AOIs,
as_prop = TRUE, trial_time = HCL_behavioural$RT)
#> pID trial AOI time
#> 1 118 1 left 0.25993316
#> 2 118 1 right 0.11867805
#> 3 118 1 centre 0.44975863
#> 4 118 2 left 0.17958618
#> 5 118 2 right 0.16573239
#> 6 118 2 centre 0.50335441
#> 7 118 3 left 0.10936432
#> 8 118 3 right 0.14107238
#> 9 118 3 centre 0.55669477
#> 10 118 4 left 0.25859894
#> 11 118 4 right 0.36060579
#> 12 118 4 centre 0.26324021
#> 13 118 5 left 0.05718548
#> 14 118 5 right 0.24885855
#> 15 118 5 centre 0.48279915
#> 16 118 6 left 0.15102791
#> 17 118 6 right 0.17399793
#> 18 118 6 centre 0.49079285
#> 19 119 1 left 0.35980711
#> 20 119 1 right 0.17153823
#> 21 119 1 centre 0.26965850
#> 22 119 2 left 0.17986747
#> 23 119 2 right 0.17355633
#> 24 119 2 centre 0.38340170
#> 25 119 3 left 0.25872657
#> 26 119 3 right 0.28421623
#> 27 119 3 centre 0.27814097
#> 28 119 4 left 0.47366037
#> 29 119 4 right 0.22567354
#> 30 119 4 centre 0.18623866
#> 31 119 5 left 0.37422118
#> 32 119 5 right 0.25467290
#> 33 119 5 centre 0.23232087
#> 34 119 6 left 0.27193121
#> 35 119 6 right 0.23035658
#> 36 119 6 centre 0.39144841
# }