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.
Usage
AOI_seq(
data,
AOIs,
AOI_names = NULL,
sample_rate = NULL,
long = TRUE,
participant_ID = "participant_ID"
)Arguments
- 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.
- sample_rate
Optional sample rate of the eye-tracker (Hz) for use with raw_data. If not supplied, the sample rate will be estimated from the time column and the number of samples.
- long
Whether to return the AOI fixations in long or wide format. Defaults to long
- participant_ID
the variable that determines the participant identifier. If no column present, assumes a single participant
Value
a dataframe containing the sequence of entries into AOIs on each trial.
If long is TRUE, then each AOI entry is returned on a new row, if FALSE, then a row per trial is returned with all AOI entries in one character string
Examples
# \donttest{
data <- combine_eyes(HCL)
fix_d <- fixation_dispersion(data, participant_ID = "pNum")
AOI_seq(fix_d, AOIs = HCL_AOIs, participant_ID = "pNum")
#> pNum trial AOI entry_n
#> 1 118 1 3 1
#> 2 118 1 1 2
#> 3 118 1 3 3
#> 4 118 1 2 4
#> 5 118 1 3 5
#> 6 118 1 2 6
#> 7 118 1 3 7
#> 8 118 1 1 8
#> 9 118 1 1 9
#> 10 118 1 3 10
#> 11 118 1 1 11
#> 12 118 1 3 12
#> 13 118 1 2 13
#> 14 118 1 3 14
#> 15 118 2 2 1
#> 16 118 2 3 2
#> 17 118 2 1 3
#> 18 118 2 3 4
#> 19 118 2 2 5
#> 20 118 2 1 6
#> 21 118 2 3 7
#> 22 118 2 1 8
#> 23 118 2 3 9
#> 24 118 2 2 10
#> 25 118 3 3 1
#> 26 118 3 2 2
#> 27 118 3 1 3
#> 28 118 3 3 4
#> 29 118 3 2 5
#> 30 118 3 3 6
#> 31 118 4 2 1
#> 32 118 4 3 2
#> 33 118 4 1 3
#> 34 118 4 2 4
#> 35 118 4 1 5
#> 36 118 4 3 6
#> 37 118 4 2 7
#> 38 118 4 1 8
#> 39 118 4 3 9
#> 40 118 4 2 10
#> 41 118 4 1 11
#> 42 118 4 2 12
#> 43 118 4 3 13
#> 44 118 5 3 1
#> 45 118 5 2 2
#> 46 118 5 3 3
#> 47 118 5 2 4
#> 48 118 5 3 5
#> 49 118 5 2 6
#> 50 118 5 3 7
#> 51 118 6 3 1
#> 52 118 6 2 2
#> 53 118 6 1 3
#> 54 118 6 3 4
#> 55 118 6 2 5
#> 56 118 6 1 6
#> 57 118 6 3 7
#> 58 119 1 3 1
#> 59 119 1 1 2
#> 60 119 1 3 3
#> 61 119 1 2 4
#> 62 119 1 1 5
#> 63 119 1 3 6
#> 64 119 1 1 7
#> 65 119 1 2 8
#> 66 119 1 1 9
#> 67 119 1 2 10
#> 68 119 1 3 11
#> 69 119 1 3 12
#> 70 119 1 1 13
#> 71 119 1 3 14
#> 72 119 2 1 1
#> 73 119 2 2 2
#> 74 119 2 3 3
#> 75 119 2 1 4
#> 76 119 2 3 5
#> 77 119 2 1 6
#> 78 119 2 2 7
#> 79 119 3 1 1
#> 80 119 3 2 2
#> 81 119 3 3 3
#> 82 119 3 2 4
#> 83 119 3 1 5
#> 84 119 3 2 6
#> 85 119 3 1 7
#> 86 119 3 3 8
#> 87 119 3 1 9
#> 88 119 4 1 1
#> 89 119 4 2 2
#> 90 119 4 1 3
#> 91 119 4 3 4
#> 92 119 4 1 5
#> 93 119 4 2 6
#> 94 119 4 1 7
#> 95 119 4 2 8
#> 96 119 4 1 9
#> 97 119 4 3 10
#> 98 119 4 3 11
#> 99 119 5 1 1
#> 100 119 5 2 2
#> 101 119 5 1 3
#> 102 119 5 2 4
#> 103 119 5 1 5
#> 104 119 5 2 6
#> 105 119 5 1 7
#> 106 119 5 3 8
#> 107 119 5 1 9
#> 108 119 5 3 10
#> 109 119 5 1 11
#> 110 119 5 3 12
#> 111 119 5 1 13
#> 112 119 6 3 1
#> 113 119 6 1 2
#> 114 119 6 2 3
#> 115 119 6 3 4
#> 116 119 6 2 5
#> 117 119 6 3 6
#> 118 119 6 2 7
#> 119 119 6 1 8
#> 120 119 6 2 9
#> 121 119 6 1 10
#> 122 119 6 3 11
# }