,
between is a thin wrapper for the between function of data.table::between.
It is equivalent to x >= lower & x <= upper when incbounds=TRUE, or x > lower & y < upper when FALSE. In comparison
with dplyr::between, it doesn't loose the class of its argument, and it's more appropriate for manipulating
the column .sample. For more information and the description of the arguments, see data.table::between.
library(dplyr)
#>
#> Attaching package: ‘dplyr’
#> The following objects are masked from ‘package:eeguana’:
#>
#> across, between, c_across
#> The following objects are masked from ‘package:stats’:
#>
#> filter, lag
#> The following objects are masked from ‘package:base’:
#>
#> intersect, setdiff, setequal, union
data_faces_ERPs %>%
eeg_filter(.sample %>% between(10, 100))
#> Warning: between() called on numeric vector with S3 class
#> # EEG data:
#>
#> # Signal table:
#> .id .sample Fp1 Fpz Fp2 F7 F3
#> 1: 1 10 1.3453968 0.9784066 0.9915191 0.5167054 1.294869
#> 2: 1 11 0.8944145 0.6989464 0.6706013 0.3141923 1.187803
#> 3: 1 12 0.6040886 0.6183362 0.5148558 0.2213581 1.175533
#> 4: 1 13 0.6393652 0.8251486 0.5712059 0.2683778 1.125829
#> 5: 1 14 0.6229659 0.8702239 0.4580129 0.2109594 0.856599
#> ---
#> 178: 2 96 -6.2044331 -7.0735023 -7.9666994 -4.7888680 -7.157626
#> 179: 2 97 -6.8542360 -7.7661741 -8.5404654 -5.0099520 -7.549616
#> 180: 2 98 -7.6500746 -8.5464032 -9.1438359 -5.3012940 -7.948112
#> 181: 2 99 -8.4903757 -9.2891036 -9.6887434 -5.6585770 -8.245455
#> 182: 2 100 -9.1003648 -9.7745572 -10.0449573 -5.9597555 -8.311005
#> Fz F4 F8 FC5 FC1 FC2
#> 1: 1.0348515 1.3014945 0.6828880 0.8954736 0.8560441 1.1417663
#> 2: 0.8187366 1.0225348 0.6046420 0.5243376 0.7858363 0.9507665
#> 3: 0.7229266 0.8372992 0.4388520 0.2801995 0.8280230 0.9010821
#> 4: 0.7545472 0.8117911 0.2781192 0.3068408 0.9338946 0.9575513
#> 5: 0.7229803 0.7598746 0.1340346 0.4451489 0.9687350 0.9583032
#> ---
#> 178: -7.3566097 -8.0668550 -6.5577388 -4.2737883 -5.3454181 -6.3432451
#> 179: -7.7839888 -8.6314991 -6.9144227 -4.5030291 -5.6156640 -6.7185450
#> 180: -8.3236178 -9.3117717 -7.2599514 -5.0112831 -5.9238289 -7.1954151
#> 181: -8.8056174 -9.9382625 -7.5721603 -5.4250756 -6.1198972 -7.6251625
#> 182: -9.0592563 -10.3349649 -7.8042054 -5.3797466 -6.1769165 -7.8938038
#> FC6 M1 T7 C3 Cz C4
#> 1: 0.7895606 0.06817941 0.6032521 0.6552598 1.261522 0.8936008
#> 2: 0.5608169 0.07282905 0.4961458 0.7580807 1.164792 0.6866566
#> 3: 0.5196839 0.10562741 0.4652340 0.9290417 1.134783 0.6800487
#> 4: 0.6858992 0.13664664 0.5610510 1.1166178 1.151285 0.8410480
#> 5: 0.7821667 0.15361261 0.6499326 1.2277140 1.137830 0.9641131
#> ---
#> 178: -5.5337369 -0.59040448 -0.7877318 -2.4096150 -3.940045 -4.5402944
#> 179: -6.0568137 -0.69911547 -0.8154876 -2.5104275 -4.116004 -4.7606690
#> 180: -6.6915519 -0.79635921 -0.9614542 -2.6048798 -4.383230 -5.0564472
#> 181: -7.2091838 -0.82770322 -1.1173848 -2.5554832 -4.587345 -5.2761233
#> 182: -7.4940664 -0.81975296 -1.1313589 -2.4424482 -4.673378 -5.3337114
#> T8 M2 CP5 CP1 CP2 CP6
#> 1: 0.9629271 -0.06817941 -0.2067426 0.4866767 0.6667189 0.366038030
#> 2: 0.9571712 -0.07282905 -0.1914062 0.4511237 0.5477906 -0.006856899
#> 3: 0.7303480 -0.10562741 -0.0334399 0.5293032 0.5738163 -0.094204161
#> 4: 0.6137383 -0.13664664 0.2441718 0.6891407 0.7240469 0.005042849
#> 5: 0.5968883 -0.15361261 0.5076659 0.8352871 0.8665894 0.081114295
#> ---
#> 178: -3.4675623 0.59040448 3.7645468 0.4870714 -1.8102504 -0.379987455
#> 179: -3.3439350 0.69911547 3.9285900 0.5650295 -1.8327076 -0.379132003
#> 180: -3.2614533 0.79635921 4.0816302 0.5892022 -1.9224388 -0.513803195
#> 181: -3.2517172 0.82770322 4.3064960 0.6818957 -1.9293534 -0.621128267
#> 182: -3.3742685 0.81975296 4.5349608 0.8238297 -1.8375839 -0.526162212
#> P7 P3 Pz P4 P8 POz
#> 1: -0.9930302 -0.7245727 0.071920581 0.02789532 -0.0161377 -0.06815066
#> 2: -0.8987255 -0.7175966 -0.038524320 -0.16931452 -0.3067263 -0.20387066
#> 3: -0.6370013 -0.5293381 0.008801139 -0.16677087 -0.3987778 -0.16345969
#> 4: -0.2289795 -0.1731266 0.208488202 0.02396739 -0.3125083 0.06343886
#> 5: 0.1695341 0.1970622 0.437265901 0.24851852 -0.1996578 0.34630682
#> ---
#> 178: 8.6304948 6.5282283 2.338998171 3.05993824 4.7261293 5.47519041
#> 179: 8.9975026 6.7868350 2.475995217 3.31014726 5.1161005 5.82858780
#> 180: 9.3946813 7.0155501 2.516374121 3.44846305 5.3379964 6.05109607
#> 181: 9.8027612 7.2960758 2.595623319 3.61217360 5.5657452 6.25929955
#> 182: 10.1594141 7.5967540 2.754193094 3.89719293 5.9427667 6.51835452
#> O1 Oz O2 EOGV EOGH
#> 1: -0.5001492 -0.13642252 -0.22082819 -1.0579165 -1.204344
#> 2: -0.3875637 -0.20043078 -0.42623018 -1.0457333 -1.336043
#> 3: -0.1766544 -0.12743899 -0.43680681 -0.9729332 -1.420421
#> 4: 0.1205775 0.04887428 -0.26410834 -0.7443320 -1.339957
#> 5: 0.4424268 0.27030956 -0.02062588 -0.5190288 -1.130928
#> ---
#> 178: 5.5443395 4.63258729 6.84455851 -0.3218142 2.127168
#> 179: 5.9066869 5.11834953 7.50909454 -0.3120248 2.136692
#> 180: 6.2893098 5.44335686 7.91290374 -0.3063231 2.191240
#> 181: 6.6632908 5.68131173 8.19870620 -0.2842934 2.330338
#> 182: 6.9816180 5.93042586 8.57571669 -0.2342150 2.583673
#>
#> # Events table:
#> No events.
#>
#> # Segments table:
#> .id .recording condition
#> 1: 1 faces.vhdr faces
#> 2: 2 faces.vhdr non-faces
# Compare with:
if (FALSE) {
data_faces_ERPs %>%
eeg_filter(.sample %>% dplyr::between(10, 100))
}