,
I address some potential sources of confusion here. To run this examples, we first load eeguana and dplyr:
The package {eeguana} allows for functions that look like {dplyr} functions but they truly are powered by {tidy.table} and {data.table}. This means that there are subtle differences between dplyr’s behavior and eeguana dplyr-like function behavior.
The default values of the arguments might be different, and some arguments might not exist for the eeguana dplyr-like functions.
Unlike dplyr’s mutate, {eeguana}’s [mutate] doesn’t allow to refer back to a recently created channel:
new_data <- data_faces_ERPs %>%
eeg_mutate(X = scale(C3), Y = X * 2)
#> Error in eval(jsub, SDenv, parent.frame()): object 'X' not foundA workaround is to use two mutates:
new_data <- data_faces_ERPs %>%
eeg_mutate(X = scale(C3)) %>%
eeg_mutate(Y = X * 2)Dplyr-like functions not only edit the eeg_lst objects but they also do book-keeping: They remove unnecessary channels, or update their information and they ensure that three tables (signal, segments, and events) match. It’s then not recommended to edit the signal and segments table directly.
Compare the correct way to filter out samples:
new_data <- data_faces_10_trials %>%
eeg_filter(.sample < 20000)
events_tbl(new_data)
#> .id .type .description .initial .final .channel
#> <int> <char> <char> <sample_int> <sample_int> <char>
#> 1: 1 Bad Interval Bad Min-Max 15000 15128 FC6
#> 2: 1 Bad Interval Bad Min-Max 15000 15146 F8
#> 3: 1 Bad Interval Bad Min-Max 15000 15142 Fp1
#> 4: 1 Bad Interval Bad Min-Max 15000 15132 Fp2
#> 5: 1 Bad Interval Bad Min-Max 15000 15140 Fpz
#> 6: 1 Bad Interval Bad Min-Max 15000 15114 F7
#> 7: 1 Bad Interval Bad Min-Max 15000 15091 FC5
#> 8: 1 Bad Interval Bad Min-Max 15000 15103 Fz
#> 9: 1 Bad Interval Bad Min-Max 15000 15103 F4
#> 10: 1 Bad Interval Bad Min-Max 15000 15093 F3
#> 11: 1 Bad Interval Bad Min-Max 15276 15905 F8
#> 12: 1 Bad Interval Bad Min-Max 15276 15556 F7
#> 13: 1 Bad Interval Bad Min-Max 15551 15956 Fp1
#> 14: 1 Bad Interval Bad Min-Max 15552 15941 Fp2
#> 15: 1 Bad Interval Bad Min-Max 15552 15946 Fpz
#> 16: 1 Bad Interval Bad Min-Max 15569 15910 Fz
#> 17: 1 Bad Interval Bad Min-Max 15569 15904 F4
#> 18: 1 Bad Interval Bad Min-Max 15571 15907 F3
#> 19: 1 Bad Interval Bad Min-Max 15575 15885 F7
#> 20: 1 Stimulus s5 15767 15767 <NA>
#> 21: 1 Bad Interval Bad Min-Max 16344 16816 F8
#> 22: 1 Bad Interval Bad Min-Max 16345 16630 Fp2
#> 23: 1 Bad Interval Bad Min-Max 16347 16628 Fpz
#> 24: 1 Bad Interval Bad Min-Max 16370 16806 F7
#> 25: 1 Stimulus s101 17162 17162 <NA>
#> 26: 1 Bad Interval Bad Min-Max 17440 18030 Fp1
#> 27: 1 Bad Interval Bad Min-Max 17525 18009 Fp2
#> 28: 1 Bad Interval Bad Min-Max 17525 18028 Fpz
#> 29: 1 Bad Interval Bad Min-Max 17531 17898 F8
#> 30: 1 Bad Interval Bad Min-Max 17533 17875 Fz
#> 31: 1 Bad Interval Bad Min-Max 17534 17863 F3
#> 32: 1 Bad Interval Bad Min-Max 17535 17877 F4
#> 33: 1 Bad Interval Bad Min-Max 17545 17861 FC6
#> 34: 1 Bad Interval Bad Min-Max 17548 17766 FC2
#> 35: 1 Bad Interval Bad Min-Max 17553 17766 FC1
#> 36: 1 Bad Interval Bad Min-Max 17559 17763 F7
#> 37: 1 Bad Interval Bad Min-Max 18084 18609 Fp1
#> 38: 1 Bad Interval Bad Min-Max 18085 18541 Fpz
#> 39: 1 Bad Interval Bad Min-Max 18086 18541 Fp2
#> 40: 1 Bad Interval Bad Min-Max 18092 18524 F7
#> 41: 1 Bad Interval Bad Min-Max 18093 18424 F8
#> 42: 1 Bad Interval Bad Min-Max 18093 18425 Fz
#> 43: 1 Bad Interval Bad Min-Max 18094 18430 F3
#> 44: 1 Bad Interval Bad Min-Max 18094 18420 F4
#> 45: 1 Bad Interval Bad Min-Max 18110 18372 FC2
#> 46: 1 Bad Interval Bad Min-Max 18821 19377 Fp1
#> 47: 1 Bad Interval Bad Min-Max 18822 19358 Fpz
#> 48: 1 Bad Interval Bad Min-Max 18822 19356 Fp2
#> 49: 1 Bad Interval Bad Min-Max 18830 19172 F8
#> 50: 1 Bad Interval Bad Min-Max 18834 19163 Fz
#> 51: 1 Bad Interval Bad Min-Max 18835 19110 F3
#> 52: 1 Bad Interval Bad Min-Max 18835 19158 F4
#> 53: 1 Bad Interval Bad Min-Max 18840 19014 F7
#> 54: 1 Bad Interval Bad Min-Max 18845 19086 FC6
#> 55: 1 Bad Interval Bad Min-Max 18849 19081 FC2
#> 56: 1 Bad Interval Bad Min-Max 19074 19357 F7
#> 57: 1 Bad Interval Bad Min-Max 19464 19849 Fp1
#> 58: 1 Bad Interval Bad Min-Max 19467 19826 Fp2
#> 59: 1 Bad Interval Bad Min-Max 19467 19999 Fpz
#> 60: 1 Bad Interval Bad Min-Max 19471 19807 F7
#> 61: 1 Bad Interval Bad Min-Max 19475 19794 F8
#> 62: 1 Bad Interval Bad Min-Max 19475 19799 F3
#> 63: 1 Bad Interval Bad Min-Max 19477 19798 Fz
#> 64: 1 Bad Interval Bad Min-Max 19479 19791 F4
#> .id .type .description .initial .final .channelwith the non-recommended version:
new_data_BAD <- data_faces_10_trials
new_data_BAD$.signal <- new_data_BAD$.signal %>%
filter(.sample < 20000)
events_tbl(new_data_BAD)
#> .id .type .description .initial .final .channel
#> <int> <char> <char> <sample_int> <sample_int> <char>
#> 1: 1 Bad Interval Bad Min-Max 15000 15128 FC6
#> 2: 1 Bad Interval Bad Min-Max 15000 15146 F8
#> 3: 1 Bad Interval Bad Min-Max 15000 15142 Fp1
#> 4: 1 Bad Interval Bad Min-Max 15000 15132 Fp2
#> 5: 1 Bad Interval Bad Min-Max 15000 15140 Fpz
#> ---
#> 540: 1 Bad Interval Bad Min-Max 55058 55400 Fpz
#> 541: 1 Bad Interval Bad Min-Max 55077 55314 F8
#> 542: 1 Stimulus s130 56380 56380 <NA>
#> 543: 1 Stimulus s70 56750 56750 <NA>
#> 544: 1 Stimulus s130 58328 58328 <NA>In some occasions, the events or the channels tables need to be
edited. In those cases, one can use
events_tbl(data_eeg) <- ... or
channels_tbl(data_eeg) <- ...).
channels_tbl(data_faces_10_trials)
#> .channel radius theta phi .x .y .z
#> <char> <num> <num> <num> <num> <num> <num>
#> 1: Fp1 1 -90 -72 -0.31 0.95 0.00
#> 2: Fpz 1 90 90 0.00 1.00 0.00
#> 3: Fp2 1 90 72 0.31 0.95 0.00
#> 4: F7 1 -90 -36 -0.81 0.59 0.00
#> 5: F3 1 -60 -51 -0.55 0.67 0.50
#> 6: Fz 1 45 90 0.00 0.71 0.71
#> 7: F4 1 60 51 0.55 0.67 0.50
#> 8: F8 1 90 36 0.81 0.59 0.00
#> 9: FC5 1 -69 -21 -0.87 0.33 0.36
#> 10: FC1 1 -31 -46 -0.36 0.37 0.86
#> 11: FC2 1 31 46 0.36 0.37 0.86
#> 12: FC6 1 69 21 0.87 0.33 0.36
#> 13: M1 0 0 0 NA NA NA
#> 14: T7 1 -90 0 -1.00 0.00 0.00
#> 15: C3 1 -45 0 -0.71 0.00 0.71
#> 16: Cz 1 0 0 0.00 0.00 1.00
#> 17: C4 1 45 0 0.71 0.00 0.71
#> 18: T8 1 90 0 1.00 0.00 0.00
#> 19: M2 0 0 0 NA NA NA
#> 20: CP5 1 -69 21 -0.87 -0.33 0.36
#> 21: CP1 1 -31 46 -0.36 -0.37 0.86
#> 22: CP2 1 31 -46 0.36 -0.37 0.86
#> 23: CP6 1 69 -21 0.87 -0.33 0.36
#> 24: P7 1 -90 36 -0.81 -0.59 0.00
#> 25: P3 1 -60 51 -0.55 -0.67 0.50
#> 26: Pz 1 45 -90 0.00 -0.71 0.71
#> 27: P4 1 60 -51 0.55 -0.67 0.50
#> 28: P8 1 90 -36 0.81 -0.59 0.00
#> 29: POz 1 67 -90 0.00 -0.92 0.39
#> 30: O1 1 -90 72 -0.31 -0.95 0.00
#> 31: Oz 1 90 -90 0.00 -1.00 0.00
#> 32: O2 1 90 -72 0.31 -0.95 0.00
#> 33: EOGV NA NA NA NA NA NA
#> 34: EOGH NA NA NA NA NA NA
#> .channel radius theta phi .x .y .z
new_data <- data_faces_10_trials
channels_tbl(new_data) <- channels_tbl(new_data) %>%
mutate(resolution = .1)
channels_tbl(new_data)
#> .channel radius theta phi .x .y .z resolution
#> <char> <num> <num> <num> <num> <num> <num> <num>
#> 1: Fp1 1 -90 -72 -0.31 0.95 0.00 0.1
#> 2: Fpz 1 90 90 0.00 1.00 0.00 0.1
#> 3: Fp2 1 90 72 0.31 0.95 0.00 0.1
#> 4: F7 1 -90 -36 -0.81 0.59 0.00 0.1
#> 5: F3 1 -60 -51 -0.55 0.67 0.50 0.1
#> 6: Fz 1 45 90 0.00 0.71 0.71 0.1
#> 7: F4 1 60 51 0.55 0.67 0.50 0.1
#> 8: F8 1 90 36 0.81 0.59 0.00 0.1
#> 9: FC5 1 -69 -21 -0.87 0.33 0.36 0.1
#> 10: FC1 1 -31 -46 -0.36 0.37 0.86 0.1
#> 11: FC2 1 31 46 0.36 0.37 0.86 0.1
#> 12: FC6 1 69 21 0.87 0.33 0.36 0.1
#> 13: M1 0 0 0 NA NA NA 0.1
#> 14: T7 1 -90 0 -1.00 0.00 0.00 0.1
#> 15: C3 1 -45 0 -0.71 0.00 0.71 0.1
#> 16: Cz 1 0 0 0.00 0.00 1.00 0.1
#> 17: C4 1 45 0 0.71 0.00 0.71 0.1
#> 18: T8 1 90 0 1.00 0.00 0.00 0.1
#> 19: M2 0 0 0 NA NA NA 0.1
#> 20: CP5 1 -69 21 -0.87 -0.33 0.36 0.1
#> 21: CP1 1 -31 46 -0.36 -0.37 0.86 0.1
#> 22: CP2 1 31 -46 0.36 -0.37 0.86 0.1
#> 23: CP6 1 69 -21 0.87 -0.33 0.36 0.1
#> 24: P7 1 -90 36 -0.81 -0.59 0.00 0.1
#> 25: P3 1 -60 51 -0.55 -0.67 0.50 0.1
#> 26: Pz 1 45 -90 0.00 -0.71 0.71 0.1
#> 27: P4 1 60 -51 0.55 -0.67 0.50 0.1
#> 28: P8 1 90 -36 0.81 -0.59 0.00 0.1
#> 29: POz 1 67 -90 0.00 -0.92 0.39 0.1
#> 30: O1 1 -90 72 -0.31 -0.95 0.00 0.1
#> 31: Oz 1 90 -90 0.00 -1.00 0.00 0.1
#> 32: O2 1 90 -72 0.31 -0.95 0.00 0.1
#> 33: EOGV NA NA NA NA NA NA 0.1
#> 34: EOGH NA NA NA NA NA NA 0.1
#> .channel radius theta phi .x .y .z resolution
events_tbl(new_data) %>%
head()
#> .id .type .description .initial .final .channel
#> <int> <char> <char> <sample_int> <sample_int> <char>
#> 1: 1 Bad Interval Bad Min-Max 15000 15128 FC6
#> 2: 1 Bad Interval Bad Min-Max 15000 15146 F8
#> 3: 1 Bad Interval Bad Min-Max 15000 15142 Fp1
#> 4: 1 Bad Interval Bad Min-Max 15000 15132 Fp2
#> 5: 1 Bad Interval Bad Min-Max 15000 15140 Fpz
#> 6: 1 Bad Interval Bad Min-Max 15000 15114 F7
events_tbl(new_data) <- events_tbl(new_data) %>%
filter(.description != "s130")
events_tbl(new_data) %>%
head()
#> .id .type .description .initial .final .channel
#> <int> <char> <char> <sample_int> <sample_int> <char>
#> 1: 1 Bad Interval Bad Min-Max 15000 15128 FC6
#> 2: 1 Bad Interval Bad Min-Max 15000 15146 F8
#> 3: 1 Bad Interval Bad Min-Max 15000 15142 Fp1
#> 4: 1 Bad Interval Bad Min-Max 15000 15132 Fp2
#> 5: 1 Bad Interval Bad Min-Max 15000 15140 Fpz
#> 6: 1 Bad Interval Bad Min-Max 15000 15114 F7Some functions keep the channel properties, e.g., mean,
scale.
head(signal_tbl(data_faces_10_trials)$Oz)
#> # radius: 1; theta: 90; phi: -90; .x: 0; .y: -1; .z: 0
#> # Values
#> [1] -16.631485 -12.549113 -8.650514 -8.433714 -12.844979 -19.946123
head(mean(signal_tbl(data_faces_10_trials)$Oz))
#> # radius: 1; theta: 90; phi: -90; .x: 0; .y: -1; .z: 0
#> # Values
#> [1] -0.7590534Some primitive functions remove the channel properties, e.g.,
min, var.
head(min(signal_tbl(data_faces_10_trials)$Oz))
#> [1] -68.00181
head(var(signal_tbl(data_faces_10_trials)$Oz))
#> [1] 107.7272A possible workaround is the following:
head(min(signal_tbl(data_faces_10_trials)$Oz)) + 0 * signal_tbl(data_faces_10_trials)$Oz[1]
#> # radius: 1; theta: 90; phi: -90; .x: 0; .y: -1; .z: 0
#> # Values
#> [1] -68.00181