Rbin-file used for this script: ~/Data/brushing_v2.Rbin
This script computes standard descriptive statistics for each group.
The groups are based on:
It computes the following statistics:
length): number of measurementsmin)max)sd)The results will be written to XLSX-files in a subfolder “Summary-stats” within the directory of the present Rmd file, i.e. “~/Summary-stats”.
The imported file is: “~/Data/brushing_v2.Rbin”
Its modification, ‘last status change’ (= ‘creation’ on Windows) and last access times are, respectively: “2020-01-21 08:58:08”, “2020-01-21 08:58:08” and “2020-01-21 08:58:08”.
The following variables will be used:
[21] epLsar
[22] Asfc
[23] Smfc
[24] HAsfc9
[25] HAsfc81
[26] Sq.SL
[27] Ssk.SL
[28] Sku.SL
[29] Sp.SL
[30] Sv.SL
[31] Sz.SL
[32] Sa.SL
[33] Smr.SL
[34] Smc.SL
[35] Sxp.SL
[36] Sal.SL
[37] Str.SL
[38] Std.SL
[39] Sdq.SL
[40] Sdr.SL
[41] Vm.SL
[42] Vv.SL
[43] Vmp.SL
[44] Vmc.SL
[45] Vvc.SL
[46] Vvv.SL
[47] Maximum.depth.of.furrows.SL
[48] Mean.depth.of.furrows.SL
[49] Mean.density.of.furrows.SL
[50] Isotropy.SL
[51] First.Direction.SL
[52] Second.Direction.SL
[53] Third.Direction.SL
[54] Isotropy.SL.1
[55] Periodicity.SL
[56] Period.SL
[57] Direction.of.period.SL
[58] Sq.SF
[59] Ssk.SF
[60] Sku.SF
[61] Sp.SF
[62] Sv.SF
[63] Sz.SF
[64] Sa.SF
[65] Smr.SF
[66] Smc.SF
[67] Sxp.SF
[68] Sal.SF
[69] Str.SF
[70] Std.SF
[71] Sdq.SF
[72] Sdr.SF
[73] Vm.SF
[74] Vv.SF
[75] Vmp.SF
[76] Vmc.SF
[77] Vvc.SF
[78] Vvv.SF
[79] Maximum.depth.of.furrows.SF
[80] Mean.depth.of.furrows.SF
[81] Mean.density.of.furrows.SF
[82] Isotropy.SF
[83] First.Direction.SF
[84] Second.Direction.SF
[85] Third.Direction.SF
[86] Isotropy.SF.1
[87] Periodicity.SF
[88] Period.SF
[89] Direction.of.period.SFnminmaxmeanmedsd <- function(x){
y <- x[!is.na(x)]
n_test <- length(y)
min_test <- min(y)
max_test <- max(y)
mean_test <- mean(y)
med_test <- median(y)
sd_test <- sd(y)
out <- c(n_test, min_test, max_test, mean_test, med_test, sd_test)
names(out) <- c("n", "min", "max", "mean", "median", "sd")
return(out)
}Warning in min(y): no non-missing arguments to min; returning InfWarning in max(y): no non-missing arguments to max; returning -InfWarning in min(y): no non-missing arguments to min; returning InfWarning in max(y): no non-missing arguments to max; returning -InfWarning in min(y): no non-missing arguments to min; returning InfWarning in max(y): no non-missing arguments to max; returning -Inf'data.frame': 2 obs. of 415 variables:
$ Before.after : Factor w/ 2 levels "Before","After": 1 2
$ epLsar.n : num 15 15
$ epLsar.min : num 0.017 0.0171
$ epLsar.max : num 0.0192 0.0184
$ epLsar.mean : num 0.0177 0.0177
$ epLsar.median : num 0.0177 0.0175
$ epLsar.sd : num 0.000565 0.000441
$ Asfc.n : num 15 15
$ Asfc.min : num 16.8 17.2
$ Asfc.max : num 50.6 55.6
$ Asfc.mean : num 32 30.7
$ Asfc.median : num 29.3 26.4
$ Asfc.sd : num 12.7 13.1
$ Smfc.n : num 15 15
$ Smfc.min : num 0.169 0.169
$ Smfc.max : num 268 102
$ Smfc.mean : num 25.2 13.6
$ Smfc.median : num 1.16 0.84
$ Smfc.sd : num 68.5 26.6
$ HAsfc9.n : num 15 15
$ HAsfc9.min : num 0.088 0.0725
$ HAsfc9.max : num 1.127 0.947
$ HAsfc9.mean : num 0.347 0.325
$ HAsfc9.median : num 0.262 0.299
$ HAsfc9.sd : num 0.268 0.232
$ HAsfc81.n : num 15 15
$ HAsfc81.min : num 0.157 0.119
$ HAsfc81.max : num 1.73 1.16
$ HAsfc81.mean : num 0.547 0.495
$ HAsfc81.median : num 0.492 0.459
$ HAsfc81.sd : num 0.409 0.315
$ Sq.SL.n : num 15 15
$ Sq.SL.min : num 1097 1102
$ Sq.SL.max : num 6943 6472
$ Sq.SL.mean : num 3546 3057
$ Sq.SL.median : num 3588 3425
$ Sq.SL.sd : num 2143 1748
$ Ssk.SL.n : num 15 15
$ Ssk.SL.min : num -1.04 -1.31
$ Ssk.SL.max : num 0.822 0.961
$ Ssk.SL.mean : num 0.0161 -0.1182
$ Ssk.SL.median : num 0.1436 0.0174
$ Ssk.SL.sd : num 0.532 0.623
$ Sku.SL.n : num 15 15
$ Sku.SL.min : num 2.77 3.41
$ Sku.SL.max : num 6.69 7.63
$ Sku.SL.mean : num 4.58 5.35
$ Sku.SL.median : num 4.54 5.08
$ Sku.SL.sd : num 1.02 1.34
$ Sp.SL.n : num 15 15
$ Sp.SL.min : num 4669 4282
$ Sp.SL.max : num 31391 33087
$ Sp.SL.mean : num 13913 12628
$ Sp.SL.median : num 13498 12428
$ Sp.SL.sd : num 8816 8150
$ Sv.SL.n : num 15 15
$ Sv.SL.min : num 4208 4502
$ Sv.SL.max : num 27808 23764
$ Sv.SL.mean : num 13587 12682
$ Sv.SL.median : num 12627 13080
$ Sv.SL.sd : num 7710 6488
$ Sz.SL.n : num 15 15
$ Sz.SL.min : num 8878 8821
$ Sz.SL.max : num 52088 56851
$ Sz.SL.mean : num 27500 25309
$ Sz.SL.median : num 29597 29340
$ Sz.SL.sd : num 15899 14011
$ Sa.SL.n : num 15 15
$ Sa.SL.min : num 824 847
$ Sa.SL.max : num 5218 4270
$ Sa.SL.mean : num 2626 2216
$ Sa.SL.median : num 2738 2383
$ Sa.SL.sd : num 1555 1234
$ Smr.SL.n : num 15 15
$ Smr.SL.min : num 0.0166 0.0133
$ Smr.SL.max : num 0.485 0.754
$ Smr.SL.mean : num 0.14 0.218
$ Smr.SL.median : num 0.072 0.158
$ Smr.SL.sd : num 0.143 0.208
$ Smc.SL.n : num 15 15
$ Smc.SL.min : num 1323 1312
$ Smc.SL.max : num 10001 6084
$ Smc.SL.mean : num 4178 3403
$ Smc.SL.median : num 4346 3380
$ Smc.SL.sd : num 2617 1930
$ Sxp.SL.n : num 15 15
$ Sxp.SL.min : num 2059 2122
$ Sxp.SL.max : num 17684 14804
$ Sxp.SL.mean : num 7567 6712
$ Sxp.SL.median : num 6617 6700
$ Sxp.SL.sd : num 4961 4116
$ Sal.SL.n : num 15 15
$ Sal.SL.min : num 12.8 15
$ Sal.SL.max : num 27.2 23.6
$ Sal.SL.mean : num 19.1 19.5
$ Sal.SL.median : num 19 19.3
$ Sal.SL.sd : num 3.76 2.81
$ Str.SL.n : num 15 15
$ Str.SL.min : num 0.142 0.521
[list output truncated]Warning in min(y): no non-missing arguments to min; returning InfWarning in max(y): no non-missing arguments to max; returning -InfWarning in min(y): no non-missing arguments to min; returning InfWarning in max(y): no non-missing arguments to max; returning -InfWarning in min(y): no non-missing arguments to min; returning InfWarning in max(y): no non-missing arguments to max; returning -InfWarning in min(y): no non-missing arguments to min; returning InfWarning in max(y): no non-missing arguments to max; returning -InfWarning in min(y): no non-missing arguments to min; returning InfWarning in max(y): no non-missing arguments to max; returning -InfWarning in min(y): no non-missing arguments to min; returning InfWarning in max(y): no non-missing arguments to max; returning -InfWarning in min(y): no non-missing arguments to min; returning InfWarning in max(y): no non-missing arguments to max; returning -InfWarning in min(y): no non-missing arguments to min; returning InfWarning in max(y): no non-missing arguments to max; returning -InfWarning in min(y): no non-missing arguments to min; returning InfWarning in max(y): no non-missing arguments to max; returning -Inf'data.frame': 4 obs. of 416 variables:
$ Brush : Factor w/ 2 levels "No","Yes": 1 1 2 2
$ Before.after : Factor w/ 2 levels "Before","After": 1 2 1 2
$ epLsar.n : num 7 7 8 8
$ epLsar.min : num 0.0171 0.0171 0.017 0.0171
$ epLsar.max : num 0.0177 0.0181 0.0192 0.0184
$ epLsar.mean : num 0.0174 0.0175 0.018 0.0178
$ epLsar.median : num 0.0174 0.0175 0.0179 0.0178
$ epLsar.sd : num 0.000179 0.000322 0.000644 0.000508
$ Asfc.n : num 7 7 8 8
$ Asfc.min : num 16.8 18.7 16.9 17.2
$ Asfc.max : num 50.6 54.6 49.7 55.6
$ Asfc.mean : num 34.3 32.6 29.9 29.1
$ Asfc.median : num 35.5 26.4 26.1 26.6
$ Asfc.sd : num 14.3 14.4 11.7 12.5
$ Smfc.n : num 7 7 8 8
$ Smfc.min : num 0.233 0.169 0.169 0.233
$ Smfc.max : num 267.6 28.4 54 102.4
$ Smfc.mean : num 42.98 6.25 9.7 20.06
$ Smfc.median : num 3.02 0.84 0.58 4.11
$ Smfc.sd : num 99.3 10.5 18.5 35
$ HAsfc9.n : num 7 7 8 8
$ HAsfc9.min : num 0.1478 0.0725 0.088 0.1023
$ HAsfc9.max : num 1.127 0.601 0.46 0.947
$ HAsfc9.mean : num 0.441 0.283 0.266 0.362
$ HAsfc9.median : num 0.262 0.299 0.219 0.286
$ HAsfc9.sd : num 0.345 0.182 0.157 0.276
$ HAsfc81.n : num 7 7 8 8
$ HAsfc81.min : num 0.261 0.119 0.157 0.175
$ HAsfc81.max : num 1.734 0.977 0.753 1.158
$ HAsfc81.mean : num 0.678 0.462 0.433 0.524
$ HAsfc81.median : num 0.492 0.459 0.39 0.454
$ HAsfc81.sd : num 0.526 0.305 0.256 0.342
$ Sq.SL.n : num 7 7 8 8
$ Sq.SL.min : num 1373 1102 1097 1328
$ Sq.SL.max : num 6118 6472 6943 5034
$ Sq.SL.mean : num 3597 3209 3502 2925
$ Sq.SL.median : num 3588 3425 2797 2713
$ Sq.SL.sd : num 2042 2060 2368 1558
$ Ssk.SL.n : num 7 7 8 8
$ Ssk.SL.min : num -1.039 -1.037 -0.607 -1.31
$ Ssk.SL.max : num 0.593 0.961 0.822 0.407
$ Ssk.SL.mean : num -0.0829 -0.0636 0.1027 -0.1659
$ Ssk.SL.median : num -0.10805 -0.00453 0.24401 0.14566
$ Ssk.SL.sd : num 0.592 0.623 0.498 0.662
$ Sku.SL.n : num 7 7 8 8
$ Sku.SL.min : num 2.77 3.41 3.72 3.9
$ Sku.SL.max : num 6.27 7.63 6.69 7.59
$ Sku.SL.mean : num 4.63 5.24 4.54 5.45
$ Sku.SL.median : num 4.54 4.95 4.15 5.39
$ Sku.SL.sd : num 1.09 1.4 1.02 1.38
$ Sp.SL.n : num 7 7 8 8
$ Sp.SL.min : num 5664 4320 4669 4282
$ Sp.SL.max : num 31391 33087 27397 21587
$ Sp.SL.mean : num 14398 13299 13489 12040
$ Sp.SL.median : num 13498 12428 11330 10907
$ Sp.SL.sd : num 9116 10175 9152 6578
$ Sv.SL.n : num 7 7 8 8
$ Sv.SL.min : num 4522 4502 4208 6431
$ Sv.SL.max : num 27808 23764 23936 22641
$ Sv.SL.mean : num 13899 13198 13314 12230
$ Sv.SL.median : num 12627 14300 12533 11082
$ Sv.SL.sd : num 8340 7642 7686 5800
$ Sz.SL.n : num 7 7 8 8
$ Sz.SL.min : num 11418 8821 8878 10713
$ Sz.SL.max : num 52088 56851 51333 38401
$ Sz.SL.mean : num 28298 26497 26803 24270
$ Sz.SL.median : num 29597 30739 23950 23181
$ Sz.SL.sd : num 16222 17274 16697 11585
$ Sa.SL.n : num 7 7 8 8
$ Sa.SL.min : num 1044 847 824 934
$ Sa.SL.max : num 4270 4270 5218 3467
$ Sa.SL.mean : num 2647 2293 2609 2148
$ Sa.SL.median : num 2738 2383 2126 1989
$ Sa.SL.sd : num 1434 1396 1753 1168
$ Smr.SL.n : num 7 7 8 8
$ Smr.SL.min : num 0.0376 0.0206 0.0166 0.0133
$ Smr.SL.max : num 0.165 0.754 0.485 0.367
$ Smr.SL.mean : num 0.0845 0.2425 0.1894 0.1962
$ Smr.SL.median : num 0.072 0.122 0.138 0.226
$ Smr.SL.sd : num 0.0511 0.2635 0.1808 0.1614
$ Smc.SL.n : num 7 7 8 8
$ Smc.SL.min : num 1623 1312 1323 1449
$ Smc.SL.max : num 6385 6084 10001 5578
$ Smc.SL.mean : num 4163 3488 4192 3328
$ Smc.SL.median : num 4346 3380 3441 2959
$ Smc.SL.sd : num 2208 2107 3085 1906
$ Sxp.SL.n : num 7 7 8 8
$ Sxp.SL.min : num 2572 2122 2059 2296
$ Sxp.SL.max : num 17684 12872 13593 14804
$ Sxp.SL.mean : num 8115 6938 7088 6515
$ Sxp.SL.median : num 6617 7570 5898 5641
$ Sxp.SL.sd : num 5504 4333 4764 4206
$ Sal.SL.n : num 7 7 8 8
$ Sal.SL.min : num 12.8 15 15.4 16.7
$ Sal.SL.max : num 23.4 23.6 27.2 23.6
$ Sal.SL.mean : num 18.3 19.1 19.9 19.9
$ Sal.SL.median : num 18.9 19.6 20 19.2
$ Sal.SL.sd : num 3.21 3.35 4.24 2.42
$ Str.SL.n : num 7 7 8 8
[list output truncated]Warning in min(y): no non-missing arguments to min; returning InfWarning in max(y): no non-missing arguments to max; returning -InfWarning in min(y): no non-missing arguments to min; returning InfWarning in max(y): no non-missing arguments to max; returning -InfWarning in min(y): no non-missing arguments to min; returning InfWarning in max(y): no non-missing arguments to max; returning -InfWarning in min(y): no non-missing arguments to min; returning InfWarning in max(y): no non-missing arguments to max; returning -InfWarning in min(y): no non-missing arguments to min; returning InfWarning in max(y): no non-missing arguments to max; returning -InfWarning in min(y): no non-missing arguments to min; returning InfWarning in max(y): no non-missing arguments to max; returning -InfWarning in min(y): no non-missing arguments to min; returning InfWarning in max(y): no non-missing arguments to max; returning -InfWarning in min(y): no non-missing arguments to min; returning InfWarning in max(y): no non-missing arguments to max; returning -InfWarning in min(y): no non-missing arguments to min; returning InfWarning in max(y): no non-missing arguments to max; returning -Inf'data.frame': 4 obs. of 416 variables:
$ Dirt : Factor w/ 2 levels "No","Yes": 1 1 2 2
$ Before.after : Factor w/ 2 levels "Before","After": 1 2 1 2
$ epLsar.n : num 7 7 8 8
$ epLsar.min : num 0.0174 0.0174 0.017 0.0171
$ epLsar.max : num 0.0185 0.0183 0.0192 0.0184
$ epLsar.mean : num 0.0178 0.0178 0.0177 0.0176
$ epLsar.median : num 0.0177 0.0177 0.0176 0.0174
$ epLsar.sd : num 0.000454 0.000389 0.000678 0.000487
$ Asfc.n : num 7 7 8 8
$ Asfc.min : num 20.9 20.6 16.8 17.2
$ Asfc.max : num 50.6 55.6 49.5 54.6
$ Asfc.mean : num 33 31.5 31.1 30.1
$ Asfc.median : num 29.3 26.4 30.3 26.1
$ Asfc.sd : num 12.8 13.1 13.4 13.9
$ Smfc.n : num 7 7 8 8
$ Smfc.min : num 0.233 0.233 0.169 0.169
$ Smfc.max : num 267.6 102.4 20.6 28.4
$ Smfc.mean : num 47.78 18.25 5.5 9.56
$ Smfc.median : num 1.16 0.84 1.93 4.07
$ Smfc.sd : num 98.88 37.83 7.32 12.35
$ HAsfc9.n : num 7 7 8 8
$ HAsfc9.min : num 0.088 0.1023 0.1228 0.0725
$ HAsfc9.max : num 1.127 0.947 0.596 0.601
$ HAsfc9.mean : num 0.409 0.366 0.293 0.289
$ HAsfc9.median : num 0.282 0.299 0.219 0.252
$ HAsfc9.sd : num 0.349 0.284 0.178 0.189
$ HAsfc81.n : num 7 7 8 8
$ HAsfc81.min : num 0.157 0.175 0.2 0.119
$ HAsfc81.max : num 1.734 1.158 0.969 0.977
$ HAsfc81.mean : num 0.625 0.516 0.479 0.477
$ HAsfc81.median : num 0.545 0.459 0.415 0.449
$ HAsfc81.sd : num 0.536 0.327 0.278 0.325
$ Sq.SL.n : num 7 7 8 8
$ Sq.SL.min : num 1405 1328 1097 1102
$ Sq.SL.max : num 6943 5034 6163 6472
$ Sq.SL.mean : num 3595 3086 3504 3032
$ Sq.SL.median : num 3588 3425 3406 2533
$ Sq.SL.sd : num 2260 1551 2191 2011
$ Ssk.SL.n : num 7 7 8 8
$ Ssk.SL.min : num -1.039 -1.31 -0.607 -0.957
$ Ssk.SL.max : num 0.822 0.274 0.593 0.961
$ Ssk.SL.mean : num -0.1 -0.419 0.118 0.145
$ Ssk.SL.median : num -0.0518 -0.4307 0.3493 0.1923
$ Ssk.SL.sd : num 0.643 0.602 0.432 0.545
$ Sku.SL.n : num 7 7 8 8
$ Sku.SL.min : num 3.72 4.11 2.77 3.41
$ Sku.SL.max : num 5.47 7.59 6.69 7.63
$ Sku.SL.mean : num 4.39 5.84 4.75 4.93
$ Sku.SL.median : num 4.54 5.87 4.52 4.53
$ Sku.SL.sd : num 0.59 1.17 1.3 1.41
$ Sp.SL.n : num 7 7 8 8
$ Sp.SL.min : num 5654 4841 4669 4282
$ Sp.SL.max : num 25103 21587 31391 33087
$ Sp.SL.mean : num 13555 12140 14227 13054
$ Sp.SL.median : num 16615 12428 10345 10329
$ Sp.SL.sd : num 7823 6026 10136 10064
$ Sv.SL.n : num 7 7 8 8
$ Sv.SL.min : num 5755 6343 4208 4502
$ Sv.SL.max : num 27808 22641 23936 23764
$ Sv.SL.mean : num 13988 13753 13236 11744
$ Sv.SL.median : num 12627 14300 12943 10165
$ Sv.SL.sd : num 8316 6445 7701 6814
$ Sz.SL.n : num 7 7 8 8
$ Sz.SL.min : num 11418 11183 8878 8821
$ Sz.SL.max : num 45969 38401 52088 56851
$ Sz.SL.mean : num 27542 25894 27464 24798
$ Sz.SL.median : num 30499 30739 23289 21685
$ Sz.SL.sd : num 15384 11598 17398 16634
$ Sa.SL.n : num 7 7 8 8
$ Sa.SL.min : num 1044 934 824 847
$ Sa.SL.max : num 5218 3467 4366 4270
$ Sa.SL.mean : num 2670 2209 2588 2221
$ Sa.SL.median : num 2738 2383 2675 1877
$ Sa.SL.sd : num 1639 1122 1590 1403
$ Smr.SL.n : num 7 7 8 8
$ Smr.SL.min : num 0.0411 0.0133 0.0166 0.0176
$ Smr.SL.max : num 0.311 0.367 0.485 0.754
$ Smr.SL.mean : num 0.141 0.163 0.14 0.266
$ Smr.SL.median : num 0.1439 0.0659 0.0601 0.2261
$ Smr.SL.sd : num 0.1 0.166 0.179 0.239
$ Smc.SL.n : num 7 7 8 8
$ Smc.SL.min : num 1623 1449 1323 1312
$ Smc.SL.max : num 10001 5358 6385 6084
$ Smc.SL.mean : num 4442 3319 3948 3476
$ Smc.SL.median : num 4346 3380 4205 2887
$ Smc.SL.sd : num 3050 1724 2365 2212
$ Sxp.SL.n : num 7 7 8 8
$ Sxp.SL.min : num 3152 2296 2059 2122
$ Sxp.SL.max : num 17684 14804 13593 12872
$ Sxp.SL.mean : num 7589 7438 7548 6077
$ Sxp.SL.median : num 6617 7570 6947 5093
$ Sxp.SL.sd : num 5163 4466 5136 3975
$ Sal.SL.n : num 7 7 8 8
$ Sal.SL.min : num 15.4 16.3 12.8 15
$ Sal.SL.max : num 23.4 23.6 27.2 23.6
$ Sal.SL.mean : num 18.9 19.3 19.4 19.7
$ Sal.SL.median : num 19 18.9 19 19.5
$ Sal.SL.sd : num 3.19 2.64 4.4 3.12
$ Str.SL.n : num 7 7 8 8
[list output truncated]Warning in min(y): no non-missing arguments to min; returning InfWarning in max(y): no non-missing arguments to max; returning -InfWarning in min(y): no non-missing arguments to min; returning InfWarning in max(y): no non-missing arguments to max; returning -InfWarning in min(y): no non-missing arguments to min; returning InfWarning in max(y): no non-missing arguments to max; returning -InfWarning in min(y): no non-missing arguments to min; returning InfWarning in max(y): no non-missing arguments to max; returning -InfWarning in min(y): no non-missing arguments to min; returning InfWarning in max(y): no non-missing arguments to max; returning -InfWarning in min(y): no non-missing arguments to min; returning InfWarning in max(y): no non-missing arguments to max; returning -InfWarning in min(y): no non-missing arguments to min; returning InfWarning in max(y): no non-missing arguments to max; returning -InfWarning in min(y): no non-missing arguments to min; returning InfWarning in max(y): no non-missing arguments to max; returning -InfWarning in min(y): no non-missing arguments to min; returning InfWarning in max(y): no non-missing arguments to max; returning -InfWarning in min(y): no non-missing arguments to min; returning InfWarning in max(y): no non-missing arguments to max; returning -InfWarning in min(y): no non-missing arguments to min; returning InfWarning in max(y): no non-missing arguments to max; returning -InfWarning in min(y): no non-missing arguments to min; returning InfWarning in max(y): no non-missing arguments to max; returning -InfWarning in min(y): no non-missing arguments to min; returning InfWarning in max(y): no non-missing arguments to max; returning -InfWarning in min(y): no non-missing arguments to min; returning InfWarning in max(y): no non-missing arguments to max; returning -InfWarning in min(y): no non-missing arguments to min; returning InfWarning in max(y): no non-missing arguments to max; returning -InfWarning in min(y): no non-missing arguments to min; returning InfWarning in max(y): no non-missing arguments to max; returning -InfWarning in min(y): no non-missing arguments to min; returning InfWarning in max(y): no non-missing arguments to max; returning -InfWarning in min(y): no non-missing arguments to min; returning InfWarning in max(y): no non-missing arguments to max; returning -InfWarning in min(y): no non-missing arguments to min; returning InfWarning in max(y): no non-missing arguments to max; returning -InfWarning in min(y): no non-missing arguments to min; returning InfWarning in max(y): no non-missing arguments to max; returning -InfWarning in min(y): no non-missing arguments to min; returning InfWarning in max(y): no non-missing arguments to max; returning -Inf'data.frame': 8 obs. of 417 variables:
$ Brush : Factor w/ 2 levels "No","Yes": 1 1 1 1 2 2 2 2
$ Dirt : Factor w/ 2 levels "No","Yes": 1 1 2 2 1 1 2 2
$ Before.after : Factor w/ 2 levels "Before","After": 1 2 1 2 1 2 1 2
$ epLsar.n : num 3 3 4 4 4 4 4 4
$ epLsar.min : num 0.0174 0.0174 0.0171 0.0171 0.0177 ...
$ epLsar.max : num 0.0175 0.0181 0.0177 0.0177 0.0185 ...
$ epLsar.mean : num 0.0174 0.0177 0.0174 0.0174 0.0181 ...
$ epLsar.median : num 0.0174 0.0177 0.0174 0.0174 0.018 ...
$ epLsar.sd : num 5.15e-05 3.52e-04 2.50e-04 2.64e-04 4.15e-04 ...
$ Asfc.n : num 3 3 4 4 4 4 4 4
$ Asfc.min : num 20.9 20.6 16.8 18.7 22 ...
$ Asfc.max : num 50.6 40.6 49.5 54.6 49.7 ...
$ Asfc.mean : num 35.6 29.2 33.4 35.2 31 ...
$ Asfc.median : num 35.5 26.4 33.5 33.7 26.1 ...
$ Asfc.sd : num 14.9 10.3 16.1 18 12.9 ...
$ Smfc.n : num 3 3 4 4 4 4 4 4
$ Smfc.min : num 0.233 0.233 0.321 0.169 0.321 ...
$ Smfc.max : num 267.6 3.02 20.65 28.44 53.96 ...
$ Smfc.mean : num 89.66 1.37 7.97 9.91 16.37 ...
$ Smfc.median : num 1.16 0.84 5.46 5.53 5.6 ...
$ Smfc.sd : num 154.1 1.47 9.01 13.34 25.55 ...
$ HAsfc9.n : num 3 3 4 4 4 4 4 4
$ HAsfc9.min : num 0.2618 0.2155 0.1478 0.0725 0.088 ...
$ HAsfc9.max : num 1.127 0.396 0.596 0.601 0.435 ...
$ HAsfc9.mean : num 0.634 0.304 0.296 0.267 0.24 ...
$ HAsfc9.median : num 0.513 0.299 0.219 0.197 0.219 ...
$ HAsfc9.sd : num 0.445 0.0903 0.2031 0.2453 0.1528 ...
$ HAsfc81.n : num 3 3 4 4 4 4 4 4
$ HAsfc81.min : num 0.314 0.323 0.261 0.119 0.157 ...
$ HAsfc81.max : num 1.734 0.591 0.969 0.977 0.753 ...
$ HAsfc81.mean : num 0.896 0.457 0.515 0.466 0.422 ...
$ HAsfc81.median : num 0.639 0.459 0.415 0.384 0.39 ...
$ HAsfc81.sd : num 0.744 0.134 0.318 0.416 0.277 ...
$ Sq.SL.n : num 3 3 4 4 4 4 4 4
$ Sq.SL.min : num 1405 1334 1373 1102 1515 ...
$ Sq.SL.max : num 6118 4074 5887 6472 6943 ...
$ Sq.SL.mean : num 3704 2944 3518 3407 3513 ...
$ Sq.SL.median : num 3588 3425 3406 3027 2797 ...
$ Sq.SL.sd : num 2359 1432 2148 2646 2547 ...
$ Ssk.SL.n : num 3 3 4 4 4 4 4 4
$ Ssk.SL.min : num -1.039 -1.037 -0.392 -0.023 -0.587 ...
$ Ssk.SL.max : num 0.468 0.129 0.593 0.961 0.822 ...
$ Ssk.SL.mean : num -0.3427 -0.4948 0.1119 0.2599 0.0817 ...
$ Ssk.SL.median : num -0.4568 -0.5772 0.1231 0.0506 0.0459 ...
$ Ssk.SL.sd : num 0.76 0.587 0.444 0.471 0.582 ...
$ Sku.SL.n : num 3 3 4 4 4 4 4 4
$ Sku.SL.min : num 4.54 4.77 2.77 3.41 3.72 ...
$ Sku.SL.max : num 5.47 5.99 6.27 7.63 4.54 ...
$ Sku.SL.mean : num 4.85 5.54 4.47 5.01 4.04 ...
$ Sku.SL.median : num 4.54 5.87 4.42 4.5 3.94 ...
$ Sku.SL.sd : num 0.537 0.67 1.447 1.857 0.353 ...
$ Sp.SL.n : num 3 3 4 4 4 4 4 4
$ Sp.SL.min : num 5664 4841 7010 4320 5654 ...
$ Sp.SL.max : num 18161 16439 31391 33087 25103 ...
$ Sp.SL.mean : num 13899 11236 14773 14846 13296 ...
$ Sp.SL.median : num 17873 12428 10345 10989 11214 ...
$ Sp.SL.sd : num 7134 5890 11482 13284 9395 ...
$ Sv.SL.n : num 3 3 4 4 4 4 4 4
$ Sv.SL.min : num 5755 6343 4522 4502 5911 ...
$ Sv.SL.max : num 27808 20018 20697 23764 20748 ...
$ Sv.SL.mean : num 15396 13554 12777 12932 12931 ...
$ Sv.SL.median : num 12627 14300 12943 11731 12533 ...
$ Sv.SL.sd : num 11284 6868 7091 9226 7067 ...
$ Sz.SL.n : num 3 3 4 4 4 4 4 4
$ Sz.SL.min : num 11418 11183 11532 8821 11724 ...
$ Sz.SL.max : num 45969 32446 52088 56851 45851 ...
$ Sz.SL.mean : num 29296 24789 27549 27778 26228 ...
$ Sz.SL.median : num 30499 30739 23289 22719 23668 ...
$ Sz.SL.sd : num 17307 11814 18024 22330 16378 ...
$ Sa.SL.n : num 3 3 4 4 4 4 4 4
$ Sa.SL.min : num 1044 994 1053 847 1170 ...
$ Sa.SL.max : num 4270 2928 4070 4270 5218 ...
$ Sa.SL.mean : num 2684 2102 2618 2436 2660 ...
$ Sa.SL.median : num 2738 2383 2675 2314 2126 ...
$ Sa.SL.sd : num 1614 997 1540 1781 1906 ...
$ Smr.SL.n : num 3 3 4 4 4 4 4 4
$ Smr.SL.min : num 0.0411 0.0206 0.0376 0.048 0.0492 ...
$ Smr.SL.max : num 0.1654 0.3132 0.0835 0.7541 0.3107 ...
$ Smr.SL.mean : num 0.1168 0.1332 0.0603 0.3245 0.1588 ...
$ Smr.SL.median : num 0.1439 0.0659 0.0601 0.2479 0.1376 ...
$ Smr.SL.sd : num 0.0664 0.1575 0.0212 0.3185 0.1269 ...
$ Smc.SL.n : num 3 3 4 4 4 4 4 4
$ Smc.SL.min : num 1623 1575 1694 1312 1875 ...
$ Smc.SL.max : num 6366 4456 6385 6084 10001 ...
$ Smc.SL.mean : num 4112 3137 4201 3751 4690 ...
$ Smc.SL.median : num 4346 3380 4362 3803 3441 ...
$ Smc.SL.sd : num 2380 1456 2443 2693 3825 ...
$ Sxp.SL.n : num 3 3 4 4 4 4 4 4
$ Sxp.SL.min : num 3547 3097 2572 2122 3152 ...
$ Sxp.SL.max : num 17684 10209 12493 12872 10330 ...
$ Sxp.SL.mean : num 9283 6959 7240 6922 6320 ...
$ Sxp.SL.median : num 6617 7570 6947 6347 5898 ...
$ Sxp.SL.sd : num 7436 3595 4620 5380 3381 ...
$ Sal.SL.n : num 3 3 4 4 4 4 4 4
$ Sal.SL.min : num 16.4 16.3 12.8 15 15.4 ...
$ Sal.SL.max : num 23.4 23.6 19.4 21.3 21.6 ...
$ Sal.SL.mean : num 19.6 20.6 17.3 18 18.4 ...
$ Sal.SL.median : num 19 21.9 18.4 17.8 18.2 ...
$ Sal.SL.sd : num 3.53 3.82 3.02 2.97 3.34 ...
[list output truncated]The results will be written to the file: “Summary-stats/brushing_v2_summary-stats.xlsx”
write.xlsx(list(B.A=ba, Brush_B.A=brush.ba, Dirt_B.A=dirt.ba, Brush_Dirt_B.A=brush.dirt.ba), file=file.out)
xlsx.info <- file.info(file.out)The exported XLSX-file is: “brushing_v2_summary-stats.xlsx”
It is saved in “~/Summary-stats”
Its modification, ‘last status change’ (= ‘creation’ on Windows) and last access times are, respectively: “2020-01-21 09:04:23”, “2019-10-24 12:03:03” and “2020-01-21 09:04:23”.
R version 3.6.2 (2019-12-12)
Platform: x86_64-w64-mingw32/x64 (64-bit)
Running under: Windows 10 x64 (build 18362)
Matrix products: default
locale:
[1] LC_COLLATE=English_United Kingdom.1252
[2] LC_CTYPE=English_United Kingdom.1252
[3] LC_MONETARY=English_United Kingdom.1252
[4] LC_NUMERIC=C
[5] LC_TIME=English_United Kingdom.1252
attached base packages:
[1] tools stats graphics grDevices utils datasets methods
[8] base
other attached packages:
[1] doBy_4.6-3 R.utils_2.9.2 R.oo_1.23.0 R.methodsS3_1.7.1
[5] openxlsx_4.1.4
loaded via a namespace (and not attached):
[1] zip_2.0.4 Rcpp_1.0.3 compiler_3.6.2 pillar_1.4.2
[5] plyr_1.8.5 zeallot_0.1.0 digest_0.6.23 lifecycle_0.1.0
[9] evaluate_0.14 tibble_2.1.3 nlme_3.1-142 lattice_0.20-38
[13] pkgconfig_2.0.3 rlang_0.4.2 Matrix_1.2-18 yaml_2.2.0
[17] xfun_0.11 dplyr_0.8.3 stringr_1.4.0 knitr_1.26
[21] generics_0.0.2 vctrs_0.2.0 grid_3.6.2 tidyselect_0.2.5
[25] glue_1.3.1 R6_2.4.1 rmarkdown_2.0 purrr_0.3.3
[29] tidyr_1.0.0 magrittr_1.5 backports_1.1.5 htmltools_0.4.0
[33] MASS_7.3-51.4 assertthat_0.2.1 Deriv_4.0 stringi_1.4.3
[37] broom_0.5.3 crayon_1.3.4 RStudio Version 1.2.5019
END OF SCRIPT