Spatial Un-replicated Diagonal Arrangement Design
Source:R/fct_diagonal_arrangement.R
diagonal_arrangement.RdRandomly generates an spatial un-replicated diagonal arrangement design.
Usage
diagonal_arrangement(
nrows = NULL,
ncols = NULL,
lines = NULL,
checks = NULL,
planter = "serpentine",
l = 1,
plotNumber = 101,
kindExpt = "SUDC",
splitBy = "row",
seed = NULL,
blocks = NULL,
exptName = NULL,
locationNames = NULL,
multiLocationData = FALSE,
data = NULL
)Arguments
- nrows
Number of rows in the field.
- ncols
Number of columns in the field.
- lines
Number of genotypes, experimental lines or treatments.
- checks
Number of genotypes checks.
- planter
Option for
serpentineorcartesianplot arrangement. By defaultplanter = 'serpentine'.- l
Number of locations or sites. By default
l = 1.- plotNumber
Numeric vector with the starting plot number for each location. By default
plotNumber = 101.- kindExpt
Type of diagonal design, with single options: Single Un-replicated Diagonal Checks
'SUDC'and Decision Blocks Un-replicated Design with Diagonal Checks'DBUDC'for multiple experiments. By defaultkindExpt = 'SUDC'.- splitBy
Option to split the field when
kindExpt = 'DBUDC'is selected. By defaultsplitBy = 'row'.- seed
(optional) Real number that specifies the starting seed to obtain reproducible designs.
- blocks
Number of experiments or blocks to generate an
DBUDCdesign. IfkindExpt = 'DBUDC'and data is null,blocksare mandatory.- exptName
(optional) Name of the experiment.
- locationNames
(optional) Names each location.
- multiLocationData
(optional) Option to pass an entry list for multiple locations. By default
multiLocationData = FALSE.- data
(optional) Data frame with 2 columns:
ENTRY | NAME.
Value
A list with five elements.
infoDesignis a list with information on the design parameters.layoutRandomis a matrix with the randomization layout.plotsNumberis a matrix with the layout plot number.data_entryis a data frame with the data input.fieldBookis a data frame with field book design. This includes the index (Row, Column).
References
Clarke, G. P. Y., & Stefanova, K. T. (2011). Optimal design for early-generation plant breeding trials with unreplicated or partially replicated test lines. Australian & New Zealand Journal of Statistics, 53(4), 461–480.
Author
Didier Murillo [aut], Salvador Gezan [aut], Ana Heilman [ctb], Thomas Walk [ctb], Johan Aparicio [ctb], Richard Horsley [ctb]
Examples
# Example 1: Generates a spatial single diagonal arrangement design in one location
# with 270 treatments and 30 check plots for a field with dimensions 15 rows x 20 cols
# in a serpentine arrangement.
spatd <- diagonal_arrangement(
nrows = 15,
ncols = 20,
lines = 270,
checks = 4,
plotNumber = 101,
kindExpt = "SUDC",
planter = "serpentine",
seed = 1987,
exptName = "20WRY1",
locationNames = "MINOT"
)
spatd$infoDesign
#> $rows
#> [1] 15
#>
#> $columns
#> [1] 20
#>
#> $treatments
#> [1] 270
#>
#> $checks
#> [1] 4
#>
#> $entry_checks
#> $entry_checks[[1]]
#> [1] 1 2 3 4
#>
#>
#> $rep_checks
#> $rep_checks[[1]]
#> [1] 8 7 8 7
#>
#>
#> $locations
#> [1] 1
#>
#> $planter
#> [1] "serpentine"
#>
#> $percent_checks
#> [1] "10%"
#>
#> $fillers
#> [1] 0
#>
#> $seed
#> [1] 1987
#>
#> $id_design
#> [1] 15
#>
spatd$layoutRandom
#> [[1]]
#> Col1 Col2 Col3 Col4 Col5 Col6 Col7 Col8 Col9 Col10 Col11 Col12 Col13
#> Row15 164 3 153 11 221 179 151 139 58 22 266 2 129
#> Row14 89 182 185 38 1 253 156 241 160 252 214 86 130
#> Row13 15 148 82 213 44 194 269 2 265 169 48 245 210
#> Row12 1 124 52 177 5 261 47 40 17 87 3 104 147
#> Row11 100 127 136 4 19 65 158 46 18 229 157 274 59
#> Row10 94 50 27 31 220 166 3 172 170 12 16 176 137
#> Row9 205 212 115 142 110 208 224 216 222 2 246 42 251
#> Row8 175 92 1 197 243 234 236 99 211 67 140 39 3
#> Row7 75 76 8 122 200 1 264 25 138 199 107 120 131
#> Row6 132 93 254 7 247 60 45 171 3 117 103 116 190
#> Row5 181 2 70 79 85 133 203 134 184 273 34 1 174
#> Row4 71 204 159 29 2 83 26 64 119 145 240 223 225
#> Row3 144 231 80 255 43 187 112 4 168 98 32 41 96
#> Row2 4 196 238 235 97 183 111 143 186 237 2 232 263
#> Row1 55 108 248 4 250 217 123 249 126 28 23 118 20
#> Col14 Col15 Col16 Col17 Col18 Col19 Col20
#> Row15 33 109 154 88 30 53 95
#> Row14 163 4 219 68 270 173 90
#> Row13 244 125 149 226 1 54 56
#> Row12 259 233 267 201 193 6 10
#> Row11 2 114 21 77 272 72 24
#> Row10 102 155 36 3 9 162 191
#> Row9 218 106 228 258 167 84 1
#> Row8 230 192 62 135 198 14 69
#> Row7 161 81 3 165 189 268 57
#> Row6 128 146 206 141 215 4 195
#> Row5 61 202 51 242 73 63 207
#> Row4 113 1 78 178 152 37 180
#> Row3 101 74 66 239 4 105 256
#> Row2 49 262 91 257 121 260 209
#> Row1 3 13 150 188 35 227 271
#>
spatd$plotsNumber
#> [[1]]
#> Col1 Col2 Col3 Col4 Col5 Col6 Col7 Col8 Col9 Col10 Col11 Col12 Col13
#> Row15 381 382 383 384 385 386 387 388 389 390 391 392 393
#> Row14 380 379 378 377 376 375 374 373 372 371 370 369 368
#> Row13 341 342 343 344 345 346 347 348 349 350 351 352 353
#> Row12 340 339 338 337 336 335 334 333 332 331 330 329 328
#> Row11 301 302 303 304 305 306 307 308 309 310 311 312 313
#> Row10 300 299 298 297 296 295 294 293 292 291 290 289 288
#> Row9 261 262 263 264 265 266 267 268 269 270 271 272 273
#> Row8 260 259 258 257 256 255 254 253 252 251 250 249 248
#> Row7 221 222 223 224 225 226 227 228 229 230 231 232 233
#> Row6 220 219 218 217 216 215 214 213 212 211 210 209 208
#> Row5 181 182 183 184 185 186 187 188 189 190 191 192 193
#> Row4 180 179 178 177 176 175 174 173 172 171 170 169 168
#> Row3 141 142 143 144 145 146 147 148 149 150 151 152 153
#> Row2 140 139 138 137 136 135 134 133 132 131 130 129 128
#> Row1 101 102 103 104 105 106 107 108 109 110 111 112 113
#> Col14 Col15 Col16 Col17 Col18 Col19 Col20
#> Row15 394 395 396 397 398 399 400
#> Row14 367 366 365 364 363 362 361
#> Row13 354 355 356 357 358 359 360
#> Row12 327 326 325 324 323 322 321
#> Row11 314 315 316 317 318 319 320
#> Row10 287 286 285 284 283 282 281
#> Row9 274 275 276 277 278 279 280
#> Row8 247 246 245 244 243 242 241
#> Row7 234 235 236 237 238 239 240
#> Row6 207 206 205 204 203 202 201
#> Row5 194 195 196 197 198 199 200
#> Row4 167 166 165 164 163 162 161
#> Row3 154 155 156 157 158 159 160
#> Row2 127 126 125 124 123 122 121
#> Row1 114 115 116 117 118 119 120
#>
head(spatd$fieldBook, 12)
#> ID EXPT LOCATION YEAR PLOT ROW COLUMN CHECKS ENTRY TREATMENT
#> 1 1 20WRY1 MINOT 2023 101 1 1 0 55 Gen-55
#> 2 2 20WRY1 MINOT 2023 102 1 2 0 108 Gen-108
#> 3 3 20WRY1 MINOT 2023 103 1 3 0 248 Gen-248
#> 4 4 20WRY1 MINOT 2023 104 1 4 4 4 Check-4
#> 5 5 20WRY1 MINOT 2023 105 1 5 0 250 Gen-250
#> 6 6 20WRY1 MINOT 2023 106 1 6 0 217 Gen-217
#> 7 7 20WRY1 MINOT 2023 107 1 7 0 123 Gen-123
#> 8 8 20WRY1 MINOT 2023 108 1 8 0 249 Gen-249
#> 9 9 20WRY1 MINOT 2023 109 1 9 0 126 Gen-126
#> 10 10 20WRY1 MINOT 2023 110 1 10 0 28 Gen-28
#> 11 11 20WRY1 MINOT 2023 111 1 11 0 23 Gen-23
#> 12 12 20WRY1 MINOT 2023 112 1 12 0 118 Gen-118
# Example 2: Generates a spatial decision block diagonal arrangement design in one location
# with 720 treatments allocated in 5 experiments or blocks for a field with dimensions
# 30 rows x 26 cols in a serpentine arrangement. In this case, we show how to set up the data
# option with the entries list.
checks <- 5;expts <- 5
list_checks <- paste("CH", 1:checks, sep = "")
treatments <- paste("G", 6:725, sep = "")
treatment_list <- data.frame(list(ENTRY = 1:725, NAME = c(list_checks, treatments)))
head(treatment_list, 12)
#> ENTRY NAME
#> 1 1 CH1
#> 2 2 CH2
#> 3 3 CH3
#> 4 4 CH4
#> 5 5 CH5
#> 6 6 G6
#> 7 7 G7
#> 8 8 G8
#> 9 9 G9
#> 10 10 G10
#> 11 11 G11
#> 12 12 G12
tail(treatment_list, 12)
#> ENTRY NAME
#> 714 714 G714
#> 715 715 G715
#> 716 716 G716
#> 717 717 G717
#> 718 718 G718
#> 719 719 G719
#> 720 720 G720
#> 721 721 G721
#> 722 722 G722
#> 723 723 G723
#> 724 724 G724
#> 725 725 G725
spatDB <- diagonal_arrangement(
nrows = 30,
ncols = 26,
checks = 5,
plotNumber = 1,
kindExpt = "DBUDC",
planter = "serpentine",
splitBy = "row",
blocks = c(150,155,95,200,120),
data = treatment_list
)
spatDB$infoDesign
#> $rows
#> [1] 30
#>
#> $columns
#> [1] 26
#>
#> $treatments
#> [1] 150 155 95 200 120
#>
#> $checks
#> [1] 5
#>
#> $entry_checks
#> $entry_checks[[1]]
#> [1] 1 2 3 4 5
#>
#>
#> $rep_checks
#> $rep_checks[[1]]
#> [1] 10 13 13 11 13
#>
#>
#> $locations
#> [1] 1
#>
#> $planter
#> [1] "serpentine"
#>
#> $percent_checks
#> [1] "7.7%"
#>
#> $fillers
#> [1] 0
#>
#> $seed
#> [1] 24210
#>
#> $id_design
#> [1] 15
#>
spatDB$layoutRandom
#> [[1]]
#> Col1 Col2 Col3 Col4 Col5 Col6 Col7 Col8 Col9 Col10 Col11 Col12 Col13
#> Row30 702 3 686 699 642 709 701 689 664 720 696 633 708
#> Row29 722 649 616 627 716 4 673 639 711 641 680 688 710
#> Row28 698 615 631 674 672 636 626 685 608 4 651 697 650
#> Row27 5 679 629 677 606 621 692 662 694 725 663 669 666
#> Row26 661 622 610 678 3 612 687 657 713 609 675 670 704
#> Row25 566 576 522 514 491 575 433 598 4 432 473 567 454
#> Row24 529 500 488 518 580 458 526 525 419 480 548 605 5
#> Row23 508 410 602 3 471 588 470 498 492 474 437 472 558
#> Row22 442 541 468 552 463 482 449 2 584 443 423 599 535
#> Row21 446 475 589 467 537 422 542 416 572 435 411 3 487
#> Row20 560 547 3 460 597 429 448 469 590 409 464 506 478
#> Row19 600 530 550 504 520 521 1 461 536 556 486 509 519
#> Row18 436 544 447 424 415 545 543 438 512 595 4 578 534
#> Row17 334 5 340 361 396 345 365 342 384 373 390 392 316
#> Row16 367 366 335 387 404 2 311 395 389 348 328 394 380
#> Row15 397 320 356 351 314 327 339 403 383 5 377 319 374
#> Row14 1 321 331 337 353 402 352 364 358 322 369 329 405
#> Row13 209 256 242 296 5 201 272 237 310 279 158 243 274
#> Row12 186 292 222 193 275 179 200 261 3 252 204 250 289
#> Row11 221 168 176 301 297 184 224 271 244 263 161 188 2
#> Row10 306 206 307 2 300 298 255 278 284 295 259 173 241
#> Row9 302 190 251 170 187 178 293 2 157 230 260 240 159
#> Row8 246 181 189 277 192 232 162 228 305 167 245 5 194
#> Row7 58 41 1 124 57 55 11 199 171 254 291 182 304
#> Row6 96 133 44 81 98 139 2 66 62 7 53 70 20
#> Row5 140 85 65 31 39 106 73 33 76 112 4 34 54
#> Row4 145 3 50 128 64 137 95 42 144 120 92 118 115
#> Row3 107 67 61 149 80 5 101 47 27 77 151 127 74
#> Row2 138 116 122 88 154 117 29 110 78 4 60 83 113
#> Row1 2 94 102 114 26 79 91 131 25 109 8 6 49
#> Col14 Col15 Col16 Col17 Col18 Col19 Col20 Col21 Col22 Col23 Col24 Col25
#> Row30 723 5 655 611 667 700 619 617 721 623 624 635
#> Row29 658 714 706 643 684 1 647 638 648 705 625 719
#> Row28 681 640 652 654 630 715 646 724 637 2 620 718
#> Row27 1 690 607 682 668 613 659 644 628 653 693 634
#> Row26 671 691 614 676 2 632 717 660 665 703 707 618
#> Row25 462 455 408 596 406 479 451 591 3 494 503 513
#> Row24 583 426 453 483 561 496 456 571 430 440 570 459
#> Row23 527 553 466 5 418 524 445 420 450 477 563 452
#> Row22 431 555 585 538 413 417 577 4 485 546 489 551
#> Row21 516 407 594 439 523 604 414 481 532 539 510 1
#> Row20 562 586 2 581 573 515 531 425 501 444 587 421
#> Row19 517 499 507 465 484 495 5 528 559 434 574 579
#> Row18 412 603 476 490 565 511 457 540 582 593 2 568
#> Row17 349 3 336 368 341 569 564 557 493 428 554 502
#> Row16 385 355 323 378 375 4 318 381 399 333 362 401
#> Row15 376 354 391 398 350 338 332 346 330 2 382 313
#> Row14 3 370 386 315 325 371 324 372 379 326 317 312
#> Row13 203 247 285 281 5 215 400 347 363 393 357 388
#> Row12 264 191 286 174 225 269 197 238 1 202 217 164
#> Row11 282 268 223 235 165 180 163 231 183 308 198 299
#> Row10 216 273 177 4 294 156 276 207 169 160 195 229
#> Row9 210 196 233 267 249 227 290 4 205 266 211 258
#> Row8 219 208 280 175 172 309 236 234 239 283 212 1
#> Row7 270 287 5 166 185 213 218 226 220 288 248 265
#> Row6 105 32 84 12 103 16 4 35 19 69 71 87
#> Row5 153 130 30 63 152 150 46 141 68 38 3 10
#> Row4 155 2 125 147 56 132 9 119 59 13 146 121
#> Row3 45 52 21 72 129 1 14 18 43 90 36 75
#> Row2 86 134 24 22 15 143 93 17 97 3 99 100
#> Row1 5 136 37 48 40 111 135 104 123 28 82 148
#> Col26
#> Row30 645
#> Row29 695
#> Row28 712
#> Row27 683
#> Row26 656
#> Row25 497
#> Row24 1
#> Row23 533
#> Row22 427
#> Row21 505
#> Row20 549
#> Row19 592
#> Row18 601
#> Row17 441
#> Row16 343
#> Row15 359
#> Row14 360
#> Row13 344
#> Row12 303
#> Row11 3
#> Row10 214
#> Row9 253
#> Row8 257
#> Row7 262
#> Row6 108
#> Row5 89
#> Row4 51
#> Row3 142
#> Row2 126
#> Row1 23
#>
spatDB$plotsNumber
#> [[1]]
#> Col1 Col2 Col3 Col4 Col5 Col6 Col7 Col8 Col9 Col10 Col11 Col12 Col13
#> Row30 780 779 778 777 776 775 774 773 772 771 770 769 768
#> Row29 729 730 731 732 733 734 735 736 737 738 739 740 741
#> Row28 728 727 726 725 724 723 722 721 720 719 718 717 716
#> Row27 677 678 679 680 681 682 683 684 685 686 687 688 689
#> Row26 676 675 674 673 672 671 670 669 668 667 666 665 664
#> Row25 625 626 627 628 629 630 631 632 633 634 635 636 637
#> Row24 624 623 622 621 620 619 618 617 616 615 614 613 612
#> Row23 573 574 575 576 577 578 579 580 581 582 583 584 585
#> Row22 572 571 570 569 568 567 566 565 564 563 562 561 560
#> Row21 521 522 523 524 525 526 527 528 529 530 531 532 533
#> Row20 520 519 518 517 516 515 514 513 512 511 510 509 508
#> Row19 469 470 471 472 473 474 475 476 477 478 479 480 481
#> Row18 468 467 466 465 464 463 462 461 460 459 458 457 456
#> Row17 417 418 419 420 421 422 423 424 425 426 427 428 429
#> Row16 416 415 414 413 412 411 410 409 408 407 406 405 404
#> Row15 365 366 367 368 369 370 371 372 373 374 375 376 377
#> Row14 364 363 362 361 360 359 358 357 356 355 354 353 352
#> Row13 313 314 315 316 317 318 319 320 321 322 323 324 325
#> Row12 312 311 310 309 308 307 306 305 304 303 302 301 300
#> Row11 261 262 263 264 265 266 267 268 269 270 271 272 273
#> Row10 260 259 258 257 256 255 254 253 252 251 250 249 248
#> Row9 209 210 211 212 213 214 215 216 217 218 219 220 221
#> Row8 208 207 206 205 204 203 202 201 200 199 198 197 196
#> Row7 157 158 159 160 161 162 163 164 165 166 167 168 169
#> Row6 156 155 154 153 152 151 150 149 148 147 146 145 144
#> Row5 105 106 107 108 109 110 111 112 113 114 115 116 117
#> Row4 104 103 102 101 100 99 98 97 96 95 94 93 92
#> Row3 53 54 55 56 57 58 59 60 61 62 63 64 65
#> Row2 52 51 50 49 48 47 46 45 44 43 42 41 40
#> Row1 1 2 3 4 5 6 7 8 9 10 11 12 13
#> Col14 Col15 Col16 Col17 Col18 Col19 Col20 Col21 Col22 Col23 Col24 Col25
#> Row30 767 766 765 764 763 762 761 760 759 758 757 756
#> Row29 742 743 744 745 746 747 748 749 750 751 752 753
#> Row28 715 714 713 712 711 710 709 708 707 706 705 704
#> Row27 690 691 692 693 694 695 696 697 698 699 700 701
#> Row26 663 662 661 660 659 658 657 656 655 654 653 652
#> Row25 638 639 640 641 642 643 644 645 646 647 648 649
#> Row24 611 610 609 608 607 606 605 604 603 602 601 600
#> Row23 586 587 588 589 590 591 592 593 594 595 596 597
#> Row22 559 558 557 556 555 554 553 552 551 550 549 548
#> Row21 534 535 536 537 538 539 540 541 542 543 544 545
#> Row20 507 506 505 504 503 502 501 500 499 498 497 496
#> Row19 482 483 484 485 486 487 488 489 490 491 492 493
#> Row18 455 454 453 452 451 450 449 448 447 446 445 444
#> Row17 430 431 432 433 434 435 436 437 438 439 440 441
#> Row16 403 402 401 400 399 398 397 396 395 394 393 392
#> Row15 378 379 380 381 382 383 384 385 386 387 388 389
#> Row14 351 350 349 348 347 346 345 344 343 342 341 340
#> Row13 326 327 328 329 330 331 332 333 334 335 336 337
#> Row12 299 298 297 296 295 294 293 292 291 290 289 288
#> Row11 274 275 276 277 278 279 280 281 282 283 284 285
#> Row10 247 246 245 244 243 242 241 240 239 238 237 236
#> Row9 222 223 224 225 226 227 228 229 230 231 232 233
#> Row8 195 194 193 192 191 190 189 188 187 186 185 184
#> Row7 170 171 172 173 174 175 176 177 178 179 180 181
#> Row6 143 142 141 140 139 138 137 136 135 134 133 132
#> Row5 118 119 120 121 122 123 124 125 126 127 128 129
#> Row4 91 90 89 88 87 86 85 84 83 82 81 80
#> Row3 66 67 68 69 70 71 72 73 74 75 76 77
#> Row2 39 38 37 36 35 34 33 32 31 30 29 28
#> Row1 14 15 16 17 18 19 20 21 22 23 24 25
#> Col26
#> Row30 755
#> Row29 754
#> Row28 703
#> Row27 702
#> Row26 651
#> Row25 650
#> Row24 599
#> Row23 598
#> Row22 547
#> Row21 546
#> Row20 495
#> Row19 494
#> Row18 443
#> Row17 442
#> Row16 391
#> Row15 390
#> Row14 339
#> Row13 338
#> Row12 287
#> Row11 286
#> Row10 235
#> Row9 234
#> Row8 183
#> Row7 182
#> Row6 131
#> Row5 130
#> Row4 79
#> Row3 78
#> Row2 27
#> Row1 26
#>
head(spatDB$fieldBook,12)
#> ID EXPT LOCATION YEAR PLOT ROW COLUMN CHECKS ENTRY TREATMENT
#> 1 1 Block1 1 2023 1 1 1 2 2 CH2
#> 2 2 Block1 1 2023 2 1 2 0 94 G94
#> 3 3 Block1 1 2023 3 1 3 0 102 G102
#> 4 4 Block1 1 2023 4 1 4 0 114 G114
#> 5 5 Block1 1 2023 5 1 5 0 26 G26
#> 6 6 Block1 1 2023 6 1 6 0 79 G79
#> 7 7 Block1 1 2023 7 1 7 0 91 G91
#> 8 8 Block1 1 2023 8 1 8 0 131 G131
#> 9 9 Block1 1 2023 9 1 9 0 25 G25
#> 10 10 Block1 1 2023 10 1 10 0 109 G109
#> 11 11 Block1 1 2023 11 1 11 0 8 G8
#> 12 12 Block1 1 2023 12 1 12 0 6 G6
# Example 3: Generates a spatial decision block diagonal arrangement design in one location
# with 270 treatments allocated in 3 experiments or blocks for a field with dimensions
# 20 rows x 15 cols in a serpentine arrangement. Which in turn is an augmented block (3 blocks).
spatAB <- diagonal_arrangement(
nrows = 20,
ncols = 15,
lines = 270,
checks = 4,
plotNumber = c(1,1001,2001),
kindExpt = "DBUDC",
planter = "serpentine",
exptName = c("20WRA", "20WRB", "20WRC"),
blocks = c(90, 90, 90),
splitBy = "column"
)
spatAB$infoDesign
#> $rows
#> [1] 20
#>
#> $columns
#> [1] 15
#>
#> $treatments
#> [1] 90 90 90
#>
#> $checks
#> [1] 4
#>
#> $entry_checks
#> $entry_checks[[1]]
#> [1] 1 2 3 4
#>
#>
#> $rep_checks
#> $rep_checks[[1]]
#> [1] 7 6 8 9
#>
#>
#> $locations
#> [1] 1
#>
#> $planter
#> [1] "serpentine"
#>
#> $percent_checks
#> [1] "10%"
#>
#> $fillers
#> [1] 0
#>
#> $seed
#> [1] 72391
#>
#> $id_design
#> [1] 15
#>
spatAB$layoutRandom
#> [[1]]
#> Col1 Col2 Col3 Col4 Col5 Col6 Col7 Col8 Col9 Col10 Col11 Col12 Col13
#> Row20 82 1 56 57 70 144 172 148 146 173 219 4 229
#> Row19 49 42 69 21 3 103 111 151 102 157 237 249 221
#> Row18 44 10 9 29 12 109 177 3 106 149 262 225 226
#> Row17 2 43 90 16 71 162 176 136 104 138 4 191 185
#> Row16 75 88 36 1 30 110 133 175 180 158 193 253 186
#> Row15 85 45 91 19 13 97 2 171 154 105 252 204 245
#> Row14 86 67 14 83 93 134 161 100 140 4 211 217 251
#> Row13 33 92 3 6 5 116 160 101 117 98 227 263 2
#> Row12 54 31 74 89 8 3 181 145 99 114 254 223 267
#> Row11 23 7 50 25 76 137 163 168 2 159 242 260 206
#> Row10 63 4 11 24 61 139 182 167 153 130 208 3 216
#> Row9 80 65 58 52 1 118 135 125 122 147 192 264 234
#> Row8 32 26 41 48 39 152 183 4 165 95 233 220 240
#> Row7 4 66 37 68 46 178 142 132 96 115 1 198 258
#> Row6 55 27 35 2 77 155 166 131 127 169 209 212 256
#> Row5 94 18 60 34 40 141 3 119 126 184 231 224 241
#> Row4 59 15 73 38 84 179 113 170 164 1 232 189 235
#> Row3 28 47 4 53 51 143 123 120 129 108 244 207 4
#> Row2 64 20 17 72 81 4 107 150 174 156 210 230 222
#> Row1 79 62 78 22 87 121 124 112 1 128 228 261 213
#> Col14 Col15
#> Row20 243 259
#> Row19 248 3
#> Row18 246 196
#> Row17 269 265
#> Row16 2 190
#> Row15 214 257
#> Row14 188 266
#> Row13 272 255
#> Row12 201 203
#> Row11 202 270
#> Row10 205 215
#> Row9 268 3
#> Row8 218 197
#> Row7 247 250
#> Row6 1 187
#> Row5 273 239
#> Row4 236 274
#> Row3 271 195
#> Row2 200 199
#> Row1 238 194
#>
spatAB$plotsNumber
#> [[1]]
#> Col1 Col2 Col3 Col4 Col5 Col6 Col7 Col8 Col9 Col10 Col11 Col12 Col13
#> Row20 100 99 98 97 96 1100 1099 1098 1097 1096 2100 2099 2098
#> Row19 91 92 93 94 95 1091 1092 1093 1094 1095 2091 2092 2093
#> Row18 90 89 88 87 86 1090 1089 1088 1087 1086 2090 2089 2088
#> Row17 81 82 83 84 85 1081 1082 1083 1084 1085 2081 2082 2083
#> Row16 80 79 78 77 76 1080 1079 1078 1077 1076 2080 2079 2078
#> Row15 71 72 73 74 75 1071 1072 1073 1074 1075 2071 2072 2073
#> Row14 70 69 68 67 66 1070 1069 1068 1067 1066 2070 2069 2068
#> Row13 61 62 63 64 65 1061 1062 1063 1064 1065 2061 2062 2063
#> Row12 60 59 58 57 56 1060 1059 1058 1057 1056 2060 2059 2058
#> Row11 51 52 53 54 55 1051 1052 1053 1054 1055 2051 2052 2053
#> Row10 50 49 48 47 46 1050 1049 1048 1047 1046 2050 2049 2048
#> Row9 41 42 43 44 45 1041 1042 1043 1044 1045 2041 2042 2043
#> Row8 40 39 38 37 36 1040 1039 1038 1037 1036 2040 2039 2038
#> Row7 31 32 33 34 35 1031 1032 1033 1034 1035 2031 2032 2033
#> Row6 30 29 28 27 26 1030 1029 1028 1027 1026 2030 2029 2028
#> Row5 21 22 23 24 25 1021 1022 1023 1024 1025 2021 2022 2023
#> Row4 20 19 18 17 16 1020 1019 1018 1017 1016 2020 2019 2018
#> Row3 11 12 13 14 15 1011 1012 1013 1014 1015 2011 2012 2013
#> Row2 10 9 8 7 6 1010 1009 1008 1007 1006 2010 2009 2008
#> Row1 1 2 3 4 5 1001 1002 1003 1004 1005 2001 2002 2003
#> Col14 Col15
#> Row20 2097 2096
#> Row19 2094 2095
#> Row18 2087 2086
#> Row17 2084 2085
#> Row16 2077 2076
#> Row15 2074 2075
#> Row14 2067 2066
#> Row13 2064 2065
#> Row12 2057 2056
#> Row11 2054 2055
#> Row10 2047 2046
#> Row9 2044 2045
#> Row8 2037 2036
#> Row7 2034 2035
#> Row6 2027 2026
#> Row5 2024 2025
#> Row4 2017 2016
#> Row3 2014 2015
#> Row2 2007 2006
#> Row1 2004 2005
#>
head(spatAB$fieldBook,12)
#> ID EXPT LOCATION YEAR PLOT ROW COLUMN CHECKS ENTRY TREATMENT
#> 1 1 20WRA 1 2023 1 1 1 0 79 Gen-79
#> 2 2 20WRA 1 2023 2 1 2 0 62 Gen-62
#> 3 3 20WRA 1 2023 3 1 3 0 78 Gen-78
#> 4 4 20WRA 1 2023 4 1 4 0 22 Gen-22
#> 5 5 20WRA 1 2023 5 1 5 0 87 Gen-87
#> 6 6 20WRB 1 2023 1001 1 6 0 121 Gen-121
#> 7 7 20WRB 1 2023 1002 1 7 0 124 Gen-124
#> 8 8 20WRB 1 2023 1003 1 8 0 112 Gen-112
#> 9 9 20WRB 1 2023 1004 1 9 1 1 Check-1
#> 10 10 20WRB 1 2023 1005 1 10 0 128 Gen-128
#> 11 11 20WRC 1 2023 2001 1 11 0 228 Gen-228
#> 12 12 20WRC 1 2023 2002 1 12 0 261 Gen-261