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,
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.
- 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
#> [1] 1 2 3 4
#>
#> $rep_checks
#> [1] 8 7 7 8
#>
#> $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 98 4 217 213 139 59 247 208 272 24 232 1 222
#> Row14 221 34 243 69 2 145 37 177 233 67 84 78 239
#> Row13 111 237 254 249 33 142 137 4 39 156 101 159 93
#> Row12 4 27 135 23 191 167 71 244 263 176 4 148 227
#> Row11 89 203 269 2 94 205 43 199 228 95 206 224 10
#> Row10 113 151 64 179 79 184 3 165 220 5 49 183 26
#> Row9 115 138 274 51 264 103 105 82 152 2 158 129 119
#> Row8 147 157 3 40 120 190 66 96 13 163 207 127 4
#> Row7 259 255 92 16 9 1 45 180 154 76 189 144 42
#> Row6 245 114 195 15 173 223 117 60 2 171 87 262 62
#> Row5 108 3 230 186 81 141 219 268 132 30 197 1 97
#> Row4 172 118 61 58 3 80 8 218 14 47 194 54 240
#> Row3 12 116 212 75 209 90 193 3 126 131 169 168 133
#> Row2 3 198 28 210 236 25 53 178 130 17 4 238 146
#> Row1 122 231 77 2 175 19 86 136 57 192 258 106 229
#> Col14 Col15 Col16 Col17 Col18 Col19 Col20
#> Row15 246 261 100 266 160 162 52
#> Row14 56 1 7 270 250 22 235
#> Row13 174 204 20 134 4 155 112
#> Row12 150 36 164 63 70 153 256
#> Row11 1 29 234 65 248 99 6
#> Row10 196 55 102 1 170 83 187
#> Row9 68 265 72 200 21 201 4
#> Row8 109 73 128 241 253 202 11
#> Row7 140 226 2 225 271 166 161
#> Row6 215 273 35 242 124 3 74
#> Row5 188 18 121 260 41 216 91
#> Row4 143 2 48 252 85 44 211
#> Row3 182 185 88 50 1 32 104
#> Row2 257 31 149 125 110 181 46
#> Row1 1 123 38 107 214 251 267
#>
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 0 122 Gen-122
#> 2 2 20WRY1 MINOT 2023 102 1 2 0 231 Gen-231
#> 3 3 20WRY1 MINOT 2023 103 1 3 0 77 Gen-77
#> 4 4 20WRY1 MINOT 2023 104 1 4 2 2 Check-2
#> 5 5 20WRY1 MINOT 2023 105 1 5 0 175 Gen-175
#> 6 6 20WRY1 MINOT 2023 106 1 6 0 19 Gen-19
#> 7 7 20WRY1 MINOT 2023 107 1 7 0 86 Gen-86
#> 8 8 20WRY1 MINOT 2023 108 1 8 0 136 Gen-136
#> 9 9 20WRY1 MINOT 2023 109 1 9 0 57 Gen-57
#> 10 10 20WRY1 MINOT 2023 110 1 10 0 192 Gen-192
#> 11 11 20WRY1 MINOT 2023 111 1 11 0 258 Gen-258
#> 12 12 20WRY1 MINOT 2023 112 1 12 0 106 Gen-106
# 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
#> [1] 1 2 3 4 5
#>
#> $rep_checks
#> [1] 14 11 12 11 12
#>
#> $locations
#> [1] 1
#>
#> $planter
#> [1] "serpentine"
#>
#> $percent_checks
#> [1] "7.7%"
#>
#> $fillers
#> [1] 0
#>
#> $seed
#> [1] 1375.622
#>
#> $id_design
#> [1] 15
#>
spatDB$layoutRandom
#> [[1]]
#> Col1 Col2 Col3 Col4 Col5 Col6 Col7 Col8 Col9 Col10 Col11 Col12 Col13
#> Row30 715 3 618 631 640 725 648 686 630 646 669 684 721
#> Row29 716 639 689 614 701 4 718 678 708 679 680 683 719
#> Row28 685 653 703 647 667 641 616 705 610 2 696 671 663
#> Row27 4 724 710 664 628 633 660 698 608 711 692 677 668
#> Row26 697 612 700 687 3 613 617 606 638 607 643 722 673
#> Row25 515 498 457 465 464 538 448 517 5 441 599 590 588
#> Row24 535 431 502 524 439 410 437 576 434 503 422 522 5
#> Row23 573 543 500 4 436 501 548 580 564 528 596 435 430
#> Row22 514 521 512 427 489 442 417 2 584 569 583 604 553
#> Row21 533 445 541 456 484 505 504 449 571 472 486 3 586
#> Row20 493 510 1 443 561 453 480 438 475 494 440 550 469
#> Row19 559 481 462 560 572 508 3 591 542 466 529 419 574
#> Row18 495 429 525 414 578 552 602 479 530 406 5 450 433
#> Row17 385 5 384 374 355 340 313 332 390 319 360 331 403
#> Row16 377 386 339 404 326 3 363 368 337 346 336 392 394
#> Row15 400 317 372 361 356 343 314 328 345 1 405 347 350
#> Row14 3 344 367 358 379 348 402 401 371 342 362 396 365
#> Row13 158 298 257 303 3 203 181 285 256 287 267 163 274
#> Row12 280 224 173 270 249 178 193 198 5 217 169 238 231
#> Row11 195 248 261 259 186 188 187 293 273 202 172 250 4
#> Row10 222 184 204 2 234 242 251 239 206 197 208 302 183
#> Row9 262 214 246 171 215 290 295 2 218 212 241 232 294
#> Row8 291 266 269 189 156 166 296 306 297 268 196 3 223
#> Row7 13 96 3 113 10 128 134 164 161 191 307 276 174
#> Row6 24 62 114 121 87 133 3 91 146 89 125 106 97
#> Row5 29 55 42 154 54 122 131 142 72 59 1 84 39
#> Row4 155 2 82 56 153 80 83 53 7 86 139 150 48
#> Row3 15 16 149 115 58 4 23 41 28 75 68 32 130
#> Row2 105 109 38 8 64 132 78 93 138 5 25 17 151
#> Row1 2 120 66 14 147 11 76 129 100 63 110 37 19
#> Col14 Col15 Col16 Col17 Col18 Col19 Col20 Col21 Col22 Col23 Col24 Col25
#> Row30 720 5 609 652 714 707 636 688 626 713 699 651
#> Row29 681 645 706 637 672 1 627 690 665 658 676 629
#> Row28 656 634 622 632 654 611 674 661 623 2 659 620
#> Row27 5 666 702 682 642 655 625 704 657 624 670 709
#> Row26 675 615 662 723 1 619 644 649 694 621 650 712
#> Row25 492 432 474 409 597 519 491 507 4 458 416 562
#> Row24 483 463 444 411 558 567 408 447 555 568 565 461
#> Row23 603 490 532 1 605 534 575 424 451 460 518 536
#> Row22 531 546 545 421 423 585 470 4 446 600 488 420
#> Row21 549 598 454 513 413 476 592 551 577 595 540 2
#> Row20 557 473 2 547 554 482 415 563 594 467 485 587
#> Row19 477 506 509 516 579 581 1 487 471 426 527 566
#> Row18 523 511 593 478 496 412 582 601 407 428 1 499
#> Row17 327 2 388 338 393 468 455 526 418 539 589 537
#> Row16 325 312 364 391 375 1 359 311 357 369 329 320
#> Row15 315 354 324 351 397 395 322 335 366 4 318 398
#> Row14 5 349 381 323 382 378 383 353 333 387 389 376
#> Row13 192 160 240 289 4 225 330 316 399 380 373 321
#> Row12 243 236 253 205 170 165 282 185 1 168 176 283
#> Row11 235 167 210 284 233 229 281 247 157 300 258 310
#> Row10 230 279 221 2 304 159 309 301 305 255 175 200
#> Row9 278 308 237 244 199 194 245 1 292 190 265 286
#> Row8 207 260 211 299 252 162 228 288 219 275 182 5
#> Row7 213 263 4 227 220 254 209 226 271 272 179 177
#> Row6 74 81 70 140 6 45 4 65 22 143 94 98
#> Row5 104 21 49 50 90 51 67 34 112 148 1 12
#> Row4 61 5 99 141 85 79 44 118 127 33 31 73
#> Row3 92 119 144 26 102 5 123 126 9 88 30 111
#> Row2 71 135 101 69 124 52 103 136 47 3 107 40
#> Row1 1 137 43 77 27 116 152 95 145 46 18 36
#> Col26
#> Row30 693
#> Row29 635
#> Row28 691
#> Row27 695
#> Row26 717
#> Row25 556
#> Row24 3
#> Row23 570
#> Row22 520
#> Row21 544
#> Row20 452
#> Row19 459
#> Row18 425
#> Row17 497
#> Row16 334
#> Row15 370
#> Row14 352
#> Row13 341
#> Row12 264
#> Row11 1
#> Row10 180
#> Row9 277
#> Row8 201
#> Row7 216
#> Row6 57
#> Row5 20
#> Row4 117
#> Row3 35
#> Row2 60
#> Row1 108
#>
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 0 120 G120
#> 3 3 Block1 1 2023 3 1 3 0 66 G66
#> 4 4 Block1 1 2023 4 1 4 0 14 G14
#> 5 5 Block1 1 2023 5 1 5 0 147 G147
#> 6 6 Block1 1 2023 6 1 6 0 11 G11
#> 7 7 Block1 1 2023 7 1 7 0 76 G76
#> 8 8 Block1 1 2023 8 1 8 0 129 G129
#> 9 9 Block1 1 2023 9 1 9 0 100 G100
#> 10 10 Block1 1 2023 10 1 10 0 63 G63
#> 11 11 Block1 1 2023 11 1 11 0 110 G110
#> 12 12 Block1 1 2023 12 1 12 0 37 G37
# 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
#> [1] 1 2 3 4
#>
#> $rep_checks
#> [1] 6 7 9 8
#>
#> $locations
#> [1] 1
#>
#> $planter
#> [1] "serpentine"
#>
#> $percent_checks
#> [1] "10%"
#>
#> $fillers
#> [1] 0
#>
#> $seed
#> [1] -6568.56
#>
#> $id_design
#> [1] 15
#>
spatAB$layoutRandom
#> [[1]]
#> Col1 Col2 Col3 Col4 Col5 Col6 Col7 Col8 Col9 Col10 Col11 Col12 Col13
#> Row20 18 3 17 69 48 100 142 120 162 180 218 3 258
#> Row19 65 70 93 83 4 104 112 116 137 171 196 215 248
#> Row18 35 25 5 42 39 109 172 3 183 140 189 247 222
#> Row17 1 15 51 19 53 96 145 133 122 170 1 224 257
#> Row16 24 16 8 3 49 178 152 176 99 166 243 214 191
#> Row15 57 84 38 71 27 174 4 129 157 169 197 241 261
#> Row14 59 31 23 36 30 134 139 123 107 1 188 236 192
#> Row13 76 10 3 14 75 153 147 98 175 136 190 193 4
#> Row12 86 64 6 41 54 3 182 177 161 126 185 201 212
#> Row11 21 33 92 26 46 151 167 164 2 146 200 204 273
#> Row10 72 4 81 63 7 117 106 95 159 132 206 4 259
#> Row9 47 62 50 44 1 149 127 173 103 156 262 253 220
#> Row8 74 78 77 66 56 163 105 1 154 135 195 274 265
#> Row7 2 40 28 9 12 130 114 101 155 124 3 202 211
#> Row6 90 79 82 4 94 110 184 115 118 119 223 255 233
#> Row5 52 85 29 89 34 131 4 113 179 168 221 226 213
#> Row4 61 37 45 88 73 165 158 143 121 2 271 227 205
#> Row3 20 55 2 22 91 108 160 128 141 144 210 242 2
#> Row2 68 80 58 60 87 3 138 148 181 111 207 217 239
#> Row1 43 32 67 13 11 125 150 97 2 102 235 268 267
#> Col14 Col15
#> Row20 229 216
#> Row19 272 1
#> Row18 270 264
#> Row17 219 228
#> Row16 2 238
#> Row15 232 187
#> Row14 269 252
#> Row13 194 249
#> Row12 225 231
#> Row11 199 237
#> Row10 260 208
#> Row9 240 3
#> Row8 246 244
#> Row7 198 251
#> Row6 4 263
#> Row5 254 250
#> Row4 230 245
#> Row3 256 234
#> Row2 209 203
#> Row1 186 266
#>
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 43 Gen-43
#> 2 2 20WRA 1 2023 2 1 2 0 32 Gen-32
#> 3 3 20WRA 1 2023 3 1 3 0 67 Gen-67
#> 4 4 20WRA 1 2023 4 1 4 0 13 Gen-13
#> 5 5 20WRA 1 2023 5 1 5 0 11 Gen-11
#> 6 6 20WRB 1 2023 1001 1 6 0 125 Gen-125
#> 7 7 20WRB 1 2023 1002 1 7 0 150 Gen-150
#> 8 8 20WRB 1 2023 1003 1 8 0 97 Gen-97
#> 9 9 20WRB 1 2023 1004 1 9 2 2 Check-2
#> 10 10 20WRB 1 2023 1005 1 10 0 102 Gen-102
#> 11 11 20WRC 1 2023 2001 1 11 0 235 Gen-235
#> 12 12 20WRC 1 2023 2002 1 12 0 268 Gen-268