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Randomly 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 serpentine or cartesian plot arrangement. By default planter = '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 default kindExpt = 'SUDC'.

splitBy

Option to split the field when kindExpt = 'DBUDC' is selected. By default splitBy = 'row'.

seed

(optional) Real number that specifies the starting seed to obtain reproducible designs.

blocks

Number of experiments or blocks to generate an DBUDC design. If kindExpt = 'DBUDC' and data is null, blocks are 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.

  • infoDesign is a list with information on the design parameters.

  • layoutRandom is a matrix with the randomization layout.

  • plotsNumber is a matrix with the layout plot number.

  • data_entry is a data frame with the data input.

  • fieldBook is 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 2024  101   1      1      0    55    Gen-55
#> 2   2 20WRY1    MINOT 2024  102   1      2      0   108   Gen-108
#> 3   3 20WRY1    MINOT 2024  103   1      3      0   248   Gen-248
#> 4   4 20WRY1    MINOT 2024  104   1      4      4     4   Check-4
#> 5   5 20WRY1    MINOT 2024  105   1      5      0   250   Gen-250
#> 6   6 20WRY1    MINOT 2024  106   1      6      0   217   Gen-217
#> 7   7 20WRY1    MINOT 2024  107   1      7      0   123   Gen-123
#> 8   8 20WRY1    MINOT 2024  108   1      8      0   249   Gen-249
#> 9   9 20WRY1    MINOT 2024  109   1      9      0   126   Gen-126
#> 10 10 20WRY1    MINOT 2024  110   1     10      0    28    Gen-28
#> 11 11 20WRY1    MINOT 2024  111   1     11      0    23    Gen-23
#> 12 12 20WRY1    MINOT 2024  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 2024    1   1      1      2     2       CH2
#> 2   2 Block1        1 2024    2   1      2      0    94       G94
#> 3   3 Block1        1 2024    3   1      3      0   102      G102
#> 4   4 Block1        1 2024    4   1      4      0   114      G114
#> 5   5 Block1        1 2024    5   1      5      0    26       G26
#> 6   6 Block1        1 2024    6   1      6      0    79       G79
#> 7   7 Block1        1 2024    7   1      7      0    91       G91
#> 8   8 Block1        1 2024    8   1      8      0   131      G131
#> 9   9 Block1        1 2024    9   1      9      0    25       G25
#> 10 10 Block1        1 2024   10   1     10      0   109      G109
#> 11 11 Block1        1 2024   11   1     11      0     8        G8
#> 12 12 Block1        1 2024   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 2024    1   1      1      0    79    Gen-79
#> 2   2 20WRA        1 2024    2   1      2      0    62    Gen-62
#> 3   3 20WRA        1 2024    3   1      3      0    78    Gen-78
#> 4   4 20WRA        1 2024    4   1      4      0    22    Gen-22
#> 5   5 20WRA        1 2024    5   1      5      0    87    Gen-87
#> 6   6 20WRB        1 2024 1001   1      6      0   121   Gen-121
#> 7   7 20WRB        1 2024 1002   1      7      0   124   Gen-124
#> 8   8 20WRB        1 2024 1003   1      8      0   112   Gen-112
#> 9   9 20WRB        1 2024 1004   1      9      1     1   Check-1
#> 10 10 20WRB        1 2024 1005   1     10      0   128   Gen-128
#> 11 11 20WRC        1 2024 2001   1     11      0   228   Gen-228
#> 12 12 20WRC        1 2024 2002   1     12      0   261   Gen-261