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Check the alignment of harvester and applicator/planter for mixed treatment problems where multiple input rates are associated with yield monitor data

Usage

check_alignment(td)

Arguments

td

trial design data created by make_exp_plots() and assign_rates()

Value

a tibble

Examples

#--- load trial design ---#
data(td_single_input)

#--- check the alignment of harvester and applicator/planter ---#
machine_alignment <- check_alignment(td_single_input)

#--- check the degree of mixed treatment problem ---#
machine_alignment$overlap_data
#> [[1]]
#>     ha_strip_id strip_id     area  ha_area total_intersecting_ha_area
#>           <int>    <int>    <num>    <num>                      <num>
#>  1:           7        1 4802.128 4914.658                   4802.128
#>  2:           8        1 4802.216 4914.095                   4802.216
#>  3:           9        1 4800.159 4913.532                   4801.744
#>  4:          10        2 4800.448 4912.968                   4800.510
#>  5:          11        2 4800.559 4912.405                   4800.559
#>  6:          12        2 4798.673 4911.842                   4800.955
#>  7:          13        3 4798.842 4911.279                   4798.946
#>  8:          14        3 4798.867 4910.715                   4798.867
#>  9:          15        3 4796.282 4910.152                   4798.333
#> 10:          16        4 4797.150 4909.589                   4797.174
#> 11:          17        4 4797.210 4909.025                   4797.210
#> 12:          18        4 4795.219 4908.462                   4797.983
#> 13:          19        5 4795.517 4907.899                   4795.556
#> 14:          20        5 4795.519 4907.336                   4795.519
#> 15:          21        5 4793.429 4906.772                   4795.620
#> 16:          22        6 4793.753 4906.209                   4793.800
#> 17:          23        6 4793.793 4905.646                   4793.793
#> 18:          24        6 4791.617 4905.082                   4793.897
#> 19:          25        7 4792.114 4904.519                   4792.184
#> 20:          26        7 4792.136 4903.956                   4792.136
#> 21:          27        7 4789.221 4903.392                   4791.600
#> 22:          28        8 4790.392 4902.829                   4790.416
#> 23:          29        8 4790.411 4902.266                   4790.411
#> 24:          30        8 4787.375 4901.703                   4790.130
#> 25:          31        9 4788.742 4901.139                   4788.785
#> 26:          32        9 4788.719 4900.576                   4788.719
#> 27:          33        9 4785.993 4900.013                   4789.221
#> 28:          34       10 4779.629 4897.277                   4779.629
#> 29:          35       10 4779.652 4892.002                   4779.652
#> 30:          36       10 4777.767 4886.723                   4779.512
#> 31:          37       11 4763.732 4881.445                   4763.820
#> 32:          38       11 4763.784 4876.166                   4763.784
#> 33:          39       11 4761.437 4871.850                   4763.524
#> 34:          40       12 4756.581 4870.037                   4756.611
#> 35:          41       12 4756.612 4868.388                   4756.612
#> 36:          42       12 4754.252 4867.832                   4756.389
#> 37:          43       13 4755.735 4867.802                   4755.755
#> 38:          44       13 4755.800 4867.771                   4755.800
#> 39:          45       13 4754.043 4867.741                   4756.146
#> 40:          46       14 4755.675 4867.710                   4755.753
#> 41:          47       14 4755.733 4867.680                   4755.733
#> 42:          48       14 4753.699 4867.649                   4756.390
#> 43:          49       15 4755.654 4867.619                   4755.702
#> 44:          50       15 4755.632 4867.588                   4755.632
#> 45:          51       15 4753.589 4867.557                   4756.247
#> 46:          52       16 4755.582 4867.527                   4755.651
#> 47:          53       16 4755.564 4867.496                   4755.564
#> 48:          54       16 4752.952 4867.466                   4754.947
#> 49:          55       17 4755.394 4867.435                   4755.396
#> 50:          56       17 4755.429 4867.405                   4755.429
#> 51:          57       17 4752.843 4867.374                   4754.830
#> 52:          58       18 4755.301 4867.344                   4755.321
#> 53:          59       18 4755.396 4867.313                   4755.396
#> 54:          60       18 4753.484 4867.283                   4756.029
#> 55:          61       19 4755.245 4867.252                   4755.335
#> 56:          62       19 4755.295 4867.222                   4755.295
#> 57:          63       19 4753.394 4867.191                   4753.394
#>     ha_strip_id strip_id     area  ha_area total_intersecting_ha_area
#>     intersecting_pct dominant_pct
#>                <num>        <num>
#>  1:        0.9771030    1.0000000
#>  2:        0.9772331    1.0000000
#>  3:        0.9772490    0.9996699
#>  4:        0.9771098    0.9999872
#>  5:        0.9772318    1.0000000
#>  6:        0.9774245    0.9995247
#>  7:        0.9771277    0.9999783
#>  8:        0.9772237    1.0000000
#>  9:        0.9772270    0.9995726
#> 10:        0.9771030    0.9999950
#> 11:        0.9772225    1.0000000
#> 12:        0.9774921    0.9994238
#> 13:        0.9771099    0.9999919
#> 14:        0.9772143    1.0000000
#> 15:        0.9773471    0.9995431
#> 16:        0.9770884    0.9999902
#> 17:        0.9771993    1.0000000
#> 18:        0.9773326    0.9995243
#> 19:        0.9770956    0.9999854
#> 20:        0.9771980    1.0000000
#> 21:        0.9772011    0.9995034
#> 22:        0.9770717    0.9999950
#> 23:        0.9771829    1.0000000
#> 24:        0.9772380    0.9994249
#> 25:        0.9770759    0.9999909
#> 26:        0.9771748    1.0000000
#> 27:        0.9773895    0.9993260
#> 28:        0.9759769    1.0000000
#> 29:        0.9770339    1.0000000
#> 30:        0.9780606    0.9996349
#> 31:        0.9759038    0.9999814
#> 32:        0.9769529    1.0000000
#> 33:        0.9777650    0.9995618
#> 34:        0.9767093    0.9999938
#> 35:        0.9770405    1.0000000
#> 36:        0.9771063    0.9995506
#> 37:        0.9769821    0.9999957
#> 38:        0.9769975    1.0000000
#> 39:        0.9770748    0.9995578
#> 40:        0.9770001    0.9999835
#> 41:        0.9770021    1.0000000
#> 42:        0.9771431    0.9994344
#> 43:        0.9770080    0.9999899
#> 44:        0.9769997    1.0000000
#> 45:        0.9771321    0.9994413
#> 46:        0.9770158    0.9999854
#> 47:        0.9770042    1.0000000
#> 48:        0.9768834    0.9995805
#> 49:        0.9769818    0.9999996
#> 50:        0.9769948    1.0000000
#> 51:        0.9768778    0.9995821
#> 52:        0.9769848    0.9999959
#> 53:        0.9770063    1.0000000
#> 54:        0.9771425    0.9994651
#> 55:        0.9770060    0.9999812
#> 56:        0.9770039    1.0000000
#> 57:        0.9766195    1.0000000
#>     intersecting_pct dominant_pct
#> 

#--- visualize the degree of mixed treatment problem ---#
machine_alignment$g_overlap[[1]]
#> `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
#> Warning: Removed 1 row containing missing values or values outside the scale range
#> (`geom_bar()`).