Example 1: Randomized Complete Block Design (RCBD)
##
## RCBD> # Example 1: Generates a RCBD design with 3 blocks and 20 treatments across 3 locations.
## RCBD> rcbd1 <- RCBD(t = LETTERS[1:20], reps = 5, l = 3,
## RCBD+ plotNumber = c(101,1001, 2001),
## RCBD+ continuous = TRUE,
## RCBD+ planter = "serpentine",
## RCBD+ seed = 1020,
## RCBD+ locationNames = c("FARGO", "MINOT", "CASSELTON"))
##
## RCBD> rcbd1$infoDesign
## $blocks
## [1] 5
##
## $number.of.treatments
## [1] 20
##
## $treatments
## [1] "A" "B" "C" "D" "E" "F" "G" "H" "I" "J" "K" "L" "M" "N" "O" "P" "Q" "R" "S"
## [20] "T"
##
## $locations
## [1] 3
##
## $plotNumber
## [1] 101 201 301 401 501 1001 1101 1201 1301 1401 2001 2101 2201 2301 2401
##
## $locationNames
## [1] "FARGO" "MINOT" "CASSELTON"
##
## $seed
## [1] 1020
##
## $id_design
## [1] 2
##
##
## RCBD> rcbd1$layoutRandom
## $Loc_FARGO
## Block --Treatments--
## [1,] "1" "P R L T E A J O M C K F I Q G D S H N B"
## [2,] "2" "Q H G M F D L P E B J N A I K C T R O S"
## [3,] "3" "R B G K H E S C F D I T P N Q M A O J L"
## [4,] "4" "M I T B N G O J Q C A L P E S R D K H F"
## [5,] "5" "M C Q O E H I A P S R L J G F B T D K N"
##
## $Loc_MINOT
## Block --Treatments--
## [1,] "1" "F O C A G D L B I S P T H K M E N R Q J"
## [2,] "2" "Q H K A G D E M N O C S J I T L P F B R"
## [3,] "3" "B K D L O E A R F S I P G T C Q J N M H"
## [4,] "4" "C P L O B K E H Q G N A T R J F S M D I"
## [5,] "5" "G S D B H L Q K A P E J T R I C O F M N"
##
## $Loc_CASSELTON
## Block --Treatments--
## [1,] "1" "P G T E L O K H D N S C M I A J Q R B F"
## [2,] "2" "C D L F A T I G S O B J M E R P H N Q K"
## [3,] "3" "C G K N B A L Q I F D H J M O P S T E R"
## [4,] "4" "E L H D F J A T S N B G Q M I O P C K R"
## [5,] "5" "T I M A H K E C Q L D J R B G S N O F P"
##
##
## RCBD> rcbd1$plotNumber
## $Loc_FARGO
## [,1] [,2] [,3] [,4] [,5] [,6] [,7] [,8] [,9] [,10] [,11] [,12] [,13] [,14]
## [1,] 101 102 103 104 105 106 107 108 109 110 111 112 113 114
## [2,] 140 139 138 137 136 135 134 133 132 131 130 129 128 127
## [3,] 141 142 143 144 145 146 147 148 149 150 151 152 153 154
## [4,] 180 179 178 177 176 175 174 173 172 171 170 169 168 167
## [5,] 181 182 183 184 185 186 187 188 189 190 191 192 193 194
## [,15] [,16] [,17] [,18] [,19] [,20]
## [1,] 115 116 117 118 119 120
## [2,] 126 125 124 123 122 121
## [3,] 155 156 157 158 159 160
## [4,] 166 165 164 163 162 161
## [5,] 195 196 197 198 199 200
##
## $Loc_MINOT
## [,1] [,2] [,3] [,4] [,5] [,6] [,7] [,8] [,9] [,10] [,11] [,12] [,13] [,14]
## [1,] 1001 1002 1003 1004 1005 1006 1007 1008 1009 1010 1011 1012 1013 1014
## [2,] 1040 1039 1038 1037 1036 1035 1034 1033 1032 1031 1030 1029 1028 1027
## [3,] 1041 1042 1043 1044 1045 1046 1047 1048 1049 1050 1051 1052 1053 1054
## [4,] 1080 1079 1078 1077 1076 1075 1074 1073 1072 1071 1070 1069 1068 1067
## [5,] 1081 1082 1083 1084 1085 1086 1087 1088 1089 1090 1091 1092 1093 1094
## [,15] [,16] [,17] [,18] [,19] [,20]
## [1,] 1015 1016 1017 1018 1019 1020
## [2,] 1026 1025 1024 1023 1022 1021
## [3,] 1055 1056 1057 1058 1059 1060
## [4,] 1066 1065 1064 1063 1062 1061
## [5,] 1095 1096 1097 1098 1099 1100
##
## $Loc_CASSELTON
## [,1] [,2] [,3] [,4] [,5] [,6] [,7] [,8] [,9] [,10] [,11] [,12] [,13] [,14]
## [1,] 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014
## [2,] 2040 2039 2038 2037 2036 2035 2034 2033 2032 2031 2030 2029 2028 2027
## [3,] 2041 2042 2043 2044 2045 2046 2047 2048 2049 2050 2051 2052 2053 2054
## [4,] 2080 2079 2078 2077 2076 2075 2074 2073 2072 2071 2070 2069 2068 2067
## [5,] 2081 2082 2083 2084 2085 2086 2087 2088 2089 2090 2091 2092 2093 2094
## [,15] [,16] [,17] [,18] [,19] [,20]
## [1,] 2015 2016 2017 2018 2019 2020
## [2,] 2026 2025 2024 2023 2022 2021
## [3,] 2055 2056 2057 2058 2059 2060
## [4,] 2066 2065 2064 2063 2062 2061
## [5,] 2095 2096 2097 2098 2099 2100
##
##
## RCBD> head(rcbd1$fieldBook)
## ID LOCATION PLOT REP TREATMENT
## 1 1 FARGO 101 1 P
## 2 2 FARGO 102 1 R
## 3 3 FARGO 103 1 L
## 4 4 FARGO 104 1 T
## 5 5 FARGO 105 1 E
## 6 6 FARGO 106 1 A
##
## RCBD> # Example 2: Generates a RCBD design with 6 blocks and 18 treatments in one location.
## RCBD> # In this case, we show how to use the option data.
## RCBD> treatments <- paste("ND-", 1:18, sep = "")
##
## RCBD> treatment_list <- data.frame(list(TREATMENT = treatments))
##
## RCBD> head(treatment_list)
## TREATMENT
## 1 ND-1
## 2 ND-2
## 3 ND-3
## 4 ND-4
## 5 ND-5
## 6 ND-6
##
## RCBD> rcbd2 <- RCBD(reps = 6, l = 1,
## RCBD+ plotNumber = 101,
## RCBD+ continuous = FALSE,
## RCBD+ planter = "serpentine",
## RCBD+ seed = 13,
## RCBD+ locationNames = "IBAGUE",
## RCBD+ data = treatment_list)
##
## RCBD> rcbd2$infoDesign
## $blocks
## [1] 6
##
## $number.of.treatments
## [1] 18
##
## $treatments
## [1] "ND-1" "ND-2" "ND-3" "ND-4" "ND-5" "ND-6" "ND-7" "ND-8" "ND-9"
## [10] "ND-10" "ND-11" "ND-12" "ND-13" "ND-14" "ND-15" "ND-16" "ND-17" "ND-18"
##
## $locations
## [1] 1
##
## $plotNumber
## [1] 101 201 301 401 501 601
##
## $locationNames
## [1] "IBAGUE"
##
## $seed
## [1] 13
##
## $id_design
## [1] 2
##
##
## RCBD> rcbd2$layoutRandom
## $Loc_IBAGUE
## Block
## [1,] "1"
## [2,] "2"
## [3,] "3"
## [4,] "4"
## [5,] "5"
## [6,] "6"
## --Treatments--
## [1,] "ND-3 ND-5 ND-10 ND-13 ND-6 ND-14 ND-4 ND-8 ND-18 ND-1 ND-11 ND-2 ND-17 ND-12 ND-9 ND-7 ND-16 ND-15"
## [2,] "ND-15 ND-17 ND-12 ND-1 ND-11 ND-4 ND-8 ND-7 ND-5 ND-3 ND-14 ND-9 ND-10 ND-13 ND-2 ND-6 ND-18 ND-16"
## [3,] "ND-17 ND-12 ND-8 ND-14 ND-10 ND-6 ND-7 ND-18 ND-2 ND-1 ND-13 ND-9 ND-11 ND-15 ND-16 ND-3 ND-4 ND-5"
## [4,] "ND-14 ND-13 ND-16 ND-1 ND-8 ND-9 ND-15 ND-6 ND-7 ND-12 ND-10 ND-18 ND-11 ND-4 ND-3 ND-5 ND-2 ND-17"
## [5,] "ND-14 ND-11 ND-9 ND-4 ND-1 ND-16 ND-3 ND-8 ND-5 ND-7 ND-10 ND-18 ND-12 ND-6 ND-2 ND-15 ND-13 ND-17"
## [6,] "ND-3 ND-5 ND-17 ND-9 ND-6 ND-18 ND-1 ND-14 ND-12 ND-8 ND-4 ND-11 ND-15 ND-2 ND-10 ND-16 ND-13 ND-7"
##
##
## RCBD> rcbd2$plotNumber
## $Loc_IBAGUE
## [,1] [,2] [,3] [,4] [,5] [,6] [,7] [,8] [,9] [,10] [,11] [,12] [,13] [,14]
## [1,] 101 102 103 104 105 106 107 108 109 110 111 112 113 114
## [2,] 218 217 216 215 214 213 212 211 210 209 208 207 206 205
## [3,] 301 302 303 304 305 306 307 308 309 310 311 312 313 314
## [4,] 418 417 416 415 414 413 412 411 410 409 408 407 406 405
## [5,] 501 502 503 504 505 506 507 508 509 510 511 512 513 514
## [6,] 618 617 616 615 614 613 612 611 610 609 608 607 606 605
## [,15] [,16] [,17] [,18]
## [1,] 115 116 117 118
## [2,] 204 203 202 201
## [3,] 315 316 317 318
## [4,] 404 403 402 401
## [5,] 515 516 517 518
## [6,] 604 603 602 601
##
##
## RCBD> head(rcbd2$fieldBook)
## ID LOCATION PLOT REP TREATMENT
## 1 1 IBAGUE 101 1 ND-3
## 2 2 IBAGUE 102 1 ND-5
## 3 3 IBAGUE 103 1 ND-10
## 4 4 IBAGUE 104 1 ND-13
## 5 5 IBAGUE 105 1 ND-6
## 6 6 IBAGUE 106 1 ND-14
p <- full_control_positions(plt$field_book, "COLUMN", "ROW", factor_name = "TREATMENT", label = "TREATMENT")
p
# Example 2: Latin Square Design
latinSq1 <- latin_square(t = 4, reps = 2, plotNumber = 101, planter = "cartesian", seed = 1980)
plt <- plot(latinSq1)
p <- full_control_positions(plt$field_book, "COLUMN", "ROW", factor_name = "TREATMENT", label = "TREATMENT")
p
# Example 3: Full Factorial Design
##
## fll_fc> # Example 1: Generates a full factorial with 3 factors each with 2 levels.
## fll_fc> # This in an RCBD arrangement with 3 reps.
## fll_fc> fullFact1 <- full_factorial(setfactors = c(2,2,2), reps = 3, l = 1, type = 2,
## fll_fc+ plotNumber = 101,
## fll_fc+ continuous = TRUE,
## fll_fc+ planter = "serpentine",
## fll_fc+ seed = 325,
## fll_fc+ locationNames = "FARGO")
##
## fll_fc> fullFact1$infoDesign
## $factors
## [1] "A" "B" "C"
##
## $levels
## [1] 0 1 0 1 0 1
##
## $runs
## [1] 8
##
## $all_treatments
## A B C
## 1 0 0 0
## 2 1 0 0
## 3 0 1 0
## 4 1 1 0
## 5 0 0 1
## 6 1 0 1
## 7 0 1 1
## 8 1 1 1
##
## $reps
## [1] 3
##
## $locations
## [1] 1
##
## $location_names
## [1] "FARGO"
##
## $kind
## [1] "RCBD"
##
## $levels_each_factor
## [1] 2 2 2
##
## $id_design
## [1] 4
##
##
## fll_fc> head(fullFact1$fieldBook,10)
## ID LOCATION PLOT REP FACTOR_A FACTOR_B FACTOR_C TRT_COMB
## 1 1 FARGO 101 1 0 1 1 0*1*1
## 2 2 FARGO 102 1 1 1 1 1*1*1
## 3 3 FARGO 103 1 1 0 0 1*0*0
## 4 4 FARGO 104 1 0 1 0 0*1*0
## 5 5 FARGO 105 1 1 1 0 1*1*0
## 6 6 FARGO 106 1 1 0 1 1*0*1
## 7 7 FARGO 107 1 0 0 0 0*0*0
## 8 8 FARGO 108 1 0 0 1 0*0*1
## 16 9 FARGO 109 2 1 1 0 1*1*0
## 15 10 FARGO 110 2 0 0 0 0*0*0
##
## fll_fc> # Example 2: Generates a full factorial with 3 factors and each with levels: 2,3,
## fll_fc> # and 2, respectively. In this case, we show how to use the option data
## fll_fc> FACTORS <- rep(c("A", "B", "C"), c(2,3,2))
##
## fll_fc> LEVELS <- c("a0", "a1", "b0", "b1", "b2", "c0", "c1")
##
## fll_fc> data_factorial <- data.frame(list(FACTOR = FACTORS, LEVEL = LEVELS))
##
## fll_fc> print(data_factorial)
## FACTOR LEVEL
## 1 A a0
## 2 A a1
## 3 B b0
## 4 B b1
## 5 B b2
## 6 C c0
## 7 C c1
##
## fll_fc> # This in an RCBD arrangement with 5 reps in 3 locations.
## fll_fc> fullFact2 <- full_factorial(setfactors = NULL, reps = 5, l = 3, type = 2,
## fll_fc+ plotNumber = c(101,1001,2001),
## fll_fc+ continuous = FALSE,
## fll_fc+ planter = "serpentine",
## fll_fc+ seed = 326,
## fll_fc+ locationNames = c("Loc1","Loc2","Loc3"),
## fll_fc+ data = data_factorial)
##
## fll_fc> fullFact2$infoDesign
## $factors
## [1] "A" "B" "C"
##
## $levels
## $levels[[1]]
## [1] "a0" "a1"
##
## $levels[[2]]
## [1] "b0" "b1" "b2"
##
## $levels[[3]]
## [1] "c0" "c1"
##
##
## $runs
## [1] 12
##
## $all_treatments
## A B C
## 1 a0 b0 c0
## 2 a1 b0 c0
## 3 a0 b1 c0
## 4 a1 b1 c0
## 5 a0 b2 c0
## 6 a1 b2 c0
## 7 a0 b0 c1
## 8 a1 b0 c1
## 9 a0 b1 c1
## 10 a1 b1 c1
## 11 a0 b2 c1
## 12 a1 b2 c1
##
## $reps
## [1] 5
##
## $locations
## [1] 3
##
## $location_names
## [1] "Loc1" "Loc2" "Loc3"
##
## $kind
## [1] "RCBD"
##
## $levels_each_factor
## [1] 2 3 2
##
## $id_design
## [1] 4
##
##
## fll_fc> head(fullFact2$fieldBook,10)
## ID LOCATION PLOT REP FACTOR_A FACTOR_B FACTOR_C TRT_COMB
## 1 1 Loc1 101 1 a0 b1 c0 a0*b1*c0
## 2 2 Loc1 102 1 a1 b0 c1 a1*b0*c1
## 3 3 Loc1 103 1 a1 b2 c1 a1*b2*c1
## 4 4 Loc1 104 1 a0 b1 c1 a0*b1*c1
## 5 5 Loc1 105 1 a1 b0 c0 a1*b0*c0
## 6 6 Loc1 106 1 a0 b0 c1 a0*b0*c1
## 7 7 Loc1 107 1 a1 b1 c0 a1*b1*c0
## 8 8 Loc1 108 1 a0 b2 c1 a0*b2*c1
## 9 9 Loc1 109 1 a1 b1 c1 a1*b1*c1
## 10 10 Loc1 110 1 a0 b0 c0 a0*b0*c0
p <- full_control_positions(plt$field_book[plt$field_book$LOCATION == "Loc1",], "COLUMN", "ROW", factor_name = "FACTOR_A", label = "FACTOR_A")
p
# Example 4: Incomplete Blocks Design
##
## incmp_> # Example 1: Generates a resolvable IBD of characteristics (t,k,r) = (12,4,2).
## incmp_> # 1-resolvable IBDs
## incmp_> ibd1 <- incomplete_blocks(t = 12,
## incmp_+ k = 4,
## incmp_+ r = 2,
## incmp_+ seed = 1984)
##
## incmp_> ibd1$infoDesign
## $Reps
## [1] 2
##
## $iBlocks
## [1] 3
##
## $NumberTreatments
## [1] 12
##
## $NumberLocations
## [1] 1
##
## $Locations
## [1] 1
##
## $seed
## [1] 1984
##
## $lambda
## [1] 0.5454545
##
## $id_design
## [1] 8
##
##
## incmp_> head(ibd1$fieldBook)
## ID LOCATION PLOT REP IBLOCK UNIT ENTRY TREATMENT
## 1 1 1 101 1 1 1 8 G-8
## 2 2 1 102 1 1 2 9 G-9
## 3 3 1 103 1 1 3 12 G-12
## 4 4 1 104 1 1 4 1 G-1
## 5 5 1 105 1 2 1 5 G-5
## 6 6 1 106 1 2 2 11 G-11
##
## incmp_> # Example 2: Generates a balanced resolvable IBD of characteristics (t,k,r) = (15,3,7).
## incmp_> # In this case, we show how to use the option data.
## incmp_> treatments <- paste("TX-", 1:15, sep = "")
##
## incmp_> ENTRY <- 1:15
##
## incmp_> treatment_list <- data.frame(list(ENTRY = ENTRY, TREATMENT = treatments))
##
## incmp_> head(treatment_list)
## ENTRY TREATMENT
## 1 1 TX-1
## 2 2 TX-2
## 3 3 TX-3
## 4 4 TX-4
## 5 5 TX-5
## 6 6 TX-6
##
## incmp_> ibd2 <- incomplete_blocks(t = 15,
## incmp_+ k = 3,
## incmp_+ r = 7,
## incmp_+ seed = 1985,
## incmp_+ data = treatment_list)
##
## incmp_> ibd2$infoDesign
## $Reps
## [1] 7
##
## $iBlocks
## [1] 5
##
## $NumberTreatments
## [1] 15
##
## $NumberLocations
## [1] 1
##
## $Locations
## [1] 1
##
## $seed
## [1] 1985
##
## $lambda
## [1] 1
##
## $id_design
## [1] 8
##
##
## incmp_> head(ibd2$fieldBook)
## ID LOCATION PLOT REP IBLOCK UNIT ENTRY TREATMENT
## 1 1 1 101 1 1 1 8 TX-8
## 2 2 1 102 1 1 2 11 TX-11
## 3 3 1 103 1 1 3 2 TX-2
## 4 4 1 104 1 2 1 4 TX-4
## 5 5 1 105 1 2 2 14 TX-14
## 6 6 1 106 1 2 3 13 TX-13
p <- full_control_positions(plt$field_book, "COLUMN", "ROW", factor_name = "TREATMENT", label = "TREATMENT")
p
# Example 5: Diagonal Arrangement Design
##
## dgnl_r> # Example 1: Generates a spatial single diagonal arrangement design in one location
## dgnl_r> # with 270 treatments and 30 check plots for a field with dimensions 15 rows x 20 cols
## dgnl_r> # in a serpentine arrangement.
## dgnl_r> spatd <- diagonal_arrangement(
## dgnl_r+ nrows = 15,
## dgnl_r+ ncols = 20,
## dgnl_r+ lines = 270,
## dgnl_r+ checks = 4,
## dgnl_r+ plotNumber = 101,
## dgnl_r+ kindExpt = "SUDC",
## dgnl_r+ planter = "serpentine",
## dgnl_r+ seed = 1987,
## dgnl_r+ exptName = "20WRY1",
## dgnl_r+ locationNames = "MINOT"
## dgnl_r+ )
##
## dgnl_r> spatd$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
##
## $id_design
## [1] 15
##
##
## dgnl_r> spatd$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
##
##
## dgnl_r> spatd$plotsNumber
## [[1]]
## Col1 Col2 Col3 Col4 Col5 Col6 Col7 Col8 Col9 Col10 Col11 Col12 Col13
## Row15 381 382 383 384 385 386 387 388 389 390 391 392 393
## Row14 380 379 378 377 376 375 374 373 372 371 370 369 368
## Row13 341 342 343 344 345 346 347 348 349 350 351 352 353
## Row12 340 339 338 337 336 335 334 333 332 331 330 329 328
## Row11 301 302 303 304 305 306 307 308 309 310 311 312 313
## Row10 300 299 298 297 296 295 294 293 292 291 290 289 288
## Row9 261 262 263 264 265 266 267 268 269 270 271 272 273
## Row8 260 259 258 257 256 255 254 253 252 251 250 249 248
## Row7 221 222 223 224 225 226 227 228 229 230 231 232 233
## Row6 220 219 218 217 216 215 214 213 212 211 210 209 208
## Row5 181 182 183 184 185 186 187 188 189 190 191 192 193
## Row4 180 179 178 177 176 175 174 173 172 171 170 169 168
## Row3 141 142 143 144 145 146 147 148 149 150 151 152 153
## Row2 140 139 138 137 136 135 134 133 132 131 130 129 128
## Row1 101 102 103 104 105 106 107 108 109 110 111 112 113
## Col14 Col15 Col16 Col17 Col18 Col19 Col20
## Row15 394 395 396 397 398 399 400
## Row14 367 366 365 364 363 362 361
## Row13 354 355 356 357 358 359 360
## Row12 327 326 325 324 323 322 321
## Row11 314 315 316 317 318 319 320
## Row10 287 286 285 284 283 282 281
## Row9 274 275 276 277 278 279 280
## Row8 247 246 245 244 243 242 241
## Row7 234 235 236 237 238 239 240
## Row6 207 206 205 204 203 202 201
## Row5 194 195 196 197 198 199 200
## Row4 167 166 165 164 163 162 161
## Row3 154 155 156 157 158 159 160
## Row2 127 126 125 124 123 122 121
## Row1 114 115 116 117 118 119 120
##
##
## dgnl_r> head(spatd$fieldBook, 12)
## ID EXPT LOCATION YEAR PLOT ROW COLUMN CHECKS ENTRY TREATMENT
## 1 1 20WRY1 MINOT 2025 101 1 1 0 55 Gen-55
## 2 2 20WRY1 MINOT 2025 102 1 2 0 108 Gen-108
## 3 3 20WRY1 MINOT 2025 103 1 3 0 248 Gen-248
## 4 4 20WRY1 MINOT 2025 104 1 4 4 4 Check-4
## 5 5 20WRY1 MINOT 2025 105 1 5 0 250 Gen-250
## 6 6 20WRY1 MINOT 2025 106 1 6 0 217 Gen-217
## 7 7 20WRY1 MINOT 2025 107 1 7 0 123 Gen-123
## 8 8 20WRY1 MINOT 2025 108 1 8 0 249 Gen-249
## 9 9 20WRY1 MINOT 2025 109 1 9 0 126 Gen-126
## 10 10 20WRY1 MINOT 2025 110 1 10 0 28 Gen-28
## 11 11 20WRY1 MINOT 2025 111 1 11 0 23 Gen-23
## 12 12 20WRY1 MINOT 2025 112 1 12 0 118 Gen-118
##
## dgnl_r> # Example 2: Generates a spatial decision block diagonal arrangement design in one location
## dgnl_r> # with 720 treatments allocated in 5 experiments or blocks for a field with dimensions
## dgnl_r> # 30 rows x 26 cols in a serpentine arrangement. In this case, we show how to set up the data
## dgnl_r> # option with the entries list.
## dgnl_r> checks <- 5;expts <- 5
##
## dgnl_r> list_checks <- paste("CH", 1:checks, sep = "")
##
## dgnl_r> treatments <- paste("G", 6:725, sep = "")
##
## dgnl_r> treatment_list <- data.frame(list(ENTRY = 1:725, NAME = c(list_checks, treatments)))
##
## dgnl_r> 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
##
## dgnl_r> 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
##
## dgnl_r> spatDB <- diagonal_arrangement(
## dgnl_r+ nrows = 30,
## dgnl_r+ ncols = 26,
## dgnl_r+ checks = 5,
## dgnl_r+ plotNumber = 1,
## dgnl_r+ kindExpt = "DBUDC",
## dgnl_r+ planter = "serpentine",
## dgnl_r+ splitBy = "row",
## dgnl_r+ blocks = c(150,155,95,200,120),
## dgnl_r+ data = treatment_list
## dgnl_r+ )
##
## dgnl_r> 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
##
## $id_design
## [1] 15
##
##
## dgnl_r> spatDB$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
##
##
## dgnl_r> 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
##
##
## dgnl_r> head(spatDB$fieldBook,12)
## ID EXPT LOCATION YEAR PLOT ROW COLUMN CHECKS ENTRY TREATMENT
## 1 1 Block1 1 2025 1 1 1 2 2 CH2
## 2 2 Block1 1 2025 2 1 2 0 94 G94
## 3 3 Block1 1 2025 3 1 3 0 102 G102
## 4 4 Block1 1 2025 4 1 4 0 114 G114
## 5 5 Block1 1 2025 5 1 5 0 26 G26
## 6 6 Block1 1 2025 6 1 6 0 79 G79
## 7 7 Block1 1 2025 7 1 7 0 91 G91
## 8 8 Block1 1 2025 8 1 8 0 131 G131
## 9 9 Block1 1 2025 9 1 9 0 25 G25
## 10 10 Block1 1 2025 10 1 10 0 109 G109
## 11 11 Block1 1 2025 11 1 11 0 8 G8
## 12 12 Block1 1 2025 12 1 12 0 6 G6
##
## dgnl_r> # Example 3: Generates a spatial decision block diagonal arrangement design in one location
## dgnl_r> # with 270 treatments allocated in 3 experiments or blocks for a field with dimensions
## dgnl_r> # 20 rows x 15 cols in a serpentine arrangement. Which in turn is an augmented block (3 blocks).
## dgnl_r> spatAB <- diagonal_arrangement(
## dgnl_r+ nrows = 20,
## dgnl_r+ ncols = 15,
## dgnl_r+ lines = 270,
## dgnl_r+ checks = 4,
## dgnl_r+ plotNumber = c(1,1001,2001),
## dgnl_r+ kindExpt = "DBUDC",
## dgnl_r+ planter = "serpentine",
## dgnl_r+ exptName = c("20WRA", "20WRB", "20WRC"),
## dgnl_r+ blocks = c(90, 90, 90),
## dgnl_r+ splitBy = "column"
## dgnl_r+ )
##
## dgnl_r> 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
##
## $id_design
## [1] 15
##
##
## dgnl_r> spatAB$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
##
##
## dgnl_r> spatAB$plotsNumber
## [[1]]
## Col1 Col2 Col3 Col4 Col5 Col6 Col7 Col8 Col9 Col10 Col11 Col12 Col13
## Row20 100 99 98 97 96 1100 1099 1098 1097 1096 2100 2099 2098
## Row19 91 92 93 94 95 1091 1092 1093 1094 1095 2091 2092 2093
## Row18 90 89 88 87 86 1090 1089 1088 1087 1086 2090 2089 2088
## Row17 81 82 83 84 85 1081 1082 1083 1084 1085 2081 2082 2083
## Row16 80 79 78 77 76 1080 1079 1078 1077 1076 2080 2079 2078
## Row15 71 72 73 74 75 1071 1072 1073 1074 1075 2071 2072 2073
## Row14 70 69 68 67 66 1070 1069 1068 1067 1066 2070 2069 2068
## Row13 61 62 63 64 65 1061 1062 1063 1064 1065 2061 2062 2063
## Row12 60 59 58 57 56 1060 1059 1058 1057 1056 2060 2059 2058
## Row11 51 52 53 54 55 1051 1052 1053 1054 1055 2051 2052 2053
## Row10 50 49 48 47 46 1050 1049 1048 1047 1046 2050 2049 2048
## Row9 41 42 43 44 45 1041 1042 1043 1044 1045 2041 2042 2043
## Row8 40 39 38 37 36 1040 1039 1038 1037 1036 2040 2039 2038
## Row7 31 32 33 34 35 1031 1032 1033 1034 1035 2031 2032 2033
## Row6 30 29 28 27 26 1030 1029 1028 1027 1026 2030 2029 2028
## Row5 21 22 23 24 25 1021 1022 1023 1024 1025 2021 2022 2023
## Row4 20 19 18 17 16 1020 1019 1018 1017 1016 2020 2019 2018
## Row3 11 12 13 14 15 1011 1012 1013 1014 1015 2011 2012 2013
## Row2 10 9 8 7 6 1010 1009 1008 1007 1006 2010 2009 2008
## Row1 1 2 3 4 5 1001 1002 1003 1004 1005 2001 2002 2003
## Col14 Col15
## Row20 2097 2096
## Row19 2094 2095
## Row18 2087 2086
## Row17 2084 2085
## Row16 2077 2076
## Row15 2074 2075
## Row14 2067 2066
## Row13 2064 2065
## Row12 2057 2056
## Row11 2054 2055
## Row10 2047 2046
## Row9 2044 2045
## Row8 2037 2036
## Row7 2034 2035
## Row6 2027 2026
## Row5 2024 2025
## Row4 2017 2016
## Row3 2014 2015
## Row2 2007 2006
## Row1 2004 2005
##
##
## dgnl_r> head(spatAB$fieldBook,12)
## ID EXPT LOCATION YEAR PLOT ROW COLUMN CHECKS ENTRY TREATMENT
## 1 1 20WRA 1 2025 1 1 1 0 79 Gen-79
## 2 2 20WRA 1 2025 2 1 2 0 62 Gen-62
## 3 3 20WRA 1 2025 3 1 3 0 78 Gen-78
## 4 4 20WRA 1 2025 4 1 4 0 22 Gen-22
## 5 5 20WRA 1 2025 5 1 5 0 87 Gen-87
## 6 6 20WRB 1 2025 1001 1 6 0 121 Gen-121
## 7 7 20WRB 1 2025 1002 1 7 0 124 Gen-124
## 8 8 20WRB 1 2025 1003 1 8 0 112 Gen-112
## 9 9 20WRB 1 2025 1004 1 9 1 1 Check-1
## 10 10 20WRB 1 2025 1005 1 10 0 128 Gen-128
## 11 11 20WRC 1 2025 2001 1 11 0 228 Gen-228
## 12 12 20WRC 1 2025 2002 1 12 0 261 Gen-261
p <- full_control_positions(plt$field_book, "COLUMN", "ROW", factor_name = "TREATMENT", label = "TREATMENT")
p
# Example 6: Augmented Randomized Complete Block Design (ARCBD)
##
## RCBD_g> # Example 1: Generates an ARCBD with 6 blocks, 3 checks for each, and 50 treatments
## RCBD_g> # in two locations.
## RCBD_g> ARCBD1 <- RCBD_augmented(lines = 50, checks = 3, b = 6, l = 2,
## RCBD_g+ planter = "cartesian",
## RCBD_g+ plotNumber = c(1,1001),
## RCBD_g+ seed = 23,
## RCBD_g+ locationNames = c("FARGO", "MINOT"))
##
## RCBD_g> ARCBD1$infoDesign
## $rows
## [1] 6
##
## $columns
## [1] 12
##
## $rows_within_blocks
## [1] 1
##
## $columns_within_blocks
## [1] 12
##
## $treatments
## [1] 50
##
## $checks
## [1] 3
##
## $blocks
## [1] 6
##
## $plots_per_block
## [1] 12 12 12 12 12 8
##
## $locations
## [1] 2
##
## $fillers
## [1] 4
##
## $seed
## [1] 23
##
## $id_design
## [1] 14
##
##
## RCBD_g> ARCBD1$layoutRandom
## Col1 Col2 Col3 Col4 Col5 Col6 Col7 Col8 Col9 Col10 Col11 Col12
## Row6 2 15 38 3 21 36 26 1 0 0 0 0
## Row5 3 1 24 46 11 2 48 37 32 31 20 42
## Row4 34 25 16 41 9 50 2 43 39 1 13 3
## Row3 18 28 5 2 40 8 30 17 53 10 3 1
## Row2 7 29 12 2 3 33 22 23 4 47 19 1
## Row1 49 14 27 3 2 45 6 35 52 44 51 1
##
## RCBD_g> ARCBD1$exptNames
## V1 V2 V3 V4 V5 V6 V7 V8 V9 V10 V11 V12
## 1 Expt1 Expt1 Expt1 Expt1 Expt1 Expt1 Expt1 Expt1 Expt1 Expt1 Expt1 Expt1
## 2 Expt1 Expt1 Expt1 Expt1 Expt1 Expt1 Expt1 Expt1 Expt1 Expt1 Expt1 Expt1
## 3 Expt1 Expt1 Expt1 Expt1 Expt1 Expt1 Expt1 Expt1 Expt1 Expt1 Expt1 Expt1
## 4 Expt1 Expt1 Expt1 Expt1 Expt1 Expt1 Expt1 Expt1 Expt1 Expt1 Expt1 Expt1
## 5 Expt1 Expt1 Expt1 Expt1 Expt1 Expt1 Expt1 Expt1 Expt1 Expt1 Expt1 Expt1
## 6 Expt1 Expt1 Expt1 Expt1 Expt1 Expt1 Expt1 Expt1 Expt1 Expt1 Expt1 Expt1
##
## RCBD_g> ARCBD1$plotNumber
## V1 V2 V3 V4 V5 V6 V7 V8 V9 V10 V11 V12
## [1,] 61 62 63 64 65 66 67 68 0 0 0 0
## [2,] 49 50 51 52 53 54 55 56 57 58 59 60
## [3,] 37 38 39 40 41 42 43 44 45 46 47 48
## [4,] 25 26 27 28 29 30 31 32 33 34 35 36
## [5,] 13 14 15 16 17 18 19 20 21 22 23 24
## [6,] 1 2 3 4 5 6 7 8 9 10 11 12
##
## RCBD_g> head(ARCBD1$fieldBook, 12)
## ID EXPT LOCATION YEAR PLOT ROW COLUMN CHECKS BLOCK ENTRY TREATMENT
## 1 1 Expt1 FARGO 2025 1 1 1 0 1 49 G49
## 2 2 Expt1 FARGO 2025 2 1 2 0 1 14 G14
## 3 3 Expt1 FARGO 2025 3 1 3 0 1 27 G27
## 4 4 Expt1 FARGO 2025 4 1 4 1 1 3 CH3
## 5 5 Expt1 FARGO 2025 5 1 5 1 1 2 CH2
## 6 6 Expt1 FARGO 2025 6 1 6 0 1 45 G45
## 7 7 Expt1 FARGO 2025 7 1 7 0 1 6 G6
## 8 8 Expt1 FARGO 2025 8 1 8 0 1 35 G35
## 9 9 Expt1 FARGO 2025 9 1 9 0 1 52 G52
## 10 10 Expt1 FARGO 2025 10 1 10 0 1 44 G44
## 11 11 Expt1 FARGO 2025 11 1 11 0 1 51 G51
## 12 12 Expt1 FARGO 2025 12 1 12 1 1 1 CH1
##
## RCBD_g> # Example 2: Generates an ARCBD with 17 blocks, 4 checks for each, and 350 treatments
## RCBD_g> # in 3 locations.
## RCBD_g> # In this case, we show how to use the option data.
## RCBD_g> checks <- 4;
##
## RCBD_g> list_checks <- paste("CH", 1:checks, sep = "")
##
## RCBD_g> treatments <- paste("G", 5:354, sep = "")
##
## RCBD_g> treatment_list <- data.frame(list(ENTRY = 1:354, NAME = c(list_checks, treatments)))
##
## RCBD_g> head(treatment_list, 12)
## ENTRY NAME
## 1 1 CH1
## 2 2 CH2
## 3 3 CH3
## 4 4 CH4
## 5 5 G5
## 6 6 G6
## 7 7 G7
## 8 8 G8
## 9 9 G9
## 10 10 G10
## 11 11 G11
## 12 12 G12
##
## RCBD_g> ARCBD2 <- RCBD_augmented(lines = 350, checks = 4, b = 17, l = 3,
## RCBD_g+ planter = "serpentine",
## RCBD_g+ plotNumber = c(101,1001,2001),
## RCBD_g+ seed = 24,
## RCBD_g+ locationNames = LETTERS[1:3],
## RCBD_g+ data = treatment_list)
##
## RCBD_g> ARCBD2$infoDesign
## $rows
## [1] 17
##
## $columns
## [1] 25
##
## $rows_within_blocks
## [1] 1
##
## $columns_within_blocks
## [1] 25
##
## $treatments
## [1] 350
##
## $checks
## [1] 4
##
## $blocks
## [1] 17
##
## $plots_per_block
## [1] 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 25 18
##
## $locations
## [1] 3
##
## $fillers
## [1] 7
##
## $seed
## [1] 24
##
## $id_design
## [1] 14
##
##
## RCBD_g> ARCBD2$layoutRandom
## Col1 Col2 Col3 Col4 Col5 Col6 Col7 Col8 Col9 Col10 Col11 Col12 Col13
## Row17 257 259 1 198 331 66 3 238 170 176 126 207 225
## Row16 17 12 1 314 22 235 77 340 188 76 101 2 16
## Row15 229 231 54 3 305 4 128 50 30 55 1 337 24
## Row14 63 45 62 40 140 322 82 228 283 142 53 211 7
## Row13 253 2 68 113 13 279 47 57 4 132 3 167 159
## Row12 282 205 192 324 315 2 247 124 179 58 105 273 31
## Row11 110 125 85 332 250 248 265 255 2 251 52 42 236
## Row10 173 154 338 327 78 3 96 177 193 4 244 191 348
## Row9 4 2 25 103 36 155 260 246 189 49 197 284 242
## Row8 107 321 186 4 163 33 71 109 100 174 309 18 135
## Row7 2 239 252 213 261 150 3 266 277 307 4 95 311
## Row6 133 75 153 102 274 2 4 1 270 285 3 240 276
## Row5 234 56 349 288 202 300 79 87 157 64 168 1 4
## Row4 160 195 2 289 161 83 143 271 141 144 94 320 3
## Row3 268 209 4 185 308 115 81 342 249 258 120 1 2
## Row2 172 347 346 215 298 86 1 116 328 224 139 3 4
## Row1 345 130 4 162 1 123 2 39 9 302 210 352 138
## Col14 Col15 Col16 Col17 Col18 Col19 Col20 Col21 Col22 Col23 Col24 Col25
## Row17 122 4 208 187 2 0 0 0 0 0 0 0
## Row16 3 219 111 291 316 4 341 169 222 237 65 281
## Row15 329 263 2 74 108 318 350 147 306 325 37 43
## Row14 136 310 5 2 199 1 4 164 46 3 158 223
## Row13 23 1 148 117 201 28 11 119 190 73 72 99
## Row12 32 1 27 243 241 21 3 303 4 106 127 254
## Row11 35 1 216 61 3 4 230 69 245 339 98 14
## Row10 1 227 323 2 203 118 181 88 104 10 272 175
## Row9 335 217 319 200 3 152 97 267 44 275 92 1
## Row8 221 333 121 2 1 3 214 226 183 15 194 351
## Row7 313 60 293 38 59 67 232 134 1 178 93 114
## Row6 156 41 165 146 51 317 292 280 343 171 334 84
## Row5 220 34 131 262 3 180 129 145 2 212 91 278
## Row4 19 353 301 6 4 206 304 1 233 354 166 20
## Row3 294 89 269 29 26 286 290 336 80 3 149 312
## Row2 48 295 151 287 2 326 70 264 204 137 296 8
## Row1 297 330 3 256 90 184 196 218 344 299 182 112
##
## RCBD_g> ARCBD2$exptNames
## V1 V2 V3 V4 V5 V6 V7 V8 V9 V10 V11 V12
## 1 Expt1 Expt1 Expt1 Expt1 Expt1 Expt1 Expt1 Expt1 Expt1 Expt1 Expt1 Expt1
## 2 Expt1 Expt1 Expt1 Expt1 Expt1 Expt1 Expt1 Expt1 Expt1 Expt1 Expt1 Expt1
## 3 Expt1 Expt1 Expt1 Expt1 Expt1 Expt1 Expt1 Expt1 Expt1 Expt1 Expt1 Expt1
## 4 Expt1 Expt1 Expt1 Expt1 Expt1 Expt1 Expt1 Expt1 Expt1 Expt1 Expt1 Expt1
## 5 Expt1 Expt1 Expt1 Expt1 Expt1 Expt1 Expt1 Expt1 Expt1 Expt1 Expt1 Expt1
## 6 Expt1 Expt1 Expt1 Expt1 Expt1 Expt1 Expt1 Expt1 Expt1 Expt1 Expt1 Expt1
## 7 Expt1 Expt1 Expt1 Expt1 Expt1 Expt1 Expt1 Expt1 Expt1 Expt1 Expt1 Expt1
## 8 Expt1 Expt1 Expt1 Expt1 Expt1 Expt1 Expt1 Expt1 Expt1 Expt1 Expt1 Expt1
## 9 Expt1 Expt1 Expt1 Expt1 Expt1 Expt1 Expt1 Expt1 Expt1 Expt1 Expt1 Expt1
## 10 Expt1 Expt1 Expt1 Expt1 Expt1 Expt1 Expt1 Expt1 Expt1 Expt1 Expt1 Expt1
## 11 Expt1 Expt1 Expt1 Expt1 Expt1 Expt1 Expt1 Expt1 Expt1 Expt1 Expt1 Expt1
## 12 Expt1 Expt1 Expt1 Expt1 Expt1 Expt1 Expt1 Expt1 Expt1 Expt1 Expt1 Expt1
## 13 Expt1 Expt1 Expt1 Expt1 Expt1 Expt1 Expt1 Expt1 Expt1 Expt1 Expt1 Expt1
## 14 Expt1 Expt1 Expt1 Expt1 Expt1 Expt1 Expt1 Expt1 Expt1 Expt1 Expt1 Expt1
## 15 Expt1 Expt1 Expt1 Expt1 Expt1 Expt1 Expt1 Expt1 Expt1 Expt1 Expt1 Expt1
## 16 Expt1 Expt1 Expt1 Expt1 Expt1 Expt1 Expt1 Expt1 Expt1 Expt1 Expt1 Expt1
## 17 Expt1 Expt1 Expt1 Expt1 Expt1 Expt1 Expt1 Expt1 Expt1 Expt1 Expt1 Expt1
## V13 V14 V15 V16 V17 V18 V19 V20 V21 V22 V23 V24
## 1 Expt1 Expt1 Expt1 Expt1 Expt1 Expt1 Expt1 Expt1 Expt1 Expt1 Expt1 Expt1
## 2 Expt1 Expt1 Expt1 Expt1 Expt1 Expt1 Expt1 Expt1 Expt1 Expt1 Expt1 Expt1
## 3 Expt1 Expt1 Expt1 Expt1 Expt1 Expt1 Expt1 Expt1 Expt1 Expt1 Expt1 Expt1
## 4 Expt1 Expt1 Expt1 Expt1 Expt1 Expt1 Expt1 Expt1 Expt1 Expt1 Expt1 Expt1
## 5 Expt1 Expt1 Expt1 Expt1 Expt1 Expt1 Expt1 Expt1 Expt1 Expt1 Expt1 Expt1
## 6 Expt1 Expt1 Expt1 Expt1 Expt1 Expt1 Expt1 Expt1 Expt1 Expt1 Expt1 Expt1
## 7 Expt1 Expt1 Expt1 Expt1 Expt1 Expt1 Expt1 Expt1 Expt1 Expt1 Expt1 Expt1
## 8 Expt1 Expt1 Expt1 Expt1 Expt1 Expt1 Expt1 Expt1 Expt1 Expt1 Expt1 Expt1
## 9 Expt1 Expt1 Expt1 Expt1 Expt1 Expt1 Expt1 Expt1 Expt1 Expt1 Expt1 Expt1
## 10 Expt1 Expt1 Expt1 Expt1 Expt1 Expt1 Expt1 Expt1 Expt1 Expt1 Expt1 Expt1
## 11 Expt1 Expt1 Expt1 Expt1 Expt1 Expt1 Expt1 Expt1 Expt1 Expt1 Expt1 Expt1
## 12 Expt1 Expt1 Expt1 Expt1 Expt1 Expt1 Expt1 Expt1 Expt1 Expt1 Expt1 Expt1
## 13 Expt1 Expt1 Expt1 Expt1 Expt1 Expt1 Expt1 Expt1 Expt1 Expt1 Expt1 Expt1
## 14 Expt1 Expt1 Expt1 Expt1 Expt1 Expt1 Expt1 Expt1 Expt1 Expt1 Expt1 Expt1
## 15 Expt1 Expt1 Expt1 Expt1 Expt1 Expt1 Expt1 Expt1 Expt1 Expt1 Expt1 Expt1
## 16 Expt1 Expt1 Expt1 Expt1 Expt1 Expt1 Expt1 Expt1 Expt1 Expt1 Expt1 Expt1
## 17 Expt1 Expt1 Expt1 Expt1 Expt1 Expt1 Expt1 Expt1 Expt1 Expt1 Expt1 Expt1
## V25
## 1 Expt1
## 2 Expt1
## 3 Expt1
## 4 Expt1
## 5 Expt1
## 6 Expt1
## 7 Expt1
## 8 Expt1
## 9 Expt1
## 10 Expt1
## 11 Expt1
## 12 Expt1
## 13 Expt1
## 14 Expt1
## 15 Expt1
## 16 Expt1
## 17 Expt1
##
## RCBD_g> ARCBD2$plotNumber
## V1 V2 V3 V4 V5 V6 V7 V8 V9 V10 V11 V12 V13 V14 V15 V16 V17 V18
## [1,] 501 502 503 504 505 506 507 508 509 510 511 512 513 514 515 516 517 518
## [2,] 500 499 498 497 496 495 494 493 492 491 490 489 488 487 486 485 484 483
## [3,] 451 452 453 454 455 456 457 458 459 460 461 462 463 464 465 466 467 468
## [4,] 450 449 448 447 446 445 444 443 442 441 440 439 438 437 436 435 434 433
## [5,] 401 402 403 404 405 406 407 408 409 410 411 412 413 414 415 416 417 418
## [6,] 400 399 398 397 396 395 394 393 392 391 390 389 388 387 386 385 384 383
## [7,] 351 352 353 354 355 356 357 358 359 360 361 362 363 364 365 366 367 368
## [8,] 350 349 348 347 346 345 344 343 342 341 340 339 338 337 336 335 334 333
## [9,] 301 302 303 304 305 306 307 308 309 310 311 312 313 314 315 316 317 318
## [10,] 300 299 298 297 296 295 294 293 292 291 290 289 288 287 286 285 284 283
## [11,] 251 252 253 254 255 256 257 258 259 260 261 262 263 264 265 266 267 268
## [12,] 250 249 248 247 246 245 244 243 242 241 240 239 238 237 236 235 234 233
## [13,] 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218
## [14,] 200 199 198 197 196 195 194 193 192 191 190 189 188 187 186 185 184 183
## [15,] 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168
## [16,] 150 149 148 147 146 145 144 143 142 141 140 139 138 137 136 135 134 133
## [17,] 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118
## V19 V20 V21 V22 V23 V24 V25
## [1,] 0 0 0 0 0 0 0
## [2,] 482 481 480 479 478 477 476
## [3,] 469 470 471 472 473 474 475
## [4,] 432 431 430 429 428 427 426
## [5,] 419 420 421 422 423 424 425
## [6,] 382 381 380 379 378 377 376
## [7,] 369 370 371 372 373 374 375
## [8,] 332 331 330 329 328 327 326
## [9,] 319 320 321 322 323 324 325
## [10,] 282 281 280 279 278 277 276
## [11,] 269 270 271 272 273 274 275
## [12,] 232 231 230 229 228 227 226
## [13,] 219 220 221 222 223 224 225
## [14,] 182 181 180 179 178 177 176
## [15,] 169 170 171 172 173 174 175
## [16,] 132 131 130 129 128 127 126
## [17,] 119 120 121 122 123 124 125
##
## RCBD_g> head(ARCBD2$fieldBook, 12)
## ID EXPT LOCATION YEAR PLOT ROW COLUMN CHECKS BLOCK ENTRY TREATMENT
## 1 1 Expt1 A 2025 101 1 1 0 1 345 G345
## 2 2 Expt1 A 2025 102 1 2 0 1 130 G130
## 3 3 Expt1 A 2025 103 1 3 1 1 4 CH4
## 4 4 Expt1 A 2025 104 1 4 0 1 162 G162
## 5 5 Expt1 A 2025 105 1 5 1 1 1 CH1
## 6 6 Expt1 A 2025 106 1 6 0 1 123 G123
## 7 7 Expt1 A 2025 107 1 7 1 1 2 CH2
## 8 8 Expt1 A 2025 108 1 8 0 1 39 G39
## 9 9 Expt1 A 2025 109 1 9 0 1 9 G9
## 10 10 Expt1 A 2025 110 1 10 0 1 302 G302
## 11 11 Expt1 A 2025 111 1 11 0 1 210 G210
## 12 12 Expt1 A 2025 112 1 12 0 1 352 G352
p <- full_control_positions(plt$field_book[plt$field_book$LOCATION == "A",], "COLUMN", "ROW", factor_name = "TREATMENT", label = "TREATMENT") + guides(fill = "none")
p
# Example 7: Rectangular Lattice Design
##
## rctng_> # Example 1: Generates a rectangular lattice design with 6 full blocks, 4 units per IBlock (k)
## rctng_> # and 20 treatments in one location.
## rctng_> rectangularLattice1 <- rectangular_lattice(t = 20, k = 4, r = 6, l = 1,
## rctng_+ plotNumber = 101,
## rctng_+ locationNames = "FARGO",
## rctng_+ seed = 126)
##
## rctng_> rectangularLattice1$infoDesign
## $Reps
## [1] 6
##
## $iBlocks
## [1] 5
##
## $NumberTreatments
## [1] 20
##
## $NumberLocations
## [1] 1
##
## $Locations
## [1] "FARGO"
##
## $seed
## [1] 126
##
## $lambda
## [1] 0.9473684
##
## $id_design
## [1] 11
##
##
## rctng_> head(rectangularLattice1$fieldBook,12)
## ID LOCATION PLOT REP IBLOCK UNIT ENTRY TREATMENT
## 1 1 FARGO 101 1 1 1 15 G-15
## 2 2 FARGO 102 1 1 2 13 G-13
## 3 3 FARGO 103 1 1 3 12 G-12
## 4 4 FARGO 104 1 1 4 16 G-16
## 5 5 FARGO 105 1 2 1 19 G-19
## 6 6 FARGO 106 1 2 2 17 G-17
## 7 7 FARGO 107 1 2 3 10 G-10
## 8 8 FARGO 108 1 2 4 14 G-14
## 9 9 FARGO 109 1 3 1 11 G-11
## 10 10 FARGO 110 1 3 2 8 G-8
## 11 11 FARGO 111 1 3 3 5 G-5
## 12 12 FARGO 112 1 3 4 20 G-20
##
## rctng_> # Example 2: Generates a rectangular lattice design with 5 full blocks, 7 units per IBlock (k)
## rctng_> # and 56 treatments across 2 locations.
## rctng_> # In this case, we show how to use the option data.
## rctng_> treatments <- paste("ND-", 1:56, sep = "")
##
## rctng_> ENTRY <- 1:56
##
## rctng_> treatment_list <- data.frame(list(ENTRY = ENTRY, TREATMENT = treatments))
##
## rctng_> head(treatment_list)
## ENTRY TREATMENT
## 1 1 ND-1
## 2 2 ND-2
## 3 3 ND-3
## 4 4 ND-4
## 5 5 ND-5
## 6 6 ND-6
##
## rctng_> rectangularLattice2 <- rectangular_lattice(t = 56, k = 7, r = 5, l = 2,
## rctng_+ plotNumber = c(1001,2001),
## rctng_+ locationNames = c("Loc1", "Loc2"),
## rctng_+ seed = 127,
## rctng_+ data = treatment_list)
##
## rctng_> rectangularLattice2$infoDesign
## $Reps
## [1] 5
##
## $iBlocks
## [1] 8
##
## $NumberTreatments
## [1] 56
##
## $NumberLocations
## [1] 2
##
## $Locations
## [1] "LOC1" "LOC2"
##
## $seed
## [1] 127
##
## $lambda
## [1] 0.5454545
##
## $id_design
## [1] 11
##
##
## rctng_> head(rectangularLattice2$fieldBook,12)
## ID LOCATION PLOT REP IBLOCK UNIT ENTRY TREATMENT
## 1 1 LOC1 1001 1 1 1 8 ND-8
## 2 2 LOC1 1002 1 1 2 35 ND-35
## 3 3 LOC1 1003 1 1 3 52 ND-52
## 4 4 LOC1 1004 1 1 4 29 ND-29
## 5 5 LOC1 1005 1 1 5 41 ND-41
## 6 6 LOC1 1006 1 1 6 22 ND-22
## 7 7 LOC1 1007 1 1 7 10 ND-10
## 8 8 LOC1 1008 1 2 1 45 ND-45
## 9 9 LOC1 1009 1 2 2 11 ND-11
## 10 10 LOC1 1010 1 2 3 6 ND-6
## 11 11 LOC1 1011 1 2 4 25 ND-25
## 12 12 LOC1 1012 1 2 5 34 ND-34
p <- full_control_positions(plt$field_book[plt$field_book$LOCATION == "LOC1",], "COLUMN", "ROW", factor_name = "TREATMENT", label = "TREATMENT")
p
# Example 8: Strip Plot Design
##
## strp_p> # Example 1: Generates a strip plot design with 5 vertical strips and 4 horizontal strips,
## strp_p> # with 3 reps in one location.
## strp_p> H <- paste("H", 1:4, sep = "")
##
## strp_p> V <- paste("V", 1:5, sep = "")
##
## strp_p> strip1 <- strip_plot(Hplots = H,
## strp_p+ Vplots = V,
## strp_p+ b = 3,
## strp_p+ l = 1,
## strp_p+ plotNumber = 101,
## strp_p+ planter = "serpentine",
## strp_p+ locationNames = "A",
## strp_p+ seed = 333)
##
## strp_p> strip1$infoDesign
## $Hplots
## [1] 4
##
## $Vplots
## [1] 5
##
## $blocks
## [1] 3
##
## $numberLocations
## [1] 1
##
## $nameLocations
## [1] "A"
##
## $seed
## [1] 333
##
## $id_design
## [1] 7
##
##
## strp_p> strip1$stripsBlockLoc
## $Loc_A
## $Loc_A$rep1
## V4 V2 V5 V1 V3
## H2 "H2|V4" "H2|V2" "H2|V5" "H2|V1" "H2|V3"
## H1 "H1|V4" "H1|V2" "H1|V5" "H1|V1" "H1|V3"
## H3 "H3|V4" "H3|V2" "H3|V5" "H3|V1" "H3|V3"
## H4 "H4|V4" "H4|V2" "H4|V5" "H4|V1" "H4|V3"
##
## $Loc_A$rep2
## V1 V3 V4 V2 V5
## H3 "H3|V1" "H3|V3" "H3|V4" "H3|V2" "H3|V5"
## H4 "H4|V1" "H4|V3" "H4|V4" "H4|V2" "H4|V5"
## H2 "H2|V1" "H2|V3" "H2|V4" "H2|V2" "H2|V5"
## H1 "H1|V1" "H1|V3" "H1|V4" "H1|V2" "H1|V5"
##
## $Loc_A$rep3
## V3 V1 V2 V4 V5
## H2 "H2|V3" "H2|V1" "H2|V2" "H2|V4" "H2|V5"
## H1 "H1|V3" "H1|V1" "H1|V2" "H1|V4" "H1|V5"
## H4 "H4|V3" "H4|V1" "H4|V2" "H4|V4" "H4|V5"
## H3 "H3|V3" "H3|V1" "H3|V2" "H3|V4" "H3|V5"
##
##
##
## strp_p> strip1$plotLayouts
## $Loc_A
## $Loc_A$rep1
## [,1] [,2] [,3] [,4] [,5]
## [1,] 101 102 103 104 105
## [2,] 110 109 108 107 106
## [3,] 111 112 113 114 115
## [4,] 120 119 118 117 116
##
## $Loc_A$rep2
## [,1] [,2] [,3] [,4] [,5]
## [1,] 201 202 203 204 205
## [2,] 210 209 208 207 206
## [3,] 211 212 213 214 215
## [4,] 220 219 218 217 216
##
## $Loc_A$rep3
## [,1] [,2] [,3] [,4] [,5]
## [1,] 301 302 303 304 305
## [2,] 310 309 308 307 306
## [3,] 311 312 313 314 315
## [4,] 320 319 318 317 316
##
##
##
## strp_p> head(strip1$fieldBook,12)
## ID LOCATION PLOT REP HSTRIP VSTRIP TRT_COMB
## 1 1 A 101 1 H2 V4 H2|V4
## 2 2 A 102 1 H2 V2 H2|V2
## 3 3 A 103 1 H2 V5 H2|V5
## 4 4 A 104 1 H2 V1 H2|V1
## 5 5 A 105 1 H2 V3 H2|V3
## 6 6 A 110 1 H1 V4 H1|V4
## 7 7 A 109 1 H1 V2 H1|V2
## 8 8 A 108 1 H1 V5 H1|V5
## 9 9 A 107 1 H1 V1 H1|V1
## 10 10 A 106 1 H1 V3 H1|V3
## 11 11 A 111 1 H3 V4 H3|V4
## 12 12 A 112 1 H3 V2 H3|V2
##
## strp_p> # Example 2: Generates a strip plot design with 5 vertical strips and 5 horizontal strips,
## strp_p> # with 6 reps across to 3 locations. In this case, we show how to use the option data.
## strp_p> Hplots <- LETTERS[1:5]
##
## strp_p> Vplots <- LETTERS[1:4]
##
## strp_p> strip_data <- data.frame(list(HPLOTS = Hplots, VPLOTS = c(Vplots, NA)))
##
## strp_p> head(strip_data)
## HPLOTS VPLOTS
## 1 A A
## 2 B B
## 3 C C
## 4 D D
## 5 E <NA>
##
## strp_p> strip2 <- strip_plot(Hplots = 5,
## strp_p+ Vplots = 5,
## strp_p+ b = 6,
## strp_p+ l = 3,
## strp_p+ plotNumber = c(101,1001,2001),
## strp_p+ planter = "cartesian",
## strp_p+ locationNames = c("A", "B", "C"),
## strp_p+ seed = 222,
## strp_p+ data = strip_data)
##
## strp_p> strip2$infoDesign
## $Hplots
## [1] 5
##
## $Vplots
## [1] 4
##
## $blocks
## [1] 6
##
## $numberLocations
## [1] 3
##
## $nameLocations
## [1] "A" "B" "C"
##
## $seed
## [1] 222
##
## $id_design
## [1] 7
##
##
## strp_p> strip2$stripsBlockLoc
## $Loc_A
## $Loc_A$rep1
## D B C A
## E "E|D" "E|B" "E|C" "E|A"
## B "B|D" "B|B" "B|C" "B|A"
## C "C|D" "C|B" "C|C" "C|A"
## D "D|D" "D|B" "D|C" "D|A"
## A "A|D" "A|B" "A|C" "A|A"
##
## $Loc_A$rep2
## D B C A
## A "A|D" "A|B" "A|C" "A|A"
## B "B|D" "B|B" "B|C" "B|A"
## E "E|D" "E|B" "E|C" "E|A"
## D "D|D" "D|B" "D|C" "D|A"
## C "C|D" "C|B" "C|C" "C|A"
##
## $Loc_A$rep3
## A D C B
## A "A|A" "A|D" "A|C" "A|B"
## D "D|A" "D|D" "D|C" "D|B"
## E "E|A" "E|D" "E|C" "E|B"
## B "B|A" "B|D" "B|C" "B|B"
## C "C|A" "C|D" "C|C" "C|B"
##
## $Loc_A$rep4
## A B C D
## A "A|A" "A|B" "A|C" "A|D"
## C "C|A" "C|B" "C|C" "C|D"
## E "E|A" "E|B" "E|C" "E|D"
## B "B|A" "B|B" "B|C" "B|D"
## D "D|A" "D|B" "D|C" "D|D"
##
## $Loc_A$rep5
## A C D B
## B "B|A" "B|C" "B|D" "B|B"
## C "C|A" "C|C" "C|D" "C|B"
## E "E|A" "E|C" "E|D" "E|B"
## A "A|A" "A|C" "A|D" "A|B"
## D "D|A" "D|C" "D|D" "D|B"
##
## $Loc_A$rep6
## B C D A
## D "D|B" "D|C" "D|D" "D|A"
## E "E|B" "E|C" "E|D" "E|A"
## B "B|B" "B|C" "B|D" "B|A"
## C "C|B" "C|C" "C|D" "C|A"
## A "A|B" "A|C" "A|D" "A|A"
##
##
## $Loc_B
## $Loc_B$rep1
## B C D A
## B "B|B" "B|C" "B|D" "B|A"
## D "D|B" "D|C" "D|D" "D|A"
## E "E|B" "E|C" "E|D" "E|A"
## A "A|B" "A|C" "A|D" "A|A"
## C "C|B" "C|C" "C|D" "C|A"
##
## $Loc_B$rep2
## D C A B
## D "D|D" "D|C" "D|A" "D|B"
## A "A|D" "A|C" "A|A" "A|B"
## C "C|D" "C|C" "C|A" "C|B"
## E "E|D" "E|C" "E|A" "E|B"
## B "B|D" "B|C" "B|A" "B|B"
##
## $Loc_B$rep3
## D A C B
## B "B|D" "B|A" "B|C" "B|B"
## E "E|D" "E|A" "E|C" "E|B"
## C "C|D" "C|A" "C|C" "C|B"
## D "D|D" "D|A" "D|C" "D|B"
## A "A|D" "A|A" "A|C" "A|B"
##
## $Loc_B$rep4
## C D A B
## D "D|C" "D|D" "D|A" "D|B"
## C "C|C" "C|D" "C|A" "C|B"
## E "E|C" "E|D" "E|A" "E|B"
## A "A|C" "A|D" "A|A" "A|B"
## B "B|C" "B|D" "B|A" "B|B"
##
## $Loc_B$rep5
## A B D C
## D "D|A" "D|B" "D|D" "D|C"
## C "C|A" "C|B" "C|D" "C|C"
## A "A|A" "A|B" "A|D" "A|C"
## B "B|A" "B|B" "B|D" "B|C"
## E "E|A" "E|B" "E|D" "E|C"
##
## $Loc_B$rep6
## C D B A
## B "B|C" "B|D" "B|B" "B|A"
## D "D|C" "D|D" "D|B" "D|A"
## A "A|C" "A|D" "A|B" "A|A"
## C "C|C" "C|D" "C|B" "C|A"
## E "E|C" "E|D" "E|B" "E|A"
##
##
## $Loc_C
## $Loc_C$rep1
## D A C B
## D "D|D" "D|A" "D|C" "D|B"
## B "B|D" "B|A" "B|C" "B|B"
## E "E|D" "E|A" "E|C" "E|B"
## A "A|D" "A|A" "A|C" "A|B"
## C "C|D" "C|A" "C|C" "C|B"
##
## $Loc_C$rep2
## B C A D
## B "B|B" "B|C" "B|A" "B|D"
## A "A|B" "A|C" "A|A" "A|D"
## D "D|B" "D|C" "D|A" "D|D"
## C "C|B" "C|C" "C|A" "C|D"
## E "E|B" "E|C" "E|A" "E|D"
##
## $Loc_C$rep3
## C D A B
## E "E|C" "E|D" "E|A" "E|B"
## C "C|C" "C|D" "C|A" "C|B"
## D "D|C" "D|D" "D|A" "D|B"
## A "A|C" "A|D" "A|A" "A|B"
## B "B|C" "B|D" "B|A" "B|B"
##
## $Loc_C$rep4
## C D B A
## D "D|C" "D|D" "D|B" "D|A"
## A "A|C" "A|D" "A|B" "A|A"
## B "B|C" "B|D" "B|B" "B|A"
## E "E|C" "E|D" "E|B" "E|A"
## C "C|C" "C|D" "C|B" "C|A"
##
## $Loc_C$rep5
## A B D C
## B "B|A" "B|B" "B|D" "B|C"
## D "D|A" "D|B" "D|D" "D|C"
## A "A|A" "A|B" "A|D" "A|C"
## E "E|A" "E|B" "E|D" "E|C"
## C "C|A" "C|B" "C|D" "C|C"
##
## $Loc_C$rep6
## C D A B
## B "B|C" "B|D" "B|A" "B|B"
## E "E|C" "E|D" "E|A" "E|B"
## A "A|C" "A|D" "A|A" "A|B"
## D "D|C" "D|D" "D|A" "D|B"
## C "C|C" "C|D" "C|A" "C|B"
##
##
##
## strp_p> strip2$plotLayouts
## $Loc_A
## $Loc_A$rep1
## [,1] [,2] [,3] [,4]
## [1,] 101 102 103 104
## [2,] 105 106 107 108
## [3,] 109 110 111 112
## [4,] 113 114 115 116
## [5,] 117 118 119 120
##
## $Loc_A$rep2
## [,1] [,2] [,3] [,4]
## [1,] 201 202 203 204
## [2,] 205 206 207 208
## [3,] 209 210 211 212
## [4,] 213 214 215 216
## [5,] 217 218 219 220
##
## $Loc_A$rep3
## [,1] [,2] [,3] [,4]
## [1,] 301 302 303 304
## [2,] 305 306 307 308
## [3,] 309 310 311 312
## [4,] 313 314 315 316
## [5,] 317 318 319 320
##
## $Loc_A$rep4
## [,1] [,2] [,3] [,4]
## [1,] 401 402 403 404
## [2,] 405 406 407 408
## [3,] 409 410 411 412
## [4,] 413 414 415 416
## [5,] 417 418 419 420
##
## $Loc_A$rep5
## [,1] [,2] [,3] [,4]
## [1,] 501 502 503 504
## [2,] 505 506 507 508
## [3,] 509 510 511 512
## [4,] 513 514 515 516
## [5,] 517 518 519 520
##
## $Loc_A$rep6
## [,1] [,2] [,3] [,4]
## [1,] 601 602 603 604
## [2,] 605 606 607 608
## [3,] 609 610 611 612
## [4,] 613 614 615 616
## [5,] 617 618 619 620
##
##
## $Loc_B
## $Loc_B$rep1
## [,1] [,2] [,3] [,4]
## [1,] 1001 1002 1003 1004
## [2,] 1005 1006 1007 1008
## [3,] 1009 1010 1011 1012
## [4,] 1013 1014 1015 1016
## [5,] 1017 1018 1019 1020
##
## $Loc_B$rep2
## [,1] [,2] [,3] [,4]
## [1,] 1101 1102 1103 1104
## [2,] 1105 1106 1107 1108
## [3,] 1109 1110 1111 1112
## [4,] 1113 1114 1115 1116
## [5,] 1117 1118 1119 1120
##
## $Loc_B$rep3
## [,1] [,2] [,3] [,4]
## [1,] 1201 1202 1203 1204
## [2,] 1205 1206 1207 1208
## [3,] 1209 1210 1211 1212
## [4,] 1213 1214 1215 1216
## [5,] 1217 1218 1219 1220
##
## $Loc_B$rep4
## [,1] [,2] [,3] [,4]
## [1,] 1301 1302 1303 1304
## [2,] 1305 1306 1307 1308
## [3,] 1309 1310 1311 1312
## [4,] 1313 1314 1315 1316
## [5,] 1317 1318 1319 1320
##
## $Loc_B$rep5
## [,1] [,2] [,3] [,4]
## [1,] 1401 1402 1403 1404
## [2,] 1405 1406 1407 1408
## [3,] 1409 1410 1411 1412
## [4,] 1413 1414 1415 1416
## [5,] 1417 1418 1419 1420
##
## $Loc_B$rep6
## [,1] [,2] [,3] [,4]
## [1,] 1501 1502 1503 1504
## [2,] 1505 1506 1507 1508
## [3,] 1509 1510 1511 1512
## [4,] 1513 1514 1515 1516
## [5,] 1517 1518 1519 1520
##
##
## $Loc_C
## $Loc_C$rep1
## [,1] [,2] [,3] [,4]
## [1,] 2001 2002 2003 2004
## [2,] 2005 2006 2007 2008
## [3,] 2009 2010 2011 2012
## [4,] 2013 2014 2015 2016
## [5,] 2017 2018 2019 2020
##
## $Loc_C$rep2
## [,1] [,2] [,3] [,4]
## [1,] 2101 2102 2103 2104
## [2,] 2105 2106 2107 2108
## [3,] 2109 2110 2111 2112
## [4,] 2113 2114 2115 2116
## [5,] 2117 2118 2119 2120
##
## $Loc_C$rep3
## [,1] [,2] [,3] [,4]
## [1,] 2201 2202 2203 2204
## [2,] 2205 2206 2207 2208
## [3,] 2209 2210 2211 2212
## [4,] 2213 2214 2215 2216
## [5,] 2217 2218 2219 2220
##
## $Loc_C$rep4
## [,1] [,2] [,3] [,4]
## [1,] 2301 2302 2303 2304
## [2,] 2305 2306 2307 2308
## [3,] 2309 2310 2311 2312
## [4,] 2313 2314 2315 2316
## [5,] 2317 2318 2319 2320
##
## $Loc_C$rep5
## [,1] [,2] [,3] [,4]
## [1,] 2401 2402 2403 2404
## [2,] 2405 2406 2407 2408
## [3,] 2409 2410 2411 2412
## [4,] 2413 2414 2415 2416
## [5,] 2417 2418 2419 2420
##
## $Loc_C$rep6
## [,1] [,2] [,3] [,4]
## [1,] 2501 2502 2503 2504
## [2,] 2505 2506 2507 2508
## [3,] 2509 2510 2511 2512
## [4,] 2513 2514 2515 2516
## [5,] 2517 2518 2519 2520
##
##
##
## strp_p> head(strip2$fieldBook,12)
## ID LOCATION PLOT REP HSTRIP VSTRIP TRT_COMB
## 1 1 A 101 1 E D E|D
## 2 2 A 102 1 E B E|B
## 3 3 A 103 1 E C E|C
## 4 4 A 104 1 E A E|A
## 5 5 A 105 1 B D B|D
## 6 6 A 106 1 B B B|B
## 7 7 A 107 1 B C B|C
## 8 8 A 108 1 B A B|A
## 9 9 A 109 1 C D C|D
## 10 10 A 110 1 C B C|B
## 11 11 A 111 1 C C C|C
## 12 12 A 112 1 C A C|A
p <- full_control_positions(plt$field_book[plt$field_book$LOCATION == "A",], "COLUMN", "ROW", factor_name = "TRT_COMB", label = "TRT_COMB")
p
# Example 9: Square Lattice Design
##
## sqr_lt> # Example 1: Generates a square lattice design with 5 full blocks, 8 units per IBlock,
## sqr_lt> # 8 IBlocks for a square number of treatmens of 64 in two locations.
## sqr_lt> squareLattice1 <- square_lattice(t = 64, k = 8, r = 5, l = 2,
## sqr_lt+ plotNumber = c(1001, 2001),
## sqr_lt+ locationNames = c("FARGO", "MINOT"),
## sqr_lt+ seed = 1986)
##
## sqr_lt> squareLattice1$infoDesign
## $Reps
## [1] 5
##
## $IBlocks
## [1] 8
##
## $NumberTreatments
## [1] 64
##
## $NumberLocations
## [1] 2
##
## $Locations
## [1] "FARGO" "MINOT"
##
## $seed
## [1] 1986
##
## $lambda
## [1] 0.5555556
##
## $id_design
## [1] 10
##
##
## sqr_lt> head(squareLattice1$fieldBook,12)
## ID LOCATION PLOT REP IBLOCK UNIT ENTRY TREATMENT
## 1 1 FARGO 1001 1 1 1 34 G-34
## 2 2 FARGO 1002 1 1 2 58 G-58
## 3 3 FARGO 1003 1 1 3 10 G-10
## 4 4 FARGO 1004 1 1 4 18 G-18
## 5 5 FARGO 1005 1 1 5 26 G-26
## 6 6 FARGO 1006 1 1 6 2 G-2
## 7 7 FARGO 1007 1 1 7 42 G-42
## 8 8 FARGO 1008 1 1 8 50 G-50
## 9 9 FARGO 1009 1 2 1 57 G-57
## 10 10 FARGO 1010 1 2 2 25 G-25
## 11 11 FARGO 1011 1 2 3 49 G-49
## 12 12 FARGO 1012 1 2 4 41 G-41
##
## sqr_lt> # Example 2: Generates a square lattice design with 3 full blocks, 7 units per IBlock,
## sqr_lt> # 7 IBlocks for a square number of treatmens of 49 in one location.
## sqr_lt> # In this case, we show how to use the option data.
## sqr_lt> treatments <- paste("G", 1:49, sep = "")
##
## sqr_lt> ENTRY <- 1:49
##
## sqr_lt> treatment_list <- data.frame(list(ENTRY = ENTRY, TREATMENT = treatments))
##
## sqr_lt> head(treatment_list)
## ENTRY TREATMENT
## 1 1 G1
## 2 2 G2
## 3 3 G3
## 4 4 G4
## 5 5 G5
## 6 6 G6
##
## sqr_lt> squareLattice2 <- square_lattice(t = 49, k = 7, r = 3, l = 1,
## sqr_lt+ plotNumber = 1001,
## sqr_lt+ locationNames = "CASSELTON",
## sqr_lt+ seed = 1986,
## sqr_lt+ data = treatment_list)
##
## sqr_lt> squareLattice2$infoDesign
## $Reps
## [1] 3
##
## $IBlocks
## [1] 7
##
## $NumberTreatments
## [1] 49
##
## $NumberLocations
## [1] 1
##
## $Locations
## [1] "CASSELTON"
##
## $seed
## [1] 1986
##
## $lambda
## [1] 0.375
##
## $id_design
## [1] 10
##
##
## sqr_lt> head(squareLattice2$fieldBook,12)
## ID LOCATION PLOT REP IBLOCK UNIT ENTRY TREATMENT
## 1 1 CASSELTON 1001 1 1 1 39 G39
## 2 2 CASSELTON 1002 1 1 2 26 G26
## 3 3 CASSELTON 1003 1 1 3 13 G13
## 4 4 CASSELTON 1004 1 1 4 49 G49
## 5 5 CASSELTON 1005 1 1 5 16 G16
## 6 6 CASSELTON 1006 1 1 6 3 G3
## 7 7 CASSELTON 1007 1 1 7 29 G29
## 8 8 CASSELTON 1008 1 2 1 14 G14
## 9 9 CASSELTON 1009 1 2 2 27 G27
## 10 10 CASSELTON 1010 1 2 3 4 G4
## 11 11 CASSELTON 1011 1 2 4 30 G30
## 12 12 CASSELTON 1012 1 2 5 40 G40
p <- full_control_positions(plt$field_book[plt$field_book$LOCATION == "CASSELTON",], "COLUMN", "ROW", factor_name = "TREATMENT", label = "TREATMENT")
p
# Example 10: Split Plot Design
##
## splt_p> # Example 1: Generates a split plot design SPD with 4 whole plots, 2 sub plots per whole plot,
## splt_p> # and 4 reps in an RCBD arrangement. This in for a single location.
## splt_p> SPDExample1 <- split_plot(wp = 4, sp = 2, reps = 5, l = 1,
## splt_p+ plotNumber = 101,
## splt_p+ seed = 14,
## splt_p+ type = 2,
## splt_p+ locationNames = "FARGO")
##
## splt_p> SPDExample1$infoDesign
## $WholePlots
## [1] 1 2 3 4
##
## $SubPlots
## [1] 1 2
##
## $locationNumber
## [1] 1
##
## $locationNames
## [1] "FARGO"
##
## $plotNumbers
## [1] 101
##
## $typeDesign
## [1] "RCBD"
##
## $seed
## [1] 14
##
## $id_design
## [1] 5
##
##
## splt_p> SPDExample1$layoutlocations
## [[1]]
## PLOT REP Whole-plot Sub-plot
## [1,] "101" "1" "1" "1 2"
## [2,] "102" "1" "4" "2 1"
## [3,] "103" "1" "3" "2 1"
## [4,] "104" "1" "2" "2 1"
## [5,] "201" "2" "3" "1 2"
## [6,] "202" "2" "2" "2 1"
## [7,] "203" "2" "4" "2 1"
## [8,] "204" "2" "1" "2 1"
## [9,] "301" "3" "4" "2 1"
## [10,] "302" "3" "2" "1 2"
## [11,] "303" "3" "1" "2 1"
## [12,] "304" "3" "3" "2 1"
## [13,] "401" "4" "1" "2 1"
## [14,] "402" "4" "3" "2 1"
## [15,] "403" "4" "2" "2 1"
## [16,] "404" "4" "4" "1 2"
## [17,] "501" "5" "3" "1 2"
## [18,] "502" "5" "1" "2 1"
## [19,] "503" "5" "2" "2 1"
## [20,] "504" "5" "4" "2 1"
##
##
## splt_p> head(SPDExample1$fieldBook,12)
## ID LOCATION PLOT REP WHOLE_PLOT SUB_PLOT TRT_COMB
## 1 1 FARGO 101 1 1 1 1|1
## 2 2 FARGO 101 1 1 2 1|2
## 3 3 FARGO 102 1 4 2 4|2
## 4 4 FARGO 102 1 4 1 4|1
## 5 5 FARGO 103 1 3 2 3|2
## 6 6 FARGO 103 1 3 1 3|1
## 7 7 FARGO 104 1 2 2 2|2
## 8 8 FARGO 104 1 2 1 2|1
## 9 9 FARGO 201 2 3 1 3|1
## 10 10 FARGO 201 2 3 2 3|2
## 11 11 FARGO 202 2 2 2 2|2
## 12 12 FARGO 202 2 2 1 2|1
##
## splt_p> # Example 2: Generates a split plot design SPD with 5 whole plots
## splt_p> # (4 types of fungicide + one control), 10 sub plots per whole plot (10 bean varieties),
## splt_p> # and 6 reps in an RCBD arrangement. This in 3 locations or sites.
## splt_p> # In this case, we show how to use the option data.
## splt_p> wp <- c("NFung", paste("Fung", 1:4, sep = "")) # Fungicides (5 Whole plots)
##
## splt_p> sp <- paste("Beans", 1:10, sep = "") # Beans varieties (10 sub plots)
##
## splt_p> split_plot_Data <- data.frame(list(WHOLPLOT = c(wp, rep(NA, 5)), SUBPLOT = sp))
##
## splt_p> head(split_plot_Data, 12)
## WHOLPLOT SUBPLOT
## 1 NFung Beans1
## 2 Fung1 Beans2
## 3 Fung2 Beans3
## 4 Fung3 Beans4
## 5 Fung4 Beans5
## 6 <NA> Beans6
## 7 <NA> Beans7
## 8 <NA> Beans8
## 9 <NA> Beans9
## 10 <NA> Beans10
##
## splt_p> SPDExample2 <- split_plot(reps = 6, l = 3,
## splt_p+ plotNumber = c(101, 1001, 2001),
## splt_p+ seed = 23,
## splt_p+ type = 2,
## splt_p+ locationNames = c("A", "B", "C"),
## splt_p+ data = split_plot_Data)
##
## splt_p> SPDExample2$infoDesign
## $WholePlots
## [1] "NFung" "Fung1" "Fung2" "Fung3" "Fung4"
##
## $SubPlots
## [1] "Beans1" "Beans2" "Beans3" "Beans4" "Beans5" "Beans6" "Beans7"
## [8] "Beans8" "Beans9" "Beans10"
##
## $locationNumber
## [1] 3
##
## $locationNames
## [1] "A" "B" "C"
##
## $plotNumbers
## [1] 101 1001 2001
##
## $typeDesign
## [1] "RCBD"
##
## $seed
## [1] 23
##
## $id_design
## [1] 5
##
##
## splt_p> SPDExample2$layoutlocations
## [[1]]
## PLOT REP Whole-plot
## [1,] "101" "1" "Fung4"
## [2,] "102" "1" "Fung3"
## [3,] "103" "1" "Fung2"
## [4,] "104" "1" "Fung1"
## [5,] "105" "1" "NFung"
## [6,] "201" "2" "Fung2"
## [7,] "202" "2" "Fung4"
## [8,] "203" "2" "NFung"
## [9,] "204" "2" "Fung1"
## [10,] "205" "2" "Fung3"
## [11,] "301" "3" "NFung"
## [12,] "302" "3" "Fung2"
## [13,] "303" "3" "Fung4"
## [14,] "304" "3" "Fung3"
## [15,] "305" "3" "Fung1"
## [16,] "401" "4" "Fung3"
## [17,] "402" "4" "Fung2"
## [18,] "403" "4" "Fung1"
## [19,] "404" "4" "Fung4"
## [20,] "405" "4" "NFung"
## [21,] "501" "5" "Fung4"
## [22,] "502" "5" "Fung1"
## [23,] "503" "5" "Fung3"
## [24,] "504" "5" "NFung"
## [25,] "505" "5" "Fung2"
## [26,] "601" "6" "Fung1"
## [27,] "602" "6" "Fung2"
## [28,] "603" "6" "Fung3"
## [29,] "604" "6" "Fung4"
## [30,] "605" "6" "NFung"
## Sub-plot
## [1,] "Beans5 Beans1 Beans2 Beans3 Beans10 Beans6 Beans7 Beans9 Beans4 Beans8"
## [2,] "Beans9 Beans10 Beans8 Beans5 Beans7 Beans4 Beans2 Beans6 Beans3 Beans1"
## [3,] "Beans7 Beans10 Beans6 Beans2 Beans8 Beans3 Beans1 Beans4 Beans9 Beans5"
## [4,] "Beans7 Beans9 Beans8 Beans2 Beans3 Beans1 Beans5 Beans4 Beans6 Beans10"
## [5,] "Beans9 Beans10 Beans4 Beans3 Beans6 Beans7 Beans1 Beans2 Beans8 Beans5"
## [6,] "Beans4 Beans3 Beans9 Beans10 Beans1 Beans8 Beans5 Beans7 Beans6 Beans2"
## [7,] "Beans8 Beans7 Beans1 Beans5 Beans2 Beans10 Beans9 Beans6 Beans4 Beans3"
## [8,] "Beans8 Beans4 Beans1 Beans2 Beans9 Beans6 Beans3 Beans5 Beans10 Beans7"
## [9,] "Beans1 Beans7 Beans5 Beans4 Beans6 Beans9 Beans2 Beans8 Beans10 Beans3"
## [10,] "Beans6 Beans1 Beans4 Beans2 Beans7 Beans10 Beans3 Beans8 Beans9 Beans5"
## [11,] "Beans5 Beans3 Beans6 Beans9 Beans4 Beans1 Beans10 Beans7 Beans2 Beans8"
## [12,] "Beans3 Beans7 Beans4 Beans2 Beans8 Beans6 Beans5 Beans9 Beans10 Beans1"
## [13,] "Beans1 Beans6 Beans7 Beans9 Beans3 Beans2 Beans4 Beans5 Beans8 Beans10"
## [14,] "Beans8 Beans4 Beans2 Beans6 Beans10 Beans3 Beans5 Beans1 Beans9 Beans7"
## [15,] "Beans3 Beans8 Beans5 Beans1 Beans7 Beans10 Beans6 Beans2 Beans9 Beans4"
## [16,] "Beans4 Beans7 Beans5 Beans8 Beans9 Beans2 Beans10 Beans6 Beans1 Beans3"
## [17,] "Beans8 Beans9 Beans2 Beans1 Beans7 Beans6 Beans5 Beans10 Beans4 Beans3"
## [18,] "Beans9 Beans8 Beans2 Beans5 Beans1 Beans6 Beans10 Beans7 Beans4 Beans3"
## [19,] "Beans10 Beans3 Beans6 Beans1 Beans5 Beans8 Beans7 Beans2 Beans4 Beans9"
## [20,] "Beans6 Beans8 Beans10 Beans1 Beans7 Beans3 Beans5 Beans4 Beans2 Beans9"
## [21,] "Beans8 Beans7 Beans9 Beans6 Beans1 Beans5 Beans2 Beans3 Beans10 Beans4"
## [22,] "Beans3 Beans9 Beans8 Beans4 Beans1 Beans7 Beans10 Beans6 Beans2 Beans5"
## [23,] "Beans2 Beans5 Beans10 Beans1 Beans7 Beans6 Beans9 Beans4 Beans8 Beans3"
## [24,] "Beans8 Beans5 Beans7 Beans1 Beans9 Beans6 Beans2 Beans4 Beans3 Beans10"
## [25,] "Beans7 Beans8 Beans10 Beans4 Beans1 Beans9 Beans3 Beans2 Beans5 Beans6"
## [26,] "Beans3 Beans8 Beans4 Beans9 Beans2 Beans6 Beans1 Beans7 Beans10 Beans5"
## [27,] "Beans4 Beans10 Beans1 Beans8 Beans3 Beans9 Beans7 Beans5 Beans6 Beans2"
## [28,] "Beans5 Beans3 Beans6 Beans4 Beans2 Beans10 Beans8 Beans1 Beans9 Beans7"
## [29,] "Beans3 Beans7 Beans2 Beans5 Beans1 Beans9 Beans4 Beans10 Beans8 Beans6"
## [30,] "Beans8 Beans6 Beans2 Beans5 Beans9 Beans10 Beans1 Beans3 Beans4 Beans7"
##
## [[2]]
## PLOT REP Whole-plot
## [1,] "1001" "1" "Fung1"
## [2,] "1002" "1" "Fung3"
## [3,] "1003" "1" "NFung"
## [4,] "1004" "1" "Fung2"
## [5,] "1005" "1" "Fung4"
## [6,] "1101" "2" "Fung3"
## [7,] "1102" "2" "Fung2"
## [8,] "1103" "2" "Fung4"
## [9,] "1104" "2" "Fung1"
## [10,] "1105" "2" "NFung"
## [11,] "1201" "3" "NFung"
## [12,] "1202" "3" "Fung2"
## [13,] "1203" "3" "Fung1"
## [14,] "1204" "3" "Fung4"
## [15,] "1205" "3" "Fung3"
## [16,] "1301" "4" "Fung3"
## [17,] "1302" "4" "NFung"
## [18,] "1303" "4" "Fung2"
## [19,] "1304" "4" "Fung4"
## [20,] "1305" "4" "Fung1"
## [21,] "1401" "5" "Fung2"
## [22,] "1402" "5" "NFung"
## [23,] "1403" "5" "Fung1"
## [24,] "1404" "5" "Fung4"
## [25,] "1405" "5" "Fung3"
## [26,] "1501" "6" "Fung2"
## [27,] "1502" "6" "Fung1"
## [28,] "1503" "6" "NFung"
## [29,] "1504" "6" "Fung4"
## [30,] "1505" "6" "Fung3"
## Sub-plot
## [1,] "Beans3 Beans6 Beans8 Beans9 Beans4 Beans5 Beans7 Beans2 Beans1 Beans10"
## [2,] "Beans2 Beans4 Beans9 Beans10 Beans8 Beans3 Beans5 Beans6 Beans1 Beans7"
## [3,] "Beans3 Beans7 Beans1 Beans6 Beans5 Beans2 Beans4 Beans10 Beans8 Beans9"
## [4,] "Beans3 Beans5 Beans7 Beans6 Beans4 Beans10 Beans2 Beans9 Beans8 Beans1"
## [5,] "Beans4 Beans9 Beans8 Beans3 Beans6 Beans7 Beans5 Beans1 Beans2 Beans10"
## [6,] "Beans6 Beans3 Beans5 Beans2 Beans7 Beans10 Beans9 Beans1 Beans8 Beans4"
## [7,] "Beans8 Beans5 Beans6 Beans7 Beans10 Beans2 Beans3 Beans9 Beans4 Beans1"
## [8,] "Beans3 Beans1 Beans10 Beans4 Beans7 Beans9 Beans5 Beans2 Beans8 Beans6"
## [9,] "Beans7 Beans3 Beans9 Beans10 Beans1 Beans5 Beans6 Beans4 Beans8 Beans2"
## [10,] "Beans10 Beans1 Beans5 Beans9 Beans6 Beans3 Beans8 Beans7 Beans4 Beans2"
## [11,] "Beans6 Beans7 Beans8 Beans3 Beans5 Beans4 Beans2 Beans1 Beans9 Beans10"
## [12,] "Beans3 Beans9 Beans8 Beans5 Beans2 Beans1 Beans4 Beans6 Beans10 Beans7"
## [13,] "Beans2 Beans5 Beans9 Beans1 Beans8 Beans3 Beans4 Beans6 Beans10 Beans7"
## [14,] "Beans10 Beans7 Beans9 Beans8 Beans5 Beans1 Beans4 Beans3 Beans2 Beans6"
## [15,] "Beans1 Beans8 Beans2 Beans3 Beans7 Beans6 Beans5 Beans10 Beans4 Beans9"
## [16,] "Beans1 Beans4 Beans3 Beans9 Beans10 Beans5 Beans6 Beans7 Beans2 Beans8"
## [17,] "Beans1 Beans8 Beans6 Beans9 Beans7 Beans2 Beans3 Beans5 Beans10 Beans4"
## [18,] "Beans2 Beans3 Beans1 Beans8 Beans7 Beans6 Beans4 Beans9 Beans5 Beans10"
## [19,] "Beans9 Beans1 Beans10 Beans8 Beans7 Beans3 Beans5 Beans6 Beans4 Beans2"
## [20,] "Beans2 Beans1 Beans3 Beans7 Beans4 Beans10 Beans8 Beans6 Beans9 Beans5"
## [21,] "Beans10 Beans9 Beans6 Beans7 Beans4 Beans3 Beans5 Beans8 Beans1 Beans2"
## [22,] "Beans3 Beans10 Beans4 Beans7 Beans1 Beans8 Beans2 Beans9 Beans5 Beans6"
## [23,] "Beans8 Beans7 Beans2 Beans3 Beans10 Beans6 Beans5 Beans4 Beans1 Beans9"
## [24,] "Beans3 Beans10 Beans5 Beans8 Beans9 Beans4 Beans2 Beans1 Beans7 Beans6"
## [25,] "Beans2 Beans8 Beans4 Beans1 Beans5 Beans6 Beans7 Beans10 Beans3 Beans9"
## [26,] "Beans10 Beans1 Beans6 Beans2 Beans9 Beans8 Beans3 Beans5 Beans7 Beans4"
## [27,] "Beans4 Beans8 Beans7 Beans5 Beans10 Beans9 Beans2 Beans3 Beans1 Beans6"
## [28,] "Beans7 Beans9 Beans6 Beans5 Beans1 Beans8 Beans3 Beans10 Beans4 Beans2"
## [29,] "Beans3 Beans9 Beans8 Beans1 Beans7 Beans10 Beans6 Beans2 Beans4 Beans5"
## [30,] "Beans3 Beans2 Beans1 Beans8 Beans9 Beans6 Beans4 Beans7 Beans10 Beans5"
##
## [[3]]
## PLOT REP Whole-plot
## [1,] "2001" "1" "NFung"
## [2,] "2002" "1" "Fung2"
## [3,] "2003" "1" "Fung1"
## [4,] "2004" "1" "Fung4"
## [5,] "2005" "1" "Fung3"
## [6,] "2101" "2" "Fung1"
## [7,] "2102" "2" "Fung2"
## [8,] "2103" "2" "Fung4"
## [9,] "2104" "2" "NFung"
## [10,] "2105" "2" "Fung3"
## [11,] "2201" "3" "Fung3"
## [12,] "2202" "3" "NFung"
## [13,] "2203" "3" "Fung4"
## [14,] "2204" "3" "Fung2"
## [15,] "2205" "3" "Fung1"
## [16,] "2301" "4" "Fung3"
## [17,] "2302" "4" "Fung4"
## [18,] "2303" "4" "Fung1"
## [19,] "2304" "4" "Fung2"
## [20,] "2305" "4" "NFung"
## [21,] "2401" "5" "Fung3"
## [22,] "2402" "5" "Fung2"
## [23,] "2403" "5" "Fung1"
## [24,] "2404" "5" "Fung4"
## [25,] "2405" "5" "NFung"
## [26,] "2501" "6" "Fung1"
## [27,] "2502" "6" "Fung3"
## [28,] "2503" "6" "Fung2"
## [29,] "2504" "6" "Fung4"
## [30,] "2505" "6" "NFung"
## Sub-plot
## [1,] "Beans3 Beans2 Beans6 Beans4 Beans5 Beans10 Beans7 Beans1 Beans8 Beans9"
## [2,] "Beans5 Beans4 Beans9 Beans1 Beans6 Beans2 Beans10 Beans7 Beans8 Beans3"
## [3,] "Beans4 Beans7 Beans6 Beans1 Beans2 Beans10 Beans9 Beans3 Beans8 Beans5"
## [4,] "Beans9 Beans8 Beans3 Beans6 Beans1 Beans7 Beans10 Beans5 Beans2 Beans4"
## [5,] "Beans7 Beans10 Beans4 Beans8 Beans2 Beans5 Beans9 Beans1 Beans3 Beans6"
## [6,] "Beans8 Beans1 Beans3 Beans10 Beans6 Beans4 Beans9 Beans5 Beans7 Beans2"
## [7,] "Beans3 Beans6 Beans1 Beans5 Beans7 Beans10 Beans9 Beans4 Beans8 Beans2"
## [8,] "Beans4 Beans2 Beans7 Beans1 Beans10 Beans9 Beans6 Beans5 Beans3 Beans8"
## [9,] "Beans2 Beans7 Beans9 Beans3 Beans4 Beans1 Beans5 Beans6 Beans8 Beans10"
## [10,] "Beans9 Beans2 Beans4 Beans5 Beans6 Beans3 Beans1 Beans8 Beans10 Beans7"
## [11,] "Beans6 Beans10 Beans1 Beans7 Beans2 Beans9 Beans4 Beans5 Beans8 Beans3"
## [12,] "Beans5 Beans3 Beans10 Beans9 Beans4 Beans2 Beans7 Beans6 Beans1 Beans8"
## [13,] "Beans1 Beans2 Beans7 Beans8 Beans5 Beans3 Beans10 Beans6 Beans9 Beans4"
## [14,] "Beans5 Beans8 Beans1 Beans3 Beans10 Beans6 Beans2 Beans9 Beans4 Beans7"
## [15,] "Beans5 Beans8 Beans3 Beans4 Beans9 Beans1 Beans10 Beans7 Beans2 Beans6"
## [16,] "Beans9 Beans5 Beans8 Beans1 Beans3 Beans7 Beans2 Beans6 Beans10 Beans4"
## [17,] "Beans2 Beans5 Beans3 Beans9 Beans6 Beans7 Beans1 Beans4 Beans8 Beans10"
## [18,] "Beans3 Beans10 Beans1 Beans8 Beans2 Beans6 Beans9 Beans4 Beans5 Beans7"
## [19,] "Beans9 Beans10 Beans1 Beans7 Beans3 Beans6 Beans5 Beans4 Beans2 Beans8"
## [20,] "Beans9 Beans1 Beans4 Beans3 Beans6 Beans2 Beans5 Beans10 Beans8 Beans7"
## [21,] "Beans1 Beans9 Beans4 Beans8 Beans2 Beans5 Beans7 Beans6 Beans3 Beans10"
## [22,] "Beans4 Beans5 Beans10 Beans7 Beans6 Beans1 Beans2 Beans9 Beans8 Beans3"
## [23,] "Beans6 Beans3 Beans10 Beans8 Beans1 Beans9 Beans2 Beans4 Beans5 Beans7"
## [24,] "Beans6 Beans7 Beans8 Beans4 Beans3 Beans1 Beans9 Beans2 Beans5 Beans10"
## [25,] "Beans3 Beans9 Beans8 Beans7 Beans1 Beans5 Beans10 Beans2 Beans4 Beans6"
## [26,] "Beans8 Beans5 Beans7 Beans10 Beans1 Beans3 Beans2 Beans6 Beans4 Beans9"
## [27,] "Beans9 Beans4 Beans10 Beans7 Beans1 Beans6 Beans8 Beans5 Beans3 Beans2"
## [28,] "Beans7 Beans8 Beans1 Beans10 Beans3 Beans4 Beans2 Beans6 Beans9 Beans5"
## [29,] "Beans3 Beans5 Beans4 Beans2 Beans7 Beans10 Beans9 Beans8 Beans1 Beans6"
## [30,] "Beans3 Beans6 Beans2 Beans9 Beans1 Beans4 Beans8 Beans7 Beans5 Beans10"
##
##
## splt_p> head(SPDExample2$fieldBook,12)
## ID LOCATION PLOT REP WHOLE_PLOT SUB_PLOT TRT_COMB
## 1 1 A 101 1 Fung4 Beans5 Fung4|Beans5
## 2 2 A 101 1 Fung4 Beans1 Fung4|Beans1
## 3 3 A 101 1 Fung4 Beans2 Fung4|Beans2
## 4 4 A 101 1 Fung4 Beans3 Fung4|Beans3
## 5 5 A 101 1 Fung4 Beans10 Fung4|Beans10
## 6 6 A 101 1 Fung4 Beans6 Fung4|Beans6
## 7 7 A 101 1 Fung4 Beans7 Fung4|Beans7
## 8 8 A 101 1 Fung4 Beans9 Fung4|Beans9
## 9 9 A 101 1 Fung4 Beans4 Fung4|Beans4
## 10 10 A 101 1 Fung4 Beans8 Fung4|Beans8
## 11 11 A 102 1 Fung3 Beans9 Fung3|Beans9
## 12 12 A 102 1 Fung3 Beans10 Fung3|Beans10
p <- full_control_positions(plt$field_book[plt$field_book$LOCATION == "A",], "COLUMN", "ROW", factor_name = "TRT_COMB", label = "TRT_COMB")
p
# Example 11: Split-Split Plot Design
##
## splt__> # Example 1: Generates a split split plot design SSPD with 5 whole plots, 2 sub-plots,
## splt__> # 3 sub-sub plots, and 3 reps in an RCBD arrangement. This is for one location.
## splt__> SSPD1 <- split_split_plot(wp = 4, sp = 2, ssp = 3, reps = 5, l = 1,
## splt__+ plotNumber = 101,
## splt__+ seed = 23,
## splt__+ type = 2,
## splt__+ locationNames = "FARGO")
##
## splt__> SSPD1$infoDesign
## $Whole.Plots
## [1] 1 2 3 4
##
## $Sub.Plots
## [1] 1 2
##
## $Sub.Sub.Plots
## [1] 1 2 3
##
## $Locations
## [1] 1
##
## $typeDesign
## [1] "RCBD"
##
## $seed
## [1] 23
##
## $id_design
## [1] 6
##
##
## splt__> head(SSPD1$fieldBook,12)
## ID LOCATION PLOT REP WHOLE_PLOT SUB_PLOT SUB_SUB_PLOT TRT_COMB
## 1 1 FARGO 101 1 1 2 2 1|2|2
## 2 2 FARGO 101 1 1 2 1 1|2|1
## 3 3 FARGO 101 1 1 2 3 1|2|3
## 4 4 FARGO 101 1 1 1 2 1|1|2
## 5 5 FARGO 101 1 1 1 1 1|1|1
## 6 6 FARGO 101 1 1 1 3 1|1|3
## 7 7 FARGO 102 1 3 1 2 3|1|2
## 8 8 FARGO 102 1 3 1 1 3|1|1
## 9 9 FARGO 102 1 3 1 3 3|1|3
## 10 10 FARGO 102 1 3 2 2 3|2|2
## 11 11 FARGO 102 1 3 2 1 3|2|1
## 12 12 FARGO 102 1 3 2 3 3|2|3
##
## splt__> # Example 2: Generates a split split plot design SSPD with 2 whole plost
## splt__> # (Irrigation, No irrigation), 5 sub plots (4 types of fungicide + one control), and
## splt__> # 10 sub-sub plots (Ten varieties of beans), and 4 reps in an RCBD arrangement.
## splt__> # This is for 3 locations. In this case, we show how to use the option data.
## splt__> wp <- paste("IRR_", c("NO", "Yes"), sep = "") #Irrigation (2 Whole plots)
##
## splt__> sp <- c("NFung", paste("Fung", 1:4, sep = "")) #Fungicides (5 Sub plots)
##
## splt__> ssp <- paste("Beans", 1:10, sep = "") #Beans varieties (10 Sub-sub plots)
##
## splt__> split_split_plot_Data <- data.frame(list(WHOLPLOT = c(wp, rep(NA, 8)),
## splt__+ SUBPLOT = c(sp, rep(NA, 5)),
## splt__+ SUB_SUBPLOTS = ssp))
##
## splt__> head(split_split_plot_Data, 10)
## WHOLPLOT SUBPLOT SUB_SUBPLOTS
## 1 IRR_NO NFung Beans1
## 2 IRR_Yes Fung1 Beans2
## 3 <NA> Fung2 Beans3
## 4 <NA> Fung3 Beans4
## 5 <NA> Fung4 Beans5
## 6 <NA> <NA> Beans6
## 7 <NA> <NA> Beans7
## 8 <NA> <NA> Beans8
## 9 <NA> <NA> Beans9
## 10 <NA> <NA> Beans10
##
## splt__> SSPD2 <- split_split_plot(reps = 4, l = 3,
## splt__+ plotNumber = c(101, 1001, 2001),
## splt__+ seed = 23,
## splt__+ type = 2,
## splt__+ locationNames = c("A", "B", "C"),
## splt__+ data = split_split_plot_Data)
##
## splt__> SSPD2$infoDesign
## $Whole.Plots
## [1] "IRR_NO" "IRR_Yes"
##
## $Sub.Plots
## [1] "NFung" "Fung1" "Fung2" "Fung3" "Fung4"
##
## $Sub.Sub.Plots
## [1] "Beans1" "Beans2" "Beans3" "Beans4" "Beans5" "Beans6" "Beans7"
## [8] "Beans8" "Beans9" "Beans10"
##
## $Locations
## [1] 3
##
## $typeDesign
## [1] "RCBD"
##
## $seed
## [1] 23
##
## $id_design
## [1] 6
##
##
## splt__> head(SSPD2$fieldBook,12)
## ID LOCATION PLOT REP WHOLE_PLOT SUB_PLOT SUB_SUB_PLOT TRT_COMB
## 1 1 A 101 1 IRR_NO Fung3 Beans3 IRR_NO|Fung3|Beans3
## 2 2 A 101 1 IRR_NO Fung3 Beans9 IRR_NO|Fung3|Beans9
## 3 3 A 101 1 IRR_NO Fung3 Beans10 IRR_NO|Fung3|Beans10
## 4 4 A 101 1 IRR_NO Fung3 Beans7 IRR_NO|Fung3|Beans7
## 5 5 A 101 1 IRR_NO Fung3 Beans8 IRR_NO|Fung3|Beans8
## 6 6 A 101 1 IRR_NO Fung3 Beans5 IRR_NO|Fung3|Beans5
## 7 7 A 101 1 IRR_NO Fung3 Beans2 IRR_NO|Fung3|Beans2
## 8 8 A 101 1 IRR_NO Fung3 Beans6 IRR_NO|Fung3|Beans6
## 9 9 A 101 1 IRR_NO Fung3 Beans4 IRR_NO|Fung3|Beans4
## 10 10 A 101 1 IRR_NO Fung3 Beans1 IRR_NO|Fung3|Beans1
## 11 11 A 101 1 IRR_NO Fung1 Beans9 IRR_NO|Fung1|Beans9
## 12 12 A 101 1 IRR_NO Fung1 Beans2 IRR_NO|Fung1|Beans2
p <- full_control_positions(plt$field_book[plt$field_book$LOCATION == "A",], "COLUMN", "ROW", factor_name = "TRT_COMB", label = "TRT_COMB") + guides(fill = "none")
p
# Example 12: Completely Randomized Design (CRD)
##
## CRD> # Example 1: Generates a CRD design with 10 treatments and 5 reps each.
## CRD> crd1 <- CRD(
## CRD+ t = 10,
## CRD+ reps = 5,
## CRD+ plotNumber = 101,
## CRD+ seed = 1987,
## CRD+ locationName = "Fargo"
## CRD+ )
##
## CRD> crd1$infoDesign
## $numberofTreatments
## [1] 10
##
## $treatments
## [1] "T1" "T2" "T3" "T4" "T5" "T6" "T7" "T8" "T9" "T10"
##
## $Reps
## [1] 5
##
## $locationName
## [1] "Fargo"
##
## $seed
## [1] 1987
##
## $id_design
## [1] 1
##
##
## CRD> head(crd1$fieldBook, 10)
## ID LOCATION PLOT REP TREATMENT
## 1 1 Fargo 101 1 T10
## 2 2 Fargo 102 4 T1
## 3 3 Fargo 103 3 T3
## 4 4 Fargo 104 3 T9
## 5 5 Fargo 105 4 T3
## 6 6 Fargo 106 5 T3
## 7 7 Fargo 107 1 T2
## 8 8 Fargo 108 3 T4
## 9 9 Fargo 109 1 T1
## 10 10 Fargo 110 3 T1
##
## CRD> # Example 2: Generates a CRD design with 15 treatments and 6 reps each.
## CRD> Gens <- paste("Wheat", 1:15, sep = "")
##
## CRD> crd2 <- CRD(
## CRD+ t = Gens,
## CRD+ reps = 6,
## CRD+ plotNumber = 1001,
## CRD+ seed = 1654,
## CRD+ locationName = "Fargo"
## CRD+ )
##
## CRD> crd2$infoDesign
## $numberofTreatments
## [1] 15
##
## $treatments
## [1] "Wheat1" "Wheat2" "Wheat3" "Wheat4" "Wheat5" "Wheat6" "Wheat7"
## [8] "Wheat8" "Wheat9" "Wheat10" "Wheat11" "Wheat12" "Wheat13" "Wheat14"
## [15] "Wheat15"
##
## $Reps
## [1] 6
##
## $locationName
## [1] "Fargo"
##
## $seed
## [1] 1654
##
## $id_design
## [1] 1
##
##
## CRD> head(crd2$fieldBook, 10)
## ID LOCATION PLOT REP TREATMENT
## 1 1 Fargo 1001 6 Wheat3
## 2 2 Fargo 1002 2 Wheat8
## 3 3 Fargo 1003 2 Wheat2
## 4 4 Fargo 1004 4 Wheat4
## 5 5 Fargo 1005 1 Wheat1
## 6 6 Fargo 1006 1 Wheat4
## 7 7 Fargo 1007 1 Wheat13
## 8 8 Fargo 1008 1 Wheat1
## 9 9 Fargo 1009 6 Wheat15
## 10 10 Fargo 1010 4 Wheat7
##
## CRD> # Example 3: Generates a CRD design with 12 treatments and 4 reps each.
## CRD> # In this case, we show how to use the option data.
## CRD> treatments <- paste("ND-", 1:12, sep = "")
##
## CRD> treatment_list <- data.frame(list(TREATMENT = treatments, REP = 4))
##
## CRD> head(treatment_list)
## TREATMENT REP
## 1 ND-1 4
## 2 ND-2 4
## 3 ND-3 4
## 4 ND-4 4
## 5 ND-5 4
## 6 ND-6 4
##
## CRD> crd3 <- CRD(
## CRD+ t = NULL,
## CRD+ reps = NULL,
## CRD+ plotNumber = 2001,
## CRD+ seed = 1655,
## CRD+ locationName = "Cali",
## CRD+ data = treatment_list
## CRD+ )
##
## CRD> crd3$infoDesign
## $numberofTreatments
## [1] 12
##
## $treatments
## [1] "ND-1" "ND-2" "ND-3" "ND-4" "ND-5" "ND-6" "ND-7" "ND-8" "ND-9"
## [10] "ND-10" "ND-11" "ND-12"
##
## $Reps
## [1] 4 4 4 4 4 4 4 4 4 4 4 4
##
## $locationName
## [1] "Cali"
##
## $seed
## [1] 1655
##
## $id_design
## [1] 1
##
##
## CRD> head(crd3$fieldBook, 10)
## ID LOCATION PLOT REP TREATMENT
## 1 1 Cali 2001 4 ND-3
## 2 2 Cali 2002 1 ND-7
## 3 3 Cali 2003 2 ND-2
## 4 4 Cali 2004 3 ND-8
## 5 5 Cali 2005 3 ND-2
## 6 6 Cali 2006 1 ND-10
## 7 7 Cali 2007 3 ND-7
## 8 8 Cali 2008 4 ND-9
## 9 9 Cali 2009 3 ND-4
## 10 10 Cali 2010 1 ND-3
p <- full_control_positions(plt$field_book, "COLUMN", "ROW", factor_name = "TREATMENT", label = "TREATMENT") + guides(fill = "none")
p
