devtools::install_github("DidierMurilloF/FielDHub")install.packages("FielDHub")
A shiny design of experiments (DOE) app that aids in the creation of traditional, un-replicated, augmented and partially-replicated designs applied to agriculture, plant breeding, forestry, animal and biological sciences.
For more details and examples of all functions present in the FielDHub package. Please, go to https://didiermurillof.github.io/FielDHub/reference/index.html.
This is a basic example which shows you how to launch the app:
library(FielDHub)
run_app()A project needs to test 270 genotypes in a field containing 20 rows and 15 columns of plots. In this example, these 270 genotypes are divided among three different experiments. In addition, four checks are included in a systematic diagonal arrangement across experiments to fill 30 plots representing 10% of the total number of experimental plots. An option to include filler plots is also available for fields where the number of experimental plots does not equal the number of available field plots.
The figure above shows a map of an experiment randomized as a Decision Block Unreplicated Design with Checks on Diagonals. Yellow, gray, and green shade the blocks of unreplicated experiments, while distinctively colored check plots are replicated throughout the field in a systematic diagonal arrangement.
To illustrate using FielDHub to build experimental designs through R code, the design produced in the R Shiny interface described above can also be created using the function diagonal_arrangement() in the R script below. Note, that to obtain identical results, users must include the same seed number in the script as was used in the Shiny app. In this case, the seed number is 1249.
diagonalExample <- diagonal_arrangement(nrows = 20,
ncols = 15,
lines = 270,
checks = 4,
plotNumber = 101,
splitBy = "row",
seed = 1249,
kindExpt = "DBUDC",
blocks = c(100, 100, 70))Users can access the returned values from diagonal_arrangement() as follow,
> diagonalExample$infoDesign
$Lines
[1] 100 100 70
$checks
[1] 4
$RepChecks
1 2 3 4
7 7 8 8
$percentChecks
[1] "10%"
$Fillers
[1] 0
$seed
[1] 1249
> diagonalExample$layoutRandom
Col1 Col2 Col3 Col4 Col5 Col6 Col7 Col8 Col9 Col10 Col11 Col12 Col13 Col14 Col15
Row20 248 2 268 225 258 223 255 226 264 239 274 1 232 235 256
Row19 214 273 244 220 3 269 247 207 243 215 245 221 208 229 4
Row18 240 252 251 272 238 212 250 3 206 242 259 253 222 213 227
Row17 2 261 210 230 254 262 266 237 236 231 4 218 219 257 265
Row16 224 263 211 1 216 209 217 270 271 205 233 260 234 2 246
Row15 166 135 201 127 194 203 1 171 190 116 156 228 241 249 267
Row14 126 191 193 162 204 137 114 178 173 1 115 130 167 155 199
Row13 111 143 4 182 150 154 117 160 136 192 122 119 3 177 131
Row12 105 197 159 157 181 1 147 107 153 158 170 152 141 148 187
Row11 161 186 112 175 183 124 200 195 2 129 120 144 146 188 118
Row10 128 3 138 165 142 184 196 121 169 132 168 4 113 172 133
Row9 145 198 139 179 2 109 180 140 125 134 149 164 176 106 3
Row8 202 189 174 163 108 185 110 4 151 123 84 70 43 57 101
Row7 4 24 15 93 22 78 87 96 18 47 3 44 100 99 53
Row6 92 19 8 1 62 49 36 85 38 5 59 33 7 2 39
Row5 66 82 104 26 34 14 4 97 73 16 65 41 77 103 42
Row4 54 86 46 75 13 69 98 10 63 2 37 71 29 45 40
Row3 89 28 3 88 17 20 72 31 35 25 56 74 3 68 55
Row2 91 23 11 51 30 1 83 64 50 90 79 21 12 6 95
Row1 48 102 9 94 52 80 76 60 4 61 32 58 67 27 81
> head(diagonalExample$fieldBook, 12)
ID EXPT LOCATION YEAR PLOT ROW COLUMN CHECKS ENTRY TREATMENT
1 1 Expt1 1 2021 101 1 1 0 48 gen 48
2 2 Expt1 1 2021 102 1 2 0 102 gen 102
3 3 Expt1 1 2021 103 1 3 0 9 gen 9
4 4 Expt1 1 2021 104 1 4 0 94 gen 94
5 5 Expt1 1 2021 105 1 5 0 52 gen 52
6 6 Expt1 1 2021 106 1 6 0 80 gen 80
7 7 Expt1 1 2021 107 1 7 0 76 gen 76
8 8 Expt1 1 2021 108 1 8 0 60 gen 60
9 9 Expt1 1 2021 109 1 9 4 4 Check 4
10 10 Expt1 1 2021 110 1 10 0 61 gen 61
11 11 Expt1 1 2021 111 1 11 0 32 gen 32
12 12 Expt1 1 2021 112 1 12 0 58 gen 58The main difference between using the FielDHub Shiny app and using the standalone function diagonal_arrangement() is that the standalone function will allocate filler only if it is necessary, while in R Shiny, filler plots are generated automatically. In cases where users include fillers, either between or after experiments, the Shiny app is preferable for filling and visualizing all field plots.
Partially replicated designs are commonly employed in early generation field trials. This type of design is characterized by replication of a portion of the entries, with the remaining entries only appearing once in the experiment. As an example, considered a field trial with 288 plots containing 75 entries appearing two times each, and 138 entries only appearing once. This field trials is arranged in a field of 16 rows by 18 columns.
In the figure above, green plots contain replicated entries, and yellow plots contain entries that only appear once.
Instead of using the Shiny FielDHub app, users can use the standalone FielDHub function partially_replicated(). The partially replicated layout described above can be produced through scripting as follows. As noted in the previous example, to obtain identical results between the script and the Shiny app, users need to use the same seed number, which, in this case, is 77.
pREPexample <- partially_replicated(nrows = 16,
ncols = 18,
repGens = c(138,75),
repUnits = c(1,2),
planter = "serpentine",
plotNumber = 1,
exptName = "ExptA",
locationNames = "FARGO",
seed = 77)
Users can access returned values from partially_replicated() as follows,
> pREPexample$layoutRandom
Col1 Col2 Col3 Col4 Col5 Col6 Col7 Col8 Col9 Col10 Col11 Col12 Col13 Col14 Col15 Col16 Col17 Col18
Row16 176 45 192 127 71 62 102 120 95 79 61 158 141 32 21 25 14 71
Row15 18 121 9 123 171 60 11 204 2 182 119 65 198 63 188 34 124 55
Row14 68 108 200 16 3 5 136 183 78 43 149 89 23 47 160 168 152 10
Row13 187 66 153 24 21 23 65 199 34 64 11 191 94 96 131 181 186 103
Row12 211 15 8 174 56 169 28 84 90 46 156 57 51 42 128 170 27 114
Row11 19 7 87 7 93 86 26 22 151 67 30 5 92 48 51 150 59 41
Row10 144 101 104 134 213 167 73 33 6 74 25 122 32 107 18 40 139 59
Row9 70 38 70 12 88 99 179 210 37 22 155 142 116 161 137 10 19 30
Row8 1 173 57 20 157 69 13 110 195 147 38 163 1 83 40 162 4 209
Row7 97 82 36 29 56 35 50 6 75 85 58 17 66 172 145 63 44 3
Row6 24 8 43 177 31 143 91 64 98 194 74 55 126 115 117 159 100 45
Row5 77 205 37 62 106 29 138 53 118 35 132 54 166 81 47 41 109 76
Row4 73 129 125 135 60 164 14 39 13 165 112 105 140 12 72 212 46 203
Row3 69 28 196 154 146 75 189 36 54 72 31 33 53 67 175 27 2 130
Row2 16 68 206 49 52 208 26 184 180 193 178 61 48 197 52 50 201 111
Row1 80 49 15 44 185 9 133 58 4 113 190 148 39 17 20 207 202 42
> head(pREPexample$fieldBook, 12)
ID EXPT LOCATION YEAR PLOT ROW COLUMN CHECKS ENTRY TREATMENT
1 1 ExptA FARGO 2021 1 1 1 0 80 G80
2 2 ExptA FARGO 2021 2 1 2 1 49 G49
4 3 ExptA FARGO 2021 3 1 3 1 15 G15
6 4 ExptA FARGO 2021 4 1 4 1 44 G44
8 5 ExptA FARGO 2021 5 1 5 0 185 G185
10 6 ExptA FARGO 2021 6 1 6 1 9 G9
11 7 ExptA FARGO 2021 7 1 7 0 133 G133
13 8 ExptA FARGO 2021 8 1 8 1 58 G58
15 9 ExptA FARGO 2021 9 1 9 1 4 G4
16 10 ExptA FARGO 2021 10 1 10 0 113 G113
17 11 ExptA FARGO 2021 11 1 11 0 190 G190
18 12 ExptA FARGO 2021 12 1 12 0 148 G148To see more examples, please go to https://didiermurillof.github.io/FielDHub/reference/index.html.