4.2. Coding details
From the comments, the same number of individual LTs are built as there are judges.
This codification preserves the information given by each judge on the wines.
Each table has as many columns as different words, called individual words, used in the corresponding individual comments.
Individual words can be homologous from one judge to another, and then correspond to the same global word.
The individual LTs describe the individual wine configurations as provided separately by each judge,
visualized by successive CAs applied to these individual LTs taken one by one.
4.2.1. Building individual LTs. In list format
French panel
Building the separate LT for French judges
The individual LTs describe the individual wine configurations as provided separately by each judge, visualized by successive CAs applied to these individual LTs taken one by one.
The 15 French judges are:
cat(names(baseFr))
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FE5 FE6 FE12 FP1 FP3 FP4 FP5 FP6 FP7 FP8 FP9 FP10 FP11 FP12 FP2 |
The 15 TextData analyses for the French judges are then performed by removing the French stopwords and saving them in a list. The same process could have been done using a loop.
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1 2 |
res.TD.Fr.list <- lapply(<span class="hljs-number">1</span>:ncol(baseFr), <span class="hljs-keyword">function</span>(i) TextData(baseFr,var.text=i, Fmin=<span class="hljs-number">1</span>, stop.word.user = str.Fr.stopworduser)) names(res.TD.Fr.list) <- names(baseFr) |
To do the 15 Correspondence analysis of the “individual tables”:
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res.LexCA.Fr.list <- lapply(<span class="hljs-number">1</span>:length(res.TD.Fr.list), <span class="hljs-keyword">function</span>(i) LexCA(res.TD.Fr.list[[i]], graph=<span class="hljs-literal">FALSE</span>)) names(res.LexCA.Fr.list) <- names(baseFr) |
To print the results of the eigenvalues for each of the 15 French judges:
unlist(lapply(1:length(res.TD.Fr.list), function(i) {
cat("\n\n", "**** French Judge ", i, "- Name: ", colnames(baseFr)[i], "\n" )
cat(summary(res.LexCA.Fr.list[[i]],nword=0,ndoc=0,nsup=0))
} ))
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1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 |
**** French Judge 1 - Name: FE5 Correspondence analysis summary Eigenvalues Variance % of var. Cumulative % of var. dim 1 1.000 30.364 30.364 dim 2 1.000 30.364 60.729 dim 3 0.806 24.483 85.212 dim 4 0.250 7.591 92.803 dim 5 0.237 7.197 100.000 Cramer's V 0.686 Inertia 3.293 **** French Judge 2 - Name: FE6 Correspondence analysis summary Eigenvalues Variance % of var. Cumulative % of var. dim 1 1 25 25 dim 2 1 25 50 dim 3 1 25 75 dim 4 1 25 100 dim 5 0 0 100 Cramer's V 0.756 Inertia 4 **** French Judge 3 - Name: FE12 Correspondence analysis summary Eigenvalues Variance % of var. Cumulative % of var. dim 1 1.000 42.056 42.056 dim 2 0.754 31.697 73.753 dim 3 0.624 26.247 100.000 dim 4 0.000 0.000 100.000 dim 5 0.000 0.000 100.000 Cramer's V 0.583 Inertia 2.378 **** French Judge 4 - Name: FP1 Correspondence analysis summary Eigenvalues Variance % of var. Cumulative % of var. dim 1 1.000 43.386 43.386 dim 2 0.884 38.347 81.733 dim 3 0.421 18.267 100.000 dim 4 0.000 0.000 100.000 dim 5 0.000 0.000 100.000 Cramer's V 0.574 Inertia 2.305 **** French Judge 5 - Name: FP3 Correspondence analysis summary Eigenvalues Variance % of var. Cumulative % of var. dim 1 1.000 38.961 38.961 dim 2 1.000 38.961 77.922 dim 3 0.567 22.078 100.000 dim 4 0.000 0.000 100.000 dim 5 0.000 0.000 100.000 Cramer's V 0.606 Inertia 2.567 **** French Judge 6 - Name: FP4 Correspondence analysis summary Eigenvalues Variance % of var. Cumulative % of var. dim 1 1.000 33.333 33.333 dim 2 1.000 33.333 66.667 dim 3 1.000 33.333 100.000 dim 4 0.000 0.000 100.000 dim 5 0.000 0.000 100.000 Cramer's V 0.655 Inertia 3 **** French Judge 7 - Name: FP5 Correspondence analysis summary Eigenvalues Variance % of var. Cumulative % of var. dim 1 0.615 24.348 24.348 dim 2 0.551 21.813 46.161 dim 3 0.424 16.801 62.962 dim 4 0.327 12.939 75.901 dim 5 0.267 10.570 86.471 Cramer's V 0.601 Inertia 2.525 **** French Judge 8 - Name: FP6 Correspondence analysis summary Eigenvalues Variance % of var. Cumulative % of var. dim 1 0.604 54.374 54.374 dim 2 0.215 19.372 73.746 dim 3 0.146 13.168 86.914 dim 4 0.089 7.995 94.909 dim 5 0.028 2.558 97.467 Cramer's V 0.398 Inertia 1.11 **** French Judge 9 - Name: FP7 Correspondence analysis summary Eigenvalues Variance % of var. Cumulative % of var. dim 1 0.919 23.546 23.546 dim 2 0.825 21.143 44.689 dim 3 0.708 18.153 62.842 dim 4 0.634 16.245 79.087 dim 5 0.407 10.431 89.518 Cramer's V 0.747 Inertia 3.903 **** French Judge 10 - Name: FP8 Correspondence analysis summary Eigenvalues Variance % of var. Cumulative % of var. dim 1 0.760 27.724 27.724 dim 2 0.581 21.197 48.921 dim 3 0.422 15.387 64.308 dim 4 0.364 13.272 77.580 dim 5 0.265 9.659 87.239 Cramer's V 0.626 Inertia 2.74 **** French Judge 11 - Name: FP9 Correspondence analysis summary Eigenvalues Variance % of var. Cumulative % of var. dim 1 0.863 52.025 52.025 dim 2 0.714 43.027 95.052 dim 3 0.082 4.948 100.000 dim 4 0.000 0.000 100.000 dim 5 0.000 0.000 100.000 Cramer's V 0.487 Inertia 1.66 **** French Judge 12 - Name: FP10 Correspondence analysis summary Eigenvalues Variance % of var. Cumulative % of var. dim 1 0.842 43.164 43.164 dim 2 0.627 32.176 75.340 dim 3 0.481 24.660 100.000 dim 4 0.000 0.000 100.000 dim 5 0.000 0.000 100.000 Cramer's V 0.528 Inertia 1.95 **** French Judge 13 - Name: FP11 Correspondence analysis summary Eigenvalues Variance % of var. Cumulative % of var. dim 1 0.780 23.629 23.629 dim 2 0.677 20.504 44.133 dim 3 0.547 16.560 60.693 dim 4 0.511 15.478 76.171 dim 5 0.459 13.887 90.058 Cramer's V 0.687 Inertia 3.303 **** French Judge 14 - Name: FP12 Correspondence analysis summary Eigenvalues Variance % of var. Cumulative % of var. dim 1 1.000 39.604 39.604 dim 2 0.917 36.321 75.925 dim 3 0.608 24.075 100.000 dim 4 0.000 0.000 100.000 dim 5 0.000 0.000 100.000 Cramer's V 0.601 Inertia 2.525 **** French Judge 15 - Name: FP2 Correspondence analysis summary Eigenvalues Variance % of var. Cumulative % of var. dim 1 1 50 50 dim 2 1 50 100 dim 3 0 0 100 dim 4 0 0 100 dim 5 0 0 100 Cramer's V 0.632 Inertia 2 |
To count the number of French judges for whom the first factorial plane explains more than 70% in order to obtain some information on the inertias of the individual tables.
cat(sum(sapply(1:length(res.TD.Fr.list), function(x) res.LexCA.Fr.list[[x]]$eig[2,3]>70)))
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8 |
Catalan panel
Building the separate LT for Catalan judges The 9 Catalan judges are names(baseCat)
cat(names(baseCat))
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CE1 CE2 CE3 CE4 CE7 CE8 CE9 CE10 CE11 |
The 9 TextData analyses for the Catalan judges are then performed by removing the Catalan stopwords and saving them in a list. The same process could have been done using a loop.
res.TD.Cat.list <- lapply(1:ncol(baseCat), function(i) TextData(baseCat,var.text=i, Fmin=1, stop.word.user = str.Cat.stopworduser))
names(res.TD.Cat.list) <- names(baseCat)
To do the 9 Correspondence analysis of the "individual tables":
res.LexCA.Cat.list <- lapply(1:length(res.TD.Cat.list), function(i) LexCA(res.TD.Cat.list[[i]], graph=FALSE))
names(res.LexCA.Cat.list) <- names(baseCat)
To print the results of the eigenvalues for each of the 9 Catalan judges:
unlist(lapply(1:length(res.TD.Cat.list), function(i) {
cat("\n\n", "**** Catalan Judge ", i, "- Name: ", colnames(baseCat)[i], "\n" )
cat(summary(res.LexCA.Cat.list[[i]],nword=0,ndoc=0,nsup=0))
} ))
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1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 |
**** Catalan Judge 1 - Name: CE1 Correspondence analysis summary Eigenvalues Variance % of var. Cumulative % of var. dim 1 1.000 40.000 40.000 dim 2 0.767 30.667 70.667 dim 3 0.733 29.333 100.000 dim 4 0.000 0.000 100.000 dim 5 0.000 0.000 100.000 Cramer's V 0.598 Inertia 2.5 **** Catalan Judge 2 - Name: CE2 Correspondence analysis summary Eigenvalues Variance % of var. Cumulative % of var. dim 1 1.000 33.333 33.333 dim 2 1.000 33.333 66.667 dim 3 1.000 33.333 100.000 dim 4 0.000 0.000 100.000 dim 5 0.000 0.000 100.000 Cramer's V 0.655 Inertia 3 **** Catalan Judge 3 - Name: CE3 Correspondence analysis summary Eigenvalues Variance % of var. Cumulative % of var. dim 1 1.000 37.500 37.500 dim 2 1.000 37.500 75.000 dim 3 0.667 25.000 100.000 dim 4 0.000 0.000 100.000 dim 5 0.000 0.000 100.000 Cramer's V 0.617 Inertia 2.667 **** Catalan Judge 4 - Name: CE4 Correspondence analysis summary Eigenvalues Variance % of var. Cumulative % of var. dim 1 0.897 52.677 52.677 dim 2 0.667 39.158 91.835 dim 3 0.139 8.165 100.000 dim 4 0.000 0.000 100.000 dim 5 0.000 0.000 100.000 Cramer's V 0.493 Inertia 1.702 **** Catalan Judge 5 - Name: CE7 Correspondence analysis summary Eigenvalues Variance % of var. Cumulative % of var. dim 1 1.000 28.504 28.504 dim 2 1.000 28.504 57.007 dim 3 0.856 24.408 81.415 dim 4 0.652 18.585 100.000 dim 5 0.000 0.000 100.000 Cramer's V 0.708 Inertia 3.508 **** Catalan Judge 6 - Name: CE8 Correspondence analysis summary Eigenvalues Variance % of var. Cumulative % of var. dim 1 0.838 38.560 38.560 dim 2 0.750 34.497 73.057 dim 3 0.586 26.943 100.000 dim 4 0.000 0.000 100.000 dim 5 0.000 0.000 100.000 Cramer's V 0.557 Inertia 2.174 **** Catalan Judge 7 - Name: CE9 Correspondence analysis summary Eigenvalues Variance % of var. Cumulative % of var. dim 1 0.719 38.011 38.011 dim 2 0.622 32.883 70.894 dim 3 0.396 20.933 91.827 dim 4 0.155 8.173 100.000 dim 5 0.000 0.000 100.000 Cramer's V 0.52 Inertia 1.893 **** Catalan Judge 8 - Name: CE10 Correspondence analysis summary Eigenvalues Variance % of var. Cumulative % of var. dim 1 1.000 33.333 33.333 dim 2 1.000 33.333 66.667 dim 3 1.000 33.333 100.000 dim 4 0.000 0.000 100.000 dim 5 0.000 0.000 100.000 Cramer's V 0.655 Inertia 3 **** Catalan Judge 9 - Name: CE11 Correspondence analysis summary Eigenvalues Variance % of var. Cumulative % of var. dim 1 1.000 38.298 38.298 dim 2 1.000 38.298 76.596 dim 3 0.611 23.404 100.000 dim 4 0.000 0.000 100.000 dim 5 0.000 0.000 100.000 Cramer's V 0.611 Inertia 2.611 |
To count the number of Catalan judges for whom the first factorial plane explains more than 70% in order to obtain some information on the inertias of the individual tables:
cat(sum(sapply(1:length(res.TD.Cat.list), function(x) res.LexCA.Cat.list[[x]]$eig[2,3]>70)))
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6 |