Clustering Jerárquico de Series Temporales en R.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### El Clustering Jerárquico, \n",
    "es una técnica muy útil cuando se desea descubrir similitudes entre individuos en base a sus características.\n",
    "\n",
    "““Dynamic Time Warping” o DTW "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "indices <- read.csv(file=\"ahppca.csv\",header=TRUE,sep=\",\",dec=\".\",row.names=1)\n",
    "# los nombres de los renglones son las fechas de cada renglon de la matriz"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table>\n",
       "<caption>A data.frame: 6 × 19</caption>\n",
       "<thead>\n",
       "\t<tr><th></th><th scope=col>Karate</th><th scope=col>Fitness</th><th scope=col>Natación</th><th scope=col>Baloncesto</th><th scope=col>Fútbol</th><th scope=col>Fitness.1</th><th scope=col>Baloncesto.1</th><th scope=col>Tae.Kwon.Do</th><th scope=col>Beisbol</th><th scope=col>Pesas</th><th scope=col>Natación.1</th><th scope=col>Baloncesto.2</th><th scope=col>Atletismo</th><th scope=col>Fútbol.1</th><th scope=col>Beisbol.1</th><th scope=col>Baloncesto.3</th><th scope=col>Baloncesto.4</th><th scope=col>Fútbol.2</th><th scope=col>Beisbol.2</th></tr>\n",
       "\t<tr><th></th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th></tr>\n",
       "</thead>\n",
       "<tbody>\n",
       "\t<tr><th scope=row>Velocidad</th><td>0.14</td><td>0.37629324</td><td>0.42793009</td><td>0.40245249</td><td>0.40782736</td><td>0.40314721</td><td>0.37764984</td><td>0.05912417</td><td>0.37323160</td><td>0.42445534</td><td>0.43613499</td><td>0.41499906</td><td>0.1552390</td><td>0.43384637</td><td>0.38762534</td><td>0.12401949</td><td>0.01059568</td><td>0.43612843</td><td>0.41850000</td></tr>\n",
       "\t<tr><th scope=row>Fuerza</th><td>0.06</td><td>0.23196298</td><td>0.24790084</td><td>0.24121734</td><td>0.24127591</td><td>0.25832405</td><td>0.21704682</td><td>0.06961118</td><td>0.22680653</td><td>0.25125748</td><td>0.23745242</td><td>0.24659269</td><td>0.2651356</td><td>0.24069868</td><td>0.22564931</td><td>0.10545469</td><td>0.02041612</td><td>0.23300949</td><td>0.25483980</td></tr>\n",
       "\t<tr><th scope=row>Potencia</th><td>0.30</td><td>0.17656429</td><td>0.13544685</td><td>0.13284924</td><td>0.14181168</td><td>0.14862630</td><td>0.17911225</td><td>0.08195827</td><td>0.15371920</td><td>0.14137882</td><td>0.13862190</td><td>0.14697833</td><td>0.1193084</td><td>0.13783184</td><td>0.14493887</td><td>0.14889525</td><td>0.03354969</td><td>0.13923632</td><td>0.14475612</td></tr>\n",
       "\t<tr><th scope=row>Equilibrio</th><td>0.07</td><td>0.09521916</td><td>0.08269160</td><td>0.08419051</td><td>0.08819383</td><td>0.08521923</td><td>0.09597850</td><td>0.09649536</td><td>0.09395042</td><td>0.08258376</td><td>0.08202029</td><td>0.08683988</td><td>0.1841829</td><td>0.07833245</td><td>0.09625857</td><td>0.05890428</td><td>0.06034982</td><td>0.08318722</td><td>0.08177855</td></tr>\n",
       "\t<tr><th scope=row>Coordinaci¢n</th><td>0.13</td><td>0.05405096</td><td>0.04973717</td><td>0.05655265</td><td>0.05711617</td><td>0.04886586</td><td>0.05715086</td><td>0.11361091</td><td>0.06377328</td><td>0.04621934</td><td>0.04084924</td><td>0.04959914</td><td>0.1118801</td><td>0.04858883</td><td>0.06194166</td><td>0.17175296</td><td>0.09163361</td><td>0.04970156</td><td>0.04642105</td></tr>\n",
       "\t<tr><th scope=row>Tiempo de reacci¢n</th><td>0.18</td><td>0.03327292</td><td>0.02781666</td><td>0.03783455</td><td>0.03165427</td><td>0.02843915</td><td>0.03476066</td><td>0.13376228</td><td>0.03977308</td><td>0.02797249</td><td>0.02665718</td><td>0.02813226</td><td>0.0715609</td><td>0.03027836</td><td>0.03948937</td><td>0.19904690</td><td>0.17124777</td><td>0.03024484</td><td>0.02832279</td></tr>\n",
       "</tbody>\n",
       "</table>\n"
      ],
      "text/latex": [
       "A data.frame: 6 × 19\n",
       "\\begin{tabular}{r|lllllllllllllllllll}\n",
       "  & Karate & Fitness & Natación & Baloncesto & Fútbol & Fitness.1 & Baloncesto.1 & Tae.Kwon.Do & Beisbol & Pesas & Natación.1 & Baloncesto.2 & Atletismo & Fútbol.1 & Beisbol.1 & Baloncesto.3 & Baloncesto.4 & Fútbol.2 & Beisbol.2\\\\\n",
       "  & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl>\\\\\n",
       "\\hline\n",
       "\tVelocidad & 0.14 & 0.37629324 & 0.42793009 & 0.40245249 & 0.40782736 & 0.40314721 & 0.37764984 & 0.05912417 & 0.37323160 & 0.42445534 & 0.43613499 & 0.41499906 & 0.1552390 & 0.43384637 & 0.38762534 & 0.12401949 & 0.01059568 & 0.43612843 & 0.41850000\\\\\n",
       "\tFuerza & 0.06 & 0.23196298 & 0.24790084 & 0.24121734 & 0.24127591 & 0.25832405 & 0.21704682 & 0.06961118 & 0.22680653 & 0.25125748 & 0.23745242 & 0.24659269 & 0.2651356 & 0.24069868 & 0.22564931 & 0.10545469 & 0.02041612 & 0.23300949 & 0.25483980\\\\\n",
       "\tPotencia & 0.30 & 0.17656429 & 0.13544685 & 0.13284924 & 0.14181168 & 0.14862630 & 0.17911225 & 0.08195827 & 0.15371920 & 0.14137882 & 0.13862190 & 0.14697833 & 0.1193084 & 0.13783184 & 0.14493887 & 0.14889525 & 0.03354969 & 0.13923632 & 0.14475612\\\\\n",
       "\tEquilibrio & 0.07 & 0.09521916 & 0.08269160 & 0.08419051 & 0.08819383 & 0.08521923 & 0.09597850 & 0.09649536 & 0.09395042 & 0.08258376 & 0.08202029 & 0.08683988 & 0.1841829 & 0.07833245 & 0.09625857 & 0.05890428 & 0.06034982 & 0.08318722 & 0.08177855\\\\\n",
       "\tCoordinaci¢n & 0.13 & 0.05405096 & 0.04973717 & 0.05655265 & 0.05711617 & 0.04886586 & 0.05715086 & 0.11361091 & 0.06377328 & 0.04621934 & 0.04084924 & 0.04959914 & 0.1118801 & 0.04858883 & 0.06194166 & 0.17175296 & 0.09163361 & 0.04970156 & 0.04642105\\\\\n",
       "\tTiempo de reacci¢n & 0.18 & 0.03327292 & 0.02781666 & 0.03783455 & 0.03165427 & 0.02843915 & 0.03476066 & 0.13376228 & 0.03977308 & 0.02797249 & 0.02665718 & 0.02813226 & 0.0715609 & 0.03027836 & 0.03948937 & 0.19904690 & 0.17124777 & 0.03024484 & 0.02832279\\\\\n",
       "\\end{tabular}\n"
      ],
      "text/markdown": [
       "\n",
       "A data.frame: 6 × 19\n",
       "\n",
       "| <!--/--> | Karate &lt;dbl&gt; | Fitness &lt;dbl&gt; | Natación &lt;dbl&gt; | Baloncesto &lt;dbl&gt; | Fútbol &lt;dbl&gt; | Fitness.1 &lt;dbl&gt; | Baloncesto.1 &lt;dbl&gt; | Tae.Kwon.Do &lt;dbl&gt; | Beisbol &lt;dbl&gt; | Pesas &lt;dbl&gt; | Natación.1 &lt;dbl&gt; | Baloncesto.2 &lt;dbl&gt; | Atletismo &lt;dbl&gt; | Fútbol.1 &lt;dbl&gt; | Beisbol.1 &lt;dbl&gt; | Baloncesto.3 &lt;dbl&gt; | Baloncesto.4 &lt;dbl&gt; | Fútbol.2 &lt;dbl&gt; | Beisbol.2 &lt;dbl&gt; |\n",
       "|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n",
       "| Velocidad | 0.14 | 0.37629324 | 0.42793009 | 0.40245249 | 0.40782736 | 0.40314721 | 0.37764984 | 0.05912417 | 0.37323160 | 0.42445534 | 0.43613499 | 0.41499906 | 0.1552390 | 0.43384637 | 0.38762534 | 0.12401949 | 0.01059568 | 0.43612843 | 0.41850000 |\n",
       "| Fuerza | 0.06 | 0.23196298 | 0.24790084 | 0.24121734 | 0.24127591 | 0.25832405 | 0.21704682 | 0.06961118 | 0.22680653 | 0.25125748 | 0.23745242 | 0.24659269 | 0.2651356 | 0.24069868 | 0.22564931 | 0.10545469 | 0.02041612 | 0.23300949 | 0.25483980 |\n",
       "| Potencia | 0.30 | 0.17656429 | 0.13544685 | 0.13284924 | 0.14181168 | 0.14862630 | 0.17911225 | 0.08195827 | 0.15371920 | 0.14137882 | 0.13862190 | 0.14697833 | 0.1193084 | 0.13783184 | 0.14493887 | 0.14889525 | 0.03354969 | 0.13923632 | 0.14475612 |\n",
       "| Equilibrio | 0.07 | 0.09521916 | 0.08269160 | 0.08419051 | 0.08819383 | 0.08521923 | 0.09597850 | 0.09649536 | 0.09395042 | 0.08258376 | 0.08202029 | 0.08683988 | 0.1841829 | 0.07833245 | 0.09625857 | 0.05890428 | 0.06034982 | 0.08318722 | 0.08177855 |\n",
       "| Coordinaci¢n | 0.13 | 0.05405096 | 0.04973717 | 0.05655265 | 0.05711617 | 0.04886586 | 0.05715086 | 0.11361091 | 0.06377328 | 0.04621934 | 0.04084924 | 0.04959914 | 0.1118801 | 0.04858883 | 0.06194166 | 0.17175296 | 0.09163361 | 0.04970156 | 0.04642105 |\n",
       "| Tiempo de reacci¢n | 0.18 | 0.03327292 | 0.02781666 | 0.03783455 | 0.03165427 | 0.02843915 | 0.03476066 | 0.13376228 | 0.03977308 | 0.02797249 | 0.02665718 | 0.02813226 | 0.0715609 | 0.03027836 | 0.03948937 | 0.19904690 | 0.17124777 | 0.03024484 | 0.02832279 |\n",
       "\n"
      ],
      "text/plain": [
       "                   Karate Fitness    Natación   Baloncesto Fútbol    \n",
       "Velocidad          0.14   0.37629324 0.42793009 0.40245249 0.40782736\n",
       "Fuerza             0.06   0.23196298 0.24790084 0.24121734 0.24127591\n",
       "Potencia           0.30   0.17656429 0.13544685 0.13284924 0.14181168\n",
       "Equilibrio         0.07   0.09521916 0.08269160 0.08419051 0.08819383\n",
       "Coordinaci¢n       0.13   0.05405096 0.04973717 0.05655265 0.05711617\n",
       "Tiempo de reacci¢n 0.18   0.03327292 0.02781666 0.03783455 0.03165427\n",
       "                   Fitness.1  Baloncesto.1 Tae.Kwon.Do Beisbol    Pesas     \n",
       "Velocidad          0.40314721 0.37764984   0.05912417  0.37323160 0.42445534\n",
       "Fuerza             0.25832405 0.21704682   0.06961118  0.22680653 0.25125748\n",
       "Potencia           0.14862630 0.17911225   0.08195827  0.15371920 0.14137882\n",
       "Equilibrio         0.08521923 0.09597850   0.09649536  0.09395042 0.08258376\n",
       "Coordinaci¢n       0.04886586 0.05715086   0.11361091  0.06377328 0.04621934\n",
       "Tiempo de reacci¢n 0.02843915 0.03476066   0.13376228  0.03977308 0.02797249\n",
       "                   Natación.1 Baloncesto.2 Atletismo Fútbol.1   Beisbol.1 \n",
       "Velocidad          0.43613499 0.41499906   0.1552390 0.43384637 0.38762534\n",
       "Fuerza             0.23745242 0.24659269   0.2651356 0.24069868 0.22564931\n",
       "Potencia           0.13862190 0.14697833   0.1193084 0.13783184 0.14493887\n",
       "Equilibrio         0.08202029 0.08683988   0.1841829 0.07833245 0.09625857\n",
       "Coordinaci¢n       0.04084924 0.04959914   0.1118801 0.04858883 0.06194166\n",
       "Tiempo de reacci¢n 0.02665718 0.02813226   0.0715609 0.03027836 0.03948937\n",
       "                   Baloncesto.3 Baloncesto.4 Fútbol.2   Beisbol.2 \n",
       "Velocidad          0.12401949   0.01059568   0.43612843 0.41850000\n",
       "Fuerza             0.10545469   0.02041612   0.23300949 0.25483980\n",
       "Potencia           0.14889525   0.03354969   0.13923632 0.14475612\n",
       "Equilibrio         0.05890428   0.06034982   0.08318722 0.08177855\n",
       "Coordinaci¢n       0.17175296   0.09163361   0.04970156 0.04642105\n",
       "Tiempo de reacci¢n 0.19904690   0.17124777   0.03024484 0.02832279"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "head(indices)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table>\n",
       "<caption>A matrix: 6 × 8 of type dbl</caption>\n",
       "<thead>\n",
       "\t<tr><th></th><th scope=col>Velocidad</th><th scope=col>Fuerza</th><th scope=col>Potencia</th><th scope=col>Equilibrio</th><th scope=col>Coordinaci¢n</th><th scope=col>Tiempo de reacci¢n</th><th scope=col>Flexibilidad</th><th scope=col>Biomec nica corporal</th></tr>\n",
       "</thead>\n",
       "<tbody>\n",
       "\t<tr><th scope=row>Karate</th><td>0.1400000</td><td>0.0600000</td><td>0.3000000</td><td>0.07000000</td><td>0.13000000</td><td>0.18000000</td><td>0.04000000</td><td>0.09000000</td></tr>\n",
       "\t<tr><th scope=row>Fitness</th><td>0.3762932</td><td>0.2319630</td><td>0.1765643</td><td>0.09521916</td><td>0.05405096</td><td>0.03327292</td><td>0.02118693</td><td>0.01144952</td></tr>\n",
       "\t<tr><th scope=row>Natación</th><td>0.4279301</td><td>0.2479008</td><td>0.1354469</td><td>0.08269160</td><td>0.04973717</td><td>0.02781666</td><td>0.01815964</td><td>0.01031714</td></tr>\n",
       "\t<tr><th scope=row>Baloncesto</th><td>0.4024525</td><td>0.2412173</td><td>0.1328492</td><td>0.08419051</td><td>0.05655265</td><td>0.03783455</td><td>0.02715826</td><td>0.01774496</td></tr>\n",
       "\t<tr><th scope=row>Fútbol</th><td>0.4078274</td><td>0.2412759</td><td>0.1418117</td><td>0.08819383</td><td>0.05711617</td><td>0.03165427</td><td>0.02049700</td><td>0.01162377</td></tr>\n",
       "\t<tr><th scope=row>Fitness.1</th><td>0.4031472</td><td>0.2583240</td><td>0.1486263</td><td>0.08521923</td><td>0.04886586</td><td>0.02843915</td><td>0.01728671</td><td>0.01009149</td></tr>\n",
       "</tbody>\n",
       "</table>\n"
      ],
      "text/latex": [
       "A matrix: 6 × 8 of type dbl\n",
       "\\begin{tabular}{r|llllllll}\n",
       "  & Velocidad & Fuerza & Potencia & Equilibrio & Coordinaci¢n & Tiempo de reacci¢n & Flexibilidad & Biomec nica corporal\\\\\n",
       "\\hline\n",
       "\tKarate & 0.1400000 & 0.0600000 & 0.3000000 & 0.07000000 & 0.13000000 & 0.18000000 & 0.04000000 & 0.09000000\\\\\n",
       "\tFitness & 0.3762932 & 0.2319630 & 0.1765643 & 0.09521916 & 0.05405096 & 0.03327292 & 0.02118693 & 0.01144952\\\\\n",
       "\tNatación & 0.4279301 & 0.2479008 & 0.1354469 & 0.08269160 & 0.04973717 & 0.02781666 & 0.01815964 & 0.01031714\\\\\n",
       "\tBaloncesto & 0.4024525 & 0.2412173 & 0.1328492 & 0.08419051 & 0.05655265 & 0.03783455 & 0.02715826 & 0.01774496\\\\\n",
       "\tFútbol & 0.4078274 & 0.2412759 & 0.1418117 & 0.08819383 & 0.05711617 & 0.03165427 & 0.02049700 & 0.01162377\\\\\n",
       "\tFitness.1 & 0.4031472 & 0.2583240 & 0.1486263 & 0.08521923 & 0.04886586 & 0.02843915 & 0.01728671 & 0.01009149\\\\\n",
       "\\end{tabular}\n"
      ],
      "text/markdown": [
       "\n",
       "A matrix: 6 × 8 of type dbl\n",
       "\n",
       "| <!--/--> | Velocidad | Fuerza | Potencia | Equilibrio | Coordinaci¢n | Tiempo de reacci¢n | Flexibilidad | Biomec nica corporal |\n",
       "|---|---|---|---|---|---|---|---|---|\n",
       "| Karate | 0.1400000 | 0.0600000 | 0.3000000 | 0.07000000 | 0.13000000 | 0.18000000 | 0.04000000 | 0.09000000 |\n",
       "| Fitness | 0.3762932 | 0.2319630 | 0.1765643 | 0.09521916 | 0.05405096 | 0.03327292 | 0.02118693 | 0.01144952 |\n",
       "| Natación | 0.4279301 | 0.2479008 | 0.1354469 | 0.08269160 | 0.04973717 | 0.02781666 | 0.01815964 | 0.01031714 |\n",
       "| Baloncesto | 0.4024525 | 0.2412173 | 0.1328492 | 0.08419051 | 0.05655265 | 0.03783455 | 0.02715826 | 0.01774496 |\n",
       "| Fútbol | 0.4078274 | 0.2412759 | 0.1418117 | 0.08819383 | 0.05711617 | 0.03165427 | 0.02049700 | 0.01162377 |\n",
       "| Fitness.1 | 0.4031472 | 0.2583240 | 0.1486263 | 0.08521923 | 0.04886586 | 0.02843915 | 0.01728671 | 0.01009149 |\n",
       "\n"
      ],
      "text/plain": [
       "           Velocidad Fuerza    Potencia  Equilibrio Coordinaci¢n\n",
       "Karate     0.1400000 0.0600000 0.3000000 0.07000000 0.13000000  \n",
       "Fitness    0.3762932 0.2319630 0.1765643 0.09521916 0.05405096  \n",
       "Natación   0.4279301 0.2479008 0.1354469 0.08269160 0.04973717  \n",
       "Baloncesto 0.4024525 0.2412173 0.1328492 0.08419051 0.05655265  \n",
       "Fútbol     0.4078274 0.2412759 0.1418117 0.08819383 0.05711617  \n",
       "Fitness.1  0.4031472 0.2583240 0.1486263 0.08521923 0.04886586  \n",
       "           Tiempo de reacci¢n Flexibilidad Biomec nica corporal\n",
       "Karate     0.18000000         0.04000000   0.09000000          \n",
       "Fitness    0.03327292         0.02118693   0.01144952          \n",
       "Natación   0.02781666         0.01815964   0.01031714          \n",
       "Baloncesto 0.03783455         0.02715826   0.01774496          \n",
       "Fútbol     0.03165427         0.02049700   0.01162377          \n",
       "Fitness.1  0.02843915         0.01728671   0.01009149          "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# ya que las características son los renglones, se debe obtener la transpuesta de la matriz\n",
    "# los renglones serán las columnas\n",
    "indices_transp <- t(indices)\n",
    "head(indices_transp)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "library(dtw)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAA0gAAANICAMAAADKOT/pAAAAMFBMVEUAAABNTU1oaGh8fHyM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\nEoCAf3tAvh08VIAUAAAAAElFTkSuQmCC",
      "text/plain": [
       "Plot with title \"Cluster Dendrogram\""
      ]
     },
     "metadata": {
      "image/png": {
       "height": 420,
       "width": 420
      },
      "text/plain": {
       "height": 420,
       "width": 420
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# crear dendograma\n",
    "modelo_dtw <- hclust(dist(indices_transp, method=\"dtw\"))\n",
    "plot(modelo_dtw)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAA0gAAANICAMAAADKOT/pAAAAM1BMVEUAAABNTU1oaGh8fHyM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\nASiASAAKIBKAAv8Ba5wuOHiEtJYAAAAASUVORK5CYII=",
      "text/plain": [
       "Plot with title \"Cluster Dendrogram\""
      ]
     },
     "metadata": {
      "image/png": {
       "height": 420,
       "width": 420
      },
      "text/plain": {
       "height": 420,
       "width": 420
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot(modelo_dtw)\n",
    "rect.hclust(modelo_dtw,k=3,border=\"red\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAA0gAAANICAMAAADKOT/pAAAANlBMVEUAAAAAAP9NTU1oaGh8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\nAhCASAACEAlAwP8BVf1RfwnmbhkAAAAASUVORK5CYII=",
      "text/plain": [
       "Plot with title \"Dendrograma\""
      ]
     },
     "metadata": {
      "image/png": {
       "height": 420,
       "width": 420
      },
      "text/plain": {
       "height": 420,
       "width": 420
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "modelo_dtw <- hclust(dist(indices_transp, method=\"dtw\"))\n",
    "# mostrar los clusters que queremos obtener\n",
    "\n",
    "fit <- modelo_dtw\n",
    "\n",
    "nodePar <- list(lab.cex = 0.8, pch = c(NA, 19), cex = 0.7, col = \"blue\")\n",
    "plot(as.dendrogram(fit), xlab = \"\", sub=\"\", ylab = \"Distancia dtw\",\n",
    "     main = \"Dendrograma\", nodePar = nodePar)\n",
    "\n",
    "rect.hclust(fit, k=3, border=\"red\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table>\n",
       "<caption>A matrix: 6 × 9 of type dbl</caption>\n",
       "<thead>\n",
       "\t<tr><th></th><th scope=col>Velocidad</th><th scope=col>Fuerza</th><th scope=col>Potencia</th><th scope=col>Equilibrio</th><th scope=col>Coordinaci¢n</th><th scope=col>Tiempo de reacci¢n</th><th scope=col>Flexibilidad</th><th scope=col>Biomec nica corporal</th><th scope=col>clusters_dtw</th></tr>\n",
       "</thead>\n",
       "<tbody>\n",
       "\t<tr><th scope=row>Karate</th><td>0.1400000</td><td>0.0600000</td><td>0.3000000</td><td>0.07000000</td><td>0.13000000</td><td>0.18000000</td><td>0.04000000</td><td>0.09000000</td><td>1</td></tr>\n",
       "\t<tr><th scope=row>Fitness</th><td>0.3762932</td><td>0.2319630</td><td>0.1765643</td><td>0.09521916</td><td>0.05405096</td><td>0.03327292</td><td>0.02118693</td><td>0.01144952</td><td>2</td></tr>\n",
       "\t<tr><th scope=row>Natación</th><td>0.4279301</td><td>0.2479008</td><td>0.1354469</td><td>0.08269160</td><td>0.04973717</td><td>0.02781666</td><td>0.01815964</td><td>0.01031714</td><td>2</td></tr>\n",
       "\t<tr><th scope=row>Baloncesto</th><td>0.4024525</td><td>0.2412173</td><td>0.1328492</td><td>0.08419051</td><td>0.05655265</td><td>0.03783455</td><td>0.02715826</td><td>0.01774496</td><td>2</td></tr>\n",
       "\t<tr><th scope=row>Fútbol</th><td>0.4078274</td><td>0.2412759</td><td>0.1418117</td><td>0.08819383</td><td>0.05711617</td><td>0.03165427</td><td>0.02049700</td><td>0.01162377</td><td>2</td></tr>\n",
       "\t<tr><th scope=row>Fitness.1</th><td>0.4031472</td><td>0.2583240</td><td>0.1486263</td><td>0.08521923</td><td>0.04886586</td><td>0.02843915</td><td>0.01728671</td><td>0.01009149</td><td>2</td></tr>\n",
       "</tbody>\n",
       "</table>\n"
      ],
      "text/latex": [
       "A matrix: 6 × 9 of type dbl\n",
       "\\begin{tabular}{r|lllllllll}\n",
       "  & Velocidad & Fuerza & Potencia & Equilibrio & Coordinaci¢n & Tiempo de reacci¢n & Flexibilidad & Biomec nica corporal & clusters\\_dtw\\\\\n",
       "\\hline\n",
       "\tKarate & 0.1400000 & 0.0600000 & 0.3000000 & 0.07000000 & 0.13000000 & 0.18000000 & 0.04000000 & 0.09000000 & 1\\\\\n",
       "\tFitness & 0.3762932 & 0.2319630 & 0.1765643 & 0.09521916 & 0.05405096 & 0.03327292 & 0.02118693 & 0.01144952 & 2\\\\\n",
       "\tNatación & 0.4279301 & 0.2479008 & 0.1354469 & 0.08269160 & 0.04973717 & 0.02781666 & 0.01815964 & 0.01031714 & 2\\\\\n",
       "\tBaloncesto & 0.4024525 & 0.2412173 & 0.1328492 & 0.08419051 & 0.05655265 & 0.03783455 & 0.02715826 & 0.01774496 & 2\\\\\n",
       "\tFútbol & 0.4078274 & 0.2412759 & 0.1418117 & 0.08819383 & 0.05711617 & 0.03165427 & 0.02049700 & 0.01162377 & 2\\\\\n",
       "\tFitness.1 & 0.4031472 & 0.2583240 & 0.1486263 & 0.08521923 & 0.04886586 & 0.02843915 & 0.01728671 & 0.01009149 & 2\\\\\n",
       "\\end{tabular}\n"
      ],
      "text/markdown": [
       "\n",
       "A matrix: 6 × 9 of type dbl\n",
       "\n",
       "| <!--/--> | Velocidad | Fuerza | Potencia | Equilibrio | Coordinaci¢n | Tiempo de reacci¢n | Flexibilidad | Biomec nica corporal | clusters_dtw |\n",
       "|---|---|---|---|---|---|---|---|---|---|\n",
       "| Karate | 0.1400000 | 0.0600000 | 0.3000000 | 0.07000000 | 0.13000000 | 0.18000000 | 0.04000000 | 0.09000000 | 1 |\n",
       "| Fitness | 0.3762932 | 0.2319630 | 0.1765643 | 0.09521916 | 0.05405096 | 0.03327292 | 0.02118693 | 0.01144952 | 2 |\n",
       "| Natación | 0.4279301 | 0.2479008 | 0.1354469 | 0.08269160 | 0.04973717 | 0.02781666 | 0.01815964 | 0.01031714 | 2 |\n",
       "| Baloncesto | 0.4024525 | 0.2412173 | 0.1328492 | 0.08419051 | 0.05655265 | 0.03783455 | 0.02715826 | 0.01774496 | 2 |\n",
       "| Fútbol | 0.4078274 | 0.2412759 | 0.1418117 | 0.08819383 | 0.05711617 | 0.03165427 | 0.02049700 | 0.01162377 | 2 |\n",
       "| Fitness.1 | 0.4031472 | 0.2583240 | 0.1486263 | 0.08521923 | 0.04886586 | 0.02843915 | 0.01728671 | 0.01009149 | 2 |\n",
       "\n"
      ],
      "text/plain": [
       "           Velocidad Fuerza    Potencia  Equilibrio Coordinaci¢n\n",
       "Karate     0.1400000 0.0600000 0.3000000 0.07000000 0.13000000  \n",
       "Fitness    0.3762932 0.2319630 0.1765643 0.09521916 0.05405096  \n",
       "Natación   0.4279301 0.2479008 0.1354469 0.08269160 0.04973717  \n",
       "Baloncesto 0.4024525 0.2412173 0.1328492 0.08419051 0.05655265  \n",
       "Fútbol     0.4078274 0.2412759 0.1418117 0.08819383 0.05711617  \n",
       "Fitness.1  0.4031472 0.2583240 0.1486263 0.08521923 0.04886586  \n",
       "           Tiempo de reacci¢n Flexibilidad Biomec nica corporal clusters_dtw\n",
       "Karate     0.18000000         0.04000000   0.09000000           1           \n",
       "Fitness    0.03327292         0.02118693   0.01144952           2           \n",
       "Natación   0.02781666         0.01815964   0.01031714           2           \n",
       "Baloncesto 0.03783455         0.02715826   0.01774496           2           \n",
       "Fútbol     0.03165427         0.02049700   0.01162377           2           \n",
       "Fitness.1  0.02843915         0.01728671   0.01009149           2           "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# a que cluster pertenece cada uno de los indices\n",
    "clusters_dtw <- cutree(modelo_dtw,k=3)\n",
    "indices_con_clusters <- cbind(indices_transp, clusters_dtw)\n",
    "head(indices_con_clusters)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "# guardar la información\n",
    "write.csv(indices_con_clusters,\"indices_con_clusters.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "R",
   "language": "R",
   "name": "ir"
  },
  "language_info": {
   "codemirror_mode": "r",
   "file_extension": ".r",
   "mimetype": "text/x-r-source",
   "name": "R",
   "pygments_lexer": "r",
   "version": "3.6.1"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}