movies_analysis/movie_analysis.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Análisis de películas:\n",
    "\n",
    "Trabajando en equipo, determinar:\n",
    "\n",
    "* ¿Cuál es el nombre de la película más popular? Obtener el top 10\n",
    "* ¿Cuantas películas vieron los H y M?\n",
    "* ¿Donde se ven las top 10 películas más populares?\n",
    "\n",
    "Use el barplot para gráficas cada punto."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Criterio de popularidad definido:\n",
    "\n",
    "Una película popular es aquella que tiene una gran cantidad de calificaciones favorables y está calificada por usuarios de diversas regiones, i.e.\n",
    "\n",
    "* +calificaciones buenas\n",
    "* +códigos postales"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "vscode": {
     "languageId": "r"
    }
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n",
      "Attaching package: 'dplyr'\n",
      "\n",
      "\n",
      "The following objects are masked from 'package:stats':\n",
      "\n",
      "    filter, lag\n",
      "\n",
      "\n",
      "The following objects are masked from 'package:base':\n",
      "\n",
      "    intersect, setdiff, setequal, union\n",
      "\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# Se cargan las librerías\n",
    "\n",
    "library(\"dplyr\")\n",
    "library(\"readr\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "vscode": {
     "languageId": "r"
    }
   },
   "outputs": [],
   "source": [
    "# Lee la información de los archivos\n",
    "usuarios <- read.table(\"./movies_data/u.user\", header = TRUE, sep = \"|\")\n",
    "peliculas <- read.table(\"./movies_data/u.item\", header = TRUE, sep = \"|\")\n",
    "calificaciones <- read.table(\"./movies_data/udata.data\", header = TRUE, sep = \"\\t\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "vscode": {
     "languageId": "r"
    }
   },
   "outputs": [],
   "source": [
    "# Unir las tablas\n",
    "datos <- left_join(calificaciones, usuarios, by = c(\"userid\"=\"userid\"))\n",
    "datos <- left_join(datos, peliculas, by = c(\"itemid\"=\"itemid\"))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "vscode": {
     "languageId": "r"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table class=\"dataframe\">\n",
       "<caption>A data.frame: 3 × 31</caption>\n",
       "<thead>\n",
       "\t<tr><th></th><th scope=col>userid</th><th scope=col>itemid</th><th scope=col>rate</th><th scope=col>timestamp</th><th scope=col>age</th><th scope=col>gender</th><th scope=col>occupation</th><th scope=col>zcode</th><th scope=col>title</th><th scope=col>rdate</th><th scope=col>⋯</th><th scope=col>Fantasy</th><th scope=col>FilmNoir</th><th scope=col>Horror</th><th scope=col>Musical</th><th scope=col>Mystery</th><th scope=col>Romance</th><th scope=col>SciFi</th><th scope=col>Thriller</th><th scope=col>War</th><th scope=col>Western</th></tr>\n",
       "\t<tr><th></th><th scope=col>&lt;int&gt;</th><th scope=col>&lt;int&gt;</th><th scope=col>&lt;int&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;int&gt;</th><th scope=col>&lt;chr&gt;</th><th scope=col>&lt;chr&gt;</th><th scope=col>&lt;chr&gt;</th><th scope=col>&lt;chr&gt;</th><th scope=col>&lt;chr&gt;</th><th scope=col>⋯</th><th scope=col>&lt;int&gt;</th><th scope=col>&lt;int&gt;</th><th scope=col>&lt;int&gt;</th><th scope=col>&lt;int&gt;</th><th scope=col>&lt;int&gt;</th><th scope=col>&lt;int&gt;</th><th scope=col>&lt;int&gt;</th><th scope=col>&lt;int&gt;</th><th scope=col>&lt;int&gt;</th><th scope=col>&lt;int&gt;</th></tr>\n",
       "</thead>\n",
       "<tbody>\n",
       "\t<tr><th scope=row>1</th><td>196</td><td>242</td><td>3</td><td>881250949</td><td>49</td><td>M</td><td>writer   </td><td>55105</td><td>Kolya (1996)            </td><td>24-Jan-1997</td><td>⋯</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td></tr>\n",
       "\t<tr><th scope=row>2</th><td>186</td><td>302</td><td>3</td><td>891717742</td><td>39</td><td>F</td><td>executive</td><td>00000</td><td>L.A. Confidential (1997)</td><td>01-Jan-1997</td><td>⋯</td><td>0</td><td>1</td><td>0</td><td>0</td><td>1</td><td>0</td><td>0</td><td>1</td><td>0</td><td>0</td></tr>\n",
       "\t<tr><th scope=row>3</th><td> 22</td><td>377</td><td>1</td><td>878887116</td><td>25</td><td>M</td><td>writer   </td><td>40206</td><td>Heavyweights (1994)     </td><td>01-Jan-1994</td><td>⋯</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td></tr>\n",
       "</tbody>\n",
       "</table>\n"
      ],
      "text/latex": [
       "A data.frame: 3 × 31\n",
       "\\begin{tabular}{r|lllllllllllllllllllll}\n",
       "  & userid & itemid & rate & timestamp & age & gender & occupation & zcode & title & rdate & ⋯ & Fantasy & FilmNoir & Horror & Musical & Mystery & Romance & SciFi & Thriller & War & Western\\\\\n",
       "  & <int> & <int> & <int> & <dbl> & <int> & <chr> & <chr> & <chr> & <chr> & <chr> & ⋯ & <int> & <int> & <int> & <int> & <int> & <int> & <int> & <int> & <int> & <int>\\\\\n",
       "\\hline\n",
       "\t1 & 196 & 242 & 3 & 881250949 & 49 & M & writer    & 55105 & Kolya (1996)             & 24-Jan-1997 & ⋯ & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0\\\\\n",
       "\t2 & 186 & 302 & 3 & 891717742 & 39 & F & executive & 00000 & L.A. Confidential (1997) & 01-Jan-1997 & ⋯ & 0 & 1 & 0 & 0 & 1 & 0 & 0 & 1 & 0 & 0\\\\\n",
       "\t3 &  22 & 377 & 1 & 878887116 & 25 & M & writer    & 40206 & Heavyweights (1994)      & 01-Jan-1994 & ⋯ & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0\\\\\n",
       "\\end{tabular}\n"
      ],
      "text/markdown": [
       "\n",
       "A data.frame: 3 × 31\n",
       "\n",
       "| <!--/--> | userid &lt;int&gt; | itemid &lt;int&gt; | rate &lt;int&gt; | timestamp &lt;dbl&gt; | age &lt;int&gt; | gender &lt;chr&gt; | occupation &lt;chr&gt; | zcode &lt;chr&gt; | title &lt;chr&gt; | rdate &lt;chr&gt; | ⋯ ⋯ | Fantasy &lt;int&gt; | FilmNoir &lt;int&gt; | Horror &lt;int&gt; | Musical &lt;int&gt; | Mystery &lt;int&gt; | Romance &lt;int&gt; | SciFi &lt;int&gt; | Thriller &lt;int&gt; | War &lt;int&gt; | Western &lt;int&gt; |\n",
       "|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n",
       "| 1 | 196 | 242 | 3 | 881250949 | 49 | M | writer    | 55105 | Kolya (1996)             | 24-Jan-1997 | ⋯ | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |\n",
       "| 2 | 186 | 302 | 3 | 891717742 | 39 | F | executive | 00000 | L.A. Confidential (1997) | 01-Jan-1997 | ⋯ | 0 | 1 | 0 | 0 | 1 | 0 | 0 | 1 | 0 | 0 |\n",
       "| 3 |  22 | 377 | 1 | 878887116 | 25 | M | writer    | 40206 | Heavyweights (1994)      | 01-Jan-1994 | ⋯ | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |\n",
       "\n"
      ],
      "text/plain": [
       "  userid itemid rate timestamp age gender occupation zcode\n",
       "1 196    242    3    881250949 49  M      writer     55105\n",
       "2 186    302    3    891717742 39  F      executive  00000\n",
       "3  22    377    1    878887116 25  M      writer     40206\n",
       "  title                    rdate       ⋯ Fantasy FilmNoir Horror Musical\n",
       "1 Kolya (1996)             24-Jan-1997 ⋯ 0       0        0      0      \n",
       "2 L.A. Confidential (1997) 01-Jan-1997 ⋯ 0       1        0      0      \n",
       "3 Heavyweights (1994)      01-Jan-1994 ⋯ 0       0        0      0      \n",
       "  Mystery Romance SciFi Thriller War Western\n",
       "1 0       0       0     0        0   0      \n",
       "2 1       0       0     1        0   0      \n",
       "3 0       0       0     0        0   0      "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "datos[1:3, ]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "vscode": {
     "languageId": "r"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>\n",
       ".list-inline {list-style: none; margin:0; padding: 0}\n",
       ".list-inline>li {display: inline-block}\n",
       ".list-inline>li:not(:last-child)::after {content: \"\\00b7\"; padding: 0 .5ex}\n",
       "</style>\n",
       "<ol class=list-inline><li>'userid'</li><li>'movieid'</li><li>'rate'</li><li>'timestamp'</li><li>'age'</li><li>'gender'</li><li>'occupation'</li><li>'zcode'</li><li>'title'</li><li>'rdate'</li><li>'vrdate'</li><li>'IMDbURL'</li><li>'unknown'</li><li>'Action'</li><li>'Adventure'</li><li>'Animation'</li><li>'Children'</li><li>'Comedy'</li><li>'Crime'</li><li>'Documentary'</li><li>'Drama'</li><li>'Fantasy'</li><li>'FilmNoir'</li><li>'Horror'</li><li>'Musical'</li><li>'Mystery'</li><li>'Romance'</li><li>'SciFi'</li><li>'Thriller'</li><li>'War'</li><li>'Western'</li></ol>\n"
      ],
      "text/latex": [
       "\\begin{enumerate*}\n",
       "\\item 'userid'\n",
       "\\item 'movieid'\n",
       "\\item 'rate'\n",
       "\\item 'timestamp'\n",
       "\\item 'age'\n",
       "\\item 'gender'\n",
       "\\item 'occupation'\n",
       "\\item 'zcode'\n",
       "\\item 'title'\n",
       "\\item 'rdate'\n",
       "\\item 'vrdate'\n",
       "\\item 'IMDbURL'\n",
       "\\item 'unknown'\n",
       "\\item 'Action'\n",
       "\\item 'Adventure'\n",
       "\\item 'Animation'\n",
       "\\item 'Children'\n",
       "\\item 'Comedy'\n",
       "\\item 'Crime'\n",
       "\\item 'Documentary'\n",
       "\\item 'Drama'\n",
       "\\item 'Fantasy'\n",
       "\\item 'FilmNoir'\n",
       "\\item 'Horror'\n",
       "\\item 'Musical'\n",
       "\\item 'Mystery'\n",
       "\\item 'Romance'\n",
       "\\item 'SciFi'\n",
       "\\item 'Thriller'\n",
       "\\item 'War'\n",
       "\\item 'Western'\n",
       "\\end{enumerate*}\n"
      ],
      "text/markdown": [
       "1. 'userid'\n",
       "2. 'movieid'\n",
       "3. 'rate'\n",
       "4. 'timestamp'\n",
       "5. 'age'\n",
       "6. 'gender'\n",
       "7. 'occupation'\n",
       "8. 'zcode'\n",
       "9. 'title'\n",
       "10. 'rdate'\n",
       "11. 'vrdate'\n",
       "12. 'IMDbURL'\n",
       "13. 'unknown'\n",
       "14. 'Action'\n",
       "15. 'Adventure'\n",
       "16. 'Animation'\n",
       "17. 'Children'\n",
       "18. 'Comedy'\n",
       "19. 'Crime'\n",
       "20. 'Documentary'\n",
       "21. 'Drama'\n",
       "22. 'Fantasy'\n",
       "23. 'FilmNoir'\n",
       "24. 'Horror'\n",
       "25. 'Musical'\n",
       "26. 'Mystery'\n",
       "27. 'Romance'\n",
       "28. 'SciFi'\n",
       "29. 'Thriller'\n",
       "30. 'War'\n",
       "31. 'Western'\n",
       "\n",
       "\n"
      ],
      "text/plain": [
       " [1] \"userid\"      \"movieid\"     \"rate\"        \"timestamp\"   \"age\"        \n",
       " [6] \"gender\"      \"occupation\"  \"zcode\"       \"title\"       \"rdate\"      \n",
       "[11] \"vrdate\"      \"IMDbURL\"     \"unknown\"     \"Action\"      \"Adventure\"  \n",
       "[16] \"Animation\"   \"Children\"    \"Comedy\"      \"Crime\"       \"Documentary\"\n",
       "[21] \"Drama\"       \"Fantasy\"     \"FilmNoir\"    \"Horror\"      \"Musical\"    \n",
       "[26] \"Mystery\"     \"Romance\"     \"SciFi\"       \"Thriller\"    \"War\"        \n",
       "[31] \"Western\"    "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Cambiamos los nombres de las columnas:\n",
    "datos <- datos %>% rename(movieid = itemid)\n",
    "colnames(datos)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Para responder a la pregunta...\n",
    "\n",
    "* ¿Cuál es el nombre de la película más popular?\n",
    "\n",
    "... vamos a conocer primero las películas con más calificaciones. A partir de ahí, obtendremos aquellas mejor calificadas"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "vscode": {
     "languageId": "r"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>\n",
       ".list-inline {list-style: none; margin:0; padding: 0}\n",
       ".list-inline>li {display: inline-block}\n",
       ".list-inline>li:not(:last-child)::after {content: \"\\00b7\"; padding: 0 .5ex}\n",
       "</style>\n",
       "<ol class=list-inline><li>'tbl_df'</li><li>'tbl'</li><li>'data.frame'</li></ol>\n"
      ],
      "text/latex": [
       "\\begin{enumerate*}\n",
       "\\item 'tbl\\_df'\n",
       "\\item 'tbl'\n",
       "\\item 'data.frame'\n",
       "\\end{enumerate*}\n"
      ],
      "text/markdown": [
       "1. 'tbl_df'\n",
       "2. 'tbl'\n",
       "3. 'data.frame'\n",
       "\n",
       "\n"
      ],
      "text/plain": [
       "[1] \"tbl_df\"     \"tbl\"        \"data.frame\""
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# De la tabla de datos, obtén las películas con más calificaciones\n",
    "mas_calif <- datos %>% group_by(movieid) %>% summarise(n_calif = n())\n",
    "class(mas_calif)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "vscode": {
     "languageId": "r"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table class=\"dataframe\">\n",
       "<caption>A tibble: 3 × 2</caption>\n",
       "<thead>\n",
       "\t<tr><th scope=col>movieid</th><th scope=col>n_calif</th></tr>\n",
       "\t<tr><th scope=col>&lt;int&gt;</th><th scope=col>&lt;int&gt;</th></tr>\n",
       "</thead>\n",
       "<tbody>\n",
       "\t<tr><td>1</td><td>452</td></tr>\n",
       "\t<tr><td>2</td><td>131</td></tr>\n",
       "\t<tr><td>3</td><td> 90</td></tr>\n",
       "</tbody>\n",
       "</table>\n"
      ],
      "text/latex": [
       "A tibble: 3 × 2\n",
       "\\begin{tabular}{ll}\n",
       " movieid & n\\_calif\\\\\n",
       " <int> & <int>\\\\\n",
       "\\hline\n",
       "\t 1 & 452\\\\\n",
       "\t 2 & 131\\\\\n",
       "\t 3 &  90\\\\\n",
       "\\end{tabular}\n"
      ],
      "text/markdown": [
       "\n",
       "A tibble: 3 × 2\n",
       "\n",
       "| movieid &lt;int&gt; | n_calif &lt;int&gt; |\n",
       "|---|---|\n",
       "| 1 | 452 |\n",
       "| 2 | 131 |\n",
       "| 3 |  90 |\n",
       "\n"
      ],
      "text/plain": [
       "  movieid n_calif\n",
       "1 1       452    \n",
       "2 2       131    \n",
       "3 3        90    "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "1682"
      ],
      "text/latex": [
       "1682"
      ],
      "text/markdown": [
       "1682"
      ],
      "text/plain": [
       "[1] 1682"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "mas_calif[1:3, ]\n",
    "length(mas_calif$movieid)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "vscode": {
     "languageId": "r"
    }
   },
   "outputs": [],
   "source": [
    "# A partir del id de la película, obtén un promedio de las calificaciones y ordénalo descendentemente\n",
    "califs_promedio <- datos %>% group_by(movieid) %>% summarise(calif_promedio = mean(rate)) %>% arrange(desc(calif_promedio))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "vscode": {
     "languageId": "r"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table class=\"dataframe\">\n",
       "<caption>A tibble: 3 × 3</caption>\n",
       "<thead>\n",
       "\t<tr><th scope=col>movieid</th><th scope=col>n_calif</th><th scope=col>calif_promedio</th></tr>\n",
       "\t<tr><th scope=col>&lt;int&gt;</th><th scope=col>&lt;int&gt;</th><th scope=col>&lt;dbl&gt;</th></tr>\n",
       "</thead>\n",
       "<tbody>\n",
       "\t<tr><td>1</td><td>452</td><td>3.878319</td></tr>\n",
       "\t<tr><td>2</td><td>131</td><td>3.206107</td></tr>\n",
       "\t<tr><td>3</td><td> 90</td><td>3.033333</td></tr>\n",
       "</tbody>\n",
       "</table>\n"
      ],
      "text/latex": [
       "A tibble: 3 × 3\n",
       "\\begin{tabular}{lll}\n",
       " movieid & n\\_calif & calif\\_promedio\\\\\n",
       " <int> & <int> & <dbl>\\\\\n",
       "\\hline\n",
       "\t 1 & 452 & 3.878319\\\\\n",
       "\t 2 & 131 & 3.206107\\\\\n",
       "\t 3 &  90 & 3.033333\\\\\n",
       "\\end{tabular}\n"
      ],
      "text/markdown": [
       "\n",
       "A tibble: 3 × 3\n",
       "\n",
       "| movieid &lt;int&gt; | n_calif &lt;int&gt; | calif_promedio &lt;dbl&gt; |\n",
       "|---|---|---|\n",
       "| 1 | 452 | 3.878319 |\n",
       "| 2 | 131 | 3.206107 |\n",
       "| 3 |  90 | 3.033333 |\n",
       "\n"
      ],
      "text/plain": [
       "  movieid n_calif calif_promedio\n",
       "1 1       452     3.878319      \n",
       "2 2       131     3.206107      \n",
       "3 3        90     3.033333      "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Unir las tablas de las películas con más calificaciones y las calificaciones promedio\n",
    "info_peliculas <- left_join(mas_calif, califs_promedio, by = c(\"movieid\"=\"movieid\"))\n",
    "info_peliculas[1:3, ]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "vscode": {
     "languageId": "r"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table class=\"dataframe\">\n",
       "<caption>A tibble: 3 × 4</caption>\n",
       "<thead>\n",
       "\t<tr><th scope=col>movieid</th><th scope=col>n_calif</th><th scope=col>calif_promedio</th><th scope=col>n_regiones</th></tr>\n",
       "\t<tr><th scope=col>&lt;int&gt;</th><th scope=col>&lt;int&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;int&gt;</th></tr>\n",
       "</thead>\n",
       "<tbody>\n",
       "\t<tr><td>1</td><td>452</td><td>3.878319</td><td>18</td></tr>\n",
       "\t<tr><td>2</td><td>131</td><td>3.206107</td><td>12</td></tr>\n",
       "\t<tr><td>3</td><td> 90</td><td>3.033333</td><td>13</td></tr>\n",
       "</tbody>\n",
       "</table>\n"
      ],
      "text/latex": [
       "A tibble: 3 × 4\n",
       "\\begin{tabular}{llll}\n",
       " movieid & n\\_calif & calif\\_promedio & n\\_regiones\\\\\n",
       " <int> & <int> & <dbl> & <int>\\\\\n",
       "\\hline\n",
       "\t 1 & 452 & 3.878319 & 18\\\\\n",
       "\t 2 & 131 & 3.206107 & 12\\\\\n",
       "\t 3 &  90 & 3.033333 & 13\\\\\n",
       "\\end{tabular}\n"
      ],
      "text/markdown": [
       "\n",
       "A tibble: 3 × 4\n",
       "\n",
       "| movieid &lt;int&gt; | n_calif &lt;int&gt; | calif_promedio &lt;dbl&gt; | n_regiones &lt;int&gt; |\n",
       "|---|---|---|---|\n",
       "| 1 | 452 | 3.878319 | 18 |\n",
       "| 2 | 131 | 3.206107 | 12 |\n",
       "| 3 |  90 | 3.033333 | 13 |\n",
       "\n"
      ],
      "text/plain": [
       "  movieid n_calif calif_promedio n_regiones\n",
       "1 1       452     3.878319       18        \n",
       "2 2       131     3.206107       12        \n",
       "3 3        90     3.033333       13        "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Aún es necesario obtener aquellas películas que han sido calificadas por usuarios\n",
    "# de una cantidad más diversa de regiones. La región se obtiene del primer dígito del\n",
    "# código postal del usuario\n",
    "\n",
    "# Se obtiene el primer dígito del código postal en una nueva columna\n",
    "datos <- datos %>% mutate(region = substr(zcode, 1, 1))\n",
    "# Se obtiene el número de regiones en las que ha sido calificada cada película\n",
    "calif_por_region <- datos %>% group_by(movieid) %>% summarise(n_regiones = n_distinct(region))\n",
    "# Se unen las tablas\n",
    "info_peliculas <- left_join(info_peliculas, calif_por_region, by = c(\"movieid\"=\"movieid\"))\n",
    "info_peliculas[1:3, ]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "vscode": {
     "languageId": "r"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "1682"
      ],
      "text/latex": [
       "1682"
      ],
      "text/markdown": [
       "1682"
      ],
      "text/plain": [
       "[1] 1682"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Conozcamos el tamaño del set de datos\n",
    "length(info_peliculas$movieid)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "vscode": {
     "languageId": "r"
    }
   },
   "outputs": [],
   "source": [
    "# Agregamos la información de las películas\n",
    "info_peliculas <- left_join(info_peliculas, peliculas, by = c(\"movieid\"=\"itemid\"))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "vscode": {
     "languageId": "r"
    }
   },
   "outputs": [],
   "source": [
    "# Tomando el cuenta que tenemos 1682 películas, se tomará una muestra de 100 películas\n",
    "# para realizar el análisis. Primero, se ordena la tabla de películas por el número de\n",
    "# calificaciones y se toma una muestra de 100 películas\n",
    "top100_mas_calif <- info_peliculas %>% arrange(desc(n_calif)) %>% slice(1:100)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "vscode": {
     "languageId": "r"
    }
   },
   "outputs": [],
   "source": [
    "# Segundo, se ordena la tabla de películas por el promedio de calificaciones y se toma\n",
    "# una muestra de 10 películas\n",
    "top10_promedio <- top100_mas_calif %>% arrange(desc(calif_promedio)) %>% slice(1:10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "vscode": {
     "languageId": "r"
    }
   },
   "outputs": [],
   "source": [
    "# Finalmente, ordenamos las 10 películas según el número de regiones en las que han sido calificadas\n",
    "top10_mas_populares <- top10_promedio %>% arrange(desc(n_regiones))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "vscode": {
     "languageId": "r"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table class=\"dataframe\">\n",
       "<caption>A tibble: 10 × 5</caption>\n",
       "<thead>\n",
       "\t<tr><th scope=col>movieid</th><th scope=col>title</th><th scope=col>n_calif</th><th scope=col>calif_promedio</th><th scope=col>n_regiones</th></tr>\n",
       "\t<tr><th scope=col>&lt;int&gt;</th><th scope=col>&lt;chr&gt;</th><th scope=col>&lt;int&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;int&gt;</th></tr>\n",
       "</thead>\n",
       "<tbody>\n",
       "\t<tr><td> 50</td><td>Star Wars (1977)                     </td><td>583</td><td>4.358491</td><td>18</td></tr>\n",
       "\t<tr><td>127</td><td>Godfather, The (1972)                </td><td>413</td><td>4.283293</td><td>16</td></tr>\n",
       "\t<tr><td>318</td><td>Schindlers List (1993)               </td><td>298</td><td>4.466443</td><td>15</td></tr>\n",
       "\t<tr><td>483</td><td>Casablanca (1942)                    </td><td>243</td><td>4.456790</td><td>15</td></tr>\n",
       "\t<tr><td> 12</td><td>Usual Suspects, The (1995)           </td><td>267</td><td>4.385768</td><td>15</td></tr>\n",
       "\t<tr><td>174</td><td>Raiders of the Lost Ark (1981)       </td><td>420</td><td>4.252381</td><td>15</td></tr>\n",
       "\t<tr><td> 64</td><td>Shawshank Redemption, The (1994)     </td><td>283</td><td>4.445230</td><td>14</td></tr>\n",
       "\t<tr><td>357</td><td>One Flew Over the Cuckoos Nest (1975)</td><td>264</td><td>4.291667</td><td>14</td></tr>\n",
       "\t<tr><td> 98</td><td>Silence of the Lambs, The (1991)     </td><td>390</td><td>4.289744</td><td>14</td></tr>\n",
       "\t<tr><td>427</td><td>To Kill a Mockingbird (1962)         </td><td>219</td><td>4.292237</td><td>13</td></tr>\n",
       "</tbody>\n",
       "</table>\n"
      ],
      "text/latex": [
       "A tibble: 10 × 5\n",
       "\\begin{tabular}{lllll}\n",
       " movieid & title & n\\_calif & calif\\_promedio & n\\_regiones\\\\\n",
       " <int> & <chr> & <int> & <dbl> & <int>\\\\\n",
       "\\hline\n",
       "\t  50 & Star Wars (1977)                      & 583 & 4.358491 & 18\\\\\n",
       "\t 127 & Godfather, The (1972)                 & 413 & 4.283293 & 16\\\\\n",
       "\t 318 & Schindlers List (1993)                & 298 & 4.466443 & 15\\\\\n",
       "\t 483 & Casablanca (1942)                     & 243 & 4.456790 & 15\\\\\n",
       "\t  12 & Usual Suspects, The (1995)            & 267 & 4.385768 & 15\\\\\n",
       "\t 174 & Raiders of the Lost Ark (1981)        & 420 & 4.252381 & 15\\\\\n",
       "\t  64 & Shawshank Redemption, The (1994)      & 283 & 4.445230 & 14\\\\\n",
       "\t 357 & One Flew Over the Cuckoos Nest (1975) & 264 & 4.291667 & 14\\\\\n",
       "\t  98 & Silence of the Lambs, The (1991)      & 390 & 4.289744 & 14\\\\\n",
       "\t 427 & To Kill a Mockingbird (1962)          & 219 & 4.292237 & 13\\\\\n",
       "\\end{tabular}\n"
      ],
      "text/markdown": [
       "\n",
       "A tibble: 10 × 5\n",
       "\n",
       "| movieid &lt;int&gt; | title &lt;chr&gt; | n_calif &lt;int&gt; | calif_promedio &lt;dbl&gt; | n_regiones &lt;int&gt; |\n",
       "|---|---|---|---|---|\n",
       "|  50 | Star Wars (1977)                      | 583 | 4.358491 | 18 |\n",
       "| 127 | Godfather, The (1972)                 | 413 | 4.283293 | 16 |\n",
       "| 318 | Schindlers List (1993)                | 298 | 4.466443 | 15 |\n",
       "| 483 | Casablanca (1942)                     | 243 | 4.456790 | 15 |\n",
       "|  12 | Usual Suspects, The (1995)            | 267 | 4.385768 | 15 |\n",
       "| 174 | Raiders of the Lost Ark (1981)        | 420 | 4.252381 | 15 |\n",
       "|  64 | Shawshank Redemption, The (1994)      | 283 | 4.445230 | 14 |\n",
       "| 357 | One Flew Over the Cuckoos Nest (1975) | 264 | 4.291667 | 14 |\n",
       "|  98 | Silence of the Lambs, The (1991)      | 390 | 4.289744 | 14 |\n",
       "| 427 | To Kill a Mockingbird (1962)          | 219 | 4.292237 | 13 |\n",
       "\n"
      ],
      "text/plain": [
       "   movieid title                                 n_calif calif_promedio\n",
       "1   50     Star Wars (1977)                      583     4.358491      \n",
       "2  127     Godfather, The (1972)                 413     4.283293      \n",
       "3  318     Schindlers List (1993)                298     4.466443      \n",
       "4  483     Casablanca (1942)                     243     4.456790      \n",
       "5   12     Usual Suspects, The (1995)            267     4.385768      \n",
       "6  174     Raiders of the Lost Ark (1981)        420     4.252381      \n",
       "7   64     Shawshank Redemption, The (1994)      283     4.445230      \n",
       "8  357     One Flew Over the Cuckoos Nest (1975) 264     4.291667      \n",
       "9   98     Silence of the Lambs, The (1991)      390     4.289744      \n",
       "10 427     To Kill a Mockingbird (1962)          219     4.292237      \n",
       "   n_regiones\n",
       "1  18        \n",
       "2  16        \n",
       "3  15        \n",
       "4  15        \n",
       "5  15        \n",
       "6  15        \n",
       "7  14        \n",
       "8  14        \n",
       "9  14        \n",
       "10 13        "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "plot without title"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 420,
       "width": 420
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Mostramos el nombre de las películas, el número de calificaciones, el promedio de calificaciones\n",
    "# y el número de regiones, así como desplegamos en un gráfico el número de regiones en el eje y y\n",
    "# el nombre de las películas en el eje x\n",
    "pelis_n_regiones <- top10_mas_populares %>% select(title, n_regiones)\n",
    "barplot(pelis_n_regiones$n_regiones, names.arg = pelis_n_regiones$title, las = 2)\n",
    "top10_mas_populares %>% select(movieid, title, n_calif, calif_promedio, n_regiones)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Ahora, respondamos a la pregunta\n",
    "\n",
    "* ¿Cuantas películas vieron los H y M?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "vscode": {
     "languageId": "r"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "100000"
      ],
      "text/latex": [
       "100000"
      ],
      "text/markdown": [
       "100000"
      ],
      "text/plain": [
       "[1] 100000"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "100000"
      ],
      "text/latex": [
       "100000"
      ],
      "text/markdown": [
       "100000"
      ],
      "text/plain": [
       "[1] 100000"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Mostrar la longitud de datos\n",
    "length(datos$movieid)\n",
    "\n",
    "# Filtramos la posibilidad de que una película haya sido calificada más de una vez por el mismo usuario\n",
    "datos_solo_una_calif <- datos %>% distinct(movieid, userid)\n",
    "length(datos_solo_una_calif$movieid)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "vscode": {
     "languageId": "r"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table class=\"dataframe\">\n",
       "<caption>A tibble: 2 × 2</caption>\n",
       "<thead>\n",
       "\t<tr><th scope=col>gender</th><th scope=col>n_pelis</th></tr>\n",
       "\t<tr><th scope=col>&lt;chr&gt;</th><th scope=col>&lt;int&gt;</th></tr>\n",
       "</thead>\n",
       "<tbody>\n",
       "\t<tr><td>F</td><td>25740</td></tr>\n",
       "\t<tr><td>M</td><td>74260</td></tr>\n",
       "</tbody>\n",
       "</table>\n"
      ],
      "text/latex": [
       "A tibble: 2 × 2\n",
       "\\begin{tabular}{ll}\n",
       " gender & n\\_pelis\\\\\n",
       " <chr> & <int>\\\\\n",
       "\\hline\n",
       "\t F & 25740\\\\\n",
       "\t M & 74260\\\\\n",
       "\\end{tabular}\n"
      ],
      "text/markdown": [
       "\n",
       "A tibble: 2 × 2\n",
       "\n",
       "| gender &lt;chr&gt; | n_pelis &lt;int&gt; |\n",
       "|---|---|\n",
       "| F | 25740 |\n",
       "| M | 74260 |\n",
       "\n"
      ],
      "text/plain": [
       "  gender n_pelis\n",
       "1 F      25740  \n",
       "2 M      74260  "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "Plot with title \"Películas vistas por género\""
      ]
     },
     "metadata": {
      "image/png": {
       "height": 420,
       "width": 420
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Sabiendo que cada usuario calificó sólo una vez cada película, podemos obtener cuántas películas vieron.\n",
    "# Ahora, obtenemos el número de películas que vieron los usuarios hombres y mujeres\n",
    "\n",
    "pelis_por_sexo <- datos %>% group_by(gender) %>% summarise(n_pelis = n())\n",
    "pelis_por_sexo\n",
    "barplot(pelis_por_sexo$n_pelis, names.arg = pelis_por_sexo$gender, xlab = \"Género\", ylab = \"Número de películas\", main = \"Películas vistas por género\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Finalmente, deebemos responder la pregunta\n",
    "\n",
    "* ¿Donde se ven las top 10 películas más populares?\n",
    "\n",
    "Primero, es necesario definir que para responder esta pregunta se tomará la región que aparezca más veces para la calificación de una película, con lo que se obtendrá una lista de 10 regiones (posiblemente con valores repetidos)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "vscode": {
     "languageId": "r"
    }
   },
   "outputs": [],
   "source": [
    "# Ya tenemos localizadas las regiones en las que se han calificado las películas en el dataframe datos.\n",
    "# Primero, es necesario filtrar las 10 películas más populares del dataframe datos, para luego\n",
    "# obtener la región en la que se ha calificado cada película y de ahí determinar cuál es la región\n",
    "# que más aparece en cada calificación\n",
    "\n",
    "# Obtenemos las calificaciones de las 10 películas más populares\n",
    "datos_top10 <- datos %>% filter(movieid %in% top10_mas_populares$movieid)\n",
    "\n",
    "# Obtenemos la región en la que se ha calificado cada película\n",
    "datos_top10_regiones <- datos_top10 %>% select(movieid, region)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "vscode": {
     "languageId": "r"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table class=\"dataframe\">\n",
       "<caption>A data.frame: 10 × 2</caption>\n",
       "<thead>\n",
       "\t<tr><th></th><th scope=col>movieid</th><th scope=col>region</th></tr>\n",
       "\t<tr><th></th><th scope=col>&lt;int&gt;</th><th scope=col>&lt;chr&gt;</th></tr>\n",
       "</thead>\n",
       "<tbody>\n",
       "\t<tr><th scope=row>1</th><td> 98</td><td>5</td></tr>\n",
       "\t<tr><th scope=row>2</th><td>427</td><td>0</td></tr>\n",
       "\t<tr><th scope=row>3</th><td>174</td><td>6</td></tr>\n",
       "\t<tr><th scope=row>4</th><td>427</td><td>9</td></tr>\n",
       "\t<tr><th scope=row>5</th><td>427</td><td>2</td></tr>\n",
       "\t<tr><th scope=row>6</th><td> 98</td><td>7</td></tr>\n",
       "\t<tr><th scope=row>7</th><td> 98</td><td>2</td></tr>\n",
       "\t<tr><th scope=row>8</th><td>174</td><td>9</td></tr>\n",
       "\t<tr><th scope=row>9</th><td>483</td><td>9</td></tr>\n",
       "\t<tr><th scope=row>10</th><td>127</td><td>4</td></tr>\n",
       "</tbody>\n",
       "</table>\n"
      ],
      "text/latex": [
       "A data.frame: 10 × 2\n",
       "\\begin{tabular}{r|ll}\n",
       "  & movieid & region\\\\\n",
       "  & <int> & <chr>\\\\\n",
       "\\hline\n",
       "\t1 &  98 & 5\\\\\n",
       "\t2 & 427 & 0\\\\\n",
       "\t3 & 174 & 6\\\\\n",
       "\t4 & 427 & 9\\\\\n",
       "\t5 & 427 & 2\\\\\n",
       "\t6 &  98 & 7\\\\\n",
       "\t7 &  98 & 2\\\\\n",
       "\t8 & 174 & 9\\\\\n",
       "\t9 & 483 & 9\\\\\n",
       "\t10 & 127 & 4\\\\\n",
       "\\end{tabular}\n"
      ],
      "text/markdown": [
       "\n",
       "A data.frame: 10 × 2\n",
       "\n",
       "| <!--/--> | movieid &lt;int&gt; | region &lt;chr&gt; |\n",
       "|---|---|---|\n",
       "| 1 |  98 | 5 |\n",
       "| 2 | 427 | 0 |\n",
       "| 3 | 174 | 6 |\n",
       "| 4 | 427 | 9 |\n",
       "| 5 | 427 | 2 |\n",
       "| 6 |  98 | 7 |\n",
       "| 7 |  98 | 2 |\n",
       "| 8 | 174 | 9 |\n",
       "| 9 | 483 | 9 |\n",
       "| 10 | 127 | 4 |\n",
       "\n"
      ],
      "text/plain": [
       "   movieid region\n",
       "1   98     5     \n",
       "2  427     0     \n",
       "3  174     6     \n",
       "4  427     9     \n",
       "5  427     2     \n",
       "6   98     7     \n",
       "7   98     2     \n",
       "8  174     9     \n",
       "9  483     9     \n",
       "10 127     4     "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "datos_top10_regiones[1:10, ]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "vscode": {
     "languageId": "r"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table class=\"dataframe\">\n",
       "<caption>A tibble: 10 × 2</caption>\n",
       "<thead>\n",
       "\t<tr><th scope=col>movieid</th><th scope=col>region_mas_frecuente</th></tr>\n",
       "\t<tr><th scope=col>&lt;int&gt;</th><th scope=col>&lt;chr&gt;</th></tr>\n",
       "</thead>\n",
       "<tbody>\n",
       "\t<tr><td> 12</td><td>9</td></tr>\n",
       "\t<tr><td> 50</td><td>9</td></tr>\n",
       "\t<tr><td> 64</td><td>9</td></tr>\n",
       "\t<tr><td> 98</td><td>9</td></tr>\n",
       "\t<tr><td>127</td><td>9</td></tr>\n",
       "\t<tr><td>174</td><td>9</td></tr>\n",
       "\t<tr><td>318</td><td>9</td></tr>\n",
       "\t<tr><td>357</td><td>9</td></tr>\n",
       "\t<tr><td>427</td><td>9</td></tr>\n",
       "\t<tr><td>483</td><td>9</td></tr>\n",
       "</tbody>\n",
       "</table>\n"
      ],
      "text/latex": [
       "A tibble: 10 × 2\n",
       "\\begin{tabular}{ll}\n",
       " movieid & region\\_mas\\_frecuente\\\\\n",
       " <int> & <chr>\\\\\n",
       "\\hline\n",
       "\t  12 & 9\\\\\n",
       "\t  50 & 9\\\\\n",
       "\t  64 & 9\\\\\n",
       "\t  98 & 9\\\\\n",
       "\t 127 & 9\\\\\n",
       "\t 174 & 9\\\\\n",
       "\t 318 & 9\\\\\n",
       "\t 357 & 9\\\\\n",
       "\t 427 & 9\\\\\n",
       "\t 483 & 9\\\\\n",
       "\\end{tabular}\n"
      ],
      "text/markdown": [
       "\n",
       "A tibble: 10 × 2\n",
       "\n",
       "| movieid &lt;int&gt; | region_mas_frecuente &lt;chr&gt; |\n",
       "|---|---|\n",
       "|  12 | 9 |\n",
       "|  50 | 9 |\n",
       "|  64 | 9 |\n",
       "|  98 | 9 |\n",
       "| 127 | 9 |\n",
       "| 174 | 9 |\n",
       "| 318 | 9 |\n",
       "| 357 | 9 |\n",
       "| 427 | 9 |\n",
       "| 483 | 9 |\n",
       "\n"
      ],
      "text/plain": [
       "   movieid region_mas_frecuente\n",
       "1   12     9                   \n",
       "2   50     9                   \n",
       "3   64     9                   \n",
       "4   98     9                   \n",
       "5  127     9                   \n",
       "6  174     9                   \n",
       "7  318     9                   \n",
       "8  357     9                   \n",
       "9  427     9                   \n",
       "10 483     9                   "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    "# Determinamos cuál es la región que más aparece en cada calificación por película\n",
    "regiones_top10 <- datos_top10_regiones %>% group_by(movieid) %>% summarise(region_mas_frecuente = names(which.max(table(region))))\n",
    "regiones_top10[1:10, ]"
   ]
  }
 ],
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