movies_analysis/rate_per_occupation_analysis.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 3,
   "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": [
    "library(\"ggplot2\")\n",
    "library(\"dplyr\")\n",
    "library(\"readr\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "vscode": {
     "languageId": "r"
    }
   },
   "outputs": [],
   "source": [
    "# Leyendo usuarios y calificaciones en un dataframe\n",
    "califs <- read.table(\"./movies_data/udata.data\", sep=\"\\t\", header=TRUE)\n",
    "users <- read.table(\"./movies_data/u.user\", sep=\"|\", header=TRUE)\n",
    "df1 <- left_join(users,califs, by=c(\"userid\"=\"userid\"), multiple=\"all\")\n",
    "\n",
    "# Extraemos la calificación y la ocupación\n",
    "df2 <- data.frame(df1$rate, df1$occupation)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "vscode": {
     "languageId": "r"
    }
   },
   "outputs": [
    {
     "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 una grafica de la concentración de las calificaciones con base a la ocupación\n",
    "ggplot(\n",
    "    data=df2,\n",
    "    aes(x=df1.occupation,y=df1.rate,color=df1.occupation) # aes() : Este método se utiliza dentro del ggplot() y se le pasan los atributos estéticos de la gráfica.\n",
    ") +\n",
    "geom_boxplot(outlier.shape = NA) + # geom_boxplot() : Este método se utiliza para agregar un boxplot a la gráfica mostrando la distribución de los datos. El argumento outlier. shape = NA se utiliza para que no se muestren los puntos considerados fuera de rango (outliers).\n",
    "geom_jitter(width = 0.1) + # geom_jitter() : Este método se utiliza para agregar un gráfico de dispersión con cierta aleatoriedad de los datos. El argumento width=e.l se utiliza para controlar el ancho de los puntos y evitar que se superpongan.\n",
    "theme_bw() + \n",
    "theme(legend.position = \"null\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "vscode": {
     "languageId": "r"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "\n",
       "Call:\n",
       "lm(formula = rate ~ occupation, data = df1)\n",
       "\n",
       "Residuals:\n",
       "    Min      1Q  Median      3Q     Max \n",
       "-2.7791 -0.5683  0.3644  0.5589  2.1038 \n",
       "\n",
       "Coefficients:\n",
       "                        Estimate Std. Error t value Pr(>|t|)    \n",
       "(Intercept)              3.63565    0.01292 281.388  < 2e-16 ***\n",
       "occupationartist         0.01773    0.02661   0.667 0.505092    \n",
       "occupationdoctor         0.05324    0.04979   1.069 0.284916    \n",
       "occupationeducator       0.03497    0.01730   2.022 0.043175 *  \n",
       "occupationengineer      -0.09424    0.01788  -5.271 1.36e-07 ***\n",
       "occupationentertainment -0.19460    0.02762  -7.045 1.86e-12 ***\n",
       "occupationexecutive     -0.28654    0.02310 -12.402  < 2e-16 ***\n",
       "occupationhealthcare    -0.73943    0.02474 -29.885  < 2e-16 ***\n",
       "occupationhomemaker     -0.33464    0.06590  -5.078 3.82e-07 ***\n",
       "occupationlawyer         0.09967    0.03309   3.012 0.002598 ** \n",
       "occupationlibrarian     -0.07487    0.02009  -3.726 0.000195 ***\n",
       "occupationmarketing     -0.15001    0.02841  -5.280 1.30e-07 ***\n",
       "occupationnone           0.14349    0.03940   3.641 0.000271 ***\n",
       "occupationother         -0.08327    0.01685  -4.941 7.79e-07 ***\n",
       "occupationprogrammer    -0.06739    0.01808  -3.727 0.000194 ***\n",
       "occupationretired       -0.16890    0.03071  -5.500 3.80e-08 ***\n",
       "occupationsalesman      -0.05270    0.04032  -1.307 0.191150    \n",
       "occupationscientist     -0.02437    0.02781  -0.876 0.380864    \n",
       "occupationstudent       -0.12050    0.01496  -8.055 8.03e-16 ***\n",
       "occupationtechnician    -0.10342    0.02287  -4.522 6.14e-06 ***\n",
       "occupationwriter        -0.25992    0.01981 -13.120  < 2e-16 ***\n",
       "---\n",
       "Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1\n",
       "\n",
       "Residual standard error: 1.117 on 99979 degrees of freedom\n",
       "Multiple R-squared:  0.01489,\tAdjusted R-squared:  0.0147 \n",
       "F-statistic: 75.58 on 20 and 99979 DF,  p-value: < 2.2e-16\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Aplicamos el modelo de regresión lineal\n",
    "modeloRL <- lm(rate ~ occupation, data=df1)\n",
    "summary(modeloRL)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "vscode": {
     "languageId": "r"
    }
   },
   "outputs": [],
   "source": [
    "# Creamos nuestro conjunto de datos sin la columna que se espera estimar\n",
    "x <- data.frame(occupation = c(\"student\", \"doctor\", \"artist\", \"educator\"))\n",
    "# Predicción\n",
    "prediction <- predict(modeloRL, x)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "vscode": {
     "languageId": "r"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "'numeric'"
      ],
      "text/latex": [
       "'numeric'"
      ],
      "text/markdown": [
       "'numeric'"
      ],
      "text/plain": [
       "[1] \"numeric\""
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "       1        2        3        4 \n",
      "3.515143 3.688889 3.653380 3.670621 \n"
     ]
    }
   ],
   "source": [
    "class(prediction)\n",
    "print(prediction)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "vscode": {
     "languageId": "r"
    }
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\u001b[1m\u001b[22m`geom_smooth()` using formula = 'y ~ x'\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "plot without title"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 420,
       "width": 420
      }
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "Plot with title \"Histogram of residuos\""
      ]
     },
     "metadata": {
      "image/png": {
       "height": 420,
       "width": 420
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Graficamos la predicción\n",
    "ggplot(modeloRL, aes(rate, occupation)) + geom_point() + geom_smooth(method = \"lm\", color = \"red\")\n",
    "residuos <- rstandard(modeloRL)\n",
    "hist(residuos)"
   ]
  }
 ],
 "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": "4.2.2"
  },
  "orig_nbformat": 4
 },
 "nbformat": 4,
 "nbformat_minor": 2
}