43 Boxplots.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Boxplots in ggplot2\n",
    "\n",
    "Boxplots are convenient, albeit ugly, way of graphically depicting groups of numerical data through their quartiles. Box plots may also have lines extending vertically from the boxes (whiskers) indicating variability outside the upper and lower quartiles, hence the terms box-and-whisker plot and box-and-whisker diagram. Outliers may be plotted as individual points.\n",
    "\n",
    "Let's see how we can create them with qplot and ggplot!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "library(ggplot2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "'data.frame':\t32 obs. of  11 variables:\n",
      " $ mpg : num  21 21 22.8 21.4 18.7 18.1 14.3 24.4 22.8 19.2 ...\n",
      " $ cyl : num  6 6 4 6 8 6 8 4 4 6 ...\n",
      " $ disp: num  160 160 108 258 360 ...\n",
      " $ hp  : num  110 110 93 110 175 105 245 62 95 123 ...\n",
      " $ drat: num  3.9 3.9 3.85 3.08 3.15 2.76 3.21 3.69 3.92 3.92 ...\n",
      " $ wt  : num  2.62 2.88 2.32 3.21 3.44 ...\n",
      " $ qsec: num  16.5 17 18.6 19.4 17 ...\n",
      " $ vs  : num  0 0 1 1 0 1 0 1 1 1 ...\n",
      " $ am  : num  1 1 1 0 0 0 0 0 0 0 ...\n",
      " $ gear: num  4 4 4 3 3 3 3 4 4 4 ...\n",
      " $ carb: num  4 4 1 1 2 1 4 2 2 4 ...\n"
     ]
    }
   ],
   "source": [
    "str(mtcars)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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xlnbEgV6KeMMzakCvRTxhkbUgX6KeOMDakC/ZRxxoZUgXbKd3d3Wx/hNwypAt2U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",
      "text/plain": [
       "plot without title"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "qplot(factor(cyl), mpg, data = mtcars, geom = \"boxplot\")\n",
    "# If you don't factor cyl, R will confuse the data for continuous"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## ggplot"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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xlnbEgV6KeMMzakCvRTxhkbUgX6KeOMDakC/ZRxxoZUgXbKd3d3Wx/hNwypAt2U\n7+5wJRlSBZop393xSjKkCjRTNqSrwDtROs2UDekq8E6UTjdlXkeGVIJ2yriODKkE/ZRxxoZU\ngX7KOGNDqkA/ZZyxIVWgnzLOeFlI++PbJwyJRD9lnPGikI79xBtDgtBPGWe8JKT9oyEx6aeM\nM170iGRIUPop44xXhfT8DeYzfplIH3xEul36KeOMDakC/ZRxxoZUgX7KOGNDqkA/ZZyxIVWg\nnzLO+IKQfGUDjn7KOONlIf2JrQ0+wDtROv2UccaGVIF+yjhjQ6pAP2WcsSFVoJ8yztiQKtBP\nGWdsSBXop4wzNqQK9FPGGRtSBfop44wNqQL9lHHGhlSBfso4Y0OqQD9lnLEhVaCfMs7YkCrQ\nTxlnbEgV6KeMMzakCvRTxhkbUgX6KeOM14eEo+H/IKyfMtjYkG6XfspgY0O6Xfopg40N6Xbp\npww2LhOSyJYYksgEDElkAoYkMgFDEpmAIYlMoE5I+/GHlOLX/4V0B9DGZUIC33EKv/2lBg1g\nG1cJac+94hzYa5UB27hISHvwFefQTPfRkK5Cx5DIXzFkYEj57B/BV5zDvp+zTzZkw/6zKod+\nzmzjGiEd2foYV4W9VhmwjUuE9Az2inNgr1UGbGNDulHYa5UB29iQbpV2n8yyjeuEJLIhhiQy\nAUMSmYAhiUzAkEQmYEgiEzAkkQkYksgEDElkAoYE5OF+t/v0h/f/e+Ib+9++/OGdu93jl28z\nTyV/w5CA7HdP/OH9f3zn4+P3P/Z1+OD995nHkr9gSEBOBHPq/ft/T33wqYcwmY4h8dgdH5C+\nf97t9l8P73j4/PyTl8ephy+73ZeH54/7sb9/fPy6j4/5efyU8PDD88cef73kY0g8jiF9e/5h\n91TCz+dP9T6/hHT8p/3Pw8fd7748RXOI5fVjvu4OXxf9t/vnGNLX3c+NZbpgSECeG/i0++/x\n8cfhp1+fcvl++MlLG0+PQve750eoQ0L/PKfz+jE/Dv/28fPu+/GDvz0VJdfAkIC8fC308O2f\n+8NPP70+rLwE9vRp3cPhU7jd4WdP0Tw8vvuYz7sfT/9m//LBD08PUnINDAnIMaT73csXS2/P\nMTz/5PhP8f73bx8Pj2Gfnx6Hvnx4v+TiPQN53v4vu0//fntYHNLhEev5CyVDuireM5AI5uff\nP7V7e2d8zNPD0df9r8HJFfCegbyE9P3x5/3xyYavx2cdPjzZcPjg49dIbx9zaOr5CQe/Rroq\nhgTkJZjXr5Eenp/afn4I2v/69Pfhg4/P2r19zOEh6fB8n8/aXRdDAnIs5MvTI8vzs96PP+6P\n34L997potgkAAAB8SURBVBDS+2/IHj7s+H2kt495fH02z+8jXRVDunm+/jbD7+9e8LrzlQ1X\nwpBun99ea3e/e3vRt6+1uxqGdPv88urv3fGphiO++vtqGFIB3v/3SPt3z9P53yNdD0MSmYAh\niUzAkEQmYEgiEzAkkQkYksgEDElkAoYkMoH/AwMk15U1W7ffAAAAAElFTkSuQmCC",
      "text/plain": [
       "plot without title"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "pl <- ggplot(df, aes(x= factor(cyl), y=mpg))\n",
    "\n",
    "print(pl + geom_boxplot())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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B4+rON7NpzTApJ+C0gLtICk35qAdMP3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\ndhiQGNthQGJsh/0fTSes0wl4ziAAAAAASUVORK5CYII=",
      "text/plain": [
       "plot without title"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "print(pl + geom_boxplot()+ coord_flip())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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0gV4NG40XmdljGkCvBo3Oi8TssYUgV4NG50XqdlDKkCPBo3Oq/TMoZUAR6NG53X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\nYEhSwP8Do5E3ehxddQYAAAAASUVORK5CYII=",
      "text/plain": [
       "plot without title"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "pl + geom_boxplot(aes(fill = factor(cyl)), color = 'cyan')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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LZahbTwpyX4h/9SvUEqer/0AUkzeiEg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\nBAGJSBCQiAT9Pw8B4RsWrLuxAAAAAElFTkSuQmCC",
      "text/plain": [
       "plot without title"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "pl + geom_violin(aes(fill = factor(cyl)))\n",
    "# Never use this. It's atrocious."
   ]
  }
 ],
 "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
}