cfljam/Jupyter Notebooks with the R kernel.ipynb

## Jupyter Notebooks with the R kernel.ipynb
{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Using R in Jupyter Notebooks\n",
    "\n",
    "![R](http://www.sciviews.org/_style/img/RLogo.png)\n",
    "\n",
    "This notebook was authored using the Jupyter [R kernel](https://github.com/IRkernel/IRkernel), running in a Docker container generated by the Dockerfile https://github.com/cfljam/pyRat/blob/master/Dockerfile "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<dl class=dl-horizontal>\n",
       "\t<dt>sysname</dt>\n",
       "\t\t<dd>'Linux'</dd>\n",
       "\t<dt>release</dt>\n",
       "\t\t<dd>'3.18.5-tinycore64'</dd>\n",
       "\t<dt>version</dt>\n",
       "\t\t<dd>'#1 SMP Sun Feb 1 06:02:30 UTC 2015'</dd>\n",
       "\t<dt>nodename</dt>\n",
       "\t\t<dd>'3931a850324e'</dd>\n",
       "\t<dt>machine</dt>\n",
       "\t\t<dd>'x86_64'</dd>\n",
       "\t<dt>login</dt>\n",
       "\t\t<dd>'unknown'</dd>\n",
       "\t<dt>user</dt>\n",
       "\t\t<dd>'root'</dd>\n",
       "\t<dt>effective_user</dt>\n",
       "\t\t<dd>'root'</dd>\n",
       "</dl>\n"
      ],
      "text/latex": [
       "\\begin{description*}\n",
       "\\item[sysname] 'Linux'\n",
       "\\item[release] '3.18.5-tinycore64'\n",
       "\\item[version] '#1 SMP Sun Feb 1 06:02:30 UTC 2015'\n",
       "\\item[nodename] '3931a850324e'\n",
       "\\item[machine] 'x86_64'\n",
       "\\item[login] 'unknown'\n",
       "\\item[user] 'root'\n",
       "\\item[effective_user] 'root'\n",
       "\\end{description*}\n"
      ],
      "text/markdown": [
       "sysname\n",
       ":   'Linux'release\n",
       ":   '3.18.5-tinycore64'version\n",
       ":   '#1 SMP Sun Feb 1 06:02:30 UTC 2015'nodename\n",
       ":   '3931a850324e'machine\n",
       ":   'x86_64'login\n",
       ":   'unknown'user\n",
       ":   'root'effective_user\n",
       ":   'root'\n",
       "\n"
      ],
      "text/plain": [
       "                             sysname                              release \n",
       "                             \"Linux\"                  \"3.18.5-tinycore64\" \n",
       "                             version                             nodename \n",
       "\"#1 SMP Sun Feb 1 06:02:30 UTC 2015\"                       \"3931a850324e\" \n",
       "                             machine                                login \n",
       "                            \"x86_64\"                            \"unknown\" \n",
       "                                user                       effective_user \n",
       "                              \"root\"                               \"root\" "
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Sys.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Exclude bulky SVG etc for now\n",
    "\n",
    "- see https://github.com/IRkernel/IRkernel/issues/145"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "options(jupyter.plot_mimetypes = 'image/png')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Do Some R Stuff"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "library(dplyr,quietly = TRUE)\n",
    "library(ggplot2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We get HTML tables 'out of the box' "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table>\n",
       "<thead><tr><th></th><th scope=col>carat</th><th scope=col>cut</th><th scope=col>color</th><th scope=col>clarity</th><th scope=col>depth</th><th scope=col>table</th><th scope=col>price</th><th scope=col>x</th><th scope=col>y</th><th scope=col>z</th></tr></thead>\n",
       "<tbody>\n",
       "\t<tr><th scope=row>1</th><td>0.23</td><td>Ideal</td><td>E</td><td>SI2</td><td>61.5</td><td>55</td><td>326</td><td>3.95</td><td>3.98</td><td>2.43</td></tr>\n",
       "\t<tr><th scope=row>2</th><td>0.21</td><td>Premium</td><td>E</td><td>SI1</td><td>59.8</td><td>61</td><td>326</td><td>3.89</td><td>3.84</td><td>2.31</td></tr>\n",
       "\t<tr><th scope=row>3</th><td>0.23</td><td>Good</td><td>E</td><td>VS1</td><td>56.9</td><td>65</td><td>327</td><td>4.05</td><td>4.07</td><td>2.31</td></tr>\n",
       "\t<tr><th scope=row>4</th><td>0.29</td><td>Premium</td><td>I</td><td>VS2</td><td>62.4</td><td>58</td><td>334</td><td>4.2</td><td>4.23</td><td>2.63</td></tr>\n",
       "\t<tr><th scope=row>5</th><td>0.31</td><td>Good</td><td>J</td><td>SI2</td><td>63.3</td><td>58</td><td>335</td><td>4.34</td><td>4.35</td><td>2.75</td></tr>\n",
       "\t<tr><th scope=row>6</th><td>0.24</td><td>Very Good</td><td>J</td><td>VVS2</td><td>62.8</td><td>57</td><td>336</td><td>3.94</td><td>3.96</td><td>2.48</td></tr>\n",
       "</tbody>\n",
       "</table>\n"
      ],
      "text/latex": [
       "\\begin{tabular}{r|llllllllll}\n",
       "  & carat & cut & color & clarity & depth & table & price & x & y & z\\\\\n",
       "\\hline\n",
       "\t1 & 0.23 & Ideal & E & SI2 & 61.5 & 55 & 326 & 3.95 & 3.98 & 2.43\\\\\n",
       "\t2 & 0.21 & Premium & E & SI1 & 59.8 & 61 & 326 & 3.89 & 3.84 & 2.31\\\\\n",
       "\t3 & 0.23 & Good & E & VS1 & 56.9 & 65 & 327 & 4.05 & 4.07 & 2.31\\\\\n",
       "\t4 & 0.29 & Premium & I & VS2 & 62.4 & 58 & 334 & 4.2 & 4.23 & 2.63\\\\\n",
       "\t5 & 0.31 & Good & J & SI2 & 63.3 & 58 & 335 & 4.34 & 4.35 & 2.75\\\\\n",
       "\t6 & 0.24 & Very Good & J & VVS2 & 62.8 & 57 & 336 & 3.94 & 3.96 & 2.48\\\\\n",
       "\\end{tabular}\n"
      ],
      "text/plain": [
       "  carat       cut color clarity depth table price    x    y    z\n",
       "1  0.23     Ideal     E     SI2  61.5    55   326 3.95 3.98 2.43\n",
       "2  0.21   Premium     E     SI1  59.8    61   326 3.89 3.84 2.31\n",
       "3  0.23      Good     E     VS1  56.9    65   327 4.05 4.07 2.31\n",
       "4  0.29   Premium     I     VS2  62.4    58   334 4.20 4.23 2.63\n",
       "5  0.31      Good     J     SI2  63.3    58   335 4.34 4.35 2.75\n",
       "6  0.24 Very Good     J    VVS2  62.8    57   336 3.94 3.96 2.48"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "diamonds %>% head"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "...and we get  inline graphics"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ggplot(diamonds,aes(x=carat,y=price)) + geom_point(aes(color=cut))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot(diamonds$carat,diamonds$price)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Read some documentation inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "\n",
       "<table width=\"100%\" summary=\"page for geom_point {ggplot2}\"><tr><td>geom_point {ggplot2}</td><td style=\"text-align: right;\">R Documentation</td></tr></table>\n",
       "\n",
       "<h2>Points, as for a scatterplot</h2>\n",
       "\n",
       "<h3>Description</h3>\n",
       "\n",
       "<p>The point geom is used to create scatterplots.\n",
       "</p>\n",
       "\n",
       "\n",
       "<h3>Usage</h3>\n",
       "\n",
       "<pre>\n",
       "geom_point(mapping = NULL, data = NULL, stat = \"identity\",\n",
       "  position = \"identity\", na.rm = FALSE, ...)\n",
       "</pre>\n",
       "\n",
       "\n",
       "<h3>Arguments</h3>\n",
       "\n",
       "<table summary=\"R argblock\">\n",
       "<tr valign=\"top\"><td><code>mapping</code></td>\n",
       "<td>\n",
       "<p>The aesthetic mapping, usually constructed with\n",
       "<code>aes</code> or <code>aes_string</code>. Only needs to be set\n",
       "at the layer level if you are overriding the plot defaults.</p>\n",
       "</td></tr>\n",
       "<tr valign=\"top\"><td><code>data</code></td>\n",
       "<td>\n",
       "<p>A layer specific dataset - only needed if you want to override\n",
       "the plot defaults.</p>\n",
       "</td></tr>\n",
       "<tr valign=\"top\"><td><code>stat</code></td>\n",
       "<td>\n",
       "<p>The statistical transformation to use on the data for this\n",
       "layer.</p>\n",
       "</td></tr>\n",
       "<tr valign=\"top\"><td><code>position</code></td>\n",
       "<td>\n",
       "<p>The position adjustment to use for overlapping points\n",
       "on this layer</p>\n",
       "</td></tr>\n",
       "<tr valign=\"top\"><td><code>na.rm</code></td>\n",
       "<td>\n",
       "<p>If <code>FALSE</code> (the default), removes missing values with\n",
       "a warning.  If <code>TRUE</code> silently removes missing values.</p>\n",
       "</td></tr>\n",
       "<tr valign=\"top\"><td><code>...</code></td>\n",
       "<td>\n",
       "<p>other arguments passed on to <code>layer</code>. This can\n",
       "include aesthetics whose values you want to set, not map. See\n",
       "<code>layer</code> for more details.</p>\n",
       "</td></tr>\n",
       "</table>\n",
       "\n",
       "\n",
       "<h3>Details</h3>\n",
       "\n",
       "<p>The scatterplot is useful for displaying the relationship between two\n",
       "continuous variables, although it can also be used with one continuous\n",
       "and one categorical variable, or two categorical variables.  See\n",
       "<code>geom_jitter</code> for possibilities.\n",
       "</p>\n",
       "<p>The <em>bubblechart</em> is a scatterplot with a third variable mapped to\n",
       "the size of points.  There are no special names for scatterplots where\n",
       "another variable is mapped to point shape or colour, however.\n",
       "</p>\n",
       "<p>The biggest potential problem with a scatterplot is overplotting: whenever\n",
       "you have more than a few points, points may be plotted on top of one\n",
       "another. This can severely distort the visual appearance of the plot.\n",
       "There is no one solution to this problem, but there are some techniques\n",
       "that can help.  You can add additional information with\n",
       "<code>stat_smooth</code>, <code>stat_quantile</code> or\n",
       "<code>stat_density2d</code>.  If you have few unique x values,\n",
       "<code>geom_boxplot</code> may also be useful.  Alternatively, you can\n",
       "summarise the number of points at each location and display that in some\n",
       "way, using <code>stat_sum</code>. Another technique is to use transparent\n",
       "points, <code>geom_point(alpha = 0.05)</code>.\n",
       "</p>\n",
       "\n",
       "\n",
       "<h3>Aesthetics</h3>\n",
       "\n",
       "<p><code>geom_point</code> understands the following aesthetics (required aesthetics are in bold):\n",
       "</p>\n",
       "\n",
       "<ul>\n",
       "<li> <p><code><strong>x</strong></code>\n",
       "</p>\n",
       "</li>\n",
       "<li> <p><code><strong>y</strong></code>\n",
       "</p>\n",
       "</li>\n",
       "<li> <p><code>alpha</code>\n",
       "</p>\n",
       "</li>\n",
       "<li> <p><code>colour</code>\n",
       "</p>\n",
       "</li>\n",
       "<li> <p><code>fill</code>\n",
       "</p>\n",
       "</li>\n",
       "<li> <p><code>shape</code>\n",
       "</p>\n",
       "</li>\n",
       "<li> <p><code>size</code>\n",
       "</p>\n",
       "</li></ul>\n",
       "\n",
       "\n",
       "\n",
       "\n",
       "<h3>See Also</h3>\n",
       "\n",
       "<p><code>scale_size</code> to see scale area of points, instead of\n",
       "radius, <code>geom_jitter</code> to jitter points to reduce (mild)\n",
       "overplotting\n",
       "</p>\n",
       "\n",
       "\n",
       "<h3>Examples</h3>\n",
       "\n",
       "<pre>\n",
       "\n",
       "p &lt;- ggplot(mtcars, aes(wt, mpg))\n",
       "p + geom_point()\n",
       "\n",
       "# Add aesthetic mappings\n",
       "p + geom_point(aes(colour = qsec))\n",
       "p + geom_point(aes(alpha = qsec))\n",
       "p + geom_point(aes(colour = factor(cyl)))\n",
       "p + geom_point(aes(shape = factor(cyl)))\n",
       "p + geom_point(aes(size = qsec))\n",
       "\n",
       "# Change scales\n",
       "p + geom_point(aes(colour = cyl)) + scale_colour_gradient(low = \"blue\")\n",
       "p + geom_point(aes(size = qsec)) + scale_size_area()\n",
       "p + geom_point(aes(shape = factor(cyl))) + scale_shape(solid = FALSE)\n",
       "\n",
       "# Set aesthetics to fixed value\n",
       "p + geom_point(colour = \"red\", size = 3)\n",
       "qplot(wt, mpg, data = mtcars, colour = I(\"red\"), size = I(3))\n",
       "\n",
       "# Varying alpha is useful for large datasets\n",
       "d &lt;- ggplot(diamonds, aes(carat, price))\n",
       "d + geom_point(alpha = 1/10)\n",
       "d + geom_point(alpha = 1/20)\n",
       "d + geom_point(alpha = 1/100)\n",
       "\n",
       "# You can create interesting shapes by layering multiple points of\n",
       "# different sizes\n",
       "p &lt;- ggplot(mtcars, aes(mpg, wt))\n",
       "p + geom_point(colour=\"grey50\", size = 4) + geom_point(aes(colour = cyl))\n",
       "p + aes(shape = factor(cyl)) +\n",
       "  geom_point(aes(colour = factor(cyl)), size = 4) +\n",
       "  geom_point(colour=\"grey90\", size = 1.5)\n",
       "p + geom_point(colour=\"black\", size = 4.5) +\n",
       "  geom_point(colour=\"pink\", size = 4) +\n",
       "  geom_point(aes(shape = factor(cyl)))\n",
       "\n",
       "# These extra layers don't usually appear in the legend, but we can\n",
       "# force their inclusion\n",
       "p + geom_point(colour=\"black\", size = 4.5, show_guide = TRUE) +\n",
       "  geom_point(colour=\"pink\", size = 4, show_guide = TRUE) +\n",
       "  geom_point(aes(shape = factor(cyl)))\n",
       "\n",
       "# Transparent points:\n",
       "qplot(mpg, wt, data = mtcars, size = I(5), alpha = I(0.2))\n",
       "\n",
       "# geom_point warns when missing values have been dropped from the data set\n",
       "# and not plotted, you can turn this off by setting na.rm = TRUE\n",
       "mtcars2 &lt;- transform(mtcars, mpg = ifelse(runif(32) &lt; 0.2, NA, mpg))\n",
       "qplot(wt, mpg, data = mtcars2)\n",
       "qplot(wt, mpg, data = mtcars2, na.rm = TRUE)\n",
       "\n",
       "# Use qplot instead\n",
       "qplot(wt, mpg, data = mtcars)\n",
       "qplot(wt, mpg, data = mtcars, colour = factor(cyl))\n",
       "qplot(wt, mpg, data = mtcars, colour = I(\"red\"))\n",
       "\n",
       "</pre>\n",
       "\n",
       "<hr /><div style=\"text-align: center;\">[Package <em>ggplot2</em> version 1.0.1 ]</div>"
      ],
      "text/latex": [
       "\\inputencoding{utf8}\n",
       "\\HeaderA{geom\\_point}{Points, as for a scatterplot}{geom.Rul.point}\n",
       "%\n",
       "\\begin{Description}\\relax\n",
       "The point geom is used to create scatterplots.\n",
       "\\end{Description}\n",
       "%\n",
       "\\begin{Usage}\n",
       "\\begin{verbatim}\n",
       "geom_point(mapping = NULL, data = NULL, stat = \"identity\",\n",
       "  position = \"identity\", na.rm = FALSE, ...)\n",
       "\\end{verbatim}\n",
       "\\end{Usage}\n",
       "%\n",
       "\\begin{Arguments}\n",
       "\\begin{ldescription}\n",
       "\\item[\\code{mapping}] The aesthetic mapping, usually constructed with\n",
       "\\code{\\LinkA{aes}{aes}} or \\code{\\LinkA{aes\\_string}{aes.Rul.string}}. Only needs to be set\n",
       "at the layer level if you are overriding the plot defaults.\n",
       "\n",
       "\\item[\\code{data}] A layer specific dataset - only needed if you want to override\n",
       "the plot defaults.\n",
       "\n",
       "\\item[\\code{stat}] The statistical transformation to use on the data for this\n",
       "layer.\n",
       "\n",
       "\\item[\\code{position}] The position adjustment to use for overlapping points\n",
       "on this layer\n",
       "\n",
       "\\item[\\code{na.rm}] If \\code{FALSE} (the default), removes missing values with\n",
       "a warning.  If \\code{TRUE} silently removes missing values.\n",
       "\n",
       "\\item[\\code{...}] other arguments passed on to \\code{\\LinkA{layer}{layer}}. This can\n",
       "include aesthetics whose values you want to set, not map. See\n",
       "\\code{\\LinkA{layer}{layer}} for more details.\n",
       "\\end{ldescription}\n",
       "\\end{Arguments}\n",
       "%\n",
       "\\begin{Details}\\relax\n",
       "The scatterplot is useful for displaying the relationship between two\n",
       "continuous variables, although it can also be used with one continuous\n",
       "and one categorical variable, or two categorical variables.  See\n",
       "\\code{\\LinkA{geom\\_jitter}{geom.Rul.jitter}} for possibilities.\n",
       "\n",
       "The \\emph{bubblechart} is a scatterplot with a third variable mapped to\n",
       "the size of points.  There are no special names for scatterplots where\n",
       "another variable is mapped to point shape or colour, however.\n",
       "\n",
       "The biggest potential problem with a scatterplot is overplotting: whenever\n",
       "you have more than a few points, points may be plotted on top of one\n",
       "another. This can severely distort the visual appearance of the plot.\n",
       "There is no one solution to this problem, but there are some techniques\n",
       "that can help.  You can add additional information with\n",
       "\\code{\\LinkA{stat\\_smooth}{stat.Rul.smooth}}, \\code{\\LinkA{stat\\_quantile}{stat.Rul.quantile}} or\n",
       "\\code{\\LinkA{stat\\_density2d}{stat.Rul.density2d}}.  If you have few unique x values,\n",
       "\\code{\\LinkA{geom\\_boxplot}{geom.Rul.boxplot}} may also be useful.  Alternatively, you can\n",
       "summarise the number of points at each location and display that in some\n",
       "way, using \\code{\\LinkA{stat\\_sum}{stat.Rul.sum}}. Another technique is to use transparent\n",
       "points, \\code{geom\\_point(alpha = 0.05)}.\n",
       "\\end{Details}\n",
       "%\n",
       "\\begin{Section}{Aesthetics}\n",
       "\n",
       "\\code{geom\\_point} understands the following aesthetics (required aesthetics are in bold):\n",
       "\n",
       "\\begin{itemize}\n",
       "\n",
       "\\item \\code{\\strong{x}}\n",
       "\\item \\code{\\strong{y}}\n",
       "\\item \\code{alpha}\n",
       "\\item \\code{colour}\n",
       "\\item \\code{fill}\n",
       "\\item \\code{shape}\n",
       "\\item \\code{size}\n",
       "\n",
       "\\end{itemize}\n",
       "\n",
       "\n",
       "\\end{Section}\n",
       "%\n",
       "\\begin{SeeAlso}\\relax\n",
       "\\code{\\LinkA{scale\\_size}{scale.Rul.size}} to see scale area of points, instead of\n",
       "radius, \\code{\\LinkA{geom\\_jitter}{geom.Rul.jitter}} to jitter points to reduce (mild)\n",
       "overplotting\n",
       "\\end{SeeAlso}\n",
       "%\n",
       "\\begin{Examples}\n",
       "\\begin{ExampleCode}\n",
       "\n",
       "p <- ggplot(mtcars, aes(wt, mpg))\n",
       "p + geom_point()\n",
       "\n",
       "# Add aesthetic mappings\n",
       "p + geom_point(aes(colour = qsec))\n",
       "p + geom_point(aes(alpha = qsec))\n",
       "p + geom_point(aes(colour = factor(cyl)))\n",
       "p + geom_point(aes(shape = factor(cyl)))\n",
       "p + geom_point(aes(size = qsec))\n",
       "\n",
       "# Change scales\n",
       "p + geom_point(aes(colour = cyl)) + scale_colour_gradient(low = \"blue\")\n",
       "p + geom_point(aes(size = qsec)) + scale_size_area()\n",
       "p + geom_point(aes(shape = factor(cyl))) + scale_shape(solid = FALSE)\n",
       "\n",
       "# Set aesthetics to fixed value\n",
       "p + geom_point(colour = \"red\", size = 3)\n",
       "qplot(wt, mpg, data = mtcars, colour = I(\"red\"), size = I(3))\n",
       "\n",
       "# Varying alpha is useful for large datasets\n",
       "d <- ggplot(diamonds, aes(carat, price))\n",
       "d + geom_point(alpha = 1/10)\n",
       "d + geom_point(alpha = 1/20)\n",
       "d + geom_point(alpha = 1/100)\n",
       "\n",
       "# You can create interesting shapes by layering multiple points of\n",
       "# different sizes\n",
       "p <- ggplot(mtcars, aes(mpg, wt))\n",
       "p + geom_point(colour=\"grey50\", size = 4) + geom_point(aes(colour = cyl))\n",
       "p + aes(shape = factor(cyl)) +\n",
       "  geom_point(aes(colour = factor(cyl)), size = 4) +\n",
       "  geom_point(colour=\"grey90\", size = 1.5)\n",
       "p + geom_point(colour=\"black\", size = 4.5) +\n",
       "  geom_point(colour=\"pink\", size = 4) +\n",
       "  geom_point(aes(shape = factor(cyl)))\n",
       "\n",
       "# These extra layers don't usually appear in the legend, but we can\n",
       "# force their inclusion\n",
       "p + geom_point(colour=\"black\", size = 4.5, show_guide = TRUE) +\n",
       "  geom_point(colour=\"pink\", size = 4, show_guide = TRUE) +\n",
       "  geom_point(aes(shape = factor(cyl)))\n",
       "\n",
       "# Transparent points:\n",
       "qplot(mpg, wt, data = mtcars, size = I(5), alpha = I(0.2))\n",
       "\n",
       "# geom_point warns when missing values have been dropped from the data set\n",
       "# and not plotted, you can turn this off by setting na.rm = TRUE\n",
       "mtcars2 <- transform(mtcars, mpg = ifelse(runif(32) < 0.2, NA, mpg))\n",
       "qplot(wt, mpg, data = mtcars2)\n",
       "qplot(wt, mpg, data = mtcars2, na.rm = TRUE)\n",
       "\n",
       "# Use qplot instead\n",
       "qplot(wt, mpg, data = mtcars)\n",
       "qplot(wt, mpg, data = mtcars, colour = factor(cyl))\n",
       "qplot(wt, mpg, data = mtcars, colour = I(\"red\"))\n",
       "\n",
       "\\end{ExampleCode}\n",
       "\\end{Examples}"
      ],
      "text/plain": [
       "geom_point               package:ggplot2               R Documentation\n",
       "\n",
       "_\bP_\bo_\bi_\bn_\bt_\bs, _\ba_\bs _\bf_\bo_\br _\ba _\bs_\bc_\ba_\bt_\bt_\be_\br_\bp_\bl_\bo_\bt\n",
       "\n",
       "_\bD_\be_\bs_\bc_\br_\bi_\bp_\bt_\bi_\bo_\bn:\n",
       "\n",
       "     The point geom is used to create scatterplots.\n",
       "\n",
       "_\bU_\bs_\ba_\bg_\be:\n",
       "\n",
       "     geom_point(mapping = NULL, data = NULL, stat = \"identity\",\n",
       "       position = \"identity\", na.rm = FALSE, ...)\n",
       "     \n",
       "_\bA_\br_\bg_\bu_\bm_\be_\bn_\bt_\bs:\n",
       "\n",
       " mapping: The aesthetic mapping, usually constructed with ΓÇÿaesΓÇÖ or\n",
       "          ΓÇÿaes_stringΓÇÖ. Only needs to be set at the layer level if you\n",
       "          are overriding the plot defaults.\n",
       "\n",
       "    data: A layer specific dataset - only needed if you want to\n",
       "          override the plot defaults.\n",
       "\n",
       "    stat: The statistical transformation to use on the data for this\n",
       "          layer.\n",
       "\n",
       "position: The position adjustment to use for overlapping points on this\n",
       "          layer\n",
       "\n",
       "   na.rm: If ΓÇÿFALSEΓÇÖ (the default), removes missing values with a\n",
       "          warning.  If ΓÇÿTRUEΓÇÖ silently removes missing values.\n",
       "\n",
       "     ...: other arguments passed on to ΓÇÿlayerΓÇÖ. This can include\n",
       "          aesthetics whose values you want to set, not map. See ΓÇÿlayerΓÇÖ\n",
       "          for more details.\n",
       "\n",
       "_\bD_\be_\bt_\ba_\bi_\bl_\bs:\n",
       "\n",
       "     The scatterplot is useful for displaying the relationship between\n",
       "     two continuous variables, although it can also be used with one\n",
       "     continuous and one categorical variable, or two categorical\n",
       "     variables.  See ΓÇÿgeom_jitterΓÇÖ for possibilities.\n",
       "\n",
       "     The _bubblechart_ is a scatterplot with a third variable mapped to\n",
       "     the size of points.  There are no special names for scatterplots\n",
       "     where another variable is mapped to point shape or colour,\n",
       "     however.\n",
       "\n",
       "     The biggest potential problem with a scatterplot is overplotting:\n",
       "     whenever you have more than a few points, points may be plotted on\n",
       "     top of one another. This can severely distort the visual\n",
       "     appearance of the plot. There is no one solution to this problem,\n",
       "     but there are some techniques that can help.  You can add\n",
       "     additional information with ΓÇÿstat_smoothΓÇÖ, ΓÇÿstat_quantileΓÇÖ or\n",
       "     ΓÇÿstat_density2dΓÇÖ.  If you have few unique x values, ΓÇÿgeom_boxplotΓÇÖ\n",
       "     may also be useful.  Alternatively, you can summarise the number\n",
       "     of points at each location and display that in some way, using\n",
       "     ΓÇÿstat_sumΓÇÖ. Another technique is to use transparent points,\n",
       "     ΓÇÿgeom_point(alpha = 0.05)ΓÇÖ.\n",
       "\n",
       "_\bA_\be_\bs_\bt_\bh_\be_\bt_\bi_\bc_\bs:\n",
       "\n",
       "     ΓÇÿgeom_pointΓÇÖ understands the following aesthetics (required\n",
       "     aesthetics are in bold):\n",
       "\n",
       "        ΓÇó ΓÇÿ*x*ΓÇÖ\n",
       "\n",
       "        ΓÇó ΓÇÿ*y*ΓÇÖ\n",
       "\n",
       "        ΓÇó ΓÇÿalphaΓÇÖ\n",
       "\n",
       "        ΓÇó ΓÇÿcolourΓÇÖ\n",
       "\n",
       "        ΓÇó ΓÇÿfillΓÇÖ\n",
       "\n",
       "        ΓÇó ΓÇÿshapeΓÇÖ\n",
       "\n",
       "        ΓÇó ΓÇÿsizeΓÇÖ\n",
       "\n",
       "_\bS_\be_\be _\bA_\bl_\bs_\bo:\n",
       "\n",
       "     ΓÇÿscale_sizeΓÇÖ to see scale area of points, instead of radius,\n",
       "     ΓÇÿgeom_jitterΓÇÖ to jitter points to reduce (mild) overplotting\n",
       "\n",
       "_\bE_\bx_\ba_\bm_\bp_\bl_\be_\bs:\n",
       "\n",
       "     p <- ggplot(mtcars, aes(wt, mpg))\n",
       "     p + geom_point()\n",
       "     \n",
       "     # Add aesthetic mappings\n",
       "     p + geom_point(aes(colour = qsec))\n",
       "     p + geom_point(aes(alpha = qsec))\n",
       "     p + geom_point(aes(colour = factor(cyl)))\n",
       "     p + geom_point(aes(shape = factor(cyl)))\n",
       "     p + geom_point(aes(size = qsec))\n",
       "     \n",
       "     # Change scales\n",
       "     p + geom_point(aes(colour = cyl)) + scale_colour_gradient(low = \"blue\")\n",
       "     p + geom_point(aes(size = qsec)) + scale_size_area()\n",
       "     p + geom_point(aes(shape = factor(cyl))) + scale_shape(solid = FALSE)\n",
       "     \n",
       "     # Set aesthetics to fixed value\n",
       "     p + geom_point(colour = \"red\", size = 3)\n",
       "     qplot(wt, mpg, data = mtcars, colour = I(\"red\"), size = I(3))\n",
       "     \n",
       "     # Varying alpha is useful for large datasets\n",
       "     d <- ggplot(diamonds, aes(carat, price))\n",
       "     d + geom_point(alpha = 1/10)\n",
       "     d + geom_point(alpha = 1/20)\n",
       "     d + geom_point(alpha = 1/100)\n",
       "     \n",
       "     # You can create interesting shapes by layering multiple points of\n",
       "     # different sizes\n",
       "     p <- ggplot(mtcars, aes(mpg, wt))\n",
       "     p + geom_point(colour=\"grey50\", size = 4) + geom_point(aes(colour = cyl))\n",
       "     p + aes(shape = factor(cyl)) +\n",
       "       geom_point(aes(colour = factor(cyl)), size = 4) +\n",
       "       geom_point(colour=\"grey90\", size = 1.5)\n",
       "     p + geom_point(colour=\"black\", size = 4.5) +\n",
       "       geom_point(colour=\"pink\", size = 4) +\n",
       "       geom_point(aes(shape = factor(cyl)))\n",
       "     \n",
       "     # These extra layers don't usually appear in the legend, but we can\n",
       "     # force their inclusion\n",
       "     p + geom_point(colour=\"black\", size = 4.5, show_guide = TRUE) +\n",
       "       geom_point(colour=\"pink\", size = 4, show_guide = TRUE) +\n",
       "       geom_point(aes(shape = factor(cyl)))\n",
       "     \n",
       "     # Transparent points:\n",
       "     qplot(mpg, wt, data = mtcars, size = I(5), alpha = I(0.2))\n",
       "     \n",
       "     # geom_point warns when missing values have been dropped from the data set\n",
       "     # and not plotted, you can turn this off by setting na.rm = TRUE\n",
       "     mtcars2 <- transform(mtcars, mpg = ifelse(runif(32) < 0.2, NA, mpg))\n",
       "     qplot(wt, mpg, data = mtcars2)\n",
       "     qplot(wt, mpg, data = mtcars2, na.rm = TRUE)\n",
       "     \n",
       "     # Use qplot instead\n",
       "     qplot(wt, mpg, data = mtcars)\n",
       "     qplot(wt, mpg, data = mtcars, colour = factor(cyl))\n",
       "     qplot(wt, mpg, data = mtcars, colour = I(\"red\"))\n",
       "     "
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "?geom_point"
   ]
  }
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