{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## R jupyter notebook demo\n",
    "\n",
    "Following https://docs.anaconda.com/anaconda/navigator/tutorials/r-lang/"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table>\n",
       "<thead><tr><th scope=col>Sepal.Length</th><th scope=col>Sepal.Width</th><th scope=col>Petal.Length</th><th scope=col>Petal.Width</th><th scope=col>Species</th></tr></thead>\n",
       "<tbody>\n",
       "\t<tr><td>5.1   </td><td>3.5   </td><td>1.4   </td><td>0.2   </td><td>setosa</td></tr>\n",
       "\t<tr><td>4.9   </td><td>3.0   </td><td>1.4   </td><td>0.2   </td><td>setosa</td></tr>\n",
       "\t<tr><td>4.7   </td><td>3.2   </td><td>1.3   </td><td>0.2   </td><td>setosa</td></tr>\n",
       "\t<tr><td>4.6   </td><td>3.1   </td><td>1.5   </td><td>0.2   </td><td>setosa</td></tr>\n",
       "\t<tr><td>5.0   </td><td>3.6   </td><td>1.4   </td><td>0.2   </td><td>setosa</td></tr>\n",
       "\t<tr><td>5.4   </td><td>3.9   </td><td>1.7   </td><td>0.4   </td><td>setosa</td></tr>\n",
       "\t<tr><td>4.6   </td><td>3.4   </td><td>1.4   </td><td>0.3   </td><td>setosa</td></tr>\n",
       "\t<tr><td>5.0   </td><td>3.4   </td><td>1.5   </td><td>0.2   </td><td>setosa</td></tr>\n",
       "\t<tr><td>4.4   </td><td>2.9   </td><td>1.4   </td><td>0.2   </td><td>setosa</td></tr>\n",
       "\t<tr><td>4.9   </td><td>3.1   </td><td>1.5   </td><td>0.1   </td><td>setosa</td></tr>\n",
       "\t<tr><td>5.4   </td><td>3.7   </td><td>1.5   </td><td>0.2   </td><td>setosa</td></tr>\n",
       "\t<tr><td>4.8   </td><td>3.4   </td><td>1.6   </td><td>0.2   </td><td>setosa</td></tr>\n",
       "\t<tr><td>4.8   </td><td>3.0   </td><td>1.4   </td><td>0.1   </td><td>setosa</td></tr>\n",
       "\t<tr><td>4.3   </td><td>3.0   </td><td>1.1   </td><td>0.1   </td><td>setosa</td></tr>\n",
       "\t<tr><td>5.8   </td><td>4.0   </td><td>1.2   </td><td>0.2   </td><td>setosa</td></tr>\n",
       "\t<tr><td>5.7   </td><td>4.4   </td><td>1.5   </td><td>0.4   </td><td>setosa</td></tr>\n",
       "\t<tr><td>5.4   </td><td>3.9   </td><td>1.3   </td><td>0.4   </td><td>setosa</td></tr>\n",
       "\t<tr><td>5.1   </td><td>3.5   </td><td>1.4   </td><td>0.3   </td><td>setosa</td></tr>\n",
       "\t<tr><td>5.7   </td><td>3.8   </td><td>1.7   </td><td>0.3   </td><td>setosa</td></tr>\n",
       "\t<tr><td>5.1   </td><td>3.8   </td><td>1.5   </td><td>0.3   </td><td>setosa</td></tr>\n",
       "\t<tr><td>5.4   </td><td>3.4   </td><td>1.7   </td><td>0.2   </td><td>setosa</td></tr>\n",
       "\t<tr><td>5.1   </td><td>3.7   </td><td>1.5   </td><td>0.4   </td><td>setosa</td></tr>\n",
       "\t<tr><td>4.6   </td><td>3.6   </td><td>1.0   </td><td>0.2   </td><td>setosa</td></tr>\n",
       "\t<tr><td>5.1   </td><td>3.3   </td><td>1.7   </td><td>0.5   </td><td>setosa</td></tr>\n",
       "\t<tr><td>4.8   </td><td>3.4   </td><td>1.9   </td><td>0.2   </td><td>setosa</td></tr>\n",
       "\t<tr><td>5.0   </td><td>3.0   </td><td>1.6   </td><td>0.2   </td><td>setosa</td></tr>\n",
       "\t<tr><td>5.0   </td><td>3.4   </td><td>1.6   </td><td>0.4   </td><td>setosa</td></tr>\n",
       "\t<tr><td>5.2   </td><td>3.5   </td><td>1.5   </td><td>0.2   </td><td>setosa</td></tr>\n",
       "\t<tr><td>5.2   </td><td>3.4   </td><td>1.4   </td><td>0.2   </td><td>setosa</td></tr>\n",
       "\t<tr><td>4.7   </td><td>3.2   </td><td>1.6   </td><td>0.2   </td><td>setosa</td></tr>\n",
       "\t<tr><td>⋮</td><td>⋮</td><td>⋮</td><td>⋮</td><td>⋮</td></tr>\n",
       "\t<tr><td>6.9      </td><td>3.2      </td><td>5.7      </td><td>2.3      </td><td>virginica</td></tr>\n",
       "\t<tr><td>5.6      </td><td>2.8      </td><td>4.9      </td><td>2.0      </td><td>virginica</td></tr>\n",
       "\t<tr><td>7.7      </td><td>2.8      </td><td>6.7      </td><td>2.0      </td><td>virginica</td></tr>\n",
       "\t<tr><td>6.3      </td><td>2.7      </td><td>4.9      </td><td>1.8      </td><td>virginica</td></tr>\n",
       "\t<tr><td>6.7      </td><td>3.3      </td><td>5.7      </td><td>2.1      </td><td>virginica</td></tr>\n",
       "\t<tr><td>7.2      </td><td>3.2      </td><td>6.0      </td><td>1.8      </td><td>virginica</td></tr>\n",
       "\t<tr><td>6.2      </td><td>2.8      </td><td>4.8      </td><td>1.8      </td><td>virginica</td></tr>\n",
       "\t<tr><td>6.1      </td><td>3.0      </td><td>4.9      </td><td>1.8      </td><td>virginica</td></tr>\n",
       "\t<tr><td>6.4      </td><td>2.8      </td><td>5.6      </td><td>2.1      </td><td>virginica</td></tr>\n",
       "\t<tr><td>7.2      </td><td>3.0      </td><td>5.8      </td><td>1.6      </td><td>virginica</td></tr>\n",
       "\t<tr><td>7.4      </td><td>2.8      </td><td>6.1      </td><td>1.9      </td><td>virginica</td></tr>\n",
       "\t<tr><td>7.9      </td><td>3.8      </td><td>6.4      </td><td>2.0      </td><td>virginica</td></tr>\n",
       "\t<tr><td>6.4      </td><td>2.8      </td><td>5.6      </td><td>2.2      </td><td>virginica</td></tr>\n",
       "\t<tr><td>6.3      </td><td>2.8      </td><td>5.1      </td><td>1.5      </td><td>virginica</td></tr>\n",
       "\t<tr><td>6.1      </td><td>2.6      </td><td>5.6      </td><td>1.4      </td><td>virginica</td></tr>\n",
       "\t<tr><td>7.7      </td><td>3.0      </td><td>6.1      </td><td>2.3      </td><td>virginica</td></tr>\n",
       "\t<tr><td>6.3      </td><td>3.4      </td><td>5.6      </td><td>2.4      </td><td>virginica</td></tr>\n",
       "\t<tr><td>6.4      </td><td>3.1      </td><td>5.5      </td><td>1.8      </td><td>virginica</td></tr>\n",
       "\t<tr><td>6.0      </td><td>3.0      </td><td>4.8      </td><td>1.8      </td><td>virginica</td></tr>\n",
       "\t<tr><td>6.9      </td><td>3.1      </td><td>5.4      </td><td>2.1      </td><td>virginica</td></tr>\n",
       "\t<tr><td>6.7      </td><td>3.1      </td><td>5.6      </td><td>2.4      </td><td>virginica</td></tr>\n",
       "\t<tr><td>6.9      </td><td>3.1      </td><td>5.1      </td><td>2.3      </td><td>virginica</td></tr>\n",
       "\t<tr><td>5.8      </td><td>2.7      </td><td>5.1      </td><td>1.9      </td><td>virginica</td></tr>\n",
       "\t<tr><td>6.8      </td><td>3.2      </td><td>5.9      </td><td>2.3      </td><td>virginica</td></tr>\n",
       "\t<tr><td>6.7      </td><td>3.3      </td><td>5.7      </td><td>2.5      </td><td>virginica</td></tr>\n",
       "\t<tr><td>6.7      </td><td>3.0      </td><td>5.2      </td><td>2.3      </td><td>virginica</td></tr>\n",
       "\t<tr><td>6.3      </td><td>2.5      </td><td>5.0      </td><td>1.9      </td><td>virginica</td></tr>\n",
       "\t<tr><td>6.5      </td><td>3.0      </td><td>5.2      </td><td>2.0      </td><td>virginica</td></tr>\n",
       "\t<tr><td>6.2      </td><td>3.4      </td><td>5.4      </td><td>2.3      </td><td>virginica</td></tr>\n",
       "\t<tr><td>5.9      </td><td>3.0      </td><td>5.1      </td><td>1.8      </td><td>virginica</td></tr>\n",
       "</tbody>\n",
       "</table>\n"
      ],
      "text/latex": [
       "\\begin{tabular}{r|lllll}\n",
       " Sepal.Length & Sepal.Width & Petal.Length & Petal.Width & Species\\\\\n",
       "\\hline\n",
       "\t 5.1    & 3.5    & 1.4    & 0.2    & setosa\\\\\n",
       "\t 4.9    & 3.0    & 1.4    & 0.2    & setosa\\\\\n",
       "\t 4.7    & 3.2    & 1.3    & 0.2    & setosa\\\\\n",
       "\t 4.6    & 3.1    & 1.5    & 0.2    & setosa\\\\\n",
       "\t 5.0    & 3.6    & 1.4    & 0.2    & setosa\\\\\n",
       "\t 5.4    & 3.9    & 1.7    & 0.4    & setosa\\\\\n",
       "\t 4.6    & 3.4    & 1.4    & 0.3    & setosa\\\\\n",
       "\t 5.0    & 3.4    & 1.5    & 0.2    & setosa\\\\\n",
       "\t 4.4    & 2.9    & 1.4    & 0.2    & setosa\\\\\n",
       "\t 4.9    & 3.1    & 1.5    & 0.1    & setosa\\\\\n",
       "\t 5.4    & 3.7    & 1.5    & 0.2    & setosa\\\\\n",
       "\t 4.8    & 3.4    & 1.6    & 0.2    & setosa\\\\\n",
       "\t 4.8    & 3.0    & 1.4    & 0.1    & setosa\\\\\n",
       "\t 4.3    & 3.0    & 1.1    & 0.1    & setosa\\\\\n",
       "\t 5.8    & 4.0    & 1.2    & 0.2    & setosa\\\\\n",
       "\t 5.7    & 4.4    & 1.5    & 0.4    & setosa\\\\\n",
       "\t 5.4    & 3.9    & 1.3    & 0.4    & setosa\\\\\n",
       "\t 5.1    & 3.5    & 1.4    & 0.3    & setosa\\\\\n",
       "\t 5.7    & 3.8    & 1.7    & 0.3    & setosa\\\\\n",
       "\t 5.1    & 3.8    & 1.5    & 0.3    & setosa\\\\\n",
       "\t 5.4    & 3.4    & 1.7    & 0.2    & setosa\\\\\n",
       "\t 5.1    & 3.7    & 1.5    & 0.4    & setosa\\\\\n",
       "\t 4.6    & 3.6    & 1.0    & 0.2    & setosa\\\\\n",
       "\t 5.1    & 3.3    & 1.7    & 0.5    & setosa\\\\\n",
       "\t 4.8    & 3.4    & 1.9    & 0.2    & setosa\\\\\n",
       "\t 5.0    & 3.0    & 1.6    & 0.2    & setosa\\\\\n",
       "\t 5.0    & 3.4    & 1.6    & 0.4    & setosa\\\\\n",
       "\t 5.2    & 3.5    & 1.5    & 0.2    & setosa\\\\\n",
       "\t 5.2    & 3.4    & 1.4    & 0.2    & setosa\\\\\n",
       "\t 4.7    & 3.2    & 1.6    & 0.2    & setosa\\\\\n",
       "\t ⋮ & ⋮ & ⋮ & ⋮ & ⋮\\\\\n",
       "\t 6.9       & 3.2       & 5.7       & 2.3       & virginica\\\\\n",
       "\t 5.6       & 2.8       & 4.9       & 2.0       & virginica\\\\\n",
       "\t 7.7       & 2.8       & 6.7       & 2.0       & virginica\\\\\n",
       "\t 6.3       & 2.7       & 4.9       & 1.8       & virginica\\\\\n",
       "\t 6.7       & 3.3       & 5.7       & 2.1       & virginica\\\\\n",
       "\t 7.2       & 3.2       & 6.0       & 1.8       & virginica\\\\\n",
       "\t 6.2       & 2.8       & 4.8       & 1.8       & virginica\\\\\n",
       "\t 6.1       & 3.0       & 4.9       & 1.8       & virginica\\\\\n",
       "\t 6.4       & 2.8       & 5.6       & 2.1       & virginica\\\\\n",
       "\t 7.2       & 3.0       & 5.8       & 1.6       & virginica\\\\\n",
       "\t 7.4       & 2.8       & 6.1       & 1.9       & virginica\\\\\n",
       "\t 7.9       & 3.8       & 6.4       & 2.0       & virginica\\\\\n",
       "\t 6.4       & 2.8       & 5.6       & 2.2       & virginica\\\\\n",
       "\t 6.3       & 2.8       & 5.1       & 1.5       & virginica\\\\\n",
       "\t 6.1       & 2.6       & 5.6       & 1.4       & virginica\\\\\n",
       "\t 7.7       & 3.0       & 6.1       & 2.3       & virginica\\\\\n",
       "\t 6.3       & 3.4       & 5.6       & 2.4       & virginica\\\\\n",
       "\t 6.4       & 3.1       & 5.5       & 1.8       & virginica\\\\\n",
       "\t 6.0       & 3.0       & 4.8       & 1.8       & virginica\\\\\n",
       "\t 6.9       & 3.1       & 5.4       & 2.1       & virginica\\\\\n",
       "\t 6.7       & 3.1       & 5.6       & 2.4       & virginica\\\\\n",
       "\t 6.9       & 3.1       & 5.1       & 2.3       & virginica\\\\\n",
       "\t 5.8       & 2.7       & 5.1       & 1.9       & virginica\\\\\n",
       "\t 6.8       & 3.2       & 5.9       & 2.3       & virginica\\\\\n",
       "\t 6.7       & 3.3       & 5.7       & 2.5       & virginica\\\\\n",
       "\t 6.7       & 3.0       & 5.2       & 2.3       & virginica\\\\\n",
       "\t 6.3       & 2.5       & 5.0       & 1.9       & virginica\\\\\n",
       "\t 6.5       & 3.0       & 5.2       & 2.0       & virginica\\\\\n",
       "\t 6.2       & 3.4       & 5.4       & 2.3       & virginica\\\\\n",
       "\t 5.9       & 3.0       & 5.1       & 1.8       & virginica\\\\\n",
       "\\end{tabular}\n"
      ],
      "text/markdown": [
       "\n",
       "| Sepal.Length | Sepal.Width | Petal.Length | Petal.Width | Species |\n",
       "|---|---|---|---|---|\n",
       "| 5.1    | 3.5    | 1.4    | 0.2    | setosa |\n",
       "| 4.9    | 3.0    | 1.4    | 0.2    | setosa |\n",
       "| 4.7    | 3.2    | 1.3    | 0.2    | setosa |\n",
       "| 4.6    | 3.1    | 1.5    | 0.2    | setosa |\n",
       "| 5.0    | 3.6    | 1.4    | 0.2    | setosa |\n",
       "| 5.4    | 3.9    | 1.7    | 0.4    | setosa |\n",
       "| 4.6    | 3.4    | 1.4    | 0.3    | setosa |\n",
       "| 5.0    | 3.4    | 1.5    | 0.2    | setosa |\n",
       "| 4.4    | 2.9    | 1.4    | 0.2    | setosa |\n",
       "| 4.9    | 3.1    | 1.5    | 0.1    | setosa |\n",
       "| 5.4    | 3.7    | 1.5    | 0.2    | setosa |\n",
       "| 4.8    | 3.4    | 1.6    | 0.2    | setosa |\n",
       "| 4.8    | 3.0    | 1.4    | 0.1    | setosa |\n",
       "| 4.3    | 3.0    | 1.1    | 0.1    | setosa |\n",
       "| 5.8    | 4.0    | 1.2    | 0.2    | setosa |\n",
       "| 5.7    | 4.4    | 1.5    | 0.4    | setosa |\n",
       "| 5.4    | 3.9    | 1.3    | 0.4    | setosa |\n",
       "| 5.1    | 3.5    | 1.4    | 0.3    | setosa |\n",
       "| 5.7    | 3.8    | 1.7    | 0.3    | setosa |\n",
       "| 5.1    | 3.8    | 1.5    | 0.3    | setosa |\n",
       "| 5.4    | 3.4    | 1.7    | 0.2    | setosa |\n",
       "| 5.1    | 3.7    | 1.5    | 0.4    | setosa |\n",
       "| 4.6    | 3.6    | 1.0    | 0.2    | setosa |\n",
       "| 5.1    | 3.3    | 1.7    | 0.5    | setosa |\n",
       "| 4.8    | 3.4    | 1.9    | 0.2    | setosa |\n",
       "| 5.0    | 3.0    | 1.6    | 0.2    | setosa |\n",
       "| 5.0    | 3.4    | 1.6    | 0.4    | setosa |\n",
       "| 5.2    | 3.5    | 1.5    | 0.2    | setosa |\n",
       "| 5.2    | 3.4    | 1.4    | 0.2    | setosa |\n",
       "| 4.7    | 3.2    | 1.6    | 0.2    | setosa |\n",
       "| ⋮ | ⋮ | ⋮ | ⋮ | ⋮ |\n",
       "| 6.9       | 3.2       | 5.7       | 2.3       | virginica |\n",
       "| 5.6       | 2.8       | 4.9       | 2.0       | virginica |\n",
       "| 7.7       | 2.8       | 6.7       | 2.0       | virginica |\n",
       "| 6.3       | 2.7       | 4.9       | 1.8       | virginica |\n",
       "| 6.7       | 3.3       | 5.7       | 2.1       | virginica |\n",
       "| 7.2       | 3.2       | 6.0       | 1.8       | virginica |\n",
       "| 6.2       | 2.8       | 4.8       | 1.8       | virginica |\n",
       "| 6.1       | 3.0       | 4.9       | 1.8       | virginica |\n",
       "| 6.4       | 2.8       | 5.6       | 2.1       | virginica |\n",
       "| 7.2       | 3.0       | 5.8       | 1.6       | virginica |\n",
       "| 7.4       | 2.8       | 6.1       | 1.9       | virginica |\n",
       "| 7.9       | 3.8       | 6.4       | 2.0       | virginica |\n",
       "| 6.4       | 2.8       | 5.6       | 2.2       | virginica |\n",
       "| 6.3       | 2.8       | 5.1       | 1.5       | virginica |\n",
       "| 6.1       | 2.6       | 5.6       | 1.4       | virginica |\n",
       "| 7.7       | 3.0       | 6.1       | 2.3       | virginica |\n",
       "| 6.3       | 3.4       | 5.6       | 2.4       | virginica |\n",
       "| 6.4       | 3.1       | 5.5       | 1.8       | virginica |\n",
       "| 6.0       | 3.0       | 4.8       | 1.8       | virginica |\n",
       "| 6.9       | 3.1       | 5.4       | 2.1       | virginica |\n",
       "| 6.7       | 3.1       | 5.6       | 2.4       | virginica |\n",
       "| 6.9       | 3.1       | 5.1       | 2.3       | virginica |\n",
       "| 5.8       | 2.7       | 5.1       | 1.9       | virginica |\n",
       "| 6.8       | 3.2       | 5.9       | 2.3       | virginica |\n",
       "| 6.7       | 3.3       | 5.7       | 2.5       | virginica |\n",
       "| 6.7       | 3.0       | 5.2       | 2.3       | virginica |\n",
       "| 6.3       | 2.5       | 5.0       | 1.9       | virginica |\n",
       "| 6.5       | 3.0       | 5.2       | 2.0       | virginica |\n",
       "| 6.2       | 3.4       | 5.4       | 2.3       | virginica |\n",
       "| 5.9       | 3.0       | 5.1       | 1.8       | virginica |\n",
       "\n"
      ],
      "text/plain": [
       "    Sepal.Length Sepal.Width Petal.Length Petal.Width Species  \n",
       "1   5.1          3.5         1.4          0.2         setosa   \n",
       "2   4.9          3.0         1.4          0.2         setosa   \n",
       "3   4.7          3.2         1.3          0.2         setosa   \n",
       "4   4.6          3.1         1.5          0.2         setosa   \n",
       "5   5.0          3.6         1.4          0.2         setosa   \n",
       "6   5.4          3.9         1.7          0.4         setosa   \n",
       "7   4.6          3.4         1.4          0.3         setosa   \n",
       "8   5.0          3.4         1.5          0.2         setosa   \n",
       "9   4.4          2.9         1.4          0.2         setosa   \n",
       "10  4.9          3.1         1.5          0.1         setosa   \n",
       "11  5.4          3.7         1.5          0.2         setosa   \n",
       "12  4.8          3.4         1.6          0.2         setosa   \n",
       "13  4.8          3.0         1.4          0.1         setosa   \n",
       "14  4.3          3.0         1.1          0.1         setosa   \n",
       "15  5.8          4.0         1.2          0.2         setosa   \n",
       "16  5.7          4.4         1.5          0.4         setosa   \n",
       "17  5.4          3.9         1.3          0.4         setosa   \n",
       "18  5.1          3.5         1.4          0.3         setosa   \n",
       "19  5.7          3.8         1.7          0.3         setosa   \n",
       "20  5.1          3.8         1.5          0.3         setosa   \n",
       "21  5.4          3.4         1.7          0.2         setosa   \n",
       "22  5.1          3.7         1.5          0.4         setosa   \n",
       "23  4.6          3.6         1.0          0.2         setosa   \n",
       "24  5.1          3.3         1.7          0.5         setosa   \n",
       "25  4.8          3.4         1.9          0.2         setosa   \n",
       "26  5.0          3.0         1.6          0.2         setosa   \n",
       "27  5.0          3.4         1.6          0.4         setosa   \n",
       "28  5.2          3.5         1.5          0.2         setosa   \n",
       "29  5.2          3.4         1.4          0.2         setosa   \n",
       "30  4.7          3.2         1.6          0.2         setosa   \n",
       "⋮   ⋮            ⋮           ⋮            ⋮           ⋮        \n",
       "121 6.9          3.2         5.7          2.3         virginica\n",
       "122 5.6          2.8         4.9          2.0         virginica\n",
       "123 7.7          2.8         6.7          2.0         virginica\n",
       "124 6.3          2.7         4.9          1.8         virginica\n",
       "125 6.7          3.3         5.7          2.1         virginica\n",
       "126 7.2          3.2         6.0          1.8         virginica\n",
       "127 6.2          2.8         4.8          1.8         virginica\n",
       "128 6.1          3.0         4.9          1.8         virginica\n",
       "129 6.4          2.8         5.6          2.1         virginica\n",
       "130 7.2          3.0         5.8          1.6         virginica\n",
       "131 7.4          2.8         6.1          1.9         virginica\n",
       "132 7.9          3.8         6.4          2.0         virginica\n",
       "133 6.4          2.8         5.6          2.2         virginica\n",
       "134 6.3          2.8         5.1          1.5         virginica\n",
       "135 6.1          2.6         5.6          1.4         virginica\n",
       "136 7.7          3.0         6.1          2.3         virginica\n",
       "137 6.3          3.4         5.6          2.4         virginica\n",
       "138 6.4          3.1         5.5          1.8         virginica\n",
       "139 6.0          3.0         4.8          1.8         virginica\n",
       "140 6.9          3.1         5.4          2.1         virginica\n",
       "141 6.7          3.1         5.6          2.4         virginica\n",
       "142 6.9          3.1         5.1          2.3         virginica\n",
       "143 5.8          2.7         5.1          1.9         virginica\n",
       "144 6.8          3.2         5.9          2.3         virginica\n",
       "145 6.7          3.3         5.7          2.5         virginica\n",
       "146 6.7          3.0         5.2          2.3         virginica\n",
       "147 6.3          2.5         5.0          1.9         virginica\n",
       "148 6.5          3.0         5.2          2.0         virginica\n",
       "149 6.2          3.4         5.4          2.3         virginica\n",
       "150 5.9          3.0         5.1          1.8         virginica"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "library(dplyr)\n",
    "iris"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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gaDQb0dHMnu9Xr9fn8gEGgPh0hqmub1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Aa8zhSL6z5CTfZCdqLYQ3XvJMRc4+Iw+G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\nCTuaphMSEpxOp9ls9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Ln3rqKZvtp4ty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\nhOktjkTwAAAAAElFTkSuQmCC",
      "text/plain": [
       "plot without title"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "library(ggplot2)\n",
    "ggplot(data=iris, aes(x=Sepal.Length, y=Sepal.Width, color=Species)) + geom_point(size=3)"
   ]
  },
  {
   "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": "3.4.4"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}