R_example_for_Jupyter.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# $\\LaTeX$ Math\n",
    "\n",
    "This is just markdown that can include latex math.\n",
    "\n",
    "$$\n",
    "\\begin{align}\n",
    "\\dot{x} & = \\sigma(y-x) \\\\\n",
    "\\dot{y} & = \\rho x - y - xz \\\\\n",
    "\\dot{z} & = -\\beta z + xy\n",
    "\\end{align}\n",
    "$$"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# System Info\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "R version 3.5.1 (2018-07-02)\n",
       "Platform: x86_64-apple-darwin15.6.0 (64-bit)\n",
       "Running under: macOS High Sierra 10.13.1\n",
       "\n",
       "Matrix products: default\n",
       "BLAS: /Library/Frameworks/R.framework/Versions/3.5/Resources/lib/libRblas.0.dylib\n",
       "LAPACK: /Library/Frameworks/R.framework/Versions/3.5/Resources/lib/libRlapack.dylib\n",
       "\n",
       "locale:\n",
       "[1] en_US.UTF-8/en_US.UTF-8/en_US.UTF-8/C/en_US.UTF-8/en_US.UTF-8\n",
       "\n",
       "attached base packages:\n",
       "[1] stats     graphics  grDevices utils     datasets  methods   base     \n",
       "\n",
       "loaded via a namespace (and not attached):\n",
       " [1] compiler_3.5.1       magrittr_1.5         IRdisplay_0.5.0     \n",
       " [4] pbdZMQ_0.3-3         tools_3.5.1          htmltools_0.3.6     \n",
       " [7] base64enc_0.1-3      crayon_1.3.4         Rcpp_0.12.18        \n",
       "[10] uuid_0.1-2           stringi_1.2.4        IRkernel_0.8.12.9000\n",
       "[13] jsonlite_1.5         stringr_1.3.1        digest_0.6.18       \n",
       "[16] repr_0.15.0          evaluate_0.11       "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# session info\n",
    "sessionInfo()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Data"
   ]
  },
  {
   "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",
       "\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": [
    "# data\n",
    "iris"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Plot"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {},
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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NFj/msmVB6mk0A0BKz57DMaQsxLAiRA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Et1KK8zl4tN/diIs0v4+kqPkpo/tasTx3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\nrkJggg==",
      "text/plain": [
       "plot without title"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "library(ggplot2)\n",
    "options(repr.plot.width=6, repr.plot.height=4)\n",
    "ggplot(iris) + \n",
    "  aes(Sepal.Length, Sepal.Width, color = Species) +\n",
    "  geom_point() + \n",
    "  theme_bw()"
   ]
  }
 ],
 "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": "3.5.1"
  },
  "toc-autonumbering": true
 },
 "nbformat": 4,
 "nbformat_minor": 2
}