Jupyter notebook examples

Julia_notebook.ipynb

Python_notebook.ipynb

R_notebook.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# R notebook"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "data <- 1:10"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<ol class=list-inline>\n",
       "\t<li>1</li>\n",
       "\t<li>2</li>\n",
       "\t<li>3</li>\n",
       "\t<li>4</li>\n",
       "\t<li>5</li>\n",
       "\t<li>6</li>\n",
       "\t<li>7</li>\n",
       "\t<li>8</li>\n",
       "\t<li>9</li>\n",
       "\t<li>10</li>\n",
       "</ol>\n"
      ],
      "text/latex": [
       "\\begin{enumerate*}\n",
       "\\item 1\n",
       "\\item 2\n",
       "\\item 3\n",
       "\\item 4\n",
       "\\item 5\n",
       "\\item 6\n",
       "\\item 7\n",
       "\\item 8\n",
       "\\item 9\n",
       "\\item 10\n",
       "\\end{enumerate*}\n"
      ],
      "text/markdown": [
       "1. 1\n",
       "2. 2\n",
       "3. 3\n",
       "4. 4\n",
       "5. 5\n",
       "6. 6\n",
       "7. 7\n",
       "8. 8\n",
       "9. 9\n",
       "10. 10\n",
       "\n",
       "\n"
      ],
      "text/plain": [
       " [1]  1  2  3  4  5  6  7  8  9 10"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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2H5Lx+r9RUM/SYkCqbekJ5e2Lfx1B0h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\nQoIAQoIAQoIA/x/EsicGaH06IgAAAABJRU5ErkJggg==",
      "text/plain": [
       "plot without title"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot(data)"
   ]
  },
  {
   "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": "3.5.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}