Initial commit

module2/exo1/toy_notebook_fr.ipynb

 {
- "cells": [],
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Utilisation Jupyter"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "4"
+      ]
+     },
+     "execution_count": 1,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "2 + 2"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Help"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Esc-m -> Cellule en Markdown  \n",
+    "Esc-y -> Cellule de code  \n",
+    "Ctrl+<enter> -> execute cellule sélectionnée\n",
+    "Alt+<enter> -> execute cellule + nouvelle cellule  "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Code R"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "%load_ext rpy2.ipython"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": "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\n"
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "%%R\n",
+    "plot(cars)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "[1] 5\n"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "%%R\n",
+    "x = 5\n",
+    "print(x)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "[1] 5\n"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "%%R\n",
+    "print(x)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "[1] 5\n"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "%%R\n",
+    "print(x)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Noyau en R"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    " Il est possible de passer de Python à R en changeant de kernel (menu Kernel->Change Kernel->R)."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [
+    {
+     "ename": "NameError",
+     "evalue": "name 'plot' is not defined",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
+      "\u001b[0;32m<ipython-input-7-63677292ba96>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mplot\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mcars\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
+      "\u001b[0;31mNameError\u001b[0m: name 'plot' is not defined"
+     ]
+    }
+   ],
+   "source": [
+    "plot(cars)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "cumul = 0\n",
+    "cumul = cumul + 100\n",
+    "print(cumul)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "print(cumul)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### End Document"
+   ]
+  }
+ ],
  "metadata": {
   "kernelspec": {
    "display_name": "Python 3",
@@ -16,10 +218,9 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.6.3"
+   "version": "3.6.4"
   }
  },
  "nbformat": 4,
  "nbformat_minor": 2
 }
-