add exercise on R+python

exercises.ipynb

@@ -141,7 +141,7 @@
     "4. The \"share\" column of the dataframe contains the number of Nobel recipients that shared the prize. Have a look at the statistics of this column using  \n",
     "\n",
     "```python\n",
-    "nobels[\"share\"].describe()\n",
+    "nobel[\"share\"].describe()\n",
     "```\n",
     "\n",
     "5. The `describe()` method is smart about data types. Try this:  \n",
@@ -161,15 +161,14 @@
     "7. Next subtract the birth date from the year of receiving the prize and insert it into a new column \"age\":\n",
     "```python\n",
     "nobel[\"age\"] = nobel[\"year\"] - nobel[\"born\"].dt.year\n",
-    "nobel[[\"surname\",\"age\"]].head(10)\n",
     "```\n",
     " - Now print the \"surname\" and \"age\" of first 10 entries using the `head()` method.\n",
     "\n",
     "8. Now plot results in two different ways:\n",
     "\n",
     "```python\n",
-    "nobel[\"age\"].plot.hist(bins=[20,30,40,50,60,70,80,\n",
-    "                             90,100],alpha=0.6);\n",
+    "nobel[\"age\"].plot.hist(bins=[20,30,40,50,60,70,80,                             90,100],alpha=0.6);\n",
+    "\n",
     "nobel.boxplot(column=\"age\", by=\"category\")\n",
     "```\n",
     "\n",
@@ -340,6 +339,39 @@
     "Final note: While parallelizing Python code is often worth it, there are other ways to get higher performance out of Python code. In particular, fast numerical packages like [Numpy](http://www.numpy.org/) should be used, and significant speedup can be obtained with just-in-time compilation with [Numba](https://numba.pydata.org/) and/or C-extensions from [Cython](http://cython.org/).\n"
    ]
   },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### <font color=\"red\"> *Exercise 6:* Mixing Python and R </font>"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Your goal now is to define a pandas dataframe, and pass it into an R cell and plot it with an R plotting library.\n",
+    "\n",
+    "1. Run the following code in a code cell and plot it with the basic plot method of pandas dataframes:\n",
+    "\n",
+    "```python\n",
+    "import pandas as pd\n",
+    "df = pd.DataFrame({\n",
+    "    'cups_of_coffee': [0, 1, 2, 3, 4, 5, 6, 7, 8, 9],\n",
+    "    'productivity': [2, 5, 6, 8, 9, 8, 0, 1, 0, -1]\n",
+    "})\n",
+    "```\n",
+    " \n",
+    "2. Now take the following R code, and use the `%%R` magic command to pass in and plot the pandas dataframe defined above:\n",
+    "\n",
+    "```R\n",
+    "library(ggplot2)\n",
+    "ggplot(df, aes(x=cups_of_coffee, y=productivity)) + geom_line()\n",
+    "```\n",
+    "\n",
+    "3. Play around with the flags for height, width, units and resolution to get a good looking graph."
+   ]
+  },
   {
    "cell_type": "code",
    "execution_count": null,