Created on Cognitive Class Labs

Module1_BasicVisualizationTools_Reviewed.ipynb

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   "source": [
    "<a href=\"https://www.bigdatauniversity.com\"><img src = \"https://ibm.box.com/shared/static/wbqvbi6o6ip0vz55ua5gp17g4f1k7ve9.png\" width = 300, align = \"center\"></a>\n",
    "\n",
    "<h1 align=center><font size = 5>BASIC PLOTS</font></h1>\n",
    "\n",
    "<hr>\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
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   "source": [
    "## Table of Contents\n",
    "\n",
    "\n",
    "<div class=\"alert alert-block alert-info\" style=\"margin-top: 20px\">\n",
    "<li><p><a href=\"#ref0\">The importance of graphs</a></p></li>\n",
    "<li style=\"margin-left: 40px;\"><p><a href=\"#ref1\">The difference between R libraries</a></p></li>\n",
    "<li style=\"margin-left: 40px;\"><p><a href=\"#ref2\">Qualitative vs Quantitative Data</a></p></li>\n",
    "<li><p><a href=\"#ref3\">The `mtcars` dataset</a></p></li>\n",
    "<li><p><a href=\"#ref4\">Bar Plots</a></p></li>\n",
    "<li><p><a href=\"#ref5\">Histograms</a></p></li>\n",
    "<li><p><a href=\"#ref6\">Pie Charts</a></p></li>\n",
    "<br>\n",
    "<p></p>\n",
    "Estimated Time Needed: <strong>20 min</strong>\n",
    "</div>\n",
    "\n",
    "<hr>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a id=\"ref0\"></a>\n",
    "<h2 align=center>The importance of graphs</h2>\n",
    "<br>\n",
    "Data visualization is the presentation of data with graphics. It's a way to summarize your findings and display it in a form that facilitates interpretation and can help in identifying patterns or trends.\n",
    "Having great data visualizations will make your work more interesting and clear.\n",
    "In this course, you will learn how to use the ggplot2 library to create beautiful graphics and charts, customizing the look and feel of them as you wish."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### The difference between R libraries<a id=\"ref1\"></a>\n",
    "\n",
    "The differences between the basic plot() library, that comes with R, and ggplot2 are many.\n",
    "Ggplot2 was created to attend design demands and was based on the book \"The Grammar of Graphics\", a book that describes the foundations for data plotting.\n",
    "\n",
    "Let's look at the differences between them using a simple example."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### plot\n",
    "The `plot` library is the default R library for plotting graphs. It's very simplistic in both syntax and aesthetics. To use it to create a bar plot, you use the `barplot` function, like so:"
   ]
  },
  {
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   "execution_count": 1,
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    "jupyter": {
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   "outputs": [
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ELqEVIRUgipR0hFSCGkHiEVIYWQeoRU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",
      "text/plain": [
       "plot without title"
      ]
     },
     "metadata": {},
     "output_type": "display_data",
     "source": "R display func"
    }
   ],
   "source": [
    "count <- table(mtcars$cyl)\n",
    "barplot(count)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### ggplot2\n",
    "`ggplot2`, as mentioned above, is a specialized library made to create visually pleasing data visualizations.\n",
    "Before we can use `ggplot2`, we need to import it into the R environment.\n",
    "The code cell below will check if your system already has ggplot, as to not run `install.packages` for no reason. Then, using the `library` function, we can then import `ggplot2`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    }
   },
   "outputs": [],
   "source": [
    "if(\"ggplot2\" %in% rownames(installed.packages()) == FALSE) {install.packages(\"ggplot2\")}\n",
    "library(ggplot2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now, let's plot our graph. To plot a simple bar graph using `ggplot2`'s `qplot`, we use this:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    }
   },
   "outputs": [
    {
     "data": {},
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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UjVZiOBJCqbDKRqs5FAEpVNBlK12Ugg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\nSEQtBCSiFgISUQsBiaiFgETUQv8Px62MM/C0I4UAAAAASUVORK5CYII=",
      "text/plain": [
       "plot without title"
      ]
     },
     "metadata": {},
     "output_type": "display_data",
     "source": "R display func"
    }
   ],
   "source": [
    "qplot(mtcars$cyl, geom = \"bar\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Don't worry - we will go back and learn these and other plotting methods during our lessons.\n",
    "\n",
    "As you can see, `ggplot2` offers us a nicer-looking graph, but has a slightly more complex syntax than the default `plot` library."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### ggplot2 have two principal functions, `qplot()` and `ggplot()`.\n",
    "\n",
    "   • `qplot()` offers a simpler syntax similar to the default `plot` function, but is limited in customization.\n",
    "\n",
    "   • `ggplot()` is the full-fledged function. It has far more possible customizations, but has a more complicated syntax than `qplot()`.\n",
    "\n",
    "In this course, we will start using `qplot()` and then change to `ggplot()` as you advance."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The next graph is a demonstration of what is possible to do with `ggplot()`. In the end of this course you will be able to create graphs like this one below and even more complex ones. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    }
   },
   "outputs": [
    {
     "data": {},
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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bJkSe+20NTUdNFFF6VSqUMOOeScc86ZNGnSLldbuXLlqaee2nn1+OOPf/TRR9et\nW1dfX9+xpLm5eePGjZ0rVFRUlJSU9G6k/CkpKdkf3vCvkD9QuACfFb2TSCSSyWTOf5yCfewS\niUQvftiOHycfd1Rgkslk7+7h/ZA7Kkt9ey/l56XYvLy820f26eSJcePG/eQnP+nd144YMeKK\nK64YOXJka2vrs88+e9ttt1188cVTp07ttlomk9m8efOgQYM6l3Rc7lpyL7300syZMzuvzpkz\n56STTurdVPlTW1sb9wh9oV+/fnGPsGtlZWVdn0UBqK6uzu0Gg3zsqqqqqqqqcjtPkMrLy+Me\noQiUlpYG9mskT/r0XsrXyRO532Sf2aewe/XVV3t9EsO4cePGjRvXcXns2LFbt2597LHHdg67\nbAwdOrTrUYADBgxoaWnp3VR5Ul5e3tbWtj/ssUulUnGPsGvpdLrQnhW9Vlpamslk2tvbc7vZ\nwB67ZDJZVlaWSqVyfkcFpmOnZltbW9yDFLqKiop0Ou2O2qvy8vLW1tbd3ZqPY6h88kQ3WYXd\n8uXLuy3ZuHHjkiVLHnjggVx9UGx9fX1DQ0MqlSot3WGkRCJRW1u7adOmziUdl+vq6jqXHHPM\nMd/5znc6rzY2NjY1NeVkqlwZNGhQU1PT/hB227dvj3uEXWtvby+0Z0WvVVdXp1KpnHdqwT52\nqVSqF49dRUVFWVlZS0tLc3NzPqYKRllZWWVlZTB/OvKnoqIipF8j+VNXV7eHeykvB8c7eWJH\nWYXdiSeeuMvlEyZMuPvuu3Myx8qVK2tra7tVXYf6+vqXX375K1/5SsfVl19+ubKycl/OxgUA\ngpDJz8kToYfdnXfe2fVqIpGoq6sbPXr0vhzKdu+999bX1x900EGtra3PPfdcQ0PDl770pY6b\nGhoannrqqVmzZnUc9DNt2rSvf/3rc+fOnTJlyrp16x5//PHPfvazTokFABxj101WYTdjxoyc\nf+Py8vJHH310w4YN5eXlw4cPnzlz5imnnNJx04YNG1auXNl5xM/o0aO/+c1vPvTQQ0uXLh04\ncOC55557wQUX5HweAKDIZPJyjN1+dFbshx9++Oabb0ZRdNhhhw0YMGBfvvEll1xyySWX7PKm\nqVOndjuL4sQTT9zdy8EAwP6rmI+Hy4ds37nqd7/73Zlnnjlo0KDjjjvuuOOOGzRo0JQpU1at\nWpXX4QAAdi8TZfLzX9HKao/d2rVrJ06cuGnTpk9+8pNjx46Noui3v/3t0qVLP0fbIc0AACAA\nSURBVPnJT/76178+8sgj8zwkAEB3mTy93Ukxh11We+yuv/76bdu2LV269IUXXpg7d+7cuXMb\nGhqWLl26bdu2WbNm5XtEAIBdy8vuur2H3T333JPY0bJly3a38uLFi8ePH19ZWTlixIhZs2al\n03k8hi+rPXbLli37x3/8xzPOOKPrwjPOOOOKK6545JFH8jMYAMAeZfKzdy27TQ4ePLhrzO3u\nBcyXXnrpnHPOufzyy+fNm/fKK69cfvnl7e3tt9xyS04m3VlWYbd58+ajjjpq5+VHHXXU5s2b\ncz0SAEA28nQ8XFbbLC0tHT9+/F5Xu/32248++uh77rkniqKxY8euWbPmzjvvvPbaa/P0QY5Z\nvRR78MEHv/DCCzsvf+GFFw4++OBcjwQAkJVMJp2H/7IKu40bNx544IF1dXUTJ05cuHDh7lZr\naGiYMmVK59UpU6Zs3br1lVdeycEPvytZ7bGbNm3anXfeecwxx8yYMaOysjKKou3bt99xxx0P\nP/zw1VdfnafJAAD2oKy05Dtfntx1ScPrf/h/X+rZW3YMra2++v+Z2HVJa9vePzt7zJgxc+bM\nOfbYY5ubmx955JHp06ffeeedO7/vbzqdfv/99w888MDOJR2X33nnnR4Nmb2swu7666//6U9/\n+o1vfOPb3/72kUcemclk3njjjS1btowdO/a6667L02QAAHvQlkp9/YdL93Ej729q6raRz06q\n//u/OXrPX3XaaaeddtppHZc/9alPNTY23nbbbdl/oEMikejFqNnI6qXY2tral1566YYbbhg1\natSaNWveeOONUaNG3XjjjS+++GJtbW2eJgMA2JNMfs6K7flhexMnTnzvvfdaW1u7LU8mk8OG\nDXvvvfc6l3RcPuigg/btJ9+tbD95orq6etasWd7cBAAoHPl5z7keb7OhoWHYsGHl5eU73zRp\n0qRnnnnmjjvu6Lj6zDPPVFdXf/zjH9/XGXejZx8pBgBQIGJ8g+JLL7305JNPPuKII5qbm+fP\nn79w4cLbb7+946YFCxbMnj178eLFAwcOjKJo5syZJ5988te+9rXLLrtsxYoV3/3ud6+++uo8\nnRIbZf8Gxccee2y3nzOdTo8ZM+bGG2/Mz2AAAHsW20eKVVVV3Xzzzaeffvo//MM/vP766/Pn\nz7/mmms6blq/fn1DQ0NbW1vH1QkTJjzxxBO//OUvTzjhhG984xvXXHPNTTfdlL97JKs9do8/\n/viZZ57Z7UC/ZDJ5+umnL1q0yOuzAEA8Yvr4r9mzZ8+ePXuXN82YMaPbWRRnn3322Wef3Sdz\nZbfH7ve///0u36D4Yx/72JtvvpnjiQAAspHJy/vYRXl4ebfPZLXHLp1Of/jhhzsv//DDDzv3\nNAIA9LWY9tgVrKz22H3sYx9bsmRJt4WZTGbJkiVHH72XN3oBAMiPTJ7E/XP1XlZhd+GFF/7i\nF7+46qqrtmzZ0rFky5Yt//zP//zss89+4QtfyOd4AAC7F9PJEwUrq5dir7zyyqeffvquu+6a\nO3fuUUcdlclk1q5d29zcfMYZZ/zTP/1TvkcEANiNIo6wfMgq7MrKypYsWXLPPfc8/PDDq1at\nSiQSxxxzzIUXXvjVr361tNQ74QEA8cjL+9gVcyxmm2VlZWVXXXXVVVddlddpAACylafj4YJ/\nKRYAoBDl5a1JhB0AQN/K5OezYov6rFhhBwAUrby8FJv7TfYZYQcAFKlMPk6eiKLQP3kCAKDg\nZOyx607YAQDFyjF23Qg7AKBoOSt2R8IOAChSeXkfO3vsAADiUMwRlg/CDgAoTvk6eaKIY1HY\nAQDFKi9vdyLsAAD6XCY/ESbsAAD6Vt4+Uiznm+w7wg4AKFr22O1I2AEAxSmTl2PsvN0JAEDf\ny88xdsUcdsm4BwAA6KVMHmTzff/t3/5t8uTJQ4cO7d+//8c//vEf/ehHu1vznnvuSexo2bJl\nubsDurPHDgAoVvl5KXbv23zwwQdPOeWUq666auDAgY899tjFF1/c1tZ2+eWX73LlwYMHd425\nI488Mmez7kTYAQBFK6aXTX/xi190Xj755JNXrFixYMGC3YVdaWnp+PHj+2YwL8UCAEUqH6/E\n9ua4ve3btw8dOnR3t27cuPHAAw+sq6ubOHHiwoUL9+1H3gt77ACAolSaLLnsvMldl7y25g8v\nrFjVo43UDqj+3JkTuy4ZOmhAj7bwb//2b7/5zW/uvvvuXd46ZsyYOXPmHHvssc3NzY888sj0\n6dPvvPPOGTNm9OhbZE/YAQBFqT2d/uUrK7su2bC5qadH3W1r3t5tI5PGj87+yx999NGvfvWr\n/+f//J8TTzxxlyucdtppp512WsflT33qU42NjbfddpuwAwDYQTqT+e3qt/ZxIy0trd02csyo\nQ7L82vvuu+/qq6/+8Y9//NnPfjbLL5k4ceKCBQtaW1vLy8t7Nmh2hB0AUKTi/KzYm2666fbb\nb3/qqacmT56897X/oqGhYdiwYXmqukjYAQDFK8u3nevhNve+zowZM+69997vfe97Q4YMWbFi\nRRRFFRUV9fX1URQtWLBg9uzZixcvHjhwYBRFl1566cknn3zEEUc0NzfPnz9/4cKFt99+e85n\n7iTsAIDilImiPLyPXTbbfOihh1Kp1BVXXNG55Igjjli7dm0URevXr29oaGhra+tYXlVVdfPN\nN69fv76ysnL06NHz58//3Oc+l/uZ/0LYAQDFKh977LLx5z//eXc3zZgxo+u5EbNnz549e3af\nDBVFwg4AKFqZLI+H6/lmi5WwAwCKVj722BVx1wk7AKBo5eWzYqM8HLfXV4QdAFC07LHbkbAD\nAIpTx0e75mGzud9mXxF2AEDRKuYIywdhBwAUL2fF7kDYAQBFKZOnkyeKeS+gsAMAilRejrET\ndgAAcchLhAk7AIA+lsnP3rUi7jphBwAUL3vsdiTsAIBilZ9j7HK+yb4j7ACAIpXJzydPFHHZ\nCTsAoFjl5wxWYQcA0McyUZSH97Gzxw4AIAZ5OcYu51vsQ8IOAChSjrHrTtgBAMXLMXY7EHYA\nQFHyWbE7E3YAQNEq5gjLB2EHABSnfH2kWBHHorADAIpUfk6eKOZj7JJxDwAA0EuZvMjqWy9e\nvHj8+PGVlZUjRoyYNWtWOr3bo/2yX3Pf2WMHABSvePbYvfTSS+ecc87ll18+b968V1555fLL\nL29vb7/lllv2Zc2cEHYAQNGK6ZMnbr/99qOPPvqee+6Jomjs2LFr1qy58847r7322n79+vV6\nzZzwUiwAUKTy9ELs3sOuoaFhypQpnVenTJmydevWV155ZV/WzAl77ACAopRMJE+d8PGuS95+\n94M33ny7RxvpV1V54vgxXZcMG1K35y9Jp9Pvv//+gQce2Lmk4/I777zT6zVzRdgBAEUpmUiM\nPOSgrku2bdv+Rg83UlZa2m0j1f2qejdPIpHI+Zo9JewAgKKUam9/cMHifdxIY9OWbhv5wv/z\n93v+kmQyOWzYsPfee69zScflgw46qNdr5opj7AAAembSpEnPPPNM59Vnnnmmurr64x//+L6s\nmRP22AEARWngwJrPnH5Kzjd73DFH7XWdmTNnnnzyyV/72tcuu+yyFStWfPe737366qs7TnRd\nsGDB7NmzFy9ePHDgwD2vmQ/CDgAoSiOHH/jAnbNi+dYTJkx44oknvvWtb91///0HHHDANddc\nc8MNN3TctH79+oaGhra2tr2umQ/CDgCgx84+++yzzz575+UzZsyYMWNGNmvmQ4Bhl0wmS0pK\n4p5iB4lEIpncLw5nLOQfs9CeFb3W8XTK+Y9TsI9dIpHoxQ/b8eMU4G+DQpNMJnt3D++H3FFZ\nci/FK8Cwq6ioqKrq5YnKeZJIJGpqauKeoi9UVlbGPcKulZaWBvMQJJPJTCZTUVGR280G9th1\nvJVARUVFWVlZHoYKR8e/E4L505FXJSUl7qi98nSKXYBh19zc3PnCdoEYNGhQY2Njtp8qXMy2\nbdsW9wi7lkqlNm/eHPcUuVFdXZ1KpVpaWnK72YJ97Nra2nrx2FVUVNTU1DQ3Nzc3N+djqmCU\nlZVVVlY2NTXFPUihGzJkSCqVamxsjHuQQldXV7eHP7BDhgzpy2H2TwX64gsAAD0l7AAAAiHs\nAAACIewAAAIh7AAAAiHsAAACIewAAAIh7AAAAiHsAAACIewAAAIh7AAAAiHsAAACIewAAAIh\n7AAAAiHsAAACIewAAAIh7AAAAiHsAAACIewAAAIh7AAAAiHsAAACIewAAAIh7AAAAiHsAAAC\nIewAAAIh7AAAAiHsAAACIewAAAIh7AAAAiHsAAACIewAAAIh7AAAAiHsAAACIewAAAIh7AAA\nAiHsAAACIewAAAIh7AAAAiHsAAACIewAAAIh7AAAAiHsAAACIewAAAIh7AAAAiHsAAACIewA\nAAIh7AAAAiHsAAACIewAAAIh7AAAAlEa9wDFIZVKPfDAA73+8qqqqubm5hzO09UXv/jF8vLy\nPG0cKHwrV6785S9/2buvTSaTZWVlLS0tuR2pw6GHHnrmmWfmY8vA7gi7rLS2tl577bVxT7Fr\n06dPF3awP/v1r39dmL+gJk+eLOygjwm7HiivGTJ49IS4p/irjWt+3dL4QdxTAAVh4GHH9Rsy\nIu4pPpJOtb6/4j/ingL2R8KuB0or+9UcUh/3FH/14R9fb2mMewigMFTVHVQ4v6DaW5uFHcTC\nyRMAAIEQdgAAgRB2AACBEHYAAIEQdgAAgRB2AACBEHYAAIEQdgAAgRB2AACBEHYAAIEQdgAA\ngRB2AACBEHYAAIEQdgAAgRB2AACBEHYAAIEQdgAAgRB2AACBEHYAAIEQdgAAgRB2AACBEHYA\nAIEQdgAAgRB2AACBEHYAAIEQdgAAgRB2AACBEHYAAIEojesbL1u27Nlnn33zzTdbWloOPvjg\nT3/606effvou11y8ePHcuXO7Lrn55puPO+64PhkTAKBoxBZ2P/vZz4455phzzjmnX79+L7zw\nwve+971UKnXWWWftcuWampqbb7658+rBBx/cV2MCABSN2MLu1ltv7bw8ZsyY3//+9w0NDbsL\nu5KSklGjRvXVaAAARSm2sOumtbV16NChu7u1qanpoosuSqVShxxyyDnnnDNp0qS+nA0AoCgU\nRNgtW7Zs7dq1l1566S5vHTFixBVXXDFy5MjW1tZnn332tttuu/jii6dOndq5ws9//vOZM2d2\nXp0zZ85JJ52U2wm3bt2a2w3m0ODBgwcNGhT3FB/p379/3CPsWllZ2ZAhQ+KeIpdqampyu8GC\nfezKy8t7/dhVV1dXV1fndp4CFORjV4DC+zWSJ+6leMUfds8///x999131VVXHXXUUbtcYdy4\ncePGjeu4PHbs2K1btz722GNdw66mpqa+vr7zamVlZSqVyu2QOd9gDqVSqcIZL51Oxz3CrmUy\nmcK5l/ZRMpnMZDKZTCa3mw3ssUskEiUlJel0umB/rhwq2J8xpD93paWlmUymvb097kEKXWlp\n6R4e9NLS+KsjeDHfxUuWLPnRj350zTXXTJgwIcsvqa+vb2hoSKVSnc+PE044Yd68eZ0rNDY2\nbt68Obdzbtu2LbcbzKHGxsZEIhH3FB8p2DsqlUrl/FkRl+rq6lQq1dLSktvNFuxj19bW1ovH\nrqKioqamprm5ubm5OR9TFZTAHrvCNGTIkFQq1djYGPcgha6urm4PD7qdeX0gzrCbP3/+okWL\nrrvuuh69d8nKlStra2tVPwBAN7Hl0f333//0009feumlNTU169ati6KorKxsxIgRURQ1NDQ8\n9dRTs2bN6tevXxRF9957b319/UEHHdTa2vrcc881NDR86UtfimtsAICCFVvY/eIXv2hvb//+\n97/fueTAAw/8wQ9+EEXRhg0bVq5c2fkifXl5+aOPPrphw4by8vLhw4fPnDnzlFNOiWdoAIAC\nFlvYPfzww7u7aerUqV3PjbjkkksuueSSPhkKAKCI+axYAIBACDsAgEAIOwCAQAg7AIBACDsA\ngEAIOwCAQAg7AIBACDsAgEAIOwCAQAg7AIBACDsAgEAIOwCAQAg7AIBACDsAgEAIOwCAQAg7\nAIBACDsAgEAIOwCAQAg7AIBACDsAgEAIOwCAQAg7AIBACDsAgEAIOwCAQAg7AIBACDsAgEAI\nOwCAQAg7AIBAlMY9AL3Xtu3DKIpmzZpVUVER9ywfWbNmTdwjdJdua42iaPXq1f/6r/8a9yx/\ntXLlys2bN5900kklJSU9/drS0tJMJtPe3p7bkVavXp3bDe67dKr3j11JSUlZWVlbW1vO76gO\nt9xyS3l5eT62DLAvhF0Ra2/dFkXRI488EvcgBa0jDt55550HHngg7lm6+93vfhf3CAUt056K\nomj9+vUF+NjNmjVL2AEFSNgVvWHHfzpZWhb3FB/ZvO6V5j+/FfcUu1AxYGjdxybGPcVfvf/y\n0+lU67BPnJ0sKZQ/g5vXvdz85z/EPcUuVAwcWje6gB67jb9raPnwT3FPAbBrhfKXCr1W3r8u\nWVYoL8WWFMwk3SRKy8prhsQ9RReJRNTx2JUWyl6fkoKZpJtkaXlBPXaJgvl3FMDOnDwBABAI\nYQcAEAhhBwAQCGEHABAIYQcAEAhhBwAQCGEHABAIYQcAEAhhBwAQCGEHABAIYQcAEAhhBwAQ\nCGEHABAIYQcAEAhhBwAQCGEHABAIYQcAEAhhBwAQCGEHABAIYQcAEAhhBwAQCGEHABAIYQcA\nEAhhBwAQCGEHABAIYQcAEAhhBwAQCGEHABAIYQcAEAhhBwAQCGEHABAIYQcAEAhhBwAQCGEH\nABAIYQcAEAhhBwAQCGEHABAIYQcAEAhhBwAQCGEHABAIYQcAEAhhBwAQCGEHABAIYQcAEAhh\nBwAQCGEHABAIYQcAEAhhBwAQCGEHABAIYQcAEIjSuAfIvX79+iWTOQ7W8vLy3G4QKGq1tbX9\n+/ePe4qP9OvXL+4Rdq2srGzQoEFxT5EzpaWlIf04eZJMJt1L8Qow7LZt29bW1pbzbeZ2g0BR\n27x5c85/z/Rawf6Camtr27RpU9xT5MaQIUNSqVRjY2PcgxS6urq6PTzoQ4YM6cth9k9eigUA\nCISwAwAIhLADAAiEsAMACISwAwAIhLADAAiEsAMACISwAwAIhLADAAiEsAMACISwAwAIhLAD\nAAiEsAMACISwAwAIhLADAAhEadwDABST1LYPoyg6//zzS0pK4p7lI++9917cI9BLzzzzzNy5\nc+OeYhdOPPHEa6+9Nu4p6A1hB9AD6fa2KIpefPHFuAchBO+8884vf/nLuKfYherq6rhHoJeE\nHUCPDTv+7ERJofz+3PzGb7Zv/GPcU9B7Aw8/vmrIoXFP8ZF0W8sHK5bEPQW9Vyi/mACKSCJZ\nkkgWykuxCQdLF7lEMllIT6dCmYTe8fsAACAQwg4AIBDCDgAgEMIOACAQwg4AIBDCDgAgEMIO\nACAQwg4AIBDCDgAgEMIOACAQwg4AIBDCDgAgEMIOACAQwg4AIBDCDgAgEMIOACAQwg4AIBDC\nDgAgEMIOACAQwg4AIBDCDgAgEMIOACAQwg4AIBDCDgAgEMIOACAQwg4AIBDCDgAgEMIOACAQ\nwg4AIBDCDgAgEMIOACAQwg4AIBDCDgAgEMIOACAQwg4AIBDCDgAgEMIOACAQwg4AIBDCDgAg\nEMIOACAQwg4AIBDCDgAgEMIOACAQwg4AIBDCDgAgEMIOACAQwg4AIBDCDgAgEMIOACAQwg4A\nIBDCDgAgEMIOACAQwg4AIBDCDgAgEMIOACAQwg4AIBDCDgAgEMIOACAQwg4AIBDCDgAgEMIO\nACAQwg4AIBDCDgAgEKUxfu/ly5fPmzfv7bffHjhw4OTJk88///xEIrGPawIA7Ldi22O3atWq\nW265ZcyYMXfccceFF164aNGihx9+eB/XBADYn8W2x27RokXDhw+/7LLLoigaOXLku+++++ST\nT06fPr2ioqLXawIA7M9i22O3cuXK448/vvPq8ccfv3379nXr1u3LmgAA+7N49thlMpnNmzcP\nGjSoc0nH5Y0bN/ZizZdeeul//a//1Xn1xhtvHDt2bG4HLi8vj6KoecP6dc/Mye2W90V76/Yo\nit55aWFUMEccpttaoyj6r3//5m9/fEvcs/xFJh1FUcvm997+5SNxj/JX6VRrFEXvvLig0B67\nN38274/P/yTuWT6SSaejKNq+6d0CfOw++K+lcQ/yV5n2tiiKPnjt53/+/56Pe5YdPP/88+PG\njYt7ih0MHz58/fr1cU/xV9u2bYui6MM3/+vDP/w27lk6ZaIoKisr6/o3b/aSyWTvvpBcifPk\niSKSTCZHjBgR9xTdbdwYbd++/cAD6pLJQjm7edu2bZs3bx5QVdqvX1ncs3wkk8m821RSUVEx\nePDguGf5qw0bNrS0tBTgY1fbv6pfv35xz/KRdDr9XsuWwnzsDho2pHBO4frosRvQv3AeuyiK\n2tpqmpqaSksL62+ZZDJZUCOVlJQMGjSof//+cQ/S3QEHHBD3CPRSPM/vRCJRW1u7adOmziUd\nl+vq6nqx5oQJE5588snOq42NjV3Xz5WXX3651187aNCgzZs3ZzKZHM4TnpqamoqKik2bNrW3\nt8c9S0Grrq5OpVItLS1xD1LQKioqampqtm7d2tzcHPcsBa2srKyysrKpqSnuQQrdkCFD2tra\nGhsb4x6k7/Tub9K6uro9fOGQIUP2YSKyEtvegvr6+q6p9PLLL1dWVo4aNWpf1gQA2J/FFnbT\npk1bv3793Llz33rrrZ///OePP/741KlTO050bWho+PrXv95x5MGe1wQAoFNshxqMHj36m9/8\n5kMPPbR06dKBAweee+65F1xwQcdNGzZsWLlyZSqV2uuaAAB0SoR34FdjY2NbW1vcU+zAMXbZ\ncIxdlhxjlw3H2GXJMXZZ2g+Pseudurq6nd/gopNj7PpAoZyRBwDAPhJ2AACBEHYAAIEQdgAA\ngRB2AACBEHYAAIEQdgAAgRB2AACBEHYAAIEQdgAAgRB2AACBEHYAAIEQdgAAgRB2AACBEHYA\nAIEQdgAAgRB2AACBEHYAAIEQdgAAgRB2AACBEHYAAIEQdgAAgRB2AACBEHYAAIEQdgAAgRB2\nAACBEHYAAIEQdgAAgRB2AACBSGQymbhngCiKoieffPL111//x3/8x9ra2rhnoeitXLny8ccf\n/7u/+7u/+Zu/iXsWil46nf7Od75zyCGHXHTRRXHPAnthjx2F4j//8z8XLVq0devWuAchBOvX\nr1+0aNGaNWviHoRALFq06Nlnn417Ctg7YQcAEAhhBwAQCGEHABAIJ08AAATCHjsAgEAIOwCA\nQAg7AIBAlMY9APuL1atXP/bYY2+88cYHH3xw+umnX3nllV1vXb58+bx5895+++2BAwdOnjz5\n/PPPTyQSe72J/dayZcueffbZN998s6Wl5eCDD/70pz99+umnd97q6USPPP/880899dT69etb\nWloGDx58yimn/Pf//t/Lyso6bvV0oriU3HDDDXHPwH5h/fr1W7ZsOfXUU998882hQ4d2/TyA\nVatWXX/99RMnTvzqV786YsSIBx98sK2tbdy4cXu+if3ZD3/4wzFjxnT0XEtLy7x582pra486\n6qjI04meW79+/SGHHPL3f//3Z5xxxtChQ3/yk59s2LDhpJNOijydKEL22NFHxo0b1/Erb9Gi\nRd1uWrRo0fDhwy+77LIoikaOHPnuu+8++eST06dPr6io2MNNff8jUDhuvfXWzstjxoz5/e9/\n39DQcNZZZ0WeTvTcxIkTOy+PHj36rbfeevXVVzuuejpRdBxjR/xWrlx5/PHHd149/vjjt2/f\nvm7duj3fBJ1aW1sHDhzYcdnTiV5Lp9Pr1q1bsWLFcccd17HE04miY48dMctkMps3bx40aFDn\nko7LGzdu3MNNfT8nBWvZsmVr16699NJLI08nequtrW369OmZTCaTyZxxxhmeThQvYQcUseef\nf/6+++676qqrOg6wg94pLS2dPXt2W1vbmjVrHnrooQEDBlx00UVxDwW9IeyIWSKRqK2t3bRp\nU+eSjst1dXV7uKnv56QALVmy5Ec/+tE111wzYcKEjiWeTvROIpEYOXJkFEVHHnlkMpmcM2fO\ntGnT+vfv7+lE0XGMHfGrr69/+eWXO6++/PLLlZWVo0aN2vNN7Ofmz5//wAMPXHfddZ1V18HT\niX2USqUymUwqlYo8nShC3u6EPtLa2vrWW29t2rTp+eefr6qqGj58eOcRKkOHDl20aFFjY+MB\nBxzwyiuvPPjgg+ecc07HUcl7uIn92f333//EE09cfPHFBx988KZNmzZt2rRly5aO8yc8neip\nH/zgB01NTc3NzR988EFDQ8PDDz88fvz4M888M/J0ogglMplM3DOwX1i3bt2MGTO6Lkkmk088\n8UTH5f/8z/986KGH/vjHP3a8z+cFF1zQ+T6fe7iJ/dbnP//5pqamrksOPPDAH/zgBx2XPZ3o\nkQcffPBXv/rVBx98kEwmhw4deuqpp37mM5/pfNcSTyeKi7ADAAiEY+wAAAIh7AAAAiHsAAAC\nIewAAAIh7AAAAiHsAAACIeyAMJ199tkrVqzYefnChQsTiUTneygC/3979xvSVN/GAfy3pTua\nbi01Ey3XtGVJpSFmRWGNCkXDRSpuOtDIsDJJNCK1dIKW5mwUpJkywRzLP0UklfXG6kWWQVaS\nMR0RJlmiudJSp/N5cZ5nz7mneXtrd3bv/n5enT/X+f3Zi3Ht+p2zA9YEiR0AzJVOp8vJyXn5\n8uV8DwQA4N8OiR0AzJVOp1MoFEjsAADmHRI7APhn+Pbt20zCXrx4IZFI3N3db9++HRQUtGzZ\nspCQkCnXZAEArA8SOwD4P/P9Z5cuXfLx8bGzs/P19a2vryeEdHZ2SiSSxYsX83g8mUw2MDBA\nX5KTk7Nnzx5CiFwuZ7FYLBZr+/bt9KmxsTGVShUQEODg4MDlctevX5+dpMS1EwAABk9JREFU\nnU2fMhgMWVlZQUFBLi4uFEV5eXmlp6cPDg5ajKSmpkahUIhEIg6Hk5ubS7dZWFi4bt06LpfL\n5XJFIlF8fLz5vbHd3d1isbitrU2pVG7durW0tLSgoMDZ2fnDhw+TJ3vlyhVfX1+Kojw9PfPy\n8pjvV6R712q1mZmZK1asoChKJBKpVKqf/XkDAPxkNvM9AAD47Zw7d66np0cul1MUVVJSEh0d\nXVtbe/jw4d27d2dnZ7e0tGg0GhaLVV1dTQiJj4+nKCojIyMjI2PXrl2EED6fTwgZGxsLDw9v\nbGwMDg4+ffo0j8d78+ZNbW2tQqEghHR1dZWVlUVGRkqlUg6H8/Dhw+Li4qdPnz548ID5GvUT\nJ054eHjk5+e7ubnZ2toSQk6ePFlUVCSTyVJSUths9rt37xoaGr58+cLlcgkhd+/e7e/vv3bt\n2s6dO6urqzds2ODv7x8bGzt5jkqlsqurSy6X8/l8jUaTlZXl7OyclJTEjElPTw8ICKirq3N0\ndKysrExNTf348eOZM2f+xo8eAGCOJgAA/qe2tpYQIhAIDAYDfeTVq1eEEBaLVVJSYg6LiIhg\ns9m9vb307q1btwghVVVVzKbOnz9PCDl69KjJZDIfHB8fpzeGh4dHR0eZ8Xl5eYSQ+/fvM0ey\natUqo9HIDBMKhTt27PjR+CsqKswjCQsLe/78+UzmODQ05OrqumbNGosYoVDI7D0mJobNZnd0\ndPyodwCAeYelWACwdOjQIR6PR2+vXbt2yZIlDg4OBw8eNAeIxWKTydTZ2TlNI1evXrW3t8/P\nz2dW4Njs/37nUBRFV+AIIUajcXh4eO/evYSQ5uZmZiMJCQk2Nn9YWODz+e3t7S0tLVN2KpFI\nPD09Dxw4IJfL9Xp9e3v7yMjIn85x4cKF27Zt0+v1JpOJGRMfH8/sPTEx0WQy4X9SAOB3hsQO\nACx5e3szd52cnAQCgTkno48QQvr6+qZpRKfTrVy50tHR8UcBlZWVW7ZscXBw4HA49vb2vr6+\nhJD+/n5mjFAotLiqqKjIaDRu3LhRIBDExsaq1WrmQxVOTk5PnjxJTk5+9uyZTqeTyWT0AqvB\nYJh+ji4uLqOjo+Z79aaM8fLyIoTo9fppZg0AML+Q2AGAJYsi2ZRHCCETjKcNpjzLrNVZKC4u\nTkhIcHFxKS8vb2pqevz4cUNDAyHEomZGUZTFhWKx+O3btzU1NWFhYa2trfv371+9enV3d7c5\nwM3NraioqL29PTQ0tKysLDExsaysTCqVzmJGFtU+eneaSQEAzDs8PAEAczVlruPj4/P69evB\nwcEpi3YVFRVCofDmzZvmax89ejTD7rhcblRUVFRUFCFEq9VKpdILFy4UFBRMjgwMDExMTOzr\n66uqqhoYGKCf6pi5tra2ybt03Q4A4PeEih0AzBX9RKrFKmpcXNz3799PnTrFPGguibHZ7ImJ\nifHxcXp3fHw8Pz9/Jn1Z9LJp0ybmwd7e3smXDA0Nza7Mplare3p66G2j0ahUKlksVkRExCya\nAgD4NVCxA4C58vPzs7Ozu3jxIofD4fP5rq6uYrH4yJEjDQ0NKpWqtbU1NDSUx+N1dHQ0NjbS\nda/IyMicnJzQ0NDo6OivX79qtdrpF3bN3N3dw8PDAwICPDw8Pn36VF5evmDBArlcTp+9fPly\nfX29VCr18/P7/PnzvXv3lErl9evX9+3b91fLdYQQb2/voKCgpKQkR0dHjUbT3Nx8/PhxkUj0\nV9sBAPhlkNgBwFwtWrRIo9EoFIpjx46NjIwEBweLxWJbW9s7d+6oVKqqqqrs7GxbW1uhUEiv\nnxJCMjMzbWxs1Gp1cnLy0qVLIyMjU1JSJj8qMVlaWlpTU1NxcbHBYHB1dQ0MDFSr1Zs3b6bP\nxsTEDA0NabXawsLCvr6+1tZWgUCQm5ublpY2i3llZGTo9frS0tL3798vX75cqVSmpqbOoh0A\ngF+GNcNfyQAA/ywhISFnz5719/efxbV1dXVRUVE3btyQSCQ/fWAAAH8f3GMHANaJ+f8sAAD/\nEvjiAwDrFBcX5+bmNt+jAAD4pXCPHQBYJ5lMNt9DAAD41XCPHQAAAICVwFIsAAAAgJVAYgcA\nAABgJZDYAQAAAFgJJHYAAAAAVgKJHQAAAICVQGIHAAAAYCWQ2AEAAABYif8ArRWVCmvv8Y0A\nAAAASUVORK5CYII=",
      "text/plain": [
       "plot without title"
      ]
     },
     "metadata": {},
     "output_type": "display_data",
     "source": "R display func"
    }
   ],
   "source": [
    "ggplot(data= mtcars, aes(mtcars$hp))  + \n",
    "  geom_histogram(breaks=seq(40, 350, by = 25),  \n",
    "                 colour = I(\"black\"),\n",
    "                 aes(fill=..count..))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Qualitative vs Quantitative Data<a id=\"ref2\"></a>\n",
    "One thing to always keep in mind is the type of data which you are trying to create graphs for. In general, we categorize data in two big groups: Qualitative data (also called Categorical data) and Quantitative data (also called Numerical data).\n",
    "\n",
    "What we refer to as **qualitative or categorical** data is data that refers to, as seen from its name, categories. This can include data for \"Yes or No\" questions, it could be the name of a location, it could be a person's favourite ice cream flavour, or it could be something else. It's something very distinct from **quantitative or numerical** data. Quantitative data is data that is, quite simply, numbers. It normally is a measurement of some sort, and can be manipulated using simple math.\n",
    "\n",
    "The plotting methods and the best types of chart for each type of data are different - choosing the best one will help you greatly in creating visually pleasant graphs."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<hr>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a id=\"ref3\"></a>\n",
    "<h2 align=center>The mtcars dataset</h2>\n",
    "<br>\n",
    "`mtcars` is an inbuilt dataset that contains data from the 1974 Motor Trend US magazine, and contains fuel consumption and 10 other aspects of automobile performance for 32 automobiles from 1973–74.\n",
    "\n",
    "Since it is already included with R, there is no need to import `mtcars`. Let's check its structure - we can do so by calling it by its name, like so:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table>\n",
       "<caption>A data.frame: 32 × 11</caption>\n",
       "<thead>\n",
       "\t<tr><th></th><th scope=col>mpg</th><th scope=col>cyl</th><th scope=col>disp</th><th scope=col>hp</th><th scope=col>drat</th><th scope=col>wt</th><th scope=col>qsec</th><th scope=col>vs</th><th scope=col>am</th><th scope=col>gear</th><th scope=col>carb</th></tr>\n",
       "\t<tr><th></th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th></tr>\n",
       "</thead>\n",
       "<tbody>\n",
       "\t<tr><th scope=row>Mazda RX4</th><td>21.0</td><td>6</td><td>160.0</td><td>110</td><td>3.90</td><td>2.620</td><td>16.46</td><td>0</td><td>1</td><td>4</td><td>4</td></tr>\n",
       "\t<tr><th scope=row>Mazda RX4 Wag</th><td>21.0</td><td>6</td><td>160.0</td><td>110</td><td>3.90</td><td>2.875</td><td>17.02</td><td>0</td><td>1</td><td>4</td><td>4</td></tr>\n",
       "\t<tr><th scope=row>Datsun 710</th><td>22.8</td><td>4</td><td>108.0</td><td> 93</td><td>3.85</td><td>2.320</td><td>18.61</td><td>1</td><td>1</td><td>4</td><td>1</td></tr>\n",
       "\t<tr><th scope=row>Hornet 4 Drive</th><td>21.4</td><td>6</td><td>258.0</td><td>110</td><td>3.08</td><td>3.215</td><td>19.44</td><td>1</td><td>0</td><td>3</td><td>1</td></tr>\n",
       "\t<tr><th scope=row>Hornet Sportabout</th><td>18.7</td><td>8</td><td>360.0</td><td>175</td><td>3.15</td><td>3.440</td><td>17.02</td><td>0</td><td>0</td><td>3</td><td>2</td></tr>\n",
       "\t<tr><th scope=row>Valiant</th><td>18.1</td><td>6</td><td>225.0</td><td>105</td><td>2.76</td><td>3.460</td><td>20.22</td><td>1</td><td>0</td><td>3</td><td>1</td></tr>\n",
       "\t<tr><th scope=row>Duster 360</th><td>14.3</td><td>8</td><td>360.0</td><td>245</td><td>3.21</td><td>3.570</td><td>15.84</td><td>0</td><td>0</td><td>3</td><td>4</td></tr>\n",
       "\t<tr><th scope=row>Merc 240D</th><td>24.4</td><td>4</td><td>146.7</td><td> 62</td><td>3.69</td><td>3.190</td><td>20.00</td><td>1</td><td>0</td><td>4</td><td>2</td></tr>\n",
       "\t<tr><th scope=row>Merc 230</th><td>22.8</td><td>4</td><td>140.8</td><td> 95</td><td>3.92</td><td>3.150</td><td>22.90</td><td>1</td><td>0</td><td>4</td><td>2</td></tr>\n",
       "\t<tr><th scope=row>Merc 280</th><td>19.2</td><td>6</td><td>167.6</td><td>123</td><td>3.92</td><td>3.440</td><td>18.30</td><td>1</td><td>0</td><td>4</td><td>4</td></tr>\n",
       "\t<tr><th scope=row>Merc 280C</th><td>17.8</td><td>6</td><td>167.6</td><td>123</td><td>3.92</td><td>3.440</td><td>18.90</td><td>1</td><td>0</td><td>4</td><td>4</td></tr>\n",
       "\t<tr><th scope=row>Merc 450SE</th><td>16.4</td><td>8</td><td>275.8</td><td>180</td><td>3.07</td><td>4.070</td><td>17.40</td><td>0</td><td>0</td><td>3</td><td>3</td></tr>\n",
       "\t<tr><th scope=row>Merc 450SL</th><td>17.3</td><td>8</td><td>275.8</td><td>180</td><td>3.07</td><td>3.730</td><td>17.60</td><td>0</td><td>0</td><td>3</td><td>3</td></tr>\n",
       "\t<tr><th scope=row>Merc 450SLC</th><td>15.2</td><td>8</td><td>275.8</td><td>180</td><td>3.07</td><td>3.780</td><td>18.00</td><td>0</td><td>0</td><td>3</td><td>3</td></tr>\n",
       "\t<tr><th scope=row>Cadillac Fleetwood</th><td>10.4</td><td>8</td><td>472.0</td><td>205</td><td>2.93</td><td>5.250</td><td>17.98</td><td>0</td><td>0</td><td>3</td><td>4</td></tr>\n",
       "\t<tr><th scope=row>Lincoln Continental</th><td>10.4</td><td>8</td><td>460.0</td><td>215</td><td>3.00</td><td>5.424</td><td>17.82</td><td>0</td><td>0</td><td>3</td><td>4</td></tr>\n",
       "\t<tr><th scope=row>Chrysler Imperial</th><td>14.7</td><td>8</td><td>440.0</td><td>230</td><td>3.23</td><td>5.345</td><td>17.42</td><td>0</td><td>0</td><td>3</td><td>4</td></tr>\n",
       "\t<tr><th scope=row>Fiat 128</th><td>32.4</td><td>4</td><td> 78.7</td><td> 66</td><td>4.08</td><td>2.200</td><td>19.47</td><td>1</td><td>1</td><td>4</td><td>1</td></tr>\n",
       "\t<tr><th scope=row>Honda Civic</th><td>30.4</td><td>4</td><td> 75.7</td><td> 52</td><td>4.93</td><td>1.615</td><td>18.52</td><td>1</td><td>1</td><td>4</td><td>2</td></tr>\n",
       "\t<tr><th scope=row>Toyota Corolla</th><td>33.9</td><td>4</td><td> 71.1</td><td> 65</td><td>4.22</td><td>1.835</td><td>19.90</td><td>1</td><td>1</td><td>4</td><td>1</td></tr>\n",
       "\t<tr><th scope=row>Toyota Corona</th><td>21.5</td><td>4</td><td>120.1</td><td> 97</td><td>3.70</td><td>2.465</td><td>20.01</td><td>1</td><td>0</td><td>3</td><td>1</td></tr>\n",
       "\t<tr><th scope=row>Dodge Challenger</th><td>15.5</td><td>8</td><td>318.0</td><td>150</td><td>2.76</td><td>3.520</td><td>16.87</td><td>0</td><td>0</td><td>3</td><td>2</td></tr>\n",
       "\t<tr><th scope=row>AMC Javelin</th><td>15.2</td><td>8</td><td>304.0</td><td>150</td><td>3.15</td><td>3.435</td><td>17.30</td><td>0</td><td>0</td><td>3</td><td>2</td></tr>\n",
       "\t<tr><th scope=row>Camaro Z28</th><td>13.3</td><td>8</td><td>350.0</td><td>245</td><td>3.73</td><td>3.840</td><td>15.41</td><td>0</td><td>0</td><td>3</td><td>4</td></tr>\n",
       "\t<tr><th scope=row>Pontiac Firebird</th><td>19.2</td><td>8</td><td>400.0</td><td>175</td><td>3.08</td><td>3.845</td><td>17.05</td><td>0</td><td>0</td><td>3</td><td>2</td></tr>\n",
       "\t<tr><th scope=row>Fiat X1-9</th><td>27.3</td><td>4</td><td> 79.0</td><td> 66</td><td>4.08</td><td>1.935</td><td>18.90</td><td>1</td><td>1</td><td>4</td><td>1</td></tr>\n",
       "\t<tr><th scope=row>Porsche 914-2</th><td>26.0</td><td>4</td><td>120.3</td><td> 91</td><td>4.43</td><td>2.140</td><td>16.70</td><td>0</td><td>1</td><td>5</td><td>2</td></tr>\n",
       "\t<tr><th scope=row>Lotus Europa</th><td>30.4</td><td>4</td><td> 95.1</td><td>113</td><td>3.77</td><td>1.513</td><td>16.90</td><td>1</td><td>1</td><td>5</td><td>2</td></tr>\n",
       "\t<tr><th scope=row>Ford Pantera L</th><td>15.8</td><td>8</td><td>351.0</td><td>264</td><td>4.22</td><td>3.170</td><td>14.50</td><td>0</td><td>1</td><td>5</td><td>4</td></tr>\n",
       "\t<tr><th scope=row>Ferrari Dino</th><td>19.7</td><td>6</td><td>145.0</td><td>175</td><td>3.62</td><td>2.770</td><td>15.50</td><td>0</td><td>1</td><td>5</td><td>6</td></tr>\n",
       "\t<tr><th scope=row>Maserati Bora</th><td>15.0</td><td>8</td><td>301.0</td><td>335</td><td>3.54</td><td>3.570</td><td>14.60</td><td>0</td><td>1</td><td>5</td><td>8</td></tr>\n",
       "\t<tr><th scope=row>Volvo 142E</th><td>21.4</td><td>4</td><td>121.0</td><td>109</td><td>4.11</td><td>2.780</td><td>18.60</td><td>1</td><td>1</td><td>4</td><td>2</td></tr>\n",
       "</tbody>\n",
       "</table>\n"
      ],
      "text/latex": [
       "A data.frame: 32 × 11\n",
       "\\begin{tabular}{r|lllllllllll}\n",
       "  & mpg & cyl & disp & hp & drat & wt & qsec & vs & am & gear & carb\\\\\n",
       "  & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl>\\\\\n",
       "\\hline\n",
       "\tMazda RX4 & 21.0 & 6 & 160.0 & 110 & 3.90 & 2.620 & 16.46 & 0 & 1 & 4 & 4\\\\\n",
       "\tMazda RX4 Wag & 21.0 & 6 & 160.0 & 110 & 3.90 & 2.875 & 17.02 & 0 & 1 & 4 & 4\\\\\n",
       "\tDatsun 710 & 22.8 & 4 & 108.0 &  93 & 3.85 & 2.320 & 18.61 & 1 & 1 & 4 & 1\\\\\n",
       "\tHornet 4 Drive & 21.4 & 6 & 258.0 & 110 & 3.08 & 3.215 & 19.44 & 1 & 0 & 3 & 1\\\\\n",
       "\tHornet Sportabout & 18.7 & 8 & 360.0 & 175 & 3.15 & 3.440 & 17.02 & 0 & 0 & 3 & 2\\\\\n",
       "\tValiant & 18.1 & 6 & 225.0 & 105 & 2.76 & 3.460 & 20.22 & 1 & 0 & 3 & 1\\\\\n",
       "\tDuster 360 & 14.3 & 8 & 360.0 & 245 & 3.21 & 3.570 & 15.84 & 0 & 0 & 3 & 4\\\\\n",
       "\tMerc 240D & 24.4 & 4 & 146.7 &  62 & 3.69 & 3.190 & 20.00 & 1 & 0 & 4 & 2\\\\\n",
       "\tMerc 230 & 22.8 & 4 & 140.8 &  95 & 3.92 & 3.150 & 22.90 & 1 & 0 & 4 & 2\\\\\n",
       "\tMerc 280 & 19.2 & 6 & 167.6 & 123 & 3.92 & 3.440 & 18.30 & 1 & 0 & 4 & 4\\\\\n",
       "\tMerc 280C & 17.8 & 6 & 167.6 & 123 & 3.92 & 3.440 & 18.90 & 1 & 0 & 4 & 4\\\\\n",
       "\tMerc 450SE & 16.4 & 8 & 275.8 & 180 & 3.07 & 4.070 & 17.40 & 0 & 0 & 3 & 3\\\\\n",
       "\tMerc 450SL & 17.3 & 8 & 275.8 & 180 & 3.07 & 3.730 & 17.60 & 0 & 0 & 3 & 3\\\\\n",
       "\tMerc 450SLC & 15.2 & 8 & 275.8 & 180 & 3.07 & 3.780 & 18.00 & 0 & 0 & 3 & 3\\\\\n",
       "\tCadillac Fleetwood & 10.4 & 8 & 472.0 & 205 & 2.93 & 5.250 & 17.98 & 0 & 0 & 3 & 4\\\\\n",
       "\tLincoln Continental & 10.4 & 8 & 460.0 & 215 & 3.00 & 5.424 & 17.82 & 0 & 0 & 3 & 4\\\\\n",
       "\tChrysler Imperial & 14.7 & 8 & 440.0 & 230 & 3.23 & 5.345 & 17.42 & 0 & 0 & 3 & 4\\\\\n",
       "\tFiat 128 & 32.4 & 4 &  78.7 &  66 & 4.08 & 2.200 & 19.47 & 1 & 1 & 4 & 1\\\\\n",
       "\tHonda Civic & 30.4 & 4 &  75.7 &  52 & 4.93 & 1.615 & 18.52 & 1 & 1 & 4 & 2\\\\\n",
       "\tToyota Corolla & 33.9 & 4 &  71.1 &  65 & 4.22 & 1.835 & 19.90 & 1 & 1 & 4 & 1\\\\\n",
       "\tToyota Corona & 21.5 & 4 & 120.1 &  97 & 3.70 & 2.465 & 20.01 & 1 & 0 & 3 & 1\\\\\n",
       "\tDodge Challenger & 15.5 & 8 & 318.0 & 150 & 2.76 & 3.520 & 16.87 & 0 & 0 & 3 & 2\\\\\n",
       "\tAMC Javelin & 15.2 & 8 & 304.0 & 150 & 3.15 & 3.435 & 17.30 & 0 & 0 & 3 & 2\\\\\n",
       "\tCamaro Z28 & 13.3 & 8 & 350.0 & 245 & 3.73 & 3.840 & 15.41 & 0 & 0 & 3 & 4\\\\\n",
       "\tPontiac Firebird & 19.2 & 8 & 400.0 & 175 & 3.08 & 3.845 & 17.05 & 0 & 0 & 3 & 2\\\\\n",
       "\tFiat X1-9 & 27.3 & 4 &  79.0 &  66 & 4.08 & 1.935 & 18.90 & 1 & 1 & 4 & 1\\\\\n",
       "\tPorsche 914-2 & 26.0 & 4 & 120.3 &  91 & 4.43 & 2.140 & 16.70 & 0 & 1 & 5 & 2\\\\\n",
       "\tLotus Europa & 30.4 & 4 &  95.1 & 113 & 3.77 & 1.513 & 16.90 & 1 & 1 & 5 & 2\\\\\n",
       "\tFord Pantera L & 15.8 & 8 & 351.0 & 264 & 4.22 & 3.170 & 14.50 & 0 & 1 & 5 & 4\\\\\n",
       "\tFerrari Dino & 19.7 & 6 & 145.0 & 175 & 3.62 & 2.770 & 15.50 & 0 & 1 & 5 & 6\\\\\n",
       "\tMaserati Bora & 15.0 & 8 & 301.0 & 335 & 3.54 & 3.570 & 14.60 & 0 & 1 & 5 & 8\\\\\n",
       "\tVolvo 142E & 21.4 & 4 & 121.0 & 109 & 4.11 & 2.780 & 18.60 & 1 & 1 & 4 & 2\\\\\n",
       "\\end{tabular}\n"
      ],
      "text/markdown": [
       "\n",
       "A data.frame: 32 × 11\n",
       "\n",
       "| <!--/--> | mpg &lt;dbl&gt; | cyl &lt;dbl&gt; | disp &lt;dbl&gt; | hp &lt;dbl&gt; | drat &lt;dbl&gt; | wt &lt;dbl&gt; | qsec &lt;dbl&gt; | vs &lt;dbl&gt; | am &lt;dbl&gt; | gear &lt;dbl&gt; | carb &lt;dbl&gt; |\n",
       "|---|---|---|---|---|---|---|---|---|---|---|---|\n",
       "| Mazda RX4 | 21.0 | 6 | 160.0 | 110 | 3.90 | 2.620 | 16.46 | 0 | 1 | 4 | 4 |\n",
       "| Mazda RX4 Wag | 21.0 | 6 | 160.0 | 110 | 3.90 | 2.875 | 17.02 | 0 | 1 | 4 | 4 |\n",
       "| Datsun 710 | 22.8 | 4 | 108.0 |  93 | 3.85 | 2.320 | 18.61 | 1 | 1 | 4 | 1 |\n",
       "| Hornet 4 Drive | 21.4 | 6 | 258.0 | 110 | 3.08 | 3.215 | 19.44 | 1 | 0 | 3 | 1 |\n",
       "| Hornet Sportabout | 18.7 | 8 | 360.0 | 175 | 3.15 | 3.440 | 17.02 | 0 | 0 | 3 | 2 |\n",
       "| Valiant | 18.1 | 6 | 225.0 | 105 | 2.76 | 3.460 | 20.22 | 1 | 0 | 3 | 1 |\n",
       "| Duster 360 | 14.3 | 8 | 360.0 | 245 | 3.21 | 3.570 | 15.84 | 0 | 0 | 3 | 4 |\n",
       "| Merc 240D | 24.4 | 4 | 146.7 |  62 | 3.69 | 3.190 | 20.00 | 1 | 0 | 4 | 2 |\n",
       "| Merc 230 | 22.8 | 4 | 140.8 |  95 | 3.92 | 3.150 | 22.90 | 1 | 0 | 4 | 2 |\n",
       "| Merc 280 | 19.2 | 6 | 167.6 | 123 | 3.92 | 3.440 | 18.30 | 1 | 0 | 4 | 4 |\n",
       "| Merc 280C | 17.8 | 6 | 167.6 | 123 | 3.92 | 3.440 | 18.90 | 1 | 0 | 4 | 4 |\n",
       "| Merc 450SE | 16.4 | 8 | 275.8 | 180 | 3.07 | 4.070 | 17.40 | 0 | 0 | 3 | 3 |\n",
       "| Merc 450SL | 17.3 | 8 | 275.8 | 180 | 3.07 | 3.730 | 17.60 | 0 | 0 | 3 | 3 |\n",
       "| Merc 450SLC | 15.2 | 8 | 275.8 | 180 | 3.07 | 3.780 | 18.00 | 0 | 0 | 3 | 3 |\n",
       "| Cadillac Fleetwood | 10.4 | 8 | 472.0 | 205 | 2.93 | 5.250 | 17.98 | 0 | 0 | 3 | 4 |\n",
       "| Lincoln Continental | 10.4 | 8 | 460.0 | 215 | 3.00 | 5.424 | 17.82 | 0 | 0 | 3 | 4 |\n",
       "| Chrysler Imperial | 14.7 | 8 | 440.0 | 230 | 3.23 | 5.345 | 17.42 | 0 | 0 | 3 | 4 |\n",
       "| Fiat 128 | 32.4 | 4 |  78.7 |  66 | 4.08 | 2.200 | 19.47 | 1 | 1 | 4 | 1 |\n",
       "| Honda Civic | 30.4 | 4 |  75.7 |  52 | 4.93 | 1.615 | 18.52 | 1 | 1 | 4 | 2 |\n",
       "| Toyota Corolla | 33.9 | 4 |  71.1 |  65 | 4.22 | 1.835 | 19.90 | 1 | 1 | 4 | 1 |\n",
       "| Toyota Corona | 21.5 | 4 | 120.1 |  97 | 3.70 | 2.465 | 20.01 | 1 | 0 | 3 | 1 |\n",
       "| Dodge Challenger | 15.5 | 8 | 318.0 | 150 | 2.76 | 3.520 | 16.87 | 0 | 0 | 3 | 2 |\n",
       "| AMC Javelin | 15.2 | 8 | 304.0 | 150 | 3.15 | 3.435 | 17.30 | 0 | 0 | 3 | 2 |\n",
       "| Camaro Z28 | 13.3 | 8 | 350.0 | 245 | 3.73 | 3.840 | 15.41 | 0 | 0 | 3 | 4 |\n",
       "| Pontiac Firebird | 19.2 | 8 | 400.0 | 175 | 3.08 | 3.845 | 17.05 | 0 | 0 | 3 | 2 |\n",
       "| Fiat X1-9 | 27.3 | 4 |  79.0 |  66 | 4.08 | 1.935 | 18.90 | 1 | 1 | 4 | 1 |\n",
       "| Porsche 914-2 | 26.0 | 4 | 120.3 |  91 | 4.43 | 2.140 | 16.70 | 0 | 1 | 5 | 2 |\n",
       "| Lotus Europa | 30.4 | 4 |  95.1 | 113 | 3.77 | 1.513 | 16.90 | 1 | 1 | 5 | 2 |\n",
       "| Ford Pantera L | 15.8 | 8 | 351.0 | 264 | 4.22 | 3.170 | 14.50 | 0 | 1 | 5 | 4 |\n",
       "| Ferrari Dino | 19.7 | 6 | 145.0 | 175 | 3.62 | 2.770 | 15.50 | 0 | 1 | 5 | 6 |\n",
       "| Maserati Bora | 15.0 | 8 | 301.0 | 335 | 3.54 | 3.570 | 14.60 | 0 | 1 | 5 | 8 |\n",
       "| Volvo 142E | 21.4 | 4 | 121.0 | 109 | 4.11 | 2.780 | 18.60 | 1 | 1 | 4 | 2 |\n",
       "\n"
      ],
      "text/plain": [
       "                    mpg  cyl disp  hp  drat wt    qsec  vs am gear carb\n",
       "Mazda RX4           21.0 6   160.0 110 3.90 2.620 16.46 0  1  4    4   \n",
       "Mazda RX4 Wag       21.0 6   160.0 110 3.90 2.875 17.02 0  1  4    4   \n",
       "Datsun 710          22.8 4   108.0  93 3.85 2.320 18.61 1  1  4    1   \n",
       "Hornet 4 Drive      21.4 6   258.0 110 3.08 3.215 19.44 1  0  3    1   \n",
       "Hornet Sportabout   18.7 8   360.0 175 3.15 3.440 17.02 0  0  3    2   \n",
       "Valiant             18.1 6   225.0 105 2.76 3.460 20.22 1  0  3    1   \n",
       "Duster 360          14.3 8   360.0 245 3.21 3.570 15.84 0  0  3    4   \n",
       "Merc 240D           24.4 4   146.7  62 3.69 3.190 20.00 1  0  4    2   \n",
       "Merc 230            22.8 4   140.8  95 3.92 3.150 22.90 1  0  4    2   \n",
       "Merc 280            19.2 6   167.6 123 3.92 3.440 18.30 1  0  4    4   \n",
       "Merc 280C           17.8 6   167.6 123 3.92 3.440 18.90 1  0  4    4   \n",
       "Merc 450SE          16.4 8   275.8 180 3.07 4.070 17.40 0  0  3    3   \n",
       "Merc 450SL          17.3 8   275.8 180 3.07 3.730 17.60 0  0  3    3   \n",
       "Merc 450SLC         15.2 8   275.8 180 3.07 3.780 18.00 0  0  3    3   \n",
       "Cadillac Fleetwood  10.4 8   472.0 205 2.93 5.250 17.98 0  0  3    4   \n",
       "Lincoln Continental 10.4 8   460.0 215 3.00 5.424 17.82 0  0  3    4   \n",
       "Chrysler Imperial   14.7 8   440.0 230 3.23 5.345 17.42 0  0  3    4   \n",
       "Fiat 128            32.4 4    78.7  66 4.08 2.200 19.47 1  1  4    1   \n",
       "Honda Civic         30.4 4    75.7  52 4.93 1.615 18.52 1  1  4    2   \n",
       "Toyota Corolla      33.9 4    71.1  65 4.22 1.835 19.90 1  1  4    1   \n",
       "Toyota Corona       21.5 4   120.1  97 3.70 2.465 20.01 1  0  3    1   \n",
       "Dodge Challenger    15.5 8   318.0 150 2.76 3.520 16.87 0  0  3    2   \n",
       "AMC Javelin         15.2 8   304.0 150 3.15 3.435 17.30 0  0  3    2   \n",
       "Camaro Z28          13.3 8   350.0 245 3.73 3.840 15.41 0  0  3    4   \n",
       "Pontiac Firebird    19.2 8   400.0 175 3.08 3.845 17.05 0  0  3    2   \n",
       "Fiat X1-9           27.3 4    79.0  66 4.08 1.935 18.90 1  1  4    1   \n",
       "Porsche 914-2       26.0 4   120.3  91 4.43 2.140 16.70 0  1  5    2   \n",
       "Lotus Europa        30.4 4    95.1 113 3.77 1.513 16.90 1  1  5    2   \n",
       "Ford Pantera L      15.8 8   351.0 264 4.22 3.170 14.50 0  1  5    4   \n",
       "Ferrari Dino        19.7 6   145.0 175 3.62 2.770 15.50 0  1  5    6   \n",
       "Maserati Bora       15.0 8   301.0 335 3.54 3.570 14.60 0  1  5    8   \n",
       "Volvo 142E          21.4 4   121.0 109 4.11 2.780 18.60 1  1  4    2   "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "mtcars"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "What kinds of insights can we get from this data?\n",
    "We have the cars' mileage per gallon of gas, the number of cylinders, and other attributes.\n",
    "\n",
    "If there's any column that you don't know what it represents, you can use the `?` function to see the helpfile for this dataset, like so:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "\n",
       "<table width=\"100%\" summary=\"page for mtcars {datasets}\"><tr><td>mtcars {datasets}</td><td style=\"text-align: right;\">R Documentation</td></tr></table>\n",
       "\n",
       "<h2>Motor Trend Car Road Tests</h2>\n",
       "\n",
       "<h3>Description</h3>\n",
       "\n",
       "<p>The data was extracted from the 1974 <em>Motor Trend</em> US magazine,\n",
       "and comprises fuel consumption and 10 aspects of\n",
       "automobile design and performance for 32 automobiles (1973&ndash;74\n",
       "models).\n",
       "</p>\n",
       "\n",
       "\n",
       "<h3>Usage</h3>\n",
       "\n",
       "<pre>mtcars</pre>\n",
       "\n",
       "\n",
       "<h3>Format</h3>\n",
       "\n",
       "<p>A data frame with 32 observations on 11 (numeric) variables.\n",
       "</p>\n",
       "\n",
       "<table summary=\"Rd table\">\n",
       "<tr>\n",
       " <td style=\"text-align: right;\">\n",
       "    [, 1] </td><td style=\"text-align: left;\"> mpg  </td><td style=\"text-align: left;\"> Miles/(US) gallon </td>\n",
       "</tr>\n",
       "<tr>\n",
       " <td style=\"text-align: right;\">\n",
       "    [, 2] </td><td style=\"text-align: left;\"> cyl  </td><td style=\"text-align: left;\"> Number of cylinders </td>\n",
       "</tr>\n",
       "<tr>\n",
       " <td style=\"text-align: right;\">\n",
       "    [, 3] </td><td style=\"text-align: left;\"> disp </td><td style=\"text-align: left;\"> Displacement (cu.in.) </td>\n",
       "</tr>\n",
       "<tr>\n",
       " <td style=\"text-align: right;\">\n",
       "    [, 4] </td><td style=\"text-align: left;\"> hp   </td><td style=\"text-align: left;\"> Gross horsepower </td>\n",
       "</tr>\n",
       "<tr>\n",
       " <td style=\"text-align: right;\">\n",
       "    [, 5] </td><td style=\"text-align: left;\"> drat </td><td style=\"text-align: left;\"> Rear axle ratio </td>\n",
       "</tr>\n",
       "<tr>\n",
       " <td style=\"text-align: right;\">\n",
       "    [, 6] </td><td style=\"text-align: left;\"> wt   </td><td style=\"text-align: left;\"> Weight (1000 lbs) </td>\n",
       "</tr>\n",
       "<tr>\n",
       " <td style=\"text-align: right;\">\n",
       "    [, 7] </td><td style=\"text-align: left;\"> qsec </td><td style=\"text-align: left;\"> 1/4 mile time </td>\n",
       "</tr>\n",
       "<tr>\n",
       " <td style=\"text-align: right;\">\n",
       "    [, 8] </td><td style=\"text-align: left;\"> vs   </td><td style=\"text-align: left;\"> Engine (0 = V-shaped, 1 = straight) </td>\n",
       "</tr>\n",
       "<tr>\n",
       " <td style=\"text-align: right;\">\n",
       "    [, 9] </td><td style=\"text-align: left;\"> am   </td><td style=\"text-align: left;\"> Transmission (0 = automatic, 1 = manual) </td>\n",
       "</tr>\n",
       "<tr>\n",
       " <td style=\"text-align: right;\">\n",
       "    [,10] </td><td style=\"text-align: left;\"> gear </td><td style=\"text-align: left;\"> Number of forward gears </td>\n",
       "</tr>\n",
       "<tr>\n",
       " <td style=\"text-align: right;\">\n",
       "    [,11] </td><td style=\"text-align: left;\"> carb </td><td style=\"text-align: left;\"> Number of carburetors\n",
       "  </td>\n",
       "</tr>\n",
       "\n",
       "</table>\n",
       "\n",
       "\n",
       "\n",
       "<h3>Source</h3>\n",
       "\n",
       "<p>Henderson and Velleman (1981),\n",
       "Building multiple regression models interactively.\n",
       "<em>Biometrics</em>, <b>37</b>, 391&ndash;411.\n",
       "</p>\n",
       "\n",
       "\n",
       "<h3>Examples</h3>\n",
       "\n",
       "<pre>\n",
       "require(graphics)\n",
       "pairs(mtcars, main = \"mtcars data\", gap = 1/4)\n",
       "coplot(mpg ~ disp | as.factor(cyl), data = mtcars,\n",
       "       panel = panel.smooth, rows = 1)\n",
       "## possibly more meaningful, e.g., for summary() or bivariate plots:\n",
       "mtcars2 &lt;- within(mtcars, {\n",
       "   vs &lt;- factor(vs, labels = c(\"V\", \"S\"))\n",
       "   am &lt;- factor(am, labels = c(\"automatic\", \"manual\"))\n",
       "   cyl  &lt;- ordered(cyl)\n",
       "   gear &lt;- ordered(gear)\n",
       "   carb &lt;- ordered(carb)\n",
       "})\n",
       "summary(mtcars2)\n",
       "</pre>\n",
       "\n",
       "<hr /><div style=\"text-align: center;\">[Package <em>datasets</em> version 3.5.1 ]</div>"
      ],
      "text/latex": [
       "\\inputencoding{utf8}\n",
       "\\HeaderA{mtcars}{Motor Trend Car Road Tests}{mtcars}\n",
       "\\keyword{datasets}{mtcars}\n",
       "%\n",
       "\\begin{Description}\\relax\n",
       "The data was extracted from the 1974 \\emph{Motor Trend} US magazine,\n",
       "and comprises fuel consumption and 10 aspects of\n",
       "automobile design and performance for 32 automobiles (1973--74\n",
       "models).\n",
       "\\end{Description}\n",
       "%\n",
       "\\begin{Usage}\n",
       "\\begin{verbatim}\n",
       "mtcars\n",
       "\\end{verbatim}\n",
       "\\end{Usage}\n",
       "%\n",
       "\\begin{Format}\n",
       "A data frame with 32 observations on 11 (numeric) variables.\n",
       "\n",
       "\\Tabular{rll}{\n",
       "[, 1] & mpg  & Miles/(US) gallon \\\\{}\n",
       "[, 2] & cyl  & Number of cylinders \\\\{}\n",
       "[, 3] & disp & Displacement (cu.in.) \\\\{}\n",
       "[, 4] & hp   & Gross horsepower \\\\{}\n",
       "[, 5] & drat & Rear axle ratio \\\\{}\n",
       "[, 6] & wt   & Weight (1000 lbs) \\\\{}\n",
       "[, 7] & qsec & 1/4 mile time \\\\{}\n",
       "[, 8] & vs   & Engine (0 = V-shaped, 1 = straight) \\\\{}\n",
       "[, 9] & am   & Transmission (0 = automatic, 1 = manual) \\\\{}\n",
       "[,10] & gear & Number of forward gears \\\\{}\n",
       "[,11] & carb & Number of carburetors\n",
       "}\n",
       "\\end{Format}\n",
       "%\n",
       "\\begin{Source}\\relax\n",
       "Henderson and Velleman (1981),\n",
       "Building multiple regression models interactively.\n",
       "\\emph{Biometrics}, \\bold{37}, 391--411.\n",
       "\\end{Source}\n",
       "%\n",
       "\\begin{Examples}\n",
       "\\begin{ExampleCode}\n",
       "require(graphics)\n",
       "pairs(mtcars, main = \"mtcars data\", gap = 1/4)\n",
       "coplot(mpg ~ disp | as.factor(cyl), data = mtcars,\n",
       "       panel = panel.smooth, rows = 1)\n",
       "## possibly more meaningful, e.g., for summary() or bivariate plots:\n",
       "mtcars2 <- within(mtcars, {\n",
       "   vs <- factor(vs, labels = c(\"V\", \"S\"))\n",
       "   am <- factor(am, labels = c(\"automatic\", \"manual\"))\n",
       "   cyl  <- ordered(cyl)\n",
       "   gear <- ordered(gear)\n",
       "   carb <- ordered(carb)\n",
       "})\n",
       "summary(mtcars2)\n",
       "\\end{ExampleCode}\n",
       "\\end{Examples}"
      ],
      "text/plain": [
       "mtcars                package:datasets                 R Documentation\n",
       "\n",
       "_\bM_\bo_\bt_\bo_\br _\bT_\br_\be_\bn_\bd _\bC_\ba_\br _\bR_\bo_\ba_\bd _\bT_\be_\bs_\bt_\bs\n",
       "\n",
       "_\bD_\be_\bs_\bc_\br_\bi_\bp_\bt_\bi_\bo_\bn:\n",
       "\n",
       "     The data was extracted from the 1974 _Motor Trend_ US magazine,\n",
       "     and comprises fuel consumption and 10 aspects of automobile design\n",
       "     and performance for 32 automobiles (1973-74 models).\n",
       "\n",
       "_\bU_\bs_\ba_\bg_\be:\n",
       "\n",
       "     mtcars\n",
       "     \n",
       "_\bF_\bo_\br_\bm_\ba_\bt:\n",
       "\n",
       "     A data frame with 32 observations on 11 (numeric) variables.\n",
       "\n",
       "       [, 1]  mpg   Miles/(US) gallon                        \n",
       "       [, 2]  cyl   Number of cylinders                      \n",
       "       [, 3]  disp  Displacement (cu.in.)                    \n",
       "       [, 4]  hp    Gross horsepower                         \n",
       "       [, 5]  drat  Rear axle ratio                          \n",
       "       [, 6]  wt    Weight (1000 lbs)                        \n",
       "       [, 7]  qsec  1/4 mile time                            \n",
       "       [, 8]  vs    Engine (0 = V-shaped, 1 = straight)      \n",
       "       [, 9]  am    Transmission (0 = automatic, 1 = manual) \n",
       "       [,10]  gear  Number of forward gears                  \n",
       "       [,11]  carb  Number of carburetors                    \n",
       "      \n",
       "_\bS_\bo_\bu_\br_\bc_\be:\n",
       "\n",
       "     Henderson and Velleman (1981), Building multiple regression models\n",
       "     interactively.  _Biometrics_, *37*, 391-411.\n",
       "\n",
       "_\bE_\bx_\ba_\bm_\bp_\bl_\be_\bs:\n",
       "\n",
       "     require(graphics)\n",
       "     pairs(mtcars, main = \"mtcars data\", gap = 1/4)\n",
       "     coplot(mpg ~ disp | as.factor(cyl), data = mtcars,\n",
       "            panel = panel.smooth, rows = 1)\n",
       "     ## possibly more meaningful, e.g., for summary() or bivariate plots:\n",
       "     mtcars2 <- within(mtcars, {\n",
       "        vs <- factor(vs, labels = c(\"V\", \"S\"))\n",
       "        am <- factor(am, labels = c(\"automatic\", \"manual\"))\n",
       "        cyl  <- ordered(cyl)\n",
       "        gear <- ordered(gear)\n",
       "        carb <- ordered(carb)\n",
       "     })\n",
       "     summary(mtcars2)\n",
       "     "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "?mtcars"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Note that this is only possible because `mtcars` is an inbuilt dataset within R.\n",
    "Almost no datasets will have helpfiles.\n",
    "Some datasets that you find on the internet will have a readme file that will describe what every column is depicting. Some will include the name of each column in the dataset. Although these are good practices, you can find datasets with undescriptive names for its columns and datasets with no names at all."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "As you may already know, we don't need to view the entire dataset to look how the data is structured. We can use the `head` function to see the first 6 rows of our data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table>\n",
       "<caption>A data.frame: 6 × 11</caption>\n",
       "<thead>\n",
       "\t<tr><th></th><th scope=col>mpg</th><th scope=col>cyl</th><th scope=col>disp</th><th scope=col>hp</th><th scope=col>drat</th><th scope=col>wt</th><th scope=col>qsec</th><th scope=col>vs</th><th scope=col>am</th><th scope=col>gear</th><th scope=col>carb</th></tr>\n",
       "\t<tr><th></th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th></tr>\n",
       "</thead>\n",
       "<tbody>\n",
       "\t<tr><th scope=row>Mazda RX4</th><td>21.0</td><td>6</td><td>160</td><td>110</td><td>3.90</td><td>2.620</td><td>16.46</td><td>0</td><td>1</td><td>4</td><td>4</td></tr>\n",
       "\t<tr><th scope=row>Mazda RX4 Wag</th><td>21.0</td><td>6</td><td>160</td><td>110</td><td>3.90</td><td>2.875</td><td>17.02</td><td>0</td><td>1</td><td>4</td><td>4</td></tr>\n",
       "\t<tr><th scope=row>Datsun 710</th><td>22.8</td><td>4</td><td>108</td><td> 93</td><td>3.85</td><td>2.320</td><td>18.61</td><td>1</td><td>1</td><td>4</td><td>1</td></tr>\n",
       "\t<tr><th scope=row>Hornet 4 Drive</th><td>21.4</td><td>6</td><td>258</td><td>110</td><td>3.08</td><td>3.215</td><td>19.44</td><td>1</td><td>0</td><td>3</td><td>1</td></tr>\n",
       "\t<tr><th scope=row>Hornet Sportabout</th><td>18.7</td><td>8</td><td>360</td><td>175</td><td>3.15</td><td>3.440</td><td>17.02</td><td>0</td><td>0</td><td>3</td><td>2</td></tr>\n",
       "\t<tr><th scope=row>Valiant</th><td>18.1</td><td>6</td><td>225</td><td>105</td><td>2.76</td><td>3.460</td><td>20.22</td><td>1</td><td>0</td><td>3</td><td>1</td></tr>\n",
       "</tbody>\n",
       "</table>\n"
      ],
      "text/latex": [
       "A data.frame: 6 × 11\n",
       "\\begin{tabular}{r|lllllllllll}\n",
       "  & mpg & cyl & disp & hp & drat & wt & qsec & vs & am & gear & carb\\\\\n",
       "  & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl>\\\\\n",
       "\\hline\n",
       "\tMazda RX4 & 21.0 & 6 & 160 & 110 & 3.90 & 2.620 & 16.46 & 0 & 1 & 4 & 4\\\\\n",
       "\tMazda RX4 Wag & 21.0 & 6 & 160 & 110 & 3.90 & 2.875 & 17.02 & 0 & 1 & 4 & 4\\\\\n",
       "\tDatsun 710 & 22.8 & 4 & 108 &  93 & 3.85 & 2.320 & 18.61 & 1 & 1 & 4 & 1\\\\\n",
       "\tHornet 4 Drive & 21.4 & 6 & 258 & 110 & 3.08 & 3.215 & 19.44 & 1 & 0 & 3 & 1\\\\\n",
       "\tHornet Sportabout & 18.7 & 8 & 360 & 175 & 3.15 & 3.440 & 17.02 & 0 & 0 & 3 & 2\\\\\n",
       "\tValiant & 18.1 & 6 & 225 & 105 & 2.76 & 3.460 & 20.22 & 1 & 0 & 3 & 1\\\\\n",
       "\\end{tabular}\n"
      ],
      "text/markdown": [
       "\n",
       "A data.frame: 6 × 11\n",
       "\n",
       "| <!--/--> | mpg &lt;dbl&gt; | cyl &lt;dbl&gt; | disp &lt;dbl&gt; | hp &lt;dbl&gt; | drat &lt;dbl&gt; | wt &lt;dbl&gt; | qsec &lt;dbl&gt; | vs &lt;dbl&gt; | am &lt;dbl&gt; | gear &lt;dbl&gt; | carb &lt;dbl&gt; |\n",
       "|---|---|---|---|---|---|---|---|---|---|---|---|\n",
       "| Mazda RX4 | 21.0 | 6 | 160 | 110 | 3.90 | 2.620 | 16.46 | 0 | 1 | 4 | 4 |\n",
       "| Mazda RX4 Wag | 21.0 | 6 | 160 | 110 | 3.90 | 2.875 | 17.02 | 0 | 1 | 4 | 4 |\n",
       "| Datsun 710 | 22.8 | 4 | 108 |  93 | 3.85 | 2.320 | 18.61 | 1 | 1 | 4 | 1 |\n",
       "| Hornet 4 Drive | 21.4 | 6 | 258 | 110 | 3.08 | 3.215 | 19.44 | 1 | 0 | 3 | 1 |\n",
       "| Hornet Sportabout | 18.7 | 8 | 360 | 175 | 3.15 | 3.440 | 17.02 | 0 | 0 | 3 | 2 |\n",
       "| Valiant | 18.1 | 6 | 225 | 105 | 2.76 | 3.460 | 20.22 | 1 | 0 | 3 | 1 |\n",
       "\n"
      ],
      "text/plain": [
       "                  mpg  cyl disp hp  drat wt    qsec  vs am gear carb\n",
       "Mazda RX4         21.0 6   160  110 3.90 2.620 16.46 0  1  4    4   \n",
       "Mazda RX4 Wag     21.0 6   160  110 3.90 2.875 17.02 0  1  4    4   \n",
       "Datsun 710        22.8 4   108   93 3.85 2.320 18.61 1  1  4    1   \n",
       "Hornet 4 Drive    21.4 6   258  110 3.08 3.215 19.44 1  0  3    1   \n",
       "Hornet Sportabout 18.7 8   360  175 3.15 3.440 17.02 0  0  3    2   \n",
       "Valiant           18.1 6   225  105 2.76 3.460 20.22 1  0  3    1   "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "head(mtcars)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "And we can use `tail` to see the last 6 rows of our data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table>\n",
       "<caption>A data.frame: 6 × 11</caption>\n",
       "<thead>\n",
       "\t<tr><th></th><th scope=col>mpg</th><th scope=col>cyl</th><th scope=col>disp</th><th scope=col>hp</th><th scope=col>drat</th><th scope=col>wt</th><th scope=col>qsec</th><th scope=col>vs</th><th scope=col>am</th><th scope=col>gear</th><th scope=col>carb</th></tr>\n",
       "\t<tr><th></th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th></tr>\n",
       "</thead>\n",
       "<tbody>\n",
       "\t<tr><th scope=row>Porsche 914-2</th><td>26.0</td><td>4</td><td>120.3</td><td> 91</td><td>4.43</td><td>2.140</td><td>16.7</td><td>0</td><td>1</td><td>5</td><td>2</td></tr>\n",
       "\t<tr><th scope=row>Lotus Europa</th><td>30.4</td><td>4</td><td> 95.1</td><td>113</td><td>3.77</td><td>1.513</td><td>16.9</td><td>1</td><td>1</td><td>5</td><td>2</td></tr>\n",
       "\t<tr><th scope=row>Ford Pantera L</th><td>15.8</td><td>8</td><td>351.0</td><td>264</td><td>4.22</td><td>3.170</td><td>14.5</td><td>0</td><td>1</td><td>5</td><td>4</td></tr>\n",
       "\t<tr><th scope=row>Ferrari Dino</th><td>19.7</td><td>6</td><td>145.0</td><td>175</td><td>3.62</td><td>2.770</td><td>15.5</td><td>0</td><td>1</td><td>5</td><td>6</td></tr>\n",
       "\t<tr><th scope=row>Maserati Bora</th><td>15.0</td><td>8</td><td>301.0</td><td>335</td><td>3.54</td><td>3.570</td><td>14.6</td><td>0</td><td>1</td><td>5</td><td>8</td></tr>\n",
       "\t<tr><th scope=row>Volvo 142E</th><td>21.4</td><td>4</td><td>121.0</td><td>109</td><td>4.11</td><td>2.780</td><td>18.6</td><td>1</td><td>1</td><td>4</td><td>2</td></tr>\n",
       "</tbody>\n",
       "</table>\n"
      ],
      "text/latex": [
       "A data.frame: 6 × 11\n",
       "\\begin{tabular}{r|lllllllllll}\n",
       "  & mpg & cyl & disp & hp & drat & wt & qsec & vs & am & gear & carb\\\\\n",
       "  & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl>\\\\\n",
       "\\hline\n",
       "\tPorsche 914-2 & 26.0 & 4 & 120.3 &  91 & 4.43 & 2.140 & 16.7 & 0 & 1 & 5 & 2\\\\\n",
       "\tLotus Europa & 30.4 & 4 &  95.1 & 113 & 3.77 & 1.513 & 16.9 & 1 & 1 & 5 & 2\\\\\n",
       "\tFord Pantera L & 15.8 & 8 & 351.0 & 264 & 4.22 & 3.170 & 14.5 & 0 & 1 & 5 & 4\\\\\n",
       "\tFerrari Dino & 19.7 & 6 & 145.0 & 175 & 3.62 & 2.770 & 15.5 & 0 & 1 & 5 & 6\\\\\n",
       "\tMaserati Bora & 15.0 & 8 & 301.0 & 335 & 3.54 & 3.570 & 14.6 & 0 & 1 & 5 & 8\\\\\n",
       "\tVolvo 142E & 21.4 & 4 & 121.0 & 109 & 4.11 & 2.780 & 18.6 & 1 & 1 & 4 & 2\\\\\n",
       "\\end{tabular}\n"
      ],
      "text/markdown": [
       "\n",
       "A data.frame: 6 × 11\n",
       "\n",
       "| <!--/--> | mpg &lt;dbl&gt; | cyl &lt;dbl&gt; | disp &lt;dbl&gt; | hp &lt;dbl&gt; | drat &lt;dbl&gt; | wt &lt;dbl&gt; | qsec &lt;dbl&gt; | vs &lt;dbl&gt; | am &lt;dbl&gt; | gear &lt;dbl&gt; | carb &lt;dbl&gt; |\n",
       "|---|---|---|---|---|---|---|---|---|---|---|---|\n",
       "| Porsche 914-2 | 26.0 | 4 | 120.3 |  91 | 4.43 | 2.140 | 16.7 | 0 | 1 | 5 | 2 |\n",
       "| Lotus Europa | 30.4 | 4 |  95.1 | 113 | 3.77 | 1.513 | 16.9 | 1 | 1 | 5 | 2 |\n",
       "| Ford Pantera L | 15.8 | 8 | 351.0 | 264 | 4.22 | 3.170 | 14.5 | 0 | 1 | 5 | 4 |\n",
       "| Ferrari Dino | 19.7 | 6 | 145.0 | 175 | 3.62 | 2.770 | 15.5 | 0 | 1 | 5 | 6 |\n",
       "| Maserati Bora | 15.0 | 8 | 301.0 | 335 | 3.54 | 3.570 | 14.6 | 0 | 1 | 5 | 8 |\n",
       "| Volvo 142E | 21.4 | 4 | 121.0 | 109 | 4.11 | 2.780 | 18.6 | 1 | 1 | 4 | 2 |\n",
       "\n"
      ],
      "text/plain": [
       "               mpg  cyl disp  hp  drat wt    qsec vs am gear carb\n",
       "Porsche 914-2  26.0 4   120.3  91 4.43 2.140 16.7 0  1  5    2   \n",
       "Lotus Europa   30.4 4    95.1 113 3.77 1.513 16.9 1  1  5    2   \n",
       "Ford Pantera L 15.8 8   351.0 264 4.22 3.170 14.5 0  1  5    4   \n",
       "Ferrari Dino   19.7 6   145.0 175 3.62 2.770 15.5 0  1  5    6   \n",
       "Maserati Bora  15.0 8   301.0 335 3.54 3.570 14.6 0  1  5    8   \n",
       "Volvo 142E     21.4 4   121.0 109 4.11 2.780 18.6 1  1  4    2   "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "tail(mtcars)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's further analyze our data...\n",
    "We can get a quick summary of each column using the `summary` function:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "      mpg             cyl             disp             hp       \n",
       " Min.   :10.40   Min.   :4.000   Min.   : 71.1   Min.   : 52.0  \n",
       " 1st Qu.:15.43   1st Qu.:4.000   1st Qu.:120.8   1st Qu.: 96.5  \n",
       " Median :19.20   Median :6.000   Median :196.3   Median :123.0  \n",
       " Mean   :20.09   Mean   :6.188   Mean   :230.7   Mean   :146.7  \n",
       " 3rd Qu.:22.80   3rd Qu.:8.000   3rd Qu.:326.0   3rd Qu.:180.0  \n",
       " Max.   :33.90   Max.   :8.000   Max.   :472.0   Max.   :335.0  \n",
       "      drat             wt             qsec             vs        \n",
       " Min.   :2.760   Min.   :1.513   Min.   :14.50   Min.   :0.0000  \n",
       " 1st Qu.:3.080   1st Qu.:2.581   1st Qu.:16.89   1st Qu.:0.0000  \n",
       " Median :3.695   Median :3.325   Median :17.71   Median :0.0000  \n",
       " Mean   :3.597   Mean   :3.217   Mean   :17.85   Mean   :0.4375  \n",
       " 3rd Qu.:3.920   3rd Qu.:3.610   3rd Qu.:18.90   3rd Qu.:1.0000  \n",
       " Max.   :4.930   Max.   :5.424   Max.   :22.90   Max.   :1.0000  \n",
       "       am              gear            carb      \n",
       " Min.   :0.0000   Min.   :3.000   Min.   :1.000  \n",
       " 1st Qu.:0.0000   1st Qu.:3.000   1st Qu.:2.000  \n",
       " Median :0.0000   Median :4.000   Median :2.000  \n",
       " Mean   :0.4062   Mean   :3.688   Mean   :2.812  \n",
       " 3rd Qu.:1.0000   3rd Qu.:4.000   3rd Qu.:4.000  \n",
       " Max.   :1.0000   Max.   :5.000   Max.   :8.000  "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "summary(mtcars)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can get the average for any column using `mean(datasetname$columnname)`, like so:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "6.1875"
      ],
      "text/latex": [
       "6.1875"
      ],
      "text/markdown": [
       "6.1875"
      ],
      "text/plain": [
       "[1] 6.1875"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "mean(mtcars$cyl)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<hr>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a id=\"ref4\"></a>\n",
    "<h2 align=center>Making Bar Plots</h2>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's start our plotting with bar plots. As the name implies, it's a plot format that shows your data using bars.\n",
    "You probably have seen a lot of them already."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Before actually creating any bar plot, let's import our plotting library, `ggplot2`.\n",
    "There's no need to execute this block if you already executed the import in \"Differences between R Libraries\"."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    }
   },
   "outputs": [],
   "source": [
    "if(\"ggplot2\" %in% rownames(installed.packages()) == FALSE) {install.packages(\"ggplot2\")}\n",
    "library(ggplot2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now that we have loaded our libraries, let's start plotting. To plot easily using `ggplot2`, we can use the `qplot` function, which has simpler syntax, like so:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    }
   },
   "outputs": [
    {
     "data": {},
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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UjVZiOBJCqbDKRqs5FAEpVNBlK12Ugg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\nDv3AV3e0e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      "text/plain": [
       "plot without title"
      ]
     },
     "metadata": {},
     "output_type": "display_data",
     "source": "R display func"
    }
   ],
   "source": [
    "qplot(mtcars$cyl, geom = \"bar\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "As we can see, we plotted a bar plot, consisting of the count of every element with the same value.\n",
    "We can now exploit some of the possibilities of `ggplot2`'s `qplot` function."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "\n",
       "<table width=\"100%\" summary=\"page for qplot {ggplot2}\"><tr><td>qplot {ggplot2}</td><td style=\"text-align: right;\">R Documentation</td></tr></table>\n",
       "\n",
       "<h2>Quick plot</h2>\n",
       "\n",
       "<h3>Description</h3>\n",
       "\n",
       "<p><code>qplot</code> is a shortcut designed to be familiar if you're used to base\n",
       "<code>plot()</code>. It's a convenient wrapper for creating a number of\n",
       "different types of plots using a consistent calling scheme. It's great\n",
       "for allowing you to produce plots quickly, but I highly recommend\n",
       "learning <code>ggplot()</code> as it makes it easier to create\n",
       "complex graphics.\n",
       "</p>\n",
       "\n",
       "\n",
       "<h3>Usage</h3>\n",
       "\n",
       "<pre>\n",
       "qplot(x, y, ..., data, facets = NULL, margins = FALSE, geom = \"auto\",\n",
       "  xlim = c(NA, NA), ylim = c(NA, NA), log = \"\", main = NULL,\n",
       "  xlab = NULL, ylab = NULL, asp = NA, stat = NULL,\n",
       "  position = NULL)\n",
       "\n",
       "quickplot(x, y, ..., data, facets = NULL, margins = FALSE,\n",
       "  geom = \"auto\", xlim = c(NA, NA), ylim = c(NA, NA), log = \"\",\n",
       "  main = NULL, xlab = NULL, ylab = NULL, asp = NA, stat = NULL,\n",
       "  position = NULL)\n",
       "</pre>\n",
       "\n",
       "\n",
       "<h3>Arguments</h3>\n",
       "\n",
       "<table summary=\"R argblock\">\n",
       "<tr valign=\"top\"><td><code>x, y, ...</code></td>\n",
       "<td>\n",
       "<p>Aesthetics passed into each layer</p>\n",
       "</td></tr>\n",
       "<tr valign=\"top\"><td><code>data</code></td>\n",
       "<td>\n",
       "<p>Data frame to use (optional).  If not specified, will create\n",
       "one, extracting vectors from the current environment.</p>\n",
       "</td></tr>\n",
       "<tr valign=\"top\"><td><code>facets</code></td>\n",
       "<td>\n",
       "<p>faceting formula to use. Picks <code>facet_wrap()</code> or\n",
       "<code>facet_grid()</code> depending on whether the formula is one-\n",
       "or two-sided</p>\n",
       "</td></tr>\n",
       "<tr valign=\"top\"><td><code>margins</code></td>\n",
       "<td>\n",
       "<p>See <code>facet_grid</code>: display marginal facets?</p>\n",
       "</td></tr>\n",
       "<tr valign=\"top\"><td><code>geom</code></td>\n",
       "<td>\n",
       "<p>Character vector specifying geom(s) to draw. Defaults to\n",
       "&quot;point&quot; if x and y are specified, and &quot;histogram&quot; if only x is specified.</p>\n",
       "</td></tr>\n",
       "<tr valign=\"top\"><td><code>xlim, ylim</code></td>\n",
       "<td>\n",
       "<p>X and y axis limits</p>\n",
       "</td></tr>\n",
       "<tr valign=\"top\"><td><code>log</code></td>\n",
       "<td>\n",
       "<p>Which variables to log transform (&quot;x&quot;, &quot;y&quot;, or &quot;xy&quot;)</p>\n",
       "</td></tr>\n",
       "<tr valign=\"top\"><td><code>main, xlab, ylab</code></td>\n",
       "<td>\n",
       "<p>Character vector (or expression) giving plot title,\n",
       "x axis label, and y axis label respectively.</p>\n",
       "</td></tr>\n",
       "<tr valign=\"top\"><td><code>asp</code></td>\n",
       "<td>\n",
       "<p>The y/x aspect ratio</p>\n",
       "</td></tr>\n",
       "<tr valign=\"top\"><td><code>stat, position</code></td>\n",
       "<td>\n",
       "<p>DEPRECATED.</p>\n",
       "</td></tr>\n",
       "</table>\n",
       "\n",
       "\n",
       "<h3>Examples</h3>\n",
       "\n",
       "<pre>\n",
       "# Use data from data.frame\n",
       "qplot(mpg, wt, data = mtcars)\n",
       "qplot(mpg, wt, data = mtcars, colour = cyl)\n",
       "qplot(mpg, wt, data = mtcars, size = cyl)\n",
       "qplot(mpg, wt, data = mtcars, facets = vs ~ am)\n",
       "\n",
       "\n",
       "qplot(1:10, rnorm(10), colour = runif(10))\n",
       "qplot(1:10, letters[1:10])\n",
       "mod &lt;- lm(mpg ~ wt, data = mtcars)\n",
       "qplot(resid(mod), fitted(mod))\n",
       "\n",
       "f &lt;- function() {\n",
       "   a &lt;- 1:10\n",
       "   b &lt;- a ^ 2\n",
       "   qplot(a, b)\n",
       "}\n",
       "f()\n",
       "\n",
       "# To set aesthetics, wrap in I()\n",
       "qplot(mpg, wt, data = mtcars, colour = I(\"red\"))\n",
       "\n",
       "# qplot will attempt to guess what geom you want depending on the input\n",
       "# both x and y supplied = scatterplot\n",
       "qplot(mpg, wt, data = mtcars)\n",
       "# just x supplied = histogram\n",
       "qplot(mpg, data = mtcars)\n",
       "# just y supplied = scatterplot, with x = seq_along(y)\n",
       "qplot(y = mpg, data = mtcars)\n",
       "\n",
       "# Use different geoms\n",
       "qplot(mpg, wt, data = mtcars, geom = \"path\")\n",
       "qplot(factor(cyl), wt, data = mtcars, geom = c(\"boxplot\", \"jitter\"))\n",
       "qplot(mpg, data = mtcars, geom = \"dotplot\")\n",
       "\n",
       "</pre>\n",
       "\n",
       "<hr /><div style=\"text-align: center;\">[Package <em>ggplot2</em> version 3.2.1 ]</div>"
      ],
      "text/latex": [
       "\\inputencoding{utf8}\n",
       "\\HeaderA{qplot}{Quick plot}{qplot}\n",
       "\\aliasA{quickplot}{qplot}{quickplot}\n",
       "%\n",
       "\\begin{Description}\\relax\n",
       "\\code{qplot} is a shortcut designed to be familiar if you're used to base\n",
       "\\code{\\LinkA{plot()}{plot}}. It's a convenient wrapper for creating a number of\n",
       "different types of plots using a consistent calling scheme. It's great\n",
       "for allowing you to produce plots quickly, but I highly recommend\n",
       "learning \\code{\\LinkA{ggplot()}{ggplot}} as it makes it easier to create\n",
       "complex graphics.\n",
       "\\end{Description}\n",
       "%\n",
       "\\begin{Usage}\n",
       "\\begin{verbatim}\n",
       "qplot(x, y, ..., data, facets = NULL, margins = FALSE, geom = \"auto\",\n",
       "  xlim = c(NA, NA), ylim = c(NA, NA), log = \"\", main = NULL,\n",
       "  xlab = NULL, ylab = NULL, asp = NA, stat = NULL,\n",
       "  position = NULL)\n",
       "\n",
       "quickplot(x, y, ..., data, facets = NULL, margins = FALSE,\n",
       "  geom = \"auto\", xlim = c(NA, NA), ylim = c(NA, NA), log = \"\",\n",
       "  main = NULL, xlab = NULL, ylab = NULL, asp = NA, stat = NULL,\n",
       "  position = NULL)\n",
       "\\end{verbatim}\n",
       "\\end{Usage}\n",
       "%\n",
       "\\begin{Arguments}\n",
       "\\begin{ldescription}\n",
       "\\item[\\code{x, y, ...}] Aesthetics passed into each layer\n",
       "\n",
       "\\item[\\code{data}] Data frame to use (optional).  If not specified, will create\n",
       "one, extracting vectors from the current environment.\n",
       "\n",
       "\\item[\\code{facets}] faceting formula to use. Picks \\code{\\LinkA{facet\\_wrap()}{facet.Rul.wrap}} or\n",
       "\\code{\\LinkA{facet\\_grid()}{facet.Rul.grid}} depending on whether the formula is one-\n",
       "or two-sided\n",
       "\n",
       "\\item[\\code{margins}] See \\code{facet\\_grid}: display marginal facets?\n",
       "\n",
       "\\item[\\code{geom}] Character vector specifying geom(s) to draw. Defaults to\n",
       "\"point\" if x and y are specified, and \"histogram\" if only x is specified.\n",
       "\n",
       "\\item[\\code{xlim, ylim}] X and y axis limits\n",
       "\n",
       "\\item[\\code{log}] Which variables to log transform (\"x\", \"y\", or \"xy\")\n",
       "\n",
       "\\item[\\code{main, xlab, ylab}] Character vector (or expression) giving plot title,\n",
       "x axis label, and y axis label respectively.\n",
       "\n",
       "\\item[\\code{asp}] The y/x aspect ratio\n",
       "\n",
       "\\item[\\code{stat, position}] DEPRECATED.\n",
       "\\end{ldescription}\n",
       "\\end{Arguments}\n",
       "%\n",
       "\\begin{Examples}\n",
       "\\begin{ExampleCode}\n",
       "# Use data from data.frame\n",
       "qplot(mpg, wt, data = mtcars)\n",
       "qplot(mpg, wt, data = mtcars, colour = cyl)\n",
       "qplot(mpg, wt, data = mtcars, size = cyl)\n",
       "qplot(mpg, wt, data = mtcars, facets = vs ~ am)\n",
       "\n",
       "\n",
       "qplot(1:10, rnorm(10), colour = runif(10))\n",
       "qplot(1:10, letters[1:10])\n",
       "mod <- lm(mpg ~ wt, data = mtcars)\n",
       "qplot(resid(mod), fitted(mod))\n",
       "\n",
       "f <- function() {\n",
       "   a <- 1:10\n",
       "   b <- a ^ 2\n",
       "   qplot(a, b)\n",
       "}\n",
       "f()\n",
       "\n",
       "# To set aesthetics, wrap in I()\n",
       "qplot(mpg, wt, data = mtcars, colour = I(\"red\"))\n",
       "\n",
       "# qplot will attempt to guess what geom you want depending on the input\n",
       "# both x and y supplied = scatterplot\n",
       "qplot(mpg, wt, data = mtcars)\n",
       "# just x supplied = histogram\n",
       "qplot(mpg, data = mtcars)\n",
       "# just y supplied = scatterplot, with x = seq_along(y)\n",
       "qplot(y = mpg, data = mtcars)\n",
       "\n",
       "# Use different geoms\n",
       "qplot(mpg, wt, data = mtcars, geom = \"path\")\n",
       "qplot(factor(cyl), wt, data = mtcars, geom = c(\"boxplot\", \"jitter\"))\n",
       "qplot(mpg, data = mtcars, geom = \"dotplot\")\n",
       "\n",
       "\\end{ExampleCode}\n",
       "\\end{Examples}"
      ],
      "text/plain": [
       "qplot                 package:ggplot2                  R Documentation\n",
       "\n",
       "_\bQ_\bu_\bi_\bc_\bk _\bp_\bl_\bo_\bt\n",
       "\n",
       "_\bD_\be_\bs_\bc_\br_\bi_\bp_\bt_\bi_\bo_\bn:\n",
       "\n",
       "     ‘qplot’ is a shortcut designed to be familiar if you're used to\n",
       "     base ‘plot()’. It's a convenient wrapper for creating a number of\n",
       "     different types of plots using a consistent calling scheme. It's\n",
       "     great for allowing you to produce plots quickly, but I highly\n",
       "     recommend learning ‘ggplot()’ as it makes it easier to create\n",
       "     complex graphics.\n",
       "\n",
       "_\bU_\bs_\ba_\bg_\be:\n",
       "\n",
       "     qplot(x, y, ..., data, facets = NULL, margins = FALSE, geom = \"auto\",\n",
       "       xlim = c(NA, NA), ylim = c(NA, NA), log = \"\", main = NULL,\n",
       "       xlab = NULL, ylab = NULL, asp = NA, stat = NULL,\n",
       "       position = NULL)\n",
       "     \n",
       "     quickplot(x, y, ..., data, facets = NULL, margins = FALSE,\n",
       "       geom = \"auto\", xlim = c(NA, NA), ylim = c(NA, NA), log = \"\",\n",
       "       main = NULL, xlab = NULL, ylab = NULL, asp = NA, stat = NULL,\n",
       "       position = NULL)\n",
       "     \n",
       "_\bA_\br_\bg_\bu_\bm_\be_\bn_\bt_\bs:\n",
       "\n",
       "x, y, ...: Aesthetics passed into each layer\n",
       "\n",
       "    data: Data frame to use (optional).  If not specified, will create\n",
       "          one, extracting vectors from the current environment.\n",
       "\n",
       "  facets: faceting formula to use. Picks ‘facet_wrap()’ or\n",
       "          ‘facet_grid()’ depending on whether the formula is one- or\n",
       "          two-sided\n",
       "\n",
       " margins: See ‘facet_grid’: display marginal facets?\n",
       "\n",
       "    geom: Character vector specifying geom(s) to draw. Defaults to\n",
       "          \"point\" if x and y are specified, and \"histogram\" if only x\n",
       "          is specified.\n",
       "\n",
       "xlim, ylim: X and y axis limits\n",
       "\n",
       "     log: Which variables to log transform (\"x\", \"y\", or \"xy\")\n",
       "\n",
       "main, xlab, ylab: Character vector (or expression) giving plot title, x\n",
       "          axis label, and y axis label respectively.\n",
       "\n",
       "     asp: The y/x aspect ratio\n",
       "\n",
       "stat, position: DEPRECATED.\n",
       "\n",
       "_\bE_\bx_\ba_\bm_\bp_\bl_\be_\bs:\n",
       "\n",
       "     # Use data from data.frame\n",
       "     qplot(mpg, wt, data = mtcars)\n",
       "     qplot(mpg, wt, data = mtcars, colour = cyl)\n",
       "     qplot(mpg, wt, data = mtcars, size = cyl)\n",
       "     qplot(mpg, wt, data = mtcars, facets = vs ~ am)\n",
       "     \n",
       "     \n",
       "     qplot(1:10, rnorm(10), colour = runif(10))\n",
       "     qplot(1:10, letters[1:10])\n",
       "     mod <- lm(mpg ~ wt, data = mtcars)\n",
       "     qplot(resid(mod), fitted(mod))\n",
       "     \n",
       "     f <- function() {\n",
       "        a <- 1:10\n",
       "        b <- a ^ 2\n",
       "        qplot(a, b)\n",
       "     }\n",
       "     f()\n",
       "     \n",
       "     # To set aesthetics, wrap in I()\n",
       "     qplot(mpg, wt, data = mtcars, colour = I(\"red\"))\n",
       "     \n",
       "     # qplot will attempt to guess what geom you want depending on the input\n",
       "     # both x and y supplied = scatterplot\n",
       "     qplot(mpg, wt, data = mtcars)\n",
       "     # just x supplied = histogram\n",
       "     qplot(mpg, data = mtcars)\n",
       "     # just y supplied = scatterplot, with x = seq_along(y)\n",
       "     qplot(y = mpg, data = mtcars)\n",
       "     \n",
       "     # Use different geoms\n",
       "     qplot(mpg, wt, data = mtcars, geom = \"path\")\n",
       "     qplot(factor(cyl), wt, data = mtcars, geom = c(\"boxplot\", \"jitter\"))\n",
       "     qplot(mpg, data = mtcars, geom = \"dotplot\")\n",
       "     "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "?qplot"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "You don't need to understand how exactly each parameter of the `qplot` function works, but you can always go to the help file to try to find what you need."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Our graph is plain as it stands right now. A plain graph is an excellent choice for academic papers, but for Internet content… simply being gray will not catch people’s attention.\n",
    "Let's give it some colour using the `colour` and the `fill` parameters.\n",
    "`colour` will modify the colour of the outline, while `fill` will change the colour of the bars."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    }
   },
   "outputs": [
    {
     "data": {},
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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2Pj+oaGbW3NZrPV1dXNzc09fzwOGTJk\nm0fp4a4BAOgjhB0AQBDCDgAgCGEHABCEsAMACELYAQAEIewAAIIQdgAAQQg7AIAghB0AQBDC\nDgAgCGEHABCEsAMACELYAQAEIewAAIIQdgAAQQg7AIAghB0AQBDCDgAgCGEHABCEsAMACELY\nAQAEkUt7AKDX1WYyDZlM2lP8gfJMpiWbbc72p98ti7PZ8iTZksk09auxc9lsaSazqY/NXJok\ng9vb054CAhJ2EN+15eUPlJSkPcUfKS5Oe4JPpawsKStLe4gdV12d9gR/4LiWloc2bkx7Cgio\nb/0OBwDApybsAACCEHYAAEEIOwCAIIQdAEAQwg4AIAhhBwAQhLADAAhC2AEABCHsAACCEHYA\nAEEIOwCAIIQdAEAQwg4AIAhhBwAQhLADAAhC2AEABCHsAACCEHYAAEEIOwCAIIQdAEAQwg4A\nIAhhBwAQhLADAAhC2AEABCHsAACCEHYAAEEIOwCAIIQdAEAQwg4AIAhhBwAQhLADAAhC2AEA\nBCHsAACCEHYAAEEIOwCAIIQdAEAQwg4AIAhhBwAQhLADAAhC2AEABCHsAACCEHYAAEEIOwCA\nIIQdAEAQwg4AIAhhBwAQhLADAAhC2AEABCHsAACCEHYAAEEIOwCAIIQdAEAQwg4AIAhhBwAQ\nhLADAAhC2AEABCHsAACCEHYAAEEIOwCAIIQdAEAQwg4AIAhhBwAQhLADAAhC2AEABCHsAACC\nEHYAAEEIOwCAIIQdAEAQwg4AIAhhBwAQhLADAAhC2AEABCHsAACCEHYAAEEIOwCAIIQdAEAQ\nwg4AIAhhBwAQhLADAAhC2AEABCHsAACCEHYAAEEIOwCAIIQdAEAQwg4AIAhhBwAQhLADAAhC\n2AEABCHsAACCEHYAAEEIOwCAIIQdAEAQwg4AIAhhBwAQhLADAAgil9aBH3/88Xnz5nW959pr\nrz300EPTmgcAoL9LLeySJKmoqLj22ms7bw4bNizFYQAA+rs0w66goGDkyJEpDgAAEEmaYbdp\n06aZM2e2trbuvffeX/nKV44++ugUhwEA6O9SC7t99tnnggsuGDFiRHNz83PPPXfDDTece+65\nJ598cueCF1988aqrruq8edNNN40fP74nR8xkMkmSVFZWdrOmrCcHgF6QzWYHDx7czYKOE7u6\nurqbNSWZzE4eC3qmsLBwe07s7tfknNj0MaWlpYNLSrpfU1RU1P2J/Yna2tq62Zpa2I0bN27c\nuHEdH48dO7ahoWHx4sVdwy6Xy1VUVHTeLCgoaG9v78kRs9lsJpPJ5/P5fH5ba/KZTOI7BX1M\n92d+x4nd/Zp8NuvEpk/J5/Pdn7QFBQXJJ538Tmz6mu05sT9xzfYcpZutaT4V29Xo0aOXL1/e\n2tqay/2/kY488sglS5Z0Lti4cWNtbW1PDlFeXl5aWlpfX9/a2rqtNY2lpUl5eU+OAjtXe3t7\n92d+ZWVlYWFhXV1dNw/1rQMGJJ/0SyTsSq2trbUbN3azoLq6Op/Pd3/yt1VVJbm+8lMMkiRp\namqqbWjY1tZsNltdXd3S0lJfX9/DAw0ZMmSbR+nhrneWlStXVlVV5TxEAQA+rdRC6vbbbx89\nevTQoUObm5uff/755cuXn3322WkNAwAQQGphV1RU9OCDD65fv76oqGj48OGXXXbZMccck9Yw\nAAABpBZ255133nnnnZfW0QEA4ukrr7EDAKCHhB0AQBDCDgAgCGEHABCEsAMACELYAQAEIewA\nAIIQdgAAQQg7AIAghB0AQBDCDgAgCGEHABCEsAMACELYAQAEIewAAIIQdgAAQQg7AIAghB0A\nQBDCDgAgCGEHABCEsAMACELYAQAEIewAAIIQdgAAQQg7AIAghB0AQBDCDgAgCGEHABCEsAMA\nCELYAQAEIewAAIIQdgAAQQg7AIAghB0AQBDCDgAgCGEHABCEsAMACELYAQAEIewAAIIQdgAA\nQQg7AIAghB0AQBDCDgAgCGEHABCEsAMACELYAQAEIewAAIIQdgAAQQg7AIAghB0AQBDCDgAg\nCGEHABCEsAMACELYAQAEIewAAIIQdgAAQQg7AIAghB0AQBDCDgAgCGEHABCEsAMACELYAQAE\nIewAAIIQdgAAQQg7AIAghB0AQBDCDgAgCGEHABCEsAMACELYAQAEIewAAIIQdgAAQQg7AIAg\nhB0AQBDCDgAgCGEHABCEsAMACELYAQAEIewAAIIQdgAAQQg7AIAghB0AQBDCDgAgCGEHABCE\nsAMACELYAQAEIewAAIIQdgAAQQg7AIAghB0AQBDCDgAgCGEHABCEsAMACELYAQAEIewAAIIQ\ndgAAQQg7AIAghB0AQBDCDgAgCGEHABCEsAMACELYAQAEIewAAIIQdgAAQQg7AIAghB0AQBDC\nDgAgCGEHABCEsAMACELYAQAEIewAAIIQdgAAQQg7AIAghB0AQBDCDgAgCGEHABCEsAMACELY\nAQAEIewAAIIQdgAAQeTSHmB7lZWVZbM9ytCOTx84cGA+n9/WmtJMpieHgJ0um80OGjSo+wVJ\nklRVVXWzprhnjx3Y6XK53Pac2N2vKXBi08eUlJQMKirqfk1hYWH3J/Ynam9v72Zrvwm7LVu2\ntLS09GQP5eXlpaWl9fX1ra2t21rTWFqalJf35Ciwc7W3t9fW1nazoLKysrCwsK6urpvfWLYO\nGJCUlPTCdPAptba21m7c2M2C6urqfD7f/cnfVlWV5PrNTzF2B01NTbUNDdvams1mq6urW1pa\n6uvre3igIUOGbPMoPdw1AAB9hLADAAhC2AEABCHsAACCEHYAAEEIOwCAIIQdAEAQwg4AIAhh\nBwAQhLADAAhC2AEABCHsAACCEHYAAEEIOwCAIIQdAEAQwg4AIAhhBwAQhLADAAhC2AEABCHs\nAACCEHYAAEEIOwCAIIQdAEAQwg4AIAhhBwAQhLADAAhC2AEABCHsAACCEHYAAEEIOwCAIIQd\nAEAQwg4AIAhhBwAQhLADAAhC2AEABCHsAACCEHYAAEEIOwCAIIQdAEAQwg4AIAhhBwAQhLAD\nAAhC2AEABCHsAACCEHYAAEEIOwCAIIQdAEAQwg4AIAhhBwAQhLADAAhC2AEABCHsAACCEHYA\nAEEIOwCAIIQdAEAQwg4AIAhhBwAQhLADAAhC2AEABCHsAACCEHYAAEEIOwCAIIQdAEAQwg4A\nIAhhBwAQhLADAAhC2AEABCHsAACCEHYAAEEIOwCAIIQdAEAQwg4AIAhhBwAQhLADAAhC2AEA\nBCHsAACCEHYAAEEIOwCAIIQdAEAQwg4AIAhhBwAQhLADAAhC2AEABCHsAACCEHYAAEEIOwCA\nIIQdAEAQwg4AIAhhBwAQhLADAAhC2AEABCHsAACCEHYAAEEIOwCAIIQdAEAQwg4AIAhhBwAQ\nhLADAAhC2AEABCHsAACCEHYAAEEIOwCAIIQdAEAQwg4AIAhhBwAQhLADAAhC2AEABCHsAACC\nEHYAAEEIOwCAIIQdAEAQwg4AIAhhBwAQhLADAAhC2AEABCHsAACCEHYAAEEIOwCAIIQdAEAQ\nwg4AIIhcisd+9dVXH3jggbVr11ZWVk6aNGnatGmZTCbFeQAA+rXUrti9+eab11133ZgxY265\n5ZYzzzyzpqZm4cKFaQ0DABBAalfsampqhg8fPmvWrCRJRowY8f777y9ZsmTq1KnFxcVpjQQA\n0K+ldsVu5cqV48eP77w5fvz4pqamNWvWpDUPAEB/l84Vu3w+X1dXN2jQoM57Oj7esGFD5z0v\nv/zyP//zP3fenD179tixY3ty0Gw2myTJwIED8/n8ttaUepEffUw2m+36SPmTC5Ikqaqq6mZN\ncdbbpOhbcrnc9pzY3a8pcGLTx5SUlAwqKup+TWFhYfcn9idqb2/vZmuab57ogyqSZJ+0Z4Cu\n9tz27yHbb7ATmz5mj52xk72c2PQxVTvjO3YPpRN2mUymqqqqtra2856Oj6urqzvvOfLII5cs\nWdJ5c+PGjV3Xfwrl5eWlpaX19fWtra3bWvOVJPlKT47RC0pKSjKZTGNjY9qD7ICCgoJBgwY1\nNTVt3rw57Vl2THV1ddfLxn1E9+d9ZWVlYWFhXV1dN5eiv5kk39zpY/XMgAEDmpubm5ub0x5k\nBxQXF1dUVDQ0NPSvx2MulysrK6uvr097kI/r/sSurq7O5/Pdf9u/d+cOtDNUVlZu3ry5ra0t\n7UF2QFlZWccZ0u8ej7lcrqGhIe1BPq6bUzabzVZXV7e0tPT88ThkyJBtHqWHu/7URo8e/dpr\nr3XefO2110pKSkaOHJnWPAAA/V1qYXfqqaeuW7du3rx577777jPPPPPII4+cfPLJ3hILAPCp\npfYau1GjRl155ZULFixYtmxZZWXlKaecMn369LSGAQAIIM03T0yYMGHChAkpDgAAEIn3igMA\nBCHsAACCEHYAAEEIOwCAIIQdAEAQwg4AIAhhBwAQhLADAAhC2AEABCHsAACCEHYAAEEIOwCA\nIIQdAEAQwg4AIAhhBwAQhLADAAhC2AEABCHsAACCEHYAAEEIOwCAIIQdAEAQwg4AIAhhBwAQ\nhLADAAhC2AEABCHsAACCEHYAAEEIOwCAIIQdAEAQmXw+n/YMu8jTTz/96quvnnXWWcOGDUt7\nluDWr18/b968sWPH/vVf/3Xas8S3cOHCd99991vf+lYul0t7luBWrlz5yCOP/NVf/dURRxyR\n9izxzZ07t6io6MILL0x7kPief/75F1988Ywzzhg5cmTaswTX0NBw6623HnjggVOnTu29o+xG\nV+xef/31mpqa2tratAeJb9OmTTU1Na+99lrag+wWXnjhhZqamra2trQHiW/dunU1NTVvvfVW\n2oPsFh5//PGnn3467Sl2C2+88UZNTc3//d//pT1IfFu3bq2pqXn55Zd79Si7UdgBAMQm7AAA\nghB2AABB7EZvngAAiM0VOwCAIIQdAEAQwg4AIIjd4i+avvDCC0uXLl23bt3WrVsHDx58zDHH\nnHHGGYWFhWnPFdkbb7xx+eWX5/P5Rx99NO1Zwnr88cfnzZvX9Z5rr7320EMPTWue2LZs2bJw\n4cKXXnqprq6uurr6+OOPP/3009MeKqB//Md/XL16ddd7MpnMokWLSktL0xopsHw+/9Of/vQX\nv/jFRx99VF5ePm7cuJkzZ37mM59Je66AWlpaFi9e/Oyzz3744YdDhgyZPHly7/0B/90i7AoK\nCiZNmjRs2LCioqLVq1f/27/9W319/de//vW05wqrvr7+pptuOuyww/yN4t5WUVFx7bXXdt70\nf1XpJc3NzVdccUVbW9vMmTOHDRu2adOmxsbGtIeK6dJLL926dWvnzRtuuGH48OGqrpfU1NT8\n5Cc/ufDCCz/3uc999NFHd9111/e///25c+emPVdA99577wsvvHDBBRd89rOffeutt+68885M\nJjN58uTeONZuEXZf+MIXOj8eNWrUu++++/rrr6c4T2z5fH7OnDmTJk0qKSkRdr2toKDA/wVo\nF1i6dOmHH3541113VVRUpD1LcMOHD+/8ePXq1e+///55552X4jyx/e53vxszZsykSZOSJBk6\ndOhJJ5101113tbS0eEZr58rn8//xH/9x2mmnHXPMMUmSDBs2bO3atQ899NCXv/zlbHbnvyJu\n93qNXXt7+5o1a1asWOHpqt6zaNGi1tbWM844I+1BdgubNm2aOXPm9OnTv/Wtby1fvjztccL6\nz//8z3Hjxi1YsODv/u7vZs2adfvtt2/atCntoeJ74okn9txzz8MPPzztQcIaO3bs6tWr33jj\njSRJamtrX3zxxfHjx6u6na69vb21tbW4uLjznpKSkrq6unXr1vXG4XaLK3ZJkrS0tEydOjWf\nz+fz+eOPP/5rX/ta2hPF9Otf//qpp56aO3duJpNJe5b49tlnnwsuuGDEiBHNzc3PPffcDTfc\ncO6555588slpzxXQ+++//z//8z9HHXXUd77znfr6+nvuuWf27Nk33XST87z3bN68+fnnn582\nbZovcu+ZMmVKa2vr5ZdfniRJW1vb+PHj/+mf/intoQIqKCg47LDDHn/88cMOO2zfffd95513\nHn/88SRJ1q9fv88+++z0w+0uYZfL5W699daWlpa33nprwYIFAwcOnDlzZtpDRX0bs9MAAAti\nSURBVFNbWztnzpxLLrlk0KBBac+yWxg3bty4ceM6Ph47dmxDQ8PixYuFXW9ob28vLy//h3/4\nh1wulyRJUVHRlVde+bvf/e5zn/tc2qOF9fOf/zyfz3c8S0gvWb58eU1NzaxZs0aPHv3RRx/d\nf//9N95443e/+10xvdNdfPHFd95558UXX5zJZCoqKo477rhHH320N56HTXafsMtkMiNGjEiS\n5IADDshms3fcccepp546YMCAtOcK5Z133qmrq7vmmms6bnZcH50yZcrpp58+ffr0dGfbHYwe\nPXr58uWtra0d8cFOVF1dPXDgwM4v7L777pskyQcffCDsekk+n3/yySePPvroysrKtGeJ7L77\n7ps4ceIJJ5yQJMmIESMGDBhw2WWXvfnmmwcffHDao0VTVVV1+eWXt7a2drytftmyZUmSDB06\ntDeOtTv+AGhtbc3n862trWkPEs2YMWNuu+22zpu/+MUvli5deuutt1ZVVaU41e5j5cqVVVVV\nqq43HHLIIa+88kpbW1tBQUGSJO+9916SJHvuuWfac4X1q1/96v3337/kkkvSHiS4rVu3dr1o\n1HGhrq2tLb2JgsvlckOGDGlvb3/iiScOOOCAXvrLMrvFz4C77777oIMO2nPPPdvb21etWrVo\n0aI/+7M/Uxs7XUlJScdl0Q4dT8h2vYed6/bbbx89evTQoUObm5uff/755cuXn3322WkPFdOU\nKVOeeeaZ22677ZRTTqmvr7/rrrsOOuig0aNHpz1XWE888cR+++3nK9zbjjrqqKeeemq//fY7\n+OCD169f/8Mf/nDPPfc84IAD0p4roNdff33t2rX7779/XV3dY4899vvf//7666/vpWPtFmFX\nUlLy8MMPf/DBB9lsdo899pg6dWrv/WFA2GWKiooefPDB9evXFxUVDR8+/LLLLut4Lz073fDh\nw6+77rr58+dfeumlAwYMGD9+/FlnneV1SL3kww8/fPXVV2fNmpX2IPGdd955AwcOXLRo0YYN\nG8rLy8eMGTNz5syub95kZ8lms08++eT//u//FhYWjhkz5oYbbui9v1SVyefzvbRrAAB2pd3r\n79gBAAQm7AAAghB2AABBCDsAgCCEHQBAEMIOACAIYQfQ6yZPnrxixYqdvtuf//znmUzm/vvv\n3+l7BvopYQeEsmrVqquvvvr1119PexCAFAg7IJRVq1bNnj1b2AG7J2EHsMO2bNmyPct+/etf\nT5kyZdiwYU888cQRRxyx9957n3DCCb3xnCxAB2EH9FE//elPM5nMo48+escdd4waNaqkpGTM\nmDGLFy9OkmT16tVTpkwZNGjQwIEDp0+fXldX1/EpV199dcf/CXrGjBmZTCaTyfzlX/5lx6bW\n1ta5c+cefvjh5eXlFRUV48aN+973vtexaePGjd/5zneOOOKIIUOGFBcXjxw58pvf/ObmzZs/\nNslDDz00e/bsAw88sKio6JprrunY54033jh27NiKioqKiooDDzzwrLPO2rRpU8dnrVu3buLE\nib/97W/nzJnzF3/xF3fdddcNN9wwePDg999/v+s/c1uDPfPMM5lM5tprr/3Yl2XGjBm5XG7t\n2rU7+csNhJBLewCA7tx0002///3vZ8yYUVxcfOedd55++ukPP/zwhRdeePzxx3/ve9/75S9/\n+eMf/ziTySxcuDBJkrPOOqu4uPiKK6644oorvvjFLyZJUlVVlSRJa2vr5MmTly1bduyxx151\n1VUDBw584403Hn744dmzZydJ8t577919992nnXbatGnTioqKnn/++VtuueWVV1557rnnMplM\n5yTf/va3hw8ffv311++1116FhYVJklx++eU333zz9OnTL7744mw2++677z722GP19fUVFRVJ\nkjz11FMbNmx48MEHJ02atHDhwsMOO+zzn//8V7/61a7/um4GO+6440aNGnXfffddeeWV2ez/\n+yW8rq5u8eLFJ5544t57772L/gMA/UseoE96+OGHkyQZMWLExo0bO+75zW9+kyRJJpO58847\nO5d95StfyWazH374YcfNf//3f0+S5IEHHui6qx/84AdJknzjG99ob2/vvLOtra3jg6ampubm\n5q7rv//97ydJ8rOf/azrJAcddFBLS0vXZfvvv/9xxx23rfnvu+++zklOOumkX/3qV3+8pvvB\n5syZkyTJsmXLOjfddtttSZIsXbq04+bPfvazJEnmz5+/rRmA3Y2nYoE+7YILLhg4cGDHx4cc\ncshnPvOZ8vLyr33ta50LJk6c2N7evnr16m52smDBgtLS0uuvv77rFbjOy2DFxcUdV+CSJGlp\naWlqajrllFOSJHn55Ze77uTss8/O5f7gWY6qqqqVK1f+8pe//JMHnTJlyr777nvuuefOmDHj\n7bffXrly5datW3dosLPOOqukpOSee+7p3HTPPffsvffeX/7yl7v5xwK7M2EH9Gmf/exnu96s\nrq4eMWJEZ/p03JMkyfr167vZyapVqw444IABAwZsa8H999//hS98oby8vKioqLS0dMyYMUmS\nbNiwoeua/fff/2OfdfPNN7e0tPz5n//5iBEjvvrVr86fP7/rmyqqq6v/67/+66KLLnr11VdX\nrVo1ffr0wYMHn3/++Rs3btzOwaqrq0877bSlS5d++OGHSZK88sorr7/++jnnnFNQUNDNPxbY\nnQk7oE/72EWyP3lPkiT5fL6bneTz+a6XxD7mlltuOfvss4cMGXLvvfc+++yzL7300mOPPZYk\nSXt7e9dlxcXFH/vEiRMnvvPOOw899NBJJ520YsWKc8455+CDD163bl3ngr322uvmm29euXLl\niSeeePfdd5933nl33333tGnTtnOwJEnOP//85ubmH/3oR0mS3HPPPdls9u///u+7WQ/s5oQd\nEMqf7KRRo0a99dZbXd/o2tV99923//77L1myZNq0accee+yRRx7Z+eTvJ6qoqJg6deodd9zx\n3//93z/5yU/ee++9f/3Xf/2TKydMmPCDH/zgzDPPfPLJJzvfxtv9YEmSHH300Ycccsi99967\nadOmRYsWfelLX9p33323czZgNyTsgFA63pH6sWdRzzzzzMbGxu9+97td7+y8yJfNZvP5fFtb\nW8fNtra266+/fnuO9bGjHHnkkV3v7Hj+9GMaGhq6pmf3g3WYNWvWG2+8cdFFF23evLnriwsB\n/pg/dwKEcuihh5aUlNx2221FRUVVVVV77LHHxIkTv/71rz/22GNz585dsWLFiSeeOHDgwLfe\nemvZsmW//e1vkyQ57bTTrr766hNPPPH000/vuDDW/RO7nYYNGzZ58uTDDz98+PDhH3zwwb33\n3ltQUDBjxoyOrfPmzVu8ePG0adMOPfTQ2trap59+es6cOTU1NX/zN3/T8UdYkiTpfrAOM2bM\n+Pa3v/2jH/1o6NChkydP3tlfMCAUYQeEUllZ+eMf/3j27NmXXHLJ1q1bjz322IkTJxYWFj75\n5JNz58594IEHvve97xUWFu6///5Tp07t+JQrr7wyl8vNnz//oosu2nPPPU877bSLL774j98q\n8ccuvfTSZ5999pZbbtm4ceMee+wxYcKE+fPnH3XUUR1bzzjjjIaGhkWLFt14443r169fsWLF\niBEjrrnmmksvvbRzD90P1vkv+tu//dv58+efc845f/L1hQCdMtv5iykAn9oJJ5zwL//yL5//\n/Oc/3aeff/7599xzz9tvv73ffvvt1LmAaLzGDqDXdf37LDuqtrZ2wYIFX/rSl1Qd8Ilc1Qfo\ndWeeeeZee+21o5+1YsWK3/zmNz/84Q+3bNlyxRVX9MZgQDCeigXoo775zW/OmTNn7733vuKK\nKy644IK0xwH6AWEHABCE19gBAAQh7AAAghB2AABBCDsAgCCEHQBAEMIOACAIYQcAEMT/D19A\n1MP8kF1qAAAAAElFTkSuQmCC",
      "text/plain": [
       "plot without title"
      ]
     },
     "metadata": {},
     "output_type": "display_data",
     "source": "R display func"
    }
   ],
   "source": [
    "qplot(mtcars$cyl, geom = \"bar\", fill = I(\"cyan1\"), colour = I(\"cyan1\"))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can just change the fill, as the outline, if no parameter is passed, will receive the same value as the fill one.\n",
    "You can change the colors to blue, pink, green, yellow...\n",
    "And there's more colours!\n",
    "Here's a list: http://sape.inf.usi.ch/quick-reference/ggplot2/colour"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can also change the name of our axes to make it more easily understandable by passing the `xlab` and `ylab` parameters (`lab` stands for \"label\"):"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    }
   },
   "outputs": [
    {
     "data": {},
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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JBsTQaSUkCyNRlISgHJ1mQgKQUkW5OBpBSQbE0GklJAsjUZSEoBydZkICkFJFuT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\nAnQi0VVQDWF4AAAAAElFTkSuQmCC",
      "text/plain": [
       "plot without title"
      ]
     },
     "metadata": {},
     "output_type": "display_data",
     "source": "R display func"
    }
   ],
   "source": [
    "qplot(mtcars$cyl, geom = \"bar\", fill = I(\"pink\"), xlab = \"Cylinders\", ylab = \"Number of Vehicles\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "To finish our bar plot, we can give a name for our graph. We can do this using the `main` parameter."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    }
   },
   "outputs": [
    {
     "data": {},
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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oAkCkg2IAUBSRSQbEAKApIoINmAFAQk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\ngdpIQG6aAAAAAElFTkSuQmCC",
      "text/plain": [
       "plot without title"
      ]
     },
     "metadata": {},
     "output_type": "display_data",
     "source": "R display func"
    }
   ],
   "source": [
    "qplot(mtcars$cyl, geom = \"bar\", fill = I(\"blue\"), xlab = \"Cylinders\", ylab = \"Number of Vehicles\", main = \"Header - Cylinders in mtcars\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<hr>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a id=\"ref5\"></a>\n",
    "<h2 align=center>Histograms</h2>\n",
    "<br>\n",
    "Histograms can be defined as a graphical visualization of data counts.\n",
    "It is a particular type of a bar plot.\n",
    "A histogram shows the distribution of a quantitative variable. Normally, bar plots are used to compare variables.\n",
    "Usually, there's no space between the columns of a histogram."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's make a simple histogram, selecting the horsepower column from `mtcars`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    }
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "`stat_bin()` using `bins = 30`. Pick better value with `binwidth`.\n"
     ]
    },
    {
     "data": {},
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "plot without title"
      ]
     },
     "metadata": {},
     "output_type": "display_data",
     "source": "R display func"
    }
   ],
   "source": [
    "qplot(mtcars$hp, geom=\"histogram\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "As you executed the code, you may receive the following error message:\n",
    "`stat_bin()` using `bins = 30`. We can pick better value with `binwidth`.\n",
    "Binwidth defines the width of your bar.\n",
    "Let's further improve our histogram, changing our binwidth."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    }
   },
   "outputs": [
    {
     "data": {},
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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AGBBBJIAYEEEkgBgQQSSAGBBBJIAYEE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\nJAUEkhQQSFJA/w/+VG3cVlZhhwAAAABJRU5ErkJggg==",
      "text/plain": [
       "plot without title"
      ]
     },
     "metadata": {},
     "output_type": "display_data",
     "source": "R display func"
    }
   ],
   "source": [
    "qplot(mtcars$hp, geom=\"histogram\", binwidth = 25)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Ok, now each bar has a width of 25 pixels. However, we can see that the visualization of data between 210 and 260 hp are not well illustrated. Again, let's add a black outline to the histogram to make it better to visualize."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    }
   },
   "outputs": [
    {
     "data": {},
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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6+/Dz6QGrYgIF117qpzLryri9u7+DX2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\nQiBJFfr/efUe2K+l0CoAAAAASUVORK5CYII=",
      "text/plain": [
       "plot without title"
      ]
     },
     "metadata": {},
     "output_type": "display_data",
     "source": "R display func"
    }
   ],
   "source": [
    "qplot(mtcars$hp, geom=\"histogram\", binwidth = 25, colour = I(\"black\"))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can also define limits for our x axis. R will fit automatically the best values according to our data, so the result will not change when you use qplot and an adequate limit."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    }
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Warning message:\n",
      "“Removed 2 rows containing missing values (geom_bar).”"
     ]
    },
    {
     "data": {},
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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gwkkDIDKTOQQMoMpEYgdR5IiYEEUmYgZQYSSJmB1AikzgMpMZBAygykzEACKTOQ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",
      "text/plain": [
       "plot without title"
      ]
     },
     "metadata": {},
     "output_type": "display_data",
     "source": "R display func"
    }
   ],
   "source": [
    "qplot(mtcars$hp, geom=\"histogram\", binwidth = 25, colour = I(\"black\"),xlim=c(50,350))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "If it happens to \"cut\" any value, R will display an error message saying how many values are not being shown.\n",
    "Let's play with xlim values, to see this message. You can put any value that you wish here."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    }
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Warning message:\n",
      "“Removed 12 rows containing non-finite values (stat_bin).”Warning message:\n",
      "“Removed 2 rows containing missing values (geom_bar).”"
     ]
    },
    {
     "data": {},
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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zz6uPL6Y88tr9/CR7vH9+iyc67dsWPHX239eTq+R8d3ZDLnaCrX0vH3ar2X0YYg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\nAAAAAElFTkSuQmCC",
      "text/plain": [
       "plot without title"
      ]
     },
     "metadata": {},
     "output_type": "display_data",
     "source": "R display func"
    }
   ],
   "source": [
    "qplot(mtcars$hp, geom=\"histogram\", binwidth = 25, colour = I(\"black\"),xlim=c(80,200))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now let's give a name for our axes. We use the `xlab` and `ylab` parameters to modify them."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    },
    "scrolled": true
   },
   "outputs": [
    {
     "data": {},
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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tSoG264YerUqRdddNHw4cNTqf/zVX/5y1/yMxsAAJ2QU9jdc88906ZNy2QypaWl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",
      "text/plain": [
       "plot without title"
      ]
     },
     "metadata": {},
     "output_type": "display_data",
     "source": "R display func"
    }
   ],
   "source": [
    "qplot(mtcars$hp, geom=\"histogram\", binwidth = 25, colour = I(\"cyan\"), xlab = \"Horsepower\", ylab= \"Number of Cars\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can remove the color that fill our bars using the parameter alpha."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    }
   },
   "outputs": [
    {
     "data": {},
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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AHJsfggPTrm/XMfWaiAVGxAciw+SKEz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\n6P8DTu0KAzeTj/kAAAAASUVORK5CYII=",
      "text/plain": [
       "plot without title"
      ]
     },
     "metadata": {},
     "output_type": "display_data",
     "source": "R display func"
    }
   ],
   "source": [
    "qplot(mtcars$hp, geom=\"histogram\", binwidth = 25, colour = I(\"black\"), xlab = \"Horsepower\", ylab= \"Number of Cars\", alpha = I(0))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "As we did with the bar plot, we can give a name for our graph. We can do this using the `main` parameter."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    }
   },
   "outputs": [
    {
     "data": {},
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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LFdA9LGYYuC2wWFxdbOHDdi1MRltYNA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\nRBEEJKIIAhJRBAGJKIKARBRB/wtdWl3JZ2yIBAAAAABJRU5ErkJggg==",
      "text/plain": [
       "plot without title"
      ]
     },
     "metadata": {},
     "output_type": "display_data",
     "source": "R display func"
    }
   ],
   "source": [
    "qplot(mtcars$hp, geom=\"histogram\", binwidth = 25, colour = I(\"black\"), xlab = \"Horsepower\", ylab= \"Number of Cars\", alpha = I(0),\n",
    "     main = \"My first Histogram\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<div class=\"alert alert-success alertsuccess\" style=\"margin-top: 20px\">\n",
    "<font size = 3><strong>Expert Tip: Sharpening your qplot skills</strong></font>\n",
    "<br>\n",
    "<br>\n",
    "Do you want to check all the qplot function parameters? Click <a href = \"http://docs.ggplot2.org/0.9.3/qplot.html\">here</a> to access the function documentation !\n",
    "<p></p>\n",
    "</div>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<hr>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a id=\"ref6\"></a>\n",
    "<h2 align=center>Pie charts</h2>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "A pie chart is a circular graph, most commonly used in business. It shows the proportion that each part of data contributes to an overall total. This is usually done as a representation of the counts of qualitative data, like demographics. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<div class=\"alert alert-success alertsuccess\" style=\"margin-top: 20px\">\n",
    "<font size = 3><strong>Expert Tip: Choosing the right chart type</strong></font>\n",
    "<br>\n",
    "<br>\n",
    "Unsure which type of chart to use for your data? Click <a href = \"http://lifehacker.com/5909501/how-to-choose-the-best-chart-for-your-data\">here</a> to learn more !\n",
    "<p></p>\n",
    "</div>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "A pie chart in ggplot2 is a transformed stacked bar plot. A stacked bar plot is a plot that stacks all the values on the vertical axis, instead of creating separate bars for each different data point.\n",
    "Let's start by creating a stacked bar plot."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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d/Yvn379u3bJ2+3a9du8+bNM2fOPOuss1J50eXLl0+ZMqXkbkFBQYsWLVL5RgBI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\ngg==",
      "text/plain": [
       "plot without title"
      ]
     },
     "metadata": {},
     "output_type": "display_data",
     "source": "R display func"
    }
   ],
   "source": [
    "barp <- ggplot(mtcars, aes(x=1, y=sort(mtcars$carb), fill=sort(mtcars$carb))) +\n",
    "        geom_bar(stat=\"identity\")\n",
    "print(barp)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now we need to transform this graph into a pie plot. We are going to plot our stacked bar graph in the polar coordinates system."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "A really nice refresher abour polar coordinates can be seen in the following gif:"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<img src=\"https://ibm.box.com/shared/static/me1bmbb54gneyr5epweqe88zg8ujkqst.gif\"</img>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Source: <a href=\"https://commons.wikimedia.org/wiki/File:Cartesian_to_polar.gif\">Wikimedia Commons</a>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We use the `coord_polar` helper function to do so:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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IaGhoJcqCPSobaAxzux1p34i+vo6Aj+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P9n/lDRs2zJ49W84MO+GEE+SDW7du1XX9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\nQBGEHQAAgCIIOwAAAEX8f8ZIZ+CYB148AAAAAElFTkSuQmCC",
      "text/plain": [
       "plot without title"
      ]
     },
     "metadata": {},
     "output_type": "display_data",
     "source": "R display func"
    }
   ],
   "source": [
    "barp <- barp + coord_polar(theta='y')\n",
    "\n",
    "print(barp)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can use the `theme` helper function so that we don't warp the labels and axes with our transformation:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    }
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Coordinate system already present. Adding new coordinate system, which will replace the existing one.\n"
     ]
    },
    {
     "data": {
      "image/png": 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9vdbjfv4ayASAf1qR/v5EF3jUbD87yZ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\nDaO6WztKO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22GOnpqbuu+++b3zjGz/7\n2c+EECeeeOJ73vOeY4899pWvfOXCwsINN9ww4kKs5b9EH//4x2+55ZaTTjrp6U9/+tatW2+7\n7bYrrrjiS1/60t/93d/Juk4IkdLApCc+8YmHHHLImWee2Wg0brjhhu9///tvfetb5TZYaTAY\n3HbbbS972ctqtdroD7skpYOdPOhkx60rE8Q8bL70PQylmNmOVActyHNPTWrszjvvvNtvv/2D\nH/zg3Nzcrrvuesghh1x33XXPec5z5O/W6/Xvfe97F1988Ze+9KUbb7xxZmbmhBNOuPjii5c8\nsmTY9PT05z//+YsuuujNb35zv99//vOfL4PdWWeddfnll3/2s59929vetpZhL/n4rut+7Wtf\nu/LKKz/zmc+8+93vdl13n332kfOnQogLL7ywVCpdd911b3zjG3fbbbcTTzzxTW960yjbPpb/\nEr361a+WR7Fceumljz766KZNm/baa6+LL774vPPOSx4hpYFJ73znOx944IGrr77697///ROe\n8IQrrrjiLW95y/Af+OpXv7ply5azzz57xAdcxhgn/WzevHntzzeWWq1Wq9UWFhbSy3YEO0Xo\nmO1UCHbfvuC4LJ+OYIfJSu+k4maz6Xne7Oxs9icqbNy4MeNnTMNrX/vab37zm/fdd595d+++\n5CUvef/73/+MZzwj74E8xhFHHCGE+O53v7v2h1J6jR0L7IpDx1V3RTv6hFQHjZi3zC5jl1xy\nyezs7DXXXJP3QCZv+HwWRdx222133HHHjveMrY66U7HJTWLpHXRCXacafVfdAVCKkafZZWm3\n3XYbPtzYJKeeeuruu++e9yge45hjjpngDm7lcmuCuq6Y9KruilPaUddBL1EUyUNP1n7cKwxz\n8sknqxbsJkvdYCffaRHsiolsBxQBV1AAE6d0sOMmsSLTq7ozG3UddCR7AYIdikbRYGfbtuM4\nqaY6FthpQYtsR2kHKEi+ghDsUDSKBjt5/Rx1HYQm1Z3B2Y66DqlKbzZW3hPgOE5Kjw+oSelg\nl+oVsdCL+tkOgGp835cHLOQ9ECA7Sgc7GjsMU7y6M7K0o66D1uSLCMEOhaJusIuiaILHuizC\nAjt9qZztACiFYLcWURRdcsklf/VXf1WtVvfcc89TTjnlt7/9bd6DwspU/OvuOI5t2xx0gp1R\n9oZZwy6Qpa5DNg4+4ZyU7haTR9zrvn/i5l8Fd/0hnPjDvuWvy49vLnfI32WXXXbRRRddffXV\nz3ve8373u9+98Y1vfPnLX/6Tn/xk4iPBZKkY7JiHxSjUvKbCsGwH6C4Mw1KpZFljXIyumq29\n+PcLkx/8IIyFWC7Y3XHHHYcffvjpp58uhNhvv/3OPvvss88+u9/vm3d7rGFUnIpl5wRGpPiq\nO61R18EM8qWE2dhVOPLII3/0ox/dddddQoiHH374pptueslLXkKqU5+Kf9fTbuxYYGcY1ao7\nA0o7Uh2MkSyz07cs2HvaPvmpj6nW/u8D4Vx/vA7v/93NfvKGx1Q5pZWKnfPOO28wGBxxxBFC\niCAIXvziF998881jPSlyoWiwC8NQ39oc2VNt1Z0B2Q7IUqrL7ITmjd3vFsLv/e4xa+y6wdiv\nkHf/Ofrlo49JhwduXKF7u/nmmy+77LKPfOQjhx122O9///u3v/3tr3zlK2+99Vau31Wccn/X\nHcexLIsFdlgF1ao7TVHXwSSyJtA62AVh3B6s9YyIQSAGYrwweO655/793//9P/7jPwohDjro\noHXr1j3nOc+56667DjvssDUOBqlSbo2d3L5EsMPqqLPqzshj7QAdBUEgD1vIeyCrFMciTsGK\nMa/T6Qx/0eQ/h+Hk9+dispR7E8MCO6wd1d2qUdfBPEEQuK5bKpV0PUUrjuNUTnVdIdmdcMIJ\nH//4x5/2tKfJqdjzzz9/n332eeYzn5nCSDBJhQt2KAgVVt2x0g5Qge/71WpV42AnYpHHovOr\nrrpql112ee973/vQQw+tW7fu8MMPv+SSS2q1WvYjwVjUqqblpX7ySMm8xwIT5D4tq9eELHUd\ncnTwCeek9Mi6759IYx52lBfZWq12ySWX3H///d1u9w9/+MNNN9203377ZfD5Yo3U+ovuOI6g\nrsNEqVDdAciRvKBS32An4ljEKUzFUqAYSq3GjnlYpCTH6k6X0o66DgYLgsC2bZ33T+TQ2EFT\nav0tl41deptu2DlRZDlumNUl2wGmki8r8iVGP3GUyi8YqljBDsh91Z2aqOtgNq2DXZzSMru8\nPy+kRLlgF8dxlMq+bmCbXLIdpR2wovT2T2gd7LatsZt8Y0e0M5Nai0lt26auQwbYUTGMug7G\n0zrYpdWvkesMpVBjJy8TI9ghMxlXd5R2QF6iKIrjWNNgJ08onvgvkp2pFAp2XFeC7GW8o0LB\nbEddh4IIw1DXXbFxnMovGEqhv+XyvVR6C+zYEoudYUcFYLwoiizL0jHbxWk1djCTQn/F2RKL\nHGVW3SlV2lHXoTg0XmZHY4dxEOyAvyhUtiPVQUFsjF1CSrtiyXaGUmhXLGedQAVsmAWMpHGw\nEyKNXbHEOlOp1dhR10ERaVd3uZd21HUoGo2DHTdPYByqNHbMw0I1VHeASfQ98SROp7GjszOV\nKo0dt8RCTelVdzmWdtR1KCZdTzxh8wTGoUpjxyF2UFZ61d3me36w8SmHTvxhASwpDMNSqWTb\ntl6LueM4jtMYMNHOUKq8d0n7EDtgjYw56466DopLb2OsfInRbzaWxg7jUCvY0dhBZWmcdZf7\nLgqgOPTdPxGngMrOVAoFO846gRa0znbUdSgyXYNdSufYwVAKrbEj1UEXbJgFdCRfZbTbP7G9\nYJv4407+IaECVf5+W5ZFsINeJljdZVPaUdeh4DQNdkKk1NiR7MykRGMnv83SOacHSBHVHaAR\n+SpjWVbeAxlTOo0dr7imUuKNiwx26TV2HGKHVE2kuku7tKOuA4QQURRp2NilszEWhlLi77d8\n/8RULPQ1kQ2z7JAF0hZFkXaNXRxHafzK+9NCWpQIdkzFwgzKnnVHXQe9pHeUXRzHlmXple3i\nOJ3jTnjNNZQSwY7GDsZYY3WXRmlHqgMS8oVGr2CX2uYJmEmJYEdjB8Oolu0ASPKFRrNldmn0\ndbzgmkuhXbE0djCJIhtmqeuAYXqeeMK0KcagxF9upmJhqtVVd5R2QEp0nIqN4zidzRMrh8W5\nubk3v/nNT3jCEzzP23vvvd/3vvdl8PlijRRq7GiGYaQcqzvqOmARPadiRSqN3UoP2ev1XvCC\nF/i+//73v3+//fbbsmXLwsLC5IeBSVMi2FmWxZQ/zLb5Vz8cK9ttvucHG59yaHrjAYpJx8ZO\niFROJ1nxFffKK6/87W9/e++9965fv37iz470KPGuxbZtUh2MN+6G2TVOyFLXATvSuLGb/AHF\nK7zs3nzzzUcdddSFF164xx577L///q9//esfffTRbD5jrIUSjZ1t20EQ5D0KIAvjVncAJkjH\nzRN7bay9+tDHD3/k//7PI/Ndf6wHOXivmSfv0Rz+SMleobZ84IEH7r777hNOOOErX/nK5s2b\nzznnnL/5m7+566679PrqFVD+wU5W4uk1dtwnBtWMnu1WPSFLXQcsScep2Edmu3fcu3n4I+3u\nII7Ge9G8/5GFh7d2hz/y/AN2Wf4/CcNwZmbm05/+dLlcFkJUKpWjjjrq+9///vOe97yxnhoZ\nyz/YcdYJCmj0HRUstgMmK45jvTqn7iB4eGtnjQ8y3xnMdwbDH1mxTtlzzz03btwoU50Q4sAD\nDxRCPPjggwQ7xeX/l5uzTlBYKV1BRl0HLEO/62JTulJspTV2RxxxxAMPPOD72+Z8f/GLXwgh\n9tlnn9Q/X6yNKsGOzRMoplF2VHCsHTBBcRxrFuxyulLsvPPOm5ubO+OMM+6+++7bb7/9rLPO\nOvTQQw877LAMPmGshSrBDiiyCVZ31HXAivR63UmjrhulTDnggAO+9a1vPfDAA8961rNOOeWU\nZz/72bfeeqtes9jFlP8aOwBipVV3rLRD0Rx8wjk/+ber8h6FGuJ4lIJtNQ+7ksMOO+yOO+6Y\n/FMjTapEb6ZiAbFsdTfKhCx1HbAiDV9ucqrsoKf8Gzu9KnEgbTleQQYUh7zxKO9RjCalK8Vg\nKFUaOwDDlqzuli/tqOsAQ6WwcyKNuV2oQZVgp807JyArS26Y3Vm2I9UBI9Lu5SZO6bgT3b4O\nGFH+wY6pWGAZKZ11BxScTi89cT7HnUBT+Qc7AMtbVN3tWNpR1wEGS6ewo64zlirBjr9kwPKo\n7oCJ0O/lRk6bTvzXSjdPQFP5Bzud+nAgV0l1N1zaUdcBq6DVS08cx9HEf+X9SSEt+Qc7AGPZ\nMdsBMFmcUmmX9+eFdOR/jp2kXzcO5GfbWXdPOZS6DhiXdi83qRVsmn0dMKL8g51WfTigkF9+\n+SPV9bt3tzyS90AA/Wj20qNbGEWO8g92AFatumGP5J9JeICZ0tnESlQ0lSrBTrtuHFBNdf3u\n8h9IeMAytHu5iUXMXgeMLv9gp1kfDiimumGP7qMPP+YjJDxgJTq99KR0S4RuARcjyj/YAVij\nHbPdto+T8AADxIQwjCH/YKddKw4oaGfZbtvvkvCA7WRXp9FLj7x5Iu9RQBuqBDvb5kQ9IHUk\nPEDSKirFQrDGDqNSJdgBWKPlS7vFf5iEh6LSrrFLaVcs+2JNpUqw02kdK6CqsbLdtv+EhIeC\n0S/YCdbYYQwEO8Aoq8h22/5DEh6KQb9gl9au2Mk/JFRAsAPwGCQ8mM2yLJ1S3baZWNbYYVQE\nO8A0qy7tFj8OCQ/5+cm/XZXSI2sX7EQ6u2JjKjtDEewAA00q2217NBIeDKJfsItjQWOHkRHs\nAIwqSXiCkAdtWZYVRTrlpFivFYHIG8EOMNNkS7slHp8aD3rSs7Fj8wRGRbADjJV2ttv2LCQ8\n6EPH15rUNk+Q7MyUf7CTdPxmA9SXTbbb9lwkPChPvtboNRUrLxXLewzQhhIXecVxnF6w+/al\nr0npkQEsqbp+d/kr74EAi2lZIsjLYict788KaTE/2AEFV92wR25PTcKDYvQ7nVgIISIRp/Br\n5KnYO++803XdUkmVKT4sj2AHmC/HbLdtACQ8qEHHYBen1NiN9jXYvHnzSSed9OIXvzjlzxIT\nQ7ADCiH3bCeR8JAvHYPdtl2xE/81giiKTjnllNNPP/3II49M+ZPExKgS7ISmSx8ArAoJD7nQ\nMtiJdLLdCN773vcOBoN3vetdaX9+mCAlpszlBiX9zhYCtJLlDtnRsZcWWdJxV+wTd53+uyMO\nHP7IN374q4VOf6wHefq+e+7/+I3DH3GcFcqUb33rW1dfffWPf/xj21aiA8KIFAp2juPo9c0G\naEfNbCeR8JABx3GEbsGu0xv8/k+zwx8JgnDcA1BmW51FDxIt+wiPPPLIqaee+qlPfWqPPZRY\nxYHRKRTseE8AQJDwkCb5QqNXsPvzbOuunz+4xgd58OEtDz68ZfgjbwqPWObPb9q06Y9//ONL\nX/pS+a9xHEdRVCqVLrzwwosuumiNg0GqCHZAsahc2i1Cwiusn/zbVSk9so7BTsSxSOXmieUc\nfvjhd999d/Kv119//ZVXXrlp06Zdd90145FgXEoEuzAMBcEOyIpG2U4a3mNByMNa2Lat3fG8\n8rSTVB545xqNxoEH/mVh3+677y6EGP4IlKVEsKOxAzKmXbZLUONhLWzbllWCTmLBlWIYnRJZ\nKtk8kdLjc6sYYB4OTMG4bNu2LEuzeVgh5Aq3if8aawTnn39+EAQpfXqYLCUaOyFEFEU0dkCW\n9C3tFqHDw4i0XGAnRFpzx5SAhlIo2HEPHZAxY7KdRMLD8jQNdqltniDZmUmVLBWGYalUsm1b\nv285QGeGZTuJhIclyWCn3xq7dDZPEOtMpUqw44xiABNHwsMwHU8nFoLNExiPWsGOZXZA9ows\n7RYh4WmEQ+x2MOrVrmM/LExEsANQiGwnkfCKTNNgF8fxuJtYR3vcyT8kVKBKsOOMYgBZIuEV\nkFzGrdfpxELIzRO6jRn5USVIcZQdkK/qhoJe9c15eMWh6f68ePuJJ5OV96eFtKjS2DEVC+Su\nOBOyS6LDM5u2pxOn19iR7cykSrATnFEMKKDg2U7ialojabrATgghb57IewzQhkLBLgxD13Ut\ny6IiBqAIajxjyKU+Gh5il1pjxyutoZQLdo7jcCEdkCNKuyWR8LKR3lkn+ga7OBb0HRidWsFO\nCFEqlQh2QL7Idssg4aUqvUkbeWuljsGOc+wwFoXWtMk8x8ZYQAWF3SQ7OrbTpmHDhg0bNmyY\nmZlpNBqe503wFcFxnDiONQ12sYhS+AUzKdTYyWAn31QBgC7o8CYoDEPbtkulUqlUqlQq8oNR\nFAVB4Pt+GIZBEKxiA4RlWRqv8+EcO4xDoRQVx3EURek1dgDGwoTsuEh4a7d161YhhG3bjuOU\nSiW58NpxnHK5XC6X5Z+RLxbBkBVnb+Uri6bBTp5jl8rjwkQKBTshRBAE5XJZ0zMkAfOQ7VaH\nhLdGURRFUeT7frfblR+ROU9ytvM8T/6unGOVrV4QBDvOt8q5IE2DHefYYSxqBTv53eg4DsEO\ngAFIeGNZZktsGIZhGPb7ffmvlmXJhCdbveVnb8Mw1HdLrEjvrlgYSq1glyyz830/jcf/9qWv\neeHbrk/jkQFTUdpNBAlvsuI49n1/+JVCdnhJq2fb9vDsraRrY5fWrliYSa1glzR2eQ8EwF+Q\n7SaIhJcS2cwNBoPkI7LSc11XRr0oinQ9DY5z7DAOFYMdG2MB1ZDtJo6Elza5r6Lf79u2vX79\nem3rOpFWY0dYNJRaEUougKWxA1AcJLy0ab3ATrDGDmNSK9gJIYIgkIdS6vtNCBiJ0i5tw2cd\nFzDkpXeZmN5bYgXn2GE8Ct08IaW9zI77J4BV4zqKzHCtxQTp3tgJsT3bTfYXDKViYyeEKJVK\nw2tgASiC3i5jTNSunc63xAohts3FTv5ROcfOUMoFOzbGAsCOSHirJtf26LyxNBZprLHT9+uB\nZakY7OI4ZmMsoCxKu3wZmfDSW2DnOI5lWRovsNtW2JHCMCrl1tgJIeTGWMuyUnp8ltkBa8Ri\nOxWwDm8U2s/Dim2XxbLGDiNSMdjJw8Qp7QBgFCS8ZciXkpRuM8pKnNqv5Xzyk588+uijd911\n10ajcfDBB1977bXZfLZYIxXDk+zMXdfV/FsRMBkTsgoycpZ2jVzXFVqfdbLt4okczrH79Kc/\n/bznPe8tb3nL9PT0LbfccsYZZ/i+f+aZZ2Y/EoxFxWAn85z8bgSgLLKdskh4kmVZpVIpCAK9\n16jlNHN6++23J/98+OGHb9q06Ytf/CLBTn0qBrsoisIwLJVKlmXp/d0IcrI9+QAAIABJREFU\nmI5spzgtEl56OydkQWDA5E8qL4VjPmSv19t7770nPwxMmorBTgjh+36lUimVSil9Q3770te8\n8G3Xp/HIAKAgLRLexJkR7HZdP/28Z/4/wx/50c/u6/T6Yz3Ik56wx+N22zD8EcceY4fiJz/5\nyR/96Ecf+tCHxnpS5ELpYMcyO0B9lHZ6KVTCMyPYlUrOVKM2/BHbHns6yyu7ix5EjJzrbrzx\nxrPPPvtTn/rUIYccMtaTIhfqBjvBxlhAE2Q7HSmS8NKbhzVkgZ0QDz2y+db/7z/X+CC/uO/B\nX9z34PBH3nDKy0b5D6+++upzzz33C1/4wvHHH7/GMSAbKh53IrYvs0t1/wSn2QETxMl2+kpO\nSzHswBQjDjoRQmw/oXjiRnjmiy+++K1vfetXvvIVUp1G1K3EkmV2Wm9TBwCNKFLjTYQBB51I\naR13slK0e/Ob3/yv//qvH/7whzdu3Lhp0yYhhOd5T3nKUyY/EkyUusEuOc3OgG9LoAiYkDWJ\nAQnPjAV2QuR23MlnP/vZIAjOOuus5CP77rvv/fffn/1IMBZ1g11yml232817LABGQrYzT6oJ\nL70FdkKIUqkUhmEU5XC072TFcTrHnaxk8+bN2T8p1k7RNXZCCPkNyTI7AFCBXuvwXNe1LMuE\nuk4IORnLXbEYkbrBTgjh+75lWY7j5D0QAKNiF4XxtEh45uyc2Cbri2KhL3WnYoUQvu97nue6\nbhiGeY8FwKiYkC2INc7SpjoPa84COyFEHKVzVyzZzkyqN3Yi5UtjmY0F0kBvVygKdniyETBg\ngd02aUzFkusMpXRjl8EyOwDApCiyl1ZeNW7MiQojnzo35sNO/BGhBqWDnRAiCIJyuew4DrOx\ngF6YkC2yfBOeUfOwQq6ISyOGEe3MpHqwGwwG5XK5XC5z6AmgHbIddpbwUl1gVy6XhRCDwSC9\np8hWSmvsYCal19iJ7d+Z8rs0JSyzA9LDYjtIma3DsyxLnmxvzgI7IdgVi9Gp3thFURQEgeu6\ntm2b9V0KAEWUdrbzPE8YVdfldvMENKV6YyeE6Pf7IuXSDkB6KO2wyJ3XviO9B5cvFvKFwwzx\n9v0Tk0VYNJUGwY7ZWEB3ZDtkQ87DhmFo1H67VM46IdUZS/WpWCGE/BaV98Pkcl8eAEAL8pXC\nqHnY1I47gak0aOyEEIPBwLIsZmMBfVHaQcpgHtawYCdEOqUdDKVHsMtgmR2zsUDayHZIW7lc\njqLInBPstkljSyzBzlh6BDu5cZ3GDtAd2Q7pkecnGFjXAePQI9iJ7bOx6V0vZlnWHVf9Y0oP\nDgAQzMMC6dMp2Il0ZmNd1200GuvWraMRBDJAaYeUlMvlOI6Nm4cFxjPGPtPNmzenOpTlWZa1\nfv36OI63bNkykQd0HMfzPM/zwjDs9/tyGd8L33b9RB4cwPK4aqyAUq3rSqXSzMzMYDCYn59P\n71lGsXHjxsk+4B//vGXr3OQ/qX2esKfnUWcYSIPjTqQ4jgeDged5pVIpCIJVP45t257nlctl\ny7L6/f7c3NzwhRbfvvQ1ZDsA0I555xIndttl/W67rM97FNCGNsFOCCGDXblcXkWwk6eleJ7n\nOM5gMGi1WkYdXwnoprphD0o7TJAMdszDApoFOyFEuVzudDqj/1eu68o46Pt+t9vl2x5QBNmu\nUFKdh7Vtu1Qq+b7PleKANpsnhBByVWypVHIcZ8U/7DhOrVZbt25dtVr1fX/Lli0LCwujpDoO\ntAMyw0YKTAT7YYGETo2dEKLf77uuWy6Xu93ukn9g+SV0AADzeJ4nCHaAEEKvxk4I0e/34ziu\nVCqLPm5Zlud5U1NT09PTtm23Wq3Z2dlut7u6VEdpB2SG0q4IUp2HdRzHdV3f91k5DQjtGjs5\nG1sul+W3sWAJHaA/FtthLeRbfSP3wwKroFmwE0L0er1yuVypVGSkk6fQtdvt0Q/kA6Aasp3B\nUq3rhBCe58VxTLADJM2mYoUQg8EgiiL5nTw7Ozs/Py/nZyf7LMzGAoD6yuWybdtpvAoAmtIv\n2IntlXsURXwnA8ZgsZ2RMqjrBPOwwBAtg12v1xPb11Wkh9IOyBjZDmORJ8+HYcjqaiChZbCT\n38au645yoB0AwEiVSsWyLPlWH4CkZbAT24t3WcKnh9IOyBilnUmYhwWyp3GwW/JAOwC6I9th\nFKVSqVQqye10eY8FUIiuwU5ubrdtW94kA8AkZDsDpF3XyTf2zMMCi+ga7ASzsQBQVPK2IXlk\nfd5jAdSicbCTF8h4nmfbGn8WAJZEaae1tOs6eSF4r9fj0CtgEb0jEaUdYDCyHXaGa8SAndE7\n2MnVFWkHOwB5IdvpKO26zrZt13WDIAiCINUnAnSkd7CLomgwGMi9Uak+EaUdACiCbRPAMvQO\ndmJ7Fc+5J4CpKO2wiNw2wTwssCQTgl0URRlsoaC0A/JCttNIBocSO44jjzJN9YkATWkf7IQQ\n3W7XsixKOwAwXrVaFUJ0u928BwIoyoRgJ3e8V6tVy7JSfSJKOyAvlHZaSLuuc11X3jYRhmGq\nTwToy4RgF8dxr9eT51XmPRYAaSHbgboOWJEJwU5s/z6X3/OporQDckS2U1nadZ3jOOVyOQgC\nbpsAlmFIsIuiqN/vO45DaQcARqrVaoK6DliJIcFOCNHpdASlHWA6Sjs1ZXAosed5YRhyygmw\nPHOCXRiG8rBi13XzHguAFJHtVJN2qhOsrgNGZk6wE6y0AwqDbFco8kArDiUGRmFUsPN9PwiC\ncrnsOE7eYwGAQsigrqtUKpZldbtdDiUGVmRUsBOUdkBhUNoVR7Valcda5T0QQAOmBbt+vx+G\nYQY3jAHIHdkudxnUdfLnubw9Mu3nAgxgYPqRhxVncMMYpR0ApI1tE8BYzAx22dwwJsh2QN4o\n7XKUQV1XLpdLpZKcikn7uQAzGBjs4jjudrvZlHYAcke2Mxh1HTAuA4OdoLQDCoZsl70M6jp5\nLqk87iDt5wKMYWawi6Ko1+vZtk1pBwATl0GqE0LU63Wx/VYhACMyM9gJIeSJR7VajdIOKAJK\nO8OUy2VZ1/m+n/dYAJ0YG+yiKJIr7eS90QCMR7bLRjZ1nfzR3W63M3guwCTGBjshRLfbjaKo\nUqlkcKYdpR2gArKdGTzPk5thWV0HjMvkYJdsj82mtCPbATBelnUdq+uAVTA52Akhut1uGIaV\nSoXbY4GCoLRLTzaprlqtOo7T6/U4uw5YBcODndj+no/SDigOsp2+LMuSN8NS1wGrY36wk6s0\n5IqNvMcCALrKrK6zbVuukM7g6QDzmB/sxPbSTh6JlDZKO0AFlHY6sm1b1nVcNQGsWiGC3WAw\n8H3fdV3XdTN4OrIdoAKy3QRltmfCsqxOpxPHcQZPBxipEMFObD8MKZvSDoAiyHYTkU2qcxzH\n8zx5b1AGTweYqijBLgiCfr9fKpU8z8vg6SjtAGAssq5rt9vUdcBaFCXYiWy3xwqyHaAGSrs1\nyqauk++6wzDs9/sZPB1gsAIFuzAMe72e4ziVSiXvsQDIDtlu1bJJdYILxIDJKVCwE0LINbmy\n8M/g6SjtAEWQ7VTmum65XPZ9fzAY5D0WQHvFCnZRFHW7Xdu2mZAFgOVlVtc1Gg3BBWLAhBQr\n2Intl4xVq1XOKwYKhdJuLFlOwsoLxHzfz+YZAbMVLtjFcSyXccj3iBmgtAMUQbZTjeM4XCAG\nTFbhgp0QYjAYyKNPMttFQbYDoJHM6rp6vW5ZVqvV4gIxYFKKGOyEEPKopHq9btsF/QoAxURp\nt6LMUp3neXLPBEecABNU0FgTRVGn07EsK7O7KCjtAEWQ7VSQ/PhttVp5jwUwSkGDnRCi2+0G\nQSDfMmbzjGQ7QBFku53JchLWtu1OpxOGYTbPCBREcYOd2P5OsdFoZHOsnSDbAVBYZqlOLnEO\nw7Db7WbzjEBxFDrYBUEgj7WrVqt5jwVApijtFsks1YnthxK0Wi2uhQUmrtDBTgjR6XSiKJIH\nKWXzjJR2gCLIdrmQx4j2+30OrgPSUPRgl/2xdoJsByiDbCdlVtfJi3+SH7wAJq7owU4I0e/3\nB4OB67qZHWsnyHYAlJHxJKxlWe12m4PrgJQQ7ITYvtSDY+2AAip4aZdlqiuXy+VyOQiCXq+X\n2ZMCRUOOEWLoWLtarZbZk1LaAYoobLbLMtVxcB2QDYLdNvJYu0ql4rpuZk9KtgNQEHKPmvxJ\nm/dYAJMR7P5Cvo9sNpuZHWsnyHaAGgpY2mVZ17muW61WwzDsdDqZPSlQTAS7vwiCoNPp2Lbd\nbDazfF6yHaCCQmW7jCdh5Q/VhYUFDq4D0kawe4xOp+P7frlcznKHLABFFCTbZZnqhBCNRkPe\nHsYkLJABgt1iyQ7ZzI4sFpR2ALKScaqrVCqe5/m+zyQskA2C3WJhGLZarWTuIDNkO0AFBSnt\nsuE4Tr1ej+OYnbBAZgh2S+j3+/1+v1Qqyc35mSHbASowONtlXNfJvWitVisMwyyfFygygt3S\n5E+iarWa5ekngmwHqMHIbJdxqqvX6/JO2H6/n+XzAgVHsFtaMneQ8ekngmwHIAUZpzp5vkkU\nRUzCAhkj2O2UXO2b/ekngmwHKMCk0i7jVMf5JkCOCHbLkfvzOf0EKCYzsl3GqU4MnW/i+37G\nTw2AYLcC+Y4z49NPBKUdgEnIPtXJ803kee8ZPzUAQbBbURiG7XY7+9NPBNkOUIAZpV1mkvNN\nFhYW8h4LUFAEu5X1ej15+kmtVsv4qcl2QO70zXbZ13WcbwLkjmA3klarFUVRrVbL+PQTQbYD\nFKBjtss+1XG+CaACgt1IkpmFZrNp21l/0ch2AMaSfarzPI/zTQAVEOxG5ft+u922bXtqairj\nk+0E2Q7Im0alXfaprlQqNRqNOI7n5+c53wTIF8FuDN1ut9fryR9h2T872Q7IlxbZLvtUJ/eW\nyaV1QRBk/OwAFiHYjafdbgdBICcdsn92sh2QL8WzXfapTggxNTXlOE6322VpHaACgt145FxD\nFEX1er1cLmc/ALIdgCXlkuoajYbruoPBoN1uZ//sAHZEsBtbFEVyHUmz2cz41GKJbAfkSM3S\nLpdU53lepVIJw5BT6wB1EOxWIwiCVqtlWVYuGykE2Q7IlWrZLpdUx4YJQE0Eu1Xq9/u9Xs9x\nnKmpqVwGQLYDIHJKdcn5AAsLC5xFDCiFYLd6rVbL933Xdev1ei4DINsBeVGktMsl1cnJCtu2\n2+32YDDIfgAAlkGwW5P5+fkwDKvVqud5uQyAbAfkJfdsl0uqE0M3THS73VwGAGAZBLs1kTdS\nxHHcaDRKpVIuYyDbAXnJMdvlleqq1WqlUpHrjHMZAIDlEezWKgiChYWFZG4ilzGQ7YBCySvV\nyZUnyRvaXMYAYHkEuwkYDAadTse27WazmdcYyHZALrIv7fJKdcleMbkEJZcxAFgRwW4yOp3O\nYDBwXZdsBxRNltkur1SXbIOVm8ZyGQOAURDsJmZhYUHeNpbXJllBtgNykk22yyvVyaUm8t6w\nXq+XyxgAjMgafZ3E5s2bUx2KAWzbnp6edhyn0+l0Op28hvHCt12f11MDhdV99OH0HjyvSCdN\nT0+7rtvv97lhYhQbN27MewgoNBq7SYqiaG5uLoqiWq1WrVbzGga9HZC99Eq7fFNds9mUt8GS\n6gAt0NhNnuM409PTtm0vLCz0+/0cR0J1B2Rs4r1dvqmu0WjIw03m5ubYBjsiGjvki8Zu8sIw\nlJcnNpvNcrmc40io7gCt5Zvq6vU6qQ7QDsEuFUEQJNnOdd0cR0K2A7I0wQnZfFNdtVqtVqvJ\n29QcRwJgLAS7tPi+nxxcnNelFBLZDsjSRLJdvqlO7u6Pomh+fj6KohxHAmBcBLsUyeXGyUkB\nOY6EbAdkaY3ZLt9UVy6Xm81mHMccRAzoiM0TqatWq/V6PQxDuWE2x5GwlwLIzOp2UeQb6YQQ\nrusm10twEPHqsHkC+SLYZaFer1erVUXWIBPvgGyMm+1yT3WlUml6etqyrPn5+cFgkO9g9EWw\nQ76Yis1Cu93u9XrJD818B8O0LJCNsSZkc0918ipYy7IWFhZIdYC+CHYZabVag8GgVCrleJls\ngmwHZGPEbJd7qpO35ti23Wq18j19E8AaMRWbHbmLIjnDnTlZoAiWn5DNPdKJ7V1d7nchGoOp\nWOSLYJcp1bKdIN4B6dtZtlMk1cmujlQ3KQQ75Iup2EzJEwQGg0G5XJbLWfIeEdOyQOqWnJBV\nIdXJhb+kOsAkNHb5mJqaKpfL8oIKFc7/pLcDUjVc2qkQ6cTQHth2u93tdvMejjlo7JAvgl1u\nms2m53kqnG+XIN4B6ZHZTpFUJ8+rsyyr1Wr1er28h2MUgh3yxVRsbhYWFnq9XrLAJe/hCMG0\nLJCm6oY9FEl1yVIQ+VMo7+EAmCQau5zJs4ujKJqbm1Pk9h56O2Di1HnXJG8M4xTi9NDYIV8E\nu/zJO8eUynaCeAdMjjqpzvO85B5YbgxLCcEO+SLYKUFmuziO5+bmgiDIezjbkO2ANVIn0gkh\nKpVKo9Eg1aWNYId8EexUkfzMVSrbCeIdsFpqpjrVfsKYh2CHfCmxZh9CiF6vt7CwYFnW9PS0\n67p5D+cvlHpxAnSh1DdOtVptNBpyvQepDjAbjZ1akhUwCt7DTXUHjEKpSCeU3KFlNho75Itg\np5xkz5qC50uR7fD/t3fvMXJVhQPHz7137p337KvFplWWdqlVQi0EES0EdAOVpiWsCphtaQLy\nCL4wRYhSH7QEqoUWqxKq/dFgUsFCKaA0YluNgtiqGKxSqekWCzGoBdvuY2bnfef3x7HX68zu\ndLqdnXvnzPfzR7Odnc6emd3tfvfce85FFX5LOk3TYrGY3/bLVB5hB28Rdn7k7B2aTqdTqZTX\nwylH3gGV/FZ1uq7H43HTNP1zhZsWQdjBW4SdTxmGkUgkDMPI5XIjIyO1f5oag7YDHH5LOuH6\nDySbzSaTSb/9B6I2wg7eIuz8S9O0RCJhmmaxWBweHvbhyTHkHeDDqnNO5/DnlL/yCDt4i7Dz\nu1gsFgqF/Lz1FHmH1uTDpBOuTTGTyWQ2m/V6OK2IsIO3CLsm4GxA5dv/qWk7tBR/Jp04/nug\nbdsjIyP+/D2wFRB28BZh1xya4tgKeYdW4M+qc87cKBQKIyMjPjxzo3UQdvAWYdc0muVsaPIO\nqnr5/5YPDg768FvP52utWg1hB28Rds3E/Uu5n/cvoO2gGDlLJ89dGx0d9Xo4/8PnuyO1IMIO\n3iLsmoyz46ht28PDw36+OhB5BwW4D7xqmtbe3u6rnX7lCbhCCB/uZ96yCDt4i7BrSk208I28\nQ/OqPJ0uFAqZpjkyMuLFcMrJa4X5ecl8ayLs4C3Crlk1xXIKB3mH5lJlhUR7e3symfR2sty5\nqoRvN7lsZYQdvEXYNbFAIBCPxw3D8Pkpdw7yDv53wkWvpmlGo9HBwcGGDGcMlmXFYjFd11kq\n4U+EHbxF2DU355S7pjgsK5F38Kfa9zGJx+O5XM6Tb7dIJBKJRIQQo6OjflvGAYmwg7cIOxUE\ng8FYLKZpms93QnEj7+AfJ7s1na7rbW1tDd76xDn8yv7DPkfYwVuEnSIMw4jH44FAoLnOuSHv\n4K0J7zbsTJvVczTjc86p5fCr/xF28BZhpw5N0yKRiFwlNzo6mk6nvR5Rrcg7NN4pXkBCbn3S\ngF+imvf7umURdvAWYaeaZjwsK5F3aIx6XRMsGAxaljWpW5+4Z+JHRkb8vG8lHIQdvEXYKaip\nfxiQd5g8db/Ma1tb2+jo6CSd7ta8v6S1OMIO3iLslOWsnkulUk13+Ia8Qx3VveccgUAgFovV\nfeuTZlztDgdhB28RdipT4IRrCg+nYvKSzhGLxQqFQh0v5+Xen3JkZKRZFkLBQdjBW4Sd4tTY\nIoG8w8lqQNJJ9d36RF4tUAjRFFeUwZgIO3iLsGsJzmHZTCaTSqWacepOkHeoQcN6zi0cDuu6\nfoodJo/qBgIB27aTyWQul6vX8NBghB28Rdi1CvePjVQq1dRn7VB4qORJ0jk6OjpOZesTZ6Iu\nl8slk0n/Xx4QVRB28BZh11rC4XAkEpFn3Snw84PCg7c957AsKxwODw0Nnew/NE0zFosZhqHA\nb1yQCDt4i7BrOYZhxGIx0zTV2O+0q6vrnBvWeT0KNJpPes6tra0tnU7XfgjV2XlYCMGGJioh\n7OAtwq5FhUKhaDSqaVo+n08mk0268s40zba2tlwuNzw8LJjAawE+7DmHYRiJRKLGVRSWZcVi\nMV3Xi8ViMpls0lVNGBNhB28Rdq1L1/VYLGZZVqlUSqfTDbvqZR3F4/FgMDg0NFT2c5HCU4+f\nk84Ri8WKxWL1WXBd16PRaDAYFELI7zsm6hRD2MFbhF2rCwaD0WhU1/VCoZBMJpvoMhWapnV1\ndZVKpSNHjox3Hwqv2TVFzznkBWSHhobGO3u1eb/dUDvCDt4i7CA0TYtGo6FQSDTVFEIwGIzH\n45lMJplMnvDOFF4Taa6YKxMOhw3DqPyaVOzcVlRB2MFbhB3+o+lO+mlrazNNc3Bw8GSnPYg8\nf2rqnnNrb28vm41zVqM39SmtqBFhB28Rdvgv9zK9XC6XSqV8+xNI1/XOzs5isXjs2LEJPwiF\n5zllYs7NNM1IJCK3PrEsKxqNGobBVV9bB2EHbxF2KOdsZSx8fGRWbuiaSqXqdUiLyGskJXvO\nLZFI5PN5y7JM0xRCZLPZVCrV7NtGokaEHbxF2GFswWAwEonImYZ0Op1Op32Vdx0dHYZhHD16\ndDJ+WBJ5k0H5mHO4172ySKIFEXbwFmGHapxzg2zbHh0dzWQyXo9ICCEMw+jo6CgUCoODg5P9\nsYi8CWudknNomhYKheS3TLFYHB0d5dhrCyLs4C3CDifgPvGuUCikUinP11XEYrFQKDQyMtL4\nn5p0XhUtWHJuzlYmrHttcYQdvEXYoSaGYUQiEXl0yfN1FV1dXZqmHTlyxPOjwy3eeS1ecg7T\nNKPRqM9PS0XDEHbwFmGHkxAIBKLRqDwfPJPJjI6ONv588LLLiPmQqrVHxlUyDCMajVqWJYTI\nZrOjo6O+XUiOhiHs4C3CDifNva5CnnjXyPmJRCJhWVblZcR8rolqj4Crha7rkUhEbuvtk1MU\n4BOEHbxF2GEiyk4ST6fTjVlXUctlxJpUI8uPdDsVuq6Hw+FQKCS/+FOpVC6X83pQ8BHCDt4i\n7DBx7kkL27Zl3k3q7N1JXUYMqC930vlqnTh8hbCDtwJeDwBNzLbtZDKZTqdDoVAoFIpGo+Fw\nOJPJTN6mdzIi+WmKBjMMQ36Ry6Tz4c6OACARdjhV8miUzDu5752Td/VdWqHrummaxWKR7V7R\nMIFAIBwOy/XgxWIxk8k0+KRSADgphB3qQx6Zknkn2y4UCuVyuTquE5Q/XJmuQ2OYphkOh+WK\nV3YbBtAsCDvUk7z+WCaTkbN3wWAwGAzWaxsIeRyWH66YbO6kKxQK6XSarzoAzYKwQ/05l5eV\nG6PIvJOzdxM+imoYhmEY+XyeK6lj8liWFYlE5FbD+Xw+nU6z4hVAcyHsMImy2Ww2m5V5Z1mW\nZVm5XC6dTk9gxy95TTOOw2KSyDlmwzCEEKf4SwgAeIiww6STeSfnQmTeFQqFTCaTzWZrPwnd\nuZrZZI4ULUcudw0Gg7quC64eAaD5EXZokFwul8vlnLOXYrFYNBrNZrOZTOaEUyOmaWqalsvl\nWI2IegkGg6FQSF4fr1QqyXXcJB2AZkfYoaHy+Xw+n9d1Xf5YlU64i4Q8DptOpxs7WCiobIpu\nApPHAOBnhB084Gzxapqm/CkbjUYjkUgul8tkMmVn4GmaZllWqVTiWpw4FZVTdLXMFgNAcyHs\n4CU5gZdKpeQPXbl+tmwCT+46wX4TmBim6AC0FMIO3qs+gcdlxDAxTNEBaEGEHXzEmcCT597J\nCTwhhG3bzK+gRoFAQH7lMEUHoAVptf9n9+9//3tShwKUsSwrFArJQ7FCiEKhkM1mc7kcSxdR\nyTRNy7KcniuVSjWuuQbqa8qUKV4PAS2NGTv4l9whRS6eCAaDlmUFAoFoNFosFuXeeBQeKnsu\nl8vJXwCYogPQggg7+J2ceslms+7Ci0QikUikWCzK+GPBbKsZc35OfjHQcwBaGWGHpjFm4YXD\n4XA4TOG1iDF7Tiad10MDAF8g7NB8KgtPXtDCKbxsNsuZVcrQNM00TdnxmqYJIWzblush6HgA\nKEPYoYlVKTy5obGcw+NUvGZkushbbNuWn256DgDGQ9hBBWWFZ1mWPGYnV9Tatp0/jsjzs0Ag\n4MScnJwTQhQKBafRvR0eAPgfYQelOIUnhDAMw6kE95Z4svByuZxt216PF//9NDlHWoUQxWLR\naXE+TQBQO8IOyioWi/LqZOJ/68GJPKceWErZYLquO58OuQxCCGHbtpyWY2IVACaMsENLcEee\nM40XCATkJS6EEIXjisVioVCg8+pL1/VAIGAYRiAQkG/I2+Vpc8QcANQLYYeWIzNCCKFpWiAQ\nkCfkyeBw7iPzzkHnnSzDMJyMCwQCzrScOL6HsPwssHgZAOqLsEPrkitnnVPy3SHiXG9Uvsu2\nbfd8HnNLlaqXnGw45zX0cJwAoDbCDvgPebhWLrwQFZ3nrLEVxzuveJxt27Ztt86snqZphmHo\nuu7EnGEYzroHcfxsOafkWP0AAA1D2AFjG7PznNpzIs9h27aMvLI/Gz7wutE0Tdab+8+yhpM4\ncg0APkHYATWRnef81akcd/Q4W+mW/UN355VKJTm9J99o4DMYg3acruuVGec+nOooezoSJQcA\nPkHYARMhD7+WbZk73hTXeM0nhCgd59TemG9PYISy1dzd5n5Dvl39Cco95NwN53mJAgCqI+yA\nuimVSmUTe5L7pLQxA0u+q5HjlKcJVkakAkeQAaCVEXbApCuVSrXs6zHe1FrlOW01ftDxJgIn\n8GgAgKZA2AF+ISf8vB4FAKCJVTvJBgAAAE2EsAMAAFAEYQcAAKCqL4YwAAANBUlEQVQIwg4A\nAEARhB0AAIAiCDsAAABFEHYAAACKIOwAAAAUQdgBAAAogrADAABQBGEHAACgCMIOAABAEYQd\nAACAIgg7AAAARRB2AAAAiiDsAAAAFEHYAQAAKIKwAwAAUARhBwAAoAjCDgAAQBGEHQAAgCII\nOwAAAEUQdgAAAIog7AAAABRB2AEAACiCsAMAAFAEYQcAAKAIwg4AAEARWqlU8noMAAAAqANm\n7AAAABRB2AEAACiCsAMAAFAEYQcAAKAIwg4AAEARhB0AAIAiCDsAAABFEHYAAACKIOyAiTh8\n+HAikXjwwQe9Hkj9LV68eO/evQ37cE8++aSmac8880yV++zcudMwjJdffrlhowKAJkXYAeM6\ncODAypUr//znP1e+6ytf+UpnZ+fNN988SY8PtwULFlx44YW33Xab1wMBAL8j7IBxHThwYNWq\nVZXh9cYbbzzyyCO33nqrZVmT8fiodNtttz3//PO//OUvvR4IAPgaYQeMYXR0tMp7N2zYoOv6\ntdde27DxnIrqz8Xxpz/9qa+vb/r06T/96U8vuOCCd77znZdffvmkHpOtcWDSokWLurq6Hnro\nockbDwAogLCDCgqFwn333Td37tx4PB6Px2fPnn3dddeNjIw4dxgcHPziF784c+bMYDD4jne8\nY+nSpQcPHnTeK0/zeuKJJ1atWjV79mzLsu6+++6VK1deccUVQohly5ZpmqZp2oc//GF5/8cf\nf/z973//aaedVvYIzzzzzEMPPTRnzpxQKHTWWWdt27ZNCHHw4MG+vr6Ojo5EIrFkyZLBwUH5\nT6o8fqFQWL9+/XnnnReNRuPx+Pve97677rpLvmtoaOirX/3qBRdcMGXKlGAwOGvWrNtvvz2Z\nTFZ/Lid8id58883e3t59+/atW7fuoosu+t73vrdmzZqurq5//vOfZa9z3Qcm2bZ9//33n3nm\nmcFgcPbs2evXry/7FJumedlll/3kJz85qRwEgFYT8HoAQB3ceeeda9euXbJkya233qrr+htv\nvLF9+/bh4eF4PC6ESKVSF1988SuvvLJ06dL58+cPDAxs2LDhueee27Nnz5w5c5wH+dKXvjRj\nxozVq1dPmzbNNM1p06YFg8EVK1asWLHisssuE0K0t7cLIQ4dOvT666/39fVVDuP+++//17/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      "text/plain": [
       "plot without title"
      ]
     },
     "metadata": {},
     "output_type": "display_data",
     "source": "R display func"
    }
   ],
   "source": [
    "barp <- barp + coord_polar(theta='y')\n",
    "barp <- barp + theme(\n",
    "    axis.line=element_blank(),\n",
    "    axis.text.x=element_blank(),\n",
    "    axis.text.y=element_blank(),\n",
    "    axis.ticks=element_blank(),\n",
    "    axis.title.y=element_blank())\n",
    "print(barp)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "You can notice that we still have an outline around our chart. We can extend the theme function using the `panel.background` parameter:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    }
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Coordinate system already present. Adding new coordinate system, which will replace the existing one.\n"
     ]
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "plot without title"
      ]
     },
     "metadata": {},
     "output_type": "display_data",
     "source": "R display func"
    }
   ],
   "source": [
    "barp <- barp + coord_polar(theta='y')\n",
    "barp <- barp + theme(\n",
    "    axis.line=element_blank(),\n",
    "    axis.text.x=element_blank(),\n",
    "    axis.text.y=element_blank(),\n",
    "    axis.ticks=element_blank(),\n",
    "    axis.title.y=element_blank(),\n",
    "    panel.background=element_blank())\n",
    "print(barp)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now we just need to change our label:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    }
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Coordinate system already present. Adding new coordinate system, which will replace the existing one.\n"
     ]
    },
    {
     "data": {
      "image/png": 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JYsdlRC2EG54njWXeJ3UQDpV4iByS5Uwg4qk+m2M9dB9sT0HDsC5TN2UDE3zAI1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\nhB0AQCCEHQBAIIQdAEAg/j+ESB/m7fwKrwAAAABJRU5ErkJggg==",
      "text/plain": [
       "plot without title"
      ]
     },
     "metadata": {},
     "output_type": "display_data",
     "source": "R display func"
    }
   ],
   "source": [
    "barp <- barp + coord_polar(theta='y')\n",
    "barp <- barp + theme(\n",
    "    axis.line=element_blank(),\n",
    "    axis.text.x=element_blank(),\n",
    "    axis.text.y=element_blank(),\n",
    "    axis.ticks=element_blank(),\n",
    "    axis.title.y=element_blank(),\n",
    "    panel.background=element_blank()) +\n",
    "    labs(y=\"Carburetors\")\n",
    "print(barp)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<hr>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### About the Author:  \n",
    "Hi! It's [Francisco Magioli](https://www.linkedin.com/in/franciscomagioli) and [Erich Natsubori Sato](https://www.linkedin.com/in/erich-natsubori-sato), the authors of this notebook. We hope you found R easy to learn! There's lots more to learn about R but you're well on your way. Feel free to connect with us if you have any questions."
   ]
  },
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   "metadata": {},
   "source": [
    "<hr>\n",
    "Copyright &copy; 2016 [Big Data University](https://bigdatauniversity.com/?utm_source=bducopyrightlink&utm_medium=dswb&utm_campaign=bdu). This notebook and its source code are released under the terms of the [MIT License](https://bigdatauniversity.com/mit-license/)."
   ]
  }
 ],
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