{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# HIDDEN\n",
    "from datascience import *\n",
    "import numpy as np\n",
    "path_data = '../../../data/'\n",
    "%matplotlib inline\n",
    "import matplotlib.pyplot as plt\n",
    "plt.style.use('fivethirtyeight')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Overlaid Graphs ###\n",
    "\n",
    "In this chapter, we have learned how to visualize data by drawing graphs. A common use of such visualizations is to compare two datasets. In this section, we will see how to *overlay* plots, that is, draw them in a single graphic on a common pair of axes.\n",
    "\n",
    "For the overlay to make sense, the graphs that are being overlaid must represent the same variables and be measured in the same units. \n",
    "\n",
    "To draw overlaid graphs, the methods `scatter`, `plot`, and `barh` can all be called in the same way. For `scatter` and `plot`, one column must serve as the common horizontal axis for all the overlaid graphs. For `barh`, one column must serve as the common axis which is the set of categories. The general call looks like:\n",
    "\n",
    "`name_of_table.method(column_label_of_common_axis, array_of_labels_of_variables_to_plot)`\n",
    "\n",
    "More commonly, we will first select only the columns needed for our graph, and then call the method by just specifying the variable on the common axis:\n",
    "\n",
    "`name_of_table.method(column_label_of_common_axis)`\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Scatter Plots ###\n",
    "\n",
    "[Franics Galton](https://en.wikipedia.org/wiki/Francis_Galton) (1822-1911) was an English polymath who was a pioneer in the analysis of relations between numerical variables. He was particularly interested in the controversial area of eugenics – indeed, he coined that term – which involves understading how physical traits are passed down from one generation to the next. \n",
    "\n",
    "Galton meticulously collected copious amounts of data, some of which we will analyze in this course. Here is a subset of Galton's data on heights of parents and their children. Specifically, the population consists of 179 men who were the first-born in their families. The data are their own heights and the heights of their parents. All heights were measured in inches."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table border=\"1\" class=\"dataframe\">\n",
       "    <thead>\n",
       "        <tr>\n",
       "            <th>father</th> <th>mother</th> <th>son</th>\n",
       "        </tr>\n",
       "    </thead>\n",
       "    <tbody>\n",
       "        <tr>\n",
       "            <td>78.5  </td> <td>67    </td> <td>73.2</td>\n",
       "        </tr>\n",
       "        <tr>\n",
       "            <td>75.5  </td> <td>66.5  </td> <td>73.5</td>\n",
       "        </tr>\n",
       "        <tr>\n",
       "            <td>75    </td> <td>64    </td> <td>71  </td>\n",
       "        </tr>\n",
       "        <tr>\n",
       "            <td>75    </td> <td>64    </td> <td>70.5</td>\n",
       "        </tr>\n",
       "        <tr>\n",
       "            <td>75    </td> <td>58.5  </td> <td>72  </td>\n",
       "        </tr>\n",
       "        <tr>\n",
       "            <td>74    </td> <td>68    </td> <td>76.5</td>\n",
       "        </tr>\n",
       "        <tr>\n",
       "            <td>74    </td> <td>62    </td> <td>74  </td>\n",
       "        </tr>\n",
       "        <tr>\n",
       "            <td>73    </td> <td>67    </td> <td>71  </td>\n",
       "        </tr>\n",
       "        <tr>\n",
       "            <td>73    </td> <td>67    </td> <td>68  </td>\n",
       "        </tr>\n",
       "        <tr>\n",
       "            <td>73    </td> <td>66.5  </td> <td>71  </td>\n",
       "        </tr>\n",
       "    </tbody>\n",
       "</table>\n",
       "<p>... (169 rows omitted)</p>"
      ],
      "text/plain": [
       "father | mother | son\n",
       "78.5   | 67     | 73.2\n",
       "75.5   | 66.5   | 73.5\n",
       "75     | 64     | 71\n",
       "75     | 64     | 70.5\n",
       "75     | 58.5   | 72\n",
       "74     | 68     | 76.5\n",
       "74     | 62     | 74\n",
       "73     | 67     | 71\n",
       "73     | 67     | 68\n",
       "73     | 66.5   | 71\n",
       "... (169 rows omitted)"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "heights = Table.read_table(path_data + 'galton_subset.csv')\n",
    "heights"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The `scatter` method allows us to visualize how the sons' heights are related to the heights of both their parents. In the graph, the sons' heights will form the common horizontal axis. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "heights.scatter('son')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Notice how we only specified the variable (sons' heights) on the common horizontal axis. Python drew two scatter plots: one each for the relation between this variable and the other two.\n",
    "\n",
    "Both the gold and the blue scatter plots slope upwards and show a positive association between the sons' heights and the heights of both their parents. The blue (fathers) plot is in general higher than the gold, because the fathers were in general taller than the mothers."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Line Plots ###\n",
    "\n",
    "Our next example involves data on children of more recent times. We will return to the Census data table `us_pop`, created below again for reference. From this, we will extract the counts of all children in each of the age categories 0 through 18 years."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table border=\"1\" class=\"dataframe\">\n",
       "    <thead>\n",
       "        <tr>\n",
       "            <th>AGE</th> <th>2010</th> <th>2014</th>\n",
       "        </tr>\n",
       "    </thead>\n",
       "    <tbody>\n",
       "        <tr>\n",
       "            <td>0   </td> <td>3951330</td> <td>3949775</td>\n",
       "        </tr>\n",
       "        <tr>\n",
       "            <td>1   </td> <td>3957888</td> <td>3949776</td>\n",
       "        </tr>\n",
       "        <tr>\n",
       "            <td>2   </td> <td>4090862</td> <td>3959664</td>\n",
       "        </tr>\n",
       "        <tr>\n",
       "            <td>3   </td> <td>4111920</td> <td>4007079</td>\n",
       "        </tr>\n",
       "        <tr>\n",
       "            <td>4   </td> <td>4077551</td> <td>4005716</td>\n",
       "        </tr>\n",
       "        <tr>\n",
       "            <td>5   </td> <td>4064653</td> <td>4006900</td>\n",
       "        </tr>\n",
       "        <tr>\n",
       "            <td>6   </td> <td>4073013</td> <td>4135930</td>\n",
       "        </tr>\n",
       "        <tr>\n",
       "            <td>7   </td> <td>4043046</td> <td>4155326</td>\n",
       "        </tr>\n",
       "        <tr>\n",
       "            <td>8   </td> <td>4025604</td> <td>4120903</td>\n",
       "        </tr>\n",
       "        <tr>\n",
       "            <td>9   </td> <td>4125415</td> <td>4108349</td>\n",
       "        </tr>\n",
       "        <tr>\n",
       "            <td>10  </td> <td>4187062</td> <td>4116942</td>\n",
       "        </tr>\n",
       "        <tr>\n",
       "            <td>11  </td> <td>4115511</td> <td>4087402</td>\n",
       "        </tr>\n",
       "        <tr>\n",
       "            <td>12  </td> <td>4113279</td> <td>4070682</td>\n",
       "        </tr>\n",
       "        <tr>\n",
       "            <td>13  </td> <td>4119666</td> <td>4171030</td>\n",
       "        </tr>\n",
       "        <tr>\n",
       "            <td>14  </td> <td>4145614</td> <td>4233839</td>\n",
       "        </tr>\n",
       "        <tr>\n",
       "            <td>15  </td> <td>4231002</td> <td>4164796</td>\n",
       "        </tr>\n",
       "        <tr>\n",
       "            <td>16  </td> <td>4313252</td> <td>4168559</td>\n",
       "        </tr>\n",
       "        <tr>\n",
       "            <td>17  </td> <td>4376367</td> <td>4186513</td>\n",
       "        </tr>\n",
       "        <tr>\n",
       "            <td>18  </td> <td>4491005</td> <td>4227920</td>\n",
       "        </tr>\n",
       "    </tbody>\n",
       "</table>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Read the full Census table\n",
    "census_url = 'http://www2.census.gov/programs-surveys/popest/datasets/2010-2015/national/asrh/nc-est2015-agesex-res.csv'\n",
    "full_census_table = Table.read_table(census_url)\n",
    "\n",
    "# Select columns from the full table and relabel some of them\n",
    "partial_census_table = full_census_table.select(['SEX', 'AGE', 'POPESTIMATE2010', 'POPESTIMATE2014'])\n",
    "us_pop = partial_census_table.relabeled('POPESTIMATE2010', '2010').relabeled('POPESTIMATE2014', '2014')\n",
    "\n",
    "# Access the rows corresponding to all children, ages 0-18\n",
    "children = us_pop.where('SEX', are.equal_to(0)).where('AGE', are.below(19)).drop('SEX')\n",
    "children.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can now draw two overlaid line plots, showing the numbers of children in the different age groups for each of the years 2010 and 2014. The method call is analogous to the `scatter` call in the previous example."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "children.plot('AGE')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "On this scale, it's important to remember that we only have data at ages 0, 1, 2, and so on; the graphs \"join the dots\" in between.\n",
    "\n",
    "The graphs cross each other in a few places: for example, there were more 4-year-olds in 2010 than in 2014, and there were more 14-year-olds in 2014 than in 2010.\n",
    "\n",
    "Of course, the 14-year-olds in 2014 mostly consist of the 10-year-olds in 2010. To see this, look at the gold graph at `AGE` 14 and the blue graph at `AGE` 10. Indeed, you will notice that the entire gold graph (2014) looks like the blue graph (2010) slid over to the right by 4 years. The slide is accompanied by a slight rise due to the net effect of children who entered the country between 2010 and 2014 outnumbering those who left. Fortunately at these ages there is not much loss of life."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Bar Charts ###\n",
    "\n",
    "For our final example of this section, we look at distributions of ethnicities of adults and children in California as well as in the entire United States.\n",
    "\n",
    "The Kaiser Family Foundation has complied Census data on the distribution of race and ethnicity in the U.S. The Foundation's website provides compilations of data for [the entire U.S. population](http://kff.org/other/state-indicator/distribution-by-raceethnicity/) in 2014, as well as for [U.S. children](http://kff.org/other/state-indicator/children-by-raceethnicity/) who were younger than 18 years old that year.\n",
    "\n",
    "Here is a table adapted from their data for the United States and California. The columns represent everyone in the U.S.A., everyone in California, children in the U.S.A., and children in California. The body of the table contains proportions in the different categories. Each column shows the distribution of ethnicities in the group of people corresponding to that column. So in each column, the entries add up to 1."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table border=\"1\" class=\"dataframe\">\n",
       "    <thead>\n",
       "        <tr>\n",
       "            <th>Ethnicity</th> <th>USA All</th> <th>CA All</th> <th>USA Children</th> <th>CA Children</th>\n",
       "        </tr>\n",
       "    </thead>\n",
       "    <tbody>\n",
       "        <tr>\n",
       "            <td>Black    </td> <td>0.12   </td> <td>0.05  </td> <td>0.14        </td> <td>0.05       </td>\n",
       "        </tr>\n",
       "        <tr>\n",
       "            <td>Hispanic </td> <td>0.18   </td> <td>0.38  </td> <td>0.24        </td> <td>0.5        </td>\n",
       "        </tr>\n",
       "        <tr>\n",
       "            <td>White    </td> <td>0.62   </td> <td>0.39  </td> <td>0.52        </td> <td>0.29       </td>\n",
       "        </tr>\n",
       "        <tr>\n",
       "            <td>Other    </td> <td>0.08   </td> <td>0.18  </td> <td>0.1         </td> <td>0.16       </td>\n",
       "        </tr>\n",
       "    </tbody>\n",
       "</table>"
      ],
      "text/plain": [
       "Ethnicity | USA All | CA All | USA Children | CA Children\n",
       "Black     | 0.12    | 0.05   | 0.14         | 0.05\n",
       "Hispanic  | 0.18    | 0.38   | 0.24         | 0.5\n",
       "White     | 0.62    | 0.39   | 0.52         | 0.29\n",
       "Other     | 0.08    | 0.18   | 0.1          | 0.16"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "usa_ca = Table.read_table(path_data + 'usa_ca_2014.csv')\n",
    "usa_ca"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "It is natural to want to compare these distributions. It makes sense to compare the columns directly, because all the entries are proportions and are therefore on the same scale.\n",
    "\n",
    "The method `barh` allows us to visualize the comparisons by drawing multiple bar charts on the same axes. The call is analogous to those for `scatter` and `plot`: we have to specify the common axis of categories. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "usa_ca.barh('Ethnicity')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "While drawing the overlaid bar charts is straightforward, there is a bit too much information on this graph for us to be able to sort out similarities and differences between populations. It seems clear that the distributions of ethnicities for everyone in the U.S. and for children in the U.S. are more similar to each other than any other pair, but it's much easier to compare the populations one pair at a time. \n",
    "\n",
    "Let's start by comparing the entire populations of the U.S.A. and California. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "usa_ca.select('Ethnicity', 'USA All', 'CA All').barh('Ethnicity')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The two distributions are quite different. California has higher proportions in the `Hispanic` and `Other` categories, and correspondingly lower proportions of `Black` and `White`. The differences are largely due to California's geographical location and patterns of immigration, both historically and in more recent decades. For example, the `Other` category in California includes a significant proportion of Asians and Pacific Islanders.\n",
    "\n",
    "As you can see from the graph, almost 40% of the Californian population in 2014 was `Hispanic`. A comparison with the population of children in the state indicates that the `Hispanic` proportion is likely to be greater in future years. Among Californian children, 50% are in the `Hispanic` category."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "usa_ca.select('Ethnicity', 'CA All', 'CA Children').barh('Ethnicity')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "More complex datasets naturally give rise to varied and interesting visualizations, including overlaid graphs of different kinds. To analyze such data, it helps to have some more skills in data manipulation, so that we can get the data into a form that allows us to use methods like those in this section. In the next chapter we will develop some of these skills."
   ]
  }
 ],
 "metadata": {
  "anaconda-cloud": {},
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.5.2"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 0
}