{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "tags": [
     "remove-input"
    ]
   },
   "outputs": [],
   "source": [
    "# Install datascience package if needed\n",
    "try:\n",
    "    import datascience\n",
    "except ImportError:\n",
    "    import micropip\n",
    "    await micropip.install('datascience')\n",
    "from datascience import *\n",
    "import numpy as np\n",
    "path_data = '../../../assets/data/'\n",
    "%matplotlib inline\n",
    "import matplotlib.pyplot as plots\n",
    "plots.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": [
    "## Overlaid Scatter Plots\n",
    "\n",
    "The table `sons_heights` is part of a historical data set on the 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": {},
   "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": [
    "sons_heights = Table.read_table(path_data + 'sons_heights.csv')\n",
    "sons_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": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sons_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",
    "Each point represents a row of the table, that is, a \"father, mother, son\" trio. For all points, the horizontal axis represents the son's height. In the blue points, the vertical axis represents the father's height. In the gold points, the vertical axis represents the mother's heights.\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": [
    "## Overlaid 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 table, 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": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table border=\"1\" class=\"dataframe\">\n",
       "    <thead>\n",
       "        <tr>\n",
       "            <th>AGE</th> <th>2014</th> <th>2019</th>\n",
       "        </tr>\n",
       "    </thead>\n",
       "    <tbody>\n",
       "        <tr>\n",
       "            <td>0   </td> <td>3954787</td> <td>3783052</td>\n",
       "        </tr>\n",
       "        <tr>\n",
       "            <td>1   </td> <td>3948891</td> <td>3829599</td>\n",
       "        </tr>\n",
       "        <tr>\n",
       "            <td>2   </td> <td>3958711</td> <td>3922044</td>\n",
       "        </tr>\n",
       "        <tr>\n",
       "            <td>3   </td> <td>4005928</td> <td>3998665</td>\n",
       "        </tr>\n",
       "        <tr>\n",
       "            <td>4   </td> <td>4004032</td> <td>4043323</td>\n",
       "        </tr>\n",
       "        <tr>\n",
       "            <td>5   </td> <td>4004576</td> <td>4028281</td>\n",
       "        </tr>\n",
       "        <tr>\n",
       "            <td>6   </td> <td>4133372</td> <td>4017227</td>\n",
       "        </tr>\n",
       "        <tr>\n",
       "            <td>7   </td> <td>4152666</td> <td>4022319</td>\n",
       "        </tr>\n",
       "        <tr>\n",
       "            <td>8   </td> <td>4118349</td> <td>4066194</td>\n",
       "        </tr>\n",
       "        <tr>\n",
       "            <td>9   </td> <td>4106068</td> <td>4061874</td>\n",
       "        </tr>\n",
       "        <tr>\n",
       "            <td>10  </td> <td>4114558</td> <td>4060940</td>\n",
       "        </tr>\n",
       "        <tr>\n",
       "            <td>11  </td> <td>4084457</td> <td>4189261</td>\n",
       "        </tr>\n",
       "        <tr>\n",
       "            <td>12  </td> <td>4067187</td> <td>4208387</td>\n",
       "        </tr>\n",
       "        <tr>\n",
       "            <td>13  </td> <td>4168095</td> <td>4175221</td>\n",
       "        </tr>\n",
       "        <tr>\n",
       "            <td>14  </td> <td>4231353</td> <td>4164459</td>\n",
       "        </tr>\n",
       "        <tr>\n",
       "            <td>15  </td> <td>4162828</td> <td>4175459</td>\n",
       "        </tr>\n",
       "        <tr>\n",
       "            <td>16  </td> <td>4165925</td> <td>4150420</td>\n",
       "        </tr>\n",
       "        <tr>\n",
       "            <td>17  </td> <td>4181940</td> <td>4142425</td>\n",
       "        </tr>\n",
       "        <tr>\n",
       "            <td>18  </td> <td>4221344</td> <td>4255827</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",
    "data = 'http://www2.census.gov/programs-surveys/popest/technical-documentation/file-layouts/2010-2019/nc-est2019-agesex-res.csv'\n",
    "full_census_table = Table.read_table(data)\n",
    "\n",
    "# Select columns from the full table and relabel some of them\n",
    "partial_census_table = full_census_table.select('SEX', 'AGE', 'POPESTIMATE2014', 'POPESTIMATE2019')\n",
    "us_pop = partial_census_table.relabeled('POPESTIMATE2014', '2014').relabeled('POPESTIMATE2019', '2019')\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 2014 and 2019. The call is analogous to the `scatter` call in the previous example."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "children.plot('AGE')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Though the horizontal axis labels include some half-integers, it's important to remember that we only have data at ages 0, 1, 2, and so on. The line plots \"join the dots\" in between.\n",
    "\n",
    "The two graphs cross each other in a few places. For example, there were more 6-year-olds in 2014 than in 2019, and there were more 12-year-olds in 2019 than in 2014.\n",
    "\n",
    "Of course, the 12-year-olds in 2019 mostly consist of the children who were 7-year-olds in 2014. To see this on the plots, compare the gold graph at `AGE` 12 and the blue graph at `AGE` 7. You will notice that the gold graph (2019) looks very much like the blue graph (2014) slid over to the right by 5 years. The slide is accompanied by a slight rise due to the net effect of children who entered the country between 2014 and 2019 outnumbering those who left. Fortunately at these ages there is not much loss of life."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Bar Charts\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 2019, 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",
    "The table `usa_ca` is 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. \n",
    "\n",
    "The body of the table contains percents in the different categories. Each column shows the distribution of the `Ethnicity/Race` variable in the group of people corresponding to that column. So in each column, the entries add up to 100. The `API` category consists of Asians and Pacific Islanders including Native Hawaiians. The `Other` category includes Native Americans, Alaskan natives, and people who identify with multiple races."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table border=\"1\" class=\"dataframe\">\n",
       "    <thead>\n",
       "        <tr>\n",
       "            <th>Ethnicity/Race</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>API           </td> <td>5.8    </td> <td>15.1  </td> <td>4.9         </td> <td>11.5       </td>\n",
       "        </tr>\n",
       "        <tr>\n",
       "            <td>Black         </td> <td>12.2   </td> <td>5.3   </td> <td>13.4        </td> <td>4.9        </td>\n",
       "        </tr>\n",
       "        <tr>\n",
       "            <td>Hispanic      </td> <td>18.5   </td> <td>39.5  </td> <td>25.6        </td> <td>52.1       </td>\n",
       "        </tr>\n",
       "        <tr>\n",
       "            <td>White         </td> <td>60.1   </td> <td>36.4  </td> <td>50          </td> <td>25.5       </td>\n",
       "        </tr>\n",
       "        <tr>\n",
       "            <td>Other         </td> <td>3.4    </td> <td>3.7   </td> <td>6.1         </td> <td>6          </td>\n",
       "        </tr>\n",
       "    </tbody>\n",
       "</table>"
      ],
      "text/plain": [
       "Ethnicity/Race | USA All | CA All | USA Children | CA Children\n",
       "API            | 5.8     | 15.1   | 4.9          | 11.5\n",
       "Black          | 12.2    | 5.3    | 13.4         | 4.9\n",
       "Hispanic       | 18.5    | 39.5   | 25.6         | 52.1\n",
       "White          | 60.1    | 36.4   | 50           | 25.5\n",
       "Other          | 3.4     | 3.7    | 6.1          | 6"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "usa_ca = Table.read_table(path_data + 'usa_ca_2019.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 percents 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": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "usa_ca.barh('Ethnicity/Race')"
   ]
  },
  {
   "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 is 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": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "usa_ca.select('Ethnicity/Race', 'USA All', 'CA All').barh('Ethnicity/Race')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The two distributions are quite different. California has higher percents in the `API` and `Hispanic` categories, and correspondingly lower percents in the`Black` and `White` categories. The percents in the `Other` category are quite similar in the two populations. The differences are largely due to California's geographical location and patterns of immigration and migration, both historically and in more recent decades. \n",
    "\n",
    "As you can see from the graph, almost 40% of the Californian population in 2019 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 in 2019, more than 50% were in the `Hispanic` category."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "usa_ca.select('Ethnicity/Race', 'CA All', 'CA Children').barh('Ethnicity/Race')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "More complex data sets 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."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "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": "3.8.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 1
}