{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Analysis: population estimates by age, sex and race/ethnicity"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "By [Christine Zhang](mailto:czhang@baltsun.com)\n",
    "\n",
    "The Baltimore Sun conducted an analysis of county-level population estimates for Baltimore — with comparison to other urban areas as well as Maryland counties — for a story published June 20, 2019 titled [\"Baltimore's white population swells with millennials, resembling D.C., Brooklyn\"](https://www.baltimoresun.com/maryland/baltimore-city/bs-md-census-estimate-population-race-20190619-story.html).\n",
    "\n",
    "Here are the key findings of the data analysis:\n",
    "\n",
    "- People in their mid-20s to mid-30s make up a substantial share of the white population in Baltimore, Brooklyn and several cities across the country. Washington, D.C. exhibits the most extreme example of this phenomenon\n",
    "- In Baltimore, more than a quarter of white residents now fall into that age category; in 2000, about 16% did\n",
    "- As of 2018, 37% of Baltimore residents within the age range were white and 51% were black; in 2000, it was 34% and 59%\n",
    "- The age distribution of blacks, who have made up a majority of Baltimore’s population since 1970s, resembles how the city’s white age distribution looked back in 1990"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## How we did it"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Run `01_processing.ipynb`"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The raw data files from the Census Bureau were pre-processed prior to the analysis. See the processing notebook `01_processing.ipynb` for more details."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Import R data analysis libraries"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "suppressMessages(library('tidyverse'))\n",
    "suppressMessages(library('stringr'))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Read in the data saved in the `output/` folder for analysis."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "data.90 <- suppressMessages(read_csv('output/data_1990.csv'))\n",
    "data.md.00 <- suppressMessages(read_csv('output/data_md_2000.csv'))\n",
    "data.18 <- suppressMessages(read.csv('output/data_2018.csv', stringsAsFactors = F))\n",
    "\n",
    "data.18 <- data.18 %>% mutate(STATE = str_pad(STATE, width = 2, pad = \"0\"),\n",
    "                              COUNTY = str_pad(COUNTY, width = 3, pad = \"0\"))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Finding: People in their mid-20s to mid-30s make up a substantial share of the white population in Baltimore, Brooklyn and several cities across the country"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [],
   "source": [
    "data.18 <- data.18 %>% \n",
    "           filter(AGEGRP != 0 & race != 'all') %>% \n",
    "           group_by(STATE, STNAME, COUNTY, YEAR) %>%\n",
    "  mutate(total.pop = sum(value)) %>%\n",
    "  ungroup() %>%\n",
    "  group_by(STATE, STNAME, COUNTY, YEAR, race_name, sex) %>%\n",
    "  mutate(total.by.race_sex = sum(value)) %>%\n",
    "  mutate(perc = (value/total.by.race_sex) * 100)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [],
   "source": [
    "data.18 <- data.18 %>% \n",
    "  mutate(fips = paste0(STATE, COUNTY),\n",
    "         name = case_when(CTYNAME == 'Baltimore city' ~ 'Baltimore',\n",
    "                          CTYNAME == 'Kings County' ~ 'Brooklyn',\n",
    "                          CTYNAME == 'New York County' ~ 'CManhattan',\n",
    "                          TRUE ~ CTYNAME)) "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Make population pyramid plots which show skewed age distributions for the cities"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {},
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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hXn+ZIACZBAIhDwlzBy7Xoo/YAnY9er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UgLJvChGOLEYQROAAJkTwcDCAAfHIUTwIiogg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\nOAAAAABJRU5ErkJggg==",
      "text/plain": [
       "plot without title"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "data.18 <- data.18 %>% \n",
    "  mutate(perc2 = ifelse(sex == 'MALE', perc*-1, perc))\n",
    "\n",
    "ggplot(data.18 %>% filter(AGEGRP != 0 & \n",
    "                          YEAR == 11 & \n",
    "                          sex != 'POP' &\n",
    "                          fips %in% c('24510', '36047', '36061', '42101', '29510', '11001') & \n",
    "                          race_name %in% c('White'))) +\n",
    "  geom_bar(aes(x = age_name, y = perc2, \n",
    "               fill = sex), stat = 'identity') +\n",
    "  facet_wrap(~name) + \n",
    "  coord_flip() + \n",
    "  scale_fill_manual(values = c('#5ab4ac',\n",
    "                               '#af8dc3')) +\n",
    "  labs(x = 'age range\\n', y = '\\npopulation') +\n",
    "  theme(panel.grid.minor.y = element_blank(),\n",
    "        panel.grid.major.y = element_blank(),\n",
    "        panel.grid.minor.x = element_blank(),\n",
    "        strip.background = element_blank(),\n",
    "        legend.position = 'top',\n",
    "        legend.title = element_blank(),\n",
    "        panel.spacing = unit(1.5, \"lines\"))\n",
    "\n",
    "plot1 <- ggplot(data.18 %>% filter(AGEGRP != 0 & \n",
    "                          YEAR == 11 & \n",
    "                          sex != 'POP' &\n",
    "                          fips %in% c('24510', '36047', '36061', '42101', '29510', '11001') & \n",
    "                          race_name %in% c('White'))) +\n",
    "  geom_bar(aes(x = age_name, y = perc2, \n",
    "               fill = sex), stat = 'identity') +\n",
    "  facet_wrap(~name, nrow = 1) + \n",
    "  coord_flip() + \n",
    "  scale_fill_manual(values = c('#5ab4ac',\n",
    "                               '#af8dc3')) +\n",
    "  labs(x = 'age range\\n', y = '\\npopulation') +\n",
    "  theme(panel.grid.minor.y = element_blank(),\n",
    "        panel.grid.major.y = element_blank(),\n",
    "        panel.grid.minor.x = element_blank(),\n",
    "        strip.background = element_blank(),\n",
    "        legend.position = 'top',\n",
    "        legend.title = element_blank(),\n",
    "        panel.spacing = unit(1.5, \"lines\"))\n",
    "\n",
    "ggsave('output/plot1.png', plot1, width = 15, height = 5)\n",
    "ggsave('output/plot1.eps', plot1, width = 15, height = 6) # for Illustrator"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Washington, D.C. is particularly skewed, with hardly any \"bulge\" in the older age brackets."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Finding: In Baltimore, more than a quarter of white residents now fall into that age category; in 2000, about 16% did"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Summarize the 2018 data to see how many white residents were in the 25-34 age category."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "balt.18.sum.white <- data.18 %>% filter(CTYNAME == 'Baltimore city' & \n",
    "                     year_name == 2018 & (race_name == 'White')) %>% \n",
    "                     ungroup() %>% group_by(age_name, race_name) %>% summarise(n = sum(value)) %>% ungroup() %>% \n",
    "                     mutate(tot = sum(n), perc = (n/tot)*100)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table>\n",
       "<thead><tr><th scope=col>age_name</th><th scope=col>race_name</th><th scope=col>n</th><th scope=col>tot</th><th scope=col>perc</th></tr></thead>\n",
       "<tbody>\n",
       "\t<tr><td>age_00-04</td><td>White    </td><td> 8324    </td><td>167248   </td><td> 4.977040</td></tr>\n",
       "\t<tr><td>age_05-09</td><td>White    </td><td> 5564    </td><td>167248   </td><td> 3.326796</td></tr>\n",
       "\t<tr><td>age_10-14</td><td>White    </td><td> 4777    </td><td>167248   </td><td> 2.856237</td></tr>\n",
       "\t<tr><td>age_15-19</td><td>White    </td><td> 6713    </td><td>167248   </td><td> 4.013800</td></tr>\n",
       "\t<tr><td>age_20-24</td><td>White    </td><td>10177    </td><td>167248   </td><td> 6.084976</td></tr>\n",
       "\t<tr><td>age_25-29</td><td>White    </td><td>21470    </td><td>167248   </td><td>12.837224</td></tr>\n",
       "\t<tr><td>age_30-34</td><td>White    </td><td>21108    </td><td>167248   </td><td>12.620779</td></tr>\n",
       "\t<tr><td>age_35-39</td><td>White    </td><td>13835    </td><td>167248   </td><td> 8.272147</td></tr>\n",
       "\t<tr><td>age_40-44</td><td>White    </td><td> 9452    </td><td>167248   </td><td> 5.651488</td></tr>\n",
       "\t<tr><td>age_45-49</td><td>White    </td><td> 8959    </td><td>167248   </td><td> 5.356716</td></tr>\n",
       "\t<tr><td>age_50-54</td><td>White    </td><td> 9602    </td><td>167248   </td><td> 5.741175</td></tr>\n",
       "\t<tr><td>age_55-59</td><td>White    </td><td>10163    </td><td>167248   </td><td> 6.076605</td></tr>\n",
       "\t<tr><td>age_60-64</td><td>White    </td><td>10429    </td><td>167248   </td><td> 6.235650</td></tr>\n",
       "\t<tr><td>age_65-69</td><td>White    </td><td> 8963    </td><td>167248   </td><td> 5.359107</td></tr>\n",
       "\t<tr><td>age_70-74</td><td>White    </td><td> 6843    </td><td>167248   </td><td> 4.091529</td></tr>\n",
       "\t<tr><td>age_75-79</td><td>White    </td><td> 4391    </td><td>167248   </td><td> 2.625442</td></tr>\n",
       "\t<tr><td>age_80-84</td><td>White    </td><td> 2813    </td><td>167248   </td><td> 1.681933</td></tr>\n",
       "\t<tr><td>age_85+  </td><td>White    </td><td> 3665    </td><td>167248   </td><td> 2.191357</td></tr>\n",
       "</tbody>\n",
       "</table>\n"
      ],
      "text/latex": [
       "\\begin{tabular}{r|lllll}\n",
       " age\\_name & race\\_name & n & tot & perc\\\\\n",
       "\\hline\n",
       "\t age\\_00-04 & White       &  8324       & 167248      &  4.977040  \\\\\n",
       "\t age\\_05-09 & White       &  5564       & 167248      &  3.326796  \\\\\n",
       "\t age\\_10-14 & White       &  4777       & 167248      &  2.856237  \\\\\n",
       "\t age\\_15-19 & White       &  6713       & 167248      &  4.013800  \\\\\n",
       "\t age\\_20-24 & White       & 10177       & 167248      &  6.084976  \\\\\n",
       "\t age\\_25-29 & White       & 21470       & 167248      & 12.837224  \\\\\n",
       "\t age\\_30-34 & White       & 21108       & 167248      & 12.620779  \\\\\n",
       "\t age\\_35-39 & White       & 13835       & 167248      &  8.272147  \\\\\n",
       "\t age\\_40-44 & White       &  9452       & 167248      &  5.651488  \\\\\n",
       "\t age\\_45-49 & White       &  8959       & 167248      &  5.356716  \\\\\n",
       "\t age\\_50-54 & White       &  9602       & 167248      &  5.741175  \\\\\n",
       "\t age\\_55-59 & White       & 10163       & 167248      &  6.076605  \\\\\n",
       "\t age\\_60-64 & White       & 10429       & 167248      &  6.235650  \\\\\n",
       "\t age\\_65-69 & White       &  8963       & 167248      &  5.359107  \\\\\n",
       "\t age\\_70-74 & White       &  6843       & 167248      &  4.091529  \\\\\n",
       "\t age\\_75-79 & White       &  4391       & 167248      &  2.625442  \\\\\n",
       "\t age\\_80-84 & White       &  2813       & 167248      &  1.681933  \\\\\n",
       "\t age\\_85+   & White       &  3665       & 167248      &  2.191357  \\\\\n",
       "\\end{tabular}\n"
      ],
      "text/markdown": [
       "\n",
       "| age_name | race_name | n | tot | perc |\n",
       "|---|---|---|---|---|\n",
       "| age_00-04 | White     |  8324     | 167248    |  4.977040 |\n",
       "| age_05-09 | White     |  5564     | 167248    |  3.326796 |\n",
       "| age_10-14 | White     |  4777     | 167248    |  2.856237 |\n",
       "| age_15-19 | White     |  6713     | 167248    |  4.013800 |\n",
       "| age_20-24 | White     | 10177     | 167248    |  6.084976 |\n",
       "| age_25-29 | White     | 21470     | 167248    | 12.837224 |\n",
       "| age_30-34 | White     | 21108     | 167248    | 12.620779 |\n",
       "| age_35-39 | White     | 13835     | 167248    |  8.272147 |\n",
       "| age_40-44 | White     |  9452     | 167248    |  5.651488 |\n",
       "| age_45-49 | White     |  8959     | 167248    |  5.356716 |\n",
       "| age_50-54 | White     |  9602     | 167248    |  5.741175 |\n",
       "| age_55-59 | White     | 10163     | 167248    |  6.076605 |\n",
       "| age_60-64 | White     | 10429     | 167248    |  6.235650 |\n",
       "| age_65-69 | White     |  8963     | 167248    |  5.359107 |\n",
       "| age_70-74 | White     |  6843     | 167248    |  4.091529 |\n",
       "| age_75-79 | White     |  4391     | 167248    |  2.625442 |\n",
       "| age_80-84 | White     |  2813     | 167248    |  1.681933 |\n",
       "| age_85+   | White     |  3665     | 167248    |  2.191357 |\n",
       "\n"
      ],
      "text/plain": [
       "   age_name  race_name n     tot    perc     \n",
       "1  age_00-04 White      8324 167248  4.977040\n",
       "2  age_05-09 White      5564 167248  3.326796\n",
       "3  age_10-14 White      4777 167248  2.856237\n",
       "4  age_15-19 White      6713 167248  4.013800\n",
       "5  age_20-24 White     10177 167248  6.084976\n",
       "6  age_25-29 White     21470 167248 12.837224\n",
       "7  age_30-34 White     21108 167248 12.620779\n",
       "8  age_35-39 White     13835 167248  8.272147\n",
       "9  age_40-44 White      9452 167248  5.651488\n",
       "10 age_45-49 White      8959 167248  5.356716\n",
       "11 age_50-54 White      9602 167248  5.741175\n",
       "12 age_55-59 White     10163 167248  6.076605\n",
       "13 age_60-64 White     10429 167248  6.235650\n",
       "14 age_65-69 White      8963 167248  5.359107\n",
       "15 age_70-74 White      6843 167248  4.091529\n",
       "16 age_75-79 White      4391 167248  2.625442\n",
       "17 age_80-84 White      2813 167248  1.681933\n",
       "18 age_85+   White      3665 167248  2.191357"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "balt.18.sum.white"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Sum the age group totals for age_25-29 and age_30-34 to get the percentage."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "25.4580024873242"
      ],
      "text/latex": [
       "25.4580024873242"
      ],
      "text/markdown": [
       "25.4580024873242"
      ],
      "text/plain": [
       "[1] 25.458"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "balt.18.sum.white[balt.18.sum.white$age_name == 'age_25-29',]$perc + \n",
    "balt.18.sum.white[balt.18.sum.white$age_name == 'age_30-34',]$perc"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Summarize the 2000 data to see how many white residents were in the 25-34 age category."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "balt.00.sum.white <- data.md.00 %>% filter(CTYNAME == 'Baltimore city' & age_name != 'total' &\n",
    "                     year_name == 2000 & (race_name == 'White')) %>% \n",
    "                     ungroup() %>% group_by(age_name, race_name) %>% summarise(n = sum(value)) %>% ungroup() %>% \n",
    "                     mutate(tot = sum(n), perc = (n/tot)*100)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Sum the age group totals for age_25-29 and age_30-34 to get the percentage."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "15.8257745668182"
      ],
      "text/latex": [
       "15.8257745668182"
      ],
      "text/markdown": [
       "15.8257745668182"
      ],
      "text/plain": [
       "[1] 15.82577"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "balt.00.sum.white[balt.00.sum.white$age_name == 'age_25-29',]$perc + \n",
    "balt.00.sum.white[balt.00.sum.white$age_name == 'age_30-34',]$perc"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Finding: As of 2018, 37% of Baltimore residents within the age range were white and 51% were black; in 2000, it was 35% and 53%"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Summarize the 2018 data to see the race distribution of people in the age categories of `age_25-29` and `age_30-34`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "balt.18.sum.age_25_34 <- data.18 %>% filter(CTYNAME == 'Baltimore city' & age_name != 'total' & \n",
    "                                            race != 'all' & \n",
    "                                            year_name == 2018) %>% \n",
    "                                 group_by(age_name, race, race_name) %>%\n",
    "                                 summarise(value = sum(value)) %>% filter(age_name == 'age_25-29' | \n",
    "                                                                          age_name == 'age_30-34') %>%\n",
    "ungroup() %>% mutate(tot.25_34 = sum(value)) %>%\n",
    "ungroup() %>% group_by(race_name, tot.25_34) %>% \n",
    "summarise(tot.race.25_34 = sum(value)) %>% mutate(perc = (tot.race.25_34/tot.25_34) * 100)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table>\n",
       "<thead><tr><th scope=col>race_name</th><th scope=col>tot.25_34</th><th scope=col>tot.race.25_34</th><th scope=col>perc</th></tr></thead>\n",
       "<tbody>\n",
       "\t<tr><td>American Indian and Alaska Native         </td><td>115483                                    </td><td>  233                                     </td><td> 0.20176130                               </td></tr>\n",
       "\t<tr><td>Asian                                     </td><td>115483                                    </td><td> 5182                                     </td><td> 4.48724055                               </td></tr>\n",
       "\t<tr><td>Black                                     </td><td>115483                                    </td><td>59204                                     </td><td>51.26642017                               </td></tr>\n",
       "\t<tr><td>Hispanic                                  </td><td>115483                                    </td><td> 6247                                     </td><td> 5.40945421                               </td></tr>\n",
       "\t<tr><td>Multiple                                  </td><td>115483                                    </td><td> 2001                                     </td><td> 1.73272257                               </td></tr>\n",
       "\t<tr><td>Native Hawaiian and Other Pacific Islander</td><td>115483                                    </td><td>   38                                     </td><td> 0.03290528                               </td></tr>\n",
       "\t<tr><td>White                                     </td><td>115483                                    </td><td>42578                                     </td><td>36.86949594                               </td></tr>\n",
       "</tbody>\n",
       "</table>\n"
      ],
      "text/latex": [
       "\\begin{tabular}{r|llll}\n",
       " race\\_name & tot.25\\_34 & tot.race.25\\_34 & perc\\\\\n",
       "\\hline\n",
       "\t American Indian and Alaska Native          & 115483                                     &   233                                      &  0.20176130                               \\\\\n",
       "\t Asian                                      & 115483                                     &  5182                                      &  4.48724055                               \\\\\n",
       "\t Black                                      & 115483                                     & 59204                                      & 51.26642017                               \\\\\n",
       "\t Hispanic                                   & 115483                                     &  6247                                      &  5.40945421                               \\\\\n",
       "\t Multiple                                   & 115483                                     &  2001                                      &  1.73272257                               \\\\\n",
       "\t Native Hawaiian and Other Pacific Islander & 115483                                     &    38                                      &  0.03290528                               \\\\\n",
       "\t White                                      & 115483                                     & 42578                                      & 36.86949594                               \\\\\n",
       "\\end{tabular}\n"
      ],
      "text/markdown": [
       "\n",
       "| race_name | tot.25_34 | tot.race.25_34 | perc |\n",
       "|---|---|---|---|\n",
       "| American Indian and Alaska Native          | 115483                                     |   233                                      |  0.20176130                                |\n",
       "| Asian                                      | 115483                                     |  5182                                      |  4.48724055                                |\n",
       "| Black                                      | 115483                                     | 59204                                      | 51.26642017                                |\n",
       "| Hispanic                                   | 115483                                     |  6247                                      |  5.40945421                                |\n",
       "| Multiple                                   | 115483                                     |  2001                                      |  1.73272257                                |\n",
       "| Native Hawaiian and Other Pacific Islander | 115483                                     |    38                                      |  0.03290528                                |\n",
       "| White                                      | 115483                                     | 42578                                      | 36.86949594                                |\n",
       "\n"
      ],
      "text/plain": [
       "  race_name                                  tot.25_34 tot.race.25_34\n",
       "1 American Indian and Alaska Native          115483      233         \n",
       "2 Asian                                      115483     5182         \n",
       "3 Black                                      115483    59204         \n",
       "4 Hispanic                                   115483     6247         \n",
       "5 Multiple                                   115483     2001         \n",
       "6 Native Hawaiian and Other Pacific Islander 115483       38         \n",
       "7 White                                      115483    42578         \n",
       "  perc       \n",
       "1  0.20176130\n",
       "2  4.48724055\n",
       "3 51.26642017\n",
       "4  5.40945421\n",
       "5  1.73272257\n",
       "6  0.03290528\n",
       "7 36.86949594"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "balt.18.sum.age_25_34"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Summarize the 2000 data to see the race distribution of people in the age category"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "balt.00.sum.age_25_34 <- data.md.00 %>% filter(CTYNAME == 'Baltimore city' & age_name != 'total' & \n",
    "                                            race != 'all' & \n",
    "                                            year_name == 2000) %>% \n",
    "                                 group_by(age_name, race, race_name) %>%\n",
    "                                 summarise(value = sum(value)) %>% filter(age_name == 'age_25-29' | \n",
    "                                                                          age_name == 'age_30-34') %>%\n",
    "ungroup() %>% mutate(tot.25_34 = sum(value)) %>%\n",
    "ungroup() %>% group_by(race_name, tot.25_34) %>% \n",
    "summarise(tot.race.25_34 = sum(value)) %>% mutate(perc = (tot.race.25_34/tot.25_34) * 100)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table>\n",
       "<thead><tr><th scope=col>race_name</th><th scope=col>tot.25_34</th><th scope=col>tot.race.25_34</th><th scope=col>perc</th></tr></thead>\n",
       "<tbody>\n",
       "\t<tr><td>American Indian and Alaska Native         </td><td>92467                                     </td><td>  244                                     </td><td> 0.26387792                               </td></tr>\n",
       "\t<tr><td>Asian                                     </td><td>92467                                     </td><td> 2710                                     </td><td> 2.93077530                               </td></tr>\n",
       "\t<tr><td>Black                                     </td><td>92467                                     </td><td>54254                                     </td><td>58.67390529                               </td></tr>\n",
       "\t<tr><td>Hispanic                                  </td><td>92467                                     </td><td> 2420                                     </td><td> 2.61714990                               </td></tr>\n",
       "\t<tr><td>Multiple                                  </td><td>92467                                     </td><td>  908                                     </td><td> 0.98197195                               </td></tr>\n",
       "\t<tr><td>Native Hawaiian and Other Pacific Islander</td><td>92467                                     </td><td>   37                                     </td><td> 0.04001428                               </td></tr>\n",
       "\t<tr><td>White                                     </td><td>92467                                     </td><td>31894                                     </td><td>34.49230536                               </td></tr>\n",
       "</tbody>\n",
       "</table>\n"
      ],
      "text/latex": [
       "\\begin{tabular}{r|llll}\n",
       " race\\_name & tot.25\\_34 & tot.race.25\\_34 & perc\\\\\n",
       "\\hline\n",
       "\t American Indian and Alaska Native          & 92467                                      &   244                                      &  0.26387792                               \\\\\n",
       "\t Asian                                      & 92467                                      &  2710                                      &  2.93077530                               \\\\\n",
       "\t Black                                      & 92467                                      & 54254                                      & 58.67390529                               \\\\\n",
       "\t Hispanic                                   & 92467                                      &  2420                                      &  2.61714990                               \\\\\n",
       "\t Multiple                                   & 92467                                      &   908                                      &  0.98197195                               \\\\\n",
       "\t Native Hawaiian and Other Pacific Islander & 92467                                      &    37                                      &  0.04001428                               \\\\\n",
       "\t White                                      & 92467                                      & 31894                                      & 34.49230536                               \\\\\n",
       "\\end{tabular}\n"
      ],
      "text/markdown": [
       "\n",
       "| race_name | tot.25_34 | tot.race.25_34 | perc |\n",
       "|---|---|---|---|\n",
       "| American Indian and Alaska Native          | 92467                                      |   244                                      |  0.26387792                                |\n",
       "| Asian                                      | 92467                                      |  2710                                      |  2.93077530                                |\n",
       "| Black                                      | 92467                                      | 54254                                      | 58.67390529                                |\n",
       "| Hispanic                                   | 92467                                      |  2420                                      |  2.61714990                                |\n",
       "| Multiple                                   | 92467                                      |   908                                      |  0.98197195                                |\n",
       "| Native Hawaiian and Other Pacific Islander | 92467                                      |    37                                      |  0.04001428                                |\n",
       "| White                                      | 92467                                      | 31894                                      | 34.49230536                                |\n",
       "\n"
      ],
      "text/plain": [
       "  race_name                                  tot.25_34 tot.race.25_34\n",
       "1 American Indian and Alaska Native          92467       244         \n",
       "2 Asian                                      92467      2710         \n",
       "3 Black                                      92467     54254         \n",
       "4 Hispanic                                   92467      2420         \n",
       "5 Multiple                                   92467       908         \n",
       "6 Native Hawaiian and Other Pacific Islander 92467        37         \n",
       "7 White                                      92467     31894         \n",
       "  perc       \n",
       "1  0.26387792\n",
       "2  2.93077530\n",
       "3 58.67390529\n",
       "4  2.61714990\n",
       "5  0.98197195\n",
       "6  0.04001428\n",
       "7 34.49230536"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "balt.00.sum.age_25_34"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Finding: The age distribution of blacks, who have made up a majority of Baltimore’s population since 1970s, resembles how the city’s white age distribution looked back in 1990"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Plot the age distribution of blacks in 2018"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "balt.18 <- data.18 %>% filter(year_name == 2018 & sex != 'POP' &\n",
    "                              AGEGRP != 0 &\n",
    "                          fips %in% c('24510'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {},
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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CCAAAIIIIAAAukhwBik9LgOHAUCCCCAAAIIIIAAAgikgQABUhpcBA4BAQQQQAAB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\nda8TJkxw446++OIL1x1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AhAvMi//p+XgRfplcXgQJTKrVKIKuJmtfiv5OVWEt/R2h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erV1r17d3fD4AYNGrhA6eGHH7bzzjsv8lBeZ4jAQw89ZLNnz3a/H7wuN23a1AYNGuS9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\nRK5CYII=",
      "text/plain": [
       "plot without title"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ggplot(data.18 %>% filter(AGEGRP != 0 & \n",
    "                          YEAR == 11 & \n",
    "                          sex != 'POP' &\n",
    "                          fips %in% c('24510') & \n",
    "                          race_name %in% c('Black'))) +\n",
    "  geom_bar(aes(x = age_name, y = perc2, \n",
    "               fill = sex), stat = 'identity') +\n",
    "  coord_flip() + \n",
    "  scale_fill_manual(values = c('#5ab4ac',\n",
    "                               '#af8dc3')) +\n",
    "  labs(x = 'age range\\n', y = '\\npopulation') +\n",
    "  theme(panel.grid.minor.y = element_blank(),\n",
    "        panel.grid.major.y = element_blank(),\n",
    "        panel.grid.minor.x = element_blank(),\n",
    "        legend.position = 'top',\n",
    "        legend.title = element_blank()) +\n",
    "  scale_y_continuous(limits = c(-12,12)) + ggtitle('Baltimore black population, 2018')\n",
    "\n",
    "ggsave('output/plot2_2018.png', width = 6, height = 8)\n",
    "ggsave('output/plot2_2018.eps', width = 6, height = 8) # Illustrator"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Plot the age distribution of whites in 1990"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "balt.data.90 <- data.90 %>% filter(fips == '24510' & \n",
    "                             race == 'White' & \n",
    "                             hisp == 'not_Hispanic' & year == 1990) %>%\n",
    "  group_by(fips, race, sex) %>%\n",
    "  mutate(race.sex.total = sum(pop)) %>%\n",
    "  mutate(perc = (pop/race.sex.total) * 100)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {},
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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kLaFuT6s+kOhgtCAAEEEEAA\nAQQQQACBjCcQdICk7FDHjh1NQ+i0wKoKMVSqVMl+/vln2717t1v49J133sl4ktwxAgikiUDD\nntFp0u/N7DQyMtIiIiLc/1TSgtM0BKKioqzdLxVgY2JiwEDACWhxev3P5aNHjyKCAAK3WCDo\nIXaLFi1yQdC+ffvcArD333+/Pf3007Z161bbtm2bFSlSxAVPt/i++HgEEEAAAQQQQAABBBBA\nIGiBoAOk77//3h5++GG744473IdVrFjR1qxZ416rkt2wYcNs4MCBQV8IJyCAAAIIIIAAAggg\ngAACt1og6AApf/78litXLt91ly1b1lTq22vVq1d3w0h+/PFHbxN/EUAAAQQQQAABBBBAAIGQ\nEAh6DlK5cuXcYrCHDx92w+k0xE6Lwh44cMBKlizphtllzpzZsmXLFhIAXCQCGVlg6ehNGfn2\nuXcEbqnAvOiDt/Tz+XAEbobApFqNbsbH8BkIpKpA0Bmk559/3mWQ7rnnHvviiy+sTp06ljt3\nbnviiSfs73//u3Xv3t0NwdNcJBoCCCCAAAIIIIAAAgggEEoCQQdIhQoVsgULFpjmHqlynYbc\nqWrdpk2bbMCAAfbDDz9Yz549Q8mAa0UAAQQQQAABBBBAAAEEnEDQQ+x01iOPPOKyR95aR889\n95zVr1/fzUUqX768lShRAl4EEEAAAQQQQAABBBBAIOQEUhQgeXeZKVMm76Wbj9SwYUPfe14g\ngAACCCCAAAIIIIAAAqEmEPQQu1C7Qa4XAQQQQAABBBBAAAEEEAhUgAApUCmOQwABBBBAAAEE\nEEAAgbAXIEAK+0fMDSKAAAIIIIAAAggggECgAgRIgUpxHAIIIIAAAggggAACCIS9AAFS2D9i\nbhABBBBAAAEEEEAAAQQCFSBAClSK4xBAAAEEEEAAAQQQQCDsBX5Xme+w10nhDV69etXWrVtn\ne/futQoVKtgDDzwQq6c9e/a4ff4bCxQoYJUrV/bfFO/1999/b2vWrLHbb7/dqlatarly5Yp1\nzK5du9znRkVFuf1axJeGAAIIIIAAAggggAACgQsQIAVuFdCRp06dsueff94KFixod955p02f\nPt2aNGli3bt3950/c+ZMW716teXNm9e3TYFUUgHS4MGDbf369Va9enVbtGiRffDBB/aPf/zD\nIiMjXR/z5s2z0aNHW9myZV2/w4cPtzFjxpgW7qUhgAACCCCAAAIIIIBAYAIESIE5BXyUApdi\nxYrZxIkT3TnK+PTt29eeeuopt5iuNirT8+KLL9qTTz4ZUL9btmyxzz//3BRYqe/Lly9b8+bN\nbcmSJdayZUs7duyYjRs3ztq0aWOdOnVyfc6ZM8cGDBhgM2bMsNy5cwf0ORyEAAIIIIAAAggg\ngEBGFwi7AEnBg4KDHTt22Pnz561kyZIuiChatKh71tqvDIyGwOXLl89atWpls2fPtvbt21uh\nQoXcMbt377b58+fboUOHrHTp0ta6dWuXEQrkH0utWrWsUaNGvkO9YW4nT550AdKlS5fswIED\nLtPjOyiZF8pGDRs2zAVHOjRr1qzu2k+cOOHO/O677+zKlSsuU+V1VbduXRs7dqxt2LDBatSo\n4W3mLwIIIIAAAggggAACCCQhEHZFGvr06WMrVqywSpUqWbVq1dywtJ49e9r169cdw8iRI23a\ntGlu6Fm2bNmsR48eLmA6e/as269hbF26dLGLFy9a7dq1bfv27da2bVuXpUnC0bdL843uuusu\nUyD09ddfu2Fv2nbvvfe6Y/bt2+euRZmlDh062DPPPGMTJkxwx/s6ifNCWSPNOVLT/CUFPqdP\nn7YGDRr4jsyUKZNlyZLF914Bk+75p59+8m3jBQIIIIAAAggggAACCCQtEFYZJAUNytgoSFLm\nR00ZJA1x09ygo0eP2uLFi93cnIoVK7r9Ko4wdepUu3HjhnuvoWoKRjTnR61p06Yuu6S5RL17\n93bbAvn10Ucf2aRJk1zgM3ToUMuc+ddYVNkpNQVQ3bp1s2+++cYWLFhgygb1798/ya51/Trn\nwoUL9qc//clKlCjhjr/vvvtMwd6sWbOsa9eupmBJGTA1HUtDAAEEEEAAAQQQQACBwATCKkBS\nwYIhQ4aYghAFQhrKtnnzZiehgGTnzp1uPo4KInhNRQ8UIKlp+J0yNKoCp6yO1xTc6NxgmuYc\naZ7QqlWrbODAgS74adiwodWvX98VY1BWSO2hhx5ymR9ltVTIYeXKlXbu3DnfRzVr1sxy5szp\n3msI4KeffuquUUGX+n3rrbfc8L+XX37Z3n77bfvss89cf2XKlLFSpUrFq3Tn65gXCCCAAAII\nIIAAAgggEE8grAIkBUHKwqiogYIg/dSrV8+2bt3qbvzMmTOmjJHm8HgtT5483ks3Z0mZJJXP\nVhbGa1WqVIlVcc7bntxffY6G6X3yySf273//2xQg5ciRwzeXyDtfQwEVIGnO0/Lly+3IkSPe\nLneOFyB5G++++257+umnTZXqNM9KRRgef/xxU1ZM967MkqrXKbhSsEdDAAEEEEAAAQQQQACB\nwAR+ixQCOz5dH6VsjeYQqehCkSJF3LUqI6OmwEdZFQUhCpRUoEFt7dq17q9+aXheRESEy8h0\n7tzZt13H+AdVvh0JvHjllVfskUcecVXrvN3KCHmfN3fuXFcgQkUXvKYslwIyZZVGjRrlbfb9\n1f1ozpIyRF5TYKQ5Rjrv559/dsPrNBywcePG7hBlvDTkMO4aTN75/EUAAQQQQAABBBBAAIH4\nAmFVpEHZkmvXrpkqxqkpGNI8IDVll5SpKV68uCt/vXHjRlu2bJkLptwB//dLw+I0jE3rFKmv\nTZs2Wb9+/Vyw4X9cYq8VHKm0thZ11WcuXLjQtm3bZo899pg7RUP6VLxB27WgrAI6vVZ2yX9d\nJP/+H330UdP1fvzxx+4cBVRa90jbFdApw6RqdVOmTHGfqftXMPXss8+aV73Pvz9eI4AAAggg\ngAACCCCAQMICmX7JrPxanSDh/SG39c0333TD2RQ0KOujogUjRoywXr16ufk/WjNIQ9O+/fZb\nN/xMi7iqKpzWGLrjjjtcgKFCDSqyoKpwGpKnanEdO3YMyEJB0euvv+6uIXv27O4alI1q0aKF\n73yVIX/33XddBkhBmPpXAQgNv0usqZDD+PHj3TmaK6WhgzrHGyKojJHuQ39VsEGlvf/85z8H\nlPlSAQtV7aOljoCGPCpjqMIb+veQntvS0ZvS8+VxbQiEtcC86INhfX/cHAISmFTrt6VPEAlc\nQCOE9D+59T3CW9Yl8LM5MjEBfbcvXLhwYrt928MuQNKdqcS1hpdp/SD/psyKymxrro43x0jZ\nGBVH0KKrXrChc5Td0T/IQBD9P8N7rWF1GsqnoX7+5be9/epfc410jQqkAmk6R1kxZco0Tyqh\npmBH9xHokED1QYCUkGTKtxEgpdyOMxHISAIESBnpaWfceyVAStmzJ0BKmVtyZwUaIIXVEDsP\nRRmUuMGR9ilbo0ySskVa90gV65SVUVlv/+BIxyrAiBsc6XwFKYn9eGst6Xz1p+F8CQVHXv/a\nH2hw5J2jLFdiwZGOue2224IKjnQODQEEEEAAAQQQQAABBH4VCKsiDck9VAVNgwYNcnN+Jk+e\n7Kq/Va5c2a2TlNy52q+FXY8fP57ooTVr1gy4r0Q7YQcCCCCAAAIIIIAAAgjcMoEMFSBJuU6d\nOu5H83iCyd7oXJXipiGAAAIIIIAAAggggED4CoTlELtAHlewwVEgfXIMAggggAACCCCAAAII\nhLZAhg2QQvuxcfUIIIAAAggggAACCCCQFgIESGmhSp8IIIAAAggggAACCCAQkgIESCH52Lho\nBBBAAAEEEEAAAQQQSAsBAqS0UKVPBBBAAAEEEEAAAQQQCEkBAqSQfGxcNAIIIIAAAggggAAC\nCKSFAAFSWqjSJwIIIIAAAggggAACCISkQIZbBykknxIXjUAaCTTsGZ1GPdOtv0BkZKRFRETY\nkSNH7Nq1a/67eJ1BBaKioqxd9uwWExOTQQW47bgChQsXths3btjRo0fj7uI9AgjcZAEySDcZ\nnI9DAAEEEEAAAQQQQACB9CtAgJR+nw1XhgACCCCAAAIIIIAAAjdZgCF2Nxmcj8tYAktHb8pY\nN8zdIoBAwALzog8GfCwHIoBA6gtMqtUo9Tulx7AQIIMUFo+Rm0AAAQQQQAABBBBAAIHUECBA\nSg1F+kAAAQQQQAABBBBAAIGwECBACovHyE0ggAACCCCAAAIIIIBAaggQIKWGIn0ggAACCCCA\nAAIIIIBAWAgQIIXFY+QmEEAAAQQQQAABBBBAIDUECJBSQ5E+EEAAAQQQQAABBBBAICwECJDC\n4jFyEwgggAACCCCAAAIIIJAaAgRIqaFIHwgggAACCCCAAAIIIBAWAgRIYfEYuQkEEEAAAQQQ\nQAABBBBIDQECpNRQpA8EEEAAAQQQQAABBBAICwECpDR4jFevXrWvvvrKZsyYYd9++22Cn3Dg\nwAGbNWuW/etf/7Jz584leEzcjd9//73r8/PPP7eLFy/G3e17f+zYMZsyZYpdu3bNt40XCCCA\nAAIIIIAAAgggkLwAAVLyRkEdcerUKWvRooVNmjTJ9u3bZ3379rWxY8fG6uODDz6w5557zrZv\n324ffvihvfTSS3by5MlYx8R9M3jwYHvllVdMgdXEiROte/fudvr06biH2Y0bN+yNN96wqVOn\nEiDF02EDAggggAACCCCAAAJJC2RNejd7gxVQ8FOsWDEXxOjcNWvWuCDpqaeesiJFirgAR8HL\n6NGjLTo62pRt6tKli82ePdv9TejztmzZYsoazZw50/V9+fJla968uS1ZssRatmwZ65S5c+e6\nwCvWRt4ggAACCCCAAAIIIIBAQAJhFyApeJgzZ47t2LHDzp8/byVLlnRBRNGiRR2I9i9atMjW\nrVtn+fLls1atWrngpH379laoUCF3zO7du23+/Pl26NAhK126tLVu3doKFiwYEGitWrWsUaNG\nvmPz58/vXitDpABp7dq1Vrx4cRccaUfWrFmtYcOGLvhRoJRQ02cPGzbMBUfeObr2EydOxDpc\nGav333/fZaSGDx8eax9vEEAAAQQQQAABBBBAIHmBsBti16dPH1uxYoVVqlTJqlWrZuvXr7ee\nPXva9evXncbIkSNt2rRpVr58ecuWLZv16NHDBUxnz551+3W8AhXN8aldu7bLxrRt29Y0ryeQ\n9sADD9hdd91lly5dsq+//tplirTt3nvvdafHxMTY7bffHqsrBUzq37vGWDt/eaOMVNWqVd3m\nPXv2uCF7Gl7XoEED36FXrlyx1157zTp16hSvf99BvEAAAQQQQAABBBBAAIEkBcIqg6SgQRkb\nBUnK/Kgpg6R5QJobdPToUVu8eLGNGTPGKlas6PYXKFDAzdfR3B21cePGuWBEc37UmjZtasou\nTZ8+3Xr37u22BfLro48+cvOQFCgNHTrUMmf+NRZVVkrZH/+WN29eFxx51++/z/+1rr9bt252\n4cIF+9Of/mQlSpTw7dacp8KFC7vrVZBHQwABBBBAAAEEEEAAgeAFwipAioyMtCFDhpiGyCkQ\nUkGDzZs3OxUFKjt37rTcuXNbhQoVfFLVq1d3AZI2aPidMjRRUVE2YcIE3zEKbnRuME1zjjRP\naNWqVTZw4EDr37+/G0qnrJXmHfk3731ERITLZvlXtWvWrJnlzJnTHa4hgJ9++qm7RgVd6vet\nt96yDRs2uPlIGl5HQwABBBBAAAEEEEAAgZQLhFWApCBIgYiKGigI0k+9evVs69atTujMmTOm\njJHm/XgtT5483ks3Z0mZpFy5clmmTJl826tUqWLK8gTb9DkapvfJJ5/Yv//9bxcgaT7R/v37\nY3Wl61LmK0eOHLZ8+XI7cuSIb7/mJ3kBkrfx7rvvtqeffto0z0jzrFTVTsGV5impedXtFEA1\nadLEatSo4Z3KXwQQQAABBBBAAAEEEEhC4LdIIYmDQmWXsjUaXqaKcCqIoLZy5Ur3V4FPmTJl\nXOEFBSTeMDcVTfCaghQFGgpiOnfu7G12hRX8gyrfjgReqBT3I488YsogeU0ZIe/zdA1Lly51\nWSSvz23btvnmDY0aNco7zfdX96NqeG+//bZvmwIjzVlSINe4ceNYZcIPHjxo6rNcuXIuIPSd\nxAsEEEAAAQQQQAABBBBIUiCsijRoaJwWR/XWFNJ8H83NUVN2SUUbVBBhwIABtnHjRlu2bJkL\npvyFNCxOw9hWr17t+tq0aZP169fPl5XxPzah1wqOtECsFnXVZy5cuNAFK4899pg7vG7duu6v\njlGAs3fvXjccUOsiJdYeffRRd70ff/yxC6w0bHDevHmm7QroNE9KhSS8n/r167uu2rRp44pR\nJNYv2xFAAAEEEEAAAQQQQCC2QFhlkFR4QdkUVa3TsDRlaLp27WojRoxw85KUvVGGRkPTNBRP\nAZUWddVCrhreptauXTtXBEHD07JkyeIyMCoFrqFygTQFKxri98ILL1j27NndNfTq1ct3vj5H\n84dUcU5Bkobz6Ro0FyqxpmyY7knFJXT9miuloYPBFI1IrG+2I4AAAggggAACCCCAwG8CmX4Z\nevZr+bbftoX8K5W81jycuGsXKbOktYIUSHlzjJSN6d69uyty4D8fSYUTtM6QKsOlpGlYnYby\nKbhRoJVQO3z4sFt7yatwl9Ax/tt0TcqKKbBTYJVaTRX+VNacljoCKgTirVO18K2vU6dTekEA\ngbATmBd9MOzuiRtCIJQEJtX6bd3K9Hbd+p6qNTw1Ginuupfp7VpD6Xr0nTyQ7/ZhNcTOe0Cq\nFBc3ONI+Db9TNmfmzJmmdY9UsW78+PGurLd/cKRjlX2KC6jzFaQk9uO/jpH603C+xIIjfYaC\np0CDI++a7rjjjlQNjtQvDQEEEEAAAQQQQAABBH4VCKshdsk9VAVNgwYNcvOCJk+e7Ep+V65c\n2a2TlNy52t+hQwc7fvx4oofWrFkz4L4S7YQdCCCAAAIIIIAAAgggcMsEMlSAJOU6deq4H83j\n0RyhYNq0adOCOZxjEUAAAQQQQAABBBBAIMQEwnKIXSDPINjgKJA+OQYBBBBAAAEEEEAAAQRC\nWyDDBkih/di4egQQQAABBBBAAAEEEEgLAQKktFClTwQQQAABBBBAAAEEEAhJAQKkkHxsXDQC\nCCCAAAIIIIAAAgikhQABUlqo0icCCCCAAAIIIIAAAgiEpAABUkg+Ni4aAQQQQAABBBBAAAEE\n0kKAACktVOkTAQQQQAABBBBAAAEEQlIgw62DFJJPiYsOWYGGPaND9tq58NQTiIyMtIiICDty\n5Ihdu3Yt9Tqmp5AViIqKsna/rMUXExMTsvfAhaeuQOHChe3GjRt29OjR1O2Y3hBAIGgBMkhB\nk3ECAggggAACCCCAAAIIhKsAAVK4PlnuCwEEEEAAAQQQQAABBIIWIEAKmowTEEAAAQQQQAAB\nBBBAIFwFmIMUrk+W+7qpAktHb7qpn8eHIYBA6AvMiz4Y+jfBHSAQpgKTajUK0zvjtgIRIIMU\niBLHIIAAAggggAACCCCAQIYQIEDKEI+Zm0QAAQQQQAABBBBAAIFABAiQAlHiGAQQQAABBBBA\nAAEEEMgQAgRIGeIxc5MIIIAAAggggAACCCAQiAABUiBKHIMAAggggAACCCCAAAIZQoAAKUM8\nZm4SAQQQQAABBBBAAAEEAhEgQApEiWMQQAABBBBAAAEEEEAgQwgQIGWIx8xNIoAAAggggAAC\nCCCAQCACBEiBKHEMAggggAACCCCAAAIIZAgBAqQM8Zi5SQQQQAABBBBAAAEEEAhEIGsgB3FM\ncALXr1+3LVu22KZNm6xIkSJWu3Zty5Ejh6+TPXv22N69e33v9aJAgQJWuXLlWNvivjl48KCt\nWrXKsmTJYtWrV7fixYvHOmTXrl22bt06i4qKsqpVq1r+/Plj7ecNAggggAACCCCAAAIIJC1A\ngJS0T9B7jx07Zh07dnQB0YMPPmhz5861999/3yZOnGj58uVz/c2cOdNWr15tefPm9fVfoUKF\nJAOkv/71r/b1119bjRo1bN++ffbOO+/Y//7v/9rDDz/s+pg3b56NHj3aypYt6/odPny4jRkz\nxsqXL+/7DF4ggAACCCCAAAIIIIBA0gIESEn7BL1XAZEyO+PHj3fnXrx40Vq0aGGzZ8+2F198\n0W1Tpkevn3zyyYD637lzp61cudLmzJljhQsXdue89tprLgBSgKSgbNy4cdamTRvr1KmT269j\nBwwYYDNmzLDcuXMH9DkchAACCCCAAAIIIIBARhcIuwDp8uXLLpDYsWOHnT9/3kqWLGktW7a0\nokWLumet/YsWLXJD0ZTRadWqlQte2rdvb4UKFXLH7N692+bPn2+HDh2y0qVLW+vWra1gwYIB\n/VuJiIiw559/3ndsrly5rFy5cqbhcWqXLl2yAwcOuEyP76BkXpw8edI6dOjgC450eMWKFe3z\nzz+3Gzdu2HfffWdXrlyxJk2a+HqqW7eujR071jZs2OCyTr4dvEAAAQQQQAABBBBAAIFEBcKu\nSEOfPn1sxYoVVqlSJatWrZqtX7/eevbsaZoXpDZy5EibNm2aG3qWLVs269GjhwuYzp496/br\n+C5dupgyP5o7tH37dmvbtq3L0rgDkvml4Eif67UTJ07Yxo0b7f7773ebNDxO17JmzRoX9Dzz\nzDM2YcIEFzh558T9q/78gy7t1z3ed999lilTJne4/mpuktcUMOlzfvrpJ28TfxFAAAEEEEAA\nAQQQQCAZgbDKIJ0+fdoVJlCQpMyPmjJIffv2tVOnTtnRo0dt8eLFbmiaMjBqKo4wdepUl4nR\new1VU4GDwYMH6601bdrUlF2aPn269e7d220L9JeyVeqnVKlS1qxZM3easlNqyiR169bNvvnm\nG1uwYIEpkOrfv7/bl9wvDdfbvHmzm9ekYxUoKdibNWuWde3a1QVNyoCpXbhwwf3lFwIIIIAA\nAggggAACCCQvEFYBUmRkpA0ZMsQUhCgQ0lA2BRJqCkg0l0fzcVQQwWuqBqcASU0BjSrMqQqc\nsjpey5w5szvXex/I3zNnzli/fv1Mf99++20XwOi8+vXru2IMxYoVc9089NBDLvOjrFb37t3d\nXKNz5875PkKBVc6cOX3vp0yZ4uYVvf76675hehr+9/LLL7vP+eyzz1x/ZcqUcYGZhvjREEAA\nAQQQQAABBBBAIDCBsAqQFAQpC6MS2wqC9FOvXj3bunWr01CwooxR1qy/3XaePHl8UpqzpDk9\nCiq8oWvaWaVKlVgV53wnJPJCRRNeeeUVF4xpHpACN6+p3LcXHHnbNIROAZLmPC1fvtyOHDni\n7bKGDRu6AEnD5TQ8UPtHjBjh5iD5DvrlxeOPP+626d5LlCjhhhAquFKwR0MAAQQQQAABBBBA\nAIHABH6LFAI7Pl0fpTWCNIdIQ9C0/pCaqr+pKfBRVkVBiAIlr+T22rVr3X790rpBKrKgjEzn\nzp1923WMf1Dl25HAi8OHD7t5TXfddZcbXue//pEOV5U7rVU0bNgw39nKcikgU+A0atQo33b/\nF0OHDnXZMJX3vvPOO/132c8//+yG12k4YOPGjd0+Zcs05PCBBx6IdSxvEEAAAQQQQAABBBBA\nIHGBsCrSoGzJtWvXTFXf1BQMTZo0yb1WdkmZGpXgVvlrFU5YtmyZC6bcAf/3q3nz5vbpp5+6\ndYrUlxZ71VA5BRuBNGV5dN5TTz1lqqSn4Ec/Ks6gpiF9Ws9o4cKFdvXqVRfQ6bUyRf7rIvl/\n1pIlS1zm6IUXXjAVk/D61F99lobgqVqdht/pPnX/Gtb37LPP+qr3+ffHawQQQAABBBBAAAEE\nEEhYINMvmZUbCe8Kza1vvvmm/fvf/3ZBg7I+KlqgIWm9evVy8380/E2LqH777bdu+JlKY2sY\nnBZvveOOO1yAoUINH330kZvLoyF5DRo0cIu/JieiUt6qSpdQU+EHXYea1ih69913XZU5BTjq\nXwUg4mabvH5U4ltrJyXUFMwp66WMke5Df1WwQQvK/vnPfw4o86UCFqraR0u5wNLRm1J+Mmci\ngECGFJgX/evyDxny5rlpBNK5wKRajW7pFWpkkZao0f/4ViEvWuoIqOKzt6ZoUj2GXYCkm1WJ\na2V84q5dpMyKMjmqYOfNMVIWRsURlKXxn4+k7I7+QQaCmBRwYvvUv+Ya6RqzZ8+e2GFBb1ew\no/sIdEigPoAAKWjmeCcQIMUjYQMCCCQjQICUDBC7EbiFAgRItxA/DT860AAprIbYeZ7KoMQN\njrRP2RplkpQt0lA1VawbP368K+vtHxzpWAUYcYMjna/AJrEfb60lnZ9cU/8a7peawZE+87bb\nbgsqOEruOtmPAAIIIIAAAggggEBGEgirIg3JPTgFTYMGDXLzfyZPnuyqzFWuXNmtk5Tcudqv\noW7Hjx9P9NCaNWsG3FeinbADAQQQQAABBBBAAAEEbplAhgqQpFynTh33ozWPgs3eqBQ3DQEE\nEEAAAQQQQAABBMJXICyH2AXyuIINjgLpk2MQQAABBBBAAAEEEEAgtAUybIAU2o+Nq0cAAQQQ\nQAABBBBAAIG0ECBASgtV+kQAAQQQQAABBBBAAIGQFCBACsnHxkUjgAACCCCAAAIIIIBAWggQ\nIKWFKn0igAACCCCAAAIIIIBASAoQIIXkY+OiEUAAAQQQQAABBBBAIC0ECJDSQpU+EUAAAQQQ\nQAABBBBAICQFMt34pYXklXPRqSZw6tQpu3jxYqr1l9E7yp07t+XLl89OnDhhly5dyugc3P8v\nApGRkRYREWFHjhyxa9euYYKARUVFubX4YmJi0EDACRQuXNj0lezo0aOIIGCZMmWyokWLuu8R\n+j5BSx2BLFmymP5bS66RQUpOiP0IIIAAAggggAACCCCQYQQIkDLMo+ZGEUAAAQQQQAABBBBA\nIDmBrMkdwH4EEEBg6ehNICCAQCoLzIs+mMo90h0C4SswqVaj8L057izdCZBBSnePhAtCAAEE\nEEAAAQQQQACBWyVAgHSr5PlcBBBAAAEEEEAAAQQQSHcCBEjp7pFwQQgggAACCCCAAAIIIHCr\nBAiQbpU8n4sAAggggAACCCCAAALpToAAKd09Ei4IAQQQQAABBBBAAAEEbpUAAdKtkudzEUAA\nAQQQQAABBBBAIN0JECClu0fCBSGAAAIIIIAAAggggMCtEiBAulXyfC4CCCCAAAIIIIAAAgik\nOwECpHT3SLggBBBAAAEEEEAAAQQQuFUCBEi3Sp7PRQABBBBAAAEEEEAAgXQnkDXdXVEYXND1\n69dty5YttmnTJitSpIjVrl3bcuTIEevODhw4YF9++aUVKFDAqlevbnny5Im1P6E3Bw8etFWr\nVlmWLFncOcWLF0/oMDt27Jh99NFH1rZtW3dsggexEQEEEEAAAQQQQAABBOIJkEGKR/L7Nig4\nadGihf3973+3n376ycaNG2cvvPCCnTlzxtfxBx98YM8995xt377dPvzwQ3vppZfs5MmTvv0J\nvfjrX//q+tm1a5ctXrzYnf/VV1/FO/TGjRv2xhtv2NSpU+3atWvx9rMBAQQQQAABBBBAAAEE\nEhcgg5S4TYr2zJ0715TZGT9+vDv/4sWLLmCaPXu2vfjii6bMkYKX0aNHW3R0tF29etW6dOli\n2q+/CbWdO3faypUrbc6cOVa4cGF3yGuvvWZjxoyxhx9+ONYp+nwFXjQEEEAAAQQQQAABBBAI\nXiDsAqTLly+7QGLHjh12/vx5K1mypLVs2dKKFi3qdLR/0aJFtm7dOsuXL5+1atXKBSft27e3\nQoUKuWN2795t8+fPt0OHDlnp0qWtdevWVrBgwYB0IyIi7Pnnn/cdmytXLitXrpxpeJza2rVr\nXQCl4Egta9as1rBhQ5s5c2aiAZKySx06dPAFRzqvYsWK9vnnn5syRpkyZdIm27dvn73//vsu\nIzV8+HC3jV8IIIAAAggggAACCCAQuEDYDbHr06ePrVixwipVqmTVqlWz9evXW8+ePU3zgtRG\njhxp06ZNs/Lly1u2bNmsR48eLmA6e/as26/jlclR5kdzh5SN0VweDZ0LpCk40ud67cSJE7Zx\n40a7//773aaYmBi7/fbbvd3urzJO6t+7xlg7f3mj/vyDLu3XPd53332+4OjKlSumrFKnTp3i\n9R+3P94jgAACCCCAAAIIIIBAwgJhlUE6ffq05c+f3xQkKfOjpgxS37597dSpU3b06FE3f0dD\n05SBUVORBA15UyZGTXOGqlataoMHD3bvmzZtasouTZ8+3Xr37u22BfpL2Sr1U6pUKWvWrJk7\nTVkpZa78W968eV1w5F2//76EXms43ubNm23ixIm+3ZMmTXIZJl2vgjwaAggggAACCCCAAAII\nBC8QVgFSZGSkDRkyxDREToUMNN9HgYTapUuXTHN5cufObRUqVPBJqYKcAiQ1BTR79uyxqKgo\nmzBhgu+YzJkzu3N9GwJ4oaIM/fr1c8UZ3n77bZet0mnKWmnekX/z3mt4nob/nTt3zrdbgVXO\nnDl976dMmWIzZsyw119/3cqWLeu2b9iwwZYsWeKG1/kO5AUCCCCAAAIIIIAAAggELRBWAZKC\noP79+7sS2wqC9FOvXj3bunWrg1HQooyR5v14zb+8tuYsKZOkeUPevB4dV6VKFVOWJ9Cm4XKv\nvPKKC8bGjh1rCty8prlM+/fv9966v7ouZb5UCnz58uV25MgR337NT1KApOF3Gh6o/SNGjPBl\nwHSgMkkKroYNG+bOUyZKbeDAgdakSROrUaOGe88vBBBAAAEEEEAAAQQQSFrgt0gh6eNCYq/W\nCNLwMg1B0/pDaqr+pqbAp0yZMq7wggISb5ibiiZ4TUGKAg0FMZ07d/Y2u8IK/kGVb0cCLw4f\nPuzmNd11111ueF3c9Y90DUuXLnVZJK/Pbdu2+eYNjRo1KoFezYYOHeqyYe+8847deeedsY5p\n3LhxrDLhKgihPlUcQgEhDQEEEEAAAQQQQAABBAITCKsiDRoap7V/vDWFNN9Hc3PUlF1SsQMV\nRBgwYIArnLBs2TIXTPlTNW/e3D799FNbvXq160uLvWqonJeV8T82odfK8ugannrqKVMlPQ3x\n048qzKnVrVvX/dUwOWWF9u7d61vXyO1I4JeGzylzpPWUVEzC61N/9Vmad6RCEt5P/fr1XS9t\n2rRxxSgS6JJNCCCAAAIIIIAAAgggkIBAWGWQVHhB2RRVrdOwNGVounbt6oakaV6SsjfK0KgE\ntobiKaDSoq4aBudletq1a2cXLlxww9OyZMniMjAqBa6Kdsk1ZW68xVt1Df5NhR80NE6fo2yQ\nKs4pSNJwPl2D5kIl1rS2kVpCpbsVzCnrRUMAAQQQQAABBBBAAIHfL5Dpl6Fnv5Zv+/19pZse\nVPJaGZ+4axcps6RMjgIpb46RsjDdu3d3RQ785yOpcIJKdHsLs6bFzWk4ntZeUhGIW9lU4U9l\nzWmpI6BCIBrCqX8/ylyGQ1s6elM43Ab3gEC6EpgX/ev6eOnqorgYBNKpwKRajdLplaXNZel7\nqtbw1PcIfZ+gpY6Akh+BfLe/td/MU+de4/WiSnFxgyMdpOFovXr1couyaqiaKtaNHz/elfX2\nD450rLJPcQF1vgKnxH4SW8dI/SXUNE/qVgdHCV0X2xBAAAEEEEAAAQQQyKgCYTXELrmHqKBp\n0KBBtnDhQps8ebKrMle5cmW3TlJy52p/hw4d7Pjx44keWrNmzYD7SrQTdiCAAAIIIIAAAggg\ngMAtE8hQAZKU69Sp43605lH27NmDgp82bVpQx3MwAggggAACCCCAAAIIhJZAWA6xC+QRBBsc\nBdInxyCAAAIIIIAAAggggEBoC2TYACm0HxtXjwACCCCAAAIIIIAAAmkhQICUFqr0iQACCCCA\nAAIIIIAAAiEpQIAUko+Ni0YAAQQQQAABBBBAAIG0ECBASgtV+kQAAQQQQAABBBBAAIGQFCBA\nCsnHxkUjgAACCCCAAAIIIIBAWggQIKWFKn0igAACCCCAAAIIIIBASApkuHWQQvIpcdEI3GKB\nhj2jb/EVhPbHR0ZGWkREhB05csSuXbsW2jfD1aeKQFRUlLX7ZS2+mJiYVOmPTkJfoHDhwnbj\nxg07evRo6N8Md4BAiAuQQQrxB8jlI4AAAggggAACCCCAQOoJECClniU9IYAAAggggAACCCCA\nQIgLECCF+APk8hFAAAEEEEAAAQQQQCD1BJiDlHqW9IQAAgjY0tGbUEAgIIF50QcDOo6Dwltg\nUq1G4X2D3B0CIShABikEHxqXjAACCCCAAAIIIIAAAmkjQICUNq70igACCCCAAAIIIIAAAiEo\nQIAUgg+NS0YAAQQQQAABBBBAAIG0ESBAShtXekUAAQQQQAABBBBAAIEQFCBACsGHxiUjgAAC\nCCCAAAIIIIBA2ggQIKWNK70igAACCCCAAAIIIIBACAoQIIXgQ+OSEUAAAQQQQAABBBBAIG0E\nCJDSxpVeEUAAAQQQQAABBBBAIAQFCJBC8KFxyQgggAACCCCAAAIIIJA2AgRIaeNKrwgggAAC\nCCCAAAIIIBCCAllD8JpD5pJ/+ukn+/LLL+2pp56Kdc179uyxvXv3xtpWoEABq1y5cqxtCb25\ndu2a/b//9/+sefPmli9fvliH7Nq1y9atW2dRUVFWtWpVy58/f6z9vEEAAQQQQAABBBBAAIGk\nBQiQkvZJ8d5z587Zq6++ajly5IgXIM2cOdNWr15tefPm9fVfoUKFgAKk8ePH24cffmj16tWL\nFSDNmzfPRo8ebWXLlnX9Dh8+3MaMGWPly5f3fQYvEEAAAQQQQAABBBBAIGkBAqSkfVK09+uv\nv7a33nrLTp06ZWXKlInXhzI9L774oj355JPx9iW24fDhwzZixAjbsGFDvEOOHTtm48aNszZt\n2linTp3c/jlz5tiAAQNsxowZljt37njnsAEBBBBAAAEEEEAAAQTiC4RdgHT58mVTcLBjxw47\nf/68lSxZ0lq2bGlFixZ1d6/9ixYtckPRNEStVatWNnv2bGvfvr0VKlTIHbN7926bP3++HTp0\nyEqXLm2tW7e2ggULxtdLYMvZs2etf//+rl/tXrNmTayjLl26ZAcOHHCZnlg7knnz5ptvWpYs\nWWzYsGHWq1evWEd/9913duXKFWvSpIlve926dW3s2LEuoKpRo4ZvOy8QQAABBBBAAAEEEEAg\ncYGwK9LQp08fW7FihVWqVMmqVatm69evt549e9r169edwsiRI23atGlu6Fm2bNmsR48eLmBS\nYKOm47t06WIXL1602rVr2/bt261t27amLE0gLVeuXG4IXMeOHS1r1vjx5759+9y1KHDq0KGD\nPfPMMzZhwgRT4JRU03A9ZZC8IC7usZkyZXIBlLddAZPuWfOgaAgggAACCCCAAAIIIBCYQPxv\n8IGdly6POn36tCtMoCBJmR81ZZD69u3rhrsdPXrUFi9e7ObmVKxY0e1XcYSpU6fajRs33HsN\nVVOBg8GDB7v3TZs2ddml6dOnW+/evd22pH4pKFKRhMSaslNqCoi6detm33zzjS1YsMBOnDjh\nMk+JnVekSJHEdtl9991nCvZmzZplXbt2NQVLyoCpXbhwIdHz2IEAAggggAACCCCAAAKxBcIq\nQIqMjLQhQ4aYghAFQhrKtnnzZnfHCkh27tzp5uOoIILXqlev7gIkvdfwO1WYU4CjrI7XMmfO\n7M713v+ev/Xr13fFGIoVK+a6eeihh1zmR1mt7t2728qVK00FHrzWrFkzy5kzp/c2wb8a/vfy\nyy/b22+/bZ999pnrT3OfSpUqZcpo0RBAAAEEEEAAAQQQQCAwgbAKkBQEaf7Pli1bTEGQflTt\nbevWrU7jzJkzpoyR/9C3PHny+KQ0Z0mZJAUVysJ4rUqVKrEqznnbU/JXVe284Mg7X0MBFSBp\nztPy5cvtyJEj3i5r2LBhsgGSDn788cdNWTHde4kSJdwQQgVXSWWzfB/CCwQQQAABBBBAAAEE\nEHACYRUgrVq1ys0hUtEFb0iaMjJqCnyUVVEQokDJW0No7dq1br9+ad2giIgIV5Chc+fOvu06\nxj+o8u1IwYu5c+e6AhEqtuA1ZbkUkClwGjVqlLc54L8///yzG16n4YCNGzd25ylbpiGHDzzw\nQMD9cCACCCCAAAIIIIAAAhldIKyKNChbooVUT5486Z6rgqFJkya518ouKVNTvHhxV/5648aN\ntmzZMlfBzv8fgRZg/fTTT906Repr06ZN1q9fPxds+B+X0tca0qcy4AsXLrSrV6+6gE6vlSny\nXxcpmP41BE/lv6dMmeLmNun+Ndzu2Wef9VXvC6Y/jkUAAQQQQAABBBBAIKMKhFUGSUPMlEFR\n1ToFDcr6qGiBqr9pXpIySMrQaBFVDcVTQNWiRQtXDltD39TatWvnChsMHDjQzeXRkDyVAldF\nu9RoCtBUnEEluLWQq4KwBg0aBFQAIqnP9/pUqW8VbFBpb1XSoyGAAAIIIIAAAggggEDgApl+\nGXr2a/m2wM9J90eqxLWGl8Vdu0iZFZXZViDlzTHS8DYVR1iyZIn5z0dSdkeV5QoXLpwm96v+\nNddI15g9e/ZU+wwtTqv7CGZIoM5RWXNa6ghoYV4N4dS/H2UuaRlLYOnoTRnrhrnbFAvMiz6Y\n4nM5MXwEJtVq5G5G3zf0lUwVd2kI6Huq1vDU9wh9n6CljoDWFA3ku31YDbHz6JRBiRscaZ+y\nNVpkdebMmaZ1j1Sxbvz48a6st39wpGMVYMQF1PkKbBL78dZa0vnJNfWvbFJqBkf6zNtuuy2o\n4Ci562Q/AggggAACCCCAAAIZSSCshtgl9+AUNA0aNMjN/5k8ebIr+V25cmW3TlJy52q/FnY9\nfvx4oofWrFkz4L4S7YQdCCCAAAIIIIAAAgggcMsEMlSAJOU6deq4H615FGz2RqW4aQgggAAC\nCCCAAAIIIBC+AmE5xC6QxxVscBRInxyDAAIIIIAAAggggAACoS2QYQOk0H5sXD0CCCCAAAII\nIIAAAgikhQABUlqo0icCCCCAAAIIIIAAAgiEpAABUkg+Ni4aAQQQQAABBBBAAAEE0kKAACkt\nVOkTAQQQQAABBBBAAAEEQlKAACkkHxsXjQACCCCAAAIIIIAAAmkhkOHKfKcFIn0igAACnkDD\nntHeS9/fyMhIi4iIsCNHjrgFq307eJFhBaKioqxd9uwWExOTYQ24cQQQQCC9CpBBSq9PhutC\nAAEEEEAAAQQQQACBmy5AgHTTyflABBBAAAEEEEAAAQQQSK8CBEjp9clwXQgggAACCCCAAAII\nIHDTBZiDdNPJ+UAEEEAgaYGlozclfQB7w0JgXvTBsLiPcLqJSbUahdPtcC8IIJBCATJIKYTj\nNAQQQAABBBBAAAEEEAg/AQKk8Hum3BECCCCAAAIIIIAAAgikUIAAKYVwnIYAAggggAACCCCA\nAALhJ0CAFH7PlDtCAAEEEEAAAQQQQACBFAoQIKUQjtMQQAABBBBAAAEEEEAg/AQIkMLvmXJH\nCCCAAAIIIIAAAgggkEIBAqQUwnEaAggggAACCCCAAAIIhJ8AAVL4PVPuCAEEEEAAAQQQQAAB\nBFIoQICUQjhOQwABBBBAAAEEEEAAgfATIEAKv2fKHSGAAAIIIIAAAggggEAKBQiQUggXyGk/\n/fSTzZkzJ8FDDxw4YLNmzbJ//etfdu7cuQSPSWjjtWvX7P3337czZ84ktNttO3bsmE2ZMsV0\nLA0BBBBAAAEEEEAAAQQCFyBACtwqqCMV9Lz66qv26aefxjvvgw8+sOeee862b99uH374ob30\n0kt28uTJeMcltGH8+PE2efLkRIOqGzdu2BtvvGFTp04lQEoIkG0IIIAAAggggAACCCQhQICU\nBE5Kd3399dfWtm1bO3jwYLwulDlS8DJ69GgbMmSITZgwwXLkyGGzZ8+Od6z/hsOHD1vfvn3t\nn//8p//meK/nzp3rAq94O9iAAAIIIIAAAggggAACyQpkTfaIEDvg8uXLbljbjh077Pz581ay\nZElr2bKlFS1a1N2J9i9atMjWrVtn+fLls1atWrngpH379laoUCF3zO7du23+/Pl26NAhK126\ntLVu3doKFiwYkMTZs2etf//+rl+dsGbNmljnrV271ooXL27R0dFue9asWa1hw4Y2c+ZM69Kl\nS6xj/d+8+eabliVLFhs2bJj16tXLf5fv9b59+9zwO2Wkhg8f7tvOCwQQQAABBBBAAAEEEAhM\nIOwySH369LEVK1ZYpUqVrFq1arZ+/Xrr2bOnXb9+3YmMHDnSpk2bZuXLl7ds2bJZjx49XMCk\nwEZNxytQuXjxotWuXdtlY5QN0ryeQFquXLncsLmOHTuagp+4LSYmxm6//fZYmxUwqX/vGmPt\n/L83Gq43YsQIXxAX95grV67Ya6+9Zp06dYrXf9xjeY8AAggggAACCCCAAAIJC8T/Bp/wcSGx\n9fTp058QZFkAADCcSURBVJY/f35TkKTMj5oySBqadurUKTt69KgtXrzYxowZYxUrVnT7CxQo\n4Ia8ae6O2rhx46xq1ao2ePBg975p06am7NL06dOtd+/ebltSvxQURUVFJXqIslLKXPm3vHnz\nuuDIu37/fd7rIkWKeC8T/Dtp0iQrXLiw6XoV5NEQQAABBBBAAAEEEEAgeIGwCpAiIyPdvB4N\nkVMgpPk+mzdvdiqXLl2ynTt3Wu7cua1ChQo+qerVq7sASRs0/G7Pnj0uwNHcIK9lzpzZneu9\n/z1/lbW6evVqrC689xERES6b5V/VrlmzZpYzZ85Yx8d9s2HDBluyZIkbXhd3H+8RQAABBBBA\nAAEEEEAgcIGwCpAUBGn+z5YtW1wQpECoXr16tnXrViei0tjKGPkPfcuTJ49PS3OWlEnSMLlM\nmTL5tlepUsWU5UmNprlM+/fvj9WVrkuZLxVrWL58uR05csS3X/OTkguQJk6caAquND9JTZko\ntYEDB1qTJk2sRo0a7j2/EEAAAQQQQAABBBBAIGmBsAqQVq1a5YaXqSKcNyRt5cqVTkCBT5ky\nZVzhBQUk3jA3FU3wmoIUBRoKYjp37uxtNh3jH1T5dqTgha5h6dKlLovk9blt2zbfvKFRo0YF\n3Wvjxo1jlQlX9Tz1Wa5cORcQBt0hJyCAAAIIIIAAAgggkEEFwqpIg+b+aHFUb00hzffR3Bw1\nZZdUtEEFEQYMGGAbN260ZcuWxSuv3bx5c7d20erVq11fmzZtsn79+vmyMr/330ndunVdFzNm\nzHDzjvbu3euGA2pdpJQ2zTtSIQnvp379+q6rNm3auGIUKe2X8xBAAAEEEEAAAQQQyGgCYZVB\nUuEFZVNUtU7D0pSh6dq1q6v+pnlJyt4oQ6MS2BqKp4CqRYsWNnbsWDe8TQ+/Xbt2duHCBTc8\nTWW1NSRPpcBV0S41mobRDR061FWcU5Ck4Xy6Bs2FoiGAAAIIIIAAAggggMCtFcj0y9CzX8u3\n3drrSNVPV8lrzcOJu3aRMktaK0iBlDfHSEUcunfv7ooc+M9HUuGEEydOuMpwqXpxfp1p8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WKf6IVEjwmFQvbQQw/Z6NGjCZCiEVlGAAEE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\nYII=",
      "text/plain": [
       "plot without title"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "balt.data.90 <- data.90 %>% filter(fips == '24510' & \n",
    "                             race == 'White' & \n",
    "                             hisp == 'not_Hispanic' & year == 1990) %>%\n",
    "  group_by(fips, race, sex) %>%\n",
    "  mutate(race.sex.total = sum(pop)) %>%\n",
    "  mutate(perc = (pop/race.sex.total) * 100)\n",
    "\n",
    "balt.data.90 <- balt.data.90 %>% \n",
    "  mutate(perc2 = ifelse(sex =='male', perc*-1, perc))\n",
    "\n",
    "ggplot(balt.data.90)  +\n",
    "         geom_bar(aes(x = age_name, y = perc2, \n",
    "                      fill = sex), stat = 'identity') +\n",
    "         coord_flip() + \n",
    "         scale_fill_manual(values = c('#5ab4ac',\n",
    "                                      '#af8dc3')) +\n",
    "         labs(x = 'age range\\n', y = '\\npercent of population') +\n",
    "         theme(axis.ticks.y = element_blank(),\n",
    "               panel.grid.minor.y = element_blank(),\n",
    "               panel.grid.major.y = element_blank(),\n",
    "               panel.grid.minor.x = element_blank(),\n",
    "               strip.background = element_blank(),\n",
    "               legend.position = 'top',\n",
    "               legend.title = element_blank()) +\n",
    "  scale_y_continuous(limits = c(-12,12)) + ggtitle('Baltimore white population, 1990')\n",
    "\n",
    "ggsave('output/plot2_1990.png', width = 6, height = 8) \n",
    "ggsave('output/plot2_1990.eps', width = 6, height = 8) #Illustrator"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Plot: White population between 25 and 34 in Maryland by county as a percentage of total white population"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "data.18.sum <- data.18 %>% filter(AGEGRP != 0 & \n",
    "                                   YEAR == 11 & \n",
    "                                   sex != 'POP' &\n",
    "                                   STATE == '24'&\n",
    "                                   race_name %in% c('White')) %>%\n",
    "  mutate(yp = case_when(age_name == 'age_25-29' | \n",
    "                        age_name == 'age_30-34' ~ 1, TRUE ~ 0))%>%\n",
    "  group_by(fips, CTYNAME, age_name, race_name, yp)  %>% \n",
    "  summarise(n = sum(value)) %>%\n",
    "  ungroup() %>%\n",
    "  group_by(CTYNAME, yp) %>%\n",
    "  summarise(n  = sum(n)) %>%\n",
    "  mutate(perc = (n/sum(n)) * 100) %>%\n",
    "  filter(yp == 1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {},
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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BRkQhS7G/daLpiA9+7dWyf3mHxDYfNamPCGHuyQjmtGjx4dcqNY1BEPA1wXLLEo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\nQAABBEJWIOxKluO/c8RC9hFT58GOHTvmgg1tx32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CZJCCOPiCAAIIIIAAAggggAACXhYgQPLy6PPsCCCAAAIIIIAAAgggECSQIegbXxBI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\n0/meeOIJ1ydtHqEsl3a+07bd2pyie/fubmvzJOsUDSGAAAIII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BBAAIEQCBAgZdHJCpA0DE6rwCkjpCxRQkKClSxZ0mVqlDnSMDcFRBr2prJq1So3XM0L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\nflaxYkXTM8MyFz5LmUV4j8DvAk2aNDH9iy7z5s2z888/341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      "text/plain": [
       "plot without title"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "data.18.sum$CTYNAME <- gsub(\"County\", \"\", data.18.sum$CTYNAME)\n",
    "\n",
    "data.18.sum$county.factor = \n",
    "  factor(data.18.sum$CTYNAME, \n",
    "         levels = data.18.sum$CTYNAME[order(data.18.sum$perc)])          \n",
    "\n",
    "ggplot(data.18.sum,\n",
    "       aes(x = county.factor, \n",
    "           y = perc)) + \n",
    "  geom_bar(stat = 'identity', fill = '#0075b0') + \n",
    "  coord_flip() +\n",
    "  theme(panel.grid.minor.y = element_blank(),\n",
    "        panel.grid.major.y = element_blank(),\n",
    "        panel.grid.minor.x = element_blank(),\n",
    "        panel.background = element_blank(),\n",
    "        legend.position = 'top',\n",
    "        axis.ticks.y = element_blank(),\n",
    "        legend.title = element_blank(),\n",
    "        panel.spacing = unit(1.5, \"lines\")) +\n",
    "  labs(x='', y = '') +\n",
    "  scale_y_continuous(limits = c(0, 30)) + ggtitle(\"Md. white population aged 25-34 by county\\nas a % of each county's total white population\")\n",
    "\n",
    "ggsave('output/plot3.png', width = 8, height = 6)\n",
    "ggsave('output/plot3.eps', width = 8, height = 6) #Illustrator"
   ]
  }
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