EDA.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Importing the data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "adult <- read.table('https://archive.ics.uci.edu/ml/machine-learning-databases/adult/adult.data', \n",
    "                    sep = ',', fill = F, strip.white = T)\n",
    "colnames(adult) <- c('age', 'workclass', 'fnlwgt', 'education', \n",
    "                     'education_num', 'marital_status', 'occupation', 'relationship', 'race', 'sex', \n",
    "                     'capital_gain', 'capital_loss', 'hours_per_week', 'native_country', 'income')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Removing redundant columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "adult$education <- NULL\n",
    "adult$fnlwgt <- NULL\n",
    "adult$relationship <- NULL"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table>\n",
       "<thead><tr><th scope=col>age</th><th scope=col>workclass</th><th scope=col>education_num</th><th scope=col>marital_status</th><th scope=col>occupation</th><th scope=col>race</th><th scope=col>sex</th><th scope=col>capital_gain</th><th scope=col>capital_loss</th><th scope=col>hours_per_week</th><th scope=col>native_country</th><th scope=col>income</th></tr></thead>\n",
       "<tbody>\n",
       "\t<tr><td>39                                                        </td><td><span style=white-space:pre-wrap>State-gov       </span>  </td><td>13                                                        </td><td><span style=white-space:pre-wrap>Never-married     </span></td><td><span style=white-space:pre-wrap>Adm-clerical     </span> </td><td>White                                                     </td><td><span style=white-space:pre-wrap>Male  </span>            </td><td>2174                                                      </td><td>0                                                         </td><td>40                                                        </td><td>United-States                                             </td><td>&lt;=50K                                                  </td></tr>\n",
       "\t<tr><td>50                                                       </td><td>Self-emp-not-inc                                         </td><td>13                                                       </td><td>Married-civ-spouse                                       </td><td><span style=white-space:pre-wrap>Exec-managerial  </span></td><td>White                                                    </td><td><span style=white-space:pre-wrap>Male  </span>           </td><td><span style=white-space:pre-wrap>   0</span>             </td><td>0                                                        </td><td>13                                                       </td><td>United-States                                            </td><td>&lt;=50K                                                 </td></tr>\n",
       "\t<tr><td>38                                                        </td><td><span style=white-space:pre-wrap>Private         </span>  </td><td> 9                                                        </td><td><span style=white-space:pre-wrap>Divorced          </span></td><td>Handlers-cleaners                                         </td><td>White                                                     </td><td><span style=white-space:pre-wrap>Male  </span>            </td><td><span style=white-space:pre-wrap>   0</span>              </td><td>0                                                         </td><td>40                                                        </td><td>United-States                                             </td><td>&lt;=50K                                                  </td></tr>\n",
       "\t<tr><td>53                                                      </td><td><span style=white-space:pre-wrap>Private         </span></td><td> 7                                                      </td><td>Married-civ-spouse                                      </td><td>Handlers-cleaners                                       </td><td>Black                                                   </td><td><span style=white-space:pre-wrap>Male  </span>          </td><td><span style=white-space:pre-wrap>   0</span>            </td><td>0                                                       </td><td>40                                                      </td><td>United-States                                           </td><td>&lt;=50K                                                </td></tr>\n",
       "\t<tr><td>28                                                       </td><td><span style=white-space:pre-wrap>Private         </span> </td><td>13                                                       </td><td>Married-civ-spouse                                       </td><td><span style=white-space:pre-wrap>Prof-specialty   </span></td><td>Black                                                    </td><td>Female                                                   </td><td><span style=white-space:pre-wrap>   0</span>             </td><td>0                                                        </td><td>40                                                       </td><td><span style=white-space:pre-wrap>Cuba         </span>    </td><td>&lt;=50K                                                 </td></tr>\n",
       "\t<tr><td>37                                                       </td><td><span style=white-space:pre-wrap>Private         </span> </td><td>14                                                       </td><td>Married-civ-spouse                                       </td><td><span style=white-space:pre-wrap>Exec-managerial  </span></td><td>White                                                    </td><td>Female                                                   </td><td><span style=white-space:pre-wrap>   0</span>             </td><td>0                                                        </td><td>40                                                       </td><td>United-States                                            </td><td>&lt;=50K                                                 </td></tr>\n",
       "</tbody>\n",
       "</table>\n"
      ],
      "text/latex": [
       "\\begin{tabular}{r|llllllllllll}\n",
       " age & workclass & education\\_num & marital\\_status & occupation & race & sex & capital\\_gain & capital\\_loss & hours\\_per\\_week & native\\_country & income\\\\\n",
       "\\hline\n",
       "\t 39                 & State-gov          & 13                 & Never-married      & Adm-clerical       & White              & Male               & 2174               & 0                  & 40                 & United-States      & <=50K             \\\\\n",
       "\t 50                 & Self-emp-not-inc   & 13                 & Married-civ-spouse & Exec-managerial    & White              & Male               &    0               & 0                  & 13                 & United-States      & <=50K             \\\\\n",
       "\t 38                 & Private            &  9                 & Divorced           & Handlers-cleaners  & White              & Male               &    0               & 0                  & 40                 & United-States      & <=50K             \\\\\n",
       "\t 53                 & Private            &  7                 & Married-civ-spouse & Handlers-cleaners  & Black              & Male               &    0               & 0                  & 40                 & United-States      & <=50K             \\\\\n",
       "\t 28                 & Private            & 13                 & Married-civ-spouse & Prof-specialty     & Black              & Female             &    0               & 0                  & 40                 & Cuba               & <=50K             \\\\\n",
       "\t 37                 & Private            & 14                 & Married-civ-spouse & Exec-managerial    & White              & Female             &    0               & 0                  & 40                 & United-States      & <=50K             \\\\\n",
       "\\end{tabular}\n"
      ],
      "text/markdown": [
       "\n",
       "| age | workclass | education_num | marital_status | occupation | race | sex | capital_gain | capital_loss | hours_per_week | native_country | income |\n",
       "|---|---|---|---|---|---|---|---|---|---|---|---|\n",
       "| 39                 | State-gov          | 13                 | Never-married      | Adm-clerical       | White              | Male               | 2174               | 0                  | 40                 | United-States      | <=50K              |\n",
       "| 50                 | Self-emp-not-inc   | 13                 | Married-civ-spouse | Exec-managerial    | White              | Male               |    0               | 0                  | 13                 | United-States      | <=50K              |\n",
       "| 38                 | Private            |  9                 | Divorced           | Handlers-cleaners  | White              | Male               |    0               | 0                  | 40                 | United-States      | <=50K              |\n",
       "| 53                 | Private            |  7                 | Married-civ-spouse | Handlers-cleaners  | Black              | Male               |    0               | 0                  | 40                 | United-States      | <=50K              |\n",
       "| 28                 | Private            | 13                 | Married-civ-spouse | Prof-specialty     | Black              | Female             |    0               | 0                  | 40                 | Cuba               | <=50K              |\n",
       "| 37                 | Private            | 14                 | Married-civ-spouse | Exec-managerial    | White              | Female             |    0               | 0                  | 40                 | United-States      | <=50K              |\n",
       "\n"
      ],
      "text/plain": [
       "  age workclass        education_num marital_status     occupation        race \n",
       "1 39  State-gov        13            Never-married      Adm-clerical      White\n",
       "2 50  Self-emp-not-inc 13            Married-civ-spouse Exec-managerial   White\n",
       "3 38  Private           9            Divorced           Handlers-cleaners White\n",
       "4 53  Private           7            Married-civ-spouse Handlers-cleaners Black\n",
       "5 28  Private          13            Married-civ-spouse Prof-specialty    Black\n",
       "6 37  Private          14            Married-civ-spouse Exec-managerial   White\n",
       "  sex    capital_gain capital_loss hours_per_week native_country income\n",
       "1 Male   2174         0            40             United-States  <=50K \n",
       "2 Male      0         0            13             United-States  <=50K \n",
       "3 Male      0         0            40             United-States  <=50K \n",
       "4 Male      0         0            40             United-States  <=50K \n",
       "5 Female    0         0            40             Cuba           <=50K \n",
       "6 Female    0         0            40             United-States  <=50K "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "head(adult)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Analyzing each column with the income"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Warning message:\n",
      "\"package 'ggplot2' was built under R version 3.6.3\"Warning message:\n",
      "\"package 'plyr' was built under R version 3.6.3\""
     ]
    }
   ],
   "source": [
    "# Importing the libraries needed for this\n",
    "library(ggplot2)\n",
    "library(plyr)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Age vs Income"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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U1p6JLTuvuOQKQi4cXNlqqmNHTJad19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\nSUVORK5CYII=",
      "text/plain": [
       "plot without title"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ggplot(adult) + aes(x=as.numeric(age), group=income, fill=income) + \n",
    "  geom_histogram(binwidth=1, color='black')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Gender vs Income"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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04hl7ryiABIP3bN/ufplfeXpnl5Pxye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\nCQISkSAgEQkCEpEgIBEJAhKRoP8H7s92bQFayGcAAAAASUVORK5CYII=",
      "text/plain": [
       "plot without title"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ggplot(adult) + aes(x=as.numeric(age), group=sex, fill=sex) + \n",
    "  geom_histogram(binwidth=1, color='black')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Working Class vs Income"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<dl class=dl-horizontal>\n",
       "\t<dt>?</dt>\n",
       "\t\t<dd>1836</dd>\n",
       "\t<dt>Federal-gov</dt>\n",
       "\t\t<dd>960</dd>\n",
       "\t<dt>Local-gov</dt>\n",
       "\t\t<dd>2093</dd>\n",
       "\t<dt>Never-worked</dt>\n",
       "\t\t<dd>7</dd>\n",
       "\t<dt>Private</dt>\n",
       "\t\t<dd>22696</dd>\n",
       "\t<dt>Self-emp-inc</dt>\n",
       "\t\t<dd>1116</dd>\n",
       "\t<dt>Self-emp-not-inc</dt>\n",
       "\t\t<dd>2541</dd>\n",
       "\t<dt>State-gov</dt>\n",
       "\t\t<dd>1298</dd>\n",
       "\t<dt>Without-pay</dt>\n",
       "\t\t<dd>14</dd>\n",
       "</dl>\n"
      ],
      "text/latex": [
       "\\begin{description*}\n",
       "\\item[?] 1836\n",
       "\\item[Federal-gov] 960\n",
       "\\item[Local-gov] 2093\n",
       "\\item[Never-worked] 7\n",
       "\\item[Private] 22696\n",
       "\\item[Self-emp-inc] 1116\n",
       "\\item[Self-emp-not-inc] 2541\n",
       "\\item[State-gov] 1298\n",
       "\\item[Without-pay] 14\n",
       "\\end{description*}\n"
      ],
      "text/markdown": [
       "?\n",
       ":   1836Federal-gov\n",
       ":   960Local-gov\n",
       ":   2093Never-worked\n",
       ":   7Private\n",
       ":   22696Self-emp-inc\n",
       ":   1116Self-emp-not-inc\n",
       ":   2541State-gov\n",
       ":   1298Without-pay\n",
       ":   14\n",
       "\n"
      ],
      "text/plain": [
       "               ?      Federal-gov        Local-gov     Never-worked \n",
       "            1836              960             2093                7 \n",
       "         Private     Self-emp-inc Self-emp-not-inc        State-gov \n",
       "           22696             1116             2541             1298 \n",
       "     Without-pay \n",
       "              14 "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "summary(adult$workclass)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### What we do next is convert them into 4 main types of classes."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "levels(adult$workclass)[1] <- 'Unknown'\n",
    "# combine into Government job\n",
    "adult$workclass <- gsub('^Federal-gov', 'Government', adult$workclass)\n",
    "adult$workclass <- gsub('^Local-gov', 'Government', adult$workclass)\n",
    "adult$workclass <- gsub('^State-gov', 'Government', adult$workclass) \n",
    "\n",
    "# combine into Sele-Employed job\n",
    "adult$workclass <- gsub('^Self-emp-inc', 'Self-Employed', adult$workclass)\n",
    "adult$workclass <- gsub('^Self-emp-not-inc', 'Self-Employed', adult$workclass)\n",
    "\n",
    "# combine into Other/Unknown\n",
    "adult$workclass <- gsub('^Never-worked', 'Other', adult$workclass)\n",
    "adult$workclass <- gsub('^Without-pay', 'Other', adult$workclass)\n",
    "adult$workclass <- gsub('^Other', 'Other/Unknown', adult$workclass)\n",
    "adult$workclass <- gsub('^Unknown', 'Other/Unknown', adult$workclass)\n",
    "\n",
    "adult$workclass <- as.factor(adult$workclass)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "\n",
       "   Government Other/Unknown       Private Self-Employed \n",
       "         4351          1857         22696          3657 "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "table(adult$workclass)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table>\n",
       "<thead><tr><th scope=col>industry</th><th scope=col>income</th><th scope=col>count</th></tr></thead>\n",
       "<tbody>\n",
       "\t<tr><td><span style=white-space:pre-wrap>Government   </span></td><td>&lt;=50K                                             </td><td> 3010                                                </td></tr>\n",
       "\t<tr><td><span style=white-space:pre-wrap>Government   </span></td><td>&gt;50K                                              </td><td> 1341                                                </td></tr>\n",
       "\t<tr><td>Other/Unknown</td><td>&lt;=50K     </td><td> 1666        </td></tr>\n",
       "\t<tr><td>Other/Unknown                                </td><td>&gt;50K                                      </td><td><span style=white-space:pre-wrap>  191</span></td></tr>\n",
       "\t<tr><td><span style=white-space:pre-wrap>Private      </span></td><td>&lt;=50K                                             </td><td>17733                                                </td></tr>\n",
       "\t<tr><td><span style=white-space:pre-wrap>Private      </span></td><td>&gt;50K                                              </td><td> 4963                                                </td></tr>\n",
       "\t<tr><td>Self-Employed</td><td>&lt;=50K     </td><td> 2311        </td></tr>\n",
       "\t<tr><td>Self-Employed</td><td>&gt;50K      </td><td> 1346        </td></tr>\n",
       "</tbody>\n",
       "</table>\n"
      ],
      "text/latex": [
       "\\begin{tabular}{r|lll}\n",
       " industry & income & count\\\\\n",
       "\\hline\n",
       "\t Government    & <=50K         &  3010        \\\\\n",
       "\t Government    & >50K          &  1341        \\\\\n",
       "\t Other/Unknown & <=50K         &  1666        \\\\\n",
       "\t Other/Unknown & >50K          &   191        \\\\\n",
       "\t Private       & <=50K         & 17733        \\\\\n",
       "\t Private       & >50K          &  4963        \\\\\n",
       "\t Self-Employed & <=50K         &  2311        \\\\\n",
       "\t Self-Employed & >50K          &  1346        \\\\\n",
       "\\end{tabular}\n"
      ],
      "text/markdown": [
       "\n",
       "| industry | income | count |\n",
       "|---|---|---|\n",
       "| Government    | <=50K         |  3010         |\n",
       "| Government    | >50K          |  1341         |\n",
       "| Other/Unknown | <=50K         |  1666         |\n",
       "| Other/Unknown | >50K          |   191         |\n",
       "| Private       | <=50K         | 17733         |\n",
       "| Private       | >50K          |  4963         |\n",
       "| Self-Employed | <=50K         |  2311         |\n",
       "| Self-Employed | >50K          |  1346         |\n",
       "\n"
      ],
      "text/plain": [
       "  industry      income count\n",
       "1 Government    <=50K   3010\n",
       "2 Government    >50K    1341\n",
       "3 Other/Unknown <=50K   1666\n",
       "4 Other/Unknown >50K     191\n",
       "5 Private       <=50K  17733\n",
       "6 Private       >50K    4963\n",
       "7 Self-Employed <=50K   2311\n",
       "8 Self-Employed >50K    1346"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# barplot of job type by income group\n",
    "# get the counts by industry and income group\n",
    "count <- table(adult[adult$workclass == 'Government',]$income)[\"<=50K\"]\n",
    "count <- c(count, table(adult[adult$workclass == 'Government',]$income)[\">50K\"])\n",
    "count <- c(count, table(adult[adult$workclass == 'Other/Unknown',]$income)[\"<=50K\"])\n",
    "count <- c(count, table(adult[adult$workclass == 'Other/Unknown',]$income)[\">50K\"])\n",
    "count <- c(count, table(adult[adult$workclass == 'Private',]$income)[\"<=50K\"])\n",
    "count <- c(count, table(adult[adult$workclass == 'Private',]$income)[\">50K\"])\n",
    "count <- c(count, table(adult[adult$workclass == 'Self-Employed',]$income)[\"<=50K\"])\n",
    "count <- c(count, table(adult[adult$workclass == 'Self-Employed',]$income)[\">50K\"])\n",
    "count <- as.numeric(count)\n",
    "\n",
    "# create a dataframe\n",
    "industry <- rep(levels(adult$workclass), each = 2)\n",
    "income <- rep(c('<=50K', '>50K'), 4)\n",
    "df <- data.frame(industry, income, count)\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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FQVXFRutIOQFIRUFVxUbrSDkBSEVBVc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\nZkBIwAwICZgBIQEzICRgBv8HjPoc/jkHUw4AAAAASUVORK5CYII=",
      "text/plain": [
       "plot without title"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# calculate the percentages\n",
    "df <- ddply(df, .(industry), transform, percent = count/sum(count) * 100)\n",
    "\n",
    "# bar plot of counts by industry with in group proportions \n",
    "ggplot(df, aes(x = industry, y = count, fill = income)) +\n",
    "  geom_bar(stat=\"identity\") +\n",
    "  ggtitle('Income by Industry')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Income vs Number of years of Education"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table>\n",
       "<thead><tr><th scope=col>income</th><th scope=col>education_num</th><th scope=col>count</th></tr></thead>\n",
       "<tbody>\n",
       "\t<tr><td>&lt;=50K                                    </td><td>1                                           </td><td><span style=white-space:pre-wrap>  51</span></td></tr>\n",
       "\t<tr><td>&gt;50K                                     </td><td>1                                           </td><td><span style=white-space:pre-wrap>   0</span></td></tr>\n",
       "\t<tr><td>&lt;=50K</td><td>2       </td><td> 162    </td></tr>\n",
       "\t<tr><td>&gt;50K                                     </td><td>2                                           </td><td><span style=white-space:pre-wrap>   6</span></td></tr>\n",
       "\t<tr><td>&lt;=50K</td><td>3       </td><td> 317    </td></tr>\n",
       "\t<tr><td>&gt;50K                                     </td><td>3                                           </td><td><span style=white-space:pre-wrap>  16</span></td></tr>\n",
       "\t<tr><td>&lt;=50K</td><td>4       </td><td> 606    </td></tr>\n",
       "\t<tr><td>&gt;50K                                     </td><td>4                                           </td><td><span style=white-space:pre-wrap>  40</span></td></tr>\n",
       "\t<tr><td>&lt;=50K</td><td>5       </td><td> 487    </td></tr>\n",
       "\t<tr><td>&gt;50K                                     </td><td>5                                           </td><td><span style=white-space:pre-wrap>  27</span></td></tr>\n",
       "\t<tr><td>&lt;=50K</td><td>6       </td><td> 871    </td></tr>\n",
       "\t<tr><td>&gt;50K                                     </td><td>6                                           </td><td><span style=white-space:pre-wrap>  62</span></td></tr>\n",
       "\t<tr><td>&lt;=50K</td><td>7       </td><td>1115    </td></tr>\n",
       "\t<tr><td>&gt;50K                                     </td><td>7                                           </td><td><span style=white-space:pre-wrap>  60</span></td></tr>\n",
       "\t<tr><td>&lt;=50K</td><td>8       </td><td> 400    </td></tr>\n",
       "\t<tr><td>&gt;50K                                     </td><td>8                                           </td><td><span style=white-space:pre-wrap>  33</span></td></tr>\n",
       "\t<tr><td>&lt;=50K</td><td>9       </td><td>8826    </td></tr>\n",
       "\t<tr><td>&gt;50K </td><td>9       </td><td>1675    </td></tr>\n",
       "\t<tr><td>&lt;=50K</td><td>10      </td><td>5904    </td></tr>\n",
       "\t<tr><td>&gt;50K </td><td>10      </td><td>1387    </td></tr>\n",
       "\t<tr><td>&lt;=50K</td><td>11      </td><td>1021    </td></tr>\n",
       "\t<tr><td>&gt;50K </td><td>11      </td><td> 361    </td></tr>\n",
       "\t<tr><td>&lt;=50K</td><td>12      </td><td> 802    </td></tr>\n",
       "\t<tr><td>&gt;50K </td><td>12      </td><td> 265    </td></tr>\n",
       "\t<tr><td>&lt;=50K</td><td>13      </td><td>3134    </td></tr>\n",
       "\t<tr><td>&gt;50K </td><td>13      </td><td>2221    </td></tr>\n",
       "\t<tr><td>&lt;=50K</td><td>14      </td><td> 764    </td></tr>\n",
       "\t<tr><td>&gt;50K </td><td>14      </td><td> 959    </td></tr>\n",
       "\t<tr><td>&lt;=50K</td><td>15      </td><td> 153    </td></tr>\n",
       "\t<tr><td>&gt;50K </td><td>15      </td><td> 423    </td></tr>\n",
       "\t<tr><td>&lt;=50K</td><td>16      </td><td> 107    </td></tr>\n",
       "\t<tr><td>&gt;50K </td><td>16      </td><td> 306    </td></tr>\n",
       "</tbody>\n",
       "</table>\n"
      ],
      "text/latex": [
       "\\begin{tabular}{r|lll}\n",
       " income & education\\_num & count\\\\\n",
       "\\hline\n",
       "\t <=50K & 1     &   51 \\\\\n",
       "\t >50K  & 1     &    0 \\\\\n",
       "\t <=50K & 2     &  162 \\\\\n",
       "\t >50K  & 2     &    6 \\\\\n",
       "\t <=50K & 3     &  317 \\\\\n",
       "\t >50K  & 3     &   16 \\\\\n",
       "\t <=50K & 4     &  606 \\\\\n",
       "\t >50K  & 4     &   40 \\\\\n",
       "\t <=50K & 5     &  487 \\\\\n",
       "\t >50K  & 5     &   27 \\\\\n",
       "\t <=50K & 6     &  871 \\\\\n",
       "\t >50K  & 6     &   62 \\\\\n",
       "\t <=50K & 7     & 1115 \\\\\n",
       "\t >50K  & 7     &   60 \\\\\n",
       "\t <=50K & 8     &  400 \\\\\n",
       "\t >50K  & 8     &   33 \\\\\n",
       "\t <=50K & 9     & 8826 \\\\\n",
       "\t >50K  & 9     & 1675 \\\\\n",
       "\t <=50K & 10    & 5904 \\\\\n",
       "\t >50K  & 10    & 1387 \\\\\n",
       "\t <=50K & 11    & 1021 \\\\\n",
       "\t >50K  & 11    &  361 \\\\\n",
       "\t <=50K & 12    &  802 \\\\\n",
       "\t >50K  & 12    &  265 \\\\\n",
       "\t <=50K & 13    & 3134 \\\\\n",
       "\t >50K  & 13    & 2221 \\\\\n",
       "\t <=50K & 14    &  764 \\\\\n",
       "\t >50K  & 14    &  959 \\\\\n",
       "\t <=50K & 15    &  153 \\\\\n",
       "\t >50K  & 15    &  423 \\\\\n",
       "\t <=50K & 16    &  107 \\\\\n",
       "\t >50K  & 16    &  306 \\\\\n",
       "\\end{tabular}\n"
      ],
      "text/markdown": [
       "\n",
       "| income | education_num | count |\n",
       "|---|---|---|\n",
       "| <=50K | 1     |   51  |\n",
       "| >50K  | 1     |    0  |\n",
       "| <=50K | 2     |  162  |\n",
       "| >50K  | 2     |    6  |\n",
       "| <=50K | 3     |  317  |\n",
       "| >50K  | 3     |   16  |\n",
       "| <=50K | 4     |  606  |\n",
       "| >50K  | 4     |   40  |\n",
       "| <=50K | 5     |  487  |\n",
       "| >50K  | 5     |   27  |\n",
       "| <=50K | 6     |  871  |\n",
       "| >50K  | 6     |   62  |\n",
       "| <=50K | 7     | 1115  |\n",
       "| >50K  | 7     |   60  |\n",
       "| <=50K | 8     |  400  |\n",
       "| >50K  | 8     |   33  |\n",
       "| <=50K | 9     | 8826  |\n",
       "| >50K  | 9     | 1675  |\n",
       "| <=50K | 10    | 5904  |\n",
       "| >50K  | 10    | 1387  |\n",
       "| <=50K | 11    | 1021  |\n",
       "| >50K  | 11    |  361  |\n",
       "| <=50K | 12    |  802  |\n",
       "| >50K  | 12    |  265  |\n",
       "| <=50K | 13    | 3134  |\n",
       "| >50K  | 13    | 2221  |\n",
       "| <=50K | 14    |  764  |\n",
       "| >50K  | 14    |  959  |\n",
       "| <=50K | 15    |  153  |\n",
       "| >50K  | 15    |  423  |\n",
       "| <=50K | 16    |  107  |\n",
       "| >50K  | 16    |  306  |\n",
       "\n"
      ],
      "text/plain": [
       "   income education_num count\n",
       "1  <=50K  1               51 \n",
       "2  >50K   1                0 \n",
       "3  <=50K  2              162 \n",
       "4  >50K   2                6 \n",
       "5  <=50K  3              317 \n",
       "6  >50K   3               16 \n",
       "7  <=50K  4              606 \n",
       "8  >50K   4               40 \n",
       "9  <=50K  5              487 \n",
       "10 >50K   5               27 \n",
       "11 <=50K  6              871 \n",
       "12 >50K   6               62 \n",
       "13 <=50K  7             1115 \n",
       "14 >50K   7               60 \n",
       "15 <=50K  8              400 \n",
       "16 >50K   8               33 \n",
       "17 <=50K  9             8826 \n",
       "18 >50K   9             1675 \n",
       "19 <=50K  10            5904 \n",
       "20 >50K   10            1387 \n",
       "21 <=50K  11            1021 \n",
       "22 >50K   11             361 \n",
       "23 <=50K  12             802 \n",
       "24 >50K   12             265 \n",
       "25 <=50K  13            3134 \n",
       "26 >50K   13            2221 \n",
       "27 <=50K  14             764 \n",
       "28 >50K   14             959 \n",
       "29 <=50K  15             153 \n",
       "30 >50K   15             423 \n",
       "31 <=50K  16             107 \n",
       "32 >50K   16             306 "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# create a dataframe\n",
    "df1 <- data.frame(table(adult$income, adult$education_num))\n",
    "names(df1) <- c('income', 'education_num', 'count')\n",
    "df1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
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aIwRJIxc5GrQaQoDJFkzFzkahApCkMk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\nSUVORK5CYII=",
      "text/plain": [
       "plot without title"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# calculate the percentages\n",
    "df1 <- ddply(df1, .(education_num), transform, percent = count/sum(count) * 100)\n",
    "\n",
    "\n",
    "# bar plot of counts by years of education with in group proportions \n",
    "ggplot(df1, aes(x = education_num, y = count, fill = income)) +\n",
    "  geom_bar(stat = \"identity\") +\n",
    "  ggtitle('Income Level with Years of Education')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Income based on class of Occupation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "\n",
       "                ?      Adm-clerical      Armed-Forces      Craft-repair \n",
       "             1843              3770                 9              4099 \n",
       "  Exec-managerial   Farming-fishing Handlers-cleaners Machine-op-inspct \n",
       "             4066               994              1370              2002 \n",
       "    Other-service   Priv-house-serv    Prof-specialty   Protective-serv \n",
       "             3295               149              4140               649 \n",
       "            Sales      Tech-support  Transport-moving \n",
       "             3650               928              1597 "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "table(adult$occupation)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Combining the occupation classes in Blue-collar, professional, sales, service and white collar."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<dl class=dl-horizontal>\n",
       "\t<dt>Blue-Collar</dt>\n",
       "\t\t<dd>10062</dd>\n",
       "\t<dt>Other/Unknown</dt>\n",
       "\t\t<dd>1852</dd>\n",
       "\t<dt>Professional</dt>\n",
       "\t\t<dd>4140</dd>\n",
       "\t<dt>Sales</dt>\n",
       "\t\t<dd>3650</dd>\n",
       "\t<dt>Service</dt>\n",
       "\t\t<dd>5021</dd>\n",
       "\t<dt>White-Collar</dt>\n",
       "\t\t<dd>7836</dd>\n",
       "</dl>\n"
      ],
      "text/latex": [
       "\\begin{description*}\n",
       "\\item[Blue-Collar] 10062\n",
       "\\item[Other/Unknown] 1852\n",
       "\\item[Professional] 4140\n",
       "\\item[Sales] 3650\n",
       "\\item[Service] 5021\n",
       "\\item[White-Collar] 7836\n",
       "\\end{description*}\n"
      ],
      "text/markdown": [
       "Blue-Collar\n",
       ":   10062Other/Unknown\n",
       ":   1852Professional\n",
       ":   4140Sales\n",
       ":   3650Service\n",
       ":   5021White-Collar\n",
       ":   7836\n",
       "\n"
      ],
      "text/plain": [
       "  Blue-Collar Other/Unknown  Professional         Sales       Service \n",
       "        10062          1852          4140          3650          5021 \n",
       " White-Collar \n",
       "         7836 "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "levels(adult$occupation)[1] <- 'Unknown'\n",
    "adult$occupation <- gsub('Adm-clerical', 'White-Collar', adult$occupation)\n",
    "adult$occupation <- gsub('Craft-repair', 'Blue-Collar', adult$occupation)\n",
    "adult$occupation <- gsub('Exec-managerial', 'White-Collar', adult$occupation)\n",
    "adult$occupation <- gsub('Farming-fishing', 'Blue-Collar', adult$occupation)\n",
    "adult$occupation <- gsub('Handlers-cleaners', 'Blue-Collar', adult$occupation)\n",
    "adult$occupation <- gsub('Machine-op-inspct', 'Blue-Collar', adult$occupation)\n",
    "adult$occupation <- gsub('Other-service', 'Service', adult$occupation)\n",
    "adult$occupation <- gsub('Priv-house-serv', 'Service', adult$occupation)\n",
    "adult$occupation <- gsub('Prof-specialty', 'Professional', adult$occupation)\n",
    "adult$occupation <- gsub('Protective-serv', 'Service', adult$occupation)\n",
    "adult$occupation <- gsub('Tech-support', 'Service', adult$occupation)\n",
    "adult$occupation <- gsub('Transport-moving', 'Blue-Collar', adult$occupation)\n",
    "adult$occupation <- gsub('Unknown', 'Other/Unknown', adult$occupation)\n",
    "adult$occupation <- gsub('Armed-Forces', 'Other/Unknown', adult$occupation)\n",
    "adult$occupation <- as.factor(adult$occupation)\n",
    "summary(adult$occupation)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table>\n",
       "<thead><tr><th scope=col>income</th><th scope=col>occupation</th><th scope=col>count</th></tr></thead>\n",
       "<tbody>\n",
       "\t<tr><td>&lt;=50K                                             </td><td><span style=white-space:pre-wrap>Blue-Collar  </span></td><td>8362                                                 </td></tr>\n",
       "\t<tr><td>&gt;50K                                              </td><td><span style=white-space:pre-wrap>Blue-Collar  </span></td><td>1700                                                 </td></tr>\n",
       "\t<tr><td>&lt;=50K     </td><td>Other/Unknown</td><td>1660         </td></tr>\n",
       "\t<tr><td>&gt;50K      </td><td>Other/Unknown</td><td> 192         </td></tr>\n",
       "\t<tr><td>&lt;=50K     </td><td>Professional </td><td>2281         </td></tr>\n",
       "\t<tr><td>&gt;50K      </td><td>Professional </td><td>1859         </td></tr>\n",
       "\t<tr><td>&lt;=50K                                             </td><td><span style=white-space:pre-wrap>Sales        </span></td><td>2667                                                 </td></tr>\n",
       "\t<tr><td>&gt;50K                                              </td><td><span style=white-space:pre-wrap>Sales        </span></td><td> 983                                                 </td></tr>\n",
       "\t<tr><td>&lt;=50K                                             </td><td><span style=white-space:pre-wrap>Service      </span></td><td>4389                                                 </td></tr>\n",
       "\t<tr><td>&gt;50K                                              </td><td><span style=white-space:pre-wrap>Service      </span></td><td> 632                                                 </td></tr>\n",
       "\t<tr><td>&lt;=50K     </td><td>White-Collar </td><td>5361         </td></tr>\n",
       "\t<tr><td>&gt;50K      </td><td>White-Collar </td><td>2475         </td></tr>\n",
       "</tbody>\n",
       "</table>\n"
      ],
      "text/latex": [
       "\\begin{tabular}{r|lll}\n",
       " income & occupation & count\\\\\n",
       "\\hline\n",
       "\t <=50K         & Blue-Collar   & 8362         \\\\\n",
       "\t >50K          & Blue-Collar   & 1700         \\\\\n",
       "\t <=50K         & Other/Unknown & 1660         \\\\\n",
       "\t >50K          & Other/Unknown &  192         \\\\\n",
       "\t <=50K         & Professional  & 2281         \\\\\n",
       "\t >50K          & Professional  & 1859         \\\\\n",
       "\t <=50K         & Sales         & 2667         \\\\\n",
       "\t >50K          & Sales         &  983         \\\\\n",
       "\t <=50K         & Service       & 4389         \\\\\n",
       "\t >50K          & Service       &  632         \\\\\n",
       "\t <=50K         & White-Collar  & 5361         \\\\\n",
       "\t >50K          & White-Collar  & 2475         \\\\\n",
       "\\end{tabular}\n"
      ],
      "text/markdown": [
       "\n",
       "| income | occupation | count |\n",
       "|---|---|---|\n",
       "| <=50K         | Blue-Collar   | 8362          |\n",
       "| >50K          | Blue-Collar   | 1700          |\n",
       "| <=50K         | Other/Unknown | 1660          |\n",
       "| >50K          | Other/Unknown |  192          |\n",
       "| <=50K         | Professional  | 2281          |\n",
       "| >50K          | Professional  | 1859          |\n",
       "| <=50K         | Sales         | 2667          |\n",
       "| >50K          | Sales         |  983          |\n",
       "| <=50K         | Service       | 4389          |\n",
       "| >50K          | Service       |  632          |\n",
       "| <=50K         | White-Collar  | 5361          |\n",
       "| >50K          | White-Collar  | 2475          |\n",
       "\n"
      ],
      "text/plain": [
       "   income occupation    count\n",
       "1  <=50K  Blue-Collar   8362 \n",
       "2  >50K   Blue-Collar   1700 \n",
       "3  <=50K  Other/Unknown 1660 \n",
       "4  >50K   Other/Unknown  192 \n",
       "5  <=50K  Professional  2281 \n",
       "6  >50K   Professional  1859 \n",
       "7  <=50K  Sales         2667 \n",
       "8  >50K   Sales          983 \n",
       "9  <=50K  Service       4389 \n",
       "10 >50K   Service        632 \n",
       "11 <=50K  White-Collar  5361 \n",
       "12 >50K   White-Collar  2475 "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# create a dataframe\n",
    "df2 <- data.frame(table(adult$income, adult$occupation))\n",
    "names(df2) <- c('income', 'occupation', 'count')\n",
    "df2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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bRQvp5SnItsBZGiaKF8PaU4F9kKIkXR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\nHEAkAAcQCcABRAJwAJEAHPh/bEI+w/v7rZgAAAAASUVORK5CYII=",
      "text/plain": [
       "plot without title"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# calculate the percentages\n",
    "df2 <- ddply(df2, .(occupation), transform, percent = count/sum(count) * 100)\n",
    "\n",
    "# bar plot of counts by occupation with in group proportions \n",
    "ggplot(df2, aes(x = occupation, y = count, fill = income)) +\n",
    "  geom_bar(stat = \"identity\") +\n",
    "  ggtitle('Income Level with Different Occupations')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Income vs Marital Status"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<dl class=dl-horizontal>\n",
       "\t<dt>Divorced</dt>\n",
       "\t\t<dd>4443</dd>\n",
       "\t<dt>Married-AF-spouse</dt>\n",
       "\t\t<dd>23</dd>\n",
       "\t<dt>Married-civ-spouse</dt>\n",
       "\t\t<dd>14976</dd>\n",
       "\t<dt>Married-spouse-absent</dt>\n",
       "\t\t<dd>418</dd>\n",
       "\t<dt>Never-married</dt>\n",
       "\t\t<dd>10683</dd>\n",
       "\t<dt>Separated</dt>\n",
       "\t\t<dd>1025</dd>\n",
       "\t<dt>Widowed</dt>\n",
       "\t\t<dd>993</dd>\n",
       "</dl>\n"
      ],
      "text/latex": [
       "\\begin{description*}\n",
       "\\item[Divorced] 4443\n",
       "\\item[Married-AF-spouse] 23\n",
       "\\item[Married-civ-spouse] 14976\n",
       "\\item[Married-spouse-absent] 418\n",
       "\\item[Never-married] 10683\n",
       "\\item[Separated] 1025\n",
       "\\item[Widowed] 993\n",
       "\\end{description*}\n"
      ],
      "text/markdown": [
       "Divorced\n",
       ":   4443Married-AF-spouse\n",
       ":   23Married-civ-spouse\n",
       ":   14976Married-spouse-absent\n",
       ":   418Never-married\n",
       ":   10683Separated\n",
       ":   1025Widowed\n",
       ":   993\n",
       "\n"
      ],
      "text/plain": [
       "             Divorced     Married-AF-spouse    Married-civ-spouse \n",
       "                 4443                    23                 14976 \n",
       "Married-spouse-absent         Never-married             Separated \n",
       "                  418                 10683                  1025 \n",
       "              Widowed \n",
       "                  993 "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "summary(adult$marital_status)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<dl class=dl-horizontal>\n",
       "\t<dt>Divorced</dt>\n",
       "\t\t<dd>4443</dd>\n",
       "\t<dt>Married</dt>\n",
       "\t\t<dd>15417</dd>\n",
       "\t<dt>Separated</dt>\n",
       "\t\t<dd>1025</dd>\n",
       "\t<dt>Single</dt>\n",
       "\t\t<dd>10683</dd>\n",
       "\t<dt>Widowed</dt>\n",
       "\t\t<dd>993</dd>\n",
       "</dl>\n"
      ],
      "text/latex": [
       "\\begin{description*}\n",
       "\\item[Divorced] 4443\n",
       "\\item[Married] 15417\n",
       "\\item[Separated] 1025\n",
       "\\item[Single] 10683\n",
       "\\item[Widowed] 993\n",
       "\\end{description*}\n"
      ],
      "text/markdown": [
       "Divorced\n",
       ":   4443Married\n",
       ":   15417Separated\n",
       ":   1025Single\n",
       ":   10683Widowed\n",
       ":   993\n",
       "\n"
      ],
      "text/plain": [
       " Divorced   Married Separated    Single   Widowed \n",
       "     4443     15417      1025     10683       993 "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "adult$marital_status <- gsub('Married-AF-spouse', 'Married', adult$marital_status)\n",
    "adult$marital_status <- gsub('Married-civ-spouse', 'Married', adult$marital_status)\n",
    "adult$marital_status <- gsub('Married-spouse-absent', 'Married', adult$marital_status)\n",
    "adult$marital_status <- gsub('Never-married', 'Single', adult$marital_status)\n",
    "adult$marital_status <- as.factor(adult$marital_status)\n",
    "summary(adult$marital_status)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table>\n",
       "<thead><tr><th scope=col>income</th><th scope=col>marital_status</th><th scope=col>count</th></tr></thead>\n",
       "<tbody>\n",
       "\t<tr><td>&lt;=50K </td><td>Divorced </td><td> 3980    </td></tr>\n",
       "\t<tr><td>&gt;50K                                      </td><td>Divorced                                     </td><td><span style=white-space:pre-wrap>  463</span></td></tr>\n",
       "\t<tr><td>&lt;=50K                                         </td><td><span style=white-space:pre-wrap>Married  </span></td><td> 8681                                            </td></tr>\n",
       "\t<tr><td>&gt;50K                                          </td><td><span style=white-space:pre-wrap>Married  </span></td><td> 6736                                            </td></tr>\n",
       "\t<tr><td>&lt;=50K                                     </td><td>Separated                                    </td><td><span style=white-space:pre-wrap>  959</span></td></tr>\n",
       "\t<tr><td>&gt;50K                                      </td><td>Separated                                    </td><td><span style=white-space:pre-wrap>   66</span></td></tr>\n",
       "\t<tr><td>&lt;=50K                                         </td><td><span style=white-space:pre-wrap>Single   </span></td><td>10192                                            </td></tr>\n",
       "\t<tr><td>&gt;50K                                          </td><td><span style=white-space:pre-wrap>Single   </span></td><td><span style=white-space:pre-wrap>  491</span>    </td></tr>\n",
       "\t<tr><td>&lt;=50K                                         </td><td><span style=white-space:pre-wrap>Widowed  </span></td><td><span style=white-space:pre-wrap>  908</span>    </td></tr>\n",
       "\t<tr><td>&gt;50K                                          </td><td><span style=white-space:pre-wrap>Widowed  </span></td><td><span style=white-space:pre-wrap>   85</span>    </td></tr>\n",
       "</tbody>\n",
       "</table>\n"
      ],
      "text/latex": [
       "\\begin{tabular}{r|lll}\n",
       " income & marital\\_status & count\\\\\n",
       "\\hline\n",
       "\t <=50K     & Divorced  &  3980    \\\\\n",
       "\t >50K      & Divorced  &   463    \\\\\n",
       "\t <=50K     & Married   &  8681    \\\\\n",
       "\t >50K      & Married   &  6736    \\\\\n",
       "\t <=50K     & Separated &   959    \\\\\n",
       "\t >50K      & Separated &    66    \\\\\n",
       "\t <=50K     & Single    & 10192    \\\\\n",
       "\t >50K      & Single    &   491    \\\\\n",
       "\t <=50K     & Widowed   &   908    \\\\\n",
       "\t >50K      & Widowed   &    85    \\\\\n",
       "\\end{tabular}\n"
      ],
      "text/markdown": [
       "\n",
       "| income | marital_status | count |\n",
       "|---|---|---|\n",
       "| <=50K     | Divorced  |  3980     |\n",
       "| >50K      | Divorced  |   463     |\n",
       "| <=50K     | Married   |  8681     |\n",
       "| >50K      | Married   |  6736     |\n",
       "| <=50K     | Separated |   959     |\n",
       "| >50K      | Separated |    66     |\n",
       "| <=50K     | Single    | 10192     |\n",
       "| >50K      | Single    |   491     |\n",
       "| <=50K     | Widowed   |   908     |\n",
       "| >50K      | Widowed   |    85     |\n",
       "\n"
      ],
      "text/plain": [
       "   income marital_status count\n",
       "1  <=50K  Divorced        3980\n",
       "2  >50K   Divorced         463\n",
       "3  <=50K  Married         8681\n",
       "4  >50K   Married         6736\n",
       "5  <=50K  Separated        959\n",
       "6  >50K   Separated         66\n",
       "7  <=50K  Single         10192\n",
       "8  >50K   Single           491\n",
       "9  <=50K  Widowed          908\n",
       "10 >50K   Widowed           85"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df3 <- data.frame(table(adult$income, adult$marital_status))\n",
    "names(df3) <- c('income', 'marital_status', 'count')\n",
    "df3"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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xApfmVEErCbiz4CkeJXRiQBu7noIxAp\nfmVEErCbiz4CkeJXRiQBu7noIxApfmVEErCbiz4CkeJXRiQBu7noIxApfmVEErCbiz4CkeJX\nRiQBu7noIxApfmVEErCbiz4CkeJXRiQBu7noIxApfmVEErCbiz4CkeJXRiQBu7noIxApfmVE\nErCbiz4CkeJXRiQBu7noIxApfmVEErCbiz4CkeJXRiQBu7noIxApfmVEErCbiz4CkeJXRiQB\nu7noIxApfmVEErCbiz4CkeJXRiQBu7noIxApfmVEErCbiz4CkeJXRiQBu7noIxApfmVEErCb\niz4CkeJXRiQBu7noIxApfmVEErCbiz4CkeJXRiQBu7noIxApfmVEErCbiz4CkeJXRiQBu7no\nIxApfmVEErCbiz4CkeJXRiQBu7noIxApfmVEErCbiz4CkeJXRiQBu7noIxApfmVEErCbiz4C\nkeJXRiQBu7noIxApfmVEErCbiz4CkeJXRiQBu7noIxApfmVEErCbiz4CkeJXRiQBu7noIxAp\nfmVEErCbiz4CkeJXRiQBu7noIxApfmVEErCbiz4CkeJXRiQBu7noIxApfmVEErCbiz6iRZEa\nbDkl+YJIy9qqBltOSb4g0rK2qsGWU5IviLSsrWqw5ZTkCyIta6sabDkl+YJIy9qqBltOSb4g\n0rK2qsGWU5IviLSsrWqw5ZTkCyIta6sabDkl+YJIy9qqBltOSb4g0rK2qsGWU5IviLSsrWqw\n5ZTkCyIta6sabDkl+YJIy9qqBltOSb4g0rK2qsGWU5IviLSsrWqw5ZTkCyIta6sabDkl+YJI\ny9qqBltOSb4g0rK2qsGWU5IviLSsrWqw5ZTkCyIta6sabDkl+YJIy9qqBltOSb4g0rK2qsGW\nU5IviLSsrWqw5ZTkCyIta6sabDkl+YJIy9qqBltOSb78RaT++PbAo/dH7Oaij6i6VEG2qsGW\nU5IvfxDp5MvpjfT+hN1c9BFVlyrIVjXYckry5d9F6veI9Dpb1WDLKcmXP39ph0gvsVUNtpyS\nfDEU6Z+BWS6ykKpL9T/vbo802HIMeEayQn+9L9ez/nJ5RkKkmFvVYMspyRdEWtZWNdhySvIF\nkZa1VQ22nJJ8QaRlbVWDLackX/jNhmVtVYMtpyRf+F27ZW1Vgy2nJF8QaVlb1WDLKckXRFrW\nVjXYckryBZGWtVUNtpySfEGkZW1Vgy2nJF8QaVlb1WDLKckXRFrWVjXYckryBZGWtVUNtpyS\nfEGkZW1Vgy2nJF8QaVlb1WDLKckXRFrWVjXYckryBZGWtVUNtpySfEGkZW1Vgy2nJF8QaVlb\n1WDLKckXRFrWVjXYckryBZGWtVUNtpySfEGkZW1Vgy2nJF8QaVlb1WDLKckXRFrWVjXYckry\nBZGWtVUNtpySfEGkZW1Vgy2nJF8QaVlb1WDLKckXRFrWVjXYckryBZGWtVUNtpySfEGkZW1V\ngy2nJF8QaVlb1WDLKckXRFrWVjXYckryBZGWtVUNtpySfEGkZW1Vgy2nJF8QaVlb1WDLKckX\nRFrWVjXYckryBZGWtVUNtpySfEGkZW1Vgy2nJF8QaVlb1WDLKckXRFrWVjXYckryBZGWtVUN\ntpySfEGkZW1Vgy2nJF8QaVlb1WDLKckXRFrWVjXYckryBZGWtVUNtpySfEGkZW1Vgy2nJF8Q\naVlb1WDLKckXRFrWVjXYckryBZGWtVUNtpySfEGkZW1Vgy2nJF8QaVlb1WDLKckXRFrWVjXY\nckryBZGWtVUNtpySfEGkZW1Vgy2nJF8QaVlb1WDLKckXRFrWVjXYckryBZGWtVUNtpySfEGk\nZW1Vgy2nJF8QaVlb1WDLKckXRFrWVjXYckryBZGWtVUNtpySHm259ZrfKWGcZzcXfUTVpQqy\nVQ22nJIebTkiaai6VEG2qsGWU5IviLSsrWqw5ZT0aMu74X/bt67fDH+8fPDede/b4wPeurf9\ndtW97Q5/3A23755afERa1lY12HJKerTlo0h9d+Ag0G784C190O+GO98OH32tDm/eD48fb189\ntfiItKytarDllPRoy0eR1rv9Z9fv95uDLD/DTZtuvd+vB7cGf74Gy76G2z+Gmzbd5zOLj0jL\n2qoGW05Jj7b8+KXd6aNVd/qqbTXctB2eesY7u26XHjB+0tszi49Iy9qqBltOSY+2vEuv3F0+\n2mc3nd90J55ZfERa1lY12HJKerTliKSh6lIF2aoGW05Jj7b8SqS7X9rtJ29WBVYg0rK2qsGW\nU9KjLb8SadNt9r+3LzbsJ2+GB+y/hjv/DiIta6sabDklPdryK5G26dXt6cvf+8mb4+3d7zOL\nj0jL2qoGW05Jj7b8SqT97/r0c9jpD2Snb4bb1z9PLT4iLWurGmw5JfmCSMvaqgZbTkm+INKy\ntqrBllOSL4i0rK1qsOWU5MuNSOmHUH1fmGc3F31E1aUKslUNtpySfJmK1HcTCvPs5qKPqLpU\nQbaqwZZTki9TYT4nHj31m68T7Oaij6i6VEG2qsGWU5Ivwpd2xdjNRR9RdamCbFWDLackX3ix\nYVlb1WDLKcmXW5E2Pd8jvfJWNdhySvLlRpgNLza89lY12HJKkvnvIwoX/ZYbYfriVxlO2M1F\nH1F1qYJsVYMtpySZ/z7oYSaReLGhGP31vlzP+stdrEhv3XP/CFGG3Vz0EdUWakR/vS/Xs/5y\nFyvStl9vVXl2c9FHVFuoEf31vlzP+stdrEj8ZkMx+ut9uZ71l4tIEnZz0UdUW6gR/fW+XM/6\ny12sSGrs5qKPqLZQI/rrfbme9ZeLSBJ2c9FHVFuoEf31vlzP+stdrEh8aVeM/npfrmf95SKS\nhN1c9BHVFmpEf70v17P+chcr0pHt+qM0z24u+ohqCzWiv96X61l/udFFSs8p6d3p1nuPvPv5\nu67UJLu56COqLdSI/npfrmf95YYXafKuS3+864zwJRxf2j2N/npfrmf95QYUqbvzhyuR7qtx\n/9avjn+z4Vn01/tyPesvN5pIly/kxo/O/9r+6c3ky7tbpBcbNkUaIZKKV+tZf7mxRMq+Duum\n3xt1k6/ucu6L1Jd6hEgaXq1n/eWGEil35PZLum78v7vwA1kr9Nf7cj3rLzeUSMIrA1OR5K/t\nEMkK/fW+XM/6y40lUvY9UlKk4MWG3WbVdatN8X+VZDcXfUS1hRrRX+/L9ay/3Ggi3RiRf2l3\nz5l7N25P//ZJX/pfJdnNRR9RbaFG9Nf7cj3rLzegSLd23LzYcEeae7e9d8N/2Lddd+9/d+cK\nu7noI6ot1Ij+el+uZ/3lBhcp/UbDzW823DNJ+Dcb+IHs0+iv9+V61l9udJH+DiJZob/el+tZ\nf7mLFYkv7YrRX+/L9ay/3MWKpH6xIRLVFmrEu9sjDbb873j8ZxS8/F2K/npfrmf95S72GUmN\n3Vz0EdUWakR/vS/Xs/5yEUnCbi76iGoLNaK/3pfrWX+5i/23v/dvx5fNV/xA9jW3qsGWU5KM\ng0ib9PMnXrV7za1qsOWUJPPf/8jM9v+N4md498vPkV50qxpsOSXJOIjED2RffKsabDklyTiI\n9Na974bXwLt1YZ7dXPQRVZcqyFY12HJKknEQ6fwD2d/CPLu56COqLlWQrWqw5ZQk4yBS+oFs\n8S822M1FH1F1qYJsVYMtpyQZD5G02M1FH1F1qYJsVYMtpyQZRNJRdamCbFWDLackGUTSUXWp\ngmxVgy2nJBlE0lF1qYJsVYMtpySZp0X6+z/5ffmUQmEk7Oaij6i6VEG2qsGWU5LM8yJN3j3+\nN09uP8UMu7noI6ouVZCtarDllCTzR5G624/+8K9w3flkG+zmoo+oulRBtqrBllOSzF+fkdK/\nZffEP/l9+dxSYwTs5qKPqLpUQbaqwZZTkswTX9pd/oHI/ZVI/+oJIi1rqxpsOSXJPPU90p1/\nWPXBP/k9/TRT7Oaij6i6VEG2qsGWU5LMs89IyYw//JPf+8nDTbGbiz6i6lIF2aoGW05JMs9+\nj5TM4MWGI1WXKshWNdhySpJ59lW7J/7J7/yTjbCbiz6i6lIF2aoGW05JMs/+HOmJf/L76nMs\nsZuLPqLqUgXZqgZbTkkyRb/ZMHn/4J/8vnxKmS8idnPRR1RdqiBb1WDLKUmG37XTUXWpgmxV\ngy2nJBlE0lF1qYJsVYMtpyQZRNJRdamCbFWDLackGUTSUXWpgmxVgy2nJBlE0lF1qYJsVYMt\npyQZRNJRdamCbFWDLackGUTSUXWpgmxVgy2nJBmPf0Rfi91c9BEP/h6aAf31IpIqyRdEQiRE\nMgCREAmRDEAkREIkAxAJkRDJAERCJEQyAJEQCZEMQCREQiQDEAmREMkAREIkRDIAkRAJkQxA\nJERCJAMQCZEQyQBEQiREMgCREAmRDEAkREIkAxAJkRDJAERCJEQyAJEQCZEMQCREQiQDEAmR\nEMkAREIkRDIAkRAJkQxAJERCJAMQCZEQyQBEQiREMgCREAmRDEAkREIkAxAJkRDJAERCJEQy\nAJEQCZEMQCREQiQDEAmREMkAREIkRDIAkRAJkQxAJERCJAMQCZEQyQBEQiREMgCREAmRDEAk\nREIkAxAJkRDJAERCJEQyAJEQCZEMQCREQiQDEAmREMkAREIkRDIAkRAJkQxAJERCJAMQCZEQ\nyQBEQiREMgCREAmRDEAkREIkAxAJkRDJAERCJEQyAJEQCZEMQCREQiQDEAmREMkAREIkRDIA\nkRAJkQxAJERCJAMQCZEQyQBEQiREMgCREAmRDEAkREIkAxAJkRDJAERCJEQyAJEQCZEMQCRE\nQiQDEAmREMkAREIkRDIAkRAJkQxAJERCJAP+LlI/cHq/v/P+iN1c9BGIhEjVeEKkybs+f3/C\nbi76CERCpGogEiIhkgF/FqmfvkckREKkK/4uUvoWab8XRPpnYIZLLKauSN7dHqkqknezkXju\nGemOQDwjndBfL89IqiRfnnv5G5EQCZHugkiIhEgG8KUdIiGSAU+J9PjFhhG7uegjEAmRqvHc\nbzY8en/Ebi76CERCpGrwu3aIhEgGIBIiIZIBiIRIiGQAIiESIhmASIiESAYgEiIhkgGIhEiI\nZAAiIRIiGYBIiIRIBiASIiGSAYiESIhkACIhEiIZgEiIhEgGIBIiIZIBiIRIiGQAIiESIhmA\nSIiESAYgEiIhkgHzi1T1aK/OFpEqDrsQRBLIG6x6tIjkNexCEEkgb7Dq0SKS17ALQSSBvMGq\nR4tIXsMuBJEE8garHi0ieQ27EEQSyBuserSI5DXsQhBJIG+w6tEiktewC0EkgbzBqkeLSF7D\nLgSRBPIGqx4tInkNuxBEEsgbrHq0iOQ17EIQSSBvsOrRIpLXsAtBJIG8wapHi0hewy4EkQTy\nBqseLSJ5DbsQRBLIG6x6tIjkNexCEEkgb7Dq0SKS17ALQSSBvMGqR4tIXsMuBJEE8garHi0i\neQ27EEQSyBuserSI5DXsQhBJIG+w6tEiktewC0EkgbzBqkeLSF7DLgSRBPIGqx4tInkNuxBE\nEsgbrHq0iOQ17EIQSSBvsOrRIpLXsAtBJIG8wapHi0hewy4EkQTyBqseLSJ5DbsQRBLIG6x6\ntIjkNexCEEkgb7Dq0SKS17ALQSSBvMGqR4tIXsMuBJEE8garHi0ieQ27EEQSyBuserSI5DXs\nQhBJIG+w6tEiktewC0EkgbzBqkeLSF7DLgSRBPIGqx4tInkNuxBEEsgbrHq0iOQ17EIQSSBv\nsOrRIpLXsAtBJIG8wapHi0hewy4EkQTyBqseLSJ5DbsQRBLIG6x6tIjkNexCEEkgb7Dq0SKS\n17ALQSSBvMGqR4tIXsMuBJEE8garHi0ieQ27EEQSyBuserSI5DXsQhBJIG+w6tEiktewC0Ek\ngbzBqkeLSF7DLgSRBPIGqx4tInkNuxBEEsgbrHq0iOQ17EIQSSBvsOrRIpLXsAtBJIG8wapH\ni0hewy4EkQTyBqseLSJ5DbsQRBLIG6x6tIjkNexCEEkgb7Dq0SKS17ALQSSBvMGqR4tIXsMu\nBJEE8garHi0ieQ27EEQSyBuserSI5DXsQhBJIG+w6tEiktewC0EkgbzBqkeLSF7DLgSRBPIG\nqx4tInkNuxBEEsgbrHq0iOQ17EIQSSBvsOrRIpLXsAtBJIG8wapHi0hewy4EkQTyBqseLSJ5\nDbsQRBLIG6x6tIjkNexCEEkgb7Dq0SKS17ALQSSBvMGqR4tIXsMuBJEE8garHi0ieQ27EEQS\nyBuserSI5DXsQhBJILkuwm4AAAs8SURBVG+w6tEiktewC0EkgbzBqkeLSF7DLgSRBPIGqx4t\nInkNuxBEEsgbrHq0iOQ17EIQSSBvsOrRIpLXsAtBJIG8wapHi0hewy4EkQTyBqseLSJ5DbsQ\nRBLIG6x6tIjkNexCEEkgb7Dq0SKS17ALQSSBvMGqR4tIXsMuBJEE8garHi0ieQ27EEQSyBus\nerSI5DXsQhBJIG+w6tEiktewC0EkgbzBqkeLSF7DLgSRBPIGqx4tInkNuxBEEsgbrHq0iOQ1\n7EIQSSBvsOrRIpLXsAtBJIG8wapHi0hewy4EkQTyBqseLSJ5DbsQRPozVY/2f9PKdUWafZB/\nwm3YrcMzkhUWf6nqI9yGXQjPSAJ5g1WPFpG8hl0IIgnkDVY9WkTyGnYhiCSQN1j1aBHJa9iF\nIJJA3mDVo0Ukr2EXgkgCeYNVjxaRvIZdCCIJ5A1WPVpE8hp2IYgkkDdY9WgRyWvYhSCSQN5g\n1aNFJK9hF4JIAnmDVY8WkbyGXQgiCeQNVj1aRPIadiGIJJA3WPVoEclr2IUgkkDeYNWjRSSv\nYReCSAJ5g1WPFpG8hl0IIgnkDVY9WkTyGnYhiCSQN1j1aBHJa9iFIJJA3mDVo0Ukr2EXgkgC\neYNVjxaRvIZdCCIJ5A1WPVpE8hp2IYgkkDdY9WgRyWvYhSCSQN5g1aNFJK9hF4JIAnmDVY8W\nkbyGXQgiCeQNVj1aRPIadiGIJJA3WPVoEclr2IUgkkDeYNWjRSSvYReCSAJ5g1WPFpG8hl0I\nIgnkDVY9WkTyGnYhiCSQN1j1aBHJa9iFIJJA3mDVo0Ukr2EXgkgCeYNVjxaRvIZdCCIJ5A1W\nPVpE8hp2IYgkkDdY9WgRyWvYhSCSQN5g1aNFJK9hF4JIAnmDVY8WkbyGXQgiCeQNVj1aRPIa\ndiGIJJA3WPVoEclr2IUgkkDeYNWjRSSvYReCSAJ5g1WPFpG8hl0IIgnkDVY9WkTyGnYhiCSQ\nN1j1aBHJa9iFIJJA3mDVo0Ukr2EXgkgCeYNVjxaRvIZdCCIJ5A1WPVpE8hp2IYgkkDdY9WgR\nyWvYhSCSQN5g1aNFJK9hF4JIAnmDVY8WkbyGXQgiCeQNVj1aRPIadiGIJJA3WPVoEclr2IUg\nkkDeYNWjRSSvYReCSAJ5g1WPFpG8hl0IIgnkDVY9WkTyGnYhiCSQN1j1aBHJa9iFIJJA3mDV\no0Ukr2EXgkgCeYNVjxaRvIZdCCIJ5A1WPVpE8hp2IYgkkDdY9WgRyWvYhSCSQN5g1aNFJK9h\nF4JIAnmDVY8WkbyGXQgiCeQNVj1aRPIadiGIJJA3WPVoEclr2IUgkkDeYNWjRSSvYReCSAJ5\ng1WPFpG8hl0IIgnkDVY9WkTyGnYhiCSQN1j1aBHJa9iFIJJA3mDVow0ikl/PboVL198g45Tk\nCyIhkklhd4WNF/lZEAmRTAojki2IhEi1KyPSzBNGpIqFEckWREKk2pURaeYJI1LFwohkCyIh\nUu3KiDTzhBGpYmFEsgWREKl2ZUSaecKIVLEwItmCSIhUuzIizTxhRKpYGJFsQSREql0ZkWae\nMCJVLIxItiASItWujEgzTxiRKhZGJFsQCZFqV0akmSeMSBULI5ItiIRItSsj0swTRqSKhRHJ\nFkRCpNqVEWnmCSNSxcKIZAsiNdQzIl1ApDm2qpGeEenC/CJVPdr/xKhcdakQCZHmPVu/ylWX\nCpEQad6z9atcdakQCZHmPVu/ylWXCpEQad6z9atcdakQCZHmPVu/ylWXCpEQad6z9atcdakQ\nCZHmPVu/ylWXCpEQad6z9atcdakQCZHmPVu/ylWXCpEQad6z9atcdakQCZHmPVu/ylWXCpEQ\nad6z9atcdakQCZHmPVu/ylWXCpEQad6z9atcdakQCZHmPVu/ylWXCpGWIVJ/4PInRGpVJL/K\nyxCpP78ZQaSGKiPSBUSisklhRFKCSO1WRqQLhiL9M6CNA3hJ5n9GKsQuicqBKxsujC+IRGXP\nyoiUQCQqhyisXWQliERlz8qIlEAkKocorF1kJfP/ZkPpXMySqBy4MiIJ2M3FLInKgSsjkoDd\nXMySqBy4MiIJ2M3FLInKgSsjkoDdXMySqBy4MiIJ2M3FLInKgSsjkoDdXMySqBy4MiIJ2M3F\nLInKgSsjkoDdXMySqBy4MiIJ2M3FLInKgSsjkoDdXMySqBy4MiIJ2M3FLInKgSsjkoDdXMyS\nqBy4MiIJ2M3FLInKgSsjkoDdXMySqBy4MiIJ2M3FLInKgSsjkoDdXMySqBy4MiIJ2M3FLInK\ngSsjkoDdXMySqBy4MiIJ2M3FLInKgSsjkoDdXMySqBy4MiIJ2M3FLInKgSsjkoDdXMySqBy4\nMiIJ2M3FLInKgSsjkoDdXMySqBy4MiIJ2M3FLInKgSsjkoDdXMySqBy4MiIJ2M3FLInKgSsj\n0tz4/f/+o3ILha1BJCp7VkakuWlwqVqsjEhz0+BStVgZkQDgAiIBGIBIAAYgEoABiARgACIB\nGOAqUn9k/PCZT7Mq/yjv9kaTotf/H+Cf/myLS7i6jjuJdkWu8s7vHg593kuZFV+Rrt49+2n6\n8v30XYWiz/2VkT3QbLEeXcc8IvXn90/UQaS/8sTfTvc+TV2+skhP/r0xm0gPr8N8e0/W3sqL\nSIZMRJr8lXX6uqMfN/z8RcjlveFC9Ze3k6KnN+ZFp2t0Kdff/Dn9YfrWuu/JR9NLOP5Z+xXo\nvWoXkfpLqdTi5FGz9FuFeCJdRrrfX43/cgxm5c8iXRft5yl62c/bHsWe5+l7eh3TSzit9XNf\ngf5rtev/XZe6+dtsnn6rEEekyaTz47x7o7785PRu86Ur0da8+/fDVfnpH2bp++o67jRbQ6Ss\n5P27XodXEam//TLApvzk6ej+lxnmRYXkyZ+nf5jpEs7XcX+r+8tzllmdByc8FWnGfuclkEjn\nZ4j91ZhvHzmPSDdnfPX8MINIt8nT8jdrN8clTK5DekayrNNP3t8teT79OfudF0QSngZnONX+\n/O42WSw/V9+XEohkQwSRpvo82um0XJYLNf3bf+6ilx+CnpOl8ucv7YwvIb+O2wq3f4VZ1Ju+\nv9vt/uoj636rEOo3G/rzrbd/mO1l4PP/+v56h/o5ip6/97jz8vflL+T0h7le/p6kXjd7WWHT\nJc6eZq5f/p6KxMvfUEK0VYl2PS8EInkSaHFf77uSWCCSJ5EW1/jrudZAJAADEAnAAEQCMACR\nAAxAJAADEAnAAESqT3cY+mef3XSHm0d98vp0WBCpPoM1N+YIIv3tURAAjsYHRFoYHM0cHDb+\nrXvbb1fd2+7wx5+3rus34+2//Xq4t+tGKSZ3XH3+R9+tPve3jzr+8fjQ8e3pYRAARJqDrjvs\nfve1Orx53++/u5FRhfXhhrNI0zumn74Zb/68fdStSOlhEABEmoPBn69h97+GhV91X/v971GC\n9PQz6jC94/rTt/ufrr//qItI54eBP4g0B8OKH97s0tpvvz/WafWvXmy43DH99L57/z7l5I+6\niHR+GPiDSHNw2fjxzfr4xdn1s8k+vyPx3XfdKil3+6hLxvlh4A8izcG1SO/d6vN7e0ek2zsu\n/K66/uf+oyYvNqSHgT+INAfXIo0f7O6IdHvHlE/pUeObbfqET14SjwHHMAe3Iv3sd+u7Il3f\nkegPt/+eX2y4eVTffZ3+eH4Y+INIc3At0vFl6luR+vyOxPH2jzuPSn/8uOR+eDQItyDSHNy8\n2PDedeufa5E+x2eSmzvObPquHwS5edTxj4c7P46fkB4G/iASgAGIBGAAIkWhO+N9JVAApxYF\nRHppODUAAxAJwABEAjAAkQAMQCQAAxAJwABEAjAAkQAM+H+kPUV0/24wGQAAAABJRU5ErkJg\ngg==",
      "text/plain": [
       "plot without title"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# calculate the percentages\n",
    "df3 <- ddply(df3, .(marital_status), transform, percent = count/sum(count) * 100)\n",
    "\n",
    "# bar plot of counts by marital status with in group proportions \n",
    "ggplot(df3, aes(x = marital_status, y = count, fill = income)) +\n",
    "  geom_bar(stat = \"identity\") + \n",
    "  ggtitle('Income Level with Marital Status')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "R",
   "language": "R",
   "name": "ir"
  },
  "language_info": {
   "codemirror_mode": "r",
   "file_extension": ".r",
   "mimetype": "text/x-r-source",
   "name": "R",
   "pygments_lexer": "r",
   "version": "3.6.1"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}