FirstTable.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "library(dplyr)\n",
    "library(readr)\n",
    "library(ggplot2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "options(repr.plot.height=5)\n",
    "options(repr.matrix.max.rows=20)\n",
    "options(repr.matrix.max.columns=10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table>\n",
       "<thead><tr><th scope=col>user</th><th scope=col>gender</th><th scope=col>age</th><th scope=col>dataset</th></tr></thead>\n",
       "<tbody>\n",
       "\t<tr><td> 1        </td><td>F         </td><td>1         </td><td>Lenskit-1m</td></tr>\n",
       "\t<tr><td> 2        </td><td>M         </td><td>56        </td><td>Lenskit-1m</td></tr>\n",
       "\t<tr><td> 3        </td><td>M         </td><td>25        </td><td>Lenskit-1m</td></tr>\n",
       "\t<tr><td> 4        </td><td>M         </td><td>45        </td><td>Lenskit-1m</td></tr>\n",
       "\t<tr><td> 5        </td><td>M         </td><td>25        </td><td>Lenskit-1m</td></tr>\n",
       "\t<tr><td> 6        </td><td>F         </td><td>50        </td><td>Lenskit-1m</td></tr>\n",
       "\t<tr><td> 7        </td><td>M         </td><td>35        </td><td>Lenskit-1m</td></tr>\n",
       "\t<tr><td> 8        </td><td>M         </td><td>25        </td><td>Lenskit-1m</td></tr>\n",
       "\t<tr><td> 9        </td><td>M         </td><td>25        </td><td>Lenskit-1m</td></tr>\n",
       "\t<tr><td>10        </td><td>F         </td><td>35        </td><td>Lenskit-1m</td></tr>\n",
       "\t<tr><td>⋮</td><td>⋮</td><td>⋮</td><td>⋮</td></tr>\n",
       "\t<tr><td>6031      </td><td>F         </td><td>18        </td><td>Lenskit-1m</td></tr>\n",
       "\t<tr><td>6032      </td><td>M         </td><td>45        </td><td>Lenskit-1m</td></tr>\n",
       "\t<tr><td>6033      </td><td>M         </td><td>50        </td><td>Lenskit-1m</td></tr>\n",
       "\t<tr><td>6034      </td><td>M         </td><td>25        </td><td>Lenskit-1m</td></tr>\n",
       "\t<tr><td>6035      </td><td>F         </td><td>25        </td><td>Lenskit-1m</td></tr>\n",
       "\t<tr><td>6036      </td><td>F         </td><td>25        </td><td>Lenskit-1m</td></tr>\n",
       "\t<tr><td>6037      </td><td>F         </td><td>45        </td><td>Lenskit-1m</td></tr>\n",
       "\t<tr><td>6038      </td><td>F         </td><td>56        </td><td>Lenskit-1m</td></tr>\n",
       "\t<tr><td>6039      </td><td>F         </td><td>45        </td><td>Lenskit-1m</td></tr>\n",
       "\t<tr><td>6040      </td><td>M         </td><td>25        </td><td>Lenskit-1m</td></tr>\n",
       "</tbody>\n",
       "</table>\n"
      ],
      "text/latex": [
       "\\begin{tabular}{r|llll}\n",
       " user & gender & age & dataset\\\\\n",
       "\\hline\n",
       "\t  1         & F          & 1          & Lenskit-1m\\\\\n",
       "\t  2         & M          & 56         & Lenskit-1m\\\\\n",
       "\t  3         & M          & 25         & Lenskit-1m\\\\\n",
       "\t  4         & M          & 45         & Lenskit-1m\\\\\n",
       "\t  5         & M          & 25         & Lenskit-1m\\\\\n",
       "\t  6         & F          & 50         & Lenskit-1m\\\\\n",
       "\t  7         & M          & 35         & Lenskit-1m\\\\\n",
       "\t  8         & M          & 25         & Lenskit-1m\\\\\n",
       "\t  9         & M          & 25         & Lenskit-1m\\\\\n",
       "\t 10         & F          & 35         & Lenskit-1m\\\\\n",
       "\t ⋮ & ⋮ & ⋮ & ⋮\\\\\n",
       "\t 6031       & F          & 18         & Lenskit-1m\\\\\n",
       "\t 6032       & M          & 45         & Lenskit-1m\\\\\n",
       "\t 6033       & M          & 50         & Lenskit-1m\\\\\n",
       "\t 6034       & M          & 25         & Lenskit-1m\\\\\n",
       "\t 6035       & F          & 25         & Lenskit-1m\\\\\n",
       "\t 6036       & F          & 25         & Lenskit-1m\\\\\n",
       "\t 6037       & F          & 45         & Lenskit-1m\\\\\n",
       "\t 6038       & F          & 56         & Lenskit-1m\\\\\n",
       "\t 6039       & F          & 45         & Lenskit-1m\\\\\n",
       "\t 6040       & M          & 25         & Lenskit-1m\\\\\n",
       "\\end{tabular}\n"
      ],
      "text/markdown": [
       "\n",
       "user | gender | age | dataset | \n",
       "|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n",
       "|  1         | F          | 1          | Lenskit-1m | \n",
       "|  2         | M          | 56         | Lenskit-1m | \n",
       "|  3         | M          | 25         | Lenskit-1m | \n",
       "|  4         | M          | 45         | Lenskit-1m | \n",
       "|  5         | M          | 25         | Lenskit-1m | \n",
       "|  6         | F          | 50         | Lenskit-1m | \n",
       "|  7         | M          | 35         | Lenskit-1m | \n",
       "|  8         | M          | 25         | Lenskit-1m | \n",
       "|  9         | M          | 25         | Lenskit-1m | \n",
       "| 10         | F          | 35         | Lenskit-1m | \n",
       "| ⋮ | ⋮ | ⋮ | ⋮ | \n",
       "| 6031       | F          | 18         | Lenskit-1m | \n",
       "| 6032       | M          | 45         | Lenskit-1m | \n",
       "| 6033       | M          | 50         | Lenskit-1m | \n",
       "| 6034       | M          | 25         | Lenskit-1m | \n",
       "| 6035       | F          | 25         | Lenskit-1m | \n",
       "| 6036       | F          | 25         | Lenskit-1m | \n",
       "| 6037       | F          | 45         | Lenskit-1m | \n",
       "| 6038       | F          | 56         | Lenskit-1m | \n",
       "| 6039       | F          | 45         | Lenskit-1m | \n",
       "| 6040       | M          | 25         | Lenskit-1m | \n",
       "\n",
       "\n"
      ],
      "text/plain": [
       "     user gender age dataset   \n",
       "1     1   F      1   Lenskit-1m\n",
       "2     2   M      56  Lenskit-1m\n",
       "3     3   M      25  Lenskit-1m\n",
       "4     4   M      45  Lenskit-1m\n",
       "5     5   M      25  Lenskit-1m\n",
       "6     6   F      50  Lenskit-1m\n",
       "7     7   M      35  Lenskit-1m\n",
       "8     8   M      25  Lenskit-1m\n",
       "9     9   M      25  Lenskit-1m\n",
       "10   10   F      35  Lenskit-1m\n",
       "⋮    ⋮    ⋮      ⋮   ⋮         \n",
       "6031 6031 F      18  Lenskit-1m\n",
       "6032 6032 M      45  Lenskit-1m\n",
       "6033 6033 M      50  Lenskit-1m\n",
       "6034 6034 M      25  Lenskit-1m\n",
       "6035 6035 F      25  Lenskit-1m\n",
       "6036 6036 F      25  Lenskit-1m\n",
       "6037 6037 F      45  Lenskit-1m\n",
       "6038 6038 F      56  Lenskit-1m\n",
       "6039 6039 F      45  Lenskit-1m\n",
       "6040 6040 M      25  Lenskit-1m"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<ol class=list-inline>\n",
       "\t<li>1</li>\n",
       "\t<li>56</li>\n",
       "\t<li>25</li>\n",
       "\t<li>45</li>\n",
       "\t<li>50</li>\n",
       "\t<li>35</li>\n",
       "\t<li>18</li>\n",
       "</ol>\n"
      ],
      "text/latex": [
       "\\begin{enumerate*}\n",
       "\\item 1\n",
       "\\item 56\n",
       "\\item 25\n",
       "\\item 45\n",
       "\\item 50\n",
       "\\item 35\n",
       "\\item 18\n",
       "\\end{enumerate*}\n"
      ],
      "text/markdown": [
       "1. 1\n",
       "2. 56\n",
       "3. 25\n",
       "4. 45\n",
       "5. 50\n",
       "6. 35\n",
       "7. 18\n",
       "\n",
       "\n"
      ],
      "text/plain": [
       "[1] 1  56 25 45 50 35 18\n",
       "Levels: 1 18 25 35 45 50 56"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "usersLK = read_delim(\"data/lk1m/users.dat\", delim=\":\",\n",
    "                   col_names=c(\"user\", \"gender\", \"age\", \"occupation\", \"zip\"),\n",
    "                   col_types=\"i_c_c_c_c\") %>%\n",
    "    mutate(gender=as.factor(gender),\n",
    "           age=as.factor(age),\n",
    "           occupation=as.factor(occupation))\n",
    "usersLK <-  subset(usersLK, select = c(\"user\", \"gender\", \"age\"))\n",
    "usersLK$dataset = as.factor(\"Lenskit-1m\")\n",
    "usersLK\n",
    "unique(usersLK$age)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<ol class=list-inline>\n",
       "\t<li>1</li>\n",
       "\t<li>56</li>\n",
       "\t<li>25</li>\n",
       "\t<li>45</li>\n",
       "\t<li>50</li>\n",
       "\t<li>35</li>\n",
       "\t<li>18</li>\n",
       "</ol>\n"
      ],
      "text/latex": [
       "\\begin{enumerate*}\n",
       "\\item 1\n",
       "\\item 56\n",
       "\\item 25\n",
       "\\item 45\n",
       "\\item 50\n",
       "\\item 35\n",
       "\\item 18\n",
       "\\end{enumerate*}\n"
      ],
      "text/markdown": [
       "1. 1\n",
       "2. 56\n",
       "3. 25\n",
       "4. 45\n",
       "5. 50\n",
       "6. 35\n",
       "7. 18\n",
       "\n",
       "\n"
      ],
      "text/plain": [
       "[1] 1  56 25 45 50 35 18\n",
       "Levels: 1 18 25 35 45 50 56"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "metadata": {
    "collapsed": false,
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Parsed with column specification:\n",
      "cols(\n",
      "  user = col_integer(),\n",
      "  key = col_character(),\n",
      "  gender = col_character(),\n",
      "  age = col_integer()\n",
      ")\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<table>\n",
       "<thead><tr><th scope=col>user</th><th scope=col>gender</th><th scope=col>age</th><th scope=col>dataset</th></tr></thead>\n",
       "<tbody>\n",
       "\t<tr><td> 1    </td><td>F     </td><td>18    </td><td>LastFM</td></tr>\n",
       "\t<tr><td> 2    </td><td>F     </td><td>NA    </td><td>LastFM</td></tr>\n",
       "\t<tr><td> 3    </td><td>NA    </td><td>NA    </td><td>LastFM</td></tr>\n",
       "\t<tr><td> 4    </td><td>M     </td><td>18    </td><td>LastFM</td></tr>\n",
       "\t<tr><td> 5    </td><td>M     </td><td>25    </td><td>LastFM</td></tr>\n",
       "\t<tr><td> 6    </td><td>M     </td><td>18    </td><td>LastFM</td></tr>\n",
       "\t<tr><td> 7    </td><td>M     </td><td>1     </td><td>LastFM</td></tr>\n",
       "\t<tr><td> 8    </td><td>F     </td><td>18    </td><td>LastFM</td></tr>\n",
       "\t<tr><td> 9    </td><td>F     </td><td>18    </td><td>LastFM</td></tr>\n",
       "\t<tr><td>10    </td><td>M     </td><td>NA    </td><td>LastFM</td></tr>\n",
       "\t<tr><td>⋮</td><td>⋮</td><td>⋮</td><td>⋮</td></tr>\n",
       "\t<tr><td>359338</td><td>F     </td><td>1     </td><td>LastFM</td></tr>\n",
       "\t<tr><td>359339</td><td>M     </td><td>25    </td><td>LastFM</td></tr>\n",
       "\t<tr><td>359340</td><td>F     </td><td>18    </td><td>LastFM</td></tr>\n",
       "\t<tr><td>359341</td><td>F     </td><td>18    </td><td>LastFM</td></tr>\n",
       "\t<tr><td>359342</td><td>M     </td><td>25    </td><td>LastFM</td></tr>\n",
       "\t<tr><td>359343</td><td>M     </td><td>25    </td><td>LastFM</td></tr>\n",
       "\t<tr><td>359344</td><td>M     </td><td>25    </td><td>LastFM</td></tr>\n",
       "\t<tr><td>359345</td><td>M     </td><td>18    </td><td>LastFM</td></tr>\n",
       "\t<tr><td>359346</td><td>M     </td><td>18    </td><td>LastFM</td></tr>\n",
       "\t<tr><td>359347</td><td>M     </td><td>18    </td><td>LastFM</td></tr>\n",
       "</tbody>\n",
       "</table>\n"
      ],
      "text/latex": [
       "\\begin{tabular}{r|llll}\n",
       " user & gender & age & dataset\\\\\n",
       "\\hline\n",
       "\t  1     & F      & 18     & LastFM\\\\\n",
       "\t  2     & F      & NA     & LastFM\\\\\n",
       "\t  3     & NA     & NA     & LastFM\\\\\n",
       "\t  4     & M      & 18     & LastFM\\\\\n",
       "\t  5     & M      & 25     & LastFM\\\\\n",
       "\t  6     & M      & 18     & LastFM\\\\\n",
       "\t  7     & M      & 1      & LastFM\\\\\n",
       "\t  8     & F      & 18     & LastFM\\\\\n",
       "\t  9     & F      & 18     & LastFM\\\\\n",
       "\t 10     & M      & NA     & LastFM\\\\\n",
       "\t ⋮ & ⋮ & ⋮ & ⋮\\\\\n",
       "\t 359338 & F      & 1      & LastFM\\\\\n",
       "\t 359339 & M      & 25     & LastFM\\\\\n",
       "\t 359340 & F      & 18     & LastFM\\\\\n",
       "\t 359341 & F      & 18     & LastFM\\\\\n",
       "\t 359342 & M      & 25     & LastFM\\\\\n",
       "\t 359343 & M      & 25     & LastFM\\\\\n",
       "\t 359344 & M      & 25     & LastFM\\\\\n",
       "\t 359345 & M      & 18     & LastFM\\\\\n",
       "\t 359346 & M      & 18     & LastFM\\\\\n",
       "\t 359347 & M      & 18     & LastFM\\\\\n",
       "\\end{tabular}\n"
      ],
      "text/markdown": [
       "\n",
       "user | gender | age | dataset | \n",
       "|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n",
       "|  1     | F      | 18     | LastFM | \n",
       "|  2     | F      | NA     | LastFM | \n",
       "|  3     | NA     | NA     | LastFM | \n",
       "|  4     | M      | 18     | LastFM | \n",
       "|  5     | M      | 25     | LastFM | \n",
       "|  6     | M      | 18     | LastFM | \n",
       "|  7     | M      | 1      | LastFM | \n",
       "|  8     | F      | 18     | LastFM | \n",
       "|  9     | F      | 18     | LastFM | \n",
       "| 10     | M      | NA     | LastFM | \n",
       "| ⋮ | ⋮ | ⋮ | ⋮ | \n",
       "| 359338 | F      | 1      | LastFM | \n",
       "| 359339 | M      | 25     | LastFM | \n",
       "| 359340 | F      | 18     | LastFM | \n",
       "| 359341 | F      | 18     | LastFM | \n",
       "| 359342 | M      | 25     | LastFM | \n",
       "| 359343 | M      | 25     | LastFM | \n",
       "| 359344 | M      | 25     | LastFM | \n",
       "| 359345 | M      | 18     | LastFM | \n",
       "| 359346 | M      | 18     | LastFM | \n",
       "| 359347 | M      | 18     | LastFM | \n",
       "\n",
       "\n"
      ],
      "text/plain": [
       "       user   gender age dataset\n",
       "1       1     F      18  LastFM \n",
       "2       2     F      NA  LastFM \n",
       "3       3     NA     NA  LastFM \n",
       "4       4     M      18  LastFM \n",
       "5       5     M      25  LastFM \n",
       "6       6     M      18  LastFM \n",
       "7       7     M      1   LastFM \n",
       "8       8     F      18  LastFM \n",
       "9       9     F      18  LastFM \n",
       "10     10     M      NA  LastFM \n",
       "⋮      ⋮      ⋮      ⋮   ⋮      \n",
       "359338 359338 F      1   LastFM \n",
       "359339 359339 M      25  LastFM \n",
       "359340 359340 F      18  LastFM \n",
       "359341 359341 F      18  LastFM \n",
       "359342 359342 M      25  LastFM \n",
       "359343 359343 M      25  LastFM \n",
       "359344 359344 M      25  LastFM \n",
       "359345 359345 M      18  LastFM \n",
       "359346 359346 M      18  LastFM \n",
       "359347 359347 M      18  LastFM "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<ol class=list-inline>\n",
       "\t<li>18</li>\n",
       "\t<li>NA</li>\n",
       "\t<li>25</li>\n",
       "\t<li>1</li>\n",
       "\t<li>45</li>\n",
       "\t<li>56</li>\n",
       "\t<li>35</li>\n",
       "</ol>\n"
      ],
      "text/latex": [
       "\\begin{enumerate*}\n",
       "\\item 18\n",
       "\\item NA\n",
       "\\item 25\n",
       "\\item 1\n",
       "\\item 45\n",
       "\\item 56\n",
       "\\item 35\n",
       "\\end{enumerate*}\n"
      ],
      "text/markdown": [
       "1. 18\n",
       "2. NA\n",
       "3. 25\n",
       "4. 1\n",
       "5. 45\n",
       "6. 56\n",
       "7. 35\n",
       "\n",
       "\n"
      ],
      "text/plain": [
       "[1] 18   <NA> 25   1    45   56   35  \n",
       "Levels: 1 18 25 35 45 56"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#id,key,gender,age\n",
    "\n",
    "usersLastfm = read_delim(\"data/lastfm/lastfm-users.csv\", delim=\",\", \n",
    "                   col_names=c(\"user\", \"key\", \"gender\", \"age\") )\n",
    "usersLastfm <-  subset(usersLastfm, select = c(\"user\", \"gender\", \"age\"))\n",
    "#usersLastfm$age <- as.integer(usersLastfm$age)\n",
    "usersLastfm$age <- as.numeric(as.character(usersLastfm$age))\n",
    "usersLastfm$gender[usersLastfm$gender == \"f\"] <- \"F\"\n",
    "usersLastfm$gender[usersLastfm$gender == \"m\"] <- \"M\"\n",
    "usersLastfm$age[ as.numeric(usersLastfm$age) < 18] <- 1\n",
    "usersLastfm$age[as.numeric(usersLastfm$age) >= 18 & as.numeric(usersLastfm$age) < 25] <- 18\n",
    "usersLastfm$age[as.numeric(usersLastfm$age) >= 25 &  as.numeric(usersLastfm$age) < 35] <- 25\n",
    "usersLastfm$age[as.numeric(usersLastfm$age) >= 35 &  as.numeric(usersLastfm$age) < 45] <- 35\n",
    "usersLastfm$age[as.numeric(usersLastfm$age) >= 45 & as.numeric(usersLastfm$age) < 56] <- 45\n",
    "usersLastfm$age[as.numeric(usersLastfm$age) >= 56 ] <- 56\n",
    "usersLastfm$dataset = as.factor(\"LastFM\")\n",
    "usersLastfm$age = as.factor(usersLastfm$age)\n",
    "usersLastfm\n",
    "unique(usersLastfm$age)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table>\n",
       "<thead><tr><th scope=col>user</th><th scope=col>gender</th><th scope=col>age</th><th scope=col>dataset</th></tr></thead>\n",
       "<tbody>\n",
       "\t<tr><td> 1        </td><td>F         </td><td>1         </td><td>Lenskit-1m</td></tr>\n",
       "\t<tr><td> 2        </td><td>M         </td><td>56        </td><td>Lenskit-1m</td></tr>\n",
       "\t<tr><td> 3        </td><td>M         </td><td>25        </td><td>Lenskit-1m</td></tr>\n",
       "\t<tr><td> 4        </td><td>M         </td><td>45        </td><td>Lenskit-1m</td></tr>\n",
       "\t<tr><td> 5        </td><td>M         </td><td>25        </td><td>Lenskit-1m</td></tr>\n",
       "\t<tr><td> 6        </td><td>F         </td><td>50        </td><td>Lenskit-1m</td></tr>\n",
       "\t<tr><td> 7        </td><td>M         </td><td>35        </td><td>Lenskit-1m</td></tr>\n",
       "\t<tr><td> 8        </td><td>M         </td><td>25        </td><td>Lenskit-1m</td></tr>\n",
       "\t<tr><td> 9        </td><td>M         </td><td>25        </td><td>Lenskit-1m</td></tr>\n",
       "\t<tr><td>10        </td><td>F         </td><td>35        </td><td>Lenskit-1m</td></tr>\n",
       "\t<tr><td>⋮</td><td>⋮</td><td>⋮</td><td>⋮</td></tr>\n",
       "\t<tr><td>359338</td><td>F     </td><td>1     </td><td>LastFM</td></tr>\n",
       "\t<tr><td>359339</td><td>M     </td><td>25    </td><td>LastFM</td></tr>\n",
       "\t<tr><td>359340</td><td>F     </td><td>18    </td><td>LastFM</td></tr>\n",
       "\t<tr><td>359341</td><td>F     </td><td>18    </td><td>LastFM</td></tr>\n",
       "\t<tr><td>359342</td><td>M     </td><td>25    </td><td>LastFM</td></tr>\n",
       "\t<tr><td>359343</td><td>M     </td><td>25    </td><td>LastFM</td></tr>\n",
       "\t<tr><td>359344</td><td>M     </td><td>25    </td><td>LastFM</td></tr>\n",
       "\t<tr><td>359345</td><td>M     </td><td>18    </td><td>LastFM</td></tr>\n",
       "\t<tr><td>359346</td><td>M     </td><td>18    </td><td>LastFM</td></tr>\n",
       "\t<tr><td>359347</td><td>M     </td><td>18    </td><td>LastFM</td></tr>\n",
       "</tbody>\n",
       "</table>\n"
      ],
      "text/latex": [
       "\\begin{tabular}{r|llll}\n",
       " user & gender & age & dataset\\\\\n",
       "\\hline\n",
       "\t  1         & F          & 1          & Lenskit-1m\\\\\n",
       "\t  2         & M          & 56         & Lenskit-1m\\\\\n",
       "\t  3         & M          & 25         & Lenskit-1m\\\\\n",
       "\t  4         & M          & 45         & Lenskit-1m\\\\\n",
       "\t  5         & M          & 25         & Lenskit-1m\\\\\n",
       "\t  6         & F          & 50         & Lenskit-1m\\\\\n",
       "\t  7         & M          & 35         & Lenskit-1m\\\\\n",
       "\t  8         & M          & 25         & Lenskit-1m\\\\\n",
       "\t  9         & M          & 25         & Lenskit-1m\\\\\n",
       "\t 10         & F          & 35         & Lenskit-1m\\\\\n",
       "\t ⋮ & ⋮ & ⋮ & ⋮\\\\\n",
       "\t 359338 & F      & 1      & LastFM\\\\\n",
       "\t 359339 & M      & 25     & LastFM\\\\\n",
       "\t 359340 & F      & 18     & LastFM\\\\\n",
       "\t 359341 & F      & 18     & LastFM\\\\\n",
       "\t 359342 & M      & 25     & LastFM\\\\\n",
       "\t 359343 & M      & 25     & LastFM\\\\\n",
       "\t 359344 & M      & 25     & LastFM\\\\\n",
       "\t 359345 & M      & 18     & LastFM\\\\\n",
       "\t 359346 & M      & 18     & LastFM\\\\\n",
       "\t 359347 & M      & 18     & LastFM\\\\\n",
       "\\end{tabular}\n"
      ],
      "text/markdown": [
       "\n",
       "user | gender | age | dataset | \n",
       "|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n",
       "|  1         | F          | 1          | Lenskit-1m | \n",
       "|  2         | M          | 56         | Lenskit-1m | \n",
       "|  3         | M          | 25         | Lenskit-1m | \n",
       "|  4         | M          | 45         | Lenskit-1m | \n",
       "|  5         | M          | 25         | Lenskit-1m | \n",
       "|  6         | F          | 50         | Lenskit-1m | \n",
       "|  7         | M          | 35         | Lenskit-1m | \n",
       "|  8         | M          | 25         | Lenskit-1m | \n",
       "|  9         | M          | 25         | Lenskit-1m | \n",
       "| 10         | F          | 35         | Lenskit-1m | \n",
       "| ⋮ | ⋮ | ⋮ | ⋮ | \n",
       "| 359338 | F      | 1      | LastFM | \n",
       "| 359339 | M      | 25     | LastFM | \n",
       "| 359340 | F      | 18     | LastFM | \n",
       "| 359341 | F      | 18     | LastFM | \n",
       "| 359342 | M      | 25     | LastFM | \n",
       "| 359343 | M      | 25     | LastFM | \n",
       "| 359344 | M      | 25     | LastFM | \n",
       "| 359345 | M      | 18     | LastFM | \n",
       "| 359346 | M      | 18     | LastFM | \n",
       "| 359347 | M      | 18     | LastFM | \n",
       "\n",
       "\n"
      ],
      "text/plain": [
       "       user   gender age dataset   \n",
       "1       1     F      1   Lenskit-1m\n",
       "2       2     M      56  Lenskit-1m\n",
       "3       3     M      25  Lenskit-1m\n",
       "4       4     M      45  Lenskit-1m\n",
       "5       5     M      25  Lenskit-1m\n",
       "6       6     F      50  Lenskit-1m\n",
       "7       7     M      35  Lenskit-1m\n",
       "8       8     M      25  Lenskit-1m\n",
       "9       9     M      25  Lenskit-1m\n",
       "10     10     F      35  Lenskit-1m\n",
       "⋮      ⋮      ⋮      ⋮   ⋮         \n",
       "365378 359338 F      1   LastFM    \n",
       "365379 359339 M      25  LastFM    \n",
       "365380 359340 F      18  LastFM    \n",
       "365381 359341 F      18  LastFM    \n",
       "365382 359342 M      25  LastFM    \n",
       "365383 359343 M      25  LastFM    \n",
       "365384 359344 M      25  LastFM    \n",
       "365385 359345 M      18  LastFM    \n",
       "365386 359346 M      18  LastFM    \n",
       "365387 359347 M      18  LastFM    "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "users <- rbind(usersLK, usersLastfm)\n",
    "users"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "metadata": {
    "collapsed": false,
    "scrolled": false
   },
   "outputs": [
    {
     "data": {},
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
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+/1Hjhw4NVXX3Uvbty4sWvXro2fLi8zm81ylyAzvV7PJPj5+cldQpPpdDqj0Sh3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\nqeD46AAAAABJRU5ErkJggg==",
      "text/plain": [
       "plot without title"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ggplot(users) +\n",
    "    aes(x=gender,fill=dataset) + scale_y_continuous(labels = scales::percent)+\n",
    "    geom_bar()+facet_grid(. ~ dataset)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {},
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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mvWrLZt25rN5muuuWbGjBnl5eXhKj/88MO4cePatGljs9nS09Pffvttdf55q1yK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",
      "text/plain": [
       "plot without title"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ggplot(users) +\n",
    "    aes(x=age,fill=dataset) + scale_y_continuous(labels = scales::percent)+\n",
    "    geom_bar()+facet_grid(. ~ dataset)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "\n"
   ]
  }
 ],
 "metadata": {
  "anaconda-cloud": {},
  "kernelspec": {
   "display_name": "R [conda env:my-r-env]",
   "language": "R",
   "name": "conda-env-my-r-env-r"
  },
  "language_info": {
   "codemirror_mode": "r",
   "file_extension": ".r",
   "mimetype": "text/x-r-source",
   "name": "R",
   "pygments_lexer": "r",
   "version": "3.3.2"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 1
}