1-intro_R-with-solutions.ipynb

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    "# Statistical foundations of Machine Learning INFO-F-422\n",
    "\n",
    "\n",
    "## TP 1 - Introduction to R\n",
    "\n",
    "#### *Yann-Aël Le Borgne, Fabrizio Carcillo and Gianluca Bontempi*\n",
    "\n",
    "####  March 7, 2017\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
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   "source": [
    "## Basics\n",
    "\n",
    "### How to start and end R\n",
    "\n",
    "*  Start R: type \"R\" (without the quotes) in a terminal.\n",
    "*  Type `q()` for ending your session.\n",
    "\n",
    "### Use R as calculator\n"
   ]
  },
  {
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   "source": [
    "2+2"
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       "[1] 0.1353353"
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   "source": [
    "exp(-2)"
   ]
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       "\\begin{enumerate*}\n",
       "\\item 0.00970814112014878\n",
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       "\\item -0.0596689333003107\n",
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       "\\end{enumerate*}\n"
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       "1. 0.00970814112014878\n",
       "2. 1.3852018804433\n",
       "3. 0.909097534154265\n",
       "4. -0.0596689333003107\n",
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       "8. -0.904336167203947\n",
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       "13. -0.835691077994559\n",
       "14. 1.57952666503733\n",
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       "\n",
       "\n"
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       " [1]  0.009708141  1.385201880  0.909097534 -0.059668933 -0.234053149\n",
       " [6]  1.686891069 -0.154427474 -0.904336167  0.533690656  0.247402283\n",
       "[11]  1.433199433 -0.453507111 -0.835691078  1.579526665 -2.097680068"
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   ],
   "source": [
    "rnorm(15)  # to generate random numbers"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Assign values to variables"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
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       "[1] 2"
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     "metadata": {},
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   ],
   "source": [
    "x<-2\n",
    "x"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
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       "4"
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       "[1] 4"
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     "metadata": {},
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   ],
   "source": [
    "y<-x+x\n",
    "y"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Installing and loading libraries"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# install.packages(\"ISwR\")\n",
    "# uncomment previous line to install package\n",
    "library(ISwR)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Naming conventions\n",
    "\n",
    "*  The variables are case-sensitive, can contain a dot (.) and numbers (but cannot start with a number or with a dot followed by a number).\n",
    "*  The names `c,q,t,C,D,F,I,T,diff,df,pt` in particular, are already used by the system and should therefore be avoided.\n",
    "\n",
    "## Vectors and matrices\n",
    "\n",
    "### Defining a vector\n",
    "\n",
    "Vectors can be of any type: integer, double, character, logical, resulting from relational expressions:"
   ]
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  {
   "cell_type": "code",
   "execution_count": 13,
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       "<ol class=list-inline>\n",
       "\t<li>FALSE</li>\n",
       "\t<li>FALSE</li>\n",
       "\t<li>FALSE</li>\n",
       "\t<li>TRUE</li>\n",
       "</ol>\n"
      ],
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       "\\begin{enumerate*}\n",
       "\\item FALSE\n",
       "\\item FALSE\n",
       "\\item FALSE\n",
       "\\item TRUE\n",
       "\\end{enumerate*}\n"
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       "1. FALSE\n",
       "2. FALSE\n",
       "3. FALSE\n",
       "4. TRUE\n",
       "\n",
       "\n"
      ],
      "text/plain": [
       "[1] FALSE FALSE FALSE  TRUE"
      ]
     },
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   ],
   "source": [
    "w<-c(1,2,3,4)\n",
    "x<-c(1.5,2.5,3.5,3.5)\n",
    "y<-c(\"Huey\",\"Dewey\",\"Loui\")\n",
    "z<-c(T,F,F,T)\n",
    "wx<- w>x\n",
    "wx"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "There exists different functions to define a vector: element wise `c(val_1,...,val_n)`, as a sequence between two values `seq(val_1,val_2)` and as $n$ repetitions of the same element `rep(val,n)`."
   ]
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  {
   "cell_type": "code",
   "execution_count": 14,
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       "<ol class=list-inline>\n",
       "\t<li>1</li>\n",
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       "\\begin{enumerate*}\n",
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       "\\item 2\n",
       "\\item 3\n",
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       "1. 1\n",
       "2. 2\n",
       "3. 3\n",
       "4. 4\n",
       "\n",
       "\n"
      ],
      "text/plain": [
       "[1] 1 2 3 4"
      ]
     },
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   ],
   "source": [
    "w<-c(1,2,3,4)\n",
    "w"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "collapsed": false
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   "outputs": [
    {
     "data": {
      "text/html": [
       "<ol class=list-inline>\n",
       "\t<li>1</li>\n",
       "\t<li>2</li>\n",
       "\t<li>3</li>\n",
       "\t<li>4</li>\n",
       "</ol>\n"
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       "\\begin{enumerate*}\n",
       "\\item 1\n",
       "\\item 2\n",
       "\\item 3\n",
       "\\item 4\n",
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       "1. 1\n",
       "2. 2\n",
       "3. 3\n",
       "4. 4\n",
       "\n",
       "\n"
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       "[1] 1 2 3 4"
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   ],
   "source": [
    "x<-seq(1,4)\n",
    "x"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
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    "collapsed": false
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    {
     "data": {
      "text/html": [
       "<ol class=list-inline>\n",
       "\t<li>1</li>\n",
       "\t<li>1</li>\n",
       "\t<li>1</li>\n",
       "\t<li>1</li>\n",
       "</ol>\n"
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       "\\begin{enumerate*}\n",
       "\\item 1\n",
       "\\item 1\n",
       "\\item 1\n",
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       "1. 1\n",
       "2. 1\n",
       "3. 1\n",
       "4. 1\n",
       "\n",
       "\n"
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       "[1] 1 1 1 1"
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   "source": [
    "y<-rep(1,4)\n",
    "y"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Exercises\n",
    "\n",
    "Generate the following vectors \n",
    "\n",
    "* 1 3 5\n",
    "* 1 2 2 2 3 3 3 3 3\n",
    "\n",
    "using only the functions `rep` and `seq` (no  `c(..)`), check the help by typing `?rep` and `?seq` for all parameter options.\n",
    "\n",
    "#### Solution"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
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    {
     "data": {
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       "<ol class=list-inline>\n",
       "\t<li>1</li>\n",
       "\t<li>3</li>\n",
       "\t<li>5</li>\n",
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       "\\begin{enumerate*}\n",
       "\\item 1\n",
       "\\item 3\n",
       "\\item 5\n",
       "\\end{enumerate*}\n"
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      "text/markdown": [
       "1. 1\n",
       "2. 3\n",
       "3. 5\n",
       "\n",
       "\n"
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      "text/plain": [
       "[1] 1 3 5"
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     "metadata": {},
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   ],
   "source": [
    "seq(1,5,2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "collapsed": false
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   "outputs": [
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      "text/html": [
       "<ol class=list-inline>\n",
       "\t<li>1</li>\n",
       "\t<li>2</li>\n",
       "\t<li>2</li>\n",
       "\t<li>2</li>\n",
       "\t<li>3</li>\n",
       "\t<li>3</li>\n",
       "\t<li>3</li>\n",
       "\t<li>3</li>\n",
       "\t<li>3</li>\n",
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       "\\begin{enumerate*}\n",
       "\\item 1\n",
       "\\item 2\n",
       "\\item 2\n",
       "\\item 2\n",
       "\\item 3\n",
       "\\item 3\n",
       "\\item 3\n",
       "\\item 3\n",
       "\\item 3\n",
       "\\end{enumerate*}\n"
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      "text/markdown": [
       "1. 1\n",
       "2. 2\n",
       "3. 2\n",
       "4. 2\n",
       "5. 3\n",
       "6. 3\n",
       "7. 3\n",
       "8. 3\n",
       "9. 3\n",
       "\n",
       "\n"
      ],
      "text/plain": [
       "[1] 1 2 2 2 3 3 3 3 3"
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     "metadata": {},
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   ],
   "source": [
    "rep(seq(1,3), seq(1,5,2))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Vector arithmetics"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
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    "collapsed": false
   },
   "outputs": [
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       "<ol class=list-inline>\n",
       "\t<li>19.5918367346939</li>\n",
       "\t<li>22.2222222222222</li>\n",
       "\t<li>20.9366391184573</li>\n",
       "\t<li>24.9307479224377</li>\n",
       "\t<li>31.3779891663364</li>\n",
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       "\\begin{enumerate*}\n",
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       "\\item 20.9366391184573\n",
       "\\item 24.9307479224377\n",
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       "1. 19.5918367346939\n",
       "2. 22.2222222222222\n",
       "3. 20.9366391184573\n",
       "4. 24.9307479224377\n",
       "5. 31.3779891663364\n",
       "6. 19.7363010882377\n",
       "7. 21.0076677987465\n",
       "\n",
       "\n"
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       "[1] 19.59184 22.22222 20.93664 24.93075 31.37799 19.73630 21.00767"
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     },
     "metadata": {},
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   ],
   "source": [
    "weight <- c(60, 72, 57, 90, 95, 72,60)\n",
    "height <- c(1.75, 1.80, 1.65, 1.90, 1.74, 1.91,1.69)\n",
    "bmi <- weight/height^2\n",
    "bmi"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Exercises\n",
    "\n",
    "*  Compute the adjusted BMI using the formula $bmi_2=\\frac{weight}{height^{2.5}/1.3}$ and store it in a variable  `bmi2` \n",
    "\n",
    "#### Solution\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "collapsed": false
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   "outputs": [
    {
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       "<ol class=list-inline>\n",
       "\t<li>19.2530474414496</li>\n",
       "\t<li>21.532506449998</li>\n",
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       "\t<li>23.5126651697705</li>\n",
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       "\\begin{enumerate*}\n",
       "\\item 19.2530474414496\n",
       "\\item 21.532506449998\n",
       "\\item 21.1888968824128\n",
       "\\item 23.5126651697705\n",
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       "1. 19.2530474414496\n",
       "2. 21.532506449998\n",
       "3. 21.1888968824128\n",
       "4. 23.5126651697705\n",
       "5. 30.9238698579715\n",
       "6. 18.5648921508102\n",
       "7. 21.0076677987465\n",
       "\n",
       "\n"
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       "[1] 19.25305 21.53251 21.18890 23.51267 30.92387 18.56489 21.00767"
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   "source": [
    "weight/(height^2.5/1.3)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Functions for vectors\n",
    "\n",
    "There exists a lot of predefined functions for a vector `v`, these include the following"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "collapsed": false
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   "outputs": [
    {
     "data": {
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       "15"
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       "15"
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      "text/markdown": [
       "15"
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       "[1] 15"
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     },
     "metadata": {},
     "output_type": "display_data"
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   ],
   "source": [
    "v<-seq(5,1)\n",
    "sum(v)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
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    "collapsed": false
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       "5"
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   "source": [
    "length(v)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "collapsed": false
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   "outputs": [
    {
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       "<ol class=list-inline>\n",
       "\t<li>1</li>\n",
       "\t<li>2</li>\n",
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       "\t<li>5</li>\n",
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       "\\begin{enumerate*}\n",
       "\\item 1\n",
       "\\item 2\n",
       "\\item 3\n",
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       "\\item 5\n",
       "\\end{enumerate*}\n"
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       "1. 1\n",
       "2. 2\n",
       "3. 3\n",
       "4. 4\n",
       "5. 5\n",
       "\n",
       "\n"
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       "[1] 1 2 3 4 5"
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   "source": [
    "sort(v)"
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  {
   "cell_type": "code",
   "execution_count": 24,
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       "\t<li>1</li>\n",
       "</ol>\n"
      ],
      "text/latex": [
       "\\begin{enumerate*}\n",
       "\\item 5\n",
       "\\item 4\n",
       "\\item 3\n",
       "\\item 2\n",
       "\\item 1\n",
       "\\end{enumerate*}\n"
      ],
      "text/markdown": [
       "1. 5\n",
       "2. 4\n",
       "3. 3\n",
       "4. 2\n",
       "5. 1\n",
       "\n",
       "\n"
      ],
      "text/plain": [
       "[1] 5 4 3 2 1"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "order(v)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "120"
      ],
      "text/latex": [
       "120"
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      "text/markdown": [
       "120"
      ],
      "text/plain": [
       "[1] 120"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "prod(v)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "3"
      ],
      "text/latex": [
       "3"
      ],
      "text/markdown": [
       "3"
      ],
      "text/plain": [
       "[1] 3"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "mean(v)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "1.58113883008419"
      ],
      "text/latex": [
       "1.58113883008419"
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      "text/markdown": [
       "1.58113883008419"
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      "text/plain": [
       "[1] 1.581139"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sd(v) "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "###  Defining matrices\n",
    "\n",
    "*  In R, matrices (2 dimensions) and arrays (more than 2 dimensions) can contain elements of any type (characters, etc.)\n",
    "*  The `dim` assignment function sets or changes the *dimension attribute* of a vector `x`, causing R to treat the vector as a matrix.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table>\n",
       "<tbody>\n",
       "\t<tr><td>1 </td><td>4 </td><td>7 </td><td>10</td></tr>\n",
       "\t<tr><td>2 </td><td>5 </td><td>8 </td><td>11</td></tr>\n",
       "\t<tr><td>3 </td><td>6 </td><td>9 </td><td>12</td></tr>\n",
       "</tbody>\n",
       "</table>\n"
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      "text/latex": [
       "\\begin{tabular}{llll}\n",
       "\t 1  & 4  & 7  & 10\\\\\n",
       "\t 2  & 5  & 8  & 11\\\\\n",
       "\t 3  & 6  & 9  & 12\\\\\n",
       "\\end{tabular}\n"
      ],
      "text/markdown": [
       "\n",
       "| 1  | 4  | 7  | 10 | \n",
       "| 2  | 5  | 8  | 11 | \n",
       "| 3  | 6  | 9  | 12 | \n",
       "\n",
       "\n"
      ],
      "text/plain": [
       "     [,1] [,2] [,3] [,4]\n",
       "[1,] 1    4    7    10  \n",
       "[2,] 2    5    8    11  \n",
       "[3,] 3    6    9    12  "
      ]
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     "metadata": {},
     "output_type": "display_data"
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   ],
   "source": [
    "x <- 1:12\n",
    "dim(x) <- c(3,4)\n",
    "x"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "*  Other possibilities to create matrices using the `matrix(...)` command:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table>\n",
       "<tbody>\n",
       "\t<tr><td>1 </td><td> 2</td><td> 3</td><td> 4</td></tr>\n",
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       "\t<tr><td>9 </td><td>10</td><td>11</td><td>12</td></tr>\n",
       "</tbody>\n",
       "</table>\n"
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      "text/latex": [
       "\\begin{tabular}{llll}\n",
       "\t 1  &  2 &  3 &  4\\\\\n",
       "\t 5  &  6 &  7 &  8\\\\\n",
       "\t 9  & 10 & 11 & 12\\\\\n",
       "\\end{tabular}\n"
      ],
      "text/markdown": [
       "\n",
       "| 1  |  2 |  3 |  4 | \n",
       "| 5  |  6 |  7 |  8 | \n",
       "| 9  | 10 | 11 | 12 | \n",
       "\n",
       "\n"
      ],
      "text/plain": [
       "     [,1] [,2] [,3] [,4]\n",
       "[1,] 1     2    3    4  \n",
       "[2,] 5     6    7    8  \n",
       "[3,] 9    10   11   12  "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
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   ],
   "source": [
    "matrix(1:12, nr=3, byrow=T)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table>\n",
       "<tbody>\n",
       "\t<tr><td>1</td><td>1</td><td>1</td><td>1</td><td>1</td><td>1</td><td>1</td></tr>\n",
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       "</table>\n"
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       "\\begin{tabular}{lllllll}\n",
       "\t 1 & 1 & 1 & 1 & 1 & 1 & 1\\\\\n",
       "\t 1 & 1 & 1 & 1 & 1 & 1 & 1\\\\\n",
       "\t 1 & 1 & 1 & 1 & 1 & 1 & 1\\\\\n",
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       "\t 1 & 1 & 1 & 1 & 1 & 1 & 1\\\\\n",
       "\t 1 & 1 & 1 & 1 & 1 & 1 & 1\\\\\n",
       "\\end{tabular}\n"
      ],
      "text/markdown": [
       "\n",
       "| 1 | 1 | 1 | 1 | 1 | 1 | 1 | \n",
       "| 1 | 1 | 1 | 1 | 1 | 1 | 1 | \n",
       "| 1 | 1 | 1 | 1 | 1 | 1 | 1 | \n",
       "| 1 | 1 | 1 | 1 | 1 | 1 | 1 | \n",
       "| 1 | 1 | 1 | 1 | 1 | 1 | 1 | \n",
       "| 1 | 1 | 1 | 1 | 1 | 1 | 1 | \n",
       "\n",
       "\n"
      ],
      "text/plain": [
       "     [,1] [,2] [,3] [,4] [,5] [,6] [,7]\n",
       "[1,] 1    1    1    1    1    1    1   \n",
       "[2,] 1    1    1    1    1    1    1   \n",
       "[3,] 1    1    1    1    1    1    1   \n",
       "[4,] 1    1    1    1    1    1    1   \n",
       "[5,] 1    1    1    1    1    1    1   \n",
       "[6,] 1    1    1    1    1    1    1   "
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   ],
   "source": [
    "matrix(1,6,7)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "*  Using the `array` command:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
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       "<table>\n",
       "<thead><tr><th></th><th scope=col>1</th><th scope=col>2</th><th scope=col>3</th><th scope=col>4</th></tr></thead>\n",
       "<tbody>\n",
       "\t<tr><th scope=row>a</th><td>1 </td><td>4 </td><td>7 </td><td>10</td></tr>\n",
       "\t<tr><th scope=row>b</th><td>2 </td><td>5 </td><td>8 </td><td>11</td></tr>\n",
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       "</tbody>\n",
       "</table>\n"
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       "\\begin{tabular}{r|llll}\n",
       "  & 1 & 2 & 3 & 4\\\\\n",
       "\\hline\n",
       "\ta & 1  & 4  & 7  & 10\\\\\n",
       "\tb & 2  & 5  & 8  & 11\\\\\n",
       "\tc & 3  & 6  & 9  & 12\\\\\n",
       "\\end{tabular}\n"
      ],
      "text/markdown": [
       "\n",
       "| <!--/--> | 1 | 2 | 3 | 4 | \n",
       "|---|---|---|\n",
       "| a | 1  | 4  | 7  | 10 | \n",
       "| b | 2  | 5  | 8  | 11 | \n",
       "| c | 3  | 6  | 9  | 12 | \n",
       "\n",
       "\n"
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       "  1 2 3 4 \n",
       "a 1 4 7 10\n",
       "b 2 5 8 11\n",
       "c 3 6 9 12"
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     "output_type": "display_data"
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   ],
   "source": [
    "array(1:12,dim=c(3,4),dimnames=list(c(\"a\",\"b\",\"c\"),seq(1,4)))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "*  Renaming of rows and transpose of a matrix"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table>\n",
       "<tbody>\n",
       "\t<tr><th scope=row>A</th><td>1 </td><td> 2</td><td> 3</td><td> 4</td></tr>\n",
       "\t<tr><th scope=row>B</th><td>5 </td><td> 6</td><td> 7</td><td> 8</td></tr>\n",
       "\t<tr><th scope=row>C</th><td>9 </td><td>10</td><td>11</td><td>12</td></tr>\n",
       "</tbody>\n",
       "</table>\n"
      ],
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       "\\begin{tabular}{r|llll}\n",
       "\tA & 1  &  2 &  3 &  4\\\\\n",
       "\tB & 5  &  6 &  7 &  8\\\\\n",
       "\tC & 9  & 10 & 11 & 12\\\\\n",
       "\\end{tabular}\n"
      ],
      "text/markdown": [
       "\n",
       "| A | 1  |  2 |  3 |  4 | \n",
       "| B | 5  |  6 |  7 |  8 | \n",
       "| C | 9  | 10 | 11 | 12 | \n",
       "\n",
       "\n"
      ],
      "text/plain": [
       "  [,1] [,2] [,3] [,4]\n",
       "A 1     2    3    4  \n",
       "B 5     6    7    8  \n",
       "C 9    10   11   12  "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "x <- matrix(1:12, nr=3, byrow=T)\n",
    "rownames(x) <- LETTERS[1:3]\n",
    "x"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "*  \"Glueing\" vectors together: functions `rbind()` and `cbind()` (row- and columnwise respectively)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table>\n",
       "<thead><tr><th scope=col>A</th><th scope=col>B</th><th scope=col>C</th></tr></thead>\n",
       "<tbody>\n",
       "\t<tr><td>1 </td><td>5 </td><td> 9</td></tr>\n",
       "\t<tr><td>2 </td><td>6 </td><td>10</td></tr>\n",
       "\t<tr><td>3 </td><td>7 </td><td>11</td></tr>\n",
       "\t<tr><td>4 </td><td>8 </td><td>12</td></tr>\n",
       "</tbody>\n",
       "</table>\n"
      ],
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       "\\begin{tabular}{lll}\n",
       " A & B & C\\\\\n",
       "\\hline\n",
       "\t 1  & 5  &  9\\\\\n",
       "\t 2  & 6  & 10\\\\\n",
       "\t 3  & 7  & 11\\\\\n",
       "\t 4  & 8  & 12\\\\\n",
       "\\end{tabular}\n"
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       "\n",
       "A | B | C | \n",
       "|---|---|---|---|\n",
       "| 1  | 5  |  9 | \n",
       "| 2  | 6  | 10 | \n",
       "| 3  | 7  | 11 | \n",
       "| 4  | 8  | 12 | \n",
       "\n",
       "\n"
      ],
      "text/plain": [
       "     A B C \n",
       "[1,] 1 5  9\n",
       "[2,] 2 6 10\n",
       "[3,] 3 7 11\n",
       "[4,] 4 8 12"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "cbind(A=1:4, B=5:8, C=9:12)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table>\n",
       "<tbody>\n",
       "\t<tr><th scope=row>A</th><td>1 </td><td> 2</td><td> 3</td><td> 4</td></tr>\n",
       "\t<tr><th scope=row>B</th><td>5 </td><td> 6</td><td> 7</td><td> 8</td></tr>\n",
       "\t<tr><th scope=row>C</th><td>9 </td><td>10</td><td>11</td><td>12</td></tr>\n",
       "</tbody>\n",
       "</table>\n"
      ],
      "text/latex": [
       "\\begin{tabular}{r|llll}\n",
       "\tA & 1  &  2 &  3 &  4\\\\\n",
       "\tB & 5  &  6 &  7 &  8\\\\\n",
       "\tC & 9  & 10 & 11 & 12\\\\\n",
       "\\end{tabular}\n"
      ],
      "text/markdown": [
       "\n",
       "| A | 1  |  2 |  3 |  4 | \n",
       "| B | 5  |  6 |  7 |  8 | \n",
       "| C | 9  | 10 | 11 | 12 | \n",
       "\n",
       "\n"
      ],
      "text/plain": [
       "  [,1] [,2] [,3] [,4]\n",
       "A 1     2    3    4  \n",
       "B 5     6    7    8  \n",
       "C 9    10   11   12  "
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     "metadata": {},
     "output_type": "display_data"
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   ],
   "source": [
    "rbind(A=1:4, B=5:8, C=9:12)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Matrix functions\n",
    "\n",
    "*  matrix product, inversion and transpose"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table>\n",
       "<tbody>\n",
       "\t<tr><td>1</td><td>2</td></tr>\n",
       "\t<tr><td>3</td><td>4</td></tr>\n",
       "</tbody>\n",
       "</table>\n"
      ],
      "text/latex": [
       "\\begin{tabular}{ll}\n",
       "\t 1 & 2\\\\\n",
       "\t 3 & 4\\\\\n",
       "\\end{tabular}\n"
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      "text/markdown": [
       "\n",
       "| 1 | 2 | \n",
       "| 3 | 4 | \n",
       "\n",
       "\n"
      ],
      "text/plain": [
       "     [,1] [,2]\n",
       "[1,] 1    2   \n",
       "[2,] 3    4   "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "a <- matrix(1:4, 2, 2, byrow = T)\n",
    "a"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table>\n",
       "<tbody>\n",
       "\t<tr><td> 7</td><td>10</td></tr>\n",
       "\t<tr><td>15</td><td>22</td></tr>\n",
       "</tbody>\n",
       "</table>\n"
      ],
      "text/latex": [
       "\\begin{tabular}{ll}\n",
       "\t  7 & 10\\\\\n",
       "\t 15 & 22\\\\\n",
       "\\end{tabular}\n"
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      "text/markdown": [
       "\n",
       "|  7 | 10 | \n",
       "| 15 | 22 | \n",
       "\n",
       "\n"
      ],
      "text/plain": [
       "     [,1] [,2]\n",
       "[1,]  7   10  \n",
       "[2,] 15   22  "
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     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "## matrix product\n",
    "a%*%a"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table>\n",
       "<tbody>\n",
       "\t<tr><td>-2.0</td><td> 1.0</td></tr>\n",
       "\t<tr><td> 1.5</td><td>-0.5</td></tr>\n",
       "</tbody>\n",
       "</table>\n"
      ],
      "text/latex": [
       "\\begin{tabular}{ll}\n",
       "\t -2.0 &  1.0\\\\\n",
       "\t  1.5 & -0.5\\\\\n",
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       "\n",
       "| -2.0 |  1.0 | \n",
       "|  1.5 | -0.5 | \n",
       "\n",
       "\n"
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      "text/plain": [
       "     [,1] [,2]\n",
       "[1,] -2.0  1.0\n",
       "[2,]  1.5 -0.5"
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     "metadata": {},
     "output_type": "display_data"
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   ],
   "source": [
    "## inverse matrix\n",
    "solve(a)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table>\n",
       "<tbody>\n",
       "\t<tr><td>1</td><td>3</td></tr>\n",
       "\t<tr><td>2</td><td>4</td></tr>\n",
       "</tbody>\n",
       "</table>\n"
      ],
      "text/latex": [
       "\\begin{tabular}{ll}\n",
       "\t 1 & 3\\\\\n",
       "\t 2 & 4\\\\\n",
       "\\end{tabular}\n"
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      "text/markdown": [
       "\n",
       "| 1 | 3 | \n",
       "| 2 | 4 | \n",
       "\n",
       "\n"
      ],
      "text/plain": [
       "     [,1] [,2]\n",
       "[1,] 1    3   \n",
       "[2,] 2    4   "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "## transpose of a matrix\n",
    "t(a)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Indexing\n",
    "\n",
    "*  Allows to select a particular element in a vector, take as example `height`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<ol class=list-inline>\n",
       "\t<li>1.75</li>\n",
       "\t<li>1.8</li>\n",
       "\t<li>1.65</li>\n",
       "\t<li>1.9</li>\n",
       "\t<li>1.74</li>\n",
       "\t<li>1.91</li>\n",
       "\t<li>1.69</li>\n",
       "</ol>\n"
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       "\\begin{enumerate*}\n",
       "\\item 1.75\n",
       "\\item 1.8\n",
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       "1. 1.75\n",
       "2. 1.8\n",
       "3. 1.65\n",
       "4. 1.9\n",
       "5. 1.74\n",
       "6. 1.91\n",
       "7. 1.69\n",
       "\n",
       "\n"
      ],
      "text/plain": [
       "[1] 1.75 1.80 1.65 1.90 1.74 1.91 1.69"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "height"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "*  To access the fifth element"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "1.74"
      ],
      "text/latex": [
       "1.74"
      ],
      "text/markdown": [
       "1.74"
      ],
      "text/plain": [
       "[1] 1.74"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "height[5]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "*  To access several elements in one step"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<ol class=list-inline>\n",
       "\t<li>1.65</li>\n",
       "\t<li>1.74</li>\n",
       "\t<li>1.69</li>\n",
       "</ol>\n"
      ],
      "text/latex": [
       "\\begin{enumerate*}\n",
       "\\item 1.65\n",
       "\\item 1.74\n",
       "\\item 1.69\n",
       "\\end{enumerate*}\n"
      ],
      "text/markdown": [
       "1. 1.65\n",
       "2. 1.74\n",
       "3. 1.69\n",
       "\n",
       "\n"
      ],
      "text/plain": [
       "[1] 1.65 1.74 1.69"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "height[c(3,5,7)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<ol class=list-inline>\n",
       "\t<li>1.65</li>\n",
       "\t<li>1.74</li>\n",
       "\t<li>1.69</li>\n",
       "</ol>\n"
      ],
      "text/latex": [
       "\\begin{enumerate*}\n",
       "\\item 1.65\n",
       "\\item 1.74\n",
       "\\item 1.69\n",
       "\\end{enumerate*}\n"
      ],
      "text/markdown": [
       "1. 1.65\n",
       "2. 1.74\n",
       "3. 1.69\n",
       "\n",
       "\n"
      ],
      "text/plain": [
       "[1] 1.65 1.74 1.69"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "v<-c(3,5,7)\n",
    "height[v]\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "*  Remove elements from a vector"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<ol class=list-inline>\n",
       "\t<li>1.75</li>\n",
       "\t<li>1.8</li>\n",
       "\t<li>1.9</li>\n",
       "\t<li>1.91</li>\n",
       "</ol>\n"
      ],
      "text/latex": [
       "\\begin{enumerate*}\n",
       "\\item 1.75\n",
       "\\item 1.8\n",
       "\\item 1.9\n",
       "\\item 1.91\n",
       "\\end{enumerate*}\n"
      ],
      "text/markdown": [
       "1. 1.75\n",
       "2. 1.8\n",
       "3. 1.9\n",
       "4. 1.91\n",
       "\n",
       "\n"
      ],
      "text/plain": [
       "[1] 1.75 1.80 1.90 1.91"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "height[-c(3,5,7)]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "*  Conditional selection"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<ol class=list-inline>\n",
       "\t<li>1.75</li>\n",
       "\t<li>1.8</li>\n",
       "\t<li>1.9</li>\n",
       "\t<li>1.74</li>\n",
       "\t<li>1.91</li>\n",
       "</ol>\n"
      ],
      "text/latex": [
       "\\begin{enumerate*}\n",
       "\\item 1.75\n",
       "\\item 1.8\n",
       "\\item 1.9\n",
       "\\item 1.74\n",
       "\\item 1.91\n",
       "\\end{enumerate*}\n"
      ],
      "text/markdown": [
       "1. 1.75\n",
       "2. 1.8\n",
       "3. 1.9\n",
       "4. 1.74\n",
       "5. 1.91\n",
       "\n",
       "\n"
      ],
      "text/plain": [
       "[1] 1.75 1.80 1.90 1.74 1.91"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "height[height>1.70]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<ol class=list-inline>\n",
       "\t<li>1.75</li>\n",
       "\t<li>1.8</li>\n",
       "\t<li>1.74</li>\n",
       "</ol>\n"
      ],
      "text/latex": [
       "\\begin{enumerate*}\n",
       "\\item 1.75\n",
       "\\item 1.8\n",
       "\\item 1.74\n",
       "\\end{enumerate*}\n"
      ],
      "text/markdown": [
       "1. 1.75\n",
       "2. 1.8\n",
       "3. 1.74\n",
       "\n",
       "\n"
      ],
      "text/plain": [
       "[1] 1.75 1.80 1.74"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "height[height>1.70 & height <1.90]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Exercises\n",
    "\n",
    "Use the `sort` function to sort the vector `height` by decreasing order of weight\n",
    "\n",
    "#### Solution"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<ol class=list-inline>\n",
       "\t<li>1.74</li>\n",
       "\t<li>1.9</li>\n",
       "\t<li>1.8</li>\n",
       "\t<li>1.91</li>\n",
       "\t<li>1.75</li>\n",
       "\t<li>1.69</li>\n",
       "\t<li>1.65</li>\n",
       "</ol>\n"
      ],
      "text/latex": [
       "\\begin{enumerate*}\n",
       "\\item 1.74\n",
       "\\item 1.9\n",
       "\\item 1.8\n",
       "\\item 1.91\n",
       "\\item 1.75\n",
       "\\item 1.69\n",
       "\\item 1.65\n",
       "\\end{enumerate*}\n"
      ],
      "text/markdown": [
       "1. 1.74\n",
       "2. 1.9\n",
       "3. 1.8\n",
       "4. 1.91\n",
       "5. 1.75\n",
       "6. 1.69\n",
       "7. 1.65\n",
       "\n",
       "\n"
      ],
      "text/plain": [
       "[1] 1.74 1.90 1.80 1.91 1.75 1.69 1.65"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "index<-sort(weight,decreasing=T,index.return=T)\n",
    "height[index$ix]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Factors, lists & data frames\n",
    "\n",
    "### Factors\n",
    "\n",
    "*  *Factors* are the categorial variables (see `enum` in C++). They make it possible to assign meaningful names to the categories.\n",
    "*  A factor is said to have a set of *levels*.\n",
    "*  Ex: a factor has four levels. It consists of two items: a vector of integers between 1 and 4 and a character vector of length 4 containing strings describing what the four levels are.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "pain <- c(0,3,2,2,1)\n",
    "fpain <- factor(pain, levels=0:3)\n",
    "levels(fpain) <- c(\"non\", \"mild\", \"medium\", \"severe\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "*    `fpain` is a categorial variable."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<ol class=list-inline>\n",
       "\t<li>non</li>\n",
       "\t<li>severe</li>\n",
       "\t<li>medium</li>\n",
       "\t<li>medium</li>\n",
       "\t<li>mild</li>\n",
       "</ol>\n"
      ],
      "text/latex": [
       "\\begin{enumerate*}\n",
       "\\item non\n",
       "\\item severe\n",
       "\\item medium\n",
       "\\item medium\n",
       "\\item mild\n",
       "\\end{enumerate*}\n"
      ],
      "text/markdown": [
       "1. non\n",
       "2. severe\n",
       "3. medium\n",
       "4. medium\n",
       "5. mild\n",
       "\n",
       "\n"
      ],
      "text/plain": [
       "[1] non    severe medium medium mild  \n",
       "Levels: non mild medium severe"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fpain"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<ol class=list-inline>\n",
       "\t<li>1</li>\n",
       "\t<li>4</li>\n",
       "\t<li>3</li>\n",
       "\t<li>3</li>\n",
       "\t<li>2</li>\n",
       "</ol>\n"
      ],
      "text/latex": [
       "\\begin{enumerate*}\n",
       "\\item 1\n",
       "\\item 4\n",
       "\\item 3\n",
       "\\item 3\n",
       "\\item 2\n",
       "\\end{enumerate*}\n"
      ],
      "text/markdown": [
       "1. 1\n",
       "2. 4\n",
       "3. 3\n",
       "4. 3\n",
       "5. 2\n",
       "\n",
       "\n"
      ],
      "text/plain": [
       "[1] 1 4 3 3 2"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "as.numeric(fpain)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<ol class=list-inline>\n",
       "\t<li>'non'</li>\n",
       "\t<li>'mild'</li>\n",
       "\t<li>'medium'</li>\n",
       "\t<li>'severe'</li>\n",
       "</ol>\n"
      ],
      "text/latex": [
       "\\begin{enumerate*}\n",
       "\\item 'non'\n",
       "\\item 'mild'\n",
       "\\item 'medium'\n",
       "\\item 'severe'\n",
       "\\end{enumerate*}\n"
      ],
      "text/markdown": [
       "1. 'non'\n",
       "2. 'mild'\n",
       "3. 'medium'\n",
       "4. 'severe'\n",
       "\n",
       "\n"
      ],
      "text/plain": [
       "[1] \"non\"    \"mild\"   \"medium\" \"severe\""
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "levels(fpain)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Lists\n",
    "\n",
    "*  Useful to combine a collection of objects into a larger composite object.\n",
    "*  The following data concern pre- and postmenstrual energy intake in a group of women.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "intake.pre <- c(5260, 5470, 5640, 6180, 6390,6515, 6805, 7515, 7515, 8230, 8770)\n",
    "intake.post <- c(3910, 4220, 3885, 5160, 5645,4680, 5265, 5975, 6790, 6900, 7335)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "*  Combine the vectors into a list\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<dl>\n",
       "\t<dt>$before</dt>\n",
       "\t\t<dd><ol class=list-inline>\n",
       "\t<li>5260</li>\n",
       "\t<li>5470</li>\n",
       "\t<li>5640</li>\n",
       "\t<li>6180</li>\n",
       "\t<li>6390</li>\n",
       "\t<li>6515</li>\n",
       "\t<li>6805</li>\n",
       "\t<li>7515</li>\n",
       "\t<li>7515</li>\n",
       "\t<li>8230</li>\n",
       "\t<li>8770</li>\n",
       "</ol>\n",
       "</dd>\n",
       "\t<dt>$after</dt>\n",
       "\t\t<dd><ol class=list-inline>\n",
       "\t<li>3910</li>\n",
       "\t<li>4220</li>\n",
       "\t<li>3885</li>\n",
       "\t<li>5160</li>\n",
       "\t<li>5645</li>\n",
       "\t<li>4680</li>\n",
       "\t<li>5265</li>\n",
       "\t<li>5975</li>\n",
       "\t<li>6790</li>\n",
       "\t<li>6900</li>\n",
       "\t<li>7335</li>\n",
       "</ol>\n",
       "</dd>\n",
       "</dl>\n"
      ],
      "text/latex": [
       "\\begin{description}\n",
       "\\item[\\$before] \\begin{enumerate*}\n",
       "\\item 5260\n",
       "\\item 5470\n",
       "\\item 5640\n",
       "\\item 6180\n",
       "\\item 6390\n",
       "\\item 6515\n",
       "\\item 6805\n",
       "\\item 7515\n",
       "\\item 7515\n",
       "\\item 8230\n",
       "\\item 8770\n",
       "\\end{enumerate*}\n",
       "\n",
       "\\item[\\$after] \\begin{enumerate*}\n",
       "\\item 3910\n",
       "\\item 4220\n",
       "\\item 3885\n",
       "\\item 5160\n",
       "\\item 5645\n",
       "\\item 4680\n",
       "\\item 5265\n",
       "\\item 5975\n",
       "\\item 6790\n",
       "\\item 6900\n",
       "\\item 7335\n",
       "\\end{enumerate*}\n",
       "\n",
       "\\end{description}\n"
      ],
      "text/markdown": [
       "$before\n",
       ":   1. 5260\n",
       "2. 5470\n",
       "3. 5640\n",
       "4. 6180\n",
       "5. 6390\n",
       "6. 6515\n",
       "7. 6805\n",
       "8. 7515\n",
       "9. 7515\n",
       "10. 8230\n",
       "11. 8770\n",
       "\n",
       "\n",
       "\n",
       "$after\n",
       ":   1. 3910\n",
       "2. 4220\n",
       "3. 3885\n",
       "4. 5160\n",
       "5. 5645\n",
       "6. 4680\n",
       "7. 5265\n",
       "8. 5975\n",
       "9. 6790\n",
       "10. 6900\n",
       "11. 7335\n",
       "\n",
       "\n",
       "\n",
       "\n",
       "\n"
      ],
      "text/plain": [
       "$before\n",
       " [1] 5260 5470 5640 6180 6390 6515 6805 7515 7515 8230 8770\n",
       "\n",
       "$after\n",
       " [1] 3910 4220 3885 5160 5645 4680 5265 5975 6790 6900 7335\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "mylist <- list(\"before\"=intake.pre, \"after\"=intake.post)\n",
    "mylist"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "*  The components of the list are named according to the arguments' names used in *list*  (in the example `before` and `after`). Named components may be extracted as follows \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<ol class=list-inline>\n",
       "\t<li>5260</li>\n",
       "\t<li>5470</li>\n",
       "\t<li>5640</li>\n",
       "\t<li>6180</li>\n",
       "\t<li>6390</li>\n",
       "\t<li>6515</li>\n",
       "\t<li>6805</li>\n",
       "\t<li>7515</li>\n",
       "\t<li>7515</li>\n",
       "\t<li>8230</li>\n",
       "\t<li>8770</li>\n",
       "</ol>\n"
      ],
      "text/latex": [
       "\\begin{enumerate*}\n",
       "\\item 5260\n",
       "\\item 5470\n",
       "\\item 5640\n",
       "\\item 6180\n",
       "\\item 6390\n",
       "\\item 6515\n",
       "\\item 6805\n",
       "\\item 7515\n",
       "\\item 7515\n",
       "\\item 8230\n",
       "\\item 8770\n",
       "\\end{enumerate*}\n"
      ],
      "text/markdown": [
       "1. 5260\n",
       "2. 5470\n",
       "3. 5640\n",
       "4. 6180\n",
       "5. 6390\n",
       "6. 6515\n",
       "7. 6805\n",
       "8. 7515\n",
       "9. 7515\n",
       "10. 8230\n",
       "11. 8770\n",
       "\n",
       "\n"
      ],
      "text/plain": [
       " [1] 5260 5470 5640 6180 6390 6515 6805 7515 7515 8230 8770"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "mylist$before"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "*  or alternatively by using the component's index\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<ol class=list-inline>\n",
       "\t<li>5260</li>\n",
       "\t<li>5470</li>\n",
       "\t<li>5640</li>\n",
       "\t<li>6180</li>\n",
       "\t<li>6390</li>\n",
       "\t<li>6515</li>\n",
       "\t<li>6805</li>\n",
       "\t<li>7515</li>\n",
       "\t<li>7515</li>\n",
       "\t<li>8230</li>\n",
       "\t<li>8770</li>\n",
       "</ol>\n"
      ],
      "text/latex": [
       "\\begin{enumerate*}\n",
       "\\item 5260\n",
       "\\item 5470\n",
       "\\item 5640\n",
       "\\item 6180\n",
       "\\item 6390\n",
       "\\item 6515\n",
       "\\item 6805\n",
       "\\item 7515\n",
       "\\item 7515\n",
       "\\item 8230\n",
       "\\item 8770\n",
       "\\end{enumerate*}\n"
      ],
      "text/markdown": [
       "1. 5260\n",
       "2. 5470\n",
       "3. 5640\n",
       "4. 6180\n",
       "5. 6390\n",
       "6. 6515\n",
       "7. 6805\n",
       "8. 7515\n",
       "9. 7515\n",
       "10. 8230\n",
       "11. 8770\n",
       "\n",
       "\n"
      ],
      "text/plain": [
       " [1] 5260 5470 5640 6180 6390 6515 6805 7515 7515 8230 8770"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "mylist[[1]]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Data frames\n",
    "\n",
    "*  A data frame corresponds to a \"data set\" in other statistical packages. It is a list of vectors and/or factors of the same length. Data in the same position come from the same experimental unit. \n",
    "*  Creation of a data frame:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table>\n",
       "<thead><tr><th scope=col>intake.pre</th><th scope=col>intake.post</th></tr></thead>\n",
       "<tbody>\n",
       "\t<tr><td>5260</td><td>3910</td></tr>\n",
       "\t<tr><td>5470</td><td>4220</td></tr>\n",
       "\t<tr><td>5640</td><td>3885</td></tr>\n",
       "\t<tr><td>6180</td><td>5160</td></tr>\n",
       "\t<tr><td>6390</td><td>5645</td></tr>\n",
       "\t<tr><td>6515</td><td>4680</td></tr>\n",
       "\t<tr><td>6805</td><td>5265</td></tr>\n",
       "\t<tr><td>7515</td><td>5975</td></tr>\n",
       "\t<tr><td>7515</td><td>6790</td></tr>\n",
       "\t<tr><td>8230</td><td>6900</td></tr>\n",
       "\t<tr><td>8770</td><td>7335</td></tr>\n",
       "</tbody>\n",
       "</table>\n"
      ],
      "text/latex": [
       "\\begin{tabular}{r|ll}\n",
       " intake.pre & intake.post\\\\\n",
       "\\hline\n",
       "\t 5260 & 3910\\\\\n",
       "\t 5470 & 4220\\\\\n",
       "\t 5640 & 3885\\\\\n",
       "\t 6180 & 5160\\\\\n",
       "\t 6390 & 5645\\\\\n",
       "\t 6515 & 4680\\\\\n",
       "\t 6805 & 5265\\\\\n",
       "\t 7515 & 5975\\\\\n",
       "\t 7515 & 6790\\\\\n",
       "\t 8230 & 6900\\\\\n",
       "\t 8770 & 7335\\\\\n",
       "\\end{tabular}\n"
      ],
      "text/markdown": [
       "\n",
       "intake.pre | intake.post | \n",
       "|---|---|---|---|---|---|---|---|---|---|---|\n",
       "| 5260 | 3910 | \n",
       "| 5470 | 4220 | \n",
       "| 5640 | 3885 | \n",
       "| 6180 | 5160 | \n",
       "| 6390 | 5645 | \n",
       "| 6515 | 4680 | \n",
       "| 6805 | 5265 | \n",
       "| 7515 | 5975 | \n",
       "| 7515 | 6790 | \n",
       "| 8230 | 6900 | \n",
       "| 8770 | 7335 | \n",
       "\n",
       "\n"
      ],
      "text/plain": [
       "   intake.pre intake.post\n",
       "1  5260       3910       \n",
       "2  5470       4220       \n",
       "3  5640       3885       \n",
       "4  6180       5160       \n",
       "5  6390       5645       \n",
       "6  6515       4680       \n",
       "7  6805       5265       \n",
       "8  7515       5975       \n",
       "9  7515       6790       \n",
       "10 8230       6900       \n",
       "11 8770       7335       "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "d <- data.frame(intake.pre, intake.post)\n",
    "d"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "*  Elements in one row correspond to the same woman\n",
    "*  As with lists, variables are accessible using the ''\\$'' notation\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<ol class=list-inline>\n",
       "\t<li>5260</li>\n",
       "\t<li>5470</li>\n",
       "\t<li>5640</li>\n",
       "\t<li>6180</li>\n",
       "\t<li>6390</li>\n",
       "\t<li>6515</li>\n",
       "\t<li>6805</li>\n",
       "\t<li>7515</li>\n",
       "\t<li>7515</li>\n",
       "\t<li>8230</li>\n",
       "\t<li>8770</li>\n",
       "</ol>\n"
      ],
      "text/latex": [
       "\\begin{enumerate*}\n",
       "\\item 5260\n",
       "\\item 5470\n",
       "\\item 5640\n",
       "\\item 6180\n",
       "\\item 6390\n",
       "\\item 6515\n",
       "\\item 6805\n",
       "\\item 7515\n",
       "\\item 7515\n",
       "\\item 8230\n",
       "\\item 8770\n",
       "\\end{enumerate*}\n"
      ],
      "text/markdown": [
       "1. 5260\n",
       "2. 5470\n",
       "3. 5640\n",
       "4. 6180\n",
       "5. 6390\n",
       "6. 6515\n",
       "7. 6805\n",
       "8. 7515\n",
       "9. 7515\n",
       "10. 8230\n",
       "11. 8770\n",
       "\n",
       "\n"
      ],
      "text/plain": [
       " [1] 5260 5470 5640 6180 6390 6515 6805 7515 7515 8230 8770"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "d$intake.pre"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Loops & conditions\n",
    "\n",
    "### Loop types\n",
    "\n",
    "*   `for(name in expr1) instructions` where  `expr1` is a vector expression\n",
    "      (ex: `1:20`)\n",
    "*   `while(condition) instructions`\n",
    "*  `repeat instructions`\n",
    "\n",
    "### Implicit loops\n",
    "\n",
    "*  Apply a function to each element of  a set of values or vectors and collect the results in a single structure.\n",
    "*  In R this can be done by using one of the two functions `lapply` and   `sapply`. The former always returns a list whereas the latter tries to simplify the result into a vector or matrix if possible.\n",
    "*  The function `apply` allows to apply a function to the rows or columns of a matrix.\n",
    "*  The function  `tapply` allows to create tables of the function's value on subgroups defined by its second argument, which can be a factor or a list of factors. (The grouping can also be defined by ordinary vectors. They will be converted to factors internally.)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Exercises\n",
    "\n",
    "*  Load the \"thuesen\" data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "data(thuesen)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "*  Compute the mean of the data frame `thuesen` to get the result as a list, and as a vector.\n",
    "\n",
    "#### Solution"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<dl>\n",
       "\t<dt>$blood.glucose</dt>\n",
       "\t\t<dd>10.3</dd>\n",
       "\t<dt>$short.velocity</dt>\n",
       "\t\t<dd>1.32565217391304</dd>\n",
       "</dl>\n"
      ],
      "text/latex": [
       "\\begin{description}\n",
       "\\item[\\$blood.glucose] 10.3\n",
       "\\item[\\$short.velocity] 1.32565217391304\n",
       "\\end{description}\n"
      ],
      "text/markdown": [
       "$blood.glucose\n",
       ":   10.3\n",
       "$short.velocity\n",
       ":   1.32565217391304\n",
       "\n",
       "\n"
      ],
      "text/plain": [
       "$blood.glucose\n",
       "[1] 10.3\n",
       "\n",
       "$short.velocity\n",
       "[1] 1.325652\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "lapply(thuesen, mean, na.rm=T)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<dl class=dl-horizontal>\n",
       "\t<dt>blood.glucose</dt>\n",
       "\t\t<dd>10.3</dd>\n",
       "\t<dt>short.velocity</dt>\n",
       "\t\t<dd>1.32565217391304</dd>\n",
       "</dl>\n"
      ],
      "text/latex": [
       "\\begin{description*}\n",
       "\\item[blood.glucose] 10.3\n",
       "\\item[short.velocity] 1.32565217391304\n",
       "\\end{description*}\n"
      ],
      "text/markdown": [
       "blood.glucose\n",
       ":   10.3short.velocity\n",
       ":   1.32565217391304\n",
       "\n"
      ],
      "text/plain": [
       " blood.glucose short.velocity \n",
       "     10.300000       1.325652 "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sapply(thuesen, mean, na.rm=T)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "*  Initialize a matrix, in order to get the same results as in this tutorial, use the same random seed\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table>\n",
       "<tbody>\n",
       "\t<tr><td> 0.5855288</td><td> 0.6058875</td><td>-0.2841597</td></tr>\n",
       "\t<tr><td> 0.7094660</td><td>-1.8179560</td><td>-0.9193220</td></tr>\n",
       "\t<tr><td>-0.1093033</td><td> 0.6300986</td><td>-0.1162478</td></tr>\n",
       "\t<tr><td>-0.4534972</td><td>-0.2761841</td><td> 1.8173120</td></tr>\n",
       "</tbody>\n",
       "</table>\n"
      ],
      "text/latex": [
       "\\begin{tabular}{lll}\n",
       "\t  0.5855288 &  0.6058875 & -0.2841597\\\\\n",
       "\t  0.7094660 & -1.8179560 & -0.9193220\\\\\n",
       "\t -0.1093033 &  0.6300986 & -0.1162478\\\\\n",
       "\t -0.4534972 & -0.2761841 &  1.8173120\\\\\n",
       "\\end{tabular}\n"
      ],
      "text/markdown": [
       "\n",
       "|  0.5855288 |  0.6058875 | -0.2841597 | \n",
       "|  0.7094660 | -1.8179560 | -0.9193220 | \n",
       "| -0.1093033 |  0.6300986 | -0.1162478 | \n",
       "| -0.4534972 | -0.2761841 |  1.8173120 | \n",
       "\n",
       "\n"
      ],
      "text/plain": [
       "     [,1]       [,2]       [,3]      \n",
       "[1,]  0.5855288  0.6058875 -0.2841597\n",
       "[2,]  0.7094660 -1.8179560 -0.9193220\n",
       "[3,] -0.1093033  0.6300986 -0.1162478\n",
       "[4,] -0.4534972 -0.2761841  1.8173120"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "set.seed(12345)\n",
    "m <- matrix(rnorm(12),4)\n",
    "m"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "and compute the mean of each row and of each column using `apply`, check the help for details `?apply`. How do you compute the mean of the entire matrix?\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<ol class=list-inline>\n",
       "\t<li>0.302418843246959</li>\n",
       "\t<li>-0.675937317556111</li>\n",
       "\t<li>0.134849143345112</li>\n",
       "\t<li>0.362543588338746</li>\n",
       "</ol>\n"
      ],
      "text/latex": [
       "\\begin{enumerate*}\n",
       "\\item 0.302418843246959\n",
       "\\item -0.675937317556111\n",
       "\\item 0.134849143345112\n",
       "\\item 0.362543588338746\n",
       "\\end{enumerate*}\n"
      ],
      "text/markdown": [
       "1. 0.302418843246959\n",
       "2. -0.675937317556111\n",
       "3. 0.134849143345112\n",
       "4. 0.362543588338746\n",
       "\n",
       "\n"
      ],
      "text/plain": [
       "[1]  0.3024188 -0.6759373  0.1348491  0.3625436"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "## mean for each row\n",
    "apply(m, 1, mean)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<ol class=list-inline>\n",
       "\t<li>0.183048586802391</li>\n",
       "\t<li>-0.21453851650504</li>\n",
       "\t<li>0.124395622733679</li>\n",
       "</ol>\n"
      ],
      "text/latex": [
       "\\begin{enumerate*}\n",
       "\\item 0.183048586802391\n",
       "\\item -0.21453851650504\n",
       "\\item 0.124395622733679\n",
       "\\end{enumerate*}\n"
      ],
      "text/markdown": [
       "1. 0.183048586802391\n",
       "2. -0.21453851650504\n",
       "3. 0.124395622733679\n",
       "\n",
       "\n"
      ],
      "text/plain": [
       "[1]  0.1830486 -0.2145385  0.1243956"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "## mean for each column\n",
    "apply(m, 2, mean)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "0.0309685643436766"
      ],
      "text/latex": [
       "0.0309685643436766"
      ],
      "text/markdown": [
       "0.0309685643436766"
      ],
      "text/plain": [
       "[1] 0.03096856"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "## matrix mean\n",
    "mean(m)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Conditions\n",
    "\n",
    "*  `if(condition) instructions else if(condition2) instructions2 else instructions3`\n",
    "*  Variants: see documentation.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[1]  0.5855288  0.6058875 -0.2841597\n"
     ]
    }
   ],
   "source": [
    "if (m[1,1]>0)\n",
    "{\n",
    "    print(m[1,])\n",
    "} else {\n",
    "    if(m[1,1]<0)\n",
    "    { \n",
    "        print(m[,1])\n",
    "    }\n",
    "    else \n",
    "    {\n",
    "        print(\"element is zero\")\n",
    "    }\n",
    "   } \n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Functions\n",
    "\n",
    "### How to write a basic function"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "*  `name <- function(arg1, arg2, ...)` expression\n",
    "*  Use  `return(variable)` to return one variable, multiple variables can be returned as a list.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<dl>\n",
       "\t<dt>$sum</dt>\n",
       "\t\t<dd>5</dd>\n",
       "\t<dt>$mean</dt>\n",
       "\t\t<dd>1.25</dd>\n",
       "</dl>\n"
      ],
      "text/latex": [
       "\\begin{description}\n",
       "\\item[\\$sum] 5\n",
       "\\item[\\$mean] 1.25\n",
       "\\end{description}\n"
      ],
      "text/markdown": [
       "$sum\n",
       ":   5\n",
       "$mean\n",
       ":   1.25\n",
       "\n",
       "\n"
      ],
      "text/plain": [
       "$sum\n",
       "[1] 5\n",
       "\n",
       "$mean\n",
       "[1] 1.25\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "## compute sum of a vector using a for loop\n",
    "computeSum<-function(x){\n",
    "sum<-0\n",
    "n<-0\n",
    "for(i in 1:length(x)){\n",
    "if(!is.na(x[i])){ ## The function is.na is used to determine\n",
    "## whether an element is missing (==NA)\n",
    "sum<-sum+x[i]\n",
    "n<-n+1\n",
    "}}\n",
    "# return(sum) will return the sum of the vector\n",
    "return(list(\"sum\"=sum,\"mean\"=sum/n))\n",
    "}\n",
    "computeSum(c(1,2,-4,NA,6))\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Compile functions\n",
    "\n",
    "*  A set of functions can be saved in a *.R file\n",
    "*  These functions can be loaded by using the  `source(filename)` command\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "## Plotting\n",
    "\n",
    "A basic two dimensional plot from using the vectors  `height` and `weight` can be generated using the `plot` command.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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4f6EoauPTUrVRRVq0vC/ia5Rt3Y5FbF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      "text/plain": [
       "Plot with title \"height vs weight\""
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot(height, weight, main=\"height vs weight\", xlab=\"weight\",ylab=\"height\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Adding pieces to the plot "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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Vy932gxS70/6aLUuxaz8TJuIyAtI6ZdESBAgAABAgQIDFSgVQGp7ZM03DI/Gt9N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\nECDQNYH/D/uEz4U9TxWaAAAAAElFTkSuQmCC",
      "text/plain": [
       "Plot with title \"height vs weight\""
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot(height, weight, main=\"height vs weight\", xlab=\"weight\",ylab=\"height\")\n",
    "hh <- c(1.65, 1.70, 1.75, 1.80, 1.85, 1.90)\n",
    "lines(hh, 22.5 * hh^2,col=\"red\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "To save a plot to a *.pdf file use the `pdf(...)` command; the following will save the file `myplot.pdf` in your working directory.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<strong>pdf:</strong> 2"
      ],
      "text/latex": [
       "\\textbf{pdf:} 2"
      ],
      "text/markdown": [
       "**pdf:** 2"
      ],
      "text/plain": [
       "pdf \n",
       "  2 "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "pdf(file=\"myplot.pdf\",width=5,height=4)\n",
    "plot(height, weight, main=\"height vs weight\", xlab=\"weight\",ylab=\"height\")\n",
    "hh <- c(1.65, 1.70, 1.75, 1.80, 1.85, 1.90)\n",
    "lines(hh, 22.5 * hh^2,col=\"red\")\n",
    "dev.off()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Probabilities and distributions\n",
    "\n",
    "### Sampling\n",
    "\n",
    "The function `sample` allows to draw random variables uniformly (parameter `prob` to make it non-uniformly) from a vector with or without replacement.\n",
    "\n",
    "### Exercises\n",
    "\n",
    "* Re-initialize random seed\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "set.seed(123456)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* Draw five random variables, uniformly distributed between 1 and 40"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<ol class=list-inline>\n",
       "\t<li>32</li>\n",
       "\t<li>24</li>\n",
       "\t<li>35</li>\n",
       "\t<li>33</li>\n",
       "\t<li>36</li>\n",
       "</ol>\n"
      ],
      "text/latex": [
       "\\begin{enumerate*}\n",
       "\\item 32\n",
       "\\item 24\n",
       "\\item 35\n",
       "\\item 33\n",
       "\\item 36\n",
       "\\end{enumerate*}\n"
      ],
      "text/markdown": [
       "1. 32\n",
       "2. 24\n",
       "3. 35\n",
       "4. 33\n",
       "5. 36\n",
       "\n",
       "\n"
      ],
      "text/plain": [
       "[1] 32 24 35 33 36"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sample(1:40,5) "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* Simulate 10 coin tosses\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<ol class=list-inline>\n",
       "\t<li>'T'</li>\n",
       "\t<li>'T'</li>\n",
       "\t<li>'H'</li>\n",
       "\t<li>'H'</li>\n",
       "\t<li>'T'</li>\n",
       "\t<li>'H'</li>\n",
       "\t<li>'H'</li>\n",
       "\t<li>'H'</li>\n",
       "\t<li>'H'</li>\n",
       "\t<li>'H'</li>\n",
       "</ol>\n"
      ],
      "text/latex": [
       "\\begin{enumerate*}\n",
       "\\item 'T'\n",
       "\\item 'T'\n",
       "\\item 'H'\n",
       "\\item 'H'\n",
       "\\item 'T'\n",
       "\\item 'H'\n",
       "\\item 'H'\n",
       "\\item 'H'\n",
       "\\item 'H'\n",
       "\\item 'H'\n",
       "\\end{enumerate*}\n"
      ],
      "text/markdown": [
       "1. 'T'\n",
       "2. 'T'\n",
       "3. 'H'\n",
       "4. 'H'\n",
       "5. 'T'\n",
       "6. 'H'\n",
       "7. 'H'\n",
       "8. 'H'\n",
       "9. 'H'\n",
       "10. 'H'\n",
       "\n",
       "\n"
      ],
      "text/plain": [
       " [1] \"T\" \"T\" \"H\" \"H\" \"T\" \"H\" \"H\" \"H\" \"H\" \"H\""
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sample(c(\"H\", \"T\"), 10, replace=T)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* Sample 10 coin tosses with for a coin in which heads appears with a probability of $0.8$\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<ol class=list-inline>\n",
       "\t<li>'H'</li>\n",
       "\t<li>'H'</li>\n",
       "\t<li>'H'</li>\n",
       "\t<li>'H'</li>\n",
       "\t<li>'H'</li>\n",
       "\t<li>'H'</li>\n",
       "\t<li>'H'</li>\n",
       "\t<li>'H'</li>\n",
       "\t<li>'T'</li>\n",
       "\t<li>'H'</li>\n",
       "</ol>\n"
      ],
      "text/latex": [
       "\\begin{enumerate*}\n",
       "\\item 'H'\n",
       "\\item 'H'\n",
       "\\item 'H'\n",
       "\\item 'H'\n",
       "\\item 'H'\n",
       "\\item 'H'\n",
       "\\item 'H'\n",
       "\\item 'H'\n",
       "\\item 'T'\n",
       "\\item 'H'\n",
       "\\end{enumerate*}\n"
      ],
      "text/markdown": [
       "1. 'H'\n",
       "2. 'H'\n",
       "3. 'H'\n",
       "4. 'H'\n",
       "5. 'H'\n",
       "6. 'H'\n",
       "7. 'H'\n",
       "8. 'H'\n",
       "9. 'T'\n",
       "10. 'H'\n",
       "\n",
       "\n"
      ],
      "text/plain": [
       " [1] \"H\" \"H\" \"H\" \"H\" \"H\" \"H\" \"H\" \"H\" \"T\" \"H\""
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sample(c(\"H\", \"T\"), 10, replace=T,prob=c(0.8,0.2))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Distributions in R\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* For a probability distribution, four fundamental values can be computed: the density, the distribution function, the quantiles and the pseudo-random numbers. Using R, each of these can be computed for the included distributions. \n",
    "* For example for a normal distribution (respectively): `dnorm, pnorm, qnorm`\n",
    "et `rnorm`. The same nomenclature is used for all distributions: the density (or the discrete probability) starts with a \"d\", the distribution function with \"p\", the quantile with \"q\" and the random number generating function with \"r\".\n",
    "* Plot normal density\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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pyKl1b8bUV+IJ5XcVFFlsnDFR5f0Xcx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AgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAAB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\nRK5CYII=",
      "text/plain": [
       "plot without title"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "x <- seq(-4,4,0.1)\n",
    "# alternatively curve(dnorm(x), from=-4, to=4)\n",
    "plot(x,dnorm(x), type=\"l\") "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Exercises\n",
    "\n",
    "* Plot the density for a binomial random variable $B(50, 0.33)$ (use the parameter `type=\"h\"` for the plot function to generate a pin diagram)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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EcLTdwiQIAAAQIECBAg0I1Ajw3SQanu\ngcl+yaPJQ8ljiUGAAAECBAgQIECAQOcCvXwG6ajU+ZLkvuSB5Lbk5mRHUk3SLcnFyZbEIECA\nAAECBAgQIECgU4EeziCdk9qeO6rvHbm8IakmqRqjOpN0cHJ4ckZycnJmcnliECBAgAABAgQI\nECBAoCmBUzKbJ5M/SF44MLON2XZ88umk9n9JsifHG/Ji9boH7MkX9VoECDylwGezx79+yr3s\nQIAAAQIE+hbYN9Ov32WPbYGh9bfYnZQi3ZrU5Y0DBauCXpuckDySnJYYBAgQWKlA/WPL+5Jn\nrfSB9idAgAABAgRmQ6D1BmlbmOstdU9Myf1g9tuebJ1yf7sRIEBgUmCf3Kgvlv3WyTtdJ0CA\nAAECBOZHoPUG6Z6U4uikfmmZZtQKd9VU1QIOBgECBAgQIECAAAECnQm03iBdmnoemVyRHDNQ\n23pbzHHJNcn+yVWJQYAAAQIECBAgQIBAZwKtr2JXq9EdkpyXnJjcldTS3vcnO5PNSa1id0Ry\nWLIrOSu5LjEIECBAgAABAgQIEOhMoPUGqRZfuDC5Ojk/qZXqFp9Jejz33Z1ckFyU3JkYBAgQ\nIECAAAECBAh0KNB6gzQuaa1kd+roRp01qu8/2pTUF8c+nBgECBAgQIAAAQIECBDY0EuDNFnq\nemtdxSBAgAABAgQIECBAgMACgdYXaVgwWTcIECBAgAABAgQIECAwJKBBGtKxjQABAgQIECBA\ngACBrgQ0SF2V22QJECBAgAABAgQIEBgS0CAN6dhGgAABAgQIECBAgEBXAhqkrsptsgQIECBA\ngAABAgQIDAlokIZ0bCNAgAABAgQIECBAoCsBDVJX5TZZAgQIECBAgAABAgSGBDRIQzq2ESBA\ngAABAgQIECDQlYAGqatymywBAgQIECBAgAABAkMCGqQhHdsIECBAgAABAgQIEOhKQIPUVblN\nlgABAgQIECBAgACBIQEN0pCObQQIECBAgAABAgQIdCWgQeqq3CZLgAABAgQIECBAgMCQgAZp\nSMc2AgQIECBAgAABAgS6EtAgdVVukyVAgAABAgQIECBAYEhAgzSkYxsBAgQIECBAgAABAl0J\naJC6KrfJEiBAgAABAgQIECAwJKBBGtKxjQABAgQIECBAgACBrgQ0SF2V22QJECBAgAABAgQI\nEBgS0CAN6dhGgAABAgQIECBAgEBXAhqkrsptsgQIECBAgAABAgQIDAlokIZ0bCNAgAABAgQI\nECBAoCsBDVJX5TZZAgQIECBAgAABAgSGBDRIQzq2ESBAgAABAgQIECDQlYAGqatymyyBrgX2\nzuwrBgECBAgQIEBgWQG/LCxLYwMBAo0JvH00nzMbm5fpECBAgAABAqsooEFaRUxPRYDATAts\nnumjc3AECBAgQIDATAh4i91MlMFBECBAgAABAgQIECAwCwIapFmogmMgQIAAAQIECBAgQGAm\nBDRIM1EGB0GAAAECBAgQIECAwCwIaJBmoQqOgQABAgQIECBAgACBmRDQIM1EGRwEAQIECBAg\nQIAAAQKzIKBBmoUqOAYCBAgQIECAAAECBGZCQIM0E2VwEAQIECBAgAABAgQIzIKABmkWquAY\nCBAgQIAAAQIECBCYCQEN0kyUwUEQIECAAAECBAgQIDALAhqkWaiCYyBAgAABAgQIECBAYCYE\nNEgzUQYHQYAAAQIECBAgQIDALAhokGahCo6BAAECBAgQIECAAIGZENAgzUQZHAQBAgQIECBA\ngAABArMgoEGahSo4BgIECBAgQIAAAQIEZkJAgzQTZXAQBAgQIECAAAECBAjMgoAGaRaq4BgI\nECBAgAABAgQIEJgJAQ3STJTBQRAgQIAAAQIECBAgMAsCGqRZqIJjIECAAAECBAgQIEBgJgQ0\nSDNRBgdBgAABAgQIECBAgMAsCGiQZqEKjoEAAQIECBAgQIAAgZkQ0CDNRBkcBAECHQs8P3P/\nfOL/xx3/EJg6AQIECMyOgL+QZ6cWjoQAgT4FtmTaRyZ79zl9syZAgAABArMloEGarXo4GgIE\nCBAgQIAAAQIE1lFAg7SO+F6aAAECBAgQIECAAIHZEtAgzVY9HA0BAgQIECBAgAABAusooEFa\nR3wvTYAAAQIECBAgQIDAbAlokGarHo6GAAECBAgQIECAAIF1FNAgrSO+lyZAgAABAgQIECBA\nYLYENEizVQ9HQ4AAAQIECBAgQIDAOgr0+L0bB8X7wGS/5NHkoeSxxCBAgAABAgQIECBAoHOB\nXs4gHZU6X5LclzyQ3JbcnOxIqkm6Jbk4qS9sNAgQIECAAAECBAgQ6FSghzNI56S2547qe0cu\nb0iqSarGqM4kHZwcnpyRnJycmVyeGAQIECBAgAABAgQIdCbQeoN0SupZzdE1ydnJjclSY2Pu\nPC65ILksuT25PjEIECBAgAABAgQIEOhIoPW32J2UWt6a1OVyzVGV+8nk2uSE5JHktMQgQIAA\nAQIECBAgQKAzgdYbpG2pZ72l7okp6/pg9tuebJ1yf7sRIECAAAECBAgQINCQQOsN0j2p1dHJ\nPlPWrFa4q6aqFnAwCBAgQIAAAQIECBDoTKD1BunS1PPI5IrkmIHajj+DVJ9V2j+5amBfmwgQ\nIECAAAECBAgQaFSg9UUaajW6Q5LzkhOTu5Idyf3JzmRzUqvYHZEcluxKzkquSwwCBAgQIECA\nAAECBDoTaL1BqsUXLkyuTs5Pjk8Wn0l6PPfdndQKdhcldyYGAQIECBAgQIAAAQIdCrTeII1L\nWivZnTq6UWeN6vuPNiX1xbEPJwYBAgQIECBAgAABAgQ29NIgTZa63lpXMQgQIECAAAECBAgQ\nILBAoPVFGhZM1g0CBAgQIECAAAECBAgMCWiQhnRsI0CAAAECBAgQIECgKwENUlflNlkCBAgQ\nIECAAAECBIYEWv8M0hsy+VqUYaXj+jzghpU+yP4ECBAgQIAAAQIECMy3QOsN0v+W8nzv0yjR\nW/MYDdLTgPMQAgQIECBAgAABAvMs0HqD9KoU58rk2KS+C+m3kmnGX0yzk30IECBAgAABAgQI\nEGhLoPUG6a9Srpcn/zWpZunc5DPJWo/6bNcPJvtO+UIvmHI/uxEgQIAAAQIECBAgsIYCrTdI\nRfdE8rrkxuTXkpcmaz2emxf4YDJtg9RDHdba3PMTIECAAAECBAgQ2G2BXlax+1yk3pLUgg17\n4mzNrXmdg5NnTJn/PfsZBAgQIECAAAECBAiss0AvDVIxX5BsSz5bNwwCBAgQIECAAAECBAgs\nFuipQVo8d7cJECBAgAABAgQIECCwQECDtIDDDQIECBAgQIAAAQIEehbQIC2s/pty86bkjQvv\ndosAAQIECBAgQIAAgR4ENEgLq3xobtbnlOrSIECAAAECBAgQIECgMwHLSy8s+Dtz88rk3oV3\nu0WAAAECBAgQIECAQA8CGqSFVa7GSHO00MQtAgQIECBAgAABAt0I9NggHZTqHpjslzyaPJQ8\nlhgECBAgQIAAAQIECHQu0MtnkI5KnS9J7kseSG5Lbk52JNUk3ZJcnGxJDAIECBAgQIAAAQIE\nOhXo4QzSOantuaP63pHLG5JqkqoxqjNJByeHJ2ckJydnJpcnBgECBAgQIECAAAECnQm03iCd\nknpWc3RNcnZyY7LU2Jg7j0suSC5Lbk+uTwwCBAgQIECAAAECBDoSaP0tdiellrcmdblcc1Tl\nfjK5NjkheSQ5LTEIECBAgAABAgQIEOhMoPUGaVvqWW+pe2LKuj6Y/bYnW6fc324ECBAgQIAA\nAQIECDQk0HqDdE9qdXSyz5Q1qxXuqqmqBRwMAgQIECBAgAABAgQ6E2i9Qbo09TwyuSI5ZqC2\n488g1WeV9k+uGtjXJgIECBAgQIAAAQIEGhVofZGGWo3ukOS85MTkrmRHcn+yM9mc1Cp2RySH\nJbuSs5LrEoMAAQIECBAgQIAAgc4EWm+QavGFC5Ork/OT45PFZ5Iez313J7WC3UXJnYlBgAAB\nAgQIECBAgECHAq03SOOS1kp2p45u1Fmj+v6jTUl9cezDiUGAAAECBAgQIECAAIENvTRIk6Wu\nt9ZVDAIECBAgQIAAAQIECCwQaH2RhgWTdYMAAQIECBAgQIAAAQJDAhqkIR3bCBAgQIAAAQIE\nCBDoSkCD1FW5TZYAAQIECBAgQIAAgSEBDdKQjm0ECBAgQIAAAQIECHQloEHqqtwmS4AAAQIE\nCBAgQIDAkIAGaUjHNgIECBAgQIAAAQIEuhLQIHVVbpMlQIAAAQIECBAgQGBIQIM0pGMbAQIE\nCBAgQIAAAQJdCWiQuiq3yRIgQIAAAQIECBAgMCSgQRrSsY0AAQIECBAgQIAAga4ENEhdldtk\nCTQjsDEzeUYzszERAgQIECBAYGYENEgzUwoHQoDACgROz74fWcH+diVAgAABAgQITCWgQZqK\nyU4ECMyYwAE5nopBgAABAgQIEFhVAQ3SqnJ6MgIECBAgQIAAAQIE5llAgzTP1XPsBAgQIECA\nAAECBAisqoAGaVU5PRkBAgQIECBAgAABAvMsoEGa5+o5dgIECBAgQIAAAQIEVlVAg7SqnJ6M\nAAECBAgQIECAAIF5FtAgzXP1HDsBAgQIECBAgAABAqsqoEFaVU5PRoAAAQIECBAgQIDAPAto\nkOa5eo6dAAECBAgQIECAAIFVFdAgrSqnJyNAgAABAgQIECBAYJ4FNEjzXD3HToAAAQIECBAg\nQIDAqgpokFaV05MRIECAAAECBAgQIDDPAhqkea6eYydAgAABAgQIECBAYFUFNEiryunJCBAg\nQIAAAQIECBCYZwEN0jxXz7ETIECAAAECBAgQILCqAhqkVeX0ZAQIECBAgAABAgQIzLOABmme\nq+fYCRAgQIAAAQIECBBYVQEN0qpyejICBAgQIECAAAECBOZZQIM0z9Vz7AQIECBAgAABAgQI\nrKqABmlVOT0ZAQIECBAgQIAAAQLzLKBBmufqOXYCBAgQIECAAAECBFZVQIO0qpyejAABAgQI\nECBAgACBeRbQIM1z9Rw7AQIECBAgQIAAAQKrKqBBWlVOT0aAAAECBAgQIECAwDwLaJDmuXqO\nnQABAgQIECBAgACBVRXQIK0qpycjQIAAAQIECBAgQGCeBTRI81w9x06AQK8CP5GJf6TXyZs3\nAQIECBBYSwEN0lrqem4CBAisjcCWPO2ha/PUnpUAAQIECPQtoEHqu/5mT4AAAQIECBAgQIDA\nhIAGaQLDVQIECBAgQIAAAQIE+hbQIPVdf7MnQIAAAQIECBAgQGBCQIM0geEqAQIECBAgQIAA\nAQJ9C2iQ+q6/2RMgQIAAAQIECBAgMCGgQZrAcJUAAQIECBAgQIAAgb4FNEh919/sCRAgQIAA\nAQIECBCYENAgTWC4SoAAAQIECBAgQIBA3wIapL7rb/YECBAgQIAAAQIECEwIaJAmMFwlQIAA\nAQIECBAgQKBvAQ1S3/U3ewIECBAgQIAAAQIEJgQ0SBMYrhIgQIAAAQIECBAg0LeABqnv+ps9\nAQIECBAgQIAAAQITAhqkCQxXCRAgQIAAAQIECBDoW0CD1Hf9zZ4AAQIECBAgQIAAgQkBDdIE\nhqsECBAgQIAAAQIECPQtoEHqu/5mT4AAAQIECBAgQIDAhIAGacOGfePxPckBEy6uEiBAgAAB\nAgQIECDQoUAvDdJPpLa/nvx88vxRnZ+Ryw8mf5P8abIzeV9yYGIQIECAAAECBAgQINChwN6N\nz7kawN9Nfnhinv8m1+uM0S8mP558LPmfyVHJTybfmhyfPJkYBAgQIECAAAECBAh0JND6GaQ3\npJbVHH1kdPmvcvlg8tHkdckpyQ8mP528OKmm6aXJaxKDAAECBAgQIECAAIHOBFo/g/QjqecD\nyYnJl0a13ZHLq5MPJb8zum988Su5ckZSTdIHxne6JECAAAECBAgQIECgD4HWzyAdkTLWW+jG\nzVFVtc4efTX587qxaNT9tyWHL7rfTQIECBAgQIAAAQIEOhBovUG6IzWst9Btmqjlq3K95v1d\nE/eNr9YZtRcmt4/vcEmAAAECBAgQIECAQD8CrTdI9Va6g5J6O92PJr+Q/D9JrVpXjdJrk/Eo\ni/cktbrdJxKDAAECBAgQIECAAIHOBFr/DNK7U89/ltRnkV4+qu1fj+775VxelvxMUp9L+sfJ\ns5MPJ1ckBgECBAgQIECAAAECnQm03iDVZ4pOSurs0UuSW5PfS+5Nfi6pL4l9dfLi5IvJryX1\nXUkGAQIECBAgQIAAAQIdCrTeII1L+ru5UpkcD+XG6Um9ta4Wc7gj+dvEIECAAAECBAgQIECg\nU4FeGqTJ8tZnkg5M9kseTapRqpXrDAIECBAgQIAAAQIEOhdofZGGcXmPypVLkvuS+l6kaohu\nTuqzR9Uk3ZJcnGxJDAIECBAgQIAAAQIEOhXo4QzSOantuaP61tvobkiqSarGqM4kHZzU9x7V\nF8SenJyZXJ4YBAgQIECAAAECBAh0JtB6g3RK6lnN0TXJ2cmNyVJjY+48LrkgqZXtbk+uTwwC\nBAgQIECAAAECBDoSaP0tdrWCXa1cV5fLNUdV7ieTa5MTkkeS0xKDAAECBAgQIECAAIHOBFpv\nkLalnvWWuiemrOuD2W97snXK/e1GgAABAgQIECBAgEBDAq03SPekVkcn+0xZs1rhrpqqWsDB\nIECAAAECBAgQIECgM4HWP4N0aer5/uSK5PzkU8lSoz6D9NLkbcn+yVXJ7oxvyYPrC2mnbcye\nuTsv5rEECBAgQIAAAQIECKyOQOsNUq1Gd0hyXnJicleyI7k/2ZlsTmoVu/qi2MOSXclZyXXJ\n7oy/zoPflew75ZMcm/1eO+W+diNAgAABAgQIECBAYI0EWm+QavGFC5OrkzqDdHxyTDI5Hs+N\nu5Nawe6i5M5kd0d95undK3iS2l+DtAIwuxIgQIAAAQIECBBYC4HWG6SxWa1kd+roRp01qu8/\n2pTUF8c+nBgECBAgQIAAAQIECBDY0EuDNFnqemtdxSBAgAABAgQIECBAgMACgdZXsVswWTcI\nECBAgAABAgQIECAwJKBBWqjzpty8KXnjwrvdIkCAAAECBAgQIECgBwEN0sIqH5qb9T1IdWkQ\nIECAAAECBAgQINCZQI+fQRoq8Tuz8crk3qGdbCNAgAABAgQIECBAoE0BDdLCulZjpDlaaOIW\nAQIECBAgQIAAgW4EemyQDkp1a5nv/ZJHk4eSxxKDAAECBAgQIECAAIHOBXr5DNJRqfMlSX3v\n0QPJbcnNyY6kmqRbkouTLYlBgAABAgQIECBAgECnAj2cQTontT13VN87cnlDUk1SNUZ1Jung\n5PDkjOTk5Mzk8sQgQIAAAQIECBAgQKAzgdYbpFNSz2qOrknOTm5Mlhobc+dxyQXJZcntyfWJ\nQYAAAQIECBAgQIBARwKtv8XupNTy1qQul2uOqtxPJtcmJySPJKclBgECBAgQIECAAAECnQm0\n3iBtSz3rLXVPTFnXB7Pf9mTrlPvbjQABAgQIECBAgACBhgRab5DuSa2OTvaZsma1wl01VbWA\ng0GAAAECBAgQIECAQGcCrTdIl6aeRyZXJMcM1Hb8GaT6rNL+yVUD+9pEgAABAgQIECBAgECj\nAq0v0lCr0R2SnJecmNyV7EjuT3Ymm5Naxe6I5LBkV3JWcl1iECBAgAABAgQIECDQmUDrDVIt\nvnBhcnVyfnJ8svhM0uO57+6kVrC7KLkzMQgQIECAAAECBAgQ6FCg9QZpXNJaye7U0Y06a1Tf\nf7QpqS+OfTgxCBAgQIAAAQIECBAgsKGXBmmy1PXWuopBgAABAgQIECBAgACBBQKtL9KwYLJu\nECBAgAABAgQIECBAYEhAgzSkYxsBAgQIECBAgAABAl0JaJC6KrfJEiBAgAABAgQIECAwJKBB\nGtKxjQABAgQIECBAgACBrgQ0SF2V22QJECBAgAABAgQIEBgS0CAN6dhGgAABAgQIECBAgEBX\nAhqkrsptsgQIECBAgAABAgQIDAlokIZ0bCNAgAABAgQIECBAoCsBDVJX5TZZAgQIECBAgAAB\nAgSGBDRIQzq2ESBAgAABAgQIECDQlYAGqatymywBAgQIECBAgAABAkMCGqQhHdsIECBAgAAB\nAgQIEOhKQIPUVblNlgABAgQIECBAgACBIQEN0pCObQQIECBAgAABAgQIdCWgQeqq3CZLgAAB\nAgQIECBAgMCQgAZpSMc2AgQIECBAgAABAgS6EtAgdVVukyVAgAABAgQIECBAYEhAgzSkYxsB\nAgQIECBAgAABAl0JaJC6KrfJEphJga05qt9NNs7k0TkoAgQIECBAoCsBDVJX5TZZAjMpcHiO\n6qRkn5k8OgdFgAABAgQIdCWgQeqq3CZLgAABAgQIECBAgMCQgAZpSMc2AgQIECBAgAABAgS6\nEtAgdVVukyVAgAABAgQIECBAYEhAgzSkYxsBAgQIECBAgAABAl0JaJC6KrfJEiBAgAABAgQI\nECAwJKBBGtKxjQABAgQIECBAgACBrgQ0SF2V22QJECBAgAABAgQIEBgS0CAN6dhGgAABAgQI\nECBAgEBXAhqkrsptsgQIECBAgAABAgQIDAlokIZ0bCNAgAABAgQIECBAoCsBDVJX5TZZAgQI\nECBAgAABAgSGBDRIQzq2ESBAgAABAgQIECDQlYAGqatymywBAgQIECBAgAABAkMCGqQhHdsI\nECBAgAABAgQIEOhKQIPUVblNlgABAgQIECBAgACBIQEN0pCObQQIECBAgAABAgQIdCWgQeqq\n3CZLgAABAgQIECBAgMCQgAZpSMc2AgQIECBAgAABAgS6EtAgdVVukyVAgAABAgQIECBAYEhA\ngzSkYxsBAgQIECBAgAABAl0JaJC6KrfJEiBAgAABAgQIECAwJKBBGtKxjQABAu0I/GKmUjEI\nECBAgACBAYG9B7bZRIAAAQLtCHxbO1MxEwIECBAgsHYCziCtna1nJkCAAAECBAgQIEBgzgQ0\nSHNWMIdLgAABAgQIECBAgMDaCWiQ1s7WMxMgQIAAAQIECBAgMGcCGqQ5K5jDJUCAAAECBAgQ\nIEBg7QQ0SGtn65kJECBAgAABAgQIEJgzAQ3SnBXM4RIgQIAAAQIECBAgsHYCGqS1s/XMBAgQ\nIECAAAECBAjMmYAGac4K5nAJECBAgAABAgQIEFg7AQ3S2tl6ZgIECBAgQIAAAQIE5kxAgzRn\nBXO4BAgQIECAAAECBAisnYAGae1sPTMBAgQIECBAgAABAnMmoEGas4I5XAIECBAgQIAAAQIE\n1k5Ag7R2tp6ZAAECBAgQIECAAIE5E+i9Qdor9Xp+8sw5q5vDJUCAAAECBAgQIEBgDQR6aJAO\nidu7kv8w4Xdgrr8zeSz5y+T+ZHtyVmIQIECAAAECBAgQINCpwN6Nz/ubM78bk63JtaO57pPL\njyUvTL6afCL5m+TFyduSOqP0r5LaZhAgQIAAAQIECBAg0JFA62eQ3pJaVnP0C8kJo7r+61xW\nc/SepLa9PDkl+fbkHckbkx9MDAIECBAgQIAAAQIEOhNovUE6NvW8Lfm/kydGtT0ulw8ldZbo\nr0b31cWXkzcndyavSAwCBAgQIECAAAECBDoTaL1BqrcQfiaZfLvc3+b2HclXksWj9rs7qbNJ\nBgECBAgQIECAAAECnQm03iD9Ser5yuQfTNT12lz/jmTLxH3jq8/KlRclN43vcEmAAAECBAgQ\nIECAQD8CrTdIl6SU+yV/mtRb62r8ZlKN039Knp2Mx/fmSjVPu5Irx3e6JECAAAECBAgQIECg\nH4HWV7H77yllLbrwG8l/TT6bVHP0+eR1ye3J/0zqDFMtB/5k8tNJ7WcQIECAAAECBAgQINCZ\nQOtnkKqc9f1Hz01+NTkoOS15fbIxqSW/vzM5IPmPybakVrczCBAgQIAAAQIECBDoUKD1M0jj\nkt6bK7XUd2WvpD5rVEt8P57sSGpVO4MAAQIECBAgQIAAgc4FemmQJstcq9jdNcrk/a4TIECA\nAAECBAgQINC5QI8NUr3N7sCkFm94NKmzR48lBgECBAgQIECAAAECnQv08BmkKvFRSa1od1/y\nQFJfHntzUm+vqybpluTiZKmlv3O3QYAAAQIECBAgQIBADwI9nEE6J4U8d1TMO3J5Q1JNUjVG\ndSbp4OTw5Izk5OTM5PLEIECAAAECBAgQIECgM4HWG6RTUs9qjq5Jzk5uTJYataJdfU/SBcll\nye3J9YlBgAABAgQIECBAgEBHAq2/xe6k1PLWpC6Xa46q3PX9R9cmJySPJLUUuEGAAAECBAgQ\nIECAQGcCrTdI21LPekvdE1PW9cHstz2pJcANAgQIECBAgAABAgQ6E2j9LXb3pJ5HJ/WFsF+Z\nora1wl01VbVgw+6Mcv2xZN8pn+SYKfezGwECBAgQIECAAAECayjQeoN0aezen1yRnJ98Kllq\n1GeQXpq8Ldk/uSrZnVFnoP59Uo3ZNKNe0yBAgAABAgQIECBAYJ0FWm+QajW6Q5LzkhOTu5Id\nyf3JzmRzUqvYHZEcluxKzkquS3ZnfCEPft4KnuAN2ffdK9jfrgQIECBAgAABAgQIrIFA6w1S\nLb5wYXJ1cn5yfLL47WyP5767k1rB7qLkzsQgQIAAAQIECBAgQKBDgdYbpHFJayW7U0c36qxR\nff/RpqS+OPbhxCBAgAABAgQIECBAgMCGXhqkyVLXW+sqBgECBAgQIECAAAECBBYItL7M94LJ\nTnHjTdnnpuSNU+xrFwIECBAgQIAAAQIEGhPQIC0s6KG5Wct816VBgAABAgQIECBAgEBnAj2+\nxW6oxO/MxiuTe4d2so0AAQIECBAgQIAAgTYFNEgL61qNkeZooYlbBAgQIECAAAECBLoR6LFB\nOijVrVXs9kseTR5KHksMAgQIECBAgAABAgQ6F+jlM0hHpc6XJLWs9wPJbcnNyY6kmqRbkouT\nLYlBgAABAgQIECBAgECnAj2cQTontT13VN87cnlDUk1SNUZ1Jung5PDkjOTk5Mzk8sQgQIAA\nAQIECBAgQKAzgdYbpFNSz2qOrknOTm5Mlhobc+dxyQXJZcntyfWJQYAAAQIECBAgQIBARwKt\nv8XupNTy1qQul2uOqtxPJtcmJySPJKclBgECBAgQIECAAAECnQm03iBtSz3rLXVPTFnXB7Pf\n9mTrlPvbjQABAgQIECBAgACBhgRab5DuSa2OTvaZsma1wl01VbWAg0GAAAECBAgQIECAQGcC\nrTdIl6aeRyZXJMcM1Hb8GaT6rNL+yVUD+9pEgAABAgQIECBAgECjAq0v0lCr0R2SnJecmNyV\n7EjuT3Ymm5Naxe6I5LBkV3JWcl1iECBAgAABAgQIECDQmUDrDVItvnBhcnVyfnJ8svhM0uO5\n7+6kVrC7KLkzMQgQIECAAAECBAgQ6FCg9QZpXNJaye7U0Y06a1Tff7QpqS+OfTgxCBAgQIAA\nAQIECBAgsKGXBmmy1PXWuopBgAABAgQIECBAgACBBQKtL9KwYLJuECBAgAABAgQIECBAYEhA\ngzSkYxsBAgQIECBAgAABAl0JaJC6KrfJEiBAgAABAgQIECAwJKBBGtKxjQABAgQIECBAgACB\nrgQ0SF2V22QJECBAgAABAgQIEBgS0CAN6dhGgAABAgQIECBAgEBXAhqkrsptsgQIECBAgAAB\nAgQIDAlokIZ0bCNAgAABAgQIECBAoCsBDVJX5TZZAgQIECBAgAABAgSGBDRIQzq2ESBAgAAB\nAgQIECDQlYAGqatymywBAgQIECBAgAABAkMCGqQhHdsIECBAgAABAgQIEOhKQIPUVblNlgAB\nAgQIECBAgACBIQEN0pCObQQIECBAgAABAgQIdCWgQeqq3CZLgAABAgQIECBAgMCQgAZpSMc2\nAgQIECBAgAABAgS6EtAgdVVukyVAgAABAgQIECBAYEhAgzSkYxsBAgQIECBAgAABAl0JaJC6\nKrfJEiBAgAABAgQIECAwJKBBGtKxjQABAgQIECBAgACBrgQ0SF2V22QJECBAgAABAgQIEBgS\n0CAN6dhGgAABAgQIECBAgEBXAhqkrsptsgQIECBAgAABAgQIDAlokIZ0bCNAgAABAgQIECBA\noCsBDVJX5TZZAgQIECBAgAABAgSGBDRIQzq2ESBAgAABAgQIECDQlYAGqatymywBAgQIECBA\ngAABAkMCGqQhHdsIEHg6AluezoM8hgABAgQIECAwCwIapFmogmMg0I7AXpnKXclR7UzJTAgQ\nIECAAIGeBDRIPVXbXAmsvUA1SPskm9b+pbwCAQIECBAgQGD1BTRIq2/qGQkQIECAAAECBAgQ\nmFMBDdKcFs5hEyBAgAABAgQIECCw+gIapNU39YwECBAgQIAAAQIECMypgAZpTgvnsAkQIECA\nAAECBAgQWH0BDdLqm3pGAgQIECBAgAABAgTmVECDNKeFc9gECBAgQIAAAQIECKy+gAZp9U09\nIwECBAgQIECAAAECcyqgQZrTwjlsAgQIECBAgAABAgRWX0CDtPqmnpEAAQIECBAgQIAAgTkV\n0CDNaeEcNgECBAgQIECAAAECqy+gQVp9U89IgACBVgQOy0T2bWUy5kGAAAECBKYR0CBNo2Qf\nAgQI9CnwnzPt0/uculkTIECAQK8CGqReK2/eBAgQeGqB/bKLM0hP7WQPAgQIEGhIQIPUUDFN\nhQABAgQIECBAgACB3RPQIO2en0cTIECAAAECBAgQINCQgAapoWKaCgECBAgQIECAAAECuyeg\nQdo9P48mQIAAAQIECBAgQKAhAQ1SQ8U0FQIECBAgQIAAAQIEdk9Ag7R7fh5NgAABAgQIECBA\ngEBDAhqkhoppKgQIECBAgAABAgQI7J6ABmn3/DyaAAECBAgQIECAAIGGBDRIDRXTVAgQIECA\nAAECBAgQ2D0BDdLu+Xk0AQIECBAgQIAAAQINCWiQGiqmqRAgQIAAAQIECBAgsHsCGqTd8/No\nAgQIECBAgAABAgQaEtAgNVRMUyFAgAABAgQIECBAYPcENEi75+fRBAgQIECAAAECBAg0JKBB\n2rBhS+p5ZMKioR9sUyFAgAABAgQIECDwdAQ0BRs2/F+B+3zyzKcD6DEECBAgQIAAAQIECLQj\nsHc7U1lyJtty7wFLbvn7O7eOrn5fLneOrt+Zyx2j6y4IECBAgAABAgQIEOhEoPUG6X2p4/dM\nWctrJvZ7a66fO3HbVQIECBAgQIAAAQIEOhBovUF6V2p4YbIp+b2k3kq3eLw8d7w4eUfyxdHG\n60aXLggQIECAAAECBAgQ6Eighwbpk6nn5ckrk48kv548mYzHr+ZKNUh1xuiB8Z0uCRAgQIAA\nAQIECBDoT6CHRRo+l7JWA/QbyUXJHybjzx3lqkGAAAECBAgQIECAAIGvC/TQINVMn0hqtbpX\nJN+ZfDZ5TWIQIECAAAECBAgQIEDg7wR6aZDGE/5YrtTKdh9OPpDUW+8OSgwCBAgQIECAAAEC\nBAhsaP0zSEuV+MHc+RPJ7yf1eaTNiUGAAAECBAgQIECAAIENvZ1Bmiz5/5sbtQT47ySfSL6S\nGAQIECBAgAABAgQIdCzQ4xmkekvdgcl+yaPJ6cljiUGAAAECBAgQIECAQOcCvZxBOip1viS5\nL6mlvG9Lbk52JNUk3ZJcnGxJDAIECBAgQIAAAQIEOhXo4QzSOaltfcdRjTuSG5JqkqoxqjNJ\nByeHJ2ckJydnJrV4g0GAAAECBAgQIECAQGcCrTdIp6Se1Rxdk5yd3JgsNTbmzuOSC5LLktuT\n6xODAAECBAgQIECAAIGOBFp/i91JqeWtSV0u1xxVuZ9Mrk1OSB5JTksMAgQIECBAgAABAgQ6\nE2i9QdqWetZb6uqLYqcZtQT49mTrNDvbhwABAgQIECBAgACBtgRab5DuSbmOTvaZsmy1wl01\nVbWAg0GAAAECBAgQIECAQGcCrX8G6dLU8/3JFcn5yaeSpUZ9BumlyduS/ZOrkt0ZR+TBn0im\nbczqNQ0CBAgQIECAAAECBNZZoPUGqVajOyQ5LzkxuSvZkdyf7Ew2J7WKXTU0hyW7krOS65Ld\nGfU6P5fsO+WT/JPs9/op97UbAQIECBAgQIAAAQJrJNB6g1SLL1yYXJ2cnxyfHJNMjsdz4+6k\nVrC7KLkz2d1RjdZvr+BJ6gySBmkFYHYlQIAAAQIECBAgsBYCrTdIY7Naye7U0Y06a1Tff7Qp\nqS+OfTgxCBAgQIAAAQIECBAgsKGXBmmy1PXWuopBgAABAgQIECBAgACBBQKtr2K3YLJuECBA\ngAABAgQIECBAYEhAg7RQ5025eVPyxoV3u0WAAAECBAgQIECAQA8CGqSFVT40N+t7kOrSIECA\nAAECBAgQIECgM4EeP4M0VOJ3ZuOVyb1DO9lGgAABAgQIECBAgECbAhqkhXWtxkhztNDELQIE\nCBAgQIAAAQLdCPTYIB2U6tYy3/sljyYPJY8lBgECBAgQIECAAAECnQv08hmko1LnS5L63qMH\nktuSm5MdSTVJtyQXJ1sSgwABAgQIECBAgACBTgV6OIN0Tmp77qi+d+TyhqSapGqM6kzSwcnh\nyRnJycmZyeWJQYAAAQIECBAgQIBAZwKtN0inpJ7VHF2TnJ3cmCw1NubO45ILksuS25PrE4MA\nAQIECBAgQIAAgY4EWn+L3Ump5a1JXS7XHFW5n0yuTU5IHklOSwwCBAgQIECAAAECBDoTaL1B\n2pZ61lvqnpiyrg9mv+3J1in3txsBAgQIECBAgAABAg0JtN4g3ZNaHZ3sM2XNaoW7aqpqAQeD\nAAECBAgQIECAAIHOBFpvkC5NPY9MrkiOGajt+DNI9Vml/ZOrBva1iQABAgQIECBAgACBRgVa\nX6ShVqM7JDkvOTG5K9mR3J/sTDYntYrdEclhya7krOS6xCBAgAABAgQIECBAoDOB1hukWnzh\nwuTq5Pzk+GTxmaTHc9/dSa1gd1FyZ2IQIECAAAECBAgQINChQOsN0riktZLdqaMbddaovv9o\nU1JfHPtwYhAgQIAAAQIECBAgQGBDLw3SZKnrrXUVgwABAgQIECBAgAABAgsEWl+kYcFk3SBA\ngAABAgQIECBAgMCQgAZpSMc2AgQIECBAgAABAgS6EtAgdVVukyVAgAABAgQIECBAYEhAgzSk\nYxsBAgQIECBAgAABAl0JaJC6KrfJEiBAgAABAgQIECAwJKBBGtKxjQABAgQIECBAgACBrgQ0\nSF2V22QJECBAgAABAgQIEBgS0CAN6dhGgAABAgQIECBAgEBXAhqkrsptsgQIECBAgAABAgQI\nDAlokIZ0bCNAgAABAgQIECBAoCsBDVJX5TZZAgQIECBAgAABAgSGBDRIQzq2ESBAgAABAgQI\nECDQlYAGqatymywBAgQIECBAgAABAkMCGqQhHdsIECBAgAABAgQIEOhKQIPUVblNlgABAgQI\nECBAgACBIQEN0pCObQQIECBAgAABAgQIdCWgQeqq3CZLgAABAgQIECBAgMCQgAZpSMc2AgQI\nECBAgAABAgS6EtAgdVVukyVAgAABAgQIECBAYEhAgzSkYxsBAgQIECBAgAABAl0JaJC6KrfJ\nEiBAgAABAgQIECAwJKBBGtKxjQCBj4bgNRgIECBAgAABAr0IaJB6qbR5Enh6AofkYd/89B7q\nUQQIECBAgACB+RPQIM1fzRwxAQIEZlngjBzcAbN8gI6NAAECBAgMCWiQhnRsI0CAAIGVCOyb\nnS9Otq3kQfYlQIAAAQKzJKBBmqVqOBYCBAgQIECAAAECBNZVQIO0rvxenAABAgQIECBAgACB\nWRLQIM1SNRwLAQIECBAgQIAAAQLrKqBBWld+L06AAAECBAgQIECAwCwJaJBmqRqOhQABAgQI\nECBAgACBdRXQIK0rvxcnQIAAAQIECBAgQGCWBDRIs1QNx0KAAAECBAgQIECAwLoKaJDWld+L\nEyBAgAABAgQIECAwSwIapFmqhmMhQIAAAQIECBAgQGBdBTRI68rvxQkQIECAAAECBAgQmCUB\nDdIsVcOxECBAgAABAgQIECCwrgIapHXl9+IECBAgQIAAAQIECMySgAZplqrhWAgQIECAAAEC\nBAgQWFcBDdK68ntxAgQIECBAgAABAgRmSUCDNEvVcCwECBAgQIAAAQIECKyrgAZpXfm9OAEC\nBAgQIECAAAECsySgQZqlajgWAgQIECBAgAABAgTWVUCDtK78XpwAAQIECBAgQIAAgVkS0CDN\nUjUcCwECBAgQIECAAAEC6yqgQVpXfi9OgAABAgQIECBAgMAsCWiQZqkajoUAAQIECBAgQIAA\ngXUV0CCtK78XJ0CAAAECBAgQIEBglgQ0SLNUDcdCgAABAgQIECBAgMC6CmiQ1pXfixMgQIAA\nAQIECBAgMEsCGqRZqoZjIUCAAAECBAgQIEBgXQU0SOvK78UJECBAgAABAgQIEJglAQ3SLFXD\nsRAgQIAAAQIECBAgsK4CGqR15ffiBAgQIECAAAECBAjMkoAGaZaq4VgIECBAgAABAgQIEFhX\nAQ3SuvJ7cQIECBAgQIAAAQIEZklAgzRL1XAsBAgQIECAAAECBAisq0BvDdJTzXevVOOgZNO6\nVsWLEyBAgAABAgQIECCwLgJP1TCsy0Gt8osemuf7YPJAsjP5ePL9yVLjBbmz9vv5pTa6jwAB\nAgQIECBAgACBtgVab5CekfJ9OvnxpM4O7UhellybnJ8YBAgQIECAAAECBAgQ+DuB1hukn81M\nn5Ocm3xLcmTyfcmfJW9J3p4YBAgQIECAAAECBAgQ+JpA6w3SSzLL+5Lzkke+NuMNG/4kl8cn\nn0zenFQTZRAgQIAAAQIECBAgQGBD6w3S1tS4GqFdi2r9cG7/82R78qtJvQXPIECAAAECBAgQ\nIECgc4HWG6QvpL6vSJZala4WbPihpD6XdGmy3MIN2WQQIECAAAECBAgQINCDQOsN0kdTxAOT\nX0mevURB78p9r0zq7XcfSl6dGAQIECBAgAABAgQIdCrQeoP066nrnyf1WaM7k9cki8df5I4T\nkq8m9VmlGhu/fuG/BAgQIECAAAECBAj0JNB6g/SlFPOY5B3JHcmXk6XGn+bOFyXXLLXRfQQI\nECBAgAABAgQI9CGwdwfTfDRz/JlRhhrCW7LPq5JaBrwaK4MAAQIECBAgQIAAgc4EemiQJkta\nb6M7KKnPJe2XVPP0UPJYMh71xbIGAQIECBAgQIAAAQIdCgydUWmJ46hM5pKkvhPpgeS25Oak\nVrCrJqnOHl2cbEkMAgQIECBAgAABAgQ6FejhDNI5qe25o/rW55BuSKpJqsaoziQdnByenJGc\nnJyZXJ4YBAgQIECAAAECBAh0JtB6g3RK6lnNUS2+cHZyY7LUqFXrjksuSC5Lbk+uTwwCBAgQ\nIECAAAECBDoSaP0tdiellrcmdblcc1TlfjK5Nqnlvus7kU5LDAIECBAgQIAAAQIEOhNovUHa\nlnrWW+qemLKuD2a/7cnWKfe3GwECBAgQIECAAAECDQm03iDdk1odnewzZc1qhbtqqmoBB4MA\nAQIECBAgQIAAgc4EWv8M0qWp5/uTK5Lzk08lS436DNJLk7cl+ydXJbsz9suDT0+mbcyO3Z0X\n81gCBAgQIECAAAECBFZHoPUGqVajOyQ5LzkxuSvZkdyf7Ew2J7WK3RHJYcmu5KzkumR3Ri0X\nfkay75RP8swp97MbAQIECBAgQIAAAQJrKNB6g1SLL1yYXJ3UGaTjk2OSyfF4btyd1Ap2FyV3\nJrs7qgmrt/ZNO96QHd897c72I0CAAAECBAgQIEBgbQRab5DGarWS3amjG3XWqL7/aFNSXxz7\ncGIQIECAAAECBAgQIEBgQy8N0mSp6611FYMAAQIECBAgQIAAAQILBFpfxW7BZN0gQIAAAQIE\nCBAgQIDAkIAGaaHOm3LzpuSNC+92iwABAgQIECBAgACBHgQ0SAurfGhu1vcg1aVBgAABAgQI\nECBAgEBnAhqkhQV/Z25+T/KuhXe7RYAAAQJrJFDfG3fyGj23pyVAgAABAisW6HGRhiGke7Ox\nYhAgQIDAnhE4Ki/zO0l9b9xX9sxLehUCBAgQILC8QI8N0kHhqGW+618tH00eSh5LDAIECBDY\n8wIbRy85vtzzR+AVCRAgQIDAhEAvb7Grf6G8JKnvPXoguS25OdmRVJN0S3JxsiUxCBAgQIAA\nAQIECBDoVKCHM0jnpLbnjup7Ry5vSKpJqsaoziQdnByenJHU++DPTC5PDAIECBAgQIAAAQIE\nOhNovUE6JfWs5uia5OzkxmSpUW/tOC65ILksuT25PjEItCTwU5nMk8n7WpqUuRAgQIAAAQIE\nVlOg9bfYnRSsW5O6XK45Ks/6pfHa5ITkkeS0xCDQmsDLM6EfaG1S5kOAAAECBAgQWE2B1huk\nbcGqt9Q9MSXag9lve7J1yv3tRoAAAQIECBAgQIBAQwKtN0j3pFZHJ/tMWbNa4a6aqlrAwSBA\ngAAB1ySMiwAAHE1JREFUAgQIECBAoDOB1hukS1PPI5MrkmMGajv+DFJ9Vmn/5KqBfW0iQIAA\nAQIECBAgQKBRgdYXaajV6A5JzktOTO5KdiT3JzuTzUmtYndEcliyKzkruS4xCBAgQIAAAQIE\nCBDoTKD1BqkWX7gwuTo5Pzk+WXwm6fHcd3dSK9hdlNyZGAQIECBAgAABAgQIdCjQeoM0Lmmt\nZHfq6EadNarvP9qU1BfHPpwYBAgQIECAAAECBAgQ2NBLgzRZ6nprXcUgQIAAAQIECBAgQIDA\nAoHWF2lYMFk3CBAgQIAAAQIECBAgMCSgQRrSsY0AAQIECBAgQIAAga4ENEhdldtkCRAgQIAA\nAQIECBAYEtAgDenYRoAAAQIECBAgQIBAVwIapK7KbbIECBAgQIAAAQIECAwJaJCGdGwjQIAA\nAQIECBAgQKArAQ1SV+U2WQIECBAgQIAAAQIEhgQ0SEM6thEgQIAAAQIECBAg0JWABqmrcpss\nAQIECBAgQIAAAQJDAhqkIR3bCBAgQIAAAQIECBDoSkCD1FW5TZYAAQIECBAgQIAAgSEBDdKQ\njm0ECBAgQIAAAQIECHQloEHqqtwmS4AAAQIECBAgQIDAkIAGaUjHNgIECBAgQIAAAQIEuhLQ\nIHVVbpMlQIAAAQIECBAgQGBIQIM0pGMbAQIECBAgQIAAAQJdCWiQuiq3yRIgQIAAAQIECBAg\nMCSgQRrSsY0AAQIECBAgQIAAga4ENEhdldtkCRAgQIAAAQIECBAYEtAgDenYRoAAAQIECBAg\nQIBAVwIapK7KbbIECBAgQIAAAQIECAwJaJCGdGwjQIAAAQIECBAgQKArAQ1SV+U2WQIECBAg\nQIAAAQIEhgQ0SEM6thEgQIAAAQIECBAg0JWABqmrcpssAQIECBAgQIAAAQJDAhqkIR3bCBAg\nQIAAAQIECBDoSkCD1FW5TZYAAQJNCPyLzOJDTczEJAgQIEBg5gQ0SDNXEgdEgAABAk8h8Kxs\nf85T7GMzAQIECBB4WgIapKfF5kEECBAgQIAAAQIECLQooEFqsarmRIAAAQIECBAgQIDA0xLQ\nID0tNg8iQIAAAQIECBAgQKBFAQ1Si1U1JwIECBAgQIAAAQIEnpaABulpsXkQAQIECBAgQIAA\nAQItCmiQWqyqOfUgsDWTPKGHiZojAQIECBAgQGBPCmiQ9qS21yKwegI/mqd62+o9nWciQIAA\nAQIECBAoAQ2SnwMC8yuwcX4P3ZETIECAAAECBGZTQIM0m3VxVAQIECBAgAABAgQIrIOABmkd\n0L0kAQIECBAgQIAAAQKzKaBBms26OCoCBAgQIECAAAECBNZBQIO0DuhekgABAgQIECBAgACB\n2RTQIM1mXRwVAQIECBAgQIAAAQLrIKBBWgd0L0mAAAECBAgQIECAwGwKaJBmsy6OigABAgQI\nECBAgACBdRDQIK0DupckQIAAAQIECBAgQGA2BTRIs1kXR0WAAAECBAgQIECAwDoIaJDWAd1L\nEiBAgAABAgQIECAwmwIapNmsi6MiQIAAAQIECBAgQGAdBDRI64DuJQkQIECAAAECBAgQmE0B\nDdJs1sVRESBAgAABAgQIECCwDgIapHVA95IECBAgsMcF6u87f+ftcXYvSIAAgfkT2Hv+DtkR\nEyBAgACBFQu8ffSI/2PFj/QAAgQIEOhKQIPUVblNlgABAt0KPLPbmZs4AQIECKxIwNsNVsRl\nZwIECBAgQIAAAQIEWhbQILVcXXMjQIAAAQIECBAgQGBFAhqkFXHZmQABAgQIECBAgACBlgU0\nSC1X19wIECBAgAABAgQIEFiRgAZpRVx2JkCAAAECBAgQIECgZYEeG6SDUtDnJv8w2ZockBgE\n1lPgyLz4XyU9/nlcT3evTYAAAQIECBD4/wn08gvZUZn5Jcl9yQPJbcnNyY7k0eSW5OJkS2IQ\n2NMC1bQfmlh2f0/Lez0CBAgQIECAwCKBHn4hOydzPnc07ztyeUNSTVI1RgcmByeHJ2ckJydn\nJpcnBgECBAgQIECAAAECnQm03iCdknpWc3RNcnZyY7LU2Jg7j0suSC5Lbk+uTwwCBAgQIECA\nAAECBDoSaP0tdiellrcmdblcc1TlfjK5NjkheSQ5LTEIECBAgAABAgQIEOhMoPUGaVvqWW+p\ne2LKuj6Y/bYntXiDQYAAAQIECBAgQIBAZwKtN0j3pJ5HJ/tMWdf6sHw1VbWAg0GAAAECfQu8\nJ9OvdxYYBAgQINCRQOsN0qWpZS2hfEVyzEBdx59Bqs8q7Z9cNbCvTQQIECDQh8A/zjS/o4+p\nmiUBAgQIjAVaX6Th8kz0kOS85MTkrmRHcn+yM9mc1Cp2RySHJbuSs5LrEoMAAQIECBAgQIAA\ngc4EWm+QavGFC5Ork/OT45PFZ5Iez313J7WC3UXJnYlBgAABAgQIECBAgECHAq03SOOS1kp2\np45u1Fmj+v6jTUl9cezDiUGAAAECBAgQIECAAIENrX8GaakS75U7KzX3ZyQHJAaB1RKoz7PV\nZ96evVpP6HkIECBAgAABAgT2nEAvDdJRIb0kqTNGDyS3JbVSXX0e6dHkluTiZEtiENgdgVox\n8ceS+lybQYBAfwL1uVeDAAECBOZYoIcG6ZzU58bkdckXk/pepP+SfDCpVev+W1Ir152RfD55\nbWIQIECAAIGVCtS7E+of3uof5QwCBAgQmFOB1j+DdErqcm5SjdDZSTVKS43xMt+1UMNlye3J\n9YlBgAABAgSmFagGqc4i12dcDQIECBCYU4HWzyCdlLrUAg11uVxzVKWr1e6uTU5IHklOSwwC\nJVDLwNcvPQYBAgTWSqCaKoMAAQIEZkSg9QZpW5zrLXVPTOn9YPbbnmydcn+7tS9QZx//1/an\naYYECKyTwD/K69Z38/mHmHUqgJclQIDAYoHWG6R7MuGjk2n/de6g7FtNVS3gYMy+wH4rPMSX\nZ//KSsY3Zmdvl1mJmH0JEFiJwObs/E3JShqkl2T/81byItm3zoZXVjJa/x1hJRb2JUCgI4HW\n/+d3aWp5ZFLLLi/+gtjJMm/MjeOSOltQCzZclezOeF4eXGejHpsyvzZ6sa+OLtfqohasqKxk\nXJmdf3glD8i+f5mUwbTj27Lj7dPuPNrvX+bywyt8zP+S/SsrGbuy89+u4AFVw3rL5koeU/vW\nY1ZS/3pMHdtKRu3/dB6zkrmYv/r7+V/7P//fkz/Lr17JH/7se/4oK3nYR7LzT6zkAdn39qT+\nnz7tqL8r6u+MlYz6O6n+blrJqL/7KisZT+fvv/+RF1jJ33+1bz1mJcP890z9L0tR/ulKCpN9\nP5N8ywoeU/vWY1Yy6pjq2FYyfi47VwwCXxOoxufNSTUq9Zd2rS70x0mtYveB0eUNubw7qe1f\nSX4m2d3xDXmCVyY/NGXOyn71+vsmazmenSevrGRUg1nfF7WS8cLsXAbTjtq3HrOSUcdUx7aS\nYf7q7+d/JX9ivv5nzJ//lZn5/5////v7b/o/M7P89//zM436R/OVjO/OzvW757Sj9q3HrGTU\nMdWxrWQcmp0raznqd9j6XfbYtXwRz726As/L01VDdFdSxZtMNU/1r1dvS56TrMeoH6Y90SCt\nx9y8JgECBAgQIECAQNsCTTVIe7ddq7+b3a25duro1uZcHphsSu5LHk4MAgQIECBAgAABAgQI\nbOilQZos9c7cqBgECBAgQIAAAQIECBBYILCS98kueKAbBAgQIECAAAECBAgQaE1Ag7Swom/K\nzZuSNy682y0CBAgQIECAAAECBHoQ0CAtrHKt8LEtWeuVPha+qlsECBAgQIAAAQIECMyEQI+f\nQRqCf2c21vce3Du0k20ECBAgQIAAAQIECLQpoEFaWNdqjDRHC03cIkCAAAECBAgQINCNQI8N\n0kGpbi3zvV/yaPJQUt+FZBAgQIAAAQIECBAg0LlAL59BOip1viSp7z16ILktuTnZkVSTdEty\ncbIlMQgQIECAAAECBAgQINCswDmZ2ZOjfCGX1ye/n/zH5A+STyX3JLXP3ySvTfb0ODYvWK9f\n30JsECBAgAABAgQIEJgngfodtn6Xrd9pjRkXOCXHV8WqRuiFA8e6MduOTz6d1P4vSfbk0CDt\nSW2vRYAAAQIECBAgsJoCGqTV1Fzj57osz19vn6vPG00z6vNJO5N3TbPzKu6jQVpFTE9FgAAB\nAgQIECCwRwWaapBa/wxSfafRDckTU/6IPJj9tidbp9zfbgQIECBAgAABAgQINCTQeoNUny06\nOtlnyprVGaRqqmoBB4MAAQIECBAgQIAAgc4EWm+QLk09j0yuSI4ZqG19Bum45Jpk/+SqxCBA\ngAABAgQIECBAoDOB1r8H6fLU85DkvOTE5K6klva+P6nPGm1ODk6OSA5LdiVnJdclBgECBAgQ\nIECAAAECBJoUeF5m9YGkGqRapW4y9SWxf5m8LXlOsh7DIg3roe41CRAgQIAAAQIEVkOgqUUa\nWj+DNC74rbly6uhGnTU6MNmU1BfHPpwYBAgQIECAAAECBAgQ2NBLgzRZ6nprXcUgQIAAAQIE\nCBAgQIDAAoEeG6QFADN2o05PrvWYdkW/tT4Oz0+AAAECBAgQILBnBL6yxi+zJ36HXeMp/P3T\na5D+3mI9r41/aB9Zz4Pw2gQIECBAgAABAgR2Q+DLu/HYmXloLW9tzIbAi3IYa3125615jWck\n702M/gROH035vf1N3YwjcPpI4b2jSxd9CZw+mu57+5q22Y4ETh9dvnd06aIvgdMz3UeTtyZr\nOao5+pO1fIE99dzOIO0p6ad+nf/+1Lvs9h71xbk13vP1C//tTOD7R/NV/84KP5qu+vdZ9/Gs\n1X8s0eel+vdZ9/Gsx/W/YXyHy2GB1r8odnj2thIgQIAAAQIECBAgQGBCQIM0geEqAQIECBAg\nQIAAAQJ9C2iQ+q6/2RMgQIAAAQIECBAgMCGgQZrAcJUAAQIECBAgQIAAgb4FNEh919/sCRAg\nQIAAAQIECBCYENAgTWC4SoAAAQIECBAgQIBA3wIapL7rb/YECBAgQIAAAQIECEwIaJAmMFwl\nQIAAAQIECBAgQKBvAQ1S3/U3ewIECBAgQIAAAQIEJgT2nrjuavsCX25/imY4IKD+AzgdbFL/\nDoo8MEX1H8DpYJP6d1DkgSmq/wCOTQQODkHF6FNA/fus+3jW6j+W6PNS/fus+3jW6j+W6PNS\n/fusu1kTIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECA\nAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECA\nAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECA\nAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECA\nAAECBAgQmAeBvebhIB3jbgtUnY9NXpzsSh5IjLYFnpvpvTr57MA0/VwM4MzppufluOvP+neN\njv/+gXmo/wDOnG76hznu45MDk/uSrybLDfVfTqaN+38g0zgsuXOZ6aj/MjBzevfhOe5nLpPH\ncv/i/xeo/5wW2mGvnsC356k+nzw5kc/l+nMSo02BzZnWnyePDEzPz8UAzhxuelaO+apk8s95\nXf9YUk3T4qH+i0Xm+/bBOfzfSybr/3hun7HMtNR/GZhG7v6hzKN+Fv5wmfmo/zIwc3r3ITnu\nyT/7i69/x6J5qf8iEDf7E9iYKV+b7Ez+ZfL85A1J/cX5heSAxGhL4KBM55qk/ge5XIPk56Kt\nmn9DpvOJpGr+weRVycuS30zqXw3/LNmUjIf6jyXaufyjTKXq/+7kxcmPJJ9M6r7XJZND/Sc1\n2ru+JVP6q6Rqv1SDpP7t1fyEUb0/nMsLl0j9TIyH+o8lXHYt8KbMvv4n+dOLFKpJWur+Rbu5\nOWcCP5rjvTup2j6RLNcg+bkITkPjZZlL1fz6Jeb0X0bbTpnYpv4TGA1cfdGoxp9eNJdvze1q\nkK9bdL/6LwJp7ObVmU+9vbL+n7BUg6T+jRU80/n5Ub3r74KnGur/VEK2dyHwqczyS0m9L3Vy\n1Fuwvpgs/gt1ch/X50vgVTnc+gvxb5IfTm5MlmuQ/FwEp6HxU5nLbcnrl5jTa3Jf/Vz80sQ2\n9Z/AaOBqfd7s3yWvWGIut+S+Bxbdr/6LQBq6eUbmUn/eTxpd1rsJFg/1Xywy/7c/kCnUP4Z8\n0xRTUf8pkOzStsA+mV6dRdi+zDQ/k/u/nNR+xvwLvDJT+OWkPotQY7kGyc/F1316+e9bMtH6\nhaneYltD/b/u0MN/j8ok/zb57YnJqv8ERmNXvz3zeTT59aTeUlt/7hc3SOoflAZHfc785uQf\nJKcmb07+afKNyeRQ/0kN17sVGH9o7+PLCHw099f/QJ+9zHZ3z7fAcg2Sn4v5rutKjv6bs/Nf\nJw8nzxo9UP1HEI1e1OcLTk/qX5Trs6f1D2TPTcZD/ccSbV3unen8t6R+Sd4/Wa5BUv/gNDaq\n3vUPIfW5s/ozX7/XjfM/cv3FyXio/1hiisv6Q2W0KbB5NK16y9VSY/y2iwOW2ui+ZgX8XDRb\n2gUTqz/Xv59Uk1Rvvau/PGuo/9cdWv3vYZnYf5iY3O/l+l0Tt9V/AqOhq7+UudQZw5ckjyfV\nIC011H8plfm+b1sOvxbqOSj5t0n9f7/+oaTeNfBzyX9OvjN5IFH/IEw7NEjTSs3ffl8aHXL9\nwVlq7DW6s/7lwehHwM9F+7Wupqh+MT4meUfym8l4qP9Yos3LBzOtw5NDk2qMfz750aR+Fh5N\n1D8IjY1qin4hqbdYf/op5qb+TwE0h5tvzTHX2+ruTK6bOP635Hr9nldN0v+ZVPOk/kEwCFTz\nWx/a+/gyFJ/I/XUatt6zarQncGOmtNQiDX4u2qv15Iy+LTf+Mqk/2+dNbhhdV/8lUBq+67cz\nt/pZOHk0R/Vvq9j1ofz6Bbn+f19nB+rtVpWDk6p7Lftct/dNaqj/1x16+e8/ykTr56BWM62h\n/l938F8CX3tbzU3LOGzP/Y8l9S8MRnsCyzVINdN6u5Wfi/Zq/t2ZUi3z/pXkDQPTU/8BnMY2\n/bPMp35B+q2Jean/BMacXz0+x1/1farUZ9LGQ/3HEu1fjj9z9MmJqar/BIar/Qp8PFOvX5bq\nLTeTY0tu1P3XTt7pelMCQw2Sn4umSv21ybwo/70/2Zmc8LV7lv+P+i9vM49bfjYHXW+t+4El\nDr5+FuqX53dMbFP/CYw5v/r8UW2rvpP5jdyuun9hdP/puRwP9R9LtHH55kzjL5J6m93iMW6g\nL5nYoP4TGK72K/BjmXr9T7Legzo5/k1u1P3/YvJO15sSGGqQ/Fw0VeqvLeV6W6ZU7y8/doqp\nqf8USHO0y4k51vr/+e8uccz11pra9iMT29R/AqPRq5syr6r7NUvMT/2XQJnju+rts1XrP0tq\ncYbxqOtV/9pWjdJ4qP9YwmXXArVAw58ntRDDLyevSM4b3b4yl0a7AkMNkp+Ltur+7zKd+kuw\nViu7apm8PvePh/qPJdq4rF+EPpTUz8AfJa9N6otCx78c/adcnxzqP6nR5vWhBkn926p5fUzi\nY0n9+f948pNJLcxS/y+o+96TTA71n9RwvWuBenvdHyRfTeoPS+UPk2clRrsCQw1SzdrPRTu1\n/0ymMv6zvdzlRYumq/6LQOb85uYc/zuSXcn4Z6A+Y1orV+2TLB7qv1ikrdtDDVLNVP3bqvdB\nmc47k8k///UVL/X226WG+i+l4r5uBWq1m6MTjVG3PwJLTtzPxZIs3dyp/m2V+hszne9NviOZ\nZgEe9W+r/iudjfqvVGy296/G+AXJc6c8TPWfEspuBAgQIECAAAECBAgQIECAAAECBAgQIECA\nAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECA\nAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECA\nAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECA\nAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECA\nAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECA\nAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECA\nAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECA\nAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECA\nAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECA\nAAECBAgQIECAAAECBAgQ6E1gr94mbL4ECBAg0IXAd2aWr06eldyyaMbH5fbLk68kf71om5sE\nCBAgQIAAAQIECBBoTuCgzOjOZFfyfROz++5c/2Jye/LMxCBAgAABAgQIECBAgEAXAj+QWX41\n+Wyyb7Jfsj2pM0fHJgYBAgQIECBAgAABAgS6EnhbZvtk8ovJ20fXfyGXBgECBAgQIECAAAEC\nBLoTqLNGNyVPJHU26cPJNyQGAQIECBAgQIAAAQIEuhR4WWZdZ5EqL+hSwKQJECBAgAABAgQI\nECAwErgql+MGqa4bBAgQIECAAAECBAgQ6FLg9Zl1NUfvSX5zdL3uMwgQIECAAAECBAgQINCV\nwPMz20eTO5LNyYHJjqTuq20GAQIECBAgQIAAAQIEuhDYO7P846TOHp0wMeP68ti6r7bVPgYB\nAgQIECBAgAABAgSaF3hrZjh+a93iyb5vtK32MQgQIECAAAECBAgQIECAAAECBAgQIECAAAEC\nBAgQIECAAAECBAgQIECAAIH/rx06EAAAAAAQ5G89yIWQAQMGDBgwYMCAAQMGDBgwYMCAAQMG\nDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMG\nDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMG\nDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMG\nDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMG\nDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMG\nDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMG\nDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMGDBgwYMCAAQMG\nDBgwYMCAAQMGDBgwYMCAAQOPgQAfhdlrIYaYEQAAAABJRU5ErkJggg==",
      "text/plain": [
       "plot without title"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "x <- 0:50\n",
    "plot(x, dbinom(x, size=50, prob=.33), type=\"h\",ylab=\" \")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* Compute the probability of a normally distributed variable with mean 132 and standard deviation 13 being smaller than 160\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "0.984373880571531"
      ],
      "text/latex": [
       "0.984373880571531"
      ],
      "text/markdown": [
       "0.984373880571531"
      ],
      "text/plain": [
       "[1] 0.9843739"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "pnorm(160, mean=132,sd=13)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "### Quantiles\n",
    "\n",
    "* Definition: the quantile is the inverse of the distribution function. The p-quantile is by definition that value having the property that there is a probability $p$ to obtain a value lower or equal to it. For example, the median is the 50% quantile.\n",
    "* In order to compute the quantiles of an empirical distribution for which samples are given in a vector, we use the function `quantile` which takes as argument this vector and the probability for which the quantile is needed.\n",
    "* The quantiles are used for computing the confidence intervals. Let $n$ observations be drawn from a normal distribution with the same mean $\\mu$ and the same standard-deviation $\\sigma$. It is well known that the observed mean $\\overline{x}$ follows a normal distribution with mean $\\mu$ and standard deviation $\\sigma/\\sqrt{n}$. A confidance interval of 95% for $\\mu$ can be obtained by\n",
    "\n",
    "\\begin{equation}\n",
    "\\overline{x} + \\sigma/\\sqrt{n} \\times N_{0.025} \\leq \\mu \\leq  \\overline{x} + \\sigma/\\sqrt{n} \\times N_{0.975}\n",
    "\\end{equation}\n",
    "     \n",
    "where $N_{0.025}$ is the 2.5% quantile of the normal distribution.\n",
    "\n",
    "### Exercises\n",
    "\n",
    "Let $\\sigma=12$ and $n=5$ with a mean of $\\overline{x}=83$, compute the confidence interval using the normal distribution\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "72.481729513081"
      ],
      "text/latex": [
       "72.481729513081"
      ],
      "text/markdown": [
       "72.481729513081"
      ],
      "text/plain": [
       "[1] 72.48173"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "93.518270486919"
      ],
      "text/latex": [
       "93.518270486919"
      ],
      "text/markdown": [
       "93.518270486919"
      ],
      "text/plain": [
       "[1] 93.51827"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "xbar <- 83\n",
    "sigma <- 12\n",
    "n<-5\n",
    "sem <- sigma/sqrt(n)\n",
    "xbar + sem * qnorm(0.025)\n",
    "xbar + sem * qnorm(0.975)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Generation of pseudo-random numbers\n",
    "\n",
    "* The first argument of a function generating random numbers is always the number of samples that should be generated. The next arguments specify the parameters fo the chosen distribution (mean, etc.) Some examples are provided to present \"typical\" choices of these parameters.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<ol class=list-inline>\n",
       "\t<li>-1.12931000667995</li>\n",
       "\t<li>-0.777516108086836</li>\n",
       "\t<li>0.309303634605778</li>\n",
       "\t<li>-3.00878868375269</li>\n",
       "\t<li>1.60265266954411</li>\n",
       "\t<li>-0.248965406830323</li>\n",
       "\t<li>0.119450444570063</li>\n",
       "\t<li>-0.990491524594122</li>\n",
       "\t<li>-0.359714161949689</li>\n",
       "\t<li>-0.865340634922727</li>\n",
       "</ol>\n"
      ],
      "text/latex": [
       "\\begin{enumerate*}\n",
       "\\item -1.12931000667995\n",
       "\\item -0.777516108086836\n",
       "\\item 0.309303634605778\n",
       "\\item -3.00878868375269\n",
       "\\item 1.60265266954411\n",
       "\\item -0.248965406830323\n",
       "\\item 0.119450444570063\n",
       "\\item -0.990491524594122\n",
       "\\item -0.359714161949689\n",
       "\\item -0.865340634922727\n",
       "\\end{enumerate*}\n"
      ],
      "text/markdown": [
       "1. -1.12931000667995\n",
       "2. -0.777516108086836\n",
       "3. 0.309303634605778\n",
       "4. -3.00878868375269\n",
       "5. 1.60265266954411\n",
       "6. -0.248965406830323\n",
       "7. 0.119450444570063\n",
       "8. -0.990491524594122\n",
       "9. -0.359714161949689\n",
       "10. -0.865340634922727\n",
       "\n",
       "\n"
      ],
      "text/plain": [
       " [1] -1.1293100 -0.7775161  0.3093036 -3.0087887  1.6026527 -0.2489654\n",
       " [7]  0.1194504 -0.9904915 -0.3597142 -0.8653406"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "rnorm(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<ol class=list-inline>\n",
       "\t<li>-1.78507154046656</li>\n",
       "\t<li>10.6639943139379</li>\n",
       "\t<li>5.68026523532456</li>\n",
       "\t<li>9.05477884228865</li>\n",
       "\t<li>2.60257442387026</li>\n",
       "\t<li>7.20959621079827</li>\n",
       "\t<li>-0.842708430634493</li>\n",
       "\t<li>8.51867096693635</li>\n",
       "\t<li>-0.854008188029752</li>\n",
       "\t<li>5.75082842396513</li>\n",
       "</ol>\n"
      ],
      "text/latex": [
       "\\begin{enumerate*}\n",
       "\\item -1.78507154046656\n",
       "\\item 10.6639943139379\n",
       "\\item 5.68026523532456\n",
       "\\item 9.05477884228865\n",
       "\\item 2.60257442387026\n",
       "\\item 7.20959621079827\n",
       "\\item -0.842708430634493\n",
       "\\item 8.51867096693635\n",
       "\\item -0.854008188029752\n",
       "\\item 5.75082842396513\n",
       "\\end{enumerate*}\n"
      ],
      "text/markdown": [
       "1. -1.78507154046656\n",
       "2. 10.6639943139379\n",
       "3. 5.68026523532456\n",
       "4. 9.05477884228865\n",
       "5. 2.60257442387026\n",
       "6. 7.20959621079827\n",
       "7. -0.842708430634493\n",
       "8. 8.51867096693635\n",
       "9. -0.854008188029752\n",
       "10. 5.75082842396513\n",
       "\n",
       "\n"
      ],
      "text/plain": [
       " [1] -1.7850715 10.6639943  5.6802652  9.0547788  2.6025744  7.2095962\n",
       " [7] -0.8427084  8.5186710 -0.8540082  5.7508284"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "rnorm(10, mean=7, sd=5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<ol class=list-inline>\n",
       "\t<li>9</li>\n",
       "\t<li>9</li>\n",
       "\t<li>8</li>\n",
       "\t<li>10</li>\n",
       "\t<li>14</li>\n",
       "\t<li>9</li>\n",
       "\t<li>8</li>\n",
       "\t<li>11</li>\n",
       "\t<li>14</li>\n",
       "\t<li>10</li>\n",
       "</ol>\n"
      ],
      "text/latex": [
       "\\begin{enumerate*}\n",
       "\\item 9\n",
       "\\item 9\n",
       "\\item 8\n",
       "\\item 10\n",
       "\\item 14\n",
       "\\item 9\n",
       "\\item 8\n",
       "\\item 11\n",
       "\\item 14\n",
       "\\item 10\n",
       "\\end{enumerate*}\n"
      ],
      "text/markdown": [
       "1. 9\n",
       "2. 9\n",
       "3. 8\n",
       "4. 10\n",
       "5. 14\n",
       "6. 9\n",
       "7. 8\n",
       "8. 11\n",
       "9. 14\n",
       "10. 10\n",
       "\n",
       "\n"
      ],
      "text/plain": [
       " [1]  9  9  8 10 14  9  8 11 14 10"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "rbinom(10, size=20, prob=.5)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* The algorithms for generating pseudo-random numbers are initialised by a seed which we can define in R using the function `set.seed(init.value)`, where `init.value` is an integer. This allows the user to reproduce computations with the same pseudo-random numbers."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Table of distributions in R\n",
    "\n",
    "* The following table provides an overview over the included distributions and their parameters.\n",
    "\n",
    "| Distribution | R name | additional arguments |\n",
    "| ------------- | ------------- | ------------- |\n",
    "|beta               | `beta`     |  `shape1, shape2, ncp`  |\n",
    "|binomial           | `binom`    |  `size, prob`  |\n",
    "|Cauchy             | `cauchy`   |  `location, scale`  |\n",
    "|chi-squared        | `chisq`    |  `df, ncp`  |\n",
    "|exponential        | `exp`      |  `rate`  |\n",
    "|F                  | `f`        |  `df1, df2, ncp`  |\n",
    "|gamma              | `gamma`    |  `shape, scale`  |\n",
    "|geometric          | `geom`     |  `prob`  |\n",
    "|hypergeometric     | `hyper`    |  `m, n, k`  |\n",
    "|log-normal         | `lnorm`    |  `meanlog, sdlog`  |\n",
    "|logistic           | `logis`    |  `location, scale`  |\n",
    "|negative binomial  | `nbinom`   |  `size, prob`  |\n",
    "|normal             | `norm`     |  `mean, sd`  |\n",
    "|Poisson            | `pois`     |  `lambda`  |\n",
    "|Student's t        | `t`        |  `df, ncp`  |\n",
    "|uniform            | `unif`     |  `min, max`  |\n",
    "|Weibull            | `weibull`  |  `shape, scale`  |\n",
    "|Wilcoxon           | `wilcox`   |  `m, n`  |\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Descriptive statistics\n",
    "\n",
    "* Computation of the mean, the variance, the standard deviation and the median:\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "0.00486067296537189"
      ],
      "text/latex": [
       "0.00486067296537189"
      ],
      "text/markdown": [
       "0.00486067296537189"
      ],
      "text/plain": [
       "[1] 0.004860673"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "x <- rnorm(50)\n",
    "mean(x)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "0.807553157638321"
      ],
      "text/latex": [
       "0.807553157638321"
      ],
      "text/markdown": [
       "0.807553157638321"
      ],
      "text/plain": [
       "[1] 0.8075532"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "var(x)  ## variance"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "0.898639614994977"
      ],
      "text/latex": [
       "0.898639614994977"
      ],
      "text/markdown": [
       "0.898639614994977"
      ],
      "text/plain": [
       "[1] 0.8986396"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sd(x)  ## standard deviation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "0.010815116849316"
      ],
      "text/latex": [
       "0.010815116849316"
      ],
      "text/markdown": [
       "0.010815116849316"
      ],
      "text/plain": [
       "[1] 0.01081512"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "median(x)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Exercises\n",
    "\n",
    "* Generate the following quantiles using `x` and the `quantile()` function\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<dl class=dl-horizontal>\n",
       "\t<dt>0%</dt>\n",
       "\t\t<dd>-1.95123976505496</dd>\n",
       "\t<dt>10%</dt>\n",
       "\t\t<dd>-1.13271529630449</dd>\n",
       "\t<dt>20%</dt>\n",
       "\t\t<dd>-0.703902433256463</dd>\n",
       "\t<dt>30%</dt>\n",
       "\t\t<dd>-0.534080669182789</dd>\n",
       "\t<dt>40%</dt>\n",
       "\t\t<dd>-0.194535187638909</dd>\n",
       "\t<dt>50%</dt>\n",
       "\t\t<dd>0.010815116849316</dd>\n",
       "\t<dt>60%</dt>\n",
       "\t\t<dd>0.271635194601255</dd>\n",
       "\t<dt>70%</dt>\n",
       "\t\t<dd>0.428684091938317</dd>\n",
       "\t<dt>80%</dt>\n",
       "\t\t<dd>0.707262231615462</dd>\n",
       "\t<dt>90%</dt>\n",
       "\t\t<dd>1.22780901706794</dd>\n",
       "\t<dt>100%</dt>\n",
       "\t\t<dd>1.80424544809948</dd>\n",
       "</dl>\n"
      ],
      "text/latex": [
       "\\begin{description*}\n",
       "\\item[0\\textbackslash{}\\%] -1.95123976505496\n",
       "\\item[10\\textbackslash{}\\%] -1.13271529630449\n",
       "\\item[20\\textbackslash{}\\%] -0.703902433256463\n",
       "\\item[30\\textbackslash{}\\%] -0.534080669182789\n",
       "\\item[40\\textbackslash{}\\%] -0.194535187638909\n",
       "\\item[50\\textbackslash{}\\%] 0.010815116849316\n",
       "\\item[60\\textbackslash{}\\%] 0.271635194601255\n",
       "\\item[70\\textbackslash{}\\%] 0.428684091938317\n",
       "\\item[80\\textbackslash{}\\%] 0.707262231615462\n",
       "\\item[90\\textbackslash{}\\%] 1.22780901706794\n",
       "\\item[100\\textbackslash{}\\%] 1.80424544809948\n",
       "\\end{description*}\n"
      ],
      "text/markdown": [
       "0%\n",
       ":   -1.9512397650549610%\n",
       ":   -1.1327152963044920%\n",
       ":   -0.70390243325646330%\n",
       ":   -0.53408066918278940%\n",
       ":   -0.19453518763890950%\n",
       ":   0.01081511684931660%\n",
       ":   0.27163519460125570%\n",
       ":   0.42868409193831780%\n",
       ":   0.70726223161546290%\n",
       ":   1.22780901706794100%\n",
       ":   1.80424544809948\n",
       "\n"
      ],
      "text/plain": [
       "         0%         10%         20%         30%         40%         50% \n",
       "-1.95123977 -1.13271530 -0.70390243 -0.53408067 -0.19453519  0.01081512 \n",
       "        60%         70%         80%         90%        100% \n",
       " 0.27163519  0.42868409  0.70726223  1.22780902  1.80424545 "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "pvec <- seq(0, 1, 0.1)\n",
    "quantile(x, pvec)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Plots of distributions\n",
    "\n",
    "* Histogram and empirical cumulative distribution (the empirical distribution function is defined as the number of data points smaller or equal to $x$ devided by the total number of points)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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8tWe3K3vPLVC7z6wZn38gXmm0WAAAEC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\nBAgQIECAAAECBAgQINBngf8Hc0d1u44MxQ0AAAAASUVORK5CYII=",
      "text/plain": [
       "Plot with title \"Histogram of x\""
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "par(mfrow=c(1,2))\n",
    "hist(x)\n",
    "n<-length(x)\n",
    "plot(sort(x), (1:n)/n, type=\"s\", ylim=c(0,1))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* In a boxplot, the box in the middle of the graph indicates the quartiles and the median.The two horizontal lines represent the largest (or the smallest) observation which falls within a distance of 1.5 times the size of the box. The observations outside this box are considered as \"extremes\" and are noted by points.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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CBAgACBeQJnzzvoGIHWBBRIre2o9RAgQIAAAQIEuhE4r5thjUqgLgGfQaprP2RD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\nAAAAAElFTkSuQmCC",
      "text/plain": [
       "plot without title"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "data(IgM)\n",
    "par(mfrow=c(1,2))\n",
    "boxplot(IgM)\n",
    "boxplot(log(IgM))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Graphs for grouped data \n",
    "\n",
    "We are presenting here some techniques for comparing plots between groups.\n",
    "\n",
    "#### Histograms\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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UxrvF/2+i7zfFj2WO2cTVdE4M7pR928LeZv8mE3Lxq0e/pwRer1Brfto27IKvUj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\nIECAAAECBAisssD/B+JYsiT/+GEBAAAAAElFTkSuQmCC",
      "text/plain": [
       "Plot with title \"Histogram of expend.obese\""
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "data(energy)\n",
    "attach(energy)\n",
    "expend.lean <- expend[stature==\"lean\"]\n",
    " expend.obese <- expend[stature==\"obese\"]\n",
    " par(mfrow=c(1,2))\n",
    " hist(expend.lean, breaks=10, xlim=c(5,13), ylim=c(0,4),col=\"white\")\n",
    " hist(expend.obese, breaks=10, xlim=c(5,13), ylim=c(0,4), col=\"grey\")\n",
    "par(mfrow=c(1,1))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Parallel boxplots\n",
    "\n",
    "It is possible to insert boxplots of several different groups in the same window\n",
    " "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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ixhEGhVwB+KbbWy1kWAAAECBAgQIECA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",
      "text/plain": [
       "plot without title"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "boxplot(expend.lean, expend.obese) "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Stripcharts\n",
    "\n",
    "*  The stripcharts provide the possibility to display the raw data in a graph. We present four variants:\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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RzbGMeVma5vYn/e0Pz6bFJ9H9K+SX2BpkaAAAECBO4QUCD5QSBAgACBeRI4PoOp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\nndJ/AX+4/qpyY/DSq2h6H+WHSrSH7jjdv0uJlwf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yguJvhJ9SXHy4XR2LP8QuTKi421PFD+YJVW3crLXUohsU/9/wIWsLlLqVC1Vhv/de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",
      "text/plain": [
       "plot without title"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "opar <- par(mfrow=c(2,2), mex=0.8, mar=c(3,3,2,1)+.1)\n",
    "stripchart(expend~stature)\n",
    "stripchart(expend~stature, method=\"stack\")\n",
    "stripchart(expend~stature, method=\"jitter\")\n",
    "stripchart(expend~stature, method=\"jitter\", jitter=0.3)\n",
    "par(mfrow=c(1,1))\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "*  The first version superimposes identical points. The parameter `stack` allows to stack identical data vertically. The parameter `jitter` allows to apply a random vertical lag to each point. The last version applies a random lag smaller than the one in the previous version.\n",
    "\n",
    "#### Generate tables\n",
    "\n",
    "*  A bi-dimensional table can be generated using `matrix`. The following example concerns the caffeine consumption while giving birth, with respect to women's civil status.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table>\n",
       "<tbody>\n",
       "\t<tr><td>652 </td><td>1537</td><td>598 </td><td>242 </td></tr>\n",
       "\t<tr><td> 36 </td><td>  46</td><td> 38 </td><td> 21 </td></tr>\n",
       "\t<tr><td>218 </td><td> 327</td><td>106 </td><td> 67 </td></tr>\n",
       "</tbody>\n",
       "</table>\n"
      ],
      "text/latex": [
       "\\begin{tabular}{llll}\n",
       "\t 652  & 1537 & 598  & 242 \\\\\n",
       "\t  36  &   46 &  38  &  21 \\\\\n",
       "\t 218  &  327 & 106  &  67 \\\\\n",
       "\\end{tabular}\n"
      ],
      "text/markdown": [
       "\n",
       "| 652  | 1537 | 598  | 242  | \n",
       "|  36  |   46 |  38  |  21  | \n",
       "| 218  |  327 | 106  |  67  | \n",
       "\n",
       "\n"
      ],
      "text/plain": [
       "     [,1] [,2] [,3] [,4]\n",
       "[1,] 652  1537 598  242 \n",
       "[2,]  36    46  38   21 \n",
       "[3,] 218   327 106   67 "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "caff.marital <- matrix(c(652, 1537, 598, 242, 36, 46, 38, 21, 218, 327, 106, 67), nrow=3, byrow=T)\n",
    "caff.marital\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "*  Adding names to the rows and columns:\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table>\n",
       "<thead><tr><th></th><th scope=col>0</th><th scope=col>1-150</th><th scope=col>151-300</th><th scope=col>&gt;300</th></tr></thead>\n",
       "<tbody>\n",
       "\t<tr><th scope=row>Married</th><td>652 </td><td>1537</td><td>598 </td><td>242 </td></tr>\n",
       "\t<tr><th scope=row>Prev.married</th><td> 36 </td><td>  46</td><td> 38 </td><td> 21 </td></tr>\n",
       "\t<tr><th scope=row>Single</th><td>218 </td><td> 327</td><td>106 </td><td> 67 </td></tr>\n",
       "</tbody>\n",
       "</table>\n"
      ],
      "text/latex": [
       "\\begin{tabular}{r|llll}\n",
       "  & 0 & 1-150 & 151-300 & >300\\\\\n",
       "\\hline\n",
       "\tMarried & 652  & 1537 & 598  & 242 \\\\\n",
       "\tPrev.married &  36  &   46 &  38  &  21 \\\\\n",
       "\tSingle & 218  &  327 & 106  &  67 \\\\\n",
       "\\end{tabular}\n"
      ],
      "text/markdown": [
       "\n",
       "| <!--/--> | 0 | 1-150 | 151-300 | >300 | \n",
       "|---|---|---|\n",
       "| Married | 652  | 1537 | 598  | 242  | \n",
       "| Prev.married |  36  |   46 |  38  |  21  | \n",
       "| Single | 218  |  327 | 106  |  67  | \n",
       "\n",
       "\n"
      ],
      "text/plain": [
       "             0   1-150 151-300 >300\n",
       "Married      652 1537  598     242 \n",
       "Prev.married  36   46   38      21 \n",
       "Single       218  327  106      67 "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "colnames(caff.marital) <- c(\"0\", \"1-150\", \"151-300\", \">300\")\n",
    "rownames(caff.marital) <- c(\"Married\",\"Prev.married\", \"Single\")\n",
    "caff.marital\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "*  In general, we want to create tables from a data set. In this case, we have to use the functions `table, xtabs,` or `ftable`. The factors' levels are directly used as the columns' names. The function `table`` is the simplest one of the three.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "library(ISwR)\n",
    "data(juul)\n",
    "attach(juul)\n",
    "juul$sex <- factor(juul$sex, labels=c(\"M\", \"F\"))\n",
    "juul$menarche <- factor(juul$menarche, labels=c(\"No\", \"Yes\"))\n",
    "juul$tanner <- factor(juul$tanner, labels=c(\"I\", \"II\", \"III\", \"IV\", \"V\"))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "      age         menarche     sex           igf1        tanner   \n",
       " Min.   : 0.170   No  :369   M   :621   Min.   : 25.0   I   :515  \n",
       " 1st Qu.: 9.053   Yes :335   F   :713   1st Qu.:202.2   II  :103  \n",
       " Median :12.560   NA's:635   NA's:  5   Median :313.5   III : 72  \n",
       " Mean   :15.095                         Mean   :340.2   IV  : 81  \n",
       " 3rd Qu.:16.855                         3rd Qu.:462.8   V   :328  \n",
       " Max.   :83.000                         Max.   :915.0   NA's:240  \n",
       " NA's   :5                              NA's   :321               \n",
       "    testvol      \n",
       " Min.   : 1.000  \n",
       " 1st Qu.: 1.000  \n",
       " Median : 3.000  \n",
       " Mean   : 7.896  \n",
       " 3rd Qu.:15.000  \n",
       " Max.   :30.000  \n",
       " NA's   :859     "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "summary(juul)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "*  Then the respective tables are\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "sex\n",
       "  1   2 \n",
       "621 713 "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "table(sex)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "   menarche\n",
       "sex   1   2\n",
       "  1   0   0\n",
       "  2 369 335"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "table(sex, menarche)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "        tanner\n",
       "menarche   1   2   3   4   5\n",
       "       1 221  43  32  14   2\n",
       "       2   1   1   5  26 202"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "table(menarche, tanner)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "*   The variable `menarche` explains that a woman already had her first period and the variable  `tanner` provides the state of puberty.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Exercises: Marginal tables and relative frequencies\n",
    "\n",
    "*  Define the following table\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "      sex\n",
       "tanner   1   2\n",
       "     1 291 224\n",
       "     2  55  48\n",
       "     3  34  38\n",
       "     4  41  40\n",
       "     5 124 204"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "tanner.sex=table(tanner, sex)\n",
    "tanner.sex"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "*  Compute the marginal tables, i.e. the sum of values with respect to one of the other table's dimensions using the function `margin.table()`\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 104,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "tanner\n",
       "  1   2   3   4   5 \n",
       "515 103  72  81 328 "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "sex\n",
       "  1   2 \n",
       "545 554 "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "margin.table(tanner.sex, 1)\n",
    "margin.table(tanner.sex, 2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "*  Using the function `prop.table()`, compute the table of relative frequencies can be computed. This contains the relative frequencies that are the proportions of the row's or column's total.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 105,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "      sex\n",
       "tanner         1         2\n",
       "     1 0.5650485 0.4349515\n",
       "     2 0.5339806 0.4660194\n",
       "     3 0.4722222 0.5277778\n",
       "     4 0.5061728 0.4938272\n",
       "     5 0.3780488 0.6219512"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "prop.table(tanner.sex, 1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "*  Compute the proportions with respect to the global total of the table:\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 106,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "      sex\n",
       "tanner          1          2\n",
       "     1 0.26478617 0.20382166\n",
       "     2 0.05004550 0.04367607\n",
       "     3 0.03093722 0.03457689\n",
       "     4 0.03730664 0.03639672\n",
       "     5 0.11282985 0.18562329"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "tanner.sex/sum(tanner.sex)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Graphical display of the tables\n",
    "\n",
    "#### Bar plots\n",
    "\n",
    "*  Returning to the caffein example.\n",
    "*  The function `barplot` takes a vector or a matrix as argument. The most basic version:\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 108,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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JwENEhzqpZjJUCAAAECBAgQIEBgUgEN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\nuncqAMtOGz0AAAAASUVORK5CYII=",
      "text/plain": [
       "plot without title"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "total.caff <- margin.table(caff.marital,2)\n",
    "barplot(total.caff, col=\"white\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "*  If the argument is a matrix then  `barplot` creates a stacked box plot in which the columns are partitioned with respect to the contributions of the table's different rows. For displaying side-by-side the contributions of a row instead of stacking them, use the parameter `beside=T`. Some examples:\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 110,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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UDrG7l+y28z4c/6nXiQ061Zs+aAlOU77jid6g+yRoNb9o9//OOxk08+eY88gw7S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\npIauONUmQOAuAhvXvX322WfdsmXL6ixeI11uvPHG8ZzWfOuRbmS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ECAAAECBAgQ\nIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQ\nIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQ\nIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQ\nIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQ\nIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQ\nIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQ\nIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQ\nIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQ\nIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQ\nIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQ\nIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQ\nIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQ\nIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQ\nIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQ\nIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQ\nIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQ\nIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQ\nIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQ\nIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQ\nIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQ\nIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQ\nIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQ\nIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQ\nIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQ\nIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQ\nIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQ\nIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQ\nIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQ\nIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQ\nIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQ\nIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQ\nIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQ\nIECAAAECBAgQIECAAAECBAgQIECAAIHFK7B08TZdywkQIECAwJwILMlS1nUtaePcPzA5IHlY\ncn1yXaIQIECAAAECBAgQIEBgZAU2S8uqc/SXHS3cJcOXtcbXY5Xbk9ckCgECBAgQIECAAAEC\nBEZWoFcH6by09prkfybbJI9J3p9UR+kZiUKAAAECBAgQIECAAIGRFOjuIFWHqDpCR/Zo7TkZ\nd3KP8UYRIECAwBAJ1HHTCgECBAgQIDA3AtU5Wpt8usfiTsq4B/cYbxQBAgQIDJGADtIQrQxV\nIUCAAIHGCuyQmm+SXJucneyWdJeVGXFF90j3CRAgQIAAAQIECBAgMCoCm6Yhq5Lac7Qm+a/k\n0qQ6SlsnVfZIvpzUNM9NFAIECBAYYoFlQ1w3VZta4M+XLl36+Uw2V3sC161Zs+ZlWd6Hp35q\nUxAgQIBABG5OqpP00OQRySNbt9U52jCpsn/yxOT1ySnJKBbbo1Fcq9pEYJEK6CA1e8Xfd5NN\nNln2iU98YsVcNOOVr3zlqksuuWS7uViWZRAgQGARCdQepItaOaHV7vHc1h6jKscl70rqWkij\nWmyPRnXNahcBAgQaJnDwve9971vXzVHZe++9b03739AwA9UlQIAAgYUXsD1a+HWgBgQIzJGA\nPUhzBGkxBAgQIEBgCoE6DO+G5Moppuv18AsycsdeD3SNq5NDvCL5Sdd4dwkQIECgTwEdpD6h\nTEaAAAECBGYpcHHmPzU5YAbLqd837drHfE/INJ9NdJD6wDIJAQIEegnoIPVSMY4AAQIECMy9\nQB3C/MMZLvZ/9jnfTZmuzqCnECBAgMAMBXSQZghnNgIECBAgME2Bo6Y5vckJECBAYAEEdJAW\nAN1TEiBAgMBICtRpvXdPtk3qNN91Frs6c933kro2UvusdhlUCBAgQGBYBXSQhnXNqBcBAgQI\nNEWgtqW1d+jwZMsJKn1Bxr8wqd8hKQQIECAwxAJzdYHRIW6iqhEgQIAAgYEK1HWOXpIcnzwu\nqZMp3Cep68rVHqU6KcOvkguTvRKFAAECBIZYwB6kIV45qkaAAAECQy+wRWp4aLJv8uUetb0y\n4+oQu1OSk5MDk/MShQABAgSGVMAepCFdMapFgAABAo0Q2CG1rN8WndVHbc/MNPv0MZ1JCBAg\nQGABBXSQFhDfUxMgQIBA4wVq79Cvk/2naEkdsVGH2l0yxXQeJkCAAIEFFnCI3QKvAE9PgAAB\nAo0WWJvaH5ucmByUnJZck1yXrEjqpA27JAcnOyV7JwoBAgQIDLGADtIQrxxVI0CAAIFGCLwx\ntTw/OSbptSdpdcbXb5AOSWqPk0KAAAECQyyggzTEK0fVCBAgQKAxAmekpjsndea67ZPNk5uS\nq1q5NbcKAQIECDRAQAepAStJFQkQIECgMQJXpKYVhQABAgQaKuAkDQ1dcapNgAABAgQIECBA\ngMDcC+ggzb2pJRIgQIAAAQIECBAg0FABHaSGrjjVJkCAAAECBAgQIEBg7gX8Bmly0zpF60aT\nT3LnozfcOWSAAAECBAgQIECAAIFGCuggTb7avpuHHz75JHc++uIMHX/nPQMECBAgQIAAAQIE\nCDROQAdp8lW2bx7eavJJ1j96Tv7WhQEVAgQIECBAgAABAgQaLKCDNPnKuzIPV6YqdSV1hQAB\nAgQIECBAgACBhgvoIDV8Bao+AQIECBAgQIDAUAk8bOnSpd8aHx9fPle1Wr169VFZ1tvmanmW\nM7mADtLkPh4lQIAAAQIECBAgMB2BbdauXXuPT3/60+PTmWmiad/2tret/fa3v73DRI8bP/cC\nOkhzb2qJBAgQIECAAAECi1gge4/W7b///nPSQTrxxBPXLWLKBWm66yAtCLsnJUCAAAECBAgQ\nIEBgGAV0kIZxragTAQIECBAgQIAAAQILIqCDtCDsnpQAAQIECBAgQIAAgWEU0EEaxrWiTgQI\nECBAgAABAgQILIiADtKCsHtSAgQIECBAgAABAgSGUUAHaRjXijoRIECAAAECBAgQILAgAjpI\nC8LuSQkQIECAAAECBAgQGEaBUbgO0oaB3T3ZNtk6qXPFX598L7m0dT83CgECBAgQIECAAAEC\nBCYXaHIHqep+VHJ4suUEzbwg41+YXDzB40YTIECAAAECBAgQIEDgToEmH2J3XFrxkuT45HHJ\nrsl9ku2S2qN0QPKr5MJkr0QhQIAAAQIECBAgQIDApAJN3YO0RVp1aLJv8uUeLbwy4+oQu1OS\nk5MDk/MShQABAgQIECBAgAABAhMKNHUP0g5pUf3W6KwJW/aHB87M4D5/uGuIAAECBAgQIECA\nAAECvQWa2kGqvUO/Tvbv3aw7x9YesjrU7pI7xxggQIAAAQLzJ/Cneaq/mr+n80wECBAgMFuB\nph5itzYNPzY5MTkoOS25JrkuWZHUSRt2SQ5Odkr2ThQCBAgQIDDfAn+RJ3xGUtsrhQABAgQa\nINDUDlLRvjE5Pzkm6bUnaXXG12+QDklqj5NCgAABAgTmWuDhWeBknZ+6/MRmSftsqvW72Vcm\nCgECBAgMqUCTO0hFekayc1Jnrts+2Ty5KbmqlVtzqxAgQIAAgUEJ1NlS63D1hyTfTn6SdJZH\n5M7y5KLWyMtbt24IECBAYEgFmt5BarNekYGKQoAAAQIE5lOgDu/+4+RtyV8nH0v+JWmXozJQ\nh9jV0QwKAQIECDRAYBQ6SBvGefdk26QOZaiz212f1GF1l7bu50YhQIAAAQIDEaijFf42+WJy\nQrJf8sLk6kQhQIAAgYYJNLmDVHWvb+YOT+qkDL3KBRlZG6n2sd+9pjGOAAECBAjMhUAd9l2/\nSfpgUtudv0kUAgQIEGiYQJM7SMfF+tnJ+5P61u7a5DfJBkn7LHaHZfjC5LGJC8UGQSFAgACB\ngQrUJSiembwo+Uhyc/LLRCFAgACBhgg0tYO0RXwPTfZN6oxA3eXKjKhD7OosdicnByY6SEFQ\nCBAgQGBeBI7Ps3wjeU9yY6IQIECAQEMEmtpB2iG+9Vujs/pwPjPTHNHHdCYhQIAAAQJzKVBn\ntHvaXC7QsggQIEBg8AJN7SDV3qE6jKGuf3TqJEzVvgOSSyaZxkMECBAgQGA+BB6aJ7khqaMc\nplv+NDPcv4+Zlmaapm7b+2ieSQgQIDB4gaa+ia4NzbFJXZzvoOS0pE61el2yItky2SU5ONkp\n2TtRCBAgQIDAQgrUiRvqS7364m665c2ZYbc+Zqrf4fbTkepjUSYhQIDA4hRoagep1tYbk/OT\nY5Lak9RdVmdE/Qaprj1Re5xmUl6amaqDNVWpTln9LkohQIAAAQITCbwhD/xwogenGP/EKR5v\nP3xTBi5v33FLgAABAtMXaHIHqVpbp1TdOdku2T7ZPKmNw1Wt1LUpZlMekJnr905TlbqKen1r\npxAgQIAAgYkE6tIUCgECBAgMuUDTO0gbxXePpDoodTrv3yXdpb51uy05u/uBPu6/uo9papLq\nlDmNa59YJiNAgMCICrhw+YiuWM0iQGBxCTS5g1QX4/ts8ketVVYnbahD4uq03p3lNblT10ea\nSQepczmGCRAgQIBAL4HaltbeocOT+g1sr3JBRr4wubjXg8YRIECAwPAI1J6XJpbxVPpjye3J\nYUmdqOG/kpOSfvf6ZFKFAAECBAjMWqAuXP6S5PjkccmuyX2SOvx796ROyvCrpI502CtRCBAg\nQGCIBZq6B+mBMa2NzlOSus5RlROTNyVHJ3U2u9pQKQQIECBAYJACLlw+SF3LJkCAwAIINLWD\ntE2s6lTf3+4y+8fc3yz5l+SK5MuJQoAAAQIEBiXgwuWDkrVcAgQILJBAUw+xuzxeVfdn9HB7\nRcbVb5Pqt0h1AgeFAAECBAgMSqAuI9G+cPlkz1FfSNahdpdMNpHHCBAgQGDhBZq6B+nq0H0h\nOSbZO3lr8oukSu1Zal889psZrjPM/VuiECBAgACBuRaobc6xSR3m3d72uHD5XCtbHgECBOZR\noKkdpCL666R+Z3REckrS7iBlcGxV8qzkfclhiUKAAAECBAYl8MYs+PykvrQb1IXLB1V3yyVA\ngACBLoEmd5DahzTcI226ratddfeWpDpR9XukeycKAQIECBAYlMCgL1w+qHpbLgECBAh0CTS5\ng9Ruym/bAxPc1rd6CgECBAgQmA+BOkFQRSFAgACBhgo09SQNDeVWbQIECBAgQIAAAQIEhlmg\nqXuQNg3qjtOArb1MP5vG9CYlQIAAAQIECBAgQGARCjS1g7Rb1tU501hfdRKHOr2qQoAAAQIE\nCBAgQIAAgQkFmtpBOjctekHy/uRbyduTyUqdclUhQIAAAQIECBAgQIDApAJN7SBVo05IxpM6\n1fdbkq8nCgECBAgQIECAAAECBGYs0PSTNHw4Lf9qcvSMBcxIgAABAgQIECBAgACBlkCT9yC1\nV2L9tmiHpNqyuj3SLQECBAgQIECAAAECBKYrMAodpDpD3UXTbbjpCRAgQIAAAQIECBAg0C3Q\n9EPsutvjPgECBAgQIECAAAECBGYsoIM0YzozEiBAgAABAgQIECAwagI6SKO2RrWHAAECBAgQ\nIECAAIEZC+ggzZjOjAQIECBAgAABAgQIjJqADtKorVHtIUCAAAECBAgQIEBgxgI6SDOmMyMB\nAgQIECBAgAABAqMmoIM0amtUewgQIECAAAECBAgQmLGADtKM6cxIgAABAgQIECBAgMCoCegg\njdoa1R4CBAgQIECAAAECBGYsoIM0YzozEiBAgAABAgQIECAwagLLRq1Bc9yeFVneRnO8TIsj\nQIAAAQIECBAgQGBIBXSQJl8x383DD598kjsf3fnOIQMECBAgQIAAAQIECDRSQAdp8tW2bx7e\navJJ1j96Tv7+uI/pTEKAAAECBAgQGJRAfa57TLJ0Dp/goizr+jlcnkURGHoBHaTJV9GVebgy\nVVk71QQeJ0CAAAECBAgMWOCpWf7nx8fH183F86xbt248yzk6ec1cLM8yCDRFwEkamrKm1JMA\nAQIECBAgMLnAsk022eT2tWvXjs9FVq5cuSpP58v0yc09OoICXvQjuFI1iQABAgSGQqBO9POE\n5IHJucnFiUKAAAECQy6ggzTkK0j1CBAgQGDoBepojCOTZyRXJ29KLku+n2ydVKlDnt6W/EPd\nUQgQIEBgeAUcYje860bNCBAgQKAZAu9ONd+Y3JT8afL55PjkmuR5yZ7JB5NXJ89OFAIECBAY\nYgF7kIZ45agaAQIECAy9QF0r74jk4OTEZOPks0ntTaqzidWhdVUuSB6UHJZ8KlEIECBAYEgF\n7EEa0hWjWgQIECDQCIG6Vl6dUrk6RVVuSaqj9Nuk3TnK4Pry6fz9o9awGwIECBAYUgF7kIZ0\nxagWAQIECDRCoA6jqy8b62QMX2zVuDpLdfavOkVy5+mWn5z7P08UAgQIEBhiAXuQhnjlqBoB\nAgQIDL1AdXiqY/TJ5D1JffFYe49qL1K7c/QnGf5Ssl9yXKIQIECAwBAL6CAN8cpRNQIECBBo\nhMBzUst/Tv4iWd2jxodk3JOS/518psfjRhEgQIDAEAk4xG6IVoaqECBAgEAjBX6fWr8jee8E\ntX97xr82uXGCx40mQGCBBVasWPHRpUuX1t7eWZc1a9ZsvHr16jrEVmmogA5SQ1ecahMgQIDA\n0AnU7456lfbvjh6aB29Iruw10RTjtszj95ximnrYh7I+kExCoFtgyZIlT95///232XPPOiv/\n7MrXvva1sS9+8YvtQ2xntzBzL4iADtKCsHtSAgQIEFiEAhenzacmB8yg7d/KPNXB6qfs3M9E\npiFA4K4CK1euHDv00EPvOnIG91atWlUdpBnMaZZhEdBBGpY1oR4ECBAgMOoCb0gDfzjDRj46\n823ex7w/yjQ/7mM6kxAgQIDABAI6SBPAGE2AAAECBOZY4KhZLK9+v9TPb5gc1jMLZLMSIECg\nBJzFzuuAAAECBAgQIECAAAECLYFR2IO0Ydqye7JtsnVS355dn3wvubR1PzcKAQIECBAYqIDt\n0UB5LZwAAQLzI9DkDlLVvQ5XODyps/v0Khdk5AuT+mGsQoAAAQIEBiFgezQIVcskQIDAAgk0\n+RC7uhr5S5Ljk8cluyb3SbZLao9SnSXoV8mFyV6JQoAAAQIEBiFgezQIVcskQIDAAgk0dQ/S\nFvE6NNk3+XIPuyszrg6xOyU5OTkwOS9RCBAgQIDAXArYHs2lpmURIEBgCASaugdph9jVb43O\n6sPwzEyzTx/TmYQAAQIECExXwPZoumKmJ0CAwJALNLWDVHuHfp3sP4Vv7SGrQ+0umWI6DxMg\nQIAAgZkI2B7NRM08BAgQGGKBph5itzamxyYnJgclpyXXJNclK5Itk12Sg5Odkr0ThQABAgQI\nzLWA7dFci1oeAQIEFligqR2kYntjcn5yTNJrT9LqjK/fIB2S1Dd8CgECBAgQGISA7dEgVC2T\nAAECCyTQ5A5SkZ2R7JzUmeu2TzZPbkquauXW3CoECBAgQGDQArZHgxa2fAIECMyTQNM7SMVU\nF+a7b7JV0r5Q7DYZrra5UGwQFAIECBCYN4Er8kwVhUDjBdatq/NhjS1PNp2jxtTRPb+fo2VZ\nDIGBCTS5g1R1d6HYgb00LJgAAQIECBBYzALf//73l6b9L29lLiiqg/TA5BdzsTDLIDAogSZ3\nkOrCfM9O3p98Mbk2+U2yQdI+ScNhGa4LxT42cR2kICgECBAgQIAAgX4E1q5dO/785z9/7OUv\nrz7S7Mp11103tnLlyvrcOVd7o2ZXIXMTmESgqR2k+bow30tjt9Mkfu2H6sx5VSeFAAECBAgQ\nIDAyAltvvfXYHnvsMev2XHNNnWxYIdAMgaZ2kKZ7Yb4jZrg6HpD56rmmKnU9qdpzpRAgQIAA\nAQIECBAg0GCBpnaQ6rTd7QvFnjqJf7VvNheKffUky+58qM6c98vOEYYJECBAgAABAgQIEGie\nQFM7SC7M17zXmhoTIECAAAECBAgQGHqBpnaQCtaF+Yb+5aWCBAgQIECAwBQCh+bxB04xTb8P\n75oJx/ud2HQECPQWaHIHqVrkwny916uxBAgQIECAQAMEli1b9n932GGHjbbaaqs6OmZW5Wc/\n+9nSG264oU7NrRAgMAuBpneQ2k2vi/JVqtQZ5R6X7JfUKb7/LVEIECBAgAABAkMnMJ7y1re+\ndcWzn11XLpldec1rXjN2zDHHrL+66+yWZG4Ci1ugzr7W1FLXOvpEckPyn8lfJJslP0y+krw7\n+Vby8cTu5iAoBAgQIECAAAECBAhMLtDkDtLH0rRnJp9NqpNUHaGPJtcnz0jun7wmqbPYvThR\nCBAgQIAAAQIECBAgMKlAUw+x2y6t2jd5evL5Vgs/k9v9k0cn326NOzq3f5I8KzmuNc4NAQIE\nCBAgQIAAAQIEego0dQ9S7R2qHzN+raNV38hwXY/ovI5xNXhmcr+uce4SIECAAAECBAgQIED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      "text/plain": [
       "plot without title"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "par(mfrow=c(2,2))\n",
    "barplot(caff.marital, col=\"white\")\n",
    "barplot(t(caff.marital), col=\"white\")\n",
    "barplot(t(caff.marital), col=\"white\", beside=T)\n",
    "barplot(prop.table(t(caff.marital), 2), col=\"white\", beside=T)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "*  We can make the plots more beautiful"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 111,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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w296Qtzu6437YYAAQIECBAgQIAAgY4ItL1A+suM4wOXOJafGNjuDZk+dmDeJAEC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",
      "text/plain": [
       "plot without title"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "par(mfrow=c(1,1))\n",
    "barplot(prop.table(t(caff.marital), 2), beside=T,legend.text=colnames(caff.marital),col=c(\"white\",\"grey80\",\"grey50\", \"black\"),args.legend=c(cex=0.4))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Exercises\n",
    "\n",
    "*  Create a dotchart with the same information as the previous bar plots (use the command `dotchart()`)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 112,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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2ro/GKGudBUgTJZodXqc4dM1CjTZK1Glx4z2YpZLFMgzQLLUwkQIECAAAECBB4S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",
      "text/plain": [
       "plot without title"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "par(mfrow=c(1,1))\n",
    "dotchart(t(caff.marital),cex=0.7)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "*  In order to represent the caffeine consumption table in function of the civil state create a set of pie charts using the function `pie()`.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 113,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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tuKuU1ozKv0A5TTEyjzUxDOYZITAREQARGITqAtvI6B3jmW+mjPPffcesghhxT1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70ataimw9ft3403Peeed52rZty3VGFVLl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\n9CeFdYG7X2HzC2q3e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HYMGCBc5FF12UvXHjxoPqdmcjzj1Vypr/oOdvVtiw3nZN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\nwP8HJ5zwOStNQ2UAAAAASUVORK5CYII=",
      "text/plain": [
       "Plot with title \"Single\""
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "par(mfrow=c(2,2), mex=0.8, mar=c(1,1,2,1))\n",
    "slices <- c(\"white\", \"grey80\",\"grey50\", \"black\")\n",
    "pie(caff.marital[\"Married\",], main=\"Married\", col=slices)\n",
    "pie(caff.marital[\"Prev.married\",], main=\"Previously married\",col=slices)\n",
    "pie(caff.marital[\"Single\",], main=\"Single\", col=slices)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 114,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "R version 3.3.2 (2016-10-31)\n",
       "Platform: x86_64-apple-darwin13.4.0 (64-bit)\n",
       "Running under: OS X Yosemite 10.10.5\n",
       "\n",
       "locale:\n",
       "[1] C\n",
       "\n",
       "attached base packages:\n",
       "[1] stats     graphics  grDevices utils     datasets  methods   base     \n",
       "\n",
       "other attached packages:\n",
       "[1] ISwR_2.0-7\n",
       "\n",
       "loaded via a namespace (and not attached):\n",
       " [1] R6_2.2.0           magrittr_1.5       IRdisplay_0.4.9000 pbdZMQ_0.2-5      \n",
       " [5] tools_3.3.2        crayon_1.3.2       uuid_0.1-2         stringi_1.1.2     \n",
       " [9] IRkernel_0.6       jsonlite_1.2       stringr_1.2.0      digest_0.6.12     \n",
       "[13] repr_0.10          evaluate_0.10     "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sessionInfo()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "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": "3.3.2"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 0
}