{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<div class=\"big_title\">LAB 3: R Basics</div>\n",
    "\n",
    "BIO3782: Biologist's Toolkit (Dalhousie University)\n",
    "\n",
    "-----------------------------------------"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In LAB 1, you learned how to identify and tell apart objects like `variables`, `functions`, `arguments`, `comments`, `data` and `packages`. In this lab we will review again all of these objects. However, this time around we will \"dive deeper\" and learn intrinsic details about each of these objects. \n",
    "\n",
    "-----------------\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's review LAB 1 by doing a spectrogram of a call from a bird named Northern Goshawk (*Accipiter gentilis*), which is a medium-large raptor that inhabits many of the temperate parts of the Northern Hemisphere (Eurasia and North America). \n",
    "\n",
    "<a href=\"Accipiter_gentilis.jpg\" target=\"_blank\"><img src=\"Accipiter_gentilis.jpg\" width=\"200px\"></a>\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "A spectrogram is a visual representation of the spectrum of frequencies of a signal as it varies with time. It is normally created using a Fast Fourier Transform (FFT) to transform audio in the time domain into power in the frequency domain. It is not too complicated to code in R, however there is an even simple solution, you can use one of multiple R packages that do spectrograms, below we use the `phonTools` package.\n",
    "\n",
    "The audio file we'll use here was downloaded from [xeno-canto](https://www.xeno-canto.org/) and is a call from a Northern Goshawk, recoded in Lunenburg, Nova Scotia."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<br><br><br>\n",
    "<div class=\"startTASK\"></div>\n",
    "\n",
    "1. Make a new directory on your Desktop, call it <span class=\"file\">lab3</span>\n",
    "2. Download the file <span class=\"file\">Mono_XC386310__Northern_Goshawk__Accipiter_gentilis.wav</span> from Brightspace, and place it in your new <span class=\"file\">lab3</span> directory\n",
    "3. Listen the the audio file by double click on <span class=\"file\">Mono_XC386310__Northern_Goshawk__Accipiter_gentilis.wav</span> to open it in your default audio player in your computer. Note that you can also listen to it directly in Brightspace.\n",
    "\n",
    "4. Open RStudio and change the **working directory** to <span class=\"file\">lab3</span>. If you don't remember how to do this, review [this section](https://diego-ibarra.github.io/biol3782/week1/index.html#RStudio's-%22Working-Directory%22)\n",
    "\n",
    "5. In the <span class=\"console\"></span>, the following to install the `phonTools` package"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "install.packages(\"phonTools\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "4. In RStudio, make a new R script file and save it in your <span class=\"file\">lab3</span> directory with the name <span class=\"file\">spectrogram.R</span>.\n",
    "\n",
    "6. Copy-paste the code below into <span class=\"file\">spectrogram.R</span> and click the <span class=\"source\"></span> button"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
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17eICHWSHP7D5CaFZByr5PnVrymGCeHuM0nfd50dloGG2rvw0hOrR7D6ahIAQUu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7bPOWa6q++tAh8o0WYRUmgt0qi6yFzzt3\nkBSHh6m+FkE7ZYXgBIhizWq5U7fUBJsR6lzqFjeOb8j5hJ9SAbDQyOzPXUpdS2DpOHf3st87\nu+oQ+J3sWHkapIXErbMBSJB1WG86jlSNcxjwq7YxnBQK47Faht+YndL4mGzk7dfRZILkOI9L\nQzgGJM9ewRkjnUyq1LJ3aE7VVlwOg++5IFETpCX5apfYwT6Oeq6sWEemKQmaC/IG8jgaJRVW\nEffNOHqGSsh4ZIPIzTmEp8gZHPVwPHp8lMRRuFjJSIn8YMFPyzyHLUMxLQmeJ06ZnU805BIy\nkrKi0tXjtj+lsJ2UDLrKDnZMweMCJCWycEuaZraNnV2XVy055Rp4BVDwACnGnoV4yK0IV1Av\nhV63K46nK0XqapkR+qweQmOXGRmfw+nssmidwsCHXVho00GMVUMkLCaUdgepV0mntZxYqLzi\nBxFrLe2DtfMYOzhBnujgqZplLX5smfLl8sceyO756F/b/tZ6hFCJ87ETnCUcxuTF9AWS62ae\niXAz2/ryjAAyEruKdsMOEjwSH6nDPSGvYSXN7BuC1EWQZs/SyB0kM422jFhi6gdIgSANyhWC\npFvB2Qz5sixpgwJjk8QVmjHuIEGYxVEvYbYcvaqjnSUu2A4SAjGfyLhUOnVYfoDEhztqB8kX\nghTDFn+AFHeQOvyUR4sY0CD3cXfLslXkvFA2zot13JSqPPPE1tbDYuIYTg4gid9AMrHHn0HH\njlzgjd1BinG1E6CH7/oBkg8rdO2aVPCKjwwAEt5PIhqtbDGruSBY/aBwQQvH0REkpO8CkNYM\nHpbUQ3EOmu8D8VmZnBl9YSA1QZIECeQngiQ4jmz6AmkgXNB6tTs9uR8g1Wx/B5KY44ZY/gAp\nTPzpDhINrVMASZag7THG7UkiQBbZOz4NJUgwMztIM0QMeLUngCTwv3LyFN72ARLo6WLpfY2G\nGzgx7CCNiDPz5EU64ZtatiEESG6ZKsQPK8XXhSciTtyjZ2uCgxgQy3JecH/6fSjGUSEWr6Xj\npvdqv0AyBhIIcvcQEkDynAkDkKqbThzYJtc/Vmv3b3gkrQcvfBwPp5kg+QEgjTtIUrtBQNTB\n+dthB4mzj4V2U9/TymgxYPFATgEkvYPE6ewcWHOESCRIjsZwggS0dTQtR8DAd3EaSQURJ4A0\nWUhnblTkMZ+3tdyXZQNIWNABnr/UvKQlJgFdI5H56Gfweq7oCpuRWRZUOGCi9LpZCkeOOHbE\n/wFS2EEKO0gTQApboozLw1KrnQNMfvEGcmapZb2v5UnHcg/r/r7IZaw2BmWuA0i97wJBsl8g\nsY6g46jIKe6Z4oTlnRl0V5aBAlaj4CgJkvsVpH1mMR84ASQhDNOjzkiRW0lygXAq8DaTc4OR\na5l/BUkvfE5cO9MTJGgp2IxpL+rdZ0koDg73uDAVwVnGHauROq8iMxEkGvyhO+Ea4WLw38l9\ngeR/BclWgJQeIEHLVvZBY3jcQYratfheHI4AkPAB4wCQCqSjzMIviWPXOAagS4ued5A01jdA\n0jtIoUUUAEiaj/Bx8QsbnRMkiEFo7DKw5FCM4QgvWYX23CNfF2VzbAjIcYjzQfT4NikvEv4r\nMgM/8SDNCiEM17zZvexoBykRpOYBks4uyXWtFiDBcYk/CNK//K+/NDyrCZDap+OwN7FpkaiH\nA/SZgVCbBcf7cNCLSGAAILXIU+PQez5kVv2j8MDoxvC8smDdtXKjGXgIi6deeL56VjNA6nkM\nXXNIt9ufuEQeEEVmZ2R0cCpd+rje1vPtfrM+369IDlB1NW8VcdSIFU4ML7hEzzaLxUAPnb5A\nEi7WwbYLhCTflDsmbG3MmeApr0vx/o5ICL2z+S2z1C3PAMngBnfcz0s+blu9Xe/rCHNxzduS\n/VJXaD1gxkeKPWLrkTMjTVV+gGoLkn4oDqR8TBI5wk4eilBhFa3sUmUjpJqyDP2sCZt1Waj3\nd5DgQyz3Dny9w2ltEG7XZbH34OAX/TLtDSG3AtELHYuPu8gFntvXwR0KdwcJEpUKIlLlVqfU\n7JOFFIr3hy5XgpOPOXy60ivFfVJNEePE9ul5rQSJaRwZDPiJRc0JIGlcY8QEpIiFJXzFcEjv\nvpvsVUkGQSSvHWcSZCW5E1ShAarFZeBWBWvdsPzxOaoeHCWkYR8IgBTr4hISS51C5WAbgoRQ\nCF2DN5k6O6UnyDOXxDwCYr+wAShCOZb/Ij03k3x0T32SB9FWy6rDIYJwDsXlcK648IAkG66x\nXQtAYtczg3h8wGXZOH0u8JWqnvosYGaXP+M80j/sImSwMiHqjoe2B0jWHQkSB1EYxabrGiBF\nthNMRhqjUqchGoZxr9ZQ3Q4SAGt4sE4LS5Bsb9hj43F8jCAh4gAkSZDY1IRDKeEeHcJyYRPS\nALmca5fez9f75/V2hSS7XlK9r2FZyn1dEf0AEp/FY+UCpN5nrCbbPUBSwnFm/LBkS82ISBrl\nbyBtIM7fsXjEDlLZQYJ2KvoJeSwQJJtu93q93DalynLBG5aw1m1Zl1TxAWKYFp1bnttmr+sd\nJO6r+DDXvEKQKoIkABKkZQBIwlHQS6V4sIPzcTfxAyTkOj3nyAmbSFS3GOJty/Nl3eI1eVc7\nvYy4+d5sWT9AKn6Zl4zkDcgO3NSY7IJMtNcfAqTAWzJzve0gebiYGR9tBiix8jDkDpIqapZQ\nRQQppzUSJGhXj3SzgOt7Torzr/PkZFo5obBwNh98qn+AxCdzGUZv9jopHvtrHyAhN/wAiT3l\n5D4uG5+6MIJA1ymAZFM4pip2kPYHfYlFxyY6M7d2jC1kgAdI/QRDsDwtHHYt0vwAaeI86qZP\nagfJAaRjTCpO+HDs9gaQIIJUunsWSwAkwVO4ec6tDztIuDxqB2nMM0H6A0fN/1FPhv8C2/+3\nZwMpgDVqDl0X2MW+wdoZh31oS+BJjn72ETmLDRM1MlKvOQ9lsllD+rX8FSsfIGVCwp2HoxE8\neGIp5eFXkdVGLBM23gScszPcX+FGijBZ8jSLTkfYg/j2ebm9ny8XgHE55/W6RVyH27bWhQrX\n0TOtme2KPcRXMZCgD5A8QIJM4YE2NkjwaQfJwYXGdK/FOoIkc9nchigVS1IEqWF9UuXTkXy9\n4Q0vNyvr8ok3LHBq923bkP+WNUFo69xDLCoSMvCQNU+1hyBrIkiaj5BlAEiQqXm1kt8t8+mZ\nIUht+gLJP0AaEkHicd8rPsrlVv3ndisX4LU049Jz49LdYS4Xv4O0TmtBoMVLngjSaDi31Ti2\nAVl4+kbafb39AMlM3PLhhFqAZDJEFwJb0fg0AKk8QOJe/w4SXsKIcocOW3aQIC9XHvsuGliz\nwGRBpsmITz1M1Ox2kKwOaVooJc0DJCg/CE2ABG1qTj9AyiZVkAWRiM+ND1/9F0ix8vgvu7NA\nrMZhLY3PYu5mZNPaLJabvnlciR6ST4imGZJ+gBTy0uAGxwGES56mRuTMWaR7iDyxk9OsEPgR\nMDnIEEHU4fKotQKkiSDNyx8cNPaPMtM/AUk9WbYpbccuwgybJoRxnqHB9rJmtqj1udmbgSCw\nycS+Tv0kZdb4347c0eN0khNCRJ7E3gRVNizrd9yk4Wk9IUZkqNqPIz2W6pn1bBG4VxOLxiDw\n6QRWFV7ePs6vH5+fPq0fH8vtfCu3dbnesKSj2x6N9LfKWipWAhc1pS+PFCCJooRmSTyfBGuj\n9mW/shfGjY+X7nwqj3Vp18p9vmSgFnUb8BkqG8eV82U5f3xe4ryu7+sFNu3Ot73WrWxbyUgC\nVQoejK8mDVBmweHexcDzTVkWW/euQgDpZAiS8kvv8sx9lfgASXJnDpKIINmG5xCLz0A3lfX9\nc1vfb+f7x1LsOoW1yXQrtxS4GVW27NZhrYgMlSVWsbiBGanu/Vr1CnaMdD1rd36AxK5BUE4p\n7W4yPUDK3IFzFSAtOW3c+SZIHFxcYYiQGPFSy5Jw9+KdBaQZGRfG3ULo4bXwQj1M1N5ljG1f\nkQYXXH58aw0fvAbNvkTsXBOz5SOlsh/it6nChFadfcs6DVZKAiTPkyFWc4CVHNWJY4KbgDs4\ncGhMbRcW70+5WesUa2ZZk27GZA6yq5AxuT7hoscGQYzPq/AbvuRxB0l7JMEBNynwzBTy6Iq1\nt8BrQM2w3fycoCv+4KCxfwekE0E6dFMXWcwAkAYh2FzWFw7SGxUrqlVb/Cw0D+o5O8xs5AaQ\nTjK6ifvaHWcZDrPDVdKiYfscFoOypRtA6uwMld+zV6JkD3A6ZYDkBo174HyOi19XE55f3z9f\n3j8+EK3f3tfr53W5buvlut3vOSBXswTuvnDG6wOkmSBFguSWCVmGT+73Hin4ZCpLt++LXnNW\ndgcp/h6kVAx0nXRLgbKsn+fl8/3znKd1fdvOt2W74W3vF/B0vyN0V7toaqqA3DOUyILSwBFg\nBEkBJNhaBOBcOhPKDlJnHyCFyMefkIyQTtYnxUKzE0/YllCX82eu97eP2/0N3/RtXdymytqX\nDdnriiW0qlS3bLdhXRBZ9420X0HiOSiClAlSl9i6DhllB2kCSPoLJBgUz25OmUNdARLrOQhS\neoDk2FvJLLfg7UohOw8BIC1INDBX2haCZJBWbOwMFFIgSDqwuQpBKvoHSCBLcUhcTLb5FST4\n1gkgyeweIBlOUHMuc0lh2bMfgzp4tZVDrFKNyKWx9ov/AqkMsfI5eVDNlOxB9RW5KtUWuWcH\nyUJC4UuEWgBSjJ4NZwDSDFptZoc7niGAmABIxcKUAKSxyv84SJI99Vw7zEM0ZpaNiyfJU66Q\nzJGdTayBs+TEnnkQQ5yN50D6JinOtJqi7dgEb2SztCNkG2AZeGw+cu1SKXohG545mVrBWiHZ\n8Iy4yZzO1yrkEIvLAbN/9+47SPr++vYW8/Xl9XZ+P98/7/fP8/16xW8Y6Cgbbhv8aOHRo6L2\nMSgxcw7kMme9RPZgyJIHzq2C4l9ZnX1h6cM907LFlSCx+NoBJDtxkAICsavr+8f6/vb+uYz3\n7fX2cd1ul+3z8/4BcXm94TcgsHyPnMhnWlMNrEQKgSWyASsdnHGnLAMkGpGVReqThmBnmzl8\nJGRRLbGIvUsyQ97j03JXf72/v+f18vx6Ob+c3z5f7lu64du55Xa/rxd8wbuOC0EaITEzC1Zl\nIEisgqlRRoIEHCBzTzw5m75AcoIj9JCjoMgeIHEfmN0STc11qSluPFaJv0RsUezysl1wae7r\ntgSIjHKNa9ZBlySZaPEzERDcW2hhG/di3aSQVZZAkAp7QK8OZFE64m5E33CvAWElV/pWX8qY\nbU8hyy1xPo7G9YKk5+lEo1Tj7FaGVJUWnP1S52U/PJv7NQMvThD0uhHJHfWI1Ak5OUG4x2Pg\n2Sue3UhpKTISJIlXjpMYWP1U2HK3PkDiASUeWJ+jH/4MkP7JZoPcp0z3oxijNuMMkA7K8JRe\n3ngsZ3IGX0qfapz7+cjCdjdr10QFAOcxQhhLwy7uPp9my1GG3ej2UjAdHiCphkWafWoAACAA\nSURBVMVAcyd5TkU2saMphAr0R2gIuFdIqfV6j+4bKPr28vaWChfY59v59nG7fXzeL5el3neQ\n3JUgLQSpKkmQAkGCJS87SLie835OVmXG7iTDhV3vIZGYR34DiXMlZ1+0Xtds1u0NIL3iPwDS\ny+0duQha6+OOpHi/XFcEtbiF2W8sYVvEAmHHUw4sbAVIPHhLkLCQi3mAFBch0z7vjCAFgCQq\nT7WzcMDzDBWpu1/f3vJ2/v5y/nj+RPy43cs1XbYITXndzvBxNxfXLfv7dIe6jDVWBZDs6HeQ\n2EOV0bYYFdodpPwAycsdJP8DJA5+JEhh1DtIkSB5XAT8N6BAhi5Jptt2X5yCgLukDfnD1kwp\nW5Y4Tx7x6AgJhjhAkLLiSUH/G0h8uqceIIXfgQTfOjkk6Wx6fBRfBC5X4tlcDgCOik2G4Sjc\nipSycNo1z3LI6r2rE4RBahAZSCZAiv6kp0otx0I7TnTk7oIID5AUQeI5khxn0SEfs2gy8gyB\n3/D1uZf5K0h/bNfuX57c17CZoLJ2mATL4dqxsZz2AQGmKjSVTJOHFEp6qEn0Q4Mlwsow1wGk\nwUkksaPnCUAJkDiRECC1E7INQFKBda4A6YlWUvQc6zIApMMOEhB7UpRhcV1v2+WW3C/fXl5/\neX59zWVfYK+f1/fr9ePjdr6s692CamMud80nmTTTmqdCd5AQIme4rAjzoBHhGGdVbgmS8GeA\nZHaQPJbEF0geIHmoTS3Xraj7/fV9fYNB26bb9nx9v9zOO0hv+FDny7bVku74Lndoqrio1TsI\nlUANEQGSr1gk4IMgOej8HSSoTgQNbxwrd9Jm5spHpDtIgZ8k13K7vL7l++e358/354+Xt29I\nfdd8vpf1crncPiEUIe/AcLzP9zsYWVJl31ikUSzimuYodpAqQOp+A4kPfVlMDDNZEkHihgKy\nCJIoQPp6OM3dErxgSIutQcfbuYzler8tvBHbJd8rFMdSvkAaRgct8DSvfCgTINx54n4HiaLW\nAyQteRxTShE5Oo3HFneh630VAOlY9LCDxJPByUqWs3tOMWNF9Mn4tcx5YRtCgqRqYCuzJNb4\nBZKDCY++MwAJYTMLpDJOiX6AlFJeQT1BUhZBQcjTAG7gifmkKz5AiglZlSCN5Q+A9M927f5J\nRpoUD7uOPOAnTfN00rHFIlXBrtcaTYF6g0vVeslqPsz4D26OQSsrcfSQ/LqFs9KSB1HEyFO0\n+5nSB0hWESTd7zVB0z4fST1FDt3gdBSOsYF4Tvfb5Xq+Fvvzz99ffvr+8lIqFtjH+8vHGcLn\n7f0GmXW7hnIPejrfjHJYCzkscFeshMZdzKlOi14gyY0ux4Tl6lQ6msXEwX3ywPiaPEuGF0h9\nxtIIR41A5aoet1sVl9vz2/YKWXmTt+073hPwIl/cX7bPC4TlbV3qFSn5ppclb+5uA0jh7FTI\nl439SQqf5FSopbg/OdF5MV3USMe4Cny3zfYACXlg5tywvdHjWi+fzy/l9v7L9/fXb+/fXn4G\nsefl/bZtnx+fl/e6Lpcl35e13tTtugABvCjrJSRkVaq4JRBHK4wRhOSIoIKAVDPPIgasYYBk\nUmFNEjffEA/ZOVOoEpdagt84gZkbL/UeVyPK5XM7rGck/SNk5fVzua2Q5WsVajVlSc1g7io2\nHaRJVhzwhiweM0DKiSAJv4qZ+9ezEMA1HvaHvYwlWMMaVgnLd8QPXDmygtFMibxxiC2HhAwq\nrkUSJD4RYWUHPv8iYHd9l3hECkqu0zEMPDfC6nO1n+kLfm95y2dSa4E2TSwOwtdTsuly5pSD\ndKL43hjfuJPLOlYevPhPj3Uxj9GTo9xPyjZNp9KoQlZg+rJCZk7IkkDf4t9iMEUNfAy2t8dt\ngjsk/LPjET/jsuK+nFSDZO8OnzW7blvHuhqCNBOkSR0BEsLlDBCQkaox+XY9Xz6viwNIzz99\ne34u9QML7O3l/fPl/Pn2xqR0veZ683r+vCF53pFVwoo3hYQxUU2/gaR16b9A6nQ1oSNICiDB\nIcPp/R6kKGzV7XZbxPn6/AqQXt5u6rp9O7+eL+8A6fX+vH2cP+DQNq7qkG4SGeIebhoRWdKj\nYWlvnF1ccIHgjdkbOy47SPYUIfOcB0i1AqS2JIIkABJ7+kVwcv74/lKubz9/e3/59vbt+ScQ\ne15fb7ft4/3j/LZu63ktN0jem75d17QuBfY5sjPsgrcGSIYg5bqfZca1DT9AgpHjiUSAxAMV\njr3RdpAkQeJpxo3t6VK+m+WW7mqul4/bhC96WSFer6DqusHsbotUmy1rOo7mBpAOWwz4Mw+R\nb2TgeSs+fHJ+dus0+TTP0wxdYOLpAZICSJa9IYooM26PtaXBFQ9mSDNbC8CBkaQJJryoshjo\nIc8NCXaxxbXVixsTn70H63vE7BEgacGyduRXFrGlSJACQYLNSlAqBl9PK4KUCNIhOoQM9vGA\noTUEycryB6aa/7ODff8EJA7msFjiUvjZjMM4J61YBp1un3f4AFwJZH4T1uqQPhY9QBdBqQQN\nlYYcLPmI0SseerD9Pkt+Vm5/1szRzWzEamas98JJ8Q62sks9QAojD6zjisNe4P59vl9W99NP\n377/7Zfn73V5//n76+vz+zuEz8vr9f39cj6v61VJ/3ENSt0Q4HmZFBQeZHMHrzCsrKJGHC0q\nsYRZxVlCowzuA/dXwxAkXmuNhIEw/gAJdlo398tqPi7fX7aX789vV3PdfgG8yIKX15ft+/r+\n8f55O9O0II7fuvt9ueULBBp7MtR5VWkN1BIAaa++KWYHqSz+gCTNB2nwM0vY3BNAgmyRPExf\nkUTByefbL8/l8vrTL6/Pv7z+/O1vH5/Xz/vz9Xx7e317f7nd7h+39VIv15u5nrd4X+rqeNLX\nRW5OVna6dyv0HE/JASTnYsVCZc8pgOSzTDVM3rJfH0fPsieF4nGn7GETodJyulhkwJsU6+f7\n2V7fPj7xVS6fH+/X893n5bYZc0fihG42NxHa9s6qJzq8yt3OSn+oIN8GgNRDXUzDyC7/cbCF\nkWJ/vFQ9cDa5xecDUk2EptOnJLA0ZGbvfz7spzYDSIKP7UOxHKu2qmgWJRM3upCmRrjt2bGP\nCaSl3gtnHiCxw0ddi1crB0vj5garjx0Pe2mfj9C0/s7HFAqrcuZJdpP1v3/U/J+AxMEcFqJr\nBwlpes4w69VAp3/ec9zmgpwBy7lxd7JAFMfC2xmMahNcoZB8tMJ2DNl1kO4jrCeulrMZwldA\n2wkjkk0s+FWcVjikiR0Z4a9sq/YzJpfzx8f7efM//Y0gfd9B+vb6+v3t/fvn28vL5e39jIC9\nXZVI79eoxBVCJwBxicQ0enuEP+l3kBa2DufTSMfZVoXzWj8yy5lD+m8g+cR9Kd3eLpt7P397\ngHSxAOkDIL2+nQHSt+Xt4+3z9nm93D9vpdy62w1a9yxK3gDSMm7IPXBrBCmwLVGqAAk2Dewc\nkaSRdSFp6uo3/1TYvoCTSCALs0/bff14/eV7Pb/87efX7z8Dp79+fFw+b9+RHt5eXt+eLzdY\ntfu5fF5uFks73ghSTSQCVz4tkDcEiTLOCz7kdLH8AKkYnwVA6glSUJMSfOplVHY8dMQh5jxu\neA73T4QmvX2+fYbL6/sn8tv5AyHr8wajd92cu6WyQciZ6xyG8e55/Jwg2RZ+0H6B1LlFtCZN\nUz9wAzZNO0gGiSrKGuDXXD7g8xmdT5HbdUfmIgRAzW58XomyECQ4P5geyDAkbLkqyjskTYKk\n9+aF7HajJiSzfeyi5qmN30AS+FbqC6Su/wLpxAOeAInFtJkzVrHAs/n3m5/8E5COlv3RRrYA\nEhzOPsG2Qp8sBfcV2UiUekPgzhtP9y8bTGlNuIn4WmPOAImPRyIn9GR/Et70CkabI1KRVh8g\n2S+Q+BxJmTHN7D/c4706iGTYi/Pnx8cbIuHfQNFfAdKyvP/07QUgvX37eHt+hi2Hcbjdr3Yu\nb5esu+tKkDhcbkHcRbxfcrcJgjQJtpJDCET0AkhWhPfCPg87SLgbXyBFgqTcosfb+R4I0n0H\nyRGk5/MnQHp53r7Vt/fXjytX+Me1lpvgQ9rlrGrZVMnLsLn9KOwOUmFh9WJhLhzeJJzgmnFR\nHiCt/gSQlsiN4hDXHDJ81/vrzwDp+Qukn//6/oGL/f3yfoVZe/1+vl7ezrfPgq/tEM3ClSAt\neQcxsQlDwAWGyPsVJPiuTOv6BdIMt//k4JWCnNQMFRQRGi3+wPu6ZJaKf6bbx3Yx7v7x9hER\nOT62Wj7f397OH1fc48vdh1sGSNba6xRmecPrMmEApIEg8dkNcuzRLuqo0jT23Q6SAEhYGJNZ\n41yjTAVrmnM0VIbp8QRpn5nEx/AcISEBkqnV9gBpQhwGSGIHaYIhXLnlmGbn9tElcso9Qjcf\nZblfQVrW7EcWMDiY44AY/gOkjv2v7yxzms0DJPVHQfrH//rHmw1PyKH7WKeZu3feIAXJurlt\nOb9fSr4qRKjz3dQ796juV5Ugtm+w+86qWps0mQWBB6ELsahBDj1qDSUb9raLnCupZyOxqJCE\nB2M4uyIpXEnbsrRVxQ3xFYri/fV8CwDpG0D6toP0/PL97fWXdyzwz9fX93dY8GsZt9dLNePl\nXtZ4rXmOd/Zo6Ooa222q8KczDANASpz0K5OROr4vkJ2R58nwA9w4S9dCkHC9F6Wvn7f0+vnL\n8w7S2V/Xn98B0svr58v37Zf8+gaQ3nEV3s+w/f58uV3uH25d7ggy63SHXnNYXIUuZcXSXSHa\nqlfLEk/cmYeBs6Vudg1jifjEfKYIC1xzgVh8gxtcPr//9aeXbz+//PWn//v+jiX8y/n18oJv\n/g2J4eXj/F7e3m8BHLvLsqwsFKwpASe/Ekm/BGo6C+m0ACH4ny+QABUyku/Yvy6KWYyBPd90\nMgu7lWZEIcD0vlzebmc4v/fXt/Lx/PK+3Zf315fXj7dLuV/P11yuS7kvMF0XDpO4we/4gsTG\ntoKlckq7QmZvLPxgn8ZhaFWClNMscPNmkHBXuD9sPMYDn1rmkQ/59DFrPo3Phof1qbkfIDV4\nm5ac+gjBjLQ3uBhWz6cYP8opEN0PWE4BEZK9qd28g7Qk3+A9BFRHYcfLsdK8hTyx39+GJSgn\nXdgQgi0G7H+mZ8OvG30c7cTPPNhpNogXAdauyGXzd+jnSylXXTdERbPcN1j860WxpvPKdlYW\n5r1Jo4WeQbK2BEkH+0RacKF18gRJqdkogIQs1/MpE6KLYtvS1g87SHCVn+/vb6/na/zbX3+A\n9EaQvj1A+o51/fb28XG+Yu2+XFarAdKWLkuZw81QRtfNd9tUclnExNY7AGnmqExExvQGcyeD\nLVlBWYkdJKwOdjAFSNICpPz68V9Bunw+E6T7z/nl7eXj+vb5hvSw1Vv6PMMwfYRtvbu1rvKW\nEUG4bGcsb4BkV/8FUmoDh5rlHSSzxBmCZ+VBVgjiO9zU9b+A9BNA+j+Qrx+XX84vZ4D0/AsS\nw/PH51t5fbtFZCr7AKnC5QCk5FbN3iU1hMqRzg+Q4H84EMrHHyDNtAWJ08UDDwxhlfNxP3Rp\nWRHJ37bz2+0zlSukZHn//vwOG/z28vz6/nau8KyXUi880xKjvcxY9FfDCg+XABK8cGUX6i+Q\nXPuUhi+QorHVR2+6eU3NklmFniTHAYo88emAOWQ2+WJJlWevbkBUWcdPkI6GIKVpRXqpA/7r\nuve3FZwO5JUHSF00U2T717S3PMK/lsrO0oDVO25UWIVouoM0s9/fZiE6Z8UWRTtIf0aDyH+Y\nkfSJpx1kb6eJPTWSk3UR6z3coZ/PuKAGqR43c7nfa9guZ7jIteJCRxi6dQFI+E8PGWXxHRoT\nbANcCBKubCRI8gukoDi23uDnWmVlOt/DTrHLz/Lx30Fa3+GWANLLL+/P379/vLwwJ52vd3l9\ngafxl1vd8mWts7vqiLWC7DlsI7L6Iqe4t43TEyvdbGfT28rxxwQJYW3OvwPJhFU4hPuyg/RK\nkMKvIH0QpPLyuoP0en77vNcbhBZC9XsCSFjUq77VNTuWv8wIBgBJbz4DJL0uUCFYsuyxVioi\nEACOeeOpVoK01nq54P0A0scPkP72f94gXy8/f758PkPb/vJ6fn9+/3jFB7ily8fVnAnSffEr\nD+ftp20yQeJEAT0CpPQrSEj9NhaZqpM7SJOcxgB3HnClFpOQ07ayLVt+vX++Xj9zJUj1/fv3\nt/t1fX35/vL2+rlczp+XZb1s9Q7/YS8SDuyqETl3kDyfJ0OZ8Rx/aM0S+gNB6hE3DbtvO9jD\ndiBIZUzQm4ptx0SeAZKyh8z2rhD91Gj8bYBUijvBcJ8Up0CnESDpOmH9rNBrqgg+ZwlsO5/H\naMY4Bm0I0kiQVizCU2ZjT4IUrd5B4sBsjcy3GZPYgIggaVqwfx+kf+yR9MDDDuLk+lH5EAuu\n+DZv93CDfv6s9WyX2+X9Zjd47Xj7/MQHhOirC2Ji3JYmTwjRvmqLYAeTFzihWO9NZ1m8K6Sc\nZ/0ASbaO89zhwmUWaoL2mFTYSl0+4EVezheCRI/0rQKk799fvr+//Pz2/O3b+/PLy8s7lvTF\nn5+vt1Au12Wr520RMwyLzHq5a7Hx4PeiJU+7IgzyVK93nc9vq4iTNDWzLHJIUDc7SHygGrYJ\n4f5aX3aQnp/fv0C6PkC6/bw+vz5/XF/30gOwu+6bHm/1duc22uau65ZZ+MoCnXVLy7SFjKxh\n1lpmLHRITWiXeseCw81E6mXr2RhYzf55Bkh/+7Z+fPvrT8/ffnr9vw+Qzj9/fv94/vk7xN7n\nK/Lxy/L9+Z4h+ebPuq7uBpZqYudgBJMSHZxR8VZgFRKkBHcowS9rlyptvlUAKY9mnPncGqtR\nV1c8Mbqt9/Jy/Xi5fNTt8vbysrx++wadtz1/hzN9/n+8vcuLJDm27X09Ivxh75FGGmikkUYa\naaKRuIOjQWuQIEiwGARBgIPjODg4hoGBYdhf/q1lHplV59xz6lb3ueer7q7qzIqMMJfpt/da\neux9X+5I/8syLPhCTP9RIsuNUPEWUUiGbWFQQFITpFomXzYAqSwFAHJa8dC5fEM6KnM88cY+\nAqxrmwCVo1qkD5YvwNi3ZisVmyOvV1GnyaLeSgmWfava2OCb9SyrGFngStiWZUWh1ff+BVCw\nSuae5cV66Dn8WX8EgpE1DxqA5BoX2MxW9kiGpSBIcGOd/VsFIv97ILGGmK7fzLHsrGPpmrzU\ny+ymmSDlb5A0QFrddL/ruVvzg4s/xi/9LtR2XU0WtLvhZKw+bCU8/W+QyrqDSdlAMi23sLnB\nXXeNOW0gJTiiJ0j+x38C0ifeMUD6QoSeB/f4mkafxzHjCda+3T1EZsOypWkJks9wo4kg1UXF\nWu21hYSpfVnyzIuT5vQLpMAjPm6t/DdI5wU/BCDN/ftvkKYf62+Qzrcp9evlOjwe14xx8HNe\n7bQSJPZHD2kFSKeVlxGtAkgtQQqRJnIR0bP50m+QFjz7E6T1P4L0YwPp4+PH1/38fr189R9f\nawRIxQbStNp5A0k+QQIr8B016215ySqzv0DyG0j6CRKvNitMfzYlNBHKctlA+hrv+G9ehsv5\nK1/e36HzFoD0df66z7f7/THPj7mfEObUpPANxy4n10uCBJ+V2GAawQkTPHENFhxVgpeGjUrs\nKfYKkOo+nXziZQ3vmsY3EaqOZTZopj1BMsEYbo/FoHlBryoJEvhrYS1aeLoe8gwg8VShbXlv\nEM76xe8sjAHy4Qtvg/SBRWisO2iCFLR6guRDp7pW9LXwvCttW3avN/8/gNSwN0i5M6+FgMPo\no+qnaoY4n+6XJ0jTcJvNMs2LG643N7ZLvmdWhotzv+Npp0XzgElilVpnMCCi4Sa8472zpj3U\nCAfaeducTM0quV5jbjdCF5ogZYB0uQOkwf/4x8fXv318fYb1jvl14aS6fr5/XPB2v26f1/mW\n7p/zIy7j0C/rfVk7cTdrHXU/n+RahgRMFAxb9K48nZD+rXThsp78aad5qkaZo8tmAwnZFIF7\n6TBLxydItzPcvZkzXNn8BGn8MRGk+Xz7uJ9hwRN+iVh9WUfMr6lf/AjTyBPNUYT8BMkRJM3Z\nZ7j9iVCb4gJvLIPTC7dFoLHmac73x3L++Abp/PHz+huk+8cNku/9H8jCSMafy/tHTsNjen3E\ntVcjYlvm4Vi9Qtd5CdMRjShPFiCxNB+LxfKD2Q0kZZ89Y/RRug66p2MxumynZVnHfo48mjTe\n+3lAKu7P7++X6b58frx/fX3cpgsc6Tg+pgXqLqvJ2NiPXZ/d2vFqA3tpCN0i+QQLE4jsCydS\n8AQmBX2qDBTcIQUBkHghycAY1oicBMm/YLKz1RNA6vCgliBBk7KxYXt0vLNU5UqWAW6r6VvZ\nCMwoli9qG4VAGOQulLatuZb4xlrovWe/QusKZXnX03ABxHPfW4LXLrNosW4jF78b9XdB+su6\nJ/8XkFqApIqd3Z0EHMaa9AaSH6cbBHMe5C+QppkLpWEUS771cAIa82kXpQdICB8srcG9vwog\nsbx3x/5PAOm1bn6DVH2D5IqGbUQAEg/wbyCNg/8JkP7xDdL58/L5+Pp520D6+vq8f1yXa3/7\nXB5pmoZ1We7zKszNLVU0/XxQ3yDpDSRbnArevdMeIB38cY95V/Omp80shkjPAJXjZ5EAUjzf\nPwjS5THAhgCahSBdPocf42+Qvi5DDNPX+X6/XZYRNhwghXFekYQt9Q7A9n21utTDuAAk9mTB\ng5gKIPF6QXDqzyDdHvMfIH1uIMHkP/4M0ufPy+fH/P7B6xbISABJjnglGOMQuRmNmfoNUoXA\nzuoGAKkCSJr7TJ0Pyj1BMnv8P0z+VvJEhR1ngjTFz8ftc7yv0wPGqD///HmZbvPHO0G6jhiK\n+4CRmQFSr2ZnoQAFQWp4qyptIDVsPgV2Imum103BagsbSEeA9HZKUT5BkvY3SKIOe8Qd0Qar\nW9NuIMG0cnPLsecL6xyEOpXyCG8nqr5W3yB1jhszVkVJ/dNUUUh12kBy7CZq2T53A8n8BolV\n3/Kh84rVW1olGv33M9Jf1RD6y8WGruNq5BEgHeH84pL1OlYT3tr4BEnAfd5mC5Am+7hc46A3\nkOC009S/Jt7UVj3iDG/LwQfWAW9363PsCFK3a+qtebKtC7aWwxs3FUBSugRI5gnS4wnSD4AE\nSefXB0H6Gp4gIR99ft7fL8tlvX2u9zwSpPmOn+pvfqmT7ee9XouQuLurcwrenk4VQTLfIJXf\nIJU2wQOyju4TJPULpMtyv/wGaR2eIP3EP78eBOkGkIIdP8+3+/XCM7RphDoCSNBZCJT6CVLz\nBAm2LzNjEKQihUVblqL7BZKfx6l/gvS53D/+8RMg3X6D9Pi4biC9gyeo2un9PfbDMLWPsPRi\nmPyANBfwgT1AauPWKrXCHCFI2m0gud8gebYDjIV9075K2rdCW+jRASlpA+l+/fcgjd8gvV+H\nXyBhnDNA8i4vo1g3kIT6BqneQIKiSo3zTXtqCZLB534xrX4rU1RrKgBSRx1ddU+Q4gkgyZqp\nw7Abr8uhJ0gIeFIU3yCd5JuXvity9QSpwJxpCJ6K4gXqtSljJ7dC0a7f+kKyKCA9L8KX2A6p\nBoIEmXdo3VYGqWMzKvjEvy/t/pV+FCwQyVLdLzv7stcpJXjadXiCBI8EkOonSPM0jfZ+vuRH\nmOOtX5co85gP2YR5hqAtQw894T07i4oDNDoPWbZdJV7aWmzuoCxNI1lC2pxsBWlQGWakniAN\n18s0uPefn5cfmLz4+dB01/N0foJ0Pn9+PH5e1vN0/UzXdZgGRNXbPJsM598kt05H2x8JknWG\nt0B1WbJxuINQvfSlh0uAlAdIjfkGCaIzWj9ZhPsxnB+f12XATB71mj4f1374uj6uX7D+Z/x9\nOd/gm8730D0+vq5IndMwrGlc5zRMa2bJE2/DuqyuF79ACkjWKcBF2tfoF5auQAxmeRNlQiCH\nV4L0A9n188fPy9f7HSDdr+Mw/ny8c1n8J0D6+HF9fx9+vvt1GCf9gButhzE8ECYib7gbuPkE\n2WJadgvLPJQLkHgkWnpPkLxiMQUVS7ezfo9oV0NNLbPhKdzhN0jLSJDyF0G6zu8/f359AqRP\niL47WBofa17VGnw/j3LJbmkjskpvQxLmaHRi2byQ6uA7ceo63iEESCfT6D3X6Fbe5E8lZgFA\nchWcYoOvxVedktJCVzo67uwr7hLDCcja4E/Cb+7EDh+m3udCtyLW/tS1roZNooYugrRtEUsJ\n4wD1vEIg8jSapPpRUBpiK0AW2HBT17FoWQ8EIHVi84nHfyoj/ednhPDv/uuMJEVt1W5n395M\nYpEEsz6qcXEbSH0ejgm5aXEEydy/zv09zf66rktCqkqnbAFS14s9QeIZrDY58RoS2eHinNjz\nRAOvDRfsd90QpKOpWb0VfwdIa3/DWyRIHz+/Lj/O17NaR4RkxP7z+/0XSMPP8/o1Xj/jdXkQ\npOE2zba/5qlLfp1Ort/zkgOkzQZSBe/rWcEOIMGQdS65KrC+UWJ5XoLE0w6j68fH6M4DQBrv\nt3HUffoarnk83zaQYI2+xt8gycfH5xXzbnogUA/rnAESjCLlrN9Akr2Dc+T6XB+RMfAgdgeQ\n8Aq567OBpP8A6fPHF0ECOR+Pf/vH/wZI4x8g/by+A6Sfj58/PSwZhICb1+LxDRL0UIRNrwiS\nbjv2jCJIlrVZWP4++NzChvhuS1L+F0iNLebJwJ7NQx7j5+36ORGk6xOk6wbS+xeE5uPza7pz\n5W64rxliI4Z1GtXS01OKI0CKqTNvTEY8OJrZKkM+QdIsxIlcc2Ap4TVWJqUjZgEDZrmB1BKk\nt4SEocsNJNcr+w0SMitB8jvxZpWr3nJhNpCODUCCPFU6dnyJbZEOCllKp18gabX14ABIki18\nBEEypgoVFyIIEptR/V2Q/jht95/B8uv3/1OP1MquBEgv9vBqc07TapZHNb4XIQAAIABJREFU\nTZAGgpSG3ROkhSBhSkFczRYgzantx1QApGlqe7EDSNtltzY7/CIZglR3hTwCJK6Hm1NlBGve\neBZbFEZUtu4cQbpfJiom9/l+vv683C5ind7vX3AjBOnj44yg+TH+AEjD5TNcIOqG+QnSeu0n\n8Q3SK0DqIW0gOb2qG/kEyV966HPhIkFymHJuA8mEjiB5gmQvA7cjH/dpgsE6j7cEkIbb1/B+\nP9/Ov0C6RXt/30Di1YqMuJ63u0qOZbr9sn6DtF2dcwSJBRLsC6ImtB/Lr83mGyRw+AskaNeP\n6/lj+Lcf//txw/D+hLT7+vG5gfSP2/vP+8+fYRlnSDqAtH8MgYulMcdEkJpkGqg7ASmV2KTC\ncsXMscEDQbIECbai8TvnWVCsqt3rBJDuyzT+BmkGSJev/wjSx+d8vxGkG0ECu+tIkOwsE0Lk\nijzTmd3WgtcDJOYaWbCvpyRIFG0saG3WUJucdr9BYk/bba9ol+EkFM+3EiTWebYEqeWtDISl\nF8FqruUeuW0D6VDzbinyq0ldE7TpyrTTUmwgsYW82ZZn3Z9BYlMbW0FxQsMTpKZj29y/m5H+\n4q//C0iqfbNy92bLF9/3aVrscq/HFSA9bkMfxzYiNyEezfOob5/nGbNOAaQpH3uWOfAAqepF\nRZAMi6v1Xr6FDOHrwqk7yLJrZds5q/eVZpsQmMw3wXb2BMmv0HZ3mB/kBHf+uMBt36/FunwO\n5/stY459fXCT8PNj+XGePx+Xr3iGK4bNuEPa+fm6Toq3TY8+7wI1cwgACa+MJebwngBSkr6V\n3yAhqEGMZC6WQjq7ATNkmPR1Ot+XfhpmeKZ4nR9hutzH+3n4uF/u53kFSPevr2tKt/ePy+N8\nHu8PyLpl7pFalmxzqcIG0qoJEmbHCnlJkHrvToHL3mz+EGatef4wLBtIy+Xz53kZv94hsb6m\nf/z8t+G+zOP7AK5+fr5vIIGi68+feZ7WKY12Xo8A6Q6QIBoQpnKEJ6iTEtIWartMyosLEK41\njxQ3bCuFaM0avjvv2ix8UQQ1DvoGXzSmKRCk5QGQbtevdP73IL1/JoL0gMoFSDGnBap3Bkjb\nVMbHS619sybyDL3NBRtM1IJ3obSxiXXST62N+OCNzpEgaeiPIljZgGfYqtfM6oMHSZBMr9nW\nGyCxBDhBMoWokdqKIhXQZMioB4SAEpFPm9QKgCgqpDnJevxsCWzghLmKhECdnVXBQejxZpRz\nFaYBrAYcGaYD2+b+j4PUqGYHkPa2egt/AsmOI3TyGkcVx+nxBEltIM2zva3r2O/6wde/QGoJ\nEm8VNH2Qh5C5WhPeujdZAyRoOAxgpfQTpH3HHioEKRCkxyXfb+voLoj4n/fH7bCun9P5cU+X\nj/H8DVL/4zJjZn8xZUBk9vfrPIfpukz6CVJ63UCKcQOplaz89ARJuU5Sp/PK6B8gNU+QJoI0\nnx9LWkbEhxRuywA+H/AOAAmWaFkv3yBtR9KH83m4P6YE6vphygTp1EG/9atddXZ95o1YHvVI\nCSDZMpgeDokgLfDifwbp6/28TOePL4A0/+PnP2D8l+nj34P04/LzZz9P/ZQJUvV4AKTes/JC\nBkjwewBJKreXidW3jUrMCCVBqj276LIzighv3rH10OkY3TDo6w0gRYJ0+1wBErzhOZ7f32/j\ndfn4BdKHxUcHSJc+zypA7Q+TgXWekW9EWMyawcoGEhJAPrGudyNYKV1rm4ztXMGyOasjSAfH\n/sidBkiqYZk7Y05ZWEOQeP+jx4MSJPkLJFXx7ZmiioUVXSzDvq5cwc6KAGm7yVGnEuj+BskZ\n6Q6MJDzP5AGS2pYBecEGX//Pg/Qf7yX9p7D9FyDVL0YcDqYpUr8mSCaClM2ElDStYXJhmgcM\nDEKmBEi8/51vXEQt82CaNQCkLnc68EQ4ZmzdR1UQpODCrnuRGGNZ8JryrmSXAoLExVJbVbbp\nWP9xHa7ucc+TvZ1v4xfwrft87q/D4C9f8+UTsur69Rl+XEaAdF7PwxUPkpDC5ogoOtkUl2kf\nQhnY3C5i/gYrBFsPe4tpfcUsltR5tWdFULh+nxW7AQOkR1yR3NxtvULIxnldkVseeXbLbZjH\n6/h1v8Ew9Zf7++Pr69IvV4A0biCNcZhh06Y4Z51fT8nNuberYROZwKvlfW8JkuMPhcDzrFS0\nKLmVMVwRnK4DQPq4rvPl6zIgH//8+DE/cj99jp+3y/vXx4+ft/d/DD9/IFMs0xyRk1j15fHw\ndx6O7TH9ABKvHCWhNExFAqncDOXPeXPRpgpz27PneVDx5J2G1jvsc3o8FMszTXinvOyVh3ka\n7rdzuLy/36HeP7hCuoFkNpDuZzCkYPbmx2znXs8+ddovZs6NrTdpxzoy+8Sr/RhjVmKwUJkC\nphSOZDUs0lQ51g3lmX+jW5YCNyweDP116EBhlL2WkGFbvZrOgL+2bTvRal21seBh1yIc2tIe\nHa8V4qezFiWtliBICFm1YRdhv8PrtgTJOZa2JEie55ZF2EDqCJIzf88j/Veg/J9f9H+CVO2N\nOB5MW6f1D5D0NEL4rH4GKfOIgVmWb5DGiduhw9qmDSQ/TTJ3xrM/iAVI6waSe4L0KluAdIKW\nlRtIDUEqa957JEhp6fM6Xs3wSBPD4HSZpqHr4yXB+9vrGRPufEbQ/jQ/LsP77XJZvh5XPEi6\nQWAmJidHkA4YsAB/hPmb+2hZ+RYvxxAkzGI2XDUEabukTJA0QbKPuMzTHG75Ni3OIasApDEu\nbr3Dl1zH8/3OS9/X+wdAOvfzEyTM/MewgTROYc4q73bZzQkg2Wz75AFS19NH9JAajZPb5Qdr\n0iI3kFxPkMYVHv+6LtfLFS4FIP1cBu74Tl+36/v548c7QBp//vh6/zlPS5iWGR5JPR7uGyRJ\nkEJyZdpKzmwg+W+QdgSp9EI7YTv2USl5ULzx+1ek/rskSEjl9ut+/4r4mMPjfvGXjw981PXj\n/fMCkIafH+nBmyv3c4qzin+AFOCA/KwJUstWITYBpDeCxDPLAEk+Qao6OBIYGEQRtuJpm1ZU\nXIlmcwr8o4ccE6eON1C6/whSU3Udu4xVXSicbMIpHEQBkOIfILWpi3+AhIQmwm+QrFNbjoJR\nbJRuO8yLZIT4fwbSX/z1v3a1qo6mO+F/bf4TSGpiHY7VL9HPy+Rtv/4B0vJY+mGV3yCNE0Ke\nZQ3PbdNgjbokSNGFPTxSKyRBUmJXCqtaD6VTE6RTZZouzxtIehziZIfbY0aiAZbh6uH9zfWC\nCXe+PG5fn/IHIuXtcp2hhQBxhESBU/kF0jGwSdZvkECONfYJEjXPBhLsOKYbTHiWWuNdbCBB\nr8Z7vM2Lk33utfez723/wNS9wSk9plvobw84ta/zuoE0EaRhCBtIMGkA6RUgLbE3G0iQK25t\nNpDWzBbpIvFi6waS0Dx2kAHSjSB9QR7frrdpuPXvn+/rGOJ05qf7gH0CSD9mgvQ+TYufZoJk\nHg97jytBanok20iQWHKm7BLvR2iFSWyeIBVPkCiFeFvKAqS33brenyABTYMocfYEaXhc3fXj\nAx91/QRI5583gMTrfg+CBGuH1Do/IPezmkPurJv1lHmUVEN1gKFnRpI8kMue0Inzut5AkgSJ\na5pN07Z/AklAW1tRsKtZZE13x5aLQrU87RCbE/xzCf0meEi1Dqd4lCd78FF4nVgtEXgkAZDY\nVXFVAClY+ECNUJIAkiFIvMITAoYGIIUGIEmelrL/8yDJstRtcVRS9usauRp2r6YkEa1nmmgE\n3H7ROvfrJO6fl+k2QPQteVi5gNys0Y2zSy1Ic9stnHJNuvTZ6ehj1ZYdO24h18tuVwEk4fAl\nXI60r6Vp236GlZ1vfp5Y1m0Y1jseQWd/b4dl1bdbvF+uVzj/L/3jdgdIdwiu65qHgAkx5+E+\nzz4CpCrQYrqey/d95OFHaHG85OhumGEa+loTJHa+lgTJZBmN4TbnsvQP8+jXUMYI5eGziDaP\ny9o/lttjXB8GE/8Tgg6G5gKQlg2kB6YgTOKC2NzlY9nbBa5ldQQJbqWvAFIkSKrRLQ/RGuSE\nhQss3DZ8DMt96pFc72uCD1mmR/48f+bJuPm2IGh8Xr/eP24fP/qfPz8/3sepD8xKQP3x0A+C\nlNMrQUrJV6lRNrYND2wGrbhklQoXTTwhOzgJkDhXnfHQeq+7eQZIl1ue+jjLy2O4qIllZ8ab\nuX19DdO9//o4X88QeT8/xjuU9XD7im4xMLGbRc5qiblzhpc6KnxzmCcHhhKULZuOIEbVGOjI\nHWq88mDWrlXJd7D9LKvTWHjVxAYfRnH/QZQ1/Ew8rFpsIHWqaa0woTrJTpema1SAPi3DIZ70\ngbVQRNC8BoV32yWZSCpBaoyMro1vQHMDSTppNC8EAiRpui6GLvGynWQT3r/pkf6V3dhfIFUA\n6aSUJkjzN0hiZq3C1fUcxFUqRCaA9LWBNExLGlfnRt8s0SI0xzaYreyLJ0imYPcQgFS3FUBS\nSNRStDvkIZYy8IYbZHa3gcTrY8stUrxYHr4ZMp26e+zGNav7PTyutyu3393PDaTHCPGzpsFf\nbxkgPeY5AKS5CUrxGvYTJFbvcRozCUYCICGGwYHWjnf9eU0qS2Wz2kBa84r01g1p5RV1ghRq\nb1lvHHHi8Zj6ARN//Jwu5695vLx/XNYzsB4fjiBNi5xzm4oaIFmA5AmS+TNIslFN2EDSeRVs\nGxS6+BhZj+F+vT56/P9xnYf8dfmChjLzfQWmX7cvKK2Pn5kgfRCkZVowSP4bpJxj+Q1SnWqA\nJGqApHiEFVMy1S7qXyB1mHk8APoN0nRDRrpj3OIirsN4EXjBSEp3fQdI8z1/fV5g0e7T+8fj\nzjIZAAmfbGVGyoinakm58/obJCM3kGQqf4NUKoAUnYpwTN8gOQGQTg3E3R8gafhnKyo2mYqv\nG0hQvJ0CawCprFSrK9u2GiCpwh9iYd70L5C4lKAFQGplYIeEDSTfxL3WvLbg2FWQG8MbSJC2\n4g+QJA/v/HdX7f4apArRQTfFSSoDkAJBehCkBZMlI86ujiMoY+5n8fi6TrfH9JiXOK5ej7He\nQAoAieODT2CqNQMkdm30seFfAIk7Sc2uYa8IgARr2niC1DXrlMOy3NO6mMX285THlKONbjjN\nfVT3h3vc7rd5uF78z/vt/XYdB4IUHt8gQWEh4q9zy95ExhOkmCPCUyRIYgMpSNPwYIrjXX+k\nRIAkf4OU8InHcuT1B8fDL5CdCOILJuvQ47unUaRh+po3kM7vH9e8gXS3rEg+rQ1ETqpagKQJ\nUv8NUv0LJFGLmlfbN5C6DaQqPFgCIiNADD0U4tQvUz7fLmERauYyxON8P398AqT0/vPj82NA\n/ljGFSCF3xkp4iemmFOoE9u1yJoNrKPClOLK1gaS5W6sldBCsWO/ttK/7JB77up6T3NMi0A0\nvHbQp0hKD8VF/fmRzgDpCpH3/nG/36A6AJKGY8R7fyS3pidIcpZjLqGgxBOkWD9BghMslHiC\nJFjgqRftVjjPuSNA6rixlSxLS5lkNUAqWT19T5C0ZimacgPp1KgWirgBSHC1J79PpX3VJx9E\nYLucb5AylxE2kLQgSAeA7FjvqyVILC671b0lSDCQEhPOSS3/x0Fqi0ZVxQljv669XzCjh2pO\ncl1SiuvWeyuEilVxl3Y432ZYh3te/NjHcsrlGs04x8BrQSxh4XSDlHLYhtjHrupKHhBqRSvK\nHdtuaCs9D4gApAKjiVlkIa54pgs/Kc0BP1NjSGYWRVWPSU+P4b5Ot2t8f9zeoeqGM0Sdfbjr\nPc553M6NxnWBmuD1ysyTdjlCKSdoONMApDsXTgv2luEiq3ddC/UmHK/m6IdfQ8JEVpNds1eW\nILHxGb5FclNaxt7NAlb/vF4vXxNX4m/hchvv093Oc7/OuZxSkRrR27VOdg29QTZk1e682pDW\n5JqqrvCpuFma+7ZjKeHGw4ANS+LaX28nto9Z8u1xd30rliXfp4HHk74en+/p4/2Dkgs5lmfe\nljg87JB6l3LQG0gR+r8S4KaMhj0xREBsR9QOKrCxOw/twimxx2GMhd8dQMbdXIe4IDZqaIqb\nhILF5xjleL1O9J3nO4+yLggYd243XT+jWPFGABKEO0/PQcE3kxz7o2OtiMwrN6FNbEpmfDCv\nMJ9smpvwT4DEFodGQG4e6mfrIl4kwtuweLNGlifLXsA9QUIubdS+tVL7V6F5AaluWQpJvfkD\nUuyOrQFaFhIEc4qVjDLdjwurbCD0ApwUQYK2F5VjkcwNJLDLe2iAGSDxckf3T0i7f8kjlU3R\nqupUtMb/GSQFmRdDbz1+7S0CCGZsOwKkxwAPvtipTy9zPq1Rj0vyLQvOWiRl3fUsRxU3kEQh\nTtUGUi2KnZAbSEjAuvCGIJUASS/5saYsVxcCrH5mtUNI8wStMsCFjONjne+39DEQpPwgSJog\nhSWP09wngqT8EyTFPZakDC1apyuon4cFY0etat5NDtsds8xiY4YgudXHHhl41msfBNiyodGW\nNeKSmSNkp1mkn9cLHM03SI4gzXe7sNJ+auZ4QORDJjpEgLQCJGSdLPJqNpCqsiptzOxenL5B\nkg5ZFBkGeQ3ffsbT9n2+j4PD3Fj6NMzjbbidz8PXR95WowlS3kBKw8MBJAuQ7AYS1AtA8tEU\nAKlNcgMJDxCk/wbJIAkGViCOJ78rBmR3C5BWA9ARgu54w0jr/SSn221ex3iFcbp/jevHx/UG\nBciTjQJSnyDxoPYGkisnMfZ7JKGaIDnBMyKaTaLxRiGfPEFSkAMmm4YVHjaQOvUNkmbEiwSp\nOELnI7cCJPzT1WrXsj3mTuoGU6Ru8d68fnXH1OD3Ko8JJjKsoObehcxNF6LdQGozQCoIkrOu\nK13LdlsEqQZIKkEQOoJk4C3+icWGf+Gv/7UrylMrT8eiwpNBj/QY6LFcEN1769jHmAYADocN\naqrp+lggeW7dYuecEdJ2G0jZdaFM0BIQqzJns7OsxAmQ9nJ3EkZ14qgOB2hHZTBKUByv3ryc\nrDxCzDVrHJaYWJ9MLswGO88VQPxKjb3monteh0f6nG4fj1sCSPe+fbjbA85tWpDAfMATs7Mk\nAIArskhpBqpD1+IQUxh8rMzeqFfeTUYQq5uQqLd4S3vgUYiMpDbLvo81r/1HLu051sWGA1x8\n1yvb51t8XM/jyDos4nqfHuvdIVv3S1Bz2EUhss1v0fWcoSyVm2EnDYsX2qKsKxMz7IFIuWFf\nVOfNQFHIkoEzOFVwZjGPCF6xE8hj0zpDOF+v4+Wzh0O6XB4AiT1BE/LvI0D4ahZvAUghe4LU\n+WiPrPaaRENjGHmO0BWa7b8s73lvIIU3v2sIUrjNodc9wuUaH6z7ucQ8S4zvmqcAPTcN5yV9\nQuINbp2vX8n0eQ55GYRJXvcpNe5tBkivAOnAeGM7x/o9bLriWTiXZ0h01qxznxxGlGZYHcvO\naARWCYsaG97xxLx+OxopoyJILDxYaoAEgbhXpsZ4NS1Tqd65E5LgTtbeVbHJ9LvKsPEpQEIs\nJEgV96pKeiT80KYmSDWeKvDIaq1y4M0tLYSSTf13u5r/yyCdTq04HsrCRh4ey71JE1cMMgek\n53HLpNtux6soxXwd1hEglQvmMSbCL5CsCEc2W0RAURtIHiCFKHdyd9hA2qu3k3qCZGVnd968\nAaT9NOVqDZvJ6r2rF7aPKnkHE5NZ6jEbrhXmfhzS13z7GO6ZlaNyDZAGuyRe9czeIwF4wc2I\nJLjhD5Bi73TZ7AHSGOKJR5V3JfLcBlJMomJ/B4KUCVKfFoJUCtgmehwunYeut2q1ddaA4x6Q\nJMbp/HUe6ivmeQ8htkaWuAdIQQKkVIY/gaRTr58gVex3my2v1mUWI4EjNiNEIWznsLIEBiZg\n5iOA6E70wi9sCzXA6l++1q/Pz+uVIPmBvTnzNMQpgdKEOGPYvxYg1QDJIR3GMm3N+nipHGNR\nKBoyyytJBCn7nd91D4AUb0tgD50AlgYe8F0Dz6WO41bchceULmvcQLLrcv3K+BJkzXVgT9sN\nJHuaxYSMJPXLN0iStynYvQo5xUu60wx1AGRcJWPX4D0eTlwsBzUWv1vDTUF8qd2JIOmePWNF\n8oV+3UA6KQNV49pvkGyRurCjpS5C9Q2SiV2CR4I65wp7mWPpKoJkjKuaP0A6trbST5BYRURW\n/+MgnQ7HVhz25ck9QYJlnwESQjupsGoxEdrsiFcTDwss8jQuV7F4yDF4KoI0rQRpj4jT7oTW\nKZs9ZvUvkN7wJjvxpl9Klqm1v0HaF1a+AqS6J0ihyd7tMbF6amP2bsSwTjxqk2f8d4znmTfR\neoD0yCVAGi0nF7vqAX3Lu9aeZYThulnAHSAdi0NIYYrxYAqrXwtPkKCcIwQn6+hrORKkNGVO\npT4WG0hUCJa1StklUBeJdVMebvwGaSxg0h/57hNB8nb2BAlDxfKr6QmSTgTJhT7YqhGVxsQV\nGIxUcSkLPmzkSqFZh4wh5gEZiNEls9+pzAdY+mVax8d9up7X89fn9U5pZ58gzUOatqo71mfD\n/dgNJJiSfbKxQHhg84lAgcP1M+5z+j+DJB/32yPee95n6mPMZuT+MVziqtMEnT573jyebn34\n+oKARay4fGXf97NL63DgTZDMW3wVQFqPTujdBhKMTfgF0msDhpGfsuHySvAAqWGh+MNROEOQ\niFfF0w0tQCo0QGI3C9d1yZ/0ocMbx5Ob0mr/BAkBGQFiA8kcwwkgSYIUOq7afYN0QghkdUOz\ntfVtAZJrrABIb60tAJJ9giTlqfrvF4j8a5COh0PbvrxUyNUItBYTMSxln1wMFScJi+PgOdqq\ntX6XH1Oap/UGjFLoY48v3EAyMhxj56pdqXm8uoJhkYEgqcNOOiS0o9nVxihlDZuhu4PHRLdy\nN85ZwdLP3lfJu9eVdbpV2DplGc1yH5j9S4IL8tf19jU/ZvjiMVUPf59gSODvwQJcw7anHXxs\neg/pZjGT8RKQhFKYU9wZNrUuPPKRTZ2MQbwiiCYFkDAZE8zQChkbDyryLB5PrXUK3hamtiuC\naZ0Z9Xy/jPPlcpkb3hsNDzYcz5CfCz6Iwpd6EQJBSlHih0PNKYevsI3QtYIGgUUPsVL4AaY3\nk+/XVebR9zzl0zqGLkxAB0fR6Yh/mZdpxA9cruev+zCMOQlmrzlD4+LTNDGATR3pEQCSDNHv\n8a6KKFj0wLBOf6tL0eI7Gs9D8ATJ7fxBP+73R34EDG0PsRGbiUeo4HMwP/FDw+pmPNryiPYC\n+QxnlC/nPsIK6tCPe2kRnhDuTDfLeYWnN0cXMVUaBZCsZiZSxyriEwAkuxUE8oWMReFc+7oT\nnpW9NbRJKtlyttaqQFxFMmNbmBYg7U3ZsWNIqyzCnmc5X4SCnamSinsJ+3TE5LPcJWJXCjaw\nzyr2slGvfTwpTC2Df+dfhQdIyJLWx5fOvul+A4ktV9Xu72Wkf/mv/7U7nA5Ns9tVR4IULMtj\nrmXGHPMlkq7pelY6lF1ZW/cCN7zVCY19xjtPeamfICGyFrF1xeuBIGnI3CdIB1U8QTrZXQuI\nCJIWlTs5fSqdIkgm23l2DiD5Y9/AKumtWRhA4kFJBNg1ctLe+9vX+ph+gfSYAVIPs5At5i2b\nI0PjxKr3JUHKBOlUw0ksBKlzBkwQpCxU8mLHpKXkBEEf40yPlfuw36qJ4OdZzd627MgH+kxt\n1KSWxxUgXa9r95jm0T+8xQTswbg9ed3wOJ+PGSBFNhfC+GW2Zfa2lTDcCJ2Nb3wsAZIyq5l5\nB6uLCPlsYNGw9VF0J2sRv9kirAeQy7wM1+V6+XpMBKndQOrXMS8pVDGATRV51L6N4PQJUhkx\nqr9A4sHPjlVKakWQdA/eT+bxuA/9YBGaAJLwBAkDwmLKlhU4egenBhXnDVvcsAc2QEoASfo8\nwehqlolqtVjUskJBmXIDCYKV22QC0g9sxJaNxHre1YOCA0iHo/WYWyJsJfKRiFLBQ/g1V3+0\nUaxO2yHeZP9mqs6xjPETJNl8g1Qj8RxU60TxGyTn69gZn+UG0g4hUPwGSf4B0kHYHUBCPpBi\na43c/E+D9Fofmmr3UhUITjyd4GFUKrxcGLyui7qBVVJAoNwbU/iptz1rn+QVmhleFa4EiSNr\n7etQu+NhB1GTNKIMeAhBVKp6w+xouorNXCwtsFLy6Gqr2exvB3vlk1kXXsAKvuiPDQ/psSp9\nwKguK3eD+uBZFSrdL2kYp9t1jvUDtsFkgAonh2GP4RukI2NvcoogHUWrU0CofdF4Al2xVZHJ\nPFYpDgnmWcgZsSLF2a8rZpJ/s5HRlc13ahE6/B9Xlxbfplm7PNym5QqLIYdlmfUAz8MixGE1\nldel1+wIlFNvAv4cQMJLxuRka1O4FDx76WofKmAq9GIxZLk/+Umz/gKUCPIScHVmjx/eKIWw\nnxDQpvvyuF0w8UdkOUgunqWa+jWxia2Aho3ea9+w7hdBsqEObHyijAtaY7TbtjV4AxWmnTAw\nupWv3ePxGNex8Tw918Piz7xZy1DiOqi07aQgqF+0vF9uj7ys9nLpc1oWZJm5btiilAcQ1KoR\nBxw7PLDLZd1KZ9xWgUFVZWo76GNeTNMsrSXjDm65BkgYLoB00iqdeC0MYyEAuQpb4SeA5F4t\ny6cgtUlHkNQGEnuAI7+fVGvL1pWJHwbK0BW+286g9GoDaYcYj98HXIVibhdIbT7UcstIeMdS\nmA4psPt7IG2XYP8lkF6aY10AJ8RyuA3L6/+pSmwtX3dd0HVi92Gpq53RlYUUS2sCSIuhQ19Z\nr3ghSK4OSDTHg2aPBGExMzxAqmV9wDSuxROkliBJ9Yr3oKsNJFbwNf1iLCxVKPNuAwlT8xsk\npXyDwfBeTvFx9cM4365LrIcw9sjZvk0Q5FsBeZYtdOEt21eEeEmQ6BM2kPaa11RqggRv6yBW\n4WA1QWK3eh5S5ZoyQZLc6WBfRRkEGwc3pTHHtu7bNN6n9Xp/eMVNnzOEAAAgAElEQVSlAjW4\nOnJZO6669ogueP1hAwn+3/IeXxLW/AGSKlzlPAwxFMlslxZf+eJ4ZwOD3D1BQs7cSe9KKE+W\nuIdmHpaB5YMJksYPzXNmUuK9Ksx7CDwedUbswmz2h8ym7QjbT5BUszX+gjP35TdIELet30Ca\nuMwGkuHl8AQOWhYBrLY0jUhQSKy9lI/rjc3d9S+QZFjqqsOAwO8ovfKUBgJAu4HU1BtIjQlm\nA4l3YJ8gSV8JgGSeIHlAal8QJw7KdQRJ1QoiHr8NkGR2LxtI+ATSlZAzT5D8EboZ/g8gwWY1\nSJ3P6h8ndgpOgtJnA2kPkPBtLcaYZlluIEEkHn+BZDtGx7+72PC//uL0918VP9m1p/r48lZX\nDIXbnWUbK9Zk55m4oOrI+hLC1Dujar3g+ftw50pXBz20KvhrgKTY2LF0xalU7A8vbeGsR+xs\n2QY+uErUcLuYmawhCLkKZaCr+gkS3kZetIXL+BNILLpvzMqqs6fes731Eh43O47L/boSpAkO\nn23YCJINT5BsQPY/bRUuAVJtRJe4OnXcLHgLkJBENRKgqDJA6uTChb7IQzC0WXvYW+50fIOE\nd6HbQptTXecmjA/o2ceg1Zj7RY62DhtIPVSbKZwCLjnRbGBmy61CI4yJZbET/JugjgDJ1QCp\nUbNdkcT7nV2gMWELO/Zz3WorvmByHOpuu2UM0zIu4+M2uXFIkesT/ZzjzBZ+DZsoBuBkCVID\n7MIRIHVe8dwIy9tJHm0DSMCipOLRdlsa849hmNaJNYWgJ7Q1iwRIgWWFTxggHQ1SuQ2pE8Pt\nPsRpEZcrdOaytq1f6lPDyGIBUm8SQOqYAPDq2kKy6UNNkOoydazK0IfAApK+3kAKzf7tG6Qd\nQNrLJ0h0jwrZlyDBxL24DSSlhatY/P4JUiFbkA6QDP8YQOI6ujcHfJpfIMEj7XY1O6zBwze/\nQNLs9WIL8wskzLf2b3aj+CuQ/vo+0k6U1cv+UNch0SWzOBdeESav6gTkZu2F4kpQ82pkCwsD\nVW+HPsxNA3nXwxaonnugtvGFrctWMREhGFjufwoh6xMbTuO7vCoPH+B4hPTkOqWbxgIkFmrT\ncWVXrBCq9NbwIlNNkJQ2a48csctuKzPuxocZh/5xS7Eew+LZdK8MmK+QlZEteJ4gQXH4liDh\n0YvEotwl1D3GFfPVKqgvmKM2S5MauXIPN65mzTYlc/Sha591dzsdoAtD1xXSVEUbSjsP0LPD\ndOAJ6F5PPAoV2P6CRTgqJ0sZ+oTvYTEGrHkeW9Mlw0PIncCL3NvSOczEUEoYE5j2XNhVbc1x\n4NIjzzNB6iKivuwbuCbXgbV5mafHUhIkN6Ye4g6ZM7NvO+sp7tjDAIKtZc/WU+bekWHpHB6L\n7dquqbtGs2CxNaSrx/9UGMZxXha4Udo5a+3SgBzHNZhdy1brmvXiHPtPPYbJT0t5vQGkuYe/\n7UsuFbG1s0Lm8qvk1hiUQDRixyRhSu1he+okTt8g4Vdsbva2g+OtTpAbCXH0KFV64WmdGg9U\ny4R3HkWnWw0hsfUmZvksBFnJHuBRQrAikvtUIoCXvDdPkDC/5Kul6RKwaJXawyNB2rEVBvyg\nDpogSeV5ErzBozCNSiggxO//cZBkVe4OR4AU2WCRZWnsEyTWWpA13iveLQulyg4JAynd8PBM\n3aaVV+mCBEiaIJ1sWwu5gQShy9XmToi6QEw+yjq8am/LJ0gVPptqnyAhLOuwNoZKsI6nNlkd\noOMRkLTpARJ9D+SgTnYa7DTmxy3GZgwrYq85NmEL/O4JkglvCbEHUiXnrf3CniAhFbAIrPwG\nCZNf8iB/quSK8JbDVp0+JV1ADbS8bikRJAPXreuu6Ex96hCx53FJj3HeqYWrXJNmg9YNJGiy\nhmfqAju64kOcLH6HN6abP4H0hqxleeOhIkh4mlxjcv8BEmKBhSAzfr9rEGVsXSW/LOsyrC8A\nKbgpEiQMR/JboVoLkLgCWrqOpc4LrgtD3PwGSbQ1j093tKKwHa5X+AdU4rQsi/8N0lpCBDOb\nh9fasbJ9kJASqutWKGiA1FzveLNz7iqTjwTJ0Plwh2BlFsJLZoeJXcdjDAWSCUGSB6/Z0Ayu\nsbRdG/cAqWsKgoS5XQqVdswX8IKuFgQJ0Uq3MLpvvpYYPonR7HiO9QlSwzN1BEk1XDrfQMIf\neWNdhtDFZCt9yLFoAFLrt8KQwVhu+UMmg6j2CVIHf4B48jdrf/9lC9m/Bgnidnc81Q17ZSGz\n8gYapMsTJCtq28Lata47GimqhM8Cp5DtXMnEKzjAJhMkU/NgCihkPwDlqidI8gnSXlbhTQdb\nEKTWYup1qm25/J0cy4T2BQtgAKRyA6n9M0gVUZEmmnn005iGe4jNFFjGSe87FmEykEdWbhsL\nx0TF4SuABLLkK7eXkQowWTFNIoDkRQuF9GZMKmUPDfENUkyqDL5hzWm8BQGQjjoUXdmY5iTs\nQS7TmgYEaR5kSnaGkiVIiDvApnWi3UCCz3Yns4HUmIqdIghSdPJVbyDBdwOkBDGV2ydIbhNT\nT5DoajBf9au1MBtmXfp16qtpAynwiJDaminj+VrrXqHgtCkRmf0GkuMejRYbSG0rEdAAUovs\nBlkuHBIwnmmYpnVZA/wdJLwFDqcnSDbuCycRdxAAYctF1yNzAaTu9iBIqWt1entjbzPFQ3VQ\ngSvLPrKGN0FqmcZOSHmqbjaQ7BOkwogmHghSV3Zx66lUdxtIGiBJ/CJrt4HUmWz23yBZBFnF\ndt8m4vtjPsFyl6rCb1lMAdPytn5z0CKYDaSSINUdQOLKqFfckLY8K+EtD3YQJLuBBHr/x4vo\n71Rzei2KusUPTY07sjdug8BiuI2l28pWtceTdIWV4hBpB5s5qbk2UEVsKtlG+GHLqo8afHSe\nR3k9JDoyF35ZAaS4wzTd40MeeWy1dsLKUgmCNEWYcqXzDukqAKCmw7sJW61wyPGclYjsDIfo\nF/WypHmEW4HVnjG6wAIjxNtsFeSGkFwTriOlsSsyD1jB5MNA9xh8l2A0aEVUNjkbfGNn4l72\n+Chs/tcHCFSoe8fS+9Lz2nSQEPfHrjrp9ij1vu6XPkxzL5DLXPSLsBSfYIDVbAQCh/ArwhDc\nZW1aa5BTTQGhJKlcKH/kyfHcGwLLbOAF2fQ16fC8HM0+zcgYQlbaVbuqgxyru9hluMc1ypkg\nzY47fGzLBeBc22pbBY1MUGJaAaQybwLOKPal8rJmiV6409BS3mpX+4xhd2lcln5Z4bIIEgJ3\nX8BNai4znPa26Tz+I1pVyy7AjAEkfR+gVxe4dhlfio4XJKVG+vHtCvGPL8aQWnVC7GS/ZEin\nqo2IGAApsnltqWQVTzuFF/kECTO6kZFrTqbdOq70mh1IOzbG1UfI3g2kE0Ig9EkLkEyUNdQG\nQl6B+IwYYUyHzBmPmDwYCXxTV5pDDp0CSIXHC+R4GN1ZwY5cDvmtZyN5pHrMOfNPFIj8r0n6\ny8UG3Rz3G0jOxtrtBbvsQa0+QWoqV5aeT0KQ3gLtYLEkMdcWwTzyQHMgSLqyhZZCtywk9ATJ\nY8C68gnSKe7xOweCxFth8qgkPu/LxD5ySiZQZggSl08FbzPiRWG+J4QeAY3Usccwl65GPz5c\nbBaMJpIH29ZC7Zyg+zpuroSGICEtJE6RpOpkAFKH5AGQALjaQLIsTWrjTkLkcT+IhUtjaJvg\nTo1h0RC56XDjy65+A0hKncq8Zj/PWQp2aA0w22wcbn2E/CZ6ZgMJ6cI2hpefQm1KgCR+gSRY\n2BrzTQAkLtZDtrDRY2abZJpEBHqhII/qXQXryM2nJq3QpUHNI0GCIF0jPGzgAmrNwlUQeFpX\nipe+AJKGjETe7p4gsVWvhKZs2bFI+5IgSZemdWWPK9dui0rG9lXEIG8gbbs4bFzVSPxJz4NX\n02ruI0HCqxThWLK2NztWavjAFd8Vr4og6armByulfIJU/QYJyqYMBEnC/oFdxcPZG0jMzgCp\n7JXxliC5pE6hIEgC5lpDVsM3bSDx4mIqxGlrirSBhDF9q2QXteEB8MrsUxAaIB3YhWoDCdlL\n0BzgJXKxwXBJkcH774H03zjZsNPt66ks6w4wh9K9QqiIlgudks1vqyqcWBLBiRIZc+8F5Ouu\nD83SeAfrjgcuMDUQEgtTIixhJINplG/ZsQ7D0Z5KTPGd2hMkc4DG1Se28tvD0pvuMLsWIJWx\nUVGxb71Azm486zm7BoIak5BX1nSr2NM29fPkphEgQaBAa3bsWh25GARrpENEKoNUQJopMPka\nk5SgHEKc5h0ern3A6+o+b+LDhp3ImBgZPGSeZ6k7PB67AodW4nVIaNOua18Aku6aAyaC6xcW\nCUbAThlat2M1+SgoaeD0BTumIa1owUyhQrV1HO4AUhuV2DUHb7hzqeSibYKYYn1Rx3wOmwBc\n4OFbzSLdu4qBuYMeimuA9zDLBOgX4VMfjrCCyNX2iAcUXjdG1dwHRVjObGbs4EmeILGbvBTs\n5oZHU2EfUkd65pzjipHDUyWil7u49aoESIU+wlC5rmo72Frb46nm3g5zH9ICsQWNWEt2aNyu\nn/pTj/wf6tpwtbStWuSYRraIBBhC5HT2hWar7hZ0nF7YSrgQFC2wUScRdh0bk4CH8q3H8+mm\nM9Lz+OMJWhXTfY98dVKaTZR1xASCfT21BbIfMgzEK68cn/CUeA/PXty7hJhfG7vjlgTUDFJ1\nAz/ihfdBGW7VQwIBpM65f6Jk8b+YkdpdUVUASalQECTeYO2klojNsqxi8UqQJEDqDviofPhQ\nr61nSXrGEChdY+XJlDwJdcLn5ckAgqQaJLsnSLvIpHNo8SaOBAljJTBii+MFy2PoJEBi3RvM\nEJbA2UDisTL8OtKcxixj6JfJzQCpZoETHrW3ASPnABJ3BCNrLrGjB8t6Yoiz5M2VzLz1DZLY\nQArG4ZcAKTHpscQBq/tUHTxXw8u1T5AaTMkOFLVH03ZvmHs2Y2rjBSIxpM6znRW8l9hal0ug\nBPMkkQblBpKvmEplC7jbKAHS3vNKoSZIBlIOjLCmI0AyBAnhHGHzBeEHIEEKdDxgxYsdyqwA\nyayNTduO8WZ63jBnASVAauAOf4FUO14rtWylg+TRESQkCXzkuANI0D1pwWz+BVJSkLkibt2T\nTT7WgIAglW1DX5w7G5beDUvmBm3lGotUhaTXsaKY9hUkbghltYHUbSC1EJPq1HkWRfkGqas1\nQHojSCdJ0QJZeuzCa0fvrLtQvFJa4zMYyI2ujgXL4gEkY+s9IsUTJAwmQGoQXXh4iy6wibHq\nAJKov0FCHoKgtm8sBYncya0tKyrPZRj9BAmxHc7E+39C2v1rq3YAqQRIwgvpTwSp7Vrbsec6\nYCqqVO7gwzeQ2qPrEKF2CGUrwg+0uW/xETzmpjjpUmAe7gES9Dm7SeOd6uZQAaSXJ0iaIKk9\nsSsJUl0s9g0gIc9hzmP2Igl6ZCyuPtmax+Isjzt+gxTcyhvpk0sV6zK4Y+Psthi3k79AgjVt\ngUCFUSdIClYDaWcDiR7sCVLcQPKvIsEnZx7AQ5T1JVf5Gp4tJkhs5+wVbLICSI18geW1cfUE\nqWQrbppGBLzw70DqkMYxBdQGEqy7bABSE4XY1U+QkORXzIyAkXCWR6GRlwIEG7sY1GYHVbmr\na3YpRgwKvW/h/dcZGXQtTez9zkTL5jSv7HD9BKn9BklRMPM2nHUYdCQPPAh3lvQTpNjx5BTE\nZ+hXjykVeB4ZIw5VqDD78qGVlXAVQOpqCHubCh3W7LZjJ6uqfG3YF0WzGCrY9m0GpeFUYkQ9\ndBjAZUs8wd7a3OhxcXP8EGhFKA4AyeFvCF+QpfsuvLVMrLrFv+p5M3y7O5vaOpZPkA7ala8b\nSHh9RmwgVTVAYsENLVUdIwwgTOQeVsQ3BMkQpJNt2I4iRFWWmF5IwQQpASRVPkEK/9MgvQKk\nqq4q6WqW2nsTrPpim0bJlvdzy1wDpBYP50VTAqTCvSEEZd79tVDg+nW7nMjWmZ0xPPitWGpW\nsUQwQHrjsc0XuaMNUodaP0GCq8UglVVv3iJlFFRYh/AmJKLpCySks1DcCHv0iOCrlilLq9Z1\nAUwuFbxO7naV5xHhZF8lvBQ54QpAI2tTw2q2LsNJVzmClpB6HvUM1At9TiZk7kJ2Eeo/WR4D\nU86V+PgHxF3LPxB5+9+xtgRihy3Ni3WwgZkdiEUNlQWPVDrKpo59RbwOmPEWv9VBTQFczXP9\nCA5lCKqKrdi3b569QBF8e3a6Uzye5szWApwgNcii1TdIPNQGL8+7iSdkdO4fyb5QPkPBBhhR\nuV041dzK5cJnxH/KpODjnKsaVqVSHQ80QjkjoLEtUtrRfL5F3u3gIiZjc4zUSPj5rxtIO+Re\n6WvbNvIEZKBbWwyRm/uM6AHzWGnVIjywPLdWeB9MIAHDD5B4z0ew4hn+2ncsEqkcDb7Dp4e2\nKECq8DvtkVfxWK+tPyC7+k43/lT1UPeYaxDWsW7x4oAp9I1y+1e2SPU8HC4NFP3x1Cr8IXw0\nBIwCwQk2MDaHFi68dW+B6kXzejpeEfy6fD3AZ7h6A4n1YFThARJy1T/hkf5zjv5vICmCVGMm\nFdIe7F40dduaplJb49xj2TcbSAog1bzKe3Jv1nTIIDJZ5Up9YOMu1xSygs82J+PYaa2UMMai\n1fUrDBNAekt75SVBkgeChFAudFH3hhVvG7aIAkgG0t6qvZUQ/LJsNpAULyZVAAlTdgNp9emY\nCNKpJEg24YmTlo4gIRoiXOEtSWjiDAcB5BAQIkFSIUJvyj7nDSQHB1xz2xR2CT/ElsqdjuwP\nEtlylrsvDk8pZHe0J/MKFad5iAK8t1BZ3HSHhxIspPsNUm94BAyZgLsHroICEuIEkOD/xLF9\n5VIV3L/Kskt+O+fpeXLJ2wiQKjicDSTkLqSTreePywjkrcrIDF1GhEsAySuoFnOCkwETvJ2C\n6P4ESavDN0hGcRFcwmVwMwYgZb49v8cY8FxNj5AHkPDgVURG3GluoO50t+21tJ06ihqyU1YR\n+mzJScceEqqkS+WpHoO05OBa2wogNQApKNU+QVIEyXyDxJNJqmJDOWhHlp1zmUuaUKXfIHGz\npAFIumobB5Cqjr1aNpCkf2FJa8wW8RukTv8G6cCDM/jyum5gHjp3JLUNdyebqNglWJ7erN5A\nYpsqjLvcQJLx74H0l3/9NUhvAKmu6wJiSdqjPXyDVAB8DAtAanfeI/4VXpT1BtIrxCo8jYhW\nYsqcWHfR1QWSvLWmgm5rWyheaGOC9EKQduJIkAR0nhZHggT1JvWpyboGSFDCbAZr4VnZ8sxK\nCH4WewhsdIDXakrB7R+x9ovrex8PXCbETwRInUl44qjUL5BaITTiD+Z67kw85FBvIG07si3C\nXJ++QeLLMC1XoXksCyDZXUGQ0hMklo8GR6I7MktDWijkPYIkUu8wm07WN913RjII8L22pcd8\nlFIogmQA0tEHXcRanLonSDVrKrTJiydI7CNJkCAEG1d/gwStuPX8cdm+aWDQZ9emFvHCMBKx\nQEhZEiS/7Z2y9hZAQsCDEqgbw8JvXARXyjxBEvIJ0immDCkcwXvrHY9xH9m26RskgxwXAJJQ\nB1EZyKEiYoRWWKnYl+yfSZCkqgiS5/H8AiBBPAWWGq5ZuIeq/MBieppVTskisGt8VW/lx9lU\n0Gpr9pj4DVvemgqKACCp8glSKVgxhRUIT0hgB4hIDIbgnXSR0qGALgFIyLNav/H8EZ6p6moI\n5A7RiyBtjvsJkqh37NUMWZ1YWdcYeSJI6v8FSH+92PAm2z1AOmpbSV3YU9cw5DV7qFwMS1Gs\n4s3jo6uTR2i1BGm3uV34OR54YE8YgIQ4yYsWBjFaIGAdEa0NAku9a5WLu65Ke+FpmHihgYpD\nk482sgQZFDFmdI3fLhHOVasFQCqLQvjou4ZnX05tznhTa7/C3viIWcGYd/DkJhuABDEE6Bxr\nfXT4DjyU7RJ80z5DZQmAxAPTAMmJHhIn5sAT6Wz/FhUX8DFzS81Qj9SEP8aid5oFFiRvNLo3\nhFHF5TpJkJAkrLQemfdUcY2e6QOzIEvTQhIhIbF+HRKLF93B80JaJcvuBZ6oDSXerEDkaLeL\nb9w1xCcIEaKq9q3BpJavjXlWZmOB+SNbh8felhFwRA3J0LHFd13waHfQPMIFkAIsoTQdBDZU\nEo/c4sM4zEhMvpYL7gCJy24RkX2rD+MwuPC8Zseat0ekU9O/OR67aA3CwqEr2Yi2RCi3PF4f\nMgxOoQxmtoBg0myaAhZP0Z9gYT0iCHULFZ8ShcBPVbxRiGGU7Mrnmo5l1vGD2brKmKNwh8Zk\nJPAT5E/PyzldDRcctjsk0ghpjp1/KZDNYAxagtSmSOvKypPclzcAySBvBgpH6QQ+DYYbthap\nMcoNJAluK3ikDSTDrWGCRK/1Tyx///0V8N9fT5COAOkAzyaRDoquKdv2CRImZQmQ9jzFpI/I\nKN8gcc/yGyTRsViFdYeqKSrLPqAAqQRImBoQcABJE6Q6HTqYEIDUlgSJZedM1QXu/rPwXJsq\nZOpSbteCedP/WB43kCoPkI4NBJkQKyR+gvYtNpDUniC1BCmI7YgsjAfmOyYYUrvxqbHhQJA6\ngOSeINnuCRJmeEuQ6siKJLXSpjS6rvGsAInnTg2ybOA2mjgApCOUKty7QHwUOq88jVoa91r+\nGSQBEwSnTpAcjzJzaQY/Zo9JUnU73vv2Bb5rV0XHGntACk4QnyBSN9ewDQRp32x3bnjxI5lC\nqy763p4CzGPUtcK8xPg3ZdeycJhTHbejAmYPAtErDAOXzNQGkpYwU7b7BglE4dPHCpwlxZ/u\nfW3eHItHbyAdPEDC6CAIHluYPwM2Ldwiq8nlN/0ECZ/rRJCixcQ9Bc8O1whPCFvcplV4moog\nSaZbTAjJYj682hjrWDqdWVPBnGDCG02QjhtIqqm7yvFQF0EST5L9vlQlT3M3EAYE6Y2KIbEm\nIYLDwQOkgwyVwegDpIYgdYIBGCA1ocdAIpBta5OG1UH/SZD+soj+X2ekvWgLgPQC4yNlZaBa\nAZJqT50oW63rYpVHZmr95rsXTCV3srDE0EB0rbDPbYcPg1lVl6eCEh3qvD2kovMNXr8kSDa8\ntF06tu54grSrCRK+Mc9VYdDxbxWmTBtPnqfbuEPOmqTmpXwTPvkGuUXZtzJn2ynIMlgCHiWC\ntLMGGSk0px4gQXl6z8sqPurW606cMEfZm/iUTa0RjfHnuoSpogkSDJdllUq4gnpruIcAok/Q\nFbwNZxNeT2DbTDyYNLU8uGM47buAjwzEogJIgAdqwsJuczmGHwHRsxFQI2wjykLB+E5ONiV8\n1luoVN3BJVBfaUzuKriSV9gCHA4SD0ESHiwognRijQMopQ5yUcFQwgf0do9YgpAD71MKgNTW\nBIln2THBCFKD11ABpA5Glu6IslGAKAhcBPEMDhkbYqD9YtTmcQrEEDakblgsuy8hUbnIgnl6\nbFj6wTUJPHq2LUt7EwoEFCUaefDKwlU6hWCIOIm8vg0pt8HwI7uGIIltcZLX9QE9ZhR8amqd\nZLVipH3lXlvVI7W/Qv3C9hYtL/Z6ZMSoEQIB0lvnTzXfhu1qgtSkuNsaLzk4qNZCysFtnRSC\nz4mbm661wkL6CEvFgGDVC/uKt4N3CI+EuLLdIw2gNeV/ombDv7Rqt+8gymooS2RWUf8GqQBI\nzZ9AMq+eFVA2kBB7EIc7R8UHkPCY7qUpjifLIufwwK+p7BDnkVueIO0bkU4NeEDsrzaQtgOK\nvBTGTVW7123Yw8Q2LY8rtDCt5lABpOxrdiixL0WG51abv9EhNBtI9kiQDr2GaGwhzO0GEgQW\nBrwkSNUGUqtqgAQbQ5BUlzeQ4Gs6DLutAJIBSFIfWZCzQ6BPPCzDnU3Du9ENXHwRTocWIBmC\nZExeFS8aAe/atdzrUQCpgV/m+kIreT7MYE47hFtk6rdQ67rdcaPAF/i2kPaWIFkekd1Agt0B\nSHIDqdhAshIABYU0gd9HoHCcJfAo9iQBUtc8QfK/QcIHKV6R7Lo/geRZAWsDqeTyneIK27ZU\nD4J4rpqvYANJmr7ifhhUMuLDsW7+P97eXVmSHLnW/iMvccNNggQBEiRIkKBAggYFWkjxGrSx\nMttm29Liyf+1kLmre84ZDoeH06SR05zqqtqZEfjclwOO5T8gjdPd0ucfkISb8adNh5YgSOYH\nJLxlgFQDdS0ECvKyHyDxFhD/3hlPlFZaJUYRCtQMQZpo8ePTbJAvAJLxNTowEeLNlk3FjSCJ\nH5DGKEC2QOHj7tx0AJAmz9wmzcQVDA6Q8KsAKT/xdrJ7g0R9Q5DK/wZIWguCxN1nI6NUGiBR\nvQ6QFEBCfEdwnjJNgmxe0rQTJDypgqyubd6YkdTCe8UhEqSpc2zob5DKqlzfZHqg9gzbn0FK\nnJ4HUADSVOiVQR2s2aG4yecACVyEdFsIUjiRrXmFXQ+Q8spkshKk/AaJFypQikQrJa9DCZQx\nB+QCuzSxgjp0iCNI3LnAUnuDpAGSQmH1pJkEXeI6sfwNkgZIe11XheUROKI+fUBCrEX6oB8z\nhB4nduNhASRFkPAJCJJU0FIzMrNUEx6CIUjRqJJEIkiKqaEQJF8Ush9BoiM2QYLOrTz/3Ks+\nIYMSN7M9RyEh/hptDUBCSc+h96jl6Pgx35HiCJJl+2rh77c0tQVI73YinvmwCwPyB38HQjQH\nWURWUfGUAAklJZRiXvHACRKPpviOclsIkgdIG0HKugcUrgJVczoHSICFHQ8ACYIGEcSwWZG9\n6PikzKLthidqxvTyKOIHJD8lRefUhx0g4dUBJEWQJlt2BaGNjPYGqbVpOHtHgsT5YXgaEsVu\nfrLVnhmaJ1w2W1OQg/Ppyhx527zUBq3AfVSZ6Ez1L4L0P+j+figlFEGCSlUqaKWFAkjSWMGm\np/Xya8GXAEh6QskJ4O+ADG/FggHUNzbd8Oifano+IpsWnFXAwF0AACAASURBVAZISufN2qDl\nU4WYpfRtl3HVKvgZUiOrSCthaPKYeODEW/nPYruma2Bmn1CUGsX1mZVl9J5v/agMgc1nQcu8\nyn0twX0Cffonm++hHLCo87j5Zp+GpkICSuVAiBatN18hzQ1edu+n7gdQwTewaWfh37SRYQYP\nLoBUFNj7GPkWW7Sga0YhBenLOIslieVwnE5zjz/Qy1PzCSCgrGXHKrFud0rg4/sBEm9Zbdzr\n3SZUVSbvHAwEclHLoMhRGxVp62MbDMptAogS+crHbOfKcyj88Po8w57YGYuKPk/IOFRPBImj\ncsMbpCjT9ABIloLKcSPDK+i5jH862x3vV0YOl4GuyHzqb2NtvL7hZhEP1VHzdi0994wMj07l\nMXbieMdzB0jWQl5MbkX+VoeriuZAeDu1oGTEU+O+Hes4ynppONVlGHBnPhwUyJ1uxVjYeKns\nzDInowZkXPPl7v3GZinef/B47DFMbFlBpsQ32bnnpludeBWKzfSWE9RRvLGpEH+2PPHVUYch\nVyvocYO4xCsjdY28bV4KTeAF8qyKFSu4n3/59jeeC0C6EyT5A5LlmDOCZADSxk7AAVIASI/4\n3C0fINs4UIbaxDHUBOn2B0gofFekLSUXgqREaAKq0UC6PReAJLl/RZB4tTHwJmTakP81P0Rx\nBEkZSZCg/tjOff+AhJC4fUCKRfL4yhKkQJB4nI9ArWq2k32DFAGS9ztAQpY4UNBr7uCpfkje\nA8f/vkGCtoWY4A3lDpB8W8MbJIrFuCTVds3ufs/rHZUgqTgmTZiIxec5wRR5S2K9O7c5sQ+Q\n4gcklMRJrr9BCm6sXVr8Koh8gHRghXEGgSFISgyQzL0FpAwXbvVxBhnHFdoEkGiND5Do6V1r\n2EOgR7J5g9SM5ayGARJTmiJQ7DdE8WQ5GzJlhj88dTVA0gQJVP8GSQCkbYSBARLeL1WrIEiG\nINm1KYBkAZJi3zozkqNe3A1B4o5rMAKJlyP3CJLitJf2ZA7rTPZR5np3+gMS6s3yCFxcKTA7\n+hXxJdwBEp7rG6Q8QHokguQDW0DfIFlm3VvBqwFILCEkN3o42T0doW6Rl2TfIG3IswSp/ltA\n+i+2v6UGSAo1ijNCIaZoKdm3aqxkIw9BqtxSRjaeuA5uYRaciYcIhKeHoh0gcTdmmrEU/A9I\nKq14p1JsvFpJn0SJWsHq6B7P3yBpesJAhBSBf4GS+dBPHlMGViKaRfsFpcIFIJ+9A6TOLe61\nZoDEsS2KIPnTzfiRBMmAclqqf0DiD5kPrEGAxMmH5xukBiXzBgkfnio7dqeXKHlZLx0opmhb\nw9mJsSfe49mSRv1DkMK4J0WQIBp5RwbfgOM8mTHuCBYEaXU7ezcDcgYDtS/4g0mskyFIgp2f\nlrbkBEmvPNWtAyRTkrnRhUpww7YAJM5gRylRl9MrhuxML43nsMZ37gPSSmdVgBREmp4/INkB\n0lYjdxMcLxwUDdGXWJARDcQb4auoQQ6QeK2OfscACZxnIXkolwQnG2v6EFZJkBAw3MNuTRZ5\nmIb1wr716wek1VDaOZ7ZahTHEPK0FrZF8cJ+WwdIBiAFmdvsFECyEy+++PJkAw/nkhOkmUJ/\ntgkgsbmZ2zJs+qnLByQD9VY5NRYROhKkLUnIAc6vkLa4D0ixbZGuD5y5GcKTIIX/Dkj/7+Yn\nT5CDR7MWrMddedSyPEhyxlg1QDqxElEfACQ5RdTjU1glo5UeFTBAig/I9FlP60QJjlRqng3F\n1gJBsaP6wtNxe2wK5YNDrJnvKIqQLCq0UeK5KjSyNtB8RZ+K/VIte/YQIMKVizd1UItaqjPN\nswSFsibrxlmhqN4TQtJpVw8hCeWswVOrCrTNjs0yYGM5UG9vkE+mlJOh2yMF7I1TTC2Wfdwy\nbUp4nMkhkqleJ95onzl1lpOroIhQm+um3Z7Yi4FX00M/7Ixl+XS8NLKyVYOjuhP3F5xf3bLQ\nScdi3SsXHZsU8yYnRVdlzrXhRrFnk3lxEqKl1pP+yXRYukHRIwoDpGqW5tje7W9NnmyZo5UE\nAw4goQ0Tj82Qle88Ok4QSTI+npxVzGrBjKHt94oPzg18U3PhzbcAFllO4E+b1QIk5GDO6ALy\nHSDh4SLacQvdNbzb/chs8OIoXujA1ewiIOqLttX9QEUlNKrYQpCCTQFZhCDRSNfpJ6SdGMmJ\nk/tCpPVnZPlbegqydOHlgWw4ceYcQOK8HTxCvE0+Q5C02ciGcAIvyphRXogaQdK8fq245DhN\nLN+R0KGk8X8iDpASD7MPiFFut9C+wgU38Zqja3sN7V/ebPgvzE/+TN0/AgnSyukNeVLxIMkg\n3lrEqOAIUv+AdEMxmR9+I0gBnxBVNdAKzyQB0mO7uw9I8wBJubQJpd4gVTYWs7lqGSDhCSva\n36WMx1SNpreGOpVtPHrzJ2syiLxX4Ph3VOO886S5BSroO0IPnTdI0C+n3fxOr3Y8MMVrtpB+\nq6MXNJ75dqCQXVvnbsQ1QMoDJA5fZI7ZmHSO6Lc8tnvbdXEDfEPtystZZVyYAUgGXwC5ESD5\ngyDdE81KvPF6CcE+8CERYcdcb1TkM0DiYAU8oOh4pl5WPQmCpPClIn3d8J+NzpJrrOVCXdZR\n5pspojTe2gBpQ6jnLcO56ZNN3Dly56DwwnWkfcwbpCn+Bml+pq54A0PrAdKEp/QGqeTC24Js\ndWBvNig0s6ko5HeCxA0VgqRSR6HIU2TPDYT9RPSSwXxA0ssOTQKQ5rodort9zCy4ygek29hs\ngJACSDdT5I7lgZVTdkNjGn2ghqwEycva8dYBkgJI6g0Sb4HbP0ASlpKZZ2n0YGZxWCRzFnQa\nEhywBPn4gHi/j6IHSMKuPEjB2iJIPTcsLoKEL0OQeMWKCfjfAdJviP4zkAT0nChIQZtwFqJq\nBUjsM0ON5NfDi4PDNm5RIPXIhHWrECXZ0k6QrPbP+ExPvYp17EYCpLXhj87Q3BvKIl6C3hNg\n0WzkENtSPfsIys5+8lSe7OE2CiJHXTbhoR89XoknvgCJtpwIaiikWld089yyrolnt3hWpiYU\nRyfkFaQcyFBO4L0JvDoILrxpVDF4aZlm+knROwsYj1EAHMxJtwxUuvhsEYtGs3cNMuv7xS43\nTUttYIWKr+EjKiTk1Ru6GnhLF2Uz58K7VQgla2Bxwx5F7vmymWl5QtrlMPYFARLUfnm4CaJM\nI+FE3ll0o4UMKbTbVMur468EGW7iBUaC5N8944p3FLo9HA+tUYbVwBuPkSkJT6/0GgjSMIZV\ncX6kQyB8CI1Elpp8/AYp17KZDbQJgISHapyedJUNirAQpIAKrVtBgRtTgUDrG2r0oxTEwn0Y\n8ddV3TcmO9mmNh9bH0VRAkh5bFf4BYGhMEs4qyZbjIAq5P3glYfx1eKd+opklBwbD4I6ihNT\nCiuS7J3FNz4kWzIiT0+QjlAaBOZsp0rh3iM9EgFSwrPl0a2XqPG4GzQXm3aUENLc8SYAEtsr\nQi9NeJ6pMNJCTxmEV1PxCuu/C6T/5N//f9OMnG0UQTIACfgQJB7oOMQkT5DkAOkexTMpCU0q\nuFGCuAiQeBsRIXkCSLvcuRGLD464SpA2gCStIUgbzyyVZ3m/rxBIKJPLBpCweu/0GbD0ldKX\nz0euB8dIIgonV1+8DMdrL0gkXbL6p99H2v4EUjmVHCAFoxw7+3eAxC0AgATJB5DMgy4ZqV10\nfkK07nidKHARlT37BAPvI7vKo6h2fr3YRoOnUXhszxtHkN0Sq22mQEDxbs7YuuHpP6KyQgpH\nEpi8TSvb2wAIwiNA4sH0KBoIUqg3P00EyfKwP/KCcLIdv6fTMvt1nIEXOfyN2zcECVW4rha5\nPxrV2Xv0AYlXewBS+gOkNKb0AaRAkDaAtGpJMxixVqSfjKpeg1VIM5Tka2LPEL6HmRRWVuIj\ntrStaabbDR/GcO4uliKkqjzwQJDnRht3WeW8RM6YAkjPYz0ix0tABwMker34LYydmsA2F4Bk\nBcoS5NG6KNZyrieq0g9ILupe3D6l+ARICAU8ddB03Yfa4L1wFHTEECDxTOMPkHJcLR6XcDuq\nO/ZOLMXyrCYpc0f0LohC7FJspUs/TvlDVsHub5A0QPpXOxv+6+3vf1hEDZB2S5C0kdvOybVm\n+4CkPiCdBiA9olgSSmr5AQmV5QBJmhtAemihJP03tVd2b2YP8+rTLh1BQi1SHDQ4Vtq+7wQJ\nFclmuaMFkMb7rq2ZK9Yzt/NIL15RyADJE6QxPLJ1AZBQ9KMuXyurhh+QpAp3rLBgJBVkZYui\n4loLCrKekfUG8bSnflnOzQ6lK9okAKTtA9LVeNEW9XO/vr7xdk/ExDdInFgWs0DN8zAKi656\nfeKTGMnRfoiFKCpj1BPvEtKdEblA2B3CKZUPSL5wEMMUAFIy3LbV3IQGSNQ7+eBx0Detx6lF\nniyi986xQbwWqLlPbro/HPNxxtPwzbNEGsiWDJAeiTfFAZIOz3s+FoD0wPK3ueEhv0GyOjbU\nOAIi6EGQ6HjkJlVU56ESQUIcs82sgNlypmEI/Ym88AbpTpBsWfcNcAFrgPQgSOIDEnQwQULF\n+wYJofhG4ykng7M/IHmClAmShRaAxOzFblOO9w9IkHIslj3qXUgArVgwhz9AStwRGXfIZ4K0\nu5Ugga+Vsxg5BcnMb5DcD0jOv0ESqLM4JN1U86+D9M/+5w/Y/hFIC6QYqJVFabkvBk/ADIHA\nlk18oe3w6tTQpSudR9UWTZAqOkQS6FfUKG6XEzLOU3N2NSpY6bXdofL9PPtxqykiaK4ZqXdl\n6SCFLN7vzmf8EO7/LoiubKvmuKXSr3JcV/6uHMjj2otJCCsXeaH2PbaIVGFL2iEVUH5jwSIp\nHlDivFZK009UIvVWqlS89O4BkusfkObYL2nC+xZZ4VhAZCTn5oQXeB00tltrPr9/fRfdzmAV\nBGZrdYxqLwui4SSEOfF6FEhvxiJFWJEXC6Ub9c3JDB3GGiFLS/+uzHQ6VsMbpPR8em5668B5\nZ5b7D2eo9USRUb6uF62QShQFWlh0JGMaN6KkrFAsRzhcthxz4xoKSEKYaXRS0lEhjggS718j\n5eXz5gJKVTZdt0WzwS6zySP0umGlTnYC87noxUSAZIYhUXIHr5GgLJrxGWwutcbQBdYuQUL5\nkcY4yW1BTYPFq+obpMTtgISgxy1D7vLQyclzrJFSi8NLcRt/cJ0VHSoCk28CSBlF1hmjbdks\nE8IhXiFqpICKa+yFDH+qZPbdIqWgdPAU23xjhtNcasNXQFxb7QRwyS8CcjS75w7NqAQML3Tz\noqTiXpGj3g5OokIRyO/Nl3/xPtK/RtI/AmlVdiFIGSCJ2YAfQ6NOJmfIzrC/QTJ+R4EUURRY\nXub1PL2G8JLJbfvkZZqRk6G05yi8dqKa1S8Pj/iC74PHuHJ3miB5JVBkuh3fXIGKPZWNIKFm\nOMfY7Kuc1yt/N0sD3fadkIQSx6VagBQai0qABBRrpo1MJEgrZz4gGygGwlIngKQ/IClPX+Pn\nAOm4dvpsQCSa0g+CpAgSpNvrOMpxPAjS377KjpftPiBlrvXyaCVN667P/AGJLpjJifyEXAVI\nD4BE030mIIk602AFM4FQoBCkNuVlXK1HNNrfIJX59K3xRLN8vV7uyHSKYecDihMHnY8anw0S\nxhOkYgpLvWYa8kNiL9sHpA3xpWLBDZDKOdHpBO9or41ZcYBEL60GfYNV+EA5x54qjbRFkPA5\noh/Ni6GqxR+Zw1AaKjd8J8tMTlti3rjN26wBUh4gPY/toF0GQQIJTA0099PMlFkqtTm8CWQN\nOlTO+gekFOUHpESQ9HwrWSANrewnmbme6IMWqstmXa3w48K+GyBF3vX1b5CQ1bbfIAka8UBV\nWt4/IEj6DVJTmlcKsPBubE13FiA5TjL8y+cjKYsKACDxWPahnUM0QaFJZUKQxAE9Q5AknmTU\nG4ezDpAy9MgAaX14HRfNJBpuv0FyAClS7lL5LgRpI0j6DZK1mYfgayqCUxMTb7CFb47DAEjl\nu7MBBSAV+n2AsQY5JnxjUQmQzB8g6Xos7Ijj3HIJaU6QkFrZ9O8UMiqnCK0AaYnHuRi8rT+B\nhJjxHCCdHNIytXx9AaRHu7g1e5KkAvlS640GK/ddvUEq7CIHSH4vD0tLST1bmc0PSEZvlrMB\nAVLkTqAeIG1itIlrP0DidtVJMxfe6vv1+nYnc5itC2QkW6MPXqJWHiCFMx7+ByTd6blCkMAd\nZ1EyM/MgFC9ngGRDYQicAZItPyD5TpODdLcrN+6qXLjDWiz+rjdInVcx1OoPPFlaU/iORcpM\nbtkPWugQuj8NQYL+ntrSdyDHf0WQ0gDJZqgtjwy3S0XjDu0WCO34Z5CC5KvcCZKrWc33knki\ntOOp6Immu36AZLN9Pt3mkbu5KQ6QdDADJHA4QNrt7QOSHO55+PssfVSBtKL3BV6PUoYg2TjR\nfdrbXReC9K95NvwPXIQ2ZUGCkUlIqR688Wst3dUs9wZ8kAcqbI2Pi2hukUshxPWOh+cKJzfs\n2W/r7iz7eK1HZIw7nqOsZnYbQHLckywpPfGbtUgopxEfANJmeHfd51uqKkI5lXi+zohy4VVf\nr+/yhTB8NcsB3ByVcF0QXl25DjwqX3+mWXXQKMxN7Q9a8NAjV9HoDjVSYeOOdlAIJnKKkEKc\nlQmoGO6lpYYa4vRZo4BAXVJ8/r5e7bwg4F6//uOrrv3ipusJerHOXxWiD9JyewpFoyB9YGlw\nEEOOG6o8JFU8EpouVTZKuMrGKsvLUbwhTC9ujTrmUQRvzLDTVWaaJiAFXPo4rwOq9df3lzlr\nRXVS91zk2VQGwzzeb6ilThrgAWuCxMk3Bn8aCi6xUcOLwnZ3y76gJ0B62ojVr+PU2or6RxX2\ntSpkJGVs2o2kzx1W2VJ35MTO28UxnPmAXuIFvINZvx/ZHZx0wFZaLNaEiID6anWoaQrCy9S2\nLg5ac+tUD17l5TxLFCgrvqvNj10KJkg7R4l6dTaRLX4cgwiBWLPZkOGjRzKaH7S64cVbm8SC\ncpPLC/pNZTvd3ey9CGGcWqNSoCuP8+XIDyyFoOzmUSMhPulGvURrJkO33WzfU6EQ5zZdq0Uu\nu9GtPuC/0ooqp+1/3tnwT0Ha5QekHSDNKC8iN74JUuK5mHqD5FnPIQ9LBlZoCus4HI4O8vvO\ni607r6Yad3+DVMzT7s8/QHrg/zhY3BkoYoLEImwL+cFZyY03YwBSQrkwQKpfh68DpN4LUHld\nx8Z5792NoMV7oAUPEE8SVXm/s+sFICGGW8dGX4gSj1AEhWBZAWgNkHQ6jtsbpDpAKhqywc8o\nEMr364VUuPf8/es/flUFeYmlcV0fkI4ues9iU/qqBKm2KjNN7NYqB0ggxyABsZvBNZTywtFB\n9A1SToiW7YlENW6ga68+INlLAaQTIP3tiyDxiBkIESRRaKmq7G+QmipXBUhbR6At+g1SOAse\n8wAJudmtt3IuNE3RJt5bw6MdIEWtw9F5vwGpUhIkbR74kASps8HiRBXYeUudrYitH/hbj8DZ\nUCxq8YIHSGL/AenWRJNHiW+QEPQHSHiqs6dwvG0SJfIASWypzjb9gGTNn0BK+/JEqEEOZzPK\nJiSiqmGAbAIgPdwj+B0gQfFUCJyNNi4E6Wk4BQavVf2A5ILA3wcYPyCpN0iLGiCFmSPNgl6x\n4mrI4a8GSUi74LtLtiGYjf7mSIsbQcoEyRzeAiSoYGQV9tkm1IoEqfHgeS1BKDYMCfotOLNT\n2qFWNzcjF2gdy+2dlO5jYxPfV3mhUC2vbHjYY0bYchzyBln1feWv72/Iuu+v+nXG9urm+D64\ne9q/vy9axKmueA+As8A4iMzyEATa+RZ4Yg9BoYODzEep7wwUKT4LxQHttRots4++4ZdriACp\nXp7HuTpsvJbx/f19XC8Ip++//cev5glSKNeLg8CO73aeBjWUQZx4QaZoDmMRhQt/qVDnurIk\n4KifAHnj+hjSR/9zNql4P3Zj2lw1O1ADfYEGSLHFlzwuZNqj/+3rl76QeUtp0Fby7Pd+nmc2\nPnXUIFdq8Q2Sa48OkgkSbwohlxCkzBNVHjk86ynYhor3IyDmEGvEG6R4dPx0lOTmGTXo8VvX\nULhHPrjPdtYrHzXhj0I+4wdfxR50YuTGIz9ry+OKmn8gYQadl6YajxQEHUC7N6NfIoTqJ/pc\n5yeSS9gIUlhWgIRCCgUwsgF9dZBUl3rieSE3bAApsMsMgKEmMslIjgFo92KfSH/B7RzNR5BW\n+8CC0SGfZTb5wCrTQdH0MXAIXuTRhB8DVQti/G5Tas08Ra08tt3Zbx7lqvh6sv6vQfqfwASQ\nhOV3l4EmkeID0jqmgrk0QHIECXqGwYeX38wze3p04r8DJERbPFA5jEsQ9aQbIE1GrtzDJEgZ\nIIVxE9rJN0iLkCis6aPErpuEUubvQbpi++76+KZpydG/vi+6/8q+gwTPAoEgGdN4D6bd4wAJ\nWpqdgNBUmRdkdqd/QPI0Ey5HE5YtXb9BylDpgiDhx0JTWtT9BCkQpEiQem3nF3KVOw/O1XAE\nyfTWeVSFBbJU1rGQlwopgifvO0FCBelZbBAkRwPEARJvztLU3LkPSPklzw9IvwjSkQsvdldx\n9Qk/8mR/KWeoXbQ3kfVV8ddMyIb4OgOk5vFbJIXqqL2cnusJNThAUr1JXkT5E0ieIX+ABN4P\nvFmCxK10gnTWBJ1xEiTUp+YNUuSuXv2AZMKz8KJE2pquPFLYBkjsxiNIiIY3h8CYl6dUcQ0G\nkfm5pLrwb8AfZDMs24DdPEBKaQVIiDwRgQUgeWGTESi28BWLRaEAkAR7FyDU42Kfih3CH5Ck\n5/gKHrYTJI3vOu6584qV1W59g7TwLBG/BRTtKUI64vXHsv9rIP2/wjRAEgTJi00apGY85fgG\niQYbwZ7OHSYmgYcCGWvoMnofINFneSvs3IC0Um+Q1ABJZ4Ck/gTSo4x70nhAXnKLZt0BEmcI\nRCxAFrgFIJWvr98gJcg6dXxfnJ4EkF40UhX9ET4gjTViGt5ebbzHhcwZuJWkM3fG2UOzO0WQ\noEioHbGIe1W/QSofkBKWQ2hvkNwA6aun86oh4fO8QULe8ADJOR9eHeV/A0jbAGlFkUWXXgj9\nyLAblmg7p1161AMtECToO/rXPP8EUmIbRWzlpc7X9Tp+g5QIH0E6pgP/hreS/h4k328HB8eq\nvwMJz4+FmGWwV4ZNP7x712h5v7wfUurd0GpGu40gxWgOO0A6I5LD2a5ytqSAVIOoBUj6yI0d\nO7wo0d4gSRvmwr2PJAASIg6+uwq/QeIjRX0W1rzdAdKGN7EOkFaCxJPklBc6+fnnB6RtfzaC\npAJA2vGuUC2AAcuMtAs3IeBCvxOktJhVZl5AeoO0BV7OoXt4cJ0eXAQp8FigWij1N0jb3gCS\npOsRQNoXMUC6/9UgSWhLyDHh5CLY5ssuaPcGCWItuNN5gERnYu5TW4RbO0FA88p+9PtoKeMc\nJ8oW8IhXpwCbmbTeeektsjsgrcURJA+QDL3Ztg3JWrGnBhG9J3t8QPq68vfrF0DKxzeq2u8L\nS+c6fn19o7z3qnP4Hk8QE/13lEO5DBG+pizZNoNF6iQtF5E3OR5byuGnUDTUUYoQT/QAw4/j\nLCqCFAtnCjeAhB97vQJA+g+AVM4T6ZOfByru/AJi8TrZcZKuXmjlebSF/jl1a9GtunOrAzqo\nej8FyzWq6EgIOeMz5C8eBTRZpU0XVpR2wze+4G1f+hggHQTpxWl/vYVad5SD1/l64SOgPorI\nF/U3SDNAGs5SqBABEh2ug2shEiS/ldPocdc0uaPqHMINiTtokwESXgReE28n9JL9iVfsero4\n5u7or3w1Dho4odku1KfcTmGvUKIRE+onC5B8WAp342nEXKCU8Y20px/zuBXC277QhuGZ5U3J\nxH223W9rrPtwZmZDRqYLQfITdAH7kHf5aCAKgsDQqGDzSUEGJttFsUrZiRcjAy88VjYBbRm5\nBSDdTTpviVeoUSDhifa0Jw5mRTWVLd0J/R3VUkMNsDf24XnoKIG1B5Xp8/9CRpIbinOAZNW8\n8+IJC3K3EaQKkLy7rO8Aaa3s3P8BCWVIbyUQJLy6H5B4rcwYhTBo7mqAhEyRGmcFWI5k4+RQ\nCwUEkCAvNC8kNw6QMkcdIP36O5B2LLUC0UeQsDS96SLQW5GuiBxM6RsP73kXYecNa+/osFko\nTDiJk7t4dNRH6uGwDJ6Q/J8g7ZxZ8QNS/L9Aos/or/P1nQASe2cHSAUgzUjGWPMtuRkgYeXw\np7o3SGxM2QhSGn7B+jdInn5wnMQ6QOqXwbf7/gOkUGhlXLfrENf5/Y0F+AYJ4DUBkBBvlg9I\n/jdIEAZ0+CNIezntGyRkG4Dk3yChhu9ds8+DA+QIEud/hgESL2d8QML7vZrBdx0gcWIa6MC/\n5cTdPcrhfMMzHYt07gdIxpVmaImS37d9ESXnrO5KciyLobyJVbDbNg2QROKFKoLEkkao+xuk\nxJhb1gFSBEiqWE2Q8MsokHwp+K16/YA0AaSJb/oD0pE2gOTZG14QEd1oQydIGiCptHnDYUNp\nXXlpE7D/1SCpxRie/Rh920Z7HW1gBOeQVEPnv0uHZkJ51MYb3g4PkCDR4p4tCqhVQh4gBYOa\nHqpQSWWjWVBvcW4VlEejq4tCRmJXFeqj4JLY6EsNIY88AJDUWc/XSZDO9H191e8B0txfXN6v\n89evL85jdnhWlhc9G13okwlVcFtJpvJknAqIswt9edjYWXQQbM+ONHXjgOxeXfyABG1xeobU\nrbLmAkjfACm1Cmn33dt58FbY+Y1/AqTr9ZVfJz2G2zW6AerRnwSpyZb9pDvnKha6TLoZIHl2\ntYuEtcjxwL5vSESQdrw9WpifaDNOf7njsseLP4PSzgAkJAektQKQDH79q+YarwFSIUjfEEII\nLIxlamwLA6RI/6adDn/FcHjBgUqiAN4cDgQP55c8/va9hAAAIABJREFUQCoAiVtgPGuzqbea\nLnw0C5C4BdMHSHgj5cJfCZCKPWrnJRQUPhwaX+yGShbSTnGSDKIBU0VfGu8GASRDE+VUd15z\nF1nPNKsJZlgVhKp8oUsfnjwbu6DDp4y06y0K6hsPwDijjRuec0hq5eZjdzRcN/cI/nlEVqCD\npXtm3p0862TjOWe6GyCA6A9IJ5fr0O8uxYeNSPbWEKT88KweS9rWG/fp67+wa/ff9uL6e5Bm\nY6GghdbTyvY6jqKBRvoDJMhhgHQDRzS+IEi3H5DcPwBJSgmQNoGw5GiOCZDwxCRB4gCeAH2W\n5O5YgRGkiqJGvkH6/vV1EKQCkM7vdgdIEJDf59/eILEOMXmA1HkAF+tGqwZOgkzWcuW6Ryq8\nickQFYSjfQiSfOV1n6MY3hJ8gxQHSIUXFj8gnVdqqJH+9gNS/juQDrret5Md2+kNEnKOGiAd\nSLWJ8yONQwg8eBMOr/0Nkh6zh32bKz146wDJv0E6T9sJUn+D1E73A1K33wSp1DBAYj0k2jf3\nZD4ghQFSIEjRPrklBpAiQZKePsqFd4EBMZ00PEFqeD2RZVDkFeCW/x6kiyB5ghTO19d3gTA8\neAklIPxccYCkUngWWVkKc9xPRqnSnIYy0UOog+TFYqmobFYt2LOkjePIQz1aJSFsIs0ufkBi\n77CegAlWCUDi8QBA2i0diuivaswjasuzZo5/xip6cBJmeoO0fkCyAyTxJ5CwJH5AsgOk6QPS\nvt0iKK3xr97+Vo/fIC3OfUBSAyRNb+GXHCBNb5AsQbqz+O4d//kHSCbgQ3sOxhEoTo3YCRL9\nChPNzfPOkVLa4pXfOUBUMEIDijdI4qz05v3+9d3T9/mFJESQpvZ6WYB0AaS3kzN+TM500OmG\nw4jqMnbvcp2SIUjFTT8gIQpKHiYh4wEkTZAU/Y/ZZw2NAZAQLlHlePZPDJDYP/q3v70AUk+u\nDpDSG6TyOiDXc3+DVM4+c15k0y0HgMQxDgCpaCcBEl6YhDZ/g6T8ccfSa+sHpDpAqlgVAMm1\n1zFA+voiSCikM2TffnX3fRCk5gdIJHhvLzYOCR4GV2aWH5CCmX5AkukIEvnSjrlLBhCLD0it\n8YoSQOKVFIBULvYp9vQqiUL1SlelPepV4wDJD5BQSv2AtLKR8Fl2ynna4NEFdm9OESSnt8Tb\nyE/Ho6BsNoCUAgcD4WkQpIB1Xx0KxorAB5Agapx6g8Q5RgQptBvNCLhFf9Dx2+gnQXKtjTnq\nErUXe33Penfx3P4OJIkQ7On5UFQgSE+AVP8ACbnSlCy2B7VI/XcMGvvnIN21410eZaYnQeIk\nKDyXYLHYkjX+tYUyQOqNnTVjChe+rwNIFrUUh5BobotHxYMmgLRLTojdjOL2g6WJYbT5QaMQ\n6axNk4MkljwO5/3wzuQiUQth/b5+fdNx+itfBKlO9bqW3r5eBAmKmTft9lhiAUi8K5rrk/3B\njv/Umo+TIDF1QREjyEW3m3AW3SpKzoMbHr4QJChXOngAJCBTbPv+fl39RLX1+vXrYuNOsDVz\nnFAESC8kyFfnpvpxdICPqvx4g2RrCYsBSDxWykUgSBj2c4sxkYl12u4PfKCAjMIAQpCGLxbv\nclz4BFf/Plv7BZCudqhy0JRS4qcPkGq3vNJxQXS1rb4KspvqvGCuUVADjngGo4xYB0jWRxV7\nEjoNkDr9WD29kLy1yGI68bq/r3hm/Wj1erdXXZzsjACG/wefleOtL4IUj3awy81CK73CUd2c\n8TfNBVWSCbwfkkI6VWU5yoMO5itIAo+n7VBPcfBhQFi2HEliOJIDZaiD3GzDIijXzv44baaK\nP40yEukp9ilmTY8xPj4HRp7sqeRAPlR5TsZ5XKY46ubDKaAk8c1oV2mPZKJASOSbl+E97Tnw\nxdiNgn7ipTRbstwXXuOu/52xLv9vIN3oGUSQ7o/fIBmW5gAJ5dNrHfgwI6Gy+wHJ/0OQ2MUr\nAZJDifgnkFBaTARJ8KIZ/f70n0FKSV+s4dPr61Xyq3/n60jHqz7KdU0DpK8BEpa6B9QDpDVy\nqt8MkJDfKoImXidBQrAGKym/QVoBEoJv3fEe4lp/g2QPTnDKAMkCpBdA4kFOeX19nQTJ/98g\n4Vc4x4Ke4Mex1JJ56lPCBpASBx/kvGLNmIN2et4x8/mYN4CUC71G6eNDkBRP6znfHIkHYoqH\nvl/f3+aqhyxHwIpQZ4sfkMyLGxwWf+NaX1n6Q3dOldbmAxLqBLUJVm/4YhEhOXFkEY2tCNKO\ngM+bdgYg0d5sgITHffQPSGGAhAAWrsIc98qZIOFJtZN91zQufSE9oUTRiBhlLuYDkk+nrnjz\nnJagBPRVyncvI761FUZxC4q+LdoiD1e6Tjb2ZSJm8R4x3o55egOQ8tiZczwvA0goq2TA4xsg\nzbw6rt4geYUIyE12urgDJBRddHPCnwNI9gckPJ4O9bi+QTI0yHmD5EpW+xZBaf3vjHX5T2n5\nZ+YnyEhWSwuQng/WSIaXACyroyIHSDNBylMdIOkfkGzv2bBpULMgIUiSSRZfc1McvEGQ6JgD\nkPDsyxS1QhVBkOwbJCx8rLJ+cLvmKrwjdH29qNW/E7ITQFrKda4E6dcPSNVvvHENkGZ2QHPD\njlPJAIqUPF3i5Fe8Kt7XR5Djplq4UA6glopH2P8AybxB2ntG5KsEqR6g+vr+OgBSdZw+/+pH\naNfXxZKt8crG2RtAcgkglR+QpAVIaoD05EyiTpAQZN4grf648UBSASR6lxWtaL3Ha/UACeUz\nQCpfr29z1i4KHgQ0zFnTdx8gaYCUL+5kLFjlgBJ/e62VbugAKRGk7aF/g+R7Ruh9gxQBkvsB\nCYuKIBkPqfQHSCjBKO0gWP2Z/wAJ/wlxS5sw9QPSA9E9rMhIKIPzmAYdT1OH2SFAknEDSE8v\nIhaxFVYPkIaXB1Z0Hf2OnDI7nGSQnwHS5ABSATsDJJ+Ytq3R4k8gCVsEHf8Sy6ONZpdxGOUe\nPyCBVPxuF3eCVGh8S5A2fAZUHmbFYyqPcWc3E6SA9Vbr8j8G6Z/bcamHspCouzLrEwIVn5wG\nnXSJKyjmrHuhFtQuPRDaAJKAnB4gMfWismK3RjHeszWl0GvZmc04vbvtDRKnylk8MTbWuyfv\nD0/I5gMk9o94gATOLjZMh5MdDGd5xV7ZXyDSedgfkDi1tTYvLa1WS194hFTwUQvvdeosBEHy\nb5AYUPFuMieHvbI/CsqFA6/YefzLyLa/To//KEcxUa7XdfKwKx/XNwcVo5rDH3+17uv5dbFk\no792vFqjLx3byAEdahh8euMOmywnSqSJHVId2kWgQsy04kl7OJ/4TdU0A1nELhUVuERBzIUi\n7sivs7Bllkc3oh6Kc+4O/EpDXYb/+vIQUQtS2KNe6Yksilq8FHr8otTIB5fj5AZIiL3atrI9\n/QCpBUQ1no+jxAXlmd0HifbhzQGkdvI4sPgBEhSB4UBZgBTKyUiGiHbiufatufiyR/O3gt8s\n8oasH9megKh6oYRZM2+uKBM3lLpbWHn6zes/eHfBBs70aVZy0EbqWAcZICXOmepBTSihJk4x\noJWrtfl88p8Sn5jeRaiwlrAbTnSqjMyUzuy1avg8/pAoHozkBaWoDp7sn3gC+G2ak+qS4OCo\nZtUMZVoW3u1zKF53fIjV1/aXg/RUVu4EaVs+IA3jYA9qIkGa6RiU5jJA2n9AYiuocpLH0z8g\n0ekmOAhlSGe/v0GS0O3Q6EV4o+wtOFnu7DcSv0E6nY3hShyRheLb1DNdofHXs4xn9719o3T6\nrrTPxeJVCMH4Y33nEVJGuC5UOibvMleCFAiS4eRlgrQo981DfAmQjOlvkDiHiSAlugeYrMr5\nOo9QsRz6+SoAKXGebL4aXg3QPgBS4XXzi+7Z9FMcIGU25vsxhcJ9QMrOtL8DSQKkmjn/hl1U\nKFEAEghEdsoXB9rG6yzpdV5sJpD4gzQh63mAlOuhX0i25y3wruIVUY1xZlehFyXvnRIk+1wo\nbAhSMCgFt91xmFpgI6siSLyrMy7qJ/a19d8gQTcUd/I+Ckp6daAoh671BOmKBEnG0qGEA0EK\nE3StV1lkxayfLXQIQdozfdyV5ZlnFGH2jgUNSmceNjIXAKSNNrMASed8RGSVGx1TxZ2XdAuy\nzgDJ5HPBm7MCeP0Bki533ucESDbTzxkCmcLmoCeVBlkhIAxCaC/ISATJ2O7bAAlxWz7eICXa\nABZaIMmn+18AaZZ63w1A2nen2KHEblsvA12zUDlfK+i3YcutoJ55cDr1neNvamM7EB2BsgVX\nw52dV0RQCdn9yRFSUBdJYIUL9hcY1MYT6v/6REqXO/UXRLDrtFtMF4SB5z6WhHzkRoxrR9Io\nddMPSAh30CHB0swXUV3v/ON4bpXDXFzZ9WjrugUs6sArTjzOc/gM32CIt1cOiUf9AxJWduW6\nPzgIIvXrOFhixdrOwEHFK91GTzaRstsPIEFgNPeiRor47XjxiasXy8D7w3PAYqejAMpnKNCO\ntcWin0MSwvlAPuABEufqpoinCxlIxyx8YdMsMqG/+qnY+dP6xvHKLZXven3HeroXZ9wuiB9T\nPr1SPTaBut5zvhRA6tbwbeG/JCzdYPbWVhYlYXi00ASKwduubORCLRpR1Ppu+gGBXrgTYg5e\nb1W1zJ2ud+Xlyom4gchVTwSogyXlC4IgTJmjODhIdh/yORtzuWxVNmF7aseJZkH5iWMPARIn\ne1LFJa8aBBwPuPBCeK0eRfEN7yGu6836qfid++YW6eXcGbo3LKGeIkFawyaz4ADsALIy51oX\nz2Nie8iQwr6OvcOdJpaPN0gUAwBJMh9X3r+tDsI6se0aIO1hn2xt8/8CSNtGkIRwUvl9KFcn\nAqdFb95cm2c/3Q71A5AmtnIQpL3WcYkPAuY3SA6BmO3PZr0F+QZp3zmUC3FBGbpUuLXOKgdB\nl3TUOij1h2/pFQ37yV9dIg+cLnvbUDFDlOVeX1Du33w7BAkgRojiZlDrNlrw4on5ROenMZ/h\n5gnS9gMSIsQX9xksXqBwxwAJeTA4toxlRMCi8R3b2fvC8RbQUw4gcWYw74hWk4/X0b7Liz37\n9sX5K0i7AImT8dhBZgdIiSAFYesbJD1A4pQkG8575dCzhh9j6TCBir82Y3iQZXVV5xnt2Q7o\nxoIEv7ORHP/vq1zfoZ4eIHl87AGSM7KnNrN3Z4BUCJK3bFPlWWP0IKat3hAkxz1VO0DS9oGv\ni4/Lxo/OMgsgdVqc0DUiGboE3rpMmSDlA3HD9VZOE8qx4W8BSD1ObPKxSSe75gGSVZdLRmcV\n14k9b9B42k9jtqLy8QckxB72ZAaJt7YDpABRfsvhiE+satJJY1eAtJeTriduBY09DpA2v+6J\nE53oe+h5BZMtwD0BfoU0syz8Smnj774DJJ4ked15gZr5uJrHxGWxDZAgl1Aj7Y9/C0j/xWbD\nIuQ6QJISAs+vb5D24IKE9NXXTndnWl0lyNSJ5mkPWiGWGnaPRbF5drEZurMbgsSuzXkCSJTp\naVu5uZYrN3T0jPq/Lir7fQmoFnjjEyDpUK4w7MeupntDMZC4a8uL1rm8QXpV/wbJr5wJjmSx\nEyQUoZUHrBChliDFG5RFx4dGnYp/QatKgkQZ2X+DxGqYIBUUOAQpVCwuxgfOPNAVRdvT8L5E\nhvrrV6/fwBxg6xdrsrQBpJk3onher4M/ENwHSMqOs5UfkPQYtnXcWtKILzoJjdwkNUBvPDs5\nrdVlO8+gz9K5SwOQJMpoGji/8kmdFS7jeS+j5ikfxome6sRtsw9IbYwHxMP4gPQESBFwRrbD\nOevsuDhnp1o8QWKTJzf++tCh+CXVCdJW4r2tACm/2Hj3jaK0ltOGfOxYBS/EszdIHIPhltEK\nla04eS0Dwv85gXWfI4TJxLFQA6QSsn+DpFcmq98gMbn5I97FTJCsYk8G0k+hksRfniRA4lh4\ngrQmXjMkSIGX7HnoCNFrOhRQuD+hTd8g2dsHpKDeIPkGdTv9gISQAmG9IcMhsf87QPpnjAGk\nfV92DbmrtJ2Vnznnx5s9OjyXe1SnsG433PeAxg4TrSGf2bkbVAYqTJUWN2ZEeh5edMspN5Ck\nE2SMoPHJBooIEi/Fqj27qa4qu/XO9w+QJCSMRVHvZ4efe1aA4BrUoaWBTDhQIpbr9X2xNuc0\nPTZ9ZQ4vh9bILUFWcFdhtNZlWrzdLEHaArRSi4VVxFcq7CRNDSCFD0guNmQYlBMHqizOZy1V\nykijwoa4fhiCJKCxsOzPml7phISr8lXYZnuH/nuyWQ1lfd5jAEil0/4OZTbnHtRuGb0L8nQK\n4ZhaQjHRdFn3UrRgq2/DZ3cHnmi+H6ffj8BUlEyvnMzm8Bdf8bhUudLlKP8A4IQwEBZ8yoW3\nzAFSbKgQzfBJhJjmoZlz99pXKLUdyY3tbjwhR4YjSJxl5VjKOSjnxs51FP6cIZs0Chy714k3\nvC5kVkgC3jY6EaAgdr15uQqQWIR6+qCv3K3E/7ecLipOfZgelu+YI2ifmgZqY2oyK2Xkd92k\nof3/R9p5iK8bQEo3qcdEBs7ZhlB5ZCjJnBGYVWg0VDZy91gfiG9HGG3ojiYZdMMpsgPXAE44\nvn2H1jO3gw60KJwklC8zPu95TDNqBgT4ZOkjb7fZbwJFwPMvzkhy2ddZKIJk7F25B13XvRbR\nWV4dl4c03ILDk0Nk8g8bkHCwbiZ82CU4mWeARMsUPOqNIKFyEfMNIO0cRrAtmdYn1e8okgRA\nKpsqFhkrDZD2etJD/HQ3PyEMFiw0KHILODOC04HEhBJ4gISXEz8gWeg2x40ghLuK/+pjRhVP\ni7dpgCT8ByRrxS+AxEF8fwcSSxI84TdIJjasypWnl6JupR36CRUia8ra0QEPIFkfqiJIMU0D\nJLxYirflTyBx7oMYIHEiWVnfIN1qWhLf7oxVKAQbq94grcjlK2Tk2l2lD4bp7GHisUO9Qr/2\nfOUTKqc6OUDa4gyQePn2ByS8JDYslA9I/lmPBXFpjUUm6zkrAyANaWczTbUAkj3F4GiAtOFP\ncWdJSVoLJpSpCdm/y6OkEyn80JBLFyuTARLlWthK/gEJ+h0/alod9xjYQrqoN0j5TyAJN+z/\nO2cccAq1R0lwpIdESp4yJ8jThfIJkAKC8sa70s5/QJo8yjRePeRREjOSQfFVRGcr4WOAVBgJ\nfoOUkLAbL0k3WfVM97e00358gPT0O5LevwGkf14jyWWbB0iOIEl3d4gTjqOKNEBKAEnbFcoP\nn6pw4q53ekkAiVNcgxf5YZHB7ADp2Y2Dkonb+gRIG2eh7ltGTMbKF8ZIRZB2gPSYuGt7pLxA\nR3isVzeFMfuaTaxINw9UGIh1h6qNm9Ln+RuklTvAPIYFSChMMqoSgkRPT5Smkzlqp3lQ/IBk\nCBKyTa6SIBWA1KsdIEGndoKkkb+Ku3F+1lYkQFIESUEvKAPWPRIEm1/1VfC704Ra/BFYAfNI\nINHbEVG7IQkQpPgDUl4iJ9P3R43TAOmBh7C9QfKINbRFjgIgcQzX6iBoeubZnY0W6RmsEqSx\n0bvX/EhtS3fOyGQhsrzv3WV62GItDjdEgLQApCrqHIsY3quSg6K1XejzQJCSa+bcG90hCqc1\nLJDEmuN2VV4hlNgi28qriYNa2HKUNUHKBIm+xTxPFQOkaLcDmiEg+T12rPxMWes3SZAMdC6U\nIDIl50jQT3c8c42X5czhURKcaUYNnTgfCX9vNvucUJIBpL2h4OPQMiMIkk0o2WiF/AYpj2s7\nonH/br0NkCRT6p0g0XwACgLSWvoGIDfBEXiCINkBktsVVsBfDZKYt/us5M5LSWaWbnKZXoua\nHTZuAur0RNEPbTgHjRMxjV4Re56IBw+6Zk8GStcEPurpkFZBk9/FFpV80DcQ7ymyGQ1Sg3Yu\ndsavFH1baFkPkG58YxZyaopzwkuAMkAuV88wRlz3ueL5nK/joEoPXLUrpy6XzG6wjnohI8Ji\nkUBWsmRKN02QLE9cOk1CXfwVK8I5n7s9/AckulI1eiN2yFAFGenpiQ+Ro2lJ1blXSk/rqLZM\nb9XQ8dJ57sJrq1jPxxQ4TVu6eE9+gOQrkhVqu42bhggpGgo+0te/b5W/qeo24YcuwvmD12yL\nbQiewdQjzF3kmTML6NCAX/L4RVuONV0F+XJL8VnzHKtkZwkig0PFWSKvN7L5m3fuaXTP6yp7\nPea+F8S3OWgv42o4P88KKEoeQbPLWp9bY3ciZ7JwbkDCO0GASwg0MVw51HiV+UzhiDofnL5x\nhnjEG8eBxMz9Cejo6pEyECzWWE3EmvGmcFyAF9LxHJ/pmdP3eALclrGNWzph6wYgzQUg7QJ/\nPUcbcWKIfm7xNC43L7uLnPKIJQaQUEin42BHFRI785AyvQEkD27UzADCmVKo1o9IkEIhSMhl\nrk1NSeh+Dq+JHFhanVitpNfgXw7Sc3ssb5CcWcUASdjNRLdae0uiKSEnPQEkmZxEJNV4v8hK\njAe/QdIAyYf5EBZBNE5yB0gTDTGlpucMPTooEIvdslRV3TeEmnbEdE94WqZ1/FUrqx4a5eOn\nrJHz5lNfeLn6ePVex5WMARKiPx7u8BMgSJRKKfFqZHYfkJzr9gNS+gJIOydaSLxIBFhPkEoh\nSDHzIo7eDUdSrBbR0bG7u6vnmFcDkJ4owNTpO+c0BICERTt3gqSRSgHS8gEJa0gQpPUPkOit\niCSwl7DFARL05I6q8QckI+nccsSly/REzjeNbqUBRNVucpfxgqxUK9RqRXarNPBBynUW6+EN\nUvgDJHprRFHPuW8E6R54ejPzrix3l98gcRikAkiRA1kGSJFz0qxTNile1z55mfwsj5MeXQpS\nzBEkf8QH/ogYIJkBkjMawmNJzSLrBYAPaecCQUJVzS5D18Z9TtOeBREplkaQmrEnsiBAEjv+\n+oWhD/+jpx0C3mZe2+ScM2+sngHSxqM/SAmPgi52fEepe6tygGSWAZIpBAlvkWFggKQ+ICn8\nPNrP8h4iQJK7UQAp/9Ug7fdt3cwACZWRsHjlZjMzRPXizJL3orZtMpO2KAncvCkt7Z6cgACW\nE93dbprXGseYBHVsqDNCnJREpMc7RRZzNBBERvLGzUi1Mild1SqhpwBS3ALWjG5N3ZMoqTGv\nxGD5rLlousIfjP3ilUIsdIRKD5B435FXVdqQdh3vgEocf9jFB11+XOANMW5KOVe/Qm3sRcGb\nYA9F8/H4gHSwI89CPpkEUBDF6KmMN9QRIzho0nt1C2lfDlNpypNPhF6fNhRhE0IDamMfBFQb\nQGog+cG+o2doZYTP4Q6ck60y01woAyQOsnmDFA2POEUk1/j7THyu0miWfZy4YGpD1W3DhRAh\nZgeQygZU6gSZGfkon/gHSgCnx/koNExMg/p6Po8VoT49PG9yLI6TvjTNVWnQrIvL8tiaH9kb\nOnZxOukN4o/zPfHMeZ3RHGlCgdSTSh3ZxRxBH3HFUl051JQzaQES6KvGPtmLbGxwGiCx+1zZ\nMYkb2LiWaB5l8ZFNYyuHQ0HbtDv9RpAkBTTiDjlKeoLktpqHtgirkd1D+ibcKrbij44Sgp7M\n3UNWqtZHEuKRE6QcL5khFD3fIPmKmrZvQKatTY258pw8pNjo4ZRCoKop/dWbDdtt28HsrqAI\ntARIPpnFPBwykjcbQJLrCpAQuVAbTKtUAkW121Gkc1zmXh4EiWNROaF15YlpnLUKap94O1X7\nP0BiNcUe+oq8NkAK3BcQCcFG3pKE2CNILPpF3vEHoAf4B9sAyWQFkNzKPQYa1wyQAFpv+M/g\nOWAPINHZiiDJ/AOS5zqnlaWCWkE5G2ngluUbJI+aRNmI7Eo3djFuMnWzjg4N59WCJHvrutJx\ngN5yWB47QYoqC7ZzqDHmvFZkoCmzBkCZ0aD4JafG0POxkHG68bYphWpEIEiJvst+x7JD/t27\nDShRjarDchMglSYdBBS+SN9mk24DJE+QOCkg1fkNkmWjARaUSZzGhIKOIC1pSWlGASPZtZql\n/4AUNYekCd4joo0NQPIrnVSfDguOBgheEyQFkA4Ue4krG4+mB9ER6zJkN9YzQMJ39UCm6gES\nzcoJEi0QlTIfkBxAQl2QAVLTja0cCGA8DzixWAjSUkRBBV4IknlI0+3ofjhjgsDWRk/CbaDN\nHf2ebAPBHQFOSE65opvG8OpiixbiGkDiHcSAbAuQnlKgMANINCOzjiAZgKQhOgBS/DeA9E/Y\nAkjTLsQPSGoHSNHM+oZntHu9pz3LBSDdFIKj/4AEjbeiSBcASZQnEqjkoG7kkHO1e4uBJvxq\nfxIkeggMkIJ1nFRgggZIu/EAqQf2zu0AqSJ9DZBKpfkdQFrxbnNHyYJK4qqFc4N3grRwLO8A\nKdUMgV6PFtnESJB8fBIkroCdMtFY1749iijI9iAFBAnvxrxB6pb93pZjaSy+WOJ8EMmRcTzs\nZc8gdKHanXtOXZYn1hLdTv8EEqcz8xIOxUZFkTFAukNfEiQxjNbYgsOJRBA6AImnMTLYN0iI\nphuNWBtAcmF57pBgtNwMklf5BRYMQKp9WXSaa9kT7VrLB6SFXtEOH1gMkDTiMWeI6no+jjmt\nKM84sIHD7LLgcNVqBkgVn/3Yqo28tx2T36Ai9ORQldAGCWKiBNTDceI+9Q9Iza+do8jDk3fP\ns+dtOxTurgCkzClQ3M+AhuDFGM0Oh8BWE9vww222UKOKINUBkvkN0lyhcvAdkFCjWZTqds/H\nAMnvWPUGSWrjgN2j39jH6AkSpGPtiD8EKXxA4rCnmQOiOMBDldhvSqomeZAHAWKQxiTquIok\nt1geh//VGWmZdq2s2gZI5g3SQ030cKbL9xbFvM/mKX2YnL0/d7HRY+qupUfO8bIsBqF2ZvuT\nDec+QFJae7XvNBjgtEVDaY2wNbnCScKmKml9zw0vS6uyRo3AjMVZ8f1L4aAjKcuDs9waxwrs\nzAUVtCwAyczp8FhJtNiqvGDEcUrM4CiVE6cr5DxCAAAgAElEQVSvnIXDafvK64TWuv6y7DVj\nyYlqxOED+APKHTy4EyBxFhhC12yTkXFTY1Brc6O05hhdrI791kS+8WZaLyh6ouj0LVKo/vB9\nDV40P8PuOdb58BN9d2QobOhEwI2I+dxsREUODlzxBKnSAxXFOGSL99zRRd332I3IknsoAvVC\n2RBT8EXKcd+2tFeEmpgGSJkgbe/ZD2JnZ03mZSGj6SRUzsd5j4BOAiRBzzdEIyjWaj8ghbXv\neFL+gbCQggCk+m5FMF5q79aO0mtpYTpQriUbGwcXVbd1FFzFLWynzxwAUt1dIuzYmZHKc4ed\n//RZsys9jJv0AGnjBPiMGMTW3tEuBQl6IupCB8hnXTJjG7OcEQpZ8sZjphOVrnF45JN2vCAo\nj7YilSOpdJbNogAkbjKBd2rFzrtvfkbgxXs3DY+hz0gHDQWcxd8seTgguBPonJ0sC7jHX1wj\nzQBJW/0nkIK5S4Kkg1RpDWIWi5lRHAOk+bmJ8ZQmvfv9VjxAgGQWj0KrzXgKggRBYbwSEiIl\nW/ppvkFyN4JkLKf5cuQCHQbNjlIaIG0DpDxAMhIgzdGLDBkOKYHqJNUoy8rtpCdAyhyHRejY\n7HtC0fNOBF4MVgJq5ooi+gOSccdlSuKsPSsebeMlTkeQ4ta5UiE8o3ecJpwgu9hyMUBSbKmB\nyFIaBeGzDpAclEXOA6S60PaRg4kIEnekNxc5pjhMpXHyYplZxBEkqiZAhSA9VYRTFUyvYwgA\n5C+NZWqRAGl7CCvSzv0vmqKXFeUiPmY+7mLmbSdIzvwGKQKkna6MIaKwpd8yQKLNC6prgnQL\nO13uCJJLIs9vkEZog1oESGwP5NEDQvwdIC10YvO0yn2iAgtLDVMfIHEUFJKGBec6cIsI5SsE\nIb2AJ8EJ3B+Q0h8gyQ9IAiCtXvAO2jFAggjmeJj0BimLZ5szzcmY940kSGwmHCDx/o2ZDECS\nyNWIIABJ0zzcSpEJkv8DJF6oAEiRH0MPkDarl6o7HyFXbqR7GIetuQkRNNq/GqTnJMwfIAn3\nB0gmCs1nssjNzCIgo9jtiToQMgsgrX5/FA79IEjTACmdcoDES/tIsxUgDWPa+AbpCWnHTvuq\ntHOdV3qUXdOCBJIGSDxbyjQJVzxY5HQymgTgbyVIomwE6ZEOxOMPSPiL3yApghRjAGvsB1d9\njYDQaYCkCzs0q92nNrPT0h40sN06kMtZvUGiLbYIM0HySIO0N+LkaGWkVmulj0Y2vB6TXcDr\nLRvH1+ODKh3x3Wk2hSRY5QekJZY70hWnjWl63nGLgIU3h+AMkHgvIMYnQKK16gBJWhFXXUrY\nrE15+YB0PlE64imKCLxv/wdIy7ImXpAwKHeUQ61EkCaCZAZIPu+ZrcN/gGSXzhsJfvIDpBs3\nG4yEsFuFdXcWp+sHJKxQguSLUT1AqhoqZWCeZS32BpCMXehpg3QbBkgua4QC5IBQ84qft/ot\nedRbsn5AQgxCBJRslRVzezBitTB8UpSo7vEDkh8gWVpaZ+RISZCkZ5edFKkV3gzhrCWgkt4g\nLTyMxctCHey7tOZeDUGCkodOTztB8t4tIC+Yvxqk2yQouFZOQVFWAiRv7oIgWY5mm922KmGe\nWwTYVq7zjoRLkJ5uXwiS8E2LiZfLdD41QXIoGdkmAZAghhxjWiFIGwcgz4gOml1kBdUM8l8g\nSGF9BoCE8h/yRUA3o+wIC/BxBPSkafyWJUKTmtJh6HsKkDKKet63Q3nAxlkkNL/Fg408AiBx\n1JSy58Eu5axQ+Ex1MvhZBiA1D8lCkNhlgwqZ41KFWxTNzrmVR+0ooDIAuhGcpD5qI5ruuq2h\nwHrPY+fQPshUyEpBLwYWGLVGhIA6YaFXVjQelWWNfrcVb5geyYo4bpxKdueMm1QVJMr+VKhV\nnhJPCSDFvMhCc+507nrCT2gSS7rObE2ktJOOTbjxsSzcFWXaQFFRPiCBoeQGSKFseQm7ytUh\ntCGqeTXjfXDxsRkhyBX5UmoJNXmbkWHYXy9yfDS8N7z6MmzbkV08Uq6Eyk4cSYWCzcwc0Q2Q\n+AUjwh4v3rpi7M4L7YgfExO+u2GdT4eqSLqok8eO+xFkVhAfK3deUNOyUcYYRbVQ3iBhsSDO\nYplBI6W1cchhrZtDNQveQoO8cczCgjLyIFZhoa2eQ65F/uzG26kYXn/Cl6VlxYovDEnrxp0S\ndf+LQZruPyDJD0iOIEGT2bSZ+LAbEq1+rJHfUO2PAZK/65vd12LpDtCUuI1bmvk0BInOZlZZ\n9wGJN5YGSJyC4B5+gGQBUrUrftrK3aF1AUjvm61cVxBrSC9sCh6eP5DYccn6ByRaaXf6cnFy\n3tnj2J82CFXuDVLc3yBFac8us0AkbXqd6kNPCIE0AHN7B548thsgOVQKwmyofLB4KtZpwGJ0\nkgOxeS5WdpeWzo4ALKBWk4oU4CWtCj9aoAKQFhkIf+XUAJL4gIRIibwMkFDYu/IgSAahM+PP\ncSuPVocyvEGagaybUVO6zZiYFlVpPBdPaQZIHPleF47tGyBZTqINz22Jb5B4+FeqfoO0Bdpw\n0q0Xldoatg9IqNWhLLgrYsNq07hLZKJWSiEJPp7KbNADFuqM9srcYs2OU/lW13hxHji+QXrU\nbNbdoTpc4m+QAkGyb5D8GyRhwQrqLc2hoon6FgVWB0iy52VvHBITm6u87SF9saqcvPCaaPGJ\nioaTWEzaajIDpPewJsTrD0hFaJECbaFQ9vEzWP8gSM0Ge89YTBoPUu8llrmJAZJx+JPyfw7S\nP99smJYd9ZhZpEMQtsrdocwe8oa14RI0y6SX3Sg1zWNYqlYTCmB8hVVNVuwACZg0KWjdhjLm\nRASnbye1og8UJX7c1oQsQOk/vFb33yChmlnc3ezJdrfunHLNDens50nqZoEB1irWdBkghTsC\nG2IUQNJYSwzYSDQA9OrB1s1nDsm2O4R0gkpqa0R1i4B61j3e6Bek56VICdWAggyPXPR0ZJ5k\ncssn+NWjMFPggg4QUE58HmHXnuck9Kgx6YEfiNiqaFoNxcbB2emBkr4uVmflAM7W2D8Uqo31\nibjBq1mciAEJ5HeXId5ztn6nR+yTF20nZg47QBKb8UbvMyLLhkcTV0Or7xhP7SaIR0RagLIT\nJM/uJjxerOFFrRwTBPErzU6QoASPyS6sIJFmYgJIIqwCjxQaYRPVLzz7wmIUmh4jUrEfT+gw\nm33dFTcH2L2H2MgZaZJT2kOcApVRXDRBAlgbgsEGkBxAYs91StwyzUxgdh923S08HP3gF0So\nieKhFhSK7g2SQJopd1knfLrUEBCxxGUChABJ98RrDxJZJVgxLgxGdh7irR00LNo5O41T/FwR\nah52sokTTXhQ7KeGZ9S40ZxM58hCa7YS6w3lbKUCHGMob3/J9vefQFrZMYUiiCAZjZRhANId\nwdqm3YYfkJ5RAzGjp3XLAGKXkxFigBSbkAxkKGMAkmwsBbTRPKncI30xxRsk/CJA4iVzrBbT\nKZ43N9MZreNFf0ByBElonnfgfROk+gZpQuBpZYDUxADJQ9MVN0ASkB8ofSwr0rTHrTFaD5Dy\nHldX1qafW9FiRhk4QJIECVpw7J1yk8LIXSsO9E1jc8LqOez08ABIiYYj6wBJI2KgevA68Iz3\ntgOkB+K45tRSgPQgSGBoHiB558w2QAJOK83GoQy5NfngRVuCtKmiUMAIgcAiBUAyAMmFjSBh\n3Z5mgMRFUor4DZIaIG16i4xoYzz8AOkYIHEcvEQQSmWBGF6R5gZIO0DaUMlAEEmN5OMld1YN\ngsVslFglVBQH8UFBW88hv4neJIFuYlD4M0FqbLJHaNt3ls9LGCBFbkFmJDBvxQekGSpCaVZy\nACnTa4mzA/AeG0B61DIpgJQ5bwcBESBBF7gBUqSrkAw0WhUITZIGGSix9HbQHRnPCkSiKgZI\nEovhoMmaZ2P0GyQHwc5vjgoPj8qYtcY21ecAiTlJ//Ugbfg01j6F3QGScfMA6aFpoiUAkpoB\nkpweUT2Q0s20DJCEmBDE6SyjY9slAq5llwpnyOMtWk7UjQMklO7b/8/e2fS4jWRZOyl+fy8K\nXBhc0Bt6w9zIG+VCQAJaJJCb1Kq0KUCAJEgQQRIUJCiNasxgGvMH+i+/z6EyXdU11TUz1WmP\n268C3S47P0QyeJ97zwkGI7LpWIPFCbJbO8d/BikJci8RSFkQ5dEAUjrNHSNIH7FGmXZWxJRW\nP2luJn1HekqN8ZOmFed6zD9O8b7/+VTkGFKsdT5Lw8kjyqkKBFJcTbTva1Bqxj8gBZM09CMO\nrFcg4t+AlKeRpyXQ8wtInL5V+alm/KWARKoOHzXHMvGJ9nwAqZiOTR+QRpk3SYaljB7H7qOm\nPlfIMEAiDZK4NaEP81n5KBGErau5yiNtgjmAFAASdiUCFfoQGvw4yUoKld4nLX/KilGlfTiw\nldNoitvINXEdQ6dHLkSuUnY1pXbm08d4/ArS+DNIcen5F5CCYEbVHycJji+KEcK4+kRL1CSF\nmyaRg8sDpKysIAjvUnqV1iYpfUDSRkOJlolGoGk7KP8CUqEnhwJpkk20QBanw/1DCnqAlXCS\nLyA9agZRNp0QDUU4NqZTI5mNBpDimZYwi+Sh/gtIEQayuoCUas1zzcCbahv5apZPo9AYJz8+\nxoAU6rUDkgHOTntV5ySZx3Gg1/m92RiQzPIFJBLVFwcp8Au0qulnmtyX0hFxbEZ2pC3Y4hSQ\n7ADdYZhV5GnCnOEEAinxDW0Cy8+n40cvdmdxWgWAFBMZQaWFmsbj2SOVl9rtppoJjdxL9Zyz\n5Mo1SVODZnERatHbp4ye47+apD3NQwttM8zdk2ounwaQtEVBqmXHx0/pYzSAhO6iRP3nE3c+\nRTtpc8iQaKss7JZFOaomeCSCTBtkzcgNkyKOfaQ0wjtLHzV+OhkGgoHVzdLYKbSRRqG1ESdB\nElqVl2l/Zc0Ty4ZEV2G/ZGQ0QU3vulQeGWPmZO5Ue2rO/NkkfNRKkuXMm2r1LW3TMarGT9q6\ng2QskFKLhFoa5TgGpKIIzIk2cIyJgtJF1c+iIIjTPMwQVFz+T3lpQ9SjVggZx3r9IRNIfimO\nY60RrwG8qTEOi+ljVAGSqe1RtBYcvT+1Adz3hw2K8gjJFkKgP8umcTwMsqG0g8xJc21kb7ha\n+iiF07DUYtKlVwok7UtUwkEwgJRRqdGNXkiHJX6u1Yi1y7LeXKR60EfZeFDNmr5EjcUKPOn5\nwAT/l061jGwRVianm04tLak2jad+lhb4waSc/qh9w8aaz4MVHydRqMXxlOKowuYTDCa2NS45\nZ723iFGvvJ+eYr2Sm8pSlOEjCkhvk2RFMhvbk8coc2fjp9HU1a4LOeU1dwPjnwbpj5Y0BqTQ\n19olIy/10ohbisuOzdhBjqSAVBjRBSRL74cLJKX2qsC8a69O0hoguQk+Nq382Y/E6qNmv8Sa\nUjmbeSWZamxrywItfiK5OCwAQ6UVSGTiaAApjeIXkIJpHjuOFk0YD/veVBeQJihnVNZjNho/\nZU+xQNJS1ANIeu0rm1iT9AUko1TOA6RpDEhhhnczACnm9usFkEdNCr6ApKXqX0FKXJR9fAFp\n7MWBPXZJ0AKJSA6qTCCNYzeiNuD2coHkc9dnfh58Bil61JMdQJppDkUASIam4Q5LYGih+iK2\nJi8gFQNI7iSc6T1WSHNSak4YILIyvbWuPPpTUTkCqdB2dshYCjSnF7tFrOc31KWSxERkTgRS\nWD3ZP1ppnL6AVE1N7VkrkGZ0HSBpVwF3ls80qTOBONJKZglbbIlNZhdI+Nm8BCT/AlKmvR5i\nLItAumyYlWOmxxVaotIQZ6llg7WrX5VyhWO9zapRxQQ6NaM0rTSrPS4uIOW412pqZFNbS6pN\nk6mniS6aUDT5cRi0HUCiz5J4yMXlsD5y4eods8QCpEA70woktzJ+BCTNlsZSIDpm2vxZIOWx\n3iim1rnT8ZM98V9Awvq/AUh/9EOAFAFSTF9q5eZEWzeG8Sh2qdAJqlkg+XLCeAbtwlHYNgoB\nLCycDGoAKTJ+tBJ3GiQDSFEy03Zr2NjJZDpzqHXBeJRM9FAn0pq32uSt5JeGuAgp4Nqc5DEh\nS0ZUiMnUnBaZb8okaKlq/lc+lQIpcXLuw2NuITGeUGCPEwpI9TSJfnr09LBpYoyjpyhU2ga5\nqaHN2qZp9lMVJRNnas0SmwSep552nov1msrkscoEkrahx5WkaVBF4AzMgOSEvj92cz0YIeU/\nFV5V6pnWOAmSCbc/ibNhO+AMe8ZVTRN00cxHoxAsE2wS4VoiUTUcOxFIeYlsFEiBR5nPjXwc\nFQaqF9uvofBUq/07WpbeD0298o8N13bSP6IIAYkTzsaaZa1lpAFppCvXm0taqydDK00jAtMv\nnqwnO804QboSkOiRtAgDVUhUpsu9JOjCASQyZOJnYeDo4WcMEK4lZjiPKk60FTuuTiDlWn2/\nCuOE+B0/hlJzeWHFAgnthSIAJL8K8U2lHsRF40n6FKkQU7DSceI/JmUGSFGhR2QogUgv1pr5\nxMMwTelBjVGmT+OomjyVkV5mAaTxABL9lGrVEE0AigEJX4G6QShoSfLEj0prAIk4SjVVkg4L\nquFF4TScCiQ386bjH/1xnE2lrNKwMPw3AOkPfuoVpNSwUi8WSFq4wYhdJ58gf5MXkELXLfxI\nIDmOpmYX2cgIEtQA6W38aKbu1L+AFH4GCQ80BaTEHxsCSVsvaWa/RsmiJH0BqUwuICWAVAkk\ng7seWANIepeM+vVU/EReir0s98qn3B7PSu2cBUjUmgEkU1PrJ8ObU8FsIpC0HiUFf5rlP1Zx\nTKQ508RJVV58mZhocgEp/QxSAEhRFQavILmhH469y5ieQHIvIFXaoXHqZDHlN9VrOICU6LmY\nFiAHpEzvdQOSowWkch+QRi8gpVrFd1L4gda7BqTwAlKq8dtiWPHChYmZF1p+IpAiDTT9qF0N\nsUdllJED9IhNWzFFBs5AKw9K+GSAZM1iQPLyR/PJ0YuVAgkKBpAi4qrSU1hAouoipPJHJDk+\nCZB8C5C0CGwZOPJdGX43jgaQoiJL9GplofmiIUlmioAf5lsXI5ShQNKqwSreZVhlAfIlQL5O\nkqdQuj3xC00C1kKrGLTwAlKlx3VauHHiDyBlE6ROlQmk8d+BBOYI9dQbQMJPDiAhkfIgDgSS\nHtX4A0hkMUCyy1RvnAwvCmtVnNFkZmurvB/J/JkevABSaXlvAdIfIHYBKUoNI/XjYHhBxRdI\nVj6JBpBC20+SyPcKPTZJSxeQogEkL8mlZgHJACQvqbwBpKlcMPobjiY2iHoCiVIUfwaJH0pj\nTQYKigGk6jEeQMInC6QidNOp3jDKtRvbK0hBmtvFU+6MZ5Vmnz5OpdyftFi2oRVKCRv/KfI1\nuvwCkuaI5T+O43hmTLwLSGWmRSyLcELQEvBY5+kAkl7fzeJx6GvUAJAqP/CjF5AKQCqdSnt0\n4rQJoomJ2wAklGJRamZD8Rkkgl4rNM/sASQNIVjT6mmqBXxfQIoGkLJXkPLSJe9rF+sLSG7o\nuHR2MgsK5B4gqVO0BFylaZcDSBOBlGlZeC2AmObag1MgudmjAUj0hN5VSTSxQCBFdLPmBXnj\ndNjnfQApBiQvC/zRBaRxif1NNIk8qpJwACnOtU9Erom1dEvm67mPqfWCkCgJ1oj7lWlHrGLi\n4nz0JlfqkxQAyX8BSZOAp3GRP2qbxnRwasivPCa5jgOtxVHlY0AaZ09oBzot1OifQEoEkjPN\nUkdD21RELR+gnWDzC0iFNr0oIkAaz0JASjHFyD2HGyuQOAlnPB1lPiBh29IBpCQoPfdLgzQC\npDxMACkI/ThDm3qxkbgGCUPPx0y8fxIn9KaTaJNs3wvGQa5p0ba2GAvcbDwzUA5ODEhPSLsJ\nQmAc4Zq0dywguUg7vI4MJFofkDI6hHuJPfFRBLlAGoaQipwKYc3KKgpTzU+YYPbLMn/KfhRI\ncUQA/kht0FqaWBA9hwSk+CftfYkncCrnKXS1A5ldImuqUvPvih9B/dHVXhg4Hu1hO6vGuYci\ngtlymCZbaJ2EGJDSccAVDkvucNtCLZ+ttb4HkGxtYFeQ2bnkiaEpy1pNVNeiCeRT7Rc+8zRK\nob2li6mpdyJI2XnuTIU8CjbVfoElWo7kY6YvIEVlTs2WnUDuaH9ZNwxHCMd4ZvEbxVOJD88m\nWjOPoqQnvJijcWTkPgZp2KluqjkNgaYaPnokjEcnHdbUC7Vec2UCUhzp/YeswJxQQBxtcETW\nisZh7GZ6ggFIXBI3pxqUXFAmPpWXm5INy79PMoRB6OZcfzUTSFlcar0gKUBJdYLfESdRMibu\nLSh/MlMtYRiWREk6iTKt0ObTTdrdIk5KrcbPDc5njyiPMtQWT0/aNFlzk8FyKKbGOE5MbKyt\n5ffwylqAcxyEEB9Gfqm3N9wsz54ACbMFtEg7kr5R6SWtIjEr7Wo2oojqOVyhfdnQVn4VWV8a\nJAuQskAgYXUHkFyBNMomvoYXAYm4SUKN8JCISt/3Kx+QXMNE2Se+NYDkDSDp4XVCzOuFRmc2\nG49HgORUpm5rHAJSegHJjy4geVWl8c9fgTR2AUkifhJp2bhSm1drk4JJgn81UoGkUTRtLqA1\nLp6q5KeZX2ndRq9yn0LnsZyMnc8gleWPkzR6DCbhBSRMJ+GYuZVeqp0WWk9O/nSsqUB8yXdf\nQCpjL1LVeAWJnDfWzkO5QCIv2NpNY9iyXU8iUVtVgiy/gFQAkvUoOqkzWhlhACkCJDJ/nAJS\nbCUCqRxGIJx8ACkDJPyPF0ZGRu2emRRJkvkkLbUdm5MVQTbVqkAvIMWvIAWae/4okHy9je7g\nTF5AykpnjJSLtaMel+7xG7mhuYsDSAEgITxeQUpjVdi88ooBpGQYcXwFKXILsyhL7a2Mti1H\naTmAFEWZ3gdxM4DSKxlWAUjhk5GRlaKI8uTThVofq3RfQIqSsrKnHjdYIOXSQQNIvnbkeQVJ\nz9zjdDTLEj3TndBfWsJsTIqPUQJeob1N3GFXoaiaGgNIvrauM7SRLCAZpR58WIi56qeUBDTR\nynuRV/1mhtAXAMkGpBRbZKTotzjV6xsCyeTe6pm8FVoOkU2VsLGi2vvcr7TzApITkGItUzEz\ncm+CmXenWhFpeOAXJfZsVlWAlNiVlQJSFLyAVKSZh60f/xqkUCDlet1Ei9to78xpMNYcLYGU\nPGFl0tTL7PTH3K3G0wtIse1cQKIAjiMtP/0Y2BR+rGyFvNNkywtIiGnc9wUkveRG7tRuIgKp\n1MYbGh0FpGzs+XrAKZASF1sSFVqi6zNIqCg8EZ9txiP8nh6XjweQJhMS+RgfhvWmFAGS86g5\nKZUW1xlAyl5BSjQHKbZfQYqrxB1GGgDJlT3zUAa5QHKKYAApQ3hWlYU/vICkSzVzT2PfryBN\nwkeoegyCASQquR6nUSEK/zNIfN/T2IChq0baRVVAYvFTSbtUIGWIUyk5V3vBI1WBAZByPVri\ngF7BfSxngbwRZkOlneIShqjyTCBlSRKOk1HhvICEy4vRrC7aLdFq5PT3VA/v+NfYB6QyzgaQ\nEL8UDA3lFJNfQIpHgGToESwgTfUsaQZI+Igoi0KXMg1IXMzjAJIGgyut+xYAUKblk4xCa9S4\nKWnwpywtkfHDDpFl8sVBwljnZGC4iB0vSkDcSVAvbjrhAr3cDi070jUk9GAUlEHolZZ2ZBNI\nZYQE5HryYDyAhO9IqoDaH8bat7owyeFWSSnLqsCLyiQeRs4RZaFCotQklxzb8ojZp1eJyirU\nEtlZWk25K3Ispd551BSHzM4CgaTnvOji6TQcGZOnMv1Ra0WNA3ozEkj5mLCuKrPU3raVdjiJ\nntJxPI49LW6QaVw9NvVYuJzkobZIB+GxhB1YOFF6WduwSF1tMc3fNSo8eaxM/ozzaap3UStb\nkzrEkTbF1cuFoAtI5lRzvvMUkIJHnHhOFUXJaAACrzKs2Fhoh84qdgGpNPgHhtzHiRfjLEUi\nwa2vF5Tpa8S/n2YzbYzOb1VUKRPXjMMghkM796Jcs/2qYfm8SfTIpc5CZ2zMbK1/OkvCaVxF\nGiMDJD2KS6qJn+YVIGnuQYzw8lI/C3I7NZIBJFSq9gum9CXRAFKkEbw8nZDSxvz4iEuYamem\nMCjdrBomvntBEk0JknR4bWtMrwbVOHiyswgHnACSXZReXM6Sipyh1+6VNCbJ1NcjRUAi6KNE\nzz64pxk2ayqhmIeT2BpHA0jUlFzTdJHxj2NXRTkMHUAq+IVUS3GUE1vbEnOZeemZWiqDz6QA\n6LlyQvH/UePtejdj7OE57S8Nkhf42tB8BEiWnmK8gOSlEy7ZyezQtgApjjjFkuIVRm5pJC8g\nFSEglRMjDwGpdCaAFFd+JpAcQMotjIJZuBqq9d0BJPJinHoBtf8FpOwFJPX/lCNoQ5Y8Laf2\nC0jxo6cXlTBl3F1AKspHzTSYTAPDmDwWgKQqEVA/4sfAfcwuIFkXkLROUPyUjePqAlIKSHk0\n0tKX2oTk8WKbtZqOQLLiF5ByQCpeQRoLpJx4hYxKIDihkYXZAJKMBCA5gDQx9AYtbpkPjR5z\nDeahce2ppjEpm2p9Ye1GQj72tErEiA+gegfY8gEkO9d232E2IjSiWZwHF5CyF5CsjNRaaOnr\n0BlAGg8ghYAUz7RcWGSPR4A0VejqVW78h/okGmviMyDpHUaLjiweozgs3DQAJEebzw8gcVsE\nkpsmmiGbaA7eK0jaOsTK4mJKdap8XLtAil5BGvuJxnoBySlCgeTpTYhEw5F2WbqaljseQEpn\niZPqkW5Q5Vk0e3zMODmB9Ih1y0rOOZ0O22nHdjWApMkReodrTNGdaQ+xASSUILZCm3I9+uXE\npftDVE1WODalXiDZ6iitPB+XP+b+2K20lpQLSF98sCHw/cxzfNtKE8MPIz3jT0aZGyRccmFm\nTugYWhQ8ikckBXorcgpDP2UamtjiG7xUdGIAACAASURBVJhoA7XvpIA01cgMVnASRC6SBgeQ\nZ+i+jJTj27F6K9OMRIEkAzKA5E/L2QBSmEw1Plsh6ZBRHqmw0IDSzNKmBAWfkmQ/ZiTup1mi\nB/d0zPhRO6nQUXolM09ngRZHm5DJSrIhkTMeP2KPn2Ciiv1c60imszINzCwPNYAYgV0Vhfi3\nTEtcVob2AC4lJ1OSp5b0qDREMJ6NTRROkE20CRDREVgakSsGTUh65dsoLUBCIs1yH5ASQEqL\nWUTdmeqVCPIQICVlUQ7vlAbZOChN7ZY6iSICT3ugTEZ4hioIcwfFExO0Id8tJoXm91Z2FntK\ntIBUcQ8ASTPhqnIyVWEHpLycxh7UWvE01nEBzI3SMiBWq1jPSid+gOn2xpOqnEVcsoMgCwsP\nkBDzw8Rd+oNErnebi2mc+JqYy1kliQwUmiLUTF76NSRPamFT9IdeY01Rb4lkl7YFpl6Gj4QK\nF5aVFIOysAPtHW9q9C3G1GIzS24PJlnv0BR+xI0sHnFXsj/TRMHhTzDVF5C0npZmQ0DhDGla\nUkMHkNLcq0hcj67eXcSVlHpb1ITvEH3DyYxVuuPEKX8sbGhG7JQWiuAtHsj+IUghIDm2a9sC\nKQiyNHIoPuSbid7lG0AKggtIuW8XiLbcCAHJMgVSAF6Vdj5009LWu9BRodeo/NADpMTV64mZ\nQCp9C5AQwvJjvh9HGtsWSKkHSKGe4wSKgnhK3dDjhqAaQCqi6UiT7gsqe5o/aYHKp6nGBbVM\nGZYk1WOaCi8NDNMgnGnFhIIbridUlKDH8gJSeQEpSmfYaYGU6O1yLYsSBvi3LEsEUjaApGmB\nXjqMppUCqZoOIPkafaEqlF5gF5ht7RsAHHrAM9KOGgIpmeXOTG8B5okC2i+NSTBBrwwgxZfh\n5PwCkqW5h4DkC6Q8pqpTFkJqBSAl2qXhAlIskNwsxrlfQMoFUpwqXAAJOzBOBpASHx0JSEk+\njbQvkYPC9gpY1pB2NfE8juy/gBShk6M8QuAZcaR8IZD0rDyM4gtIrt5JTJNJGuvV/5ICpp0N\nqtLEXmm7XlUkn9uJSNUPc/0k24QPeIwEEt3GD1aF7VdTf2ILJEAhwwJSBAmOQMpd6sY4nyUj\n7XYQX0DyJiieKBlAKgSSX+VkzbEPSKkfOnpLgoSQkN5sbtwAklKSQZAFY20oFGntafKDifsy\nyXEpgWBq4tUXBwmPbVqOI5CCwH8FKUy4ZEByUW8Sf7FA8qw8jK1c79TGkFeQI0zuIiCVA0i4\n77BwMKl+6AMSegqQkOMCyQQkn/KReBEghdkrSK7eMBFIHvc/TwAJWREjTqphW/R4alYV7qPQ\nDMen1OaPqdb7mVqGVc2y9Gmsh/IeEZdPwugXkPiCXhTEjD5p3mUcCKQQkCLPyvC9A0hJVXJt\n5QBSdgGJBscXkEqlM70wYaYCaewqmAufuqwJAJreAByoRVPJGJDKeJqPtKgvIIXFLEQET7SW\nlq+5R1l0AQnxn1NBEQBYsmEpmQtIGYIWkLyCckkxj8TKpIj0Lo2vneC0xlVEshVICZUi1yOu\nRLvDzlLENAd0pxbhmk3DaBIUtpcBZah3/KJgPPHcC0hlIZBSatxnkLTaPpaPbEN5BtxiEicI\n9ViTG7JYeyuXns6ZfkaLvIAUv4CkeQ9UJJyx9viposdYIKG1tD9QYQGSO3E1aBROUk9LJwik\nYjS5gBRrEZTY0Lh2PKxZnrmvIKWR1PW08LTa5JTMA0jEIiBFA0jVDM8eTzTMiK2nBqQwl2Rx\nIJASQLIGkEY4sawwNXPmS4MUOX5ijmzXSVKqk52moZNYuR9i0IsCsxR6hu9RemMjTW1L0x4y\nA2GcuCSiLIgsYs0o6Ou0sPTIEwuLtqa0PWJC/CzLDZIu9yAwSHIu+iCywsAbZjWWehEg0fQz\nyerIDqZulmhDvTL1oakM9aw0lWkl/VL+M2SAmeRP9DWyxjCcapqmjxoUrlzt9FBJHOaaL4G3\n05Ielw14nlBhJQGbk5e1RKUD6agKtL9emQtJZWFGyci0JgUgaWXEhNuCPSmJJYJ/PLYI2CAd\nGxXWaHCJFBzq2DjW9tKzEqFUVsZUq2NnxpSInWWJUzz6dmmMSRa5FWu+ezBsKFqlBGXlFXYS\n84lxgqrRi6ETUxuBB6FeDAiR+XGMx6EKBINMSol9TEsRcikeIKVYaYrhZKKXhrjoQaiSza0Q\nMTbx4omfm3ae4//9wi89fzz2nEJv63B904h7aZM5YkLTiEgpelqnIfoS7U0+0huxpnbOxu9q\nvgmVxoticKNPjFgg0WsRdikVSGTYUE+bRVOuBbDyGKLIl8jd0vSriTMJyEiFq40x86kEu6a+\na+g1QEmW2YTA8tCJkxgLmzmTiDoKSIlKJRXH1ZzKScXJBalF4Gh6ua8HzlOsRULJ9goEbGQE\n2MqKTgsCTi9K9ZLWY2GNjZxinttJmCVfHiQvsQHJHUAacZ/txL6ApHIikDwPYgSSaQuk1NA7\nsx5dlwaRnSQZ9RO9XZhSRX6mveI9VNaEnhJIoa/pJKFAsid5GhpB6CVBQrj7JSCNJvlEAz2R\n5U8wtReQHPQdEl/jYpWTDSBpfd3HZBQDUmgLJNMtMcN6j7rkZ+CGgNPQUojbcCjlsXaySn8F\nUjnGjKeu7SoBYB4izcSJyNwDSKnWpBgTAiS6F5AKgYR7GNtxVYVpBUg5sjDSPp56D7tCJAkk\nT4OtJiB52oQNHwNIZvHoWaVdcY2Enx73B9kAUpLgD93CiSNPIGn6qEBSmcyDsAzyXNlfIEFK\nQfHJSuIi1vsqw27WnpMNIMX0jubhV3Iy8Bln/tgKJvh3N554GSBlRuG7hVc6+E1Ju+wFJC7e\nGUCiInErUi0ARQ0JBRIFitsRWQmdAkiyKANIiUDiE5MXkMIqGEAq9UKkVmPU7KSs/AwSVQmQ\nRoBkTyLVcXs8jMtONCYvkGZZFg4gjQkox7yAlLyAFA8gZReQ+DQslkAaBT4g2QNI+cQAXUo2\nIJEajEDrXsRJFGrUPEzJPRk/Ojay2M0y56uAZHuxawBSrG41PoMUDSAlbugbnksKjw0KqKMn\nTomhWXmUdoIjJrMmZi6Q8hEGgpts5fHY8wWSr5fYDX8AKRJIVJ80EEhalF8gcf+NiRY2Jufa\n3mSUxRNy1vDKMN8enmWUDqVFaxql2mfdiPKncWiSpDjlEhHyqAUDK4dQ09w1kjMBngsktMZ4\nMonT8An5VcaEfDkekb0d2wMk1CEghblsfC6QVFgHkLIU4eIPsUAMxRooGTsUvQtIZIYIX48V\nHBNRoeYWoePToMDql4U3SZ2xQEIm54+uXbqVtukxIoGEahZIsUByAClwBFIKYFUejp00815A\n0tLJkBNpJM3V9FD+rqkEegkpS1w70zR9baYokLRlnl5igi2/EkjhxKH/M9PJUyOnfLml7QKS\nfwFJW2z7UUTpFx/ccfKnVq3gulBP6QtIegMyHIbCQ4oVP8h18t0sfQEpF0iY/wtICYFSoLkT\nunmWARIpQE8USoE0tvUyTpGPPoPEt80KkFL8jEbZEyOxjWDYEzCm++Qa45FW8OYax8hTZQyB\nFMp6xI/FKBtAGhtxBkihi332lZ01bTaOQo/TCwApRsbbyPXI0tgRevOLg2S6kW+4nhslI4EU\nB1ZC4aF4I3fM2A0Dw3UoTBF62vASn/8a2JwEo4Pcj+04DBFzyA2t6VBmNrowrDwvnI0zV77T\nsImgFIlKTTdwA67hhW6qPoQU9JUxlj5IwtC18CLxRNNNU0Nr7HgkNc2hILAyCeEMM2X4xZOe\nvtGPrpcj5mYku7J04jAqNKU0RcQTC26iclhNxqTNRyGRDC9xGPEktCw/z8LYJwRmAYYf+QlI\nEZnf0EMb7m+JZE/0NnCued56zuOGhfZJsfBMaZYkXoVXH3tp6QexdukIMIN69qpUGWh+4JSc\nk88sr8Si6PmNr3fvXXkOvemN0zFzVXpNttZL1Nx5Pi01Uw9pB9dFVWgSeM51W4kPzkmslVgA\nSQrYSvXiWGnRO+NUU7bHkd5LQY+WFifgjkca+hm5Oedgc6DCsatKVwBpY2FCGvQEUuESmtgH\nzCDXlXpaHz9MJI59X2vLaQKrX6gDXL05hOIl5tGDFCl5Lz8H25xqkOkpGR4v5g5PMw3246sy\nSUaDVGnreRr5dEiF00mVZtq/bjJTP4IWVsyIRkbEVYzTYAAJHzGaaeIE2j/3SHPIfCn92PDD\ncJYbAkkv68tG5r5D19uBEQbI7TAijBCqfupjFqZEhoECgljNW/oaIAWoN8q3GbpG8gpSqBfO\nLG1DZjh26gBSRBgLJHgKvZggQo8BUhAAUuYRiFxxasYmYtX1wmmVOWSV1DAHkOIXkCLHFEie\nViXE5wukWCBRpgykV6SVosrU0ox4QgFTkjluVhFraJd8yokOIJGkTM/LyyAGJLMonQh7QdXR\nJGT6MKe8BtzxccUJP5IeAUk51YjGPiAVKQUVkKY+CgoXQr7F96ZKBIXycBj6Mb/E/zU6LGYE\nUlI6JbY2fQXJpQzrecEUFenmVKNCj1NCLRs+jZNRPht52LyQ5GJJiyVuQs7VFg5RUYxyL3Au\nIMUBPRGM/SQZDSClA0g+pTQDJBMPMIAU4/uH4Dat1M1CCVH0ZjrW69eRptMnsV9YXpWQhQEp\nGXl0eWYbuZu7KGEXKNLhAfgAkg9I6QCSIZCQsEniUoQFEuLYCy4g5aWvcYPC8QaQpEro3pJE\n5H8GKQiHp2QCKcS0CKSwzDSjp3gFCVgyo9Ryh3oVhFKL3p3RJYBEukhHoWHE2hQj9ZMLSKE5\n1bPL0hNIPqkNX0W5GflRMMteQCqNUEMurp0XFpfBLYvxmIBUCCTyejzJrQtIkbD68iCNnABC\nPDrPCh1smW8KpCAoEPB25AgkK7H4GUQeuTc0iWbfBaRIK4ZYkes4kIKY54xJL6GZuaXrhFOq\nE+GTjCx6Ag+EKIu1G7PpasFJFwujcfE4NKqg4HM8jlO6SagVLXDUFaLL1pvDRWyjJ1B71BuC\n17CLxzKw8rRyfNSkE01zLSBm+16oxytV6hQumt2NyLoJIstLHHIfICV6QmKGle3YvmZZ2nrF\nQMP9YYphS7kFJPAMFRgHRRDoSQ0qXTOxsyDFWJOXE51YKDHmVXoT1Upy2+c7uOjIzHIf2RpB\nZ651d6LYyaZGoGWZUkoryjONqZpIOfwGJs7IPN8kKgEJpaWVQ0iojjZiwPxjVgqPGo3bTozI\nUpGMUTrDQtFhaJiJw0mTmJG0SaVR55A0wE/4meWUsVGSslyyd0YI2UZG9bcqlCS0aH/6bBxp\nEx+9kZnbMREIhQQ/d8ApMoEUZ1XghEnmxfjKCg1YxLnl8tNFLDVS0q1ZGnpFiBIdI8n9AElN\njuSX/Vw1VwtUplq4IAckJO841WIgFjDEiRZCiumlvNL+ha6Wyk4z6glOuwrLxI+dKkQoB+YE\n7R8Wjjy3o6WjUYc4Oy8a5vF7WgalwBbFVWZa9CaXEXhhknvcUKRe5qcO3Vhp+7okGPF5Wh7u\niw9/o73waq4XxIDEWfuj2E4HkOLECUWZTZmRDPU4dze0VL8cOhCRgi0NHcuRMIgTrb5Kjhul\nduE6AQnaombFpuOnGkwYXUDyR54duImLhUHrFzH4eDmf43Gc0tPANkko9cl6kZ1FWu3V4u7p\nTRYEWOUYVvFYBLaGZH18shVOUA1FbnsIQBtXm9rAFWlk9RUka5rKYJCx+bGgMl9AsjDMEx/N\nElGDkUW+hiMzDEHkFz5Youq07S1n7iWV7wukPChSbt4FpHRiJpnteT5VUAtY5FovBHERZxeQ\n3GwikDSKm/m2lpqkaym9hRuGwzMBjzRMriK5hwNIceTGdpAjmCPNgomG8RjSj6nF3waQqswL\n/WAACdzMlCjl1Ih0rEMkkFLLKWKjMOLcE0gBLh6QksAqc0DSnlbI9SqycRIqt9wfpXKN1MXc\nAXQF0k7vWgU2l+3qwS3mU6sdmoBEZXKjgdEBpDx0BVLCjVMVjY00C12qpl4f9lGRGbVMIGH4\nU1wPiUsgjcdar2kAKQ4yQELaZI5vOMg/6tkFpMS/gBQUtpiwo8FkA5LrRe4EiUzdLbL8ApJh\n0psDSPQJQewjDjI/sfj1ErFOTNJRgWagfTWQfIHkxuASWwgjX4ulOsEA0oiqGhi+i6VzA1uj\nDxeQQoFkmQ6pmzpgxKQkwx+lVu7YF5AkRfQInDAehRnZMvdG/gWkMNN7JQLJIRFGLqQiaLAY\nuaPVd/I0tAaQAtNAbGl14zQrLcMsZuhi6WeqItgCkgUhjhPkpiSGldlRgK/CdWrLbT8eTaEh\n15kUmeuXI9fmLJGx6PyJb2ncPPLjxPf8aKQnifwr9z2tnER2xrzqygAJExLnoTaQQo64VarB\n8BjNhu78BSTEBR/FPdZrCunEoFrwEWBioSUjAqIKkUmAlBkJnUnAAxIRkF1A4gYAEsRodfgo\nDsgLkRGOCu11qJOpMjfwfMOMbS38RmJGGatk5EEQEVREoGXjRQEpE0hpYCQCCa0ucUDZMeX0\nqsgaQEJFmK8gcQPCCNOlp+LYL9/8DJJNcogyGS6km30BCfmLySL1BxW96vklyEeAFDgZkQ2i\nHioyg73S8HLtaOZlIcJRc+u02F8YlVpVMwoyBwfrRNwVwwWkIOeSLyD5lqZ7+7mViwkOlmnv\njxAfb49jD2mNfc0M+fjMGP0CUmq7gTwTEnHEr0tvApKldbC+AkikODJN4HCWVmBTPF3kRBJ4\nnsLb8R0IsridulrbCAK+4hqe5YSeT9hHCL2R4ZC5HRxUSE8a2Cy0ObZXQxUIRSvwY/Sr7iw3\nOXNHgQkCjocBdPGpvlGYSUhF9vR+QOiVGCQMuB7cEn6g5KINh20KPOTIyDCKaeZrLCsMXA7p\njdPEKVLLMpW6siyzEzPgLiI8famZIDImhEqugdE8Jl2ZnjOANMoCf0xU5kSsR9zbbjiSJYoD\nN/Nsx9NAahzEZGgrLCl+EIapTlG6KRIpCeKxAY2eZacVp28ABGVKu1kj+hFHWsjU0MBXBHh8\ncpCEduSWVB8bNZiScxyD72tbUXoCZ0dXcq6BnoTHw6JOkfQkYnpELaS+BlqGy3EtzzAjK6VD\ng8RONZKs4Tr6LYy4DMvEEmRGjI2XnTVij6IUBnaRoLIztGsRahMRTkq6lXKnCPQhiRsQ6gUI\nDY0nhWdEEiMCacQfkRZHSaBtFJkRiEv+uinO2C+TkNyLkKD6UbopvKVcD3XRzkiPheGS5uA/\n8RI/L7gMrcrma7eZYsxdsv3E1SV7BiZSXigK7JJw8Tyrosddqi4/bXIwbj1uzHciE9BiV1sc\npAaXjZVARxiWT5bHZZpIfIeM4cf0ratBRjKWnVi+FnD50iD5xshBtjkUGcsHJBeQTILLzfCR\njieQTECi/tqYOn4CkFzzFSSoMAApJt9yT2xAcgzKvG1iyLMRIHmAFJHdYjor1DL8gASzjoep\ncokkz8hHyEnfdakIvgYzUwvXgkJwEZhpWNgjx82JfZn1DJDyKbKIqAakwNe6lSS71DRHPjlU\nT/QIIUc1HpDQY1oDLRh2bKdORaQrigg2PMLQ6X0QPi6KfH6YKwpM2SIiPPNMB7eGUfIBKRwF\nJeTDhKbtOBQngeQLpNj0TFuzJnzCiPTskxMR9AkghQNIqWp2Kr/rJ4EdInkDqhgGxYh8mzwq\nzeu69ATyKMSm8RU97gckPCmpLHcp8FolLpaXLhPbGQFSiFiJ8XRWUkSAFFFOEQrqD3OUAC0n\n5mmAFR48ilKgKU16JkyCywPU5wAS50a5AyTCkQMIJD7pF5DiAaRS08NwjwKJPEhSyW3qLNAm\nvpX7iLHQBqQoowcS104AiVsDSBoRCfIRCJglILmaWZIEUfEKUj7G4gKSgwT1BVKklfMBqVC4\neBZWDBRHaO6QvJvoDePI9ymJFeXXKYa0wH3DShhZquAUSLFhoZUuIAWU7GQAyUlMXzsFfGmQ\nCCabDrVdm1O2PGwSIEWB66YIcvDyBFIwsj3D4r8ebsSh6jiIDC/2Q38ACV1h63mTSWfbgJRY\nI3wEIMWBY2ECkDMROhiQUsekugGSCxeU9gEkco3EopVp9k2RmNqmKYsdm8gMChMxlnvIJqR9\nahL5k2RYjTyi61ytWxl7RTLSm+/a+A7dTb3MXFcgId0JzgrVRuoPMQ2QbvsDSLFcSsm1Z0hv\nZKUzsgMdF83vpO7ItgldwlOpwvABiY8OtcgSCSBNbb35NzbCeOSNACmSGqaokHdjM/ZJ7FQe\nj2KhcQyNqIWGrgQRXHDFpmSjQaU2KBoUA9vFXgokfwDJEUhpKpBcfDh9WlAzYRV7FZPKAAkX\n6pHk8AE5ICEePQ+hQL72TCPGEhhRiMbwKEfck8T2qB2RhUaNtc9DBEjQjQPjdpAkRkojfHyA\no+Lq/RAt6gk1h0vXcIGfCSQ9aAu1olRmqc4G3PuRQKIKOiVeB2ITx0lgJA4zjscpYo1GeJ9R\nmboa282k3LR6hRfq8VzF5VlewgUOICUhcpQqbGFQKXJWQTc7XEniaUdJDVqScwVS6Q8gafTK\nc2Ul9I6J6+kfcYS4chxNuJP+sYaSS0WKtSqC/8WnCKHVLYqJ5XCDPMsNLNvQAJ1tJ8gt23bQ\ndESpaQ7T0H1Hs/KoOi63zYnd0B0JRKKAAiyGQi3KD4kjZQ0j5mZadMBICQ9eDCLEwnNhvRxH\nj2BjPiBFi3kOwNnEtmbKjMhM3GbLJly9XHU7c7RGJQKeNJ6hkomjmNLhEoxlrAnUHNVVbMUp\nytQxSI6+npvjXvBgthdqHx6XIMWo+87gyZA9TsEHUNlksGycogVTcehYsWNYI6qXwz3wId0r\nPJvwlqOHUCdJrSJygwqNzsU7WvOKIo4ajW1iM9Kz4dzTKKLBSbqkEL4qYW8GBuVI8oNr8T2K\nK9o0TkYOyhJv5Huq6fjUkOqXjDzfQWryM2YRRkChQSktX+0apC51fBTbYRZqWIraRZklX1sj\nMp0FpS4aQ5MkfX8UWQj2PBhpBgK4uxQYmYvE070JzMByKVmhhrs4SeQsV5lxOWHITRVIjit5\njDxJqc3UCu4W6YGT9TStP7GDkVUOEg1RorqjEUFT4xx+YmcmyY/k6QajWA+9PQ3v4VgoaWlp\nuwAQWwafj8QBbBfl4JoZp2I5Vu6mVFxD1WkEWpxwErq+NncgZdhafoYrtAkLvUdncwPI+PSf\n4RC35FjOOEaV2lyU6HPjN1nX7o9BQquPqIyWw58uIJn2UO8tQCJS4UYgudQFHd6zTduxIMb1\npAUBQl+3SIcjtIpl6FpkYE3DHkCKXIHkST8YQ35MBJJFiNo294pIckZIWI8+MUNHDjzLw1Go\n9YMhOtIsRTBFKpLo+P2Ik80qOhcdBEiOLBMeFJBMamSoISdOwwYkSyDp6ZRvFpYbIo6oNFY4\nigVSKpAiz87JCdgD0wYigRQiUEPbjGyJWRkl6gvh6OqJH+DGUZR5joUUL0LHrww/EEiIe8+h\nOgWZypERQhM/RpHIDOSYzirhdmITACC1nNi0fHk7daejczQoGRH52Ndz8SAyw0GIqoMNUqrh\nmAXCM+aSk0yLhTqGSf1zA8yYTY0tqEGoB9fmiJbqsmuh2MhUhkt8ksJCRK+defQpVTgij2Sh\n3pbBn9KZGFjLsRHWHtrCQIFDFH2IJiG9CKSCe5UqLDPqMTSiI0Z4ER+uuVU4G0srHlpe7EIq\nt1PTa3H6UnrkBLJmJBVCLkZCZGQfjfo4QZaECXeFHBJZmizjkI74CLyCY6Z4RtNBUwASpYTq\nRE7R7caXkZANPXKwgJpOdU0r0Qup1Ek+AVeCOLK1I0CIsgUf/BsHJj8KJOdNluP6I5D0Ci5d\nPrIk2EiXfEETHUZEhWUTn8M6Rqg6R4d3zJHJF03DdUkbmCprANElLlxuIx5KtEkumlIEhKVh\nEcIqvIZG/VBD6EjD8WzLJPNwsxS31OMR+DqxZ+HatRcnHoAz8h2frEQuSSgWfNnRmHFa4tzQ\nXQlQG/I4vpdrPMQiI5ENOc6IkBjJQ4eQCC6mjfePIECewObQ8tYGCGe2i57xdDmQ79vYwSAg\nq+tzfXdkkZmRLoaT25wsHxEFGZxGCaIGZ2Bo/IWqkrmkFg7NTSU2A0uPqh05H8IgUo4RXi5l\nDgBsi8D2SDu2upNvRhF/+kGYOi55Azb50UhPPh1kHKWMns9JwfQRRVpZCsjBlqsgSTka2KJY\nwZGsq9bktTkC94/4RoPzf7IjUjfVMJFmBsSmBDa6N6HygR3ZwzIFIlqU9OcjQrCFaBIqG2el\nRzQ2VlZlI6VrSD7RSLXMRWZpQDCWxS4w2e4I9LwQH0xC0No5GoZA4Wsj78QTmUhDG40dxg78\nBVEGSEQ7CSwO9YcTIX5iyyRleOCQkO1GpFzOT6+ewlJI1aULUxy2RdmjntKFkd4vxX1y+01l\nDsMcIWIDN7RGetrJgWPRZ+twXxokY7g7HuHnGLapUSE6hwhD3IxMvjhoOvMFJHs0GgEVJcox\nHTMyvdHwdeKG/3IjPW/Akl8Z0WVcpyWQLBVevk6YvYDkAgGZh9C1qETUY04hcCNEfpJRv0gp\nAYHs2e4oRqMYiRkoymxvWCkiwLdhIV179CuQRqZmNwRDsPFR/I8ioc0GnQxxE7qhzUcRY3yo\niV8CS0s35TIObVDbqJIgiyPE3w8ilmQMSFBlZ5ayIvl6AElPbj2cgcJ0AMnhRFQAyYEQZXJC\nCgpHZx5ZspKukoRAiuwRn0fx4GzVq6pFSDXye0qhV4kgnNG8FEwyDUEASCN0IinL9VP9k4yB\nZOBULVQxWjiziTxuBmQNN8g0YciXxrC5J5Yn5e1SGgAp0NSukRUHXCrJALE9gGSOAsd2VHkp\nsICEreMaEdkvIKF0+Q0sj0fNomprG2HSBDc1RV5q3cUC0QBI3J4AiYbX1uQ57BbWSJU3SfjZ\nKEzSAJmQKskAUpgKJJOAp2iZKNAApwAAIABJREFUHARlT7keEQ38imXpoTO3a+QRghCiO07V\nJbBSpRjMFJ2l4RpuFxmVTxiNXH/oASvklpLAVQ10YF95BXftfA2QTIU+9whORBRVSSDpr/Bx\nuUHG8M67SdmxhhqGxhtF+jV93bEFkhT8EJXD1zS9NQrUR9DmKiGAi2quQHL4ECQvXU9CpGck\nC0ll/GSMsPDEFF/iBtOZHhbDUMfh4AiLpFDEe3T1cEYZpjz3deYWTtm/gKQnXrh2MIXS1LaC\nIWkJJNf0LDORq/FNIx0JJOczSJRjKYAXEauIRY1wwRkXgJgKtcIbwMdG7pp2SScBkkeyw6pJ\nbEaEIh9L6gxMrjCS2hxAAhBHQcVJqdxRQUZDF42I6MAYURHCRN1pD0WbWsFHkRk0ek2PDyCZ\ngBS+gGSpOwHJM7UiTIRZpaC/gDSSpeTXCB6He+iOHIGUcF564uxenCriMhli0KXkU4ER8H40\nhB5qHjlsAZJrIsf1iMaMbYHkSqBFGlsCH4fMSfe5wQtIlgdIrtwL3SkPZ0nVk0/0yCShKAik\nxDf1DpMGkbgzieXGxii0TDMkIMxoRCQgx0x5by4xdqRMjCH43JBA8PDrg/1VisG5RgoZroqA\ncOxhgXxkzgBSAEiBBsRsVwdWGTPD1HiL5bj+eH+koV14pVddDS0M/w494zMVryAN90pn7ZJK\nDA3RDV+nw3SHFRoX9cGfw6y8QBlNDA3f4EdeQEK3KKHbdmBSqkmjqoOyx0ZE9sR6hRzFFLPe\nBST6mGhXmCf5AJLxGSTXzREDpla6DLwLSKQfNFJMDg1JfNxaTzdcw70SOiNA0vAXN0WPWuzh\nzEBINUNSaOhy1zJ0hfKoVqpu8O0wcDP9ouawYbF1QpqBmw0/DpuhPtODC6UkRyOdfkiIOjp1\nJx6ksyN+KWejoYsU0RwPo0mVMzV3k3/pcRzVQt+PLf1chs4GSkqCdyEFaY2t05NzQEISARKh\nbL3cIEt5W8EjKeEMEYaoNfS0QopvAEirFSkGMcCWPK2eyaurCWNHj0ONwHNGLyCNIshw0lFq\nCSSNMwUxMe0ogqm6nkCiHIwCyppvKmuSfzh30AglNIxYlVRzjzgxB6fpjPiKr6EJLpYkJPus\n+ZumooMrAySSopM5r6sMO5BNkZJMUjbk9looDBkMXayRjOxhRE7mfehTvEQwyCiFSyQJqP79\nTfsCO/b9FiTjFST390EyLiAZvwLJGkCyXj7HGn5CICmHvoBkDx9ivIJk/gKSO4Akn3wBScXB\nCYcft6QcfYFEH+tDBpA0tghItvUCkvMKkmYNBS+XM4Ak3Q9IIxUgvj/6DJI31OABJA39vYLk\nKpVfYpyMO4Ckfw8gedYLSO6vQHJeQXICqR53AGlIJgLJ0gDUABJxP/SRo3jxqD5/DxLnKmdn\nXEAKqKBDF6J0BBLuZwDJcV9Bolja0QAS8uoFJPO3II1eQbIHkDzuh0DyX0DyfgPSkD81WOt5\n5u+AhA4eQLIEkvUCkjWAlEvqj3Sp/khFVOO2elUAHaCOjQBpFIUaJXQ1EOtoMNe7gBTIAl1A\nUodr0AjtfQHJ/gUkXfFIfWpeQCI2w0sXDunwFaShT81fQKKUvoL0m6dIbwfSzbVd2//X7Y0r\n0ldrX+3Y39+BvsNL+vb67grS93+g7/CSvr2++2cHG75a+/7u0bcXDNcD/fkj/Z/C8b9p3989\n+vaC4XqgP3+kK0jf/4G+w0v69vruCtL3f6Dv8JK+vb67gvT9H+g7vKRvr++uIH3/B/oOL+nb\n67t/GZCu7dq+5XYF6dqu7Q3aFaRru7Y3aFeQru3a3qBdQbq2a3uDdgXp2q7tDdoVpGu7tjdo\nV5Cu7dreoF1BurZre4N2Benaru0N2hWka7u2N2jfNkjD2b28Uvj3/3nbw/zeEf6lDzQc7Ksc\n6eWNz++o727+1CV90yBdrmD422/+87aH+b0j/EsfaPjIm6/Rdy+f95313d/+9333LYN0Of2v\n1nXf2YG+R5C+YjR8TyD97QrSP3mY7yi+vypIfyLuriB9PtTXONDNV6oTXw+km690STdfse+u\nIP1TR/q+iP1Kl/S7R/gyxH6lA/3tb7/62CtI3+qBvjeQ/vZ7R/iXJvaXY/2vjnQF6fMHfkdR\n9zqE+x1d0lcE6eYfH/C/+6VvtX0lkP5Uz/3Z43zDQ7h/7iBXkL7IWbxpu5z+l34E9+eewP3Z\nI/2j/7x5+zpH+g4fZt/8wQH/u1+6tmu7tn+mXUG6tmt7g3YF6dqu7Q3aFaRru7Y3aFeQru3a\n3qBdQbq2a3uDdgXp2q7tDdoVpGu7tjdoV5Cu7dreoF1BurZre4N2Benaru0N2hWka7u2N2hX\nkK7t2t6gXUG6tmt7g3YF6dqu7Q3aFaRru7Y3aFeQru3a3qBdQbq2a3uDdgXp2q7tDdoVpGu7\ntjdoV5Cu7dreoF1B+qbbza8XqPsHP/I/+tK1feF27fNvvf03d+h3v329q1+9Xbv8W29XkP4l\n2rXLv/X2usbnzWXHo9c1M3/13d/5zvW2fu127fFvvX0G6fX/v1rJ+XU51f/ynett/drt2uPf\nevulIn3+/29B+vvv/O1v19v69du1x7/19nsg/bIQ9c1v/3+Vdv837drj33r7BxXpV9/9jZ67\nSrv/i3bt8W+9/a+l3RWk/4t27fFvvf0OSP9l1O4q7f7P27XH/8Xb9TnSt9GuXf4v3q4gfRvt\n2uX/6u061+6baNc+v7Zre4N2Benaru0N2v8IpJvX2fzXdm3X9rvtfwLHzX/5y7Vd27X9XbuC\ndG3X9gbtCtK1XdsbtCtI13Ztb9Cugw3Xdm1v0K5wXNu1vUG7VqRru7Y3aFePdG3X9gbtCtK1\nXdsbtCtI13Ztb9CuIF3btb1Buw42XNu1vUH7s3DcXNu1/X/d/gxI/+B3L99ar25W9X3zYXlz\nv9l/WLW73WbX7G/qd/t6s7vd1vVN129X69XDrp5vV4dm29XtZn9fb9r2Ybvb7+7rtq3Xu4fT\noe26m/rQr99t3/X1fntb7zft/apv+rppP973ddttdx/3i83yw27XPmx29eZ+u9vVfV3vu8Oy\nrvmjW/P3/W7b7OumaY58zKFdn5v6cFi2p9Npd6yPp3qzOvXHens4dMuuPd4fj7v+2PXtpjse\nV4dDc39uu8Xhua1/7nY/b7t91x0+dZ8W3bqZ9/322Dfb50P9oE+fd91f2rb7od6v2rb91By6\nfrnZHtvDZsmh2vqweG7W9e54aJ7/8pf/aPrjoT3tbtvtfreqj13d9Rxxfzw2q/vdQ7Pdtjfz\nfnFecAr9qW33yzOfd24XddvUN9u+e9j2D+1p3x/2p+25adYf+u72/rndtetuzmfs9/tbemC/\nP/FH1+yOx+PpMH/eHI7bff186j/Wh+1hfqq39alvlrvDvtl3u/Vpd+j3y9N+3+5OnF7bLOv+\n3Oz69f5wavfz7vhz/df2+T/qQ7s/9Z06+Ux/7Dq65/h8vL/nXLmfdd2emvb4vL3vdoftJ3r6\nWK/X7f78qd/tut2+Weyau23TdpvD7tB1q3Xb3/TdoX1+v+y33XP37+/P854w2HHzNk3NRe3P\nzbk/7riErt3Xx8Nqwxm3W25TdzjuVv39suv2Wy66OW623Yq7/Pzw0O/q5b5pum63aeab5YKw\n2Hxcd4TQ/ZYbdHPsD3XXbm665lT3q37fbD/dn28+csvq7ni+ISw548Xz+cNqd5q33d3h1DT9\ngRvJXd/t7v9pkP7II93crNYXkFY3d5v9O4G03Te7m/pGIN1t6uaGjl+vl/Nt/bBZH5odHb++\ngHQnkOYCabMHJDr6Zn/oN++2H/qm3n2s9+v2fglI+6a7v+vrrtvs76BRIDVLwmMDuvtmAKkn\ngvrDql/td4AEyXBUC6S+XZ7bRiCdT+ftoQGk7cOxP+43/aFfANKcYP47kOaEygqQ9p+63afd\nr0FaHT6DtGyJx3nXCqT7pl51A0htv95tj91htSJAAWn+XK8V2c15AIlccd7N1UMCiQ/uTsf9\n4dhsHnYfB5AW/fwkkA6nrtmvnvsOoqCibW43fXe37RctEXCoTxvu8Oru0L6fC6RltxhA2r3r\nDottfT6sAYk8cTgd7p9XxxPgns/d+wtI+626ZbmnU3f9bnPaAtLiRN6pAYkjrQCp3tKPh3O3\nW7THn/d/bc4CqT51gLR/AYk+BaT5nEvum5Yvn2pA2i+67WF/AWm5AaTnnvux1d1quP9tu+Yc\n2m694U53Aulm2QnKf/9wvgekvUBaNc0zv3KuAWkDSG27A6QFJ7QTSA0gbTfdxzU/vlkLpPW2\nI0g2zw8LQFpw4/uOTLpYA1J/2Nyu++UFpE4gNV27fd835KNlv283gPTuViC1AmlfX0B6v9of\nV9xXbk3d9/xBJ+22D18cpHer/X0rkNb7H5Ykky3pDZCoGLuHdd2+bw/7Dflh29yvX0Ba7u8b\neuIDIO2Xe0Da/hqk99vbAaTmAlIrkB7uegJKID3sBpDWuz1ErOtaBWvX9asdOKwJgO0LSPUA\n0q5vF7+AtD60R2rRD4f+uF0CEiQcF3RXf+xfQKJ6LM59vwGk3QBSy2EFEsWu2QikQ7v9dNiv\nOwJr0QNS0y+J6r4DJNLldi+Q5qszIDWA1GwEUn3+eQCp784Ur/1WFYlU3LWnAyC12/nuh2a3\naX9Y9PcCqRZI9foC0kogPay77sOmXymV9vV5faqb5fzQvF8IpPtueQHppj08rJvzcbNvOVdA\nOt4+LwTS9nxuKfa7/uG0u4BUd+RjQKLL4AWQ6oboJ5jXTX8mySwBqd8t2wMg1QNIzan9LyAt\nFmDBLyEzTiT153pFz9WfTmcSxGLTvIC0plCsYQmQVocNsbzeNq8gLbpd+9z8+8fnW1LRDpDq\nxa9AWh3PAomq/bCo+61Aoo4fN7vudiMds6EUHpdbbkS9fX5YdXtAatueD2oX69USpXEBqb3b\nvILEx90D0q4HO0B6ON9eQDqdbtqGM/60fD7fAtKm6R+Ilf0LSN2XB2m5uV3t7toP65uPq93N\nUsJFIDU322a7Xy6b7mN7qLebOUnpfrVRjdi3SJkWkG42Kis71fT6nnBrQItT/rAlPe0F0qq7\nJ9b7bd0vbrttB4Dz5mO9+rDTQep697BqmxbNR0xulnT39rDdrXuBtGuBSdWjV4VpB5DOz4tD\nJ5DuqNfru+7Y33M310g8/to36wGkY7s6HQ77/rlbP3e7ZwQQd+ZT+7zp0BwkptMFpC3hLJB+\nJmftCA1i8FPT1cTjjtv1fi2Q2p44Jx2fe4H0VyK0AyQSyXq3bo4H2GgAiXja03E1X7ub9++O\nC0g5nFQwnjuBtKEYteslcbDuds2pISmfl4ctkXLc364F0g2KCZDq7fumv1u2z0e0VLM+9P3p\nePdpAGlxPiO2CcLFabVDgdaLtiEXHXa744LitTxKNZ1PUteb5vC8XXcLQCLZNIeft3/dCSQ4\naTrSVCuQiLu2PT0fV+uh75p2JZCUfPolOvZwPh/396j604kI7xa7ejW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gWWuXe3+BdEvTknuB5Pvsl9LKikcarfJ6H2P0XcZL7rcdEbdH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\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fHPvJaGhexucZ2ziwSJg8/wR7R0a9t9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\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sTLPBmAjwIevNzMkBPPhfXad23BJn5A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\nQmCC",
      "text/plain": [
       "plot without title"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "library(phonTools)\n",
    "\n",
    "# Load audio file\n",
    "sound <- loadsound(\"Mono_XC386310__Northern_Goshawk__Accipiter_gentilis.wav\")\n",
    "\n",
    "# Make 3 spectrograms\n",
    "par(mfrow = c(3,1), mar = c(4,4,1,1))\n",
    "spectrogram(sound)\n",
    "spectrogram(sound, color = 'alternate')\n",
    "spectrogram(sound, color = FALSE)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "All three plots are spectrographs showing the same thing, but using different colormaps. In all plots, you can see 5 brighter \"blobs\" that represent the 5 calls from the Northern Goshawk, as you can hear from the audio file.<br><br>\n",
    "\n",
    "<div class=\"endTASK\"></div>\n",
    "<br><br><br>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "It is time to dissect the code above, if your need to refresh how to tell apart objects like `variables`, `functions`, `arguments`, etc., take a look at [Lab1 Micro-introduction to coding](https://diego-ibarra.github.io/biol3782/week1/index.html#Micro-introduction-to-coding-in-R)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<div class=\"Q\"><br><br>\n",
    "\n",
    "From the code above, in line 1...\n",
    "    \n",
    "What is: <br><br>\n",
    "\n",
    "<span class=\"codeX\">phonTools</span>\n",
    "\n",
    "\n",
    "<br><br><div class=\"qhelp\"></div></div>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<div class=\"Q\"><br><br>\n",
    "\n",
    "From the code above, in line 3...\n",
    "    \n",
    "What is: <br><br>\n",
    "\n",
    "<span class=\"codeX\"># Load audio file</span>\n",
    "\n",
    "\n",
    "<br><br><div class=\"qhelp\"></div></div>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<div class=\"Q\"><br><br>\n",
    "\n",
    "From the code above, in line 4...\n",
    "    \n",
    "What is: <br><br>\n",
    "\n",
    "<span class=\"codeX\">loadsound</span>\n",
    "\n",
    "\n",
    "<br><br><div class=\"qhelp\"></div></div>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<div class=\"Q\"><br><br>\n",
    "\n",
    "From the code above, in line 4...\n",
    "    \n",
    "What is: <br><br>\n",
    "\n",
    "<span class=\"codeX\">sound</span>\n",
    "\n",
    "\n",
    "<br><br><div class=\"qhelp\"></div></div>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<div class=\"Q\"><br><br>\n",
    "\n",
    "From the code above, in line <b>8</b>...\n",
    "    \n",
    "What is: <br><br>\n",
    "\n",
    "<span class=\"codeX\">sound</span>\n",
    "\n",
    "\n",
    "<br><br><div class=\"qhelp\"></div></div>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<div class=\"Q\"><br><br>\n",
    "\n",
    "From the code above, in line 8...\n",
    "    \n",
    "What is: <br><br>\n",
    "\n",
    "<span class=\"codeX\">spectrogram</span>\n",
    "\n",
    "\n",
    "<br><br><div class=\"qhelp\"></div></div>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<div class=\"Q\"><br><br>\n",
    "\n",
    "From the code above, in line 10...\n",
    "    \n",
    "What is: <br><br>\n",
    "\n",
    "<span class=\"codeX\">color = FALSE</span>\n",
    "\n",
    "\n",
    "<br><br><div class=\"qhelp\"></div></div>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<div class=\"Q\"><br><br>\n",
    "\n",
    "From the code above, in line 7...\n",
    "    \n",
    "What is: <br><br>\n",
    "\n",
    "<span class=\"codeX\">par</span>\n",
    "\n",
    "\n",
    "<br><br><div class=\"qhelp\"></div></div>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# How to get Help\n",
    "\n",
    "Arguably, programming in any language is mainly knowing **\"where and how to get help\"**. Even experienced programmers find themselves searching Google many times per day. If you run into a problem, this is the suggested steps you may want to do:\n",
    "\n",
    "1. Use the `help()` function and/or RStudio's \"Help panel\" (more on this [below](#Help-files-(i.e.-Function-manuals)))\n",
    "1. Google your question followed by `R`. Example: \"How to do functions in R\"\n",
    "    1. Note that Google Results from [Stack overflow](https://stackoverflow.com/) are usually high quality answers\n",
    "1. Search using https://rseek.org/\n",
    "1. Search directly in [Stack Overflow within the [r] tag](https://stackoverflow.com/questions/tagged/r)\n",
    "\n",
    "Other ports of call:\n",
    "* [Quick R](https://www.statmethods.net/)\n",
    "* [RStudio cheat sheets](https://rstudio.com/resources/cheatsheets/)\n",
    "* [Cookbook for R](http://www.cookbook-r.com/)\n",
    "\n",
    "You should also take a look at the page **\"Getting Help with R\"** from the R project: https://www.r-project.org/help.html"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Comments and statements\n",
    "\n",
    "* **Comments** are lines of text meant to be read ONLY by humans (i.e. the computer ignores these lines). Comments usually contain annotations and additional information to make easier to understand the code to the programmer. **Comments** are preceded by a `#` (hashtag)\n",
    "\n",
    "* A **Statement** is a line of text read by the computer. This is \"the code\". A statement contains instructions for the computer to do a simple task.\n",
    "\n",
    "Take a look to the sequence of comments and statements below:\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[1] 5\n"
     ]
    }
   ],
   "source": [
    "# This is a Comment because it is preceded by a # (hashtag)\n",
    "x <- 5\n",
    "\n",
    "# Lets print our variables to screen\n",
    "print(x) # Note that comments can be written after a statement (but not before)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Above...\n",
    "* The 1st line is a **comment**\n",
    "* The 2nd line is a **statement** assigning the value of variable `x` to be equal to `5` (see \"Variables\" section below for more on this)\n",
    "* The 3rd line is a blank line. Blank lines are ignored by R (they exist to space the code and make it easier to read)\n",
    "* The 4th line is a **comment**\n",
    "* The 5th line is a **statement** asking R to print-to-screen the value of `x` to screen. After the statement, there is another comment \n",
    "\n",
    "If you copy-paste the code above to the **RStudio's** <span class=\"editor\"></span> and click <span class=\"source\"></span>, it will print a `5` to the <span class=\"console\"></span>.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<div class=\"note\"> Once <span class=\"codeX\">x &lt;- 5</span> has been executed, the value of <span class=\"codeX\">x</span> will be stored <b>\"in memory\"</b> until you turn off R, until you delete <span class=\"codeX\">x</span>, or until you update <span class=\"codeX\">x</span> with a different value.</div>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## \"Commenting out\" code\n",
    "\n",
    "The beauty of: <span class=\"nomenclature\">Control</span> + <span class=\"nomenclature\">Shift</span> + <span class=\"nomenclature\">c</span>\n",
    "\n",
    "In RStudio, you can comment many lines at once by selecting the lines that you want to \"comment out\" and then click  `Ctr` + `Shift` + `c`. This will insert a `#` at the beginning of all the selected lines. Note that you can do the reverse (i.e. **\"uncomment code\"**) by selecting several lines beginning with a `#` and again click `Ctr` + `Shift` + `c`. Instead of keyboard shortcuts, you can also click on [Code > Comment/Uncomment Lines].\n",
    "\n",
    "Note that **\"commenting out\"** code is very useful during code development or testing. You want to \"disable\" a few lines of code while you run tests or diagnostics; then, at a later time, you can \"enable\" (i.e. uncomment) your code without ever having to delete or re-write your code."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<br><br><br>\n",
    "<div class=\"startTASK\"></div>\n",
    "\n",
    "In the file that you created above (i.e. <span class=\"file\">spectrogram.R</span>). Select all lines and click  `Ctr` + `Shift` + `c`. \n",
    "\n",
    "* What happened to your code?\n",
    "\n",
    "Select all lines again and click  `Ctr` + `Shift` + `c` a second time. \n",
    "\n",
    "* What happened to your code?\n",
    "\n",
    "<div class=\"endTASK\"></div>\n",
    "<br><br><br>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Functions and their arguments\n",
    "\n",
    "As we said before, most of the \"magic\" done in R is done through functions `Functions`! \n",
    "\n",
    "`Functions` are **\"commands\"** to do things like calculate means, make graphs, do stats, etc. Technically speaking, a `function` is a \"program\" that does some manipulations to an \"input\" and returns an \"output\", as represented in the diagram below:\n",
    "\n",
    "<img src=\"./function_diagram.png\" width=\"500px\">\n",
    "\n",
    "`functions` can be divided in three:\n",
    "\n",
    "1. **Built-in** `functions`: These functions come included in R. The \"programs\" that make these `functions` were developed (i.e. coded or written) by the core developers of the R programming language.\n",
    "2. **Imported** `functions`: These functions are made available to you when you **\"load a library\"**. These `functions` were developed (i.e. coded or written) by \"community members\" that usually develop them to solve their own problems, but that also don't mind sharing their `functions` with the rest of the world. \n",
    "3. **Your own** `functions`: Yes! you can make your own functions (we'll learn how to that later). This is the essence of \"code reutilization\". If you find yourself re-writing over and over very similar code, you can pack that code in a `function` so that the next time, you can simply \"execute\" your `function`.\n",
    "\n",
    "\n",
    "Regardless of how you got your `functions` (i.e. built-in, from a library, or made by you), in this section you will learn how to use, or \"execute\", these `functions` or \"commands\". However, before that, we need to learn the basic syntax or \"anatomy\" of a function: \n",
    "\n",
    "To use `Functions`, you must follow one the syntaxes below: \n",
    "\n",
    "<ul>\n",
    "  <li><span class=\"nomenclature\">FunctionName()</span></li>\n",
    "  <li><span class=\"nomenclature\">FunctionName(arguments)</span></li>\n",
    "  <li><span class=\"nomenclature\">Function.Name(arguments)</span></li>\n",
    "</ul>\n",
    "\n",
    "1. Note that **parentheses `( )` ALWAYS accompany** `functions`.\n",
    "2. Before the parentheses is the **Function name**, which may or may not have a \"dot\" in between. Function names often refer to what the function does. For example, the function `print` prints text to the screen.\n",
    "3. Inside the parentheses are `arguments`, which are explained below.\n",
    "\n",
    "\n",
    "## Arguments\n",
    "\n",
    "`Arguments` are the function's `input` and other instructions to guide the `function` on how to do their task properly.  \n",
    "\n",
    "`Functions` can have zero, one or more `arguments`. If a `Function` has more than one `argument`, they will always be separated by **commas** `,` .  \n",
    "\n",
    "If a `function` has **zero** `arguments` (i.e., nothing inside the parentheses), that means that the `function` will take its `input` directly from the computer. For example:\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[1] \"2020-12-28 16:47:33 AST\""
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# This function returns the computer's current date and time \n",
    "Sys.time()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "--------\n",
    "\n",
    "If a `function` has **ONE** `argument`, this argumnet is most likely the function's `input`. For example:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[1] \"Hello world!\"\n"
     ]
    }
   ],
   "source": [
    "# This function prints-to-screen its input\n",
    "print(\"Hello world!\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "where `\"Hello world!\"` is the input for the `print` functions above.\n",
    "\n",
    "-----------------"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "If a `function` has **many** `arguments`, the first `argument` is most likely the `input` of the `function`, and the rest of the `arguments` are instructions to guide the `function` on how to do their task properly, or fine-tune the output. For example: "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[1] 2.8\n"
     ]
    }
   ],
   "source": [
    "# This function prints-to-screen its input (i.e. first argument), but also rounds the output to \"3\" significant digits\n",
    "print(2.797337826, digits=3)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "where `2.797337826` is the input for the `print` functions above, and `digits=3` is an argument that tells the `print` function to round the output to 3 significant digits."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### \"Ordered\" arguments\n",
    "\n",
    "These are `arguments` where **the order** in which you type them inside the parentheses `( )` **is important**. \n",
    "\n",
    "In the example above `2.797337826` is an **\"Ordered\" argument**."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### \"Named\" arguments\n",
    "\n",
    "\n",
    "These are `arguments` where you must specify the `ArgumentName` following by an equal sign `=`. If you have multiple \"named\" arguments inside the parentheses `( )`one `function`, the order in which you type them is **NOT important**.\n",
    "\n",
    "\n",
    "In the example above `digits=3` is a **\"Named\" argument**."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "--------------------\n",
    "\n",
    "Now that you know the general \"anatomy\" of `functions`, you just need two things before you start using any specific `function`:\n",
    "\n",
    "1. You need to know the `FunctionName` of the function you want to use\n",
    "2. You need to get the \"Function manual\", where you can read instructions on how to use the function \n",
    "\n",
    "In the section below we'll address (1). Two sections below (i.e. Help Files), we'll address (2)."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## How to find Functions\n",
    "\n",
    "There are literally thousands of `functions` in R. Finding the `FunctionName` of the function that you need can be tricky. \n",
    "\n",
    "As you find yourself needing to \"do things\" in R, you will need to find out if there is an R function that \"does that thing\" that you need to do in R. For example: Image that you need calculate the \"standard deviation\" of your data. You quickly arrive to the question \"what is the `FunctionName` of the function to calculate standard deviation in R?\n",
    "\n",
    "This is actually a hard question with no easy solution. Below are a few pointers on how to find the function that will solve your problem:\n",
    "\n",
    "1. Search in Google or in https://rseek.org/ <br>\n",
    "For example, you can search in Google \"How to calculate a mean in R\". The results should point to tutorials or [stackoverflow](https://stackoverflow.com/questions/tagged/r) answers using a function to calculate a mean.<br><br>\n",
    "\n",
    "2. You can use the `help.search()` function to scan the documentation for packages installed in your library.<br><br>\n",
    "\n",
    "3. You can take a look at the lists below, which include some of the most used built-in `functions` in R."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### General use functions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "builtins() # List all built-in functions\n",
    "options()  # Set options to control how R computes & displays results\n",
    "\n",
    "# General\n",
    "print()      # Print to screen\n",
    "?NA          # Help page on handling of missing data values\n",
    "abs(x)       # The absolute value of \"x\"\n",
    "append()     # Add elements to a vector\n",
    "cat(x)       # Prints the arguments\n",
    "cbind()      # Combine vectors by row/column (cf. \"paste\" in Unix)\n",
    "diff(x)      # Returns suitably lagged and iterated differences\n",
    "gl()         # Generate factors with the pattern of their levels\n",
    "grep()       # Pattern matching\n",
    "identical()  # Test if 2 objects are *exactly* equal\n",
    "jitter()     # Add a small amount of noise to a numeric vector\n",
    "julian()     # Return Julian date\n",
    "length(x)    # Return no. of elements in vector x\n",
    "mat.or.vec() # Create a matrix or vector\n",
    "paste(x)     # Concatenate vectors after converting to character\n",
    "range(x)     # Returns the minimum and maximum of x\n",
    "rep(1,5)     # Repeat the number 1 five times\n",
    "rev(x)       # List the elements of \"x\" in reverse order\n",
    "seq(1,10,0.4)  # Generate a sequence (1 -> 10, spaced by 0.4)\n",
    "sequence()     # Create a vector of sequences\n",
    "sign(x)        # Returns the signs of the elements of x\n",
    "sort(x)        # Sort the vector x\n",
    "order(x)       # list sorted element numbers of x\n",
    "tolower(),toupper()  # Convert string to lower/upper case letters\n",
    "unique(x)      # Remove duplicate entries from vector\n",
    "system(\"cmd\")  # Execute \"cmd\" in operating system (outside of R)\n",
    "floor(x), ceiling(x), round(x), signif(x), trunc(x)   # rounding functions\n",
    "# Container objects\n",
    "c(x)         # Combine values into a vector or List\n",
    "vector()     # Produces a vector of given length and mode\n",
    "matrix()     # Makes a matrix\n",
    "data.frame() # Makes data frame\n",
    "\n",
    "# Environment and working directory\n",
    "ls()         # List objects in current environment\n",
    "getwd()        # Return working directory\n",
    "setwd()        # Set working directory\n",
    "Sys.getenv(x)  # Get the value of the environment variable \"x\"\n",
    "Sys.putenv(x)  # Set the value of the environment variable \"x\"\n",
    "Sys.time()     # Return system time\n",
    "Sys.Date()     # Return system date\n",
    "?files         # Help on low-level interface to file system\n",
    "list.files()   # List files in a give directory\n",
    "file.info()    # Get information about files"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Math functions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "log(x),logb(),log10(),log2(),exp(),expm1(),log1p(),sqrt()   # Fairly obvious\n",
    "cos(),sin(),tan(),acos(),asin(),atan(),atan2()       # Usual stuff\n",
    "cosh(),sinh(),tanh(),acosh(),asinh(),atanh()         # Hyperbolic functions\n",
    "union(),intersect(),setdiff(),setequal()             # Set operations\n",
    "eigen() # Computes eigenvalues and eigenvectors\n",
    "sqrt()  # Square root\n",
    "sum()   # Sum\n",
    "\n",
    "pi      # Pi constant\n",
    "\n",
    "deriv()      # Symbolic and algorithmic derivatives of simple expressions\n",
    "integrate()  # Adaptive quadrature over a finite or infinite interval.\n",
    "\n",
    "?Control     # Help on control flow statements (e.g. if, for, while)\n",
    "?Extract     # Help on operators acting to extract or replace subsets of vectors\n",
    "?Logic       # Help on logical operators\n",
    "?regex       # Help on regular expressions used in R\n",
    "?Syntax      # Help on R syntax and giving the precedence of operators"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Graphical functions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "help(package=graphics) # List all graphics functions\n",
    "\n",
    "plot()                # Generic function for plotting of R objects\n",
    "par()                 # Set or query graphical parameters\n",
    "curve(5*x^3,add=T)    # Plot an equation as a curve\n",
    "points(x,y)           # Add another set of points to an existing graph\n",
    "arrows()              # Draw arrows [see errorbar script]\n",
    "abline()              # Adds a straight line to an existing graph\n",
    "lines()               # Join specified points with line segments\n",
    "segments()            # Draw line segments between pairs of points\n",
    "hist(x)               # Plot a histogram of x\n",
    "pairs()               # Plot matrix of scatter plots\n",
    "matplot()             # Plot columns of matrices\n",
    "\n",
    "?device               # Help page on available graphical devices\n",
    "postscript()          # Plot to postscript file\n",
    "pdf()                 # Plot to pdf file\n",
    "png()                 # Plot to PNG file\n",
    "jpeg()                # Plot to JPEG file\n",
    "persp()               # Draws perspective plot\n",
    "contour()             # Contour plot\n",
    "image()               # Plot an image"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Statistical functions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "help(package=stats)   # List all stats functions\n",
    "\n",
    "?Chisquare            # Help on chi-squared distribution functions\n",
    "?Poisson              # Help on Poisson distribution functions\n",
    "help(package=survival) # Survival analysis\n",
    "\n",
    "cor.test()            # Perform correlation test\n",
    "cumsum(); cumprod(); cummin(); cummax()   # Cumuluative functions for vectors\n",
    "density(x)            # Compute kernel density estimates\n",
    "ks.test()             # Performs one or two sample Kolmogorov-Smirnov tests\n",
    "loess(), lowess()     # Scatter plot smoothing\n",
    "mad()                 # Calculate median absolute deviation\n",
    "mean(x), weighted.mean(x), median(x), min(x), max(x), quantile(x)\n",
    "rnorm(), runif()  # Generate random data with Gaussian/uniform distribution\n",
    "splinefun()           # Perform spline interpolation\n",
    "smooth.spline()       # Fits a cubic smoothing spline\n",
    "sd()                  # Calculate standard deviation\n",
    "summary(x)            # Returns a summary of x: mean, min, max etc.\n",
    "t.test()              # Student's t-test\n",
    "var()                 # Calculate variance\n",
    "sample()              # Random samples & permutations\n",
    "ecdf()                # Empirical Cumulative Distribution Function\n",
    "qqplot()              # quantile-quantile plot\n",
    "\n",
    "lm      # Fit liner model"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Help files (i.e. Function manuals)\n",
    "\n",
    "Once you find a function that you want to use, a big questions arise: How do I use this function? What arguments do I need to provide? What kind of input it takes?\n",
    "\n",
    "Luckily, each `function` has its **\"Help File\"**, which is like an \"instruction manual\" with specific instructions on how to use the `function`.\n",
    "\n",
    "If you search in Google for a specific `function`, you should be able to find the **\"Help File\"** of the function and read in directly in your browser. However, most often you probably will RStudio's help panel as shown below.\n",
    "\n",
    "Regardless on where you visualize or read a **help file** of a function, all **help files** are broken down into sections:\n",
    "\n",
    "* Description: An extended description of what the function does.\n",
    "* Usage: The arguments of the function and their default values.\n",
    "* Arguments: An explanation of the data each argument is expecting.\n",
    "* Details: Any important details to be aware of.\n",
    "* Value: The data the function returns.\n",
    "* See Also: Any related functions you might find useful.\n",
    "* Examples: Some examples for how to use the function.\n",
    "\n",
    "Different functions might have different sections, but these are the main ones you should be aware of.\n",
    "\n",
    "<span class=\"note\"></span> From within the function help page, you can highlight code in the Examples and hit `Ctrl`+`Return` to run it in RStudio console. This gives you a quick way to get a feel for how a function works.\n",
    "\n",
    "To display a **help file** in RStudio's Help panel, you can type in the <span class=\"console\"></span> the function name preceded by a `?`. For example: "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "?print"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "...or you can use the `help()` function. For example:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "help(print)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "... or you can use RStudio's **Help panel** by clicking on the **Help tab**; and typing the name of the `function` in question in the search bar within the Help panel.\n",
    "\n",
    "<img src=\"./help_panel.png\" width=\"500px\">"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<br><br><br>\n",
    "<div class=\"startTASK\"></div>\n",
    "\n",
    "Use the `?` or the `help()` function to obtain the **\"Help files\"** of 5 functions from the list above. Can you understand what are the required `arguments`? If not, that is ok, I just want you to start getting used to reading and using **\"Help files\"**.\n",
    "\n",
    "<div class=\"endTASK\"></div>\n",
    "<br><br><br>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Variables\n",
    "\n",
    "<img src=\"./sugar_cube.png\" width=\"250px\">\n",
    "\n",
    "A **Variable** is a user-defined label that can be used to name anything. I like to think \"Variables\" as **stickers** that you can glue to anything to give it a name. In the photo above, the \"Variable **Name**\" is what you wrote on the sticker (in this case, \"Sugar\") and the \"Variable **Value**\" is whatever you glued your sticker to (in this case, a 1 gr sugar cube). \n",
    "\n",
    "In R, the way to create variables is with `<-`, `->` and `=`. See examples below:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "x <- 5 # Example 1\n",
    "\n",
    "5 -> x # Example 2\n",
    "\n",
    "x = 5  # Example 3"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The convention of using the \"arrow\" symbols (i.e. `<-` and `->`) comes from the precursor of R, the [S Programming Language](https://en.wikipedia.org/wiki/S_(programming_language)). I personally prefer using `=` because `<-` requires you to type two characters (one needing the \"shift\" key); also the majority of other languages (e.g. Python, C++, etc.) use `=`. However, note that `=` only declares the variable in the current workspace (more on this later). \n",
    "\n",
    "Either way, by far `<-` is the most used way to create variables by the R community, so we'll stick with this in this course.\n",
    "\n",
    "In generic terms, the proper **nomenclature** to \"declare\" a variable is as follows:\n",
    "\n",
    "<span class=\"nomenclature\">VariableName <- VariableValue</span>\n",
    "\n",
    "    \n",
    "\n",
    "<br><br>\n",
    "<div class=\"startTASK\"></div>\n",
    "    \n",
    "Lets do a variable:\n",
    "    \n",
    "\n",
    "Type in the <span class=\"console\"></span> the line below and click [Enter]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "x <- 5 "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Even though it looks like nothing happened, when you clicked [Enter], R create the new variable `x` in memory and assigned the value `5` to it. You can see the new variable `x`, and its value `5`, in the **Environment panel**. Alternatively, you can type the \"Variable **Name**\" in the <span class=\"console\"></span> to get the \"Variable **Value**\":\n",
    "\n",
    "Type in the <span class=\"console\"></span> the line below and click [Enter]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "5"
      ],
      "text/latex": [
       "5"
      ],
      "text/markdown": [
       "5"
      ],
      "text/plain": [
       "[1] 5"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "x "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Lets do another variable:\n",
    "\n",
    "Copy-paste the code below into the <span class=\"console\"></span> and click [Enter]:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "3000"
      ],
      "text/latex": [
       "3000"
      ],
      "text/markdown": [
       "3000"
      ],
      "text/plain": [
       "[1] 3000"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "y <- 3000\n",
    "\n",
    "y"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<span class=\"note\"></span> Above, we bundled two steps in one <span class=\"console\"></span> entry: (1) we create the variable `y` with the value `3000`, and (2) we display the value of `y` to screen."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Lets do two more:\n",
    "\n",
    "Copy-paste the code below into the <span class=\"console\"></span> and click [Enter]: "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "0"
      ],
      "text/latex": [
       "0"
      ],
      "text/markdown": [
       "0"
      ],
      "text/plain": [
       "[1] 0"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "35"
      ],
      "text/latex": [
       "35"
      ],
      "text/markdown": [
       "35"
      ],
      "text/plain": [
       "[1] 35"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "a = 0\n",
    "\n",
    "a\n",
    "\n",
    "my_cute_variable <- 35\n",
    "\n",
    "my_cute_variable"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<div class=\"endTASK\"></div>\n",
    "<br><br><br>\n",
    "\n",
    "<span class=\"important\"></span> While you can name your variables anything you want, it is a good practice to choose variable names that describe what is stored within the variable. `Temperature` is better than `col1`, `number_eggs` is better than `e`, `TransectDensity` is better than `td`."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "-------------\n",
    "\n",
    "## Using \"Variables\" and \"Functions\" together"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "You can use `variables` to \"label\" the **output** from a `funtion`. In this case, the following syntax applies:\n",
    "\n",
    "<span class=\"nomenclature\">VariableName <- FunctionName()</span>\n",
    "\n",
    "For example: "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[1] \"2020-12-29 17:35:31 AST\"\n"
     ]
    }
   ],
   "source": [
    "x <- Sys.time()\n",
    "\n",
    "print(x)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<div class=\"Q\"><br><br>\n",
    "\n",
    "Take a look at the code to do the spectrogram of the Northern Goshawk call (below, or the beginning of this lab).<br><br>\n",
    "    \n",
    "<span class=\"codeX\">library(phonTools)<br>\n",
    "<br>\n",
    "#Load audio file<br>\n",
    "sound <- loadsound(\"Mono_XC386310__Northern_Goshawk__Accipiter_gentilis.wav\")<br>\n",
    "<br>\n",
    "#Make 3 spectrograms<br>\n",
    "par(mfrow = c(3,1), mar = c(4,4,1,1))<br>\n",
    "spectrogram(sound)<br>\n",
    "spectrogram(sound, color = 'alternate')<br>\n",
    "spectrogram(sound, color = FALSE)</span>\n",
    "  \n",
    "    \n",
    "<br><br> \n",
    "Which of the following are <b>variables</b>?\n",
    "    \n",
    "    \n",
    "<br><br><div class=\"qhelp\"></div></div>\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Check \"Variables\" loaded in memory\n",
    "\n",
    "As we saw in the previous lab, you can use RStudio's ***Environment panel*** to see what **variables** are currently loaded into **memory**, and well as their values.\n",
    "\n",
    "\n",
    "<a href=\"environment_panel.png\" target=\"_blank\"><img src=\"environment_panel.png\" width=\"300px\"></a>\n",
    "\n",
    "You can also see a list of what **variables** are currently loaded into **memory** by executing the function `ls()` on RStudio's **console**:\n",
    "\n",
    "<br><br><br>\n",
    "<div class=\"startTASK\"></div>\n",
    "\n",
    "Type in the <span class=\"console\"></span> the line below and click [Enter]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<ol class=list-inline>\n",
       "\t<li>'a'</li>\n",
       "\t<li>'my_cute_variable'</li>\n",
       "\t<li>'x'</li>\n",
       "\t<li>'y'</li>\n",
       "</ol>\n"
      ],
      "text/latex": [
       "\\begin{enumerate*}\n",
       "\\item 'a'\n",
       "\\item 'my\\_cute\\_variable'\n",
       "\\item 'x'\n",
       "\\item 'y'\n",
       "\\end{enumerate*}\n"
      ],
      "text/markdown": [
       "1. 'a'\n",
       "2. 'my_cute_variable'\n",
       "3. 'x'\n",
       "4. 'y'\n",
       "\n",
       "\n"
      ],
      "text/plain": [
       "[1] \"a\"                \"my_cute_variable\" \"x\"                \"y\"               "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ls()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In the <span class=\"console\"></span>, you should see the variables currently loaded in the memory of your computer.\n",
    "\n",
    "<div class=\"endTASK\"></div>\n",
    "<br><br><br>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Overwriting \"Variables\" values\n",
    "\n",
    "When you create a **Variable**, you assign a Variable **Value** to it. However, you can change its Variable **Value** at anytime simply by assigning to it a different value (kinda' \"overwriting\" or \"updating\" its value). See below:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[1] 4\n",
      "[1] 2986\n"
     ]
    }
   ],
   "source": [
    "x <- 4 # Assign value of 4 to variable \"a\"\n",
    "print(x) # Check the value of \"a\"\n",
    "\n",
    "# Lets change the value of \"a\"\n",
    "x <- 2986 # Assign value 4\n",
    "print(x) # Check again the value of \"a\""
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Deleting \"Variables\"\n",
    "\n",
    "### Deleting ONE variable\n",
    "\n",
    "* To remove one variable, use the `rm()` function:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<ol class=list-inline>\n",
       "\t<li>'a'</li>\n",
       "\t<li>'my_cute_variable'</li>\n",
       "\t<li>'x'</li>\n",
       "\t<li>'y'</li>\n",
       "</ol>\n"
      ],
      "text/latex": [
       "\\begin{enumerate*}\n",
       "\\item 'a'\n",
       "\\item 'my\\_cute\\_variable'\n",
       "\\item 'x'\n",
       "\\item 'y'\n",
       "\\end{enumerate*}\n"
      ],
      "text/markdown": [
       "1. 'a'\n",
       "2. 'my_cute_variable'\n",
       "3. 'x'\n",
       "4. 'y'\n",
       "\n",
       "\n"
      ],
      "text/plain": [
       "[1] \"a\"                \"my_cute_variable\" \"x\"                \"y\"               "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "a <- 4 # Make variable \"a\"\n",
    "ls() # Check which variables exist in memory"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<ol class=list-inline>\n",
       "\t<li>'my_cute_variable'</li>\n",
       "\t<li>'x'</li>\n",
       "\t<li>'y'</li>\n",
       "</ol>\n"
      ],
      "text/latex": [
       "\\begin{enumerate*}\n",
       "\\item 'my\\_cute\\_variable'\n",
       "\\item 'x'\n",
       "\\item 'y'\n",
       "\\end{enumerate*}\n"
      ],
      "text/markdown": [
       "1. 'my_cute_variable'\n",
       "2. 'x'\n",
       "3. 'y'\n",
       "\n",
       "\n"
      ],
      "text/plain": [
       "[1] \"my_cute_variable\" \"x\"                \"y\"               "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "rm(a) # Remove variable \"a\"\n",
    "ls() # Check again which variables exist in memory"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<br><br><br>\n",
    "<div class=\"startTASK\"></div>\n",
    "\n",
    "* Create a variable in RStudio... make the Variable **Name** `X` and the Variable **Value** `1000`.\n",
    "* Check the that your new variable in displayed in the **Environment Panel**\n",
    "* Use the `ls()` function to double-check that your new variable `X` is indeed present in **memory**\n",
    "* Use the `rm()` command to remove `X`\n",
    "* Verify that `X` is no longer in the **Environment Panel**\n",
    "* Use the `ls()` function to double-check that the variable `X` is indeed not present in **memory**\n",
    "\n",
    "<div class=\"endTASK\"></div>\n",
    "<br><br><br>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Deleting ALL variables\n",
    "\n",
    "* To remove ALL variables, use: `rm(list = ls())`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<ol class=list-inline>\n",
       "\t<li>'my_cute_variable'</li>\n",
       "\t<li>'x'</li>\n",
       "\t<li>'y'</li>\n",
       "</ol>\n"
      ],
      "text/latex": [
       "\\begin{enumerate*}\n",
       "\\item 'my\\_cute\\_variable'\n",
       "\\item 'x'\n",
       "\\item 'y'\n",
       "\\end{enumerate*}\n"
      ],
      "text/markdown": [
       "1. 'my_cute_variable'\n",
       "2. 'x'\n",
       "3. 'y'\n",
       "\n",
       "\n"
      ],
      "text/plain": [
       "[1] \"my_cute_variable\" \"x\"                \"y\"               "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ls() # Check which variables exist in memory"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [],
      "text/latex": [],
      "text/markdown": [],
      "text/plain": [
       "character(0)"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "rm(list = ls()) # Remove ALL variables\n",
    "\n",
    "ls() # Check again which variables exist in memory"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<div class=\"Q\"><br><br>\n",
    "\n",
    "    \n",
    "In R, what function is used to delete <b>one</b> variable?\n",
    "    \n",
    "    \n",
    "<br><br><div class=\"qhelp\"></div></div>\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<div class=\"Q\"><br><br>\n",
    "\n",
    "    \n",
    "In R, what statement is used to delete <b>ALL</b> variables in your environment?\n",
    "    \n",
    "    \n",
    "<br><br><div class=\"qhelp\"></div></div>\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\n",
    "\n",
    "-------------------------------\n",
    "\n",
    "#  Basic data types\n",
    "\n",
    "I mentioned earlier that R is an object-oriented language, where everything is an object. The \"basic data types\" are the simplest and most basic kinds of objects. All your data must be represented with one of these data types. Each data type behaves under different rules, thus it is important to always be aware of what data type each statement is working with. Below are some of the most common data types used in R:\n",
    "\n",
    "* **double** or **numeric**: `2`, `15.5` (these are numbers allowed to have decimals)\n",
    "* **integer**: `2L` (these are \"round\" numbers; the L tells R to store this as an integer)\n",
    "* **character**: `\"a\"`, `\"2\"`, `\"hello\"` (these are letters)\n",
    "* **logical**: `TRUE` or `FALSE`\n",
    "\n",
    "You can use the `typeof()` function to query any variable to see what data type are they made of. \n",
    "\n",
    "Lets dive a bit deeper into each different **basic data type**."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Double (or numeric) \n",
    "**Double (or numeric)** objects are numbers that are allowed to have a decimal point. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "'double'"
      ],
      "text/latex": [
       "'double'"
      ],
      "text/markdown": [
       "'double'"
      ],
      "text/plain": [
       "[1] \"double\""
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "x <- 4.67\n",
    "\n",
    "typeof(x)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Integers\n",
    "\n",
    "**Integers** are \"round\" numbers (i.e. no decimal point). Note that you have to add a capital `L` at the end to specify the number is an integer. If you do not ad an `L`, R will think it is a **double** with .0 as a decimal."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "'integer'"
      ],
      "text/latex": [
       "'integer'"
      ],
      "text/markdown": [
       "'integer'"
      ],
      "text/plain": [
       "[1] \"integer\""
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "y <- 4L\n",
    "\n",
    "typeof(y)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Character\n",
    "**Characters** are letters. Note that you need to wrap the content of the string within quotes to tell R they are a character "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "'character'"
      ],
      "text/latex": [
       "'character'"
      ],
      "text/markdown": [
       "'character'"
      ],
      "text/plain": [
       "[1] \"character\""
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "'character'"
      ],
      "text/latex": [
       "'character'"
      ],
      "text/markdown": [
       "'character'"
      ],
      "text/plain": [
       "[1] \"character\""
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "a <- \"hello\" # Example 1\n",
    "b <- \"2.3\"   # Example 2\n",
    "\n",
    "typeof(a)\n",
    "typeof(b)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Note that even though `2.3` is a number, because we declared it with quotes (i.e. `\"2.3\"`), it will be treated by R as \"letters\". Therefore, you won't be able to do math with \"2.3\"."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Logical\n",
    "\n",
    "Logicals can only have one of two values `TRUE` or `FALSE`. They are used to represent \"true or false\" statements."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "'logical'"
      ],
      "text/latex": [
       "'logical'"
      ],
      "text/markdown": [
       "'logical'"
      ],
      "text/plain": [
       "[1] \"logical\""
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "x <- FALSE\n",
    "\n",
    "typeof(x)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<div class=\"Q\"><br><br>\n",
    "\n",
    "What data type is the following: <br><br>\n",
    "324.91\n",
    "\n",
    "<br><br><div class=\"qhelp\"></div></div>\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<div class=\"Q\"><br><br>\n",
    "\n",
    "What data type is the following: <br><br>\n",
    "\n",
    "<span class=\"codeX\">\"the house is green\"</span><br>\n",
    "\n",
    "<br><br><div class=\"qhelp\"></div></div>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<div class=\"Q\"><br><br>\n",
    "\n",
    "What data type is the following: <br><br>\n",
    "    \n",
    "<span class=\"codeX\">87572L</span><br>\n",
    "\n",
    "\n",
    "<br><br><div class=\"qhelp\"></div></div>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<div class=\"Q\"><br><br>\n",
    "\n",
    "If you execute the following line in R... <br><br>\n",
    "    \n",
    "<span class=\"codeX\">a = 32</span><br><br>\n",
    "\n",
    "Then, what data type is \"<b>a</b>\"?\n",
    "\n",
    "<br><br><div class=\"qhelp\"></div></div>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<div class=\"Q\"><br><br>\n",
    "\n",
    "If you execute the following line in R... <br><br>\n",
    "    \n",
    "<span class=\"codeX\">a = 32L</span><br><br>\n",
    "\n",
    "Then, what data type is \"<b>a</b>\"?\n",
    "\n",
    "<br><br><div class=\"qhelp\"></div></div>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "----------------------------\n",
    "\n",
    "# R Operators\n",
    "\n",
    "Now that you know how to do `numeric`, `integer`, `character` and `logical` elements, you can start doing arithmetic, relational and logical operations. Below is a \"cheat-sheet\" of all the operators in R:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Arithmetic Operators -------\n",
    "+   # Addition\n",
    "-   # Subtraction\n",
    "*   # Multiplication\n",
    "/   # Division\n",
    "^   # Exponent\n",
    "%%  # Modulus (Remainder from division)\n",
    "%/% # Integer Division\n",
    "\n",
    "# Relational Operators --------\n",
    "<  # Less than\n",
    ">  # Greater than\n",
    "<= # Less than or equal to\n",
    ">= # Greater than or equal to\n",
    "== # Equal to\n",
    "!= # Not equal to\n",
    "\n",
    "# Logical Operators -----------\n",
    "!  # Logical NOT\n",
    "&  # Element-wise logical AND\n",
    "&& # Logical AND\n",
    "|  # Element-wise logical OR\n",
    "|| # Logical OR\n",
    "\n",
    "# Assignment Operators ---------\n",
    "<-, <<-, = # Leftwards assignment\n",
    "->, ->> # Rightwards assignment"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We won't dive in all of the operators at this point, but here are few examples:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "14"
      ],
      "text/latex": [
       "14"
      ],
      "text/markdown": [
       "14"
      ],
      "text/plain": [
       "[1] 14"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "7 * 2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "5"
      ],
      "text/latex": [
       "5"
      ],
      "text/markdown": [
       "5"
      ],
      "text/plain": [
       "[1] 5"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "x <- 8 - 3\n",
    "\n",
    "x"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "8"
      ],
      "text/latex": [
       "8"
      ],
      "text/markdown": [
       "8"
      ],
      "text/plain": [
       "[1] 8"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "2^3"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "TRUE"
      ],
      "text/latex": [
       "TRUE"
      ],
      "text/markdown": [
       "TRUE"
      ],
      "text/plain": [
       "[1] TRUE"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "7 > 2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "FALSE"
      ],
      "text/latex": [
       "FALSE"
      ],
      "text/markdown": [
       "FALSE"
      ],
      "text/plain": [
       "[1] FALSE"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "9 >= 18"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Data Structures (i.e. \"Container\" objects)\n",
    "\n",
    "<img src=\"./sugar_jar.png\" width=\"400px\">\n",
    "\n",
    "We mentioned that everything in R is an object, and that the \"Basic Data Types\" are the simplest type of objects. **\"Data Structures\"** are also objects, however a bit more complicated. **\"Data Structures\"** are designed to contain groups of other objects... thus, I like to call them **\"Container\"** objects.\n",
    "\n",
    "In the photo above (i.e. jar of sugar cubes), the \"Variable **Name**\" is what you wrote on the sticker (in this case, \"Sugar\"), the \"Variable **Value**\" is whatever you glued your sticker to (in this case, a jar with 28 one-gram sugar cubes). The jar is a **\"Container\"** object and each of the sugar cubes is a different object, each with its own \"Basic Data Type\" and value.\n",
    "\n",
    "The main **\"Data Structures\"** that we will discuss here are:\n",
    "\n",
    "* Vector\n",
    "* Matrix\n",
    "* List\n",
    "* Data Frame\n",
    "* Factors\n",
    "\n",
    "You can use the function `class()` to query the type of \"Data Structure\" of a variable, for example:\n",
    "\n",
    "* `class(x)` will return `matrix`, if `x` is a **Matrix** Data Structure\n",
    "* `class(x)` will return `list`, if `x` is a **List** Data Structure\n",
    "* `class(x)` will return `data.frame`, if `x` is a **Data Frame** Data Structure\n",
    "* `class(x)` will return `factor`, if `x` is a **Factor** Data Structure\n",
    "* **Vectors** are the odd one here. `class(x)` will return the **Basic Data Type** of the elements within `x` (see the two examples below)."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Vector\n",
    "\n",
    "A vector is a collection of elements of **the same \"Basic Data Type\"** (i.e. `numeric`, `integer`, `character`, or `logical`). You can think of a \"vector\" as **one single column** in a spread-sheet. Technically, vectors can be either (1) **atomic vectors** (i.e. all elements of the same \"Basic Data Type\"), or (2) **lists** (i.e. elements can be of different types). However, the term “vector” most commonly refers to the \"atomic vectors\" and \"list vectors\" are referred simply as \"lists\", which we will discuss in a section below.\n",
    "\n",
    "Vectors are particularly well suited to do matrix algebra.\n",
    "\n",
    "To make a vector, use the \"combine\" function, `c`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<ol class=list-inline>\n",
       "\t<li>5</li>\n",
       "\t<li>2</li>\n",
       "\t<li>7</li>\n",
       "\t<li>4</li>\n",
       "</ol>\n"
      ],
      "text/latex": [
       "\\begin{enumerate*}\n",
       "\\item 5\n",
       "\\item 2\n",
       "\\item 7\n",
       "\\item 4\n",
       "\\end{enumerate*}\n"
      ],
      "text/markdown": [
       "1. 5\n",
       "2. 2\n",
       "3. 7\n",
       "4. 4\n",
       "\n",
       "\n"
      ],
      "text/plain": [
       "[1] 5 2 7 4"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "'numeric'"
      ],
      "text/latex": [
       "'numeric'"
      ],
      "text/markdown": [
       "'numeric'"
      ],
      "text/plain": [
       "[1] \"numeric\""
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "x <- c(5, 2, 7, 4)\n",
    "\n",
    "x\n",
    "\n",
    "class(x)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Note that `class(x)` returned `'numeric'` because ALL the elements within `x` are of **Numeric** Basic Data Type."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<ol class=list-inline>\n",
       "\t<li>'a'</li>\n",
       "\t<li>'b'</li>\n",
       "\t<li>'c'</li>\n",
       "\t<li>'d'</li>\n",
       "</ol>\n"
      ],
      "text/latex": [
       "\\begin{enumerate*}\n",
       "\\item 'a'\n",
       "\\item 'b'\n",
       "\\item 'c'\n",
       "\\item 'd'\n",
       "\\end{enumerate*}\n"
      ],
      "text/markdown": [
       "1. 'a'\n",
       "2. 'b'\n",
       "3. 'c'\n",
       "4. 'd'\n",
       "\n",
       "\n"
      ],
      "text/plain": [
       "[1] \"a\" \"b\" \"c\" \"d\""
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "'character'"
      ],
      "text/latex": [
       "'character'"
      ],
      "text/markdown": [
       "'character'"
      ],
      "text/plain": [
       "[1] \"character\""
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "x <- c(\"a\", \"b\", \"c\", \"d\")\n",
    "\n",
    "x\n",
    "\n",
    "class(x)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Note that `class(x)` returned `'character'` because ALL the elements within `x` are of **Character** Basic Data Type.\n",
    "\n",
    "----------------\n",
    "\n",
    "In the example below, lets insert a 'numeric' element among a bunch of 'character' elements to see what happens:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<ol class=list-inline>\n",
       "\t<li>'a'</li>\n",
       "\t<li>'b'</li>\n",
       "\t<li>'7'</li>\n",
       "\t<li>'d'</li>\n",
       "</ol>\n"
      ],
      "text/latex": [
       "\\begin{enumerate*}\n",
       "\\item 'a'\n",
       "\\item 'b'\n",
       "\\item '7'\n",
       "\\item 'd'\n",
       "\\end{enumerate*}\n"
      ],
      "text/markdown": [
       "1. 'a'\n",
       "2. 'b'\n",
       "3. '7'\n",
       "4. 'd'\n",
       "\n",
       "\n"
      ],
      "text/plain": [
       "[1] \"a\" \"b\" \"7\" \"d\""
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "'character'"
      ],
      "text/latex": [
       "'character'"
      ],
      "text/markdown": [
       "'character'"
      ],
      "text/plain": [
       "[1] \"character\""
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "x <- c(\"a\", \"b\", 7, \"d\")\n",
    "\n",
    "x\n",
    "\n",
    "class(x)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Note that R changed the \"number\" `7` for a \"character\" `'7'`. In this example, R assumed that, since you added three 'character' elements and only one 'numeric' element, you probably want to have an all 'character' vector and thus changed the numeric `7` for the character `'7'`. This behaviour of R is called **\"coercion\"** and is handy to correct for potential errors, but it can also get the programmer into troubles, by changing the data type of an element without warning."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Matrix\n",
    "\n",
    "**Matrices** are an extension of vectors. They are simply a vector with dimensions of \"number of rows\" by \"number of columns\". As with vectors, the elements of a matrix must be of the same Data Type.\n",
    "\n",
    "Just like vectors, matrices are particularly well suited to do matrix algebra.\n",
    "\n",
    "To make a **Matrix**, use the function `matrix()`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table>\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"
      ],
      "text/latex": [
       "\\begin{tabular}{lll}\n",
       "\t 1  & 5  &  9\\\\\n",
       "\t 2  & 6  & 10\\\\\n",
       "\t 3  & 7  & 11\\\\\n",
       "\t 4  & 8  & 12\\\\\n",
       "\\end{tabular}\n"
      ],
      "text/markdown": [
       "\n",
       "| 1  | 5  |  9 |\n",
       "| 2  | 6  | 10 |\n",
       "| 3  | 7  | 11 |\n",
       "| 4  | 8  | 12 |\n",
       "\n"
      ],
      "text/plain": [
       "     [,1] [,2] [,3]\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"
    },
    {
     "data": {
      "text/html": [
       "'matrix'"
      ],
      "text/latex": [
       "'matrix'"
      ],
      "text/markdown": [
       "'matrix'"
      ],
      "text/plain": [
       "[1] \"matrix\""
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "x <- matrix(c(1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12), nrow=4, ncol=3)\n",
    "\n",
    "x\n",
    "\n",
    "class(x)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## List\n",
    "\n",
    "**Lists** are like vectors, but **without the restriction** of having their contents of a single Data Type. You can mix `numeric`, `integer`, `character`, and `logical` elements within a single **List**. Lists are sometimes called \"generic vectors\", because the elements of a list can be of any type of R object, even lists containing further lists. This property makes them fundamentally different from the atomic vectors that we discussed above.\n",
    "\n",
    "To make a list, use the function `list()`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<ol>\n",
       "\t<li>1</li>\n",
       "\t<li>'a'</li>\n",
       "\t<li>4</li>\n",
       "\t<li>6.87</li>\n",
       "\t<li>TRUE</li>\n",
       "\t<li>'hello'</li>\n",
       "</ol>\n"
      ],
      "text/latex": [
       "\\begin{enumerate}\n",
       "\\item 1\n",
       "\\item 'a'\n",
       "\\item 4\n",
       "\\item 6.87\n",
       "\\item TRUE\n",
       "\\item 'hello'\n",
       "\\end{enumerate}\n"
      ],
      "text/markdown": [
       "1. 1\n",
       "2. 'a'\n",
       "3. 4\n",
       "4. 6.87\n",
       "5. TRUE\n",
       "6. 'hello'\n",
       "\n",
       "\n"
      ],
      "text/plain": [
       "[[1]]\n",
       "[1] 1\n",
       "\n",
       "[[2]]\n",
       "[1] \"a\"\n",
       "\n",
       "[[3]]\n",
       "[1] 4\n",
       "\n",
       "[[4]]\n",
       "[1] 6.87\n",
       "\n",
       "[[5]]\n",
       "[1] TRUE\n",
       "\n",
       "[[6]]\n",
       "[1] \"hello\"\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "'list'"
      ],
      "text/latex": [
       "'list'"
      ],
      "text/markdown": [
       "'list'"
      ],
      "text/plain": [
       "[1] \"list\""
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "x <- list(1L, \"a\", 4, 6.87, TRUE, \"hello\")\n",
    "\n",
    "x\n",
    "\n",
    "class(x)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Below is an example of a list that contains another list. We'll insert the list created in the code above as the 3rd element of the list below, `y`:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<ol>\n",
       "\t<li>4</li>\n",
       "\t<li>'world'</li>\n",
       "\t<li><ol>\n",
       "\t<li>1</li>\n",
       "\t<li>'a'</li>\n",
       "\t<li>4</li>\n",
       "\t<li>6.87</li>\n",
       "\t<li>TRUE</li>\n",
       "\t<li>'hello'</li>\n",
       "</ol>\n",
       "</li>\n",
       "\t<li>3.67</li>\n",
       "</ol>\n"
      ],
      "text/latex": [
       "\\begin{enumerate}\n",
       "\\item 4\n",
       "\\item 'world'\n",
       "\\item \\begin{enumerate}\n",
       "\\item 1\n",
       "\\item 'a'\n",
       "\\item 4\n",
       "\\item 6.87\n",
       "\\item TRUE\n",
       "\\item 'hello'\n",
       "\\end{enumerate}\n",
       "\n",
       "\\item 3.67\n",
       "\\end{enumerate}\n"
      ],
      "text/markdown": [
       "1. 4\n",
       "2. 'world'\n",
       "3. 1. 1\n",
       "2. 'a'\n",
       "3. 4\n",
       "4. 6.87\n",
       "5. TRUE\n",
       "6. 'hello'\n",
       "\n",
       "\n",
       "\n",
       "4. 3.67\n",
       "\n",
       "\n"
      ],
      "text/plain": [
       "[[1]]\n",
       "[1] 4\n",
       "\n",
       "[[2]]\n",
       "[1] \"world\"\n",
       "\n",
       "[[3]]\n",
       "[[3]][[1]]\n",
       "[1] 1\n",
       "\n",
       "[[3]][[2]]\n",
       "[1] \"a\"\n",
       "\n",
       "[[3]][[3]]\n",
       "[1] 4\n",
       "\n",
       "[[3]][[4]]\n",
       "[1] 6.87\n",
       "\n",
       "[[3]][[5]]\n",
       "[1] TRUE\n",
       "\n",
       "[[3]][[6]]\n",
       "[1] \"hello\"\n",
       "\n",
       "\n",
       "[[4]]\n",
       "[1] 3.67\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "'list'"
      ],
      "text/latex": [
       "'list'"
      ],
      "text/markdown": [
       "'list'"
      ],
      "text/plain": [
       "[1] \"list\""
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Example of lists within a list\n",
    "y <- list(4, \"world\", x, 3.67)\n",
    "\n",
    "y\n",
    "\n",
    "class(y)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Data Frame\n",
    "\n",
    "**Data frames** are a very important data type in R. It’s pretty much the *de facto* data structure for most tabular data and what we use in the biology field and in plotting in general. **Data frames** can be created by hand, but most commonly they are generated when importing spreadsheets from your hard-drive or the web.\n",
    "\n",
    "Remember that a \"matrix\" is a special type of \"vector\" with multiple rows and columns? Well, similarly, a **Data Frame** is a special type of **list** with multiple rows and column, where every element of the list has same length (i.e. data frame is a “rectangular” list). Same as lists, the elements of a **Data frames** can be of any type of R object (i.e. `numeric`, `integer`, `character`, `logical`, lists and other Data Frames). \n",
    "\n",
    "To make a list, use the `data.frame()` function:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table>\n",
       "<thead><tr><th scope=col>id</th><th scope=col>x</th><th scope=col>y</th></tr></thead>\n",
       "<tbody>\n",
       "\t<tr><td> 1</td><td>11</td><td>21</td></tr>\n",
       "\t<tr><td> 2</td><td>12</td><td>22</td></tr>\n",
       "\t<tr><td> 3</td><td>13</td><td>23</td></tr>\n",
       "\t<tr><td> 4</td><td>14</td><td>24</td></tr>\n",
       "\t<tr><td> 5</td><td>15</td><td>25</td></tr>\n",
       "\t<tr><td> 6</td><td>16</td><td>26</td></tr>\n",
       "\t<tr><td> 7</td><td>17</td><td>27</td></tr>\n",
       "\t<tr><td> 8</td><td>18</td><td>28</td></tr>\n",
       "\t<tr><td> 9</td><td>19</td><td>29</td></tr>\n",
       "\t<tr><td>10</td><td>20</td><td>30</td></tr>\n",
       "</tbody>\n",
       "</table>\n"
      ],
      "text/latex": [
       "\\begin{tabular}{r|lll}\n",
       " id & x & y\\\\\n",
       "\\hline\n",
       "\t  1 & 11 & 21\\\\\n",
       "\t  2 & 12 & 22\\\\\n",
       "\t  3 & 13 & 23\\\\\n",
       "\t  4 & 14 & 24\\\\\n",
       "\t  5 & 15 & 25\\\\\n",
       "\t  6 & 16 & 26\\\\\n",
       "\t  7 & 17 & 27\\\\\n",
       "\t  8 & 18 & 28\\\\\n",
       "\t  9 & 19 & 29\\\\\n",
       "\t 10 & 20 & 30\\\\\n",
       "\\end{tabular}\n"
      ],
      "text/markdown": [
       "\n",
       "| id | x | y |\n",
       "|---|---|---|\n",
       "|  1 | 11 | 21 |\n",
       "|  2 | 12 | 22 |\n",
       "|  3 | 13 | 23 |\n",
       "|  4 | 14 | 24 |\n",
       "|  5 | 15 | 25 |\n",
       "|  6 | 16 | 26 |\n",
       "|  7 | 17 | 27 |\n",
       "|  8 | 18 | 28 |\n",
       "|  9 | 19 | 29 |\n",
       "| 10 | 20 | 30 |\n",
       "\n"
      ],
      "text/plain": [
       "   id x  y \n",
       "1   1 11 21\n",
       "2   2 12 22\n",
       "3   3 13 23\n",
       "4   4 14 24\n",
       "5   5 15 25\n",
       "6   6 16 26\n",
       "7   7 17 27\n",
       "8   8 18 28\n",
       "9   9 19 29\n",
       "10 10 20 30"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "'data.frame'"
      ],
      "text/latex": [
       "'data.frame'"
      ],
      "text/markdown": [
       "'data.frame'"
      ],
      "text/plain": [
       "[1] \"data.frame\""
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "x <- data.frame(id = 1:10, x = 11:20, y = 21:30)\n",
    "\n",
    "x\n",
    "\n",
    "class(x)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Factors\n",
    "\n",
    "**Factors** are used to represent categorical data. Conceptually, **factors** are variables in R which take on a limited number of different values (e.g. \"male\" and \"female\"). One of the most important uses of factors is in statistical modeling; since categorical variables enter into statistical models differently than continuous variables, storing data as factors insures that the modeling functions will treat such data correctly.\n",
    "\n",
    "Factors in R are stored as a vector of integer values with a corresponding set of character values to use when the factor is displayed. The `factor` function is used to create a factor. The only required argument to factor is a vector of values which will be returned as a vector of factor values. Both numeric and character variables can be made into factors, but a factor's levels will always be character values. Factors represent a very efficient way to store character values, because each unique character value is stored only once, and the data itself is stored as a vector of integers.\n",
    "\n",
    "Below is an example:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<ol class=list-inline>\n",
       "\t<li>beluga</li>\n",
       "\t<li>dolphin</li>\n",
       "\t<li>narwhal</li>\n",
       "\t<li>dolphin</li>\n",
       "\t<li>dolphin</li>\n",
       "\t<li>narwhal</li>\n",
       "\t<li>beluga</li>\n",
       "\t<li>beluga</li>\n",
       "</ol>\n",
       "\n",
       "<details>\n",
       "\t<summary style=display:list-item;cursor:pointer>\n",
       "\t\t<strong>Levels</strong>:\n",
       "\t</summary>\n",
       "\t<ol class=list-inline>\n",
       "\t\t<li>'beluga'</li>\n",
       "\t\t<li>'dolphin'</li>\n",
       "\t\t<li>'narwhal'</li>\n",
       "\t</ol>\n",
       "</details>"
      ],
      "text/latex": [
       "\\begin{enumerate*}\n",
       "\\item beluga\n",
       "\\item dolphin\n",
       "\\item narwhal\n",
       "\\item dolphin\n",
       "\\item dolphin\n",
       "\\item narwhal\n",
       "\\item beluga\n",
       "\\item beluga\n",
       "\\end{enumerate*}\n",
       "\n",
       "\\emph{Levels}: \\begin{enumerate*}\n",
       "\\item 'beluga'\n",
       "\\item 'dolphin'\n",
       "\\item 'narwhal'\n",
       "\\end{enumerate*}\n"
      ],
      "text/markdown": [
       "1. beluga\n",
       "2. dolphin\n",
       "3. narwhal\n",
       "4. dolphin\n",
       "5. dolphin\n",
       "6. narwhal\n",
       "7. beluga\n",
       "8. beluga\n",
       "\n",
       "\n",
       "\n",
       "**Levels**: 1. 'beluga'\n",
       "2. 'dolphin'\n",
       "3. 'narwhal'\n",
       "\n",
       "\n"
      ],
      "text/plain": [
       "[1] beluga  dolphin narwhal dolphin dolphin narwhal beluga  beluga \n",
       "Levels: beluga dolphin narwhal"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "data = c(\"beluga\", \"dolphin\", \"narwhal\", \"dolphin\", \"dolphin\", \"narwhal\", \"beluga\", \"beluga\")\n",
    "\n",
    "fdata = factor(data)\n",
    "\n",
    "fdata"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "You can see the possible levels for a factor through the `levels()` function:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<ol class=list-inline>\n",
       "\t<li>'beluga'</li>\n",
       "\t<li>'dolphin'</li>\n",
       "\t<li>'narwhal'</li>\n",
       "</ol>\n"
      ],
      "text/latex": [
       "\\begin{enumerate*}\n",
       "\\item 'beluga'\n",
       "\\item 'dolphin'\n",
       "\\item 'narwhal'\n",
       "\\end{enumerate*}\n"
      ],
      "text/markdown": [
       "1. 'beluga'\n",
       "2. 'dolphin'\n",
       "3. 'narwhal'\n",
       "\n",
       "\n"
      ],
      "text/plain": [
       "[1] \"beluga\"  \"dolphin\" \"narwhal\""
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "levels(fdata)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "... and the number of levels using the `nlevels()` function:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "3"
      ],
      "text/latex": [
       "3"
      ],
      "text/markdown": [
       "3"
      ],
      "text/plain": [
       "[1] 3"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "nlevels(fdata)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<div class=\"Q\"><br><br>\n",
    "\n",
    "    \n",
    "Consider the statement below:<br><br>\n",
    "    \n",
    "    \n",
    "<span class=\"codeX\">x <- data.frame(x = 1:20, y = 21:40)</span><br><br>\n",
    "    \n",
    "\n",
    "What is class, or Data Structure is <b>x</b>?<br><br>\n",
    "         \n",
    "<br><div class=\"qhelp\"></div></div>\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<div class=\"Q\"><br><br>\n",
    "\n",
    "    \n",
    "Consider the statement below:<br><br>\n",
    "    \n",
    "<span class=\"codeX\">x <- matrix(c(1, 2, 3, 4), nrow=2, ncol=2)</span><br><br>\n",
    "    \n",
    "What is class, or Data Structure is <b>x</b>?<br><br>\n",
    "         \n",
    "<br><div class=\"qhelp\"></div></div>\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<div class=\"Q\"><br><br>\n",
    "\n",
    "    \n",
    "Consider the statement below:<br><br>\n",
    "    \n",
    "<span class=\"codeX\">x <- list(1, 2, 3, 4)</span><br><br>\n",
    "    \n",
    "What is class, or Data Structure is <b>x</b>?<br><br>\n",
    "         \n",
    "<br><div class=\"qhelp\"></div></div>\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<div class=\"Q\"><br><br>\n",
    "\n",
    "    \n",
    "Consider the statement below:<br><br>\n",
    "    \n",
    "<span class=\"codeX\">x <- c(1, 2, 3, 4)</span><br><br>\n",
    "    \n",
    "<br>\n",
    "What is class, or Data Structure is <b>x</b>?<br><br>\n",
    "         \n",
    "<br><div class=\"qhelp\"></div></div>\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<div class=\"Q\"><br><br>\n",
    "\n",
    "    \n",
    "Consider the statement below:<br><br>\n",
    "    \n",
    "<span class=\"codeX\">x <- factor(c(1, 2, 3, 4))</span><br><br>\n",
    "    \n",
    "<br>\n",
    "What is class, or Data Structure is <b>x</b>?<br><br>\n",
    "         \n",
    "<br><div class=\"qhelp\"></div></div>\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<div class=\"Q\"><br><br>\n",
    "\n",
    "    \n",
    "Which of the Data Structures below <b>MUST</b> have all of their elements be of the same Data Type?<br><br>\n",
    "         \n",
    "<br><div class=\"qhelp\"></div></div>\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<div class=\"Q\"><br><br>\n",
    "\n",
    "    \n",
    "Which of the Data Structures below <b>CAN</b> have their elements be of any type of R object, including multiple Data Types?<br><br>\n",
    "         \n",
    "<br><div class=\"qhelp\"></div></div>\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Indexing (slicing)\n",
    "\n",
    "There are multiple ways to access or replace values inside data structures. The most common approach is to use “indexing”. This is also referred to as “slicing”.\n",
    "\n",
    "Note that brackets `[ ]` are used for indexing, whereas parentheses `( )` are used to call a function.\n",
    "\n",
    "\n",
    "## Navigating within a vectors and lists\n",
    "\n",
    "Consider the following vector `x` and list `y`:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [],
   "source": [
    "x <- c(5, 2, 7, 4)\n",
    "\n",
    "y <- list(6, \"hello\", FALSE, x, 3.67, \"world\", 8L)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "To get the first element of vector `x`:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "5"
      ],
      "text/latex": [
       "5"
      ],
      "text/markdown": [
       "5"
      ],
      "text/plain": [
       "[1] 5"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "x[1]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "If the vector `x` is graphically represented by the <span style=\"color:blue\">blue grid</span> below... then `x[1]` would access the <span style=\"color:blue\">red box</span>:\n",
    "\n",
    "<img src=\"./sample_vector1.png\" width=\"50px\">\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "To get the second element of list `y`:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<ol>\n",
       "\t<li>'hello'</li>\n",
       "</ol>\n"
      ],
      "text/latex": [
       "\\begin{enumerate}\n",
       "\\item 'hello'\n",
       "\\end{enumerate}\n"
      ],
      "text/markdown": [
       "1. 'hello'\n",
       "\n",
       "\n"
      ],
      "text/plain": [
       "[[1]]\n",
       "[1] \"hello\"\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "y[2]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "If the list `y` is graphically represented by the <span style=\"color:blue\">blue grid</span> below... then `y[2]` would access the <span style=\"color:blue\">red box</span>:\n",
    "\n",
    "<img src=\"./sample_list1.png\" width=\"130px\">\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<span class=\"important\"></span> Elements within a **list** are also **lists!** To get to the contents inside a \"list element\" you need to use double brackets `[[ ]]`:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<ol>\n",
       "\t<li>'hello'</li>\n",
       "</ol>\n"
      ],
      "text/latex": [
       "\\begin{enumerate}\n",
       "\\item 'hello'\n",
       "\\end{enumerate}\n"
      ],
      "text/markdown": [
       "1. 'hello'\n",
       "\n",
       "\n"
      ],
      "text/plain": [
       "[[1]]\n",
       "[1] \"hello\"\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "'list'"
      ],
      "text/latex": [
       "'list'"
      ],
      "text/markdown": [
       "'list'"
      ],
      "text/plain": [
       "[1] \"list\""
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "'hello'"
      ],
      "text/latex": [
       "'hello'"
      ],
      "text/markdown": [
       "'hello'"
      ],
      "text/plain": [
       "[1] \"hello\""
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "'character'"
      ],
      "text/latex": [
       "'character'"
      ],
      "text/markdown": [
       "'character'"
      ],
      "text/plain": [
       "[1] \"character\""
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "y[2]\n",
    "class(y[2])\n",
    "\n",
    "y[[2]]\n",
    "class(y[[2]])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "So, for example, `y[1] + y[5]` will return an error, because you cannot \"add\" two lists. To add the two values you need to do:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "9.67"
      ],
      "text/latex": [
       "9.67"
      ],
      "text/markdown": [
       "9.67"
      ],
      "text/plain": [
       "[1] 9.67"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "y[[1]] + y[[5]]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "To get a **range** of elements use the `:`. For example, to get the first 3 elements of vector `x`:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<ol class=list-inline>\n",
       "\t<li>5</li>\n",
       "\t<li>2</li>\n",
       "\t<li>7</li>\n",
       "</ol>\n"
      ],
      "text/latex": [
       "\\begin{enumerate*}\n",
       "\\item 5\n",
       "\\item 2\n",
       "\\item 7\n",
       "\\end{enumerate*}\n"
      ],
      "text/markdown": [
       "1. 5\n",
       "2. 2\n",
       "3. 7\n",
       "\n",
       "\n"
      ],
      "text/plain": [
       "[1] 5 2 7"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "x[1:3]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "If the vector `x` is graphically represented by the <span style=\"color:blue\">blue grid</span> below... then `x[1:3]` would access the <span style=\"color:blue\">red box</span>:\n",
    "\n",
    "<img src=\"./sample_vector2.png\" width=\"50px\">\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "To get the 5th, 6th and 7th elements of list `y`:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<ol>\n",
       "\t<li>3.67</li>\n",
       "\t<li>'world'</li>\n",
       "\t<li>8</li>\n",
       "</ol>\n"
      ],
      "text/latex": [
       "\\begin{enumerate}\n",
       "\\item 3.67\n",
       "\\item 'world'\n",
       "\\item 8\n",
       "\\end{enumerate}\n"
      ],
      "text/markdown": [
       "1. 3.67\n",
       "2. 'world'\n",
       "3. 8\n",
       "\n",
       "\n"
      ],
      "text/plain": [
       "[[1]]\n",
       "[1] 3.67\n",
       "\n",
       "[[2]]\n",
       "[1] \"world\"\n",
       "\n",
       "[[3]]\n",
       "[1] 8\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "y[5:7]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "If the list `y` is graphically represented by the <span style=\"color:blue\">blue grid</span> below... then `y[5:7]` would access the <span style=\"color:blue\">red box</span>:\n",
    "\n",
    "<img src=\"./sample_list2.png\" width=\"130px\">\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "To access a **list within list**, you have to first dive into the values of the first list, using double brackets `[[ ]]`: "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<ol>\n",
       "\t<li><ol class=list-inline>\n",
       "\t<li>5</li>\n",
       "\t<li>2</li>\n",
       "\t<li>7</li>\n",
       "\t<li>4</li>\n",
       "</ol>\n",
       "</li>\n",
       "</ol>\n"
      ],
      "text/latex": [
       "\\begin{enumerate}\n",
       "\\item \\begin{enumerate*}\n",
       "\\item 5\n",
       "\\item 2\n",
       "\\item 7\n",
       "\\item 4\n",
       "\\end{enumerate*}\n",
       "\n",
       "\\end{enumerate}\n"
      ],
      "text/markdown": [
       "1. 1. 5\n",
       "2. 2\n",
       "3. 7\n",
       "4. 4\n",
       "\n",
       "\n",
       "\n",
       "\n",
       "\n"
      ],
      "text/plain": [
       "[[1]]\n",
       "[1] 5 2 7 4\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "7"
      ],
      "text/latex": [
       "7"
      ],
      "text/markdown": [
       "7"
      ],
      "text/plain": [
       "[1] 7"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Here is the list stored in element 3\n",
    "y[4]\n",
    "\n",
    "# Here is the 3rd element of the list stored inside element 3 of list 'y'\n",
    "y[[4]][3]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "If the list `y` is graphically represented by the <span style=\"color:blue\">blue grid</span> below... then `y[[4]][3]` would access the <span style=\"color:blue\">red box</span>:\n",
    "\n",
    "<img src=\"./sample_list3.png\" width=\"130px\">\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "If you need the last element of a vector or list, but don't know how long is the vector or list, use the function `length()`:  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "4"
      ],
      "text/latex": [
       "4"
      ],
      "text/markdown": [
       "4"
      ],
      "text/plain": [
       "[1] 4"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# To retrieve the last element of x\n",
    "x[length(x)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<ol class=list-inline>\n",
       "\t<li>2</li>\n",
       "\t<li>7</li>\n",
       "\t<li>4</li>\n",
       "</ol>\n"
      ],
      "text/latex": [
       "\\begin{enumerate*}\n",
       "\\item 2\n",
       "\\item 7\n",
       "\\item 4\n",
       "\\end{enumerate*}\n"
      ],
      "text/markdown": [
       "1. 2\n",
       "2. 7\n",
       "3. 4\n",
       "\n",
       "\n"
      ],
      "text/plain": [
       "[1] 2 7 4"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# To retrieve the last 3 elements of x\n",
    "x[(length(x)-2):length(x)]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Logical Indexing** is used when you need to retrieve elements according to a logical expression. This only works with vectors and list that **ONLY** contain numeric or integer data types."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<ol class=list-inline>\n",
       "\t<li>5</li>\n",
       "\t<li>7</li>\n",
       "\t<li>4</li>\n",
       "</ol>\n"
      ],
      "text/latex": [
       "\\begin{enumerate*}\n",
       "\\item 5\n",
       "\\item 7\n",
       "\\item 4\n",
       "\\end{enumerate*}\n"
      ],
      "text/markdown": [
       "1. 5\n",
       "2. 7\n",
       "3. 4\n",
       "\n",
       "\n"
      ],
      "text/plain": [
       "[1] 5 7 4"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "x[x > 3]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<ol class=list-inline>\n",
       "\t<li>2</li>\n",
       "\t<li>4</li>\n",
       "</ol>\n"
      ],
      "text/latex": [
       "\\begin{enumerate*}\n",
       "\\item 2\n",
       "\\item 4\n",
       "\\end{enumerate*}\n"
      ],
      "text/markdown": [
       "1. 2\n",
       "2. 4\n",
       "\n",
       "\n"
      ],
      "text/plain": [
       "[1] 2 4"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "x[x <= 4]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<div class=\"Q\"><br><br>\n",
    "\n",
    "    \n",
    "Given the following vector <span class=\"codeX\">v</span>:<br><br>\n",
    "    \n",
    "<span class=\"codeX\">v <- c(8, 5, 3, 1, 9, 2)</span><br><br>\n",
    "         \n",
    "...which is represented visually with the diagram below,\n",
    "\n",
    "<img src=\"./vector1.png\" width=\"50px\">\n",
    "         \n",
    "How would you access the value highlighted in the red box?<br><br>\n",
    "         \n",
    "<br><div class=\"qhelp\"></div></div>\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<div class=\"Q\"><br><br>\n",
    "\n",
    "    \n",
    "Given the following vector <span class=\"codeX\">v</span>:<br><br>\n",
    "    \n",
    "<span class=\"codeX\">v <- c(8, 5, 3, 1, 9, 2)</span><br><br>\n",
    "         \n",
    "...which is represented visually with the diagram below,\n",
    "\n",
    "<img src=\"./vector2.png\" width=\"50px\">\n",
    "         \n",
    "How would you access the value highlighted in the red box?<br><br>\n",
    "         \n",
    "<br><div class=\"qhelp\"></div></div>\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<div class=\"Q\"><br><br>\n",
    "\n",
    "    \n",
    "Given the following vector <span class=\"codeX\">v</span>:<br><br>\n",
    "    \n",
    "<span class=\"codeX\">v <- c(8, 5, 3, 1, 9, 2)</span><br><br>\n",
    "         \n",
    "...which is represented visually with the diagram below,\n",
    "\n",
    "<img src=\"./vector3.png\" width=\"50px\">\n",
    "         \n",
    "How would you access the values highlighted within the red box?<br><br>\n",
    "         \n",
    "<br><div class=\"qhelp\"></div></div>\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<div class=\"Q\"><br><br>\n",
    "\n",
    "    \n",
    "Given the following vector <span class=\"codeX\">v</span>:<br><br>\n",
    "    \n",
    "<span class=\"codeX\">v <- c(8, 5, 3, 1, 9, 2)</span><br><br>\n",
    "         \n",
    "...which is represented visually with the diagram below,\n",
    "\n",
    "<img src=\"./vector4.png\" width=\"50px\">\n",
    "         \n",
    "How would you access the value highlighted in the red box?<br><br>\n",
    "         \n",
    "<br><div class=\"qhelp\"></div></div>\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<div class=\"Q\"><br><br>\n",
    "\n",
    "    \n",
    "Given the following vector <span class=\"codeX\">v</span>:<br><br>\n",
    "    \n",
    "<span class=\"codeX\">v <- c(8, 5, 3, 1, 9, 2)</span><br><br>\n",
    "         \n",
    "...which is represented visually with the diagram below,\n",
    "\n",
    "<img src=\"./vector5.png\" width=\"50px\">\n",
    "         \n",
    "How would you access the values highlighted within the red box?<br><br>\n",
    "         \n",
    "<br><div class=\"qhelp\"></div></div>\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<div class=\"Q\"><br><br>\n",
    "\n",
    "    \n",
    "Given the following list <span class=\"codeX\">L</span>:<br><br>\n",
    "    \n",
    "<span class=\"codeX\">L <- list(7L, \"hello\", 3.6, c(7, 5, 2), 9, \"world\")</span><br><br>\n",
    "         \n",
    "...which is represented visually with the diagram below,\n",
    "\n",
    "<img src=\"./list1.png\" width=\"150px\">\n",
    "         \n",
    "How would you access the value highlighted in the red box?<br><br>\n",
    "         \n",
    "<br><div class=\"qhelp\"></div></div>\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<div class=\"Q\"><br><br>\n",
    "\n",
    "Given the following list <span class=\"codeX\">L</span>:<br><br>\n",
    "    \n",
    "<span class=\"codeX\">L <- list(7L, \"hello\", 3.6, c(7, 5, 2), 9, \"world\")</span><br><br>\n",
    "         \n",
    "...which is represented visually with the diagram below,\n",
    "\n",
    "<img src=\"./list2.png\" width=\"150px\">\n",
    "         \n",
    "How would you access the values highlighted in the red box?<br><br>\n",
    "         \n",
    "<br><div class=\"qhelp\"></div></div>\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<div class=\"Q\"><br><br>\n",
    "\n",
    "    \n",
    "Given the following list <span class=\"codeX\">L</span>:<br><br>\n",
    "    \n",
    "<span class=\"codeX\">L <- list(7L, \"hello\", 3.6, c(7, 5, 2), 9, \"world\")</span><br><br>\n",
    "         \n",
    "...which is represented visually with the diagram below,\n",
    "\n",
    "<img src=\"./list3.png\" width=\"150px\">\n",
    "         \n",
    "How would you access the value highlighted in the red box?<br><br>\n",
    "         \n",
    "<br><div class=\"qhelp\"></div></div>\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<div class=\"Q\"><br><br>\n",
    "\n",
    "    \n",
    "Given the following list <span class=\"codeX\">L</span>:<br><br>\n",
    "    \n",
    "<span class=\"codeX\">L <- list(7L, \"hello\", 3.6, c(7, 5, 2), 9, \"world\")</span><br><br>\n",
    "         \n",
    "...which is represented visually with the diagram below,\n",
    "\n",
    "<img src=\"./list4.png\" width=\"150px\">\n",
    "         \n",
    "How would you access the value highlighted in the red box?<br><br>\n",
    "         \n",
    "<br><div class=\"qhelp\"></div></div>\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<div class=\"Q\"><br><br>\n",
    "\n",
    "    \n",
    "Given the following list <span class=\"codeX\">L</span>:<br><br>\n",
    "    \n",
    "<span class=\"codeX\">L <- list(7L, \"hello\", 3.6, c(7, 5, 2), 9, \"world\")</span><br><br>\n",
    "         \n",
    "...which is represented visually with the diagram below,\n",
    "\n",
    "<img src=\"./list5.png\" width=\"150px\">\n",
    "         \n",
    "How would you access the values highlighted in the red box?<br><br>\n",
    "         \n",
    "<br><div class=\"qhelp\"></div></div>\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Navigating within Matrices\n",
    "\n",
    "Consider the following matrix `m`:\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table>\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"
      ],
      "text/latex": [
       "\\begin{tabular}{lll}\n",
       "\t 1  & 5  &  9\\\\\n",
       "\t 2  & 6  & 10\\\\\n",
       "\t 3  & 7  & 11\\\\\n",
       "\t 4  & 8  & 12\\\\\n",
       "\\end{tabular}\n"
      ],
      "text/markdown": [
       "\n",
       "| 1  | 5  |  9 |\n",
       "| 2  | 6  | 10 |\n",
       "| 3  | 7  | 11 |\n",
       "| 4  | 8  | 12 |\n",
       "\n"
      ],
      "text/plain": [
       "     [,1] [,2] [,3]\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": [
    "m <- matrix(c(1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12), nrow=4, ncol=3)\n",
    "\n",
    "m"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "To get to a single number within the matrix, use a pair of indices where the **first index is the row number** and the **second index is the column number**:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "5"
      ],
      "text/latex": [
       "5"
      ],
      "text/markdown": [
       "5"
      ],
      "text/plain": [
       "[1] 5"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "m[1,2]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Rather than using pairs, you can also get single index. You can think of this index as a “cell number”. Cells are numbered column-wise (i.e., first the rows in the first column, then the second column, etc.). Thus,"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "6"
      ],
      "text/latex": [
       "6"
      ],
      "text/markdown": [
       "6"
      ],
      "text/plain": [
       "[1] 6"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "m[6]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "You can also get multiple values at once:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table>\n",
       "<tbody>\n",
       "\t<tr><td>5 </td><td> 9</td></tr>\n",
       "\t<tr><td>6 </td><td>10</td></tr>\n",
       "</tbody>\n",
       "</table>\n"
      ],
      "text/latex": [
       "\\begin{tabular}{ll}\n",
       "\t 5  &  9\\\\\n",
       "\t 6  & 10\\\\\n",
       "\\end{tabular}\n"
      ],
      "text/markdown": [
       "\n",
       "| 5  |  9 |\n",
       "| 6  | 10 |\n",
       "\n"
      ],
      "text/plain": [
       "     [,1] [,2]\n",
       "[1,] 5     9  \n",
       "[2,] 6    10  "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "m[1:2,2:3]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "To get a whole **column**, use a coma **before** the column index. The statement below read *return all rows of column 1*:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "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"
      ],
      "text/latex": [
       "\\begin{enumerate*}\n",
       "\\item 1\n",
       "\\item 2\n",
       "\\item 3\n",
       "\\item 4\n",
       "\\end{enumerate*}\n"
      ],
      "text/markdown": [
       "1. 1\n",
       "2. 2\n",
       "3. 3\n",
       "4. 4\n",
       "\n",
       "\n"
      ],
      "text/plain": [
       "[1] 1 2 3 4"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "m[, 1]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "To get a whole **row**, use a coma **after** the row index: The statement below read *return row 1... all columns:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<ol class=list-inline>\n",
       "\t<li>1</li>\n",
       "\t<li>5</li>\n",
       "\t<li>9</li>\n",
       "</ol>\n"
      ],
      "text/latex": [
       "\\begin{enumerate*}\n",
       "\\item 1\n",
       "\\item 5\n",
       "\\item 9\n",
       "\\end{enumerate*}\n"
      ],
      "text/markdown": [
       "1. 1\n",
       "2. 5\n",
       "3. 9\n",
       "\n",
       "\n"
      ],
      "text/plain": [
       "[1] 1 5 9"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "m[1,]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<span class=\"note\"></span> Getting a whole **row** or **column** from a matrix returns a `vector`:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "'matrix'"
      ],
      "text/latex": [
       "'matrix'"
      ],
      "text/markdown": [
       "'matrix'"
      ],
      "text/plain": [
       "[1] \"matrix\""
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "'numeric'"
      ],
      "text/latex": [
       "'numeric'"
      ],
      "text/markdown": [
       "'numeric'"
      ],
      "text/plain": [
       "[1] \"numeric\""
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "class(m)\n",
    "\n",
    "class(m[, 1])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<div class=\"Q\"><br><br>\n",
    "\n",
    "    \n",
    "Given the following matrix <span class=\"codeX\">m</span>:<br><br>\n",
    "    \n",
    "<span class=\"codeX\">m <- matrix(c(2,4,7,3,5,3,9,7,0,6,8,2),nrow=4, ncol=3)</span><br><br>\n",
    "         \n",
    "...which is represented visually with the diagram below,\n",
    "\n",
    "<img src=\"./matrix1.png\" width=\"150px\">\n",
    "         \n",
    "How would you access the value highlighted in the red box?<br><br>\n",
    "         \n",
    "<br><div class=\"qhelp\"></div></div>\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<div class=\"Q\"><br><br>\n",
    "\n",
    "    \n",
    "Given the following matrix <span class=\"codeX\">m</span>:<br><br>\n",
    "    \n",
    "<span class=\"codeX\">m <- matrix(c(2,4,7,3,5,3,9,7,0,6,8,2),nrow=4, ncol=3)</span><br><br>\n",
    "         \n",
    "...which is represented visually with the diagram below,\n",
    "\n",
    "<img src=\"./matrix2.png\" width=\"150px\">\n",
    "         \n",
    "How would you access the values highlighted in the red box?<br><br>\n",
    "         \n",
    "<br><div class=\"qhelp\"></div></div>\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<div class=\"Q\"><br><br>\n",
    "\n",
    "    \n",
    "Given the following matrix <span class=\"codeX\">m</span>:<br><br>\n",
    "    \n",
    "<span class=\"codeX\">m <- matrix(c(2,4,7,3,5,3,9,7,0,6,8,2),nrow=4, ncol=3)</span><br><br>\n",
    "         \n",
    "...which is represented visually with the diagram below,\n",
    "\n",
    "<img src=\"./matrix3.png\" width=\"150px\">\n",
    "         \n",
    "How would you access the values highlighted in the red box?<br><br>\n",
    "         \n",
    "<br><div class=\"qhelp\"></div></div>\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<div class=\"Q\"><br><br>\n",
    "\n",
    "    \n",
    "Given the following matrix <span class=\"codeX\">m</span>:<br><br>\n",
    "    \n",
    "<span class=\"codeX\">m <- matrix(c(2,4,7,3,5,3,9,7,0,6,8,2),nrow=4, ncol=3)</span><br><br>\n",
    "         \n",
    "...which is represented visually with the diagram below,\n",
    "\n",
    "<img src=\"./matrix3.png\" width=\"150px\">\n",
    "         \n",
    "If you make a <b>new variable</b> with the values highlighted in the red box (using your answer to the question above)...\n",
    "    \n",
    "...what class or Data Structure will be asigned to your <b>new variable</b>?<br><br>\n",
    "         \n",
    "<br><div class=\"qhelp\"></div></div>\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Navigating within Data Frames\n",
    "\n",
    "Consider the following data frame `d`:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table>\n",
       "<thead><tr><th scope=col>id</th><th scope=col>x</th><th scope=col>y</th></tr></thead>\n",
       "<tbody>\n",
       "\t<tr><td> 1</td><td>11</td><td>21</td></tr>\n",
       "\t<tr><td> 2</td><td>12</td><td>22</td></tr>\n",
       "\t<tr><td> 3</td><td>13</td><td>23</td></tr>\n",
       "\t<tr><td> 4</td><td>14</td><td>24</td></tr>\n",
       "\t<tr><td> 5</td><td>15</td><td>25</td></tr>\n",
       "\t<tr><td> 6</td><td>16</td><td>26</td></tr>\n",
       "\t<tr><td> 7</td><td>17</td><td>27</td></tr>\n",
       "\t<tr><td> 8</td><td>18</td><td>28</td></tr>\n",
       "\t<tr><td> 9</td><td>19</td><td>29</td></tr>\n",
       "\t<tr><td>10</td><td>20</td><td>30</td></tr>\n",
       "</tbody>\n",
       "</table>\n"
      ],
      "text/latex": [
       "\\begin{tabular}{r|lll}\n",
       " id & x & y\\\\\n",
       "\\hline\n",
       "\t  1 & 11 & 21\\\\\n",
       "\t  2 & 12 & 22\\\\\n",
       "\t  3 & 13 & 23\\\\\n",
       "\t  4 & 14 & 24\\\\\n",
       "\t  5 & 15 & 25\\\\\n",
       "\t  6 & 16 & 26\\\\\n",
       "\t  7 & 17 & 27\\\\\n",
       "\t  8 & 18 & 28\\\\\n",
       "\t  9 & 19 & 29\\\\\n",
       "\t 10 & 20 & 30\\\\\n",
       "\\end{tabular}\n"
      ],
      "text/markdown": [
       "\n",
       "| id | x | y |\n",
       "|---|---|---|\n",
       "|  1 | 11 | 21 |\n",
       "|  2 | 12 | 22 |\n",
       "|  3 | 13 | 23 |\n",
       "|  4 | 14 | 24 |\n",
       "|  5 | 15 | 25 |\n",
       "|  6 | 16 | 26 |\n",
       "|  7 | 17 | 27 |\n",
       "|  8 | 18 | 28 |\n",
       "|  9 | 19 | 29 |\n",
       "| 10 | 20 | 30 |\n",
       "\n"
      ],
      "text/plain": [
       "   id x  y \n",
       "1   1 11 21\n",
       "2   2 12 22\n",
       "3   3 13 23\n",
       "4   4 14 24\n",
       "5   5 15 25\n",
       "6   6 16 26\n",
       "7   7 17 27\n",
       "8   8 18 28\n",
       "9   9 19 29\n",
       "10 10 20 30"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "d <- data.frame(id = 1:10, x = 11:20, y = 21:30)\n",
    "\n",
    "d"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "You can extract a column by column number:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<ol class=list-inline>\n",
       "\t<li>11</li>\n",
       "\t<li>12</li>\n",
       "\t<li>13</li>\n",
       "\t<li>14</li>\n",
       "\t<li>15</li>\n",
       "\t<li>16</li>\n",
       "\t<li>17</li>\n",
       "\t<li>18</li>\n",
       "\t<li>19</li>\n",
       "\t<li>20</li>\n",
       "</ol>\n"
      ],
      "text/latex": [
       "\\begin{enumerate*}\n",
       "\\item 11\n",
       "\\item 12\n",
       "\\item 13\n",
       "\\item 14\n",
       "\\item 15\n",
       "\\item 16\n",
       "\\item 17\n",
       "\\item 18\n",
       "\\item 19\n",
       "\\item 20\n",
       "\\end{enumerate*}\n"
      ],
      "text/markdown": [
       "1. 11\n",
       "2. 12\n",
       "3. 13\n",
       "4. 14\n",
       "5. 15\n",
       "6. 16\n",
       "7. 17\n",
       "8. 18\n",
       "9. 19\n",
       "10. 20\n",
       "\n",
       "\n"
      ],
      "text/plain": [
       " [1] 11 12 13 14 15 16 17 18 19 20"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "d[,2]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Here is an alternative way to address the column number in a data frame:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table>\n",
       "<thead><tr><th scope=col>x</th></tr></thead>\n",
       "<tbody>\n",
       "\t<tr><td>11</td></tr>\n",
       "\t<tr><td>12</td></tr>\n",
       "\t<tr><td>13</td></tr>\n",
       "\t<tr><td>14</td></tr>\n",
       "\t<tr><td>15</td></tr>\n",
       "\t<tr><td>16</td></tr>\n",
       "\t<tr><td>17</td></tr>\n",
       "\t<tr><td>18</td></tr>\n",
       "\t<tr><td>19</td></tr>\n",
       "\t<tr><td>20</td></tr>\n",
       "</tbody>\n",
       "</table>\n"
      ],
      "text/latex": [
       "\\begin{tabular}{r|l}\n",
       " x\\\\\n",
       "\\hline\n",
       "\t 11\\\\\n",
       "\t 12\\\\\n",
       "\t 13\\\\\n",
       "\t 14\\\\\n",
       "\t 15\\\\\n",
       "\t 16\\\\\n",
       "\t 17\\\\\n",
       "\t 18\\\\\n",
       "\t 19\\\\\n",
       "\t 20\\\\\n",
       "\\end{tabular}\n"
      ],
      "text/markdown": [
       "\n",
       "| x |\n",
       "|---|\n",
       "| 11 |\n",
       "| 12 |\n",
       "| 13 |\n",
       "| 14 |\n",
       "| 15 |\n",
       "| 16 |\n",
       "| 17 |\n",
       "| 18 |\n",
       "| 19 |\n",
       "| 20 |\n",
       "\n"
      ],
      "text/plain": [
       "   x \n",
       "1  11\n",
       "2  12\n",
       "3  13\n",
       "4  14\n",
       "5  15\n",
       "6  16\n",
       "7  17\n",
       "8  18\n",
       "9  19\n",
       "10 20"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "d[2]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Note that whereas `[2]` would be the second element in a matrix, it refers to the second *column* in a `data.frame`. This is because a `data.frame` is a special kind of list and not a special kind of matrix.\n",
    "\n",
    "You can also use the column name to get the values of a column: "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<ol class=list-inline>\n",
       "\t<li>11</li>\n",
       "\t<li>12</li>\n",
       "\t<li>13</li>\n",
       "\t<li>14</li>\n",
       "\t<li>15</li>\n",
       "\t<li>16</li>\n",
       "\t<li>17</li>\n",
       "\t<li>18</li>\n",
       "\t<li>19</li>\n",
       "\t<li>20</li>\n",
       "</ol>\n"
      ],
      "text/latex": [
       "\\begin{enumerate*}\n",
       "\\item 11\n",
       "\\item 12\n",
       "\\item 13\n",
       "\\item 14\n",
       "\\item 15\n",
       "\\item 16\n",
       "\\item 17\n",
       "\\item 18\n",
       "\\item 19\n",
       "\\item 20\n",
       "\\end{enumerate*}\n"
      ],
      "text/markdown": [
       "1. 11\n",
       "2. 12\n",
       "3. 13\n",
       "4. 14\n",
       "5. 15\n",
       "6. 16\n",
       "7. 17\n",
       "8. 18\n",
       "9. 19\n",
       "10. 20\n",
       "\n",
       "\n"
      ],
      "text/plain": [
       " [1] 11 12 13 14 15 16 17 18 19 20"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "d[,\"x\"]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    " In addition to `d[,\"x\"]` above, you can also use the `$` symbol. \n",
    "\n",
    "<span class=\"important\"></span> Using the `$` symbol is a very common practice to slice a column of a `data.frame`:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<ol class=list-inline>\n",
       "\t<li>11</li>\n",
       "\t<li>12</li>\n",
       "\t<li>13</li>\n",
       "\t<li>14</li>\n",
       "\t<li>15</li>\n",
       "\t<li>16</li>\n",
       "\t<li>17</li>\n",
       "\t<li>18</li>\n",
       "\t<li>19</li>\n",
       "\t<li>20</li>\n",
       "</ol>\n"
      ],
      "text/latex": [
       "\\begin{enumerate*}\n",
       "\\item 11\n",
       "\\item 12\n",
       "\\item 13\n",
       "\\item 14\n",
       "\\item 15\n",
       "\\item 16\n",
       "\\item 17\n",
       "\\item 18\n",
       "\\item 19\n",
       "\\item 20\n",
       "\\end{enumerate*}\n"
      ],
      "text/markdown": [
       "1. 11\n",
       "2. 12\n",
       "3. 13\n",
       "4. 14\n",
       "5. 15\n",
       "6. 16\n",
       "7. 17\n",
       "8. 18\n",
       "9. 19\n",
       "10. 20\n",
       "\n",
       "\n"
      ],
      "text/plain": [
       " [1] 11 12 13 14 15 16 17 18 19 20"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "d$x"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table>\n",
       "<thead><tr><th scope=col>x</th></tr></thead>\n",
       "<tbody>\n",
       "\t<tr><td>11</td></tr>\n",
       "\t<tr><td>12</td></tr>\n",
       "\t<tr><td>13</td></tr>\n",
       "\t<tr><td>14</td></tr>\n",
       "\t<tr><td>15</td></tr>\n",
       "\t<tr><td>16</td></tr>\n",
       "\t<tr><td>17</td></tr>\n",
       "\t<tr><td>18</td></tr>\n",
       "\t<tr><td>19</td></tr>\n",
       "\t<tr><td>20</td></tr>\n",
       "</tbody>\n",
       "</table>\n"
      ],
      "text/latex": [
       "\\begin{tabular}{r|l}\n",
       " x\\\\\n",
       "\\hline\n",
       "\t 11\\\\\n",
       "\t 12\\\\\n",
       "\t 13\\\\\n",
       "\t 14\\\\\n",
       "\t 15\\\\\n",
       "\t 16\\\\\n",
       "\t 17\\\\\n",
       "\t 18\\\\\n",
       "\t 19\\\\\n",
       "\t 20\\\\\n",
       "\\end{tabular}\n"
      ],
      "text/markdown": [
       "\n",
       "| x |\n",
       "|---|\n",
       "| 11 |\n",
       "| 12 |\n",
       "| 13 |\n",
       "| 14 |\n",
       "| 15 |\n",
       "| 16 |\n",
       "| 17 |\n",
       "| 18 |\n",
       "| 19 |\n",
       "| 20 |\n",
       "\n"
      ],
      "text/plain": [
       "   x \n",
       "1  11\n",
       "2  12\n",
       "3  13\n",
       "4  14\n",
       "5  15\n",
       "6  16\n",
       "7  17\n",
       "8  18\n",
       "9  19\n",
       "10 20"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "d[\"x\"]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Note that both `d[,\"x\"]` and `d$x` return a `vector`. That is, the complexity of the `data.frame` structure was `dropped`. This does not happen when you do `d[\"x\"]`, where the outputs remains a `data.frame`. Take a look:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "'integer'"
      ],
      "text/latex": [
       "'integer'"
      ],
      "text/markdown": [
       "'integer'"
      ],
      "text/plain": [
       "[1] \"integer\""
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "'integer'"
      ],
      "text/latex": [
       "'integer'"
      ],
      "text/markdown": [
       "'integer'"
      ],
      "text/plain": [
       "[1] \"integer\""
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "'data.frame'"
      ],
      "text/latex": [
       "'data.frame'"
      ],
      "text/markdown": [
       "'data.frame'"
      ],
      "text/plain": [
       "[1] \"data.frame\""
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "class(d[,\"x\"]) # This drops the data.frame and returns a vector\n",
    "class(d$x)     # This drops the data.frame and returns a vector\n",
    "class(d[\"x\"])  # This preserves the data.frame"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Why should you care about this `drop` business? In many cases R functions want a specific data type, such as a `matrix` or `data.frame` and report an error if they get something else. One common situation is that you think you provide data of the right type, such as a `data.frame`, but that in fact you are providing a vector, because the structure dropped.\n",
    "\n",
    "Either way, you can use `[ ]` with all three approaches to get to a specific value within a column:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "12"
      ],
      "text/latex": [
       "12"
      ],
      "text/markdown": [
       "12"
      ],
      "text/plain": [
       "[1] 12"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "d[[\"x\"]][2]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "12"
      ],
      "text/latex": [
       "12"
      ],
      "text/markdown": [
       "12"
      ],
      "text/plain": [
       "[1] 12"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "d$x[2]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<ol class=list-inline>\n",
       "\t<li>12</li>\n",
       "\t<li>13</li>\n",
       "\t<li>14</li>\n",
       "</ol>\n"
      ],
      "text/latex": [
       "\\begin{enumerate*}\n",
       "\\item 12\n",
       "\\item 13\n",
       "\\item 14\n",
       "\\end{enumerate*}\n"
      ],
      "text/markdown": [
       "1. 12\n",
       "2. 13\n",
       "3. 14\n",
       "\n",
       "\n"
      ],
      "text/plain": [
       "[1] 12 13 14"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "d$x[2:4]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<div class=\"Q\"><br><br>\n",
    "\n",
    "    \n",
    "Given the following Data Frame <span class=\"codeX\">d</span>:<br><br>\n",
    "    \n",
    "<span class=\"codeX\">d <- data.frame(x=2:6, y=3:7, z=4:8)</span><br><br>\n",
    "         \n",
    "...which is represented visually with the diagram below,\n",
    "\n",
    "<img src=\"./dataframe0.png\" width=\"150px\">\n",
    "         \n",
    "How would you access the values highlighted in the red box?<br><br>\n",
    "         \n",
    "<br><div class=\"qhelp\"></div></div>\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<div class=\"Q\"><br><br>\n",
    "\n",
    "    \n",
    "Given the following Data Frame <span class=\"codeX\">d</span>:<br><br>\n",
    "    \n",
    "<span class=\"codeX\">d <- data.frame(x=2:6, y=3:7, z=4:8)</span><br><br>\n",
    "         \n",
    "...which is represented visually with the diagram below,\n",
    "\n",
    "<img src=\"./dataframe1.png\" width=\"150px\">\n",
    "         \n",
    "Select all the choices that you could use to access the values highlighted in the red box?<br><br>\n",
    "         \n",
    "<br><div class=\"qhelp\"></div></div>\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<div class=\"Q\"><br><br>\n",
    "\n",
    "    \n",
    "Given the following Data Frame <span class=\"codeX\">d</span>:<br><br>\n",
    "    \n",
    "<span class=\"codeX\">d <- data.frame(x=2:6, y=3:7, z=4:8)</span><br><br>\n",
    "         \n",
    "...which is represented visually with the diagram below,\n",
    "\n",
    "<img src=\"./dataframe2.png\" width=\"150px\">\n",
    "         \n",
    "How would you access the value highlighted in the red box?<br><br>\n",
    "         \n",
    "<br><div class=\"qhelp\"></div></div>\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<div class=\"Q\"><br><br>\n",
    "\n",
    "    \n",
    "    \n",
    "Given the following Data Frame <span class=\"codeX\">d</span>:<br><br>\n",
    "    \n",
    "<span class=\"codeX\">d <- data.frame(x=2:6, y=3:7, z=4:8)</span><br><br>\n",
    "         \n",
    "and given the following subset of <span class=\"codeX\">d</span>, created as follows:<br><br>\n",
    "         \n",
    "<span class=\"codeX\">subset_d <- d[,\"y\"]</span><br><br>\n",
    "         \n",
    "What class or Data Structure would be <span class=\"codeX\">subset_d</span>?<br><br>\n",
    "         \n",
    "<br><div class=\"qhelp\"></div></div>\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<div class=\"Q\"><br><br>\n",
    "\n",
    "    \n",
    "Given the following Data Frame <span class=\"codeX\">d</span>:<br><br>\n",
    "    \n",
    "<span class=\"codeX\">d <- data.frame(x=2:6, y=3:7, z=4:8)</span><br><br>\n",
    "         \n",
    "and given the following subset of <span class=\"codeX\">d</span>, created as follows:<br><br>\n",
    "         \n",
    "<span class=\"codeX\">subset_d <- d$y</span><br><br>\n",
    "         \n",
    "What class or Data Structure would be <span class=\"codeX\">subset_d</span>?<br><br>\n",
    "         \n",
    "<br><div class=\"qhelp\"></div></div>\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<div class=\"Q\"><br><br>\n",
    "\n",
    "    \n",
    "Given the following Data Frame <span class=\"codeX\">d</span>:<br><br>\n",
    "    \n",
    "<span class=\"codeX\">d <- data.frame(x=2:6, y=3:7, z=4:8)</span><br><br>\n",
    "         \n",
    "and given the following subset of <span class=\"codeX\">d</span>, created as follows:<br><br>\n",
    "         \n",
    "<span class=\"codeX\">subset_d <- d[\"y\"]</span><br><br>\n",
    "         \n",
    "What class or Data Structure would be <span class=\"codeX\">subset_d</span>?\n",
    "         \n",
    "<br><div class=\"qhelp\"></div></div>\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "-------------------\n",
    "\n",
    "Some parts of this lab where borrowed from:\n",
    "\n",
    "* [Software Carpentry](https://software-carpentry.org/)\n",
    "* [Data Carpentry](https://datacarpentry.org/)\n",
    "* [Spatial Data Science with R](https://rspatial.org/)\n",
    "* [Using R](http://www.sr.bham.ac.uk/~ajrs/R/)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<div class=\"big_title\">This is the end of lab</div>\n",
    "\n",
    "*******************\n",
    "*******************\n",
    "\n",
    "Code below is for formatting of this lab. Do not alter!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>\n",
       "\n",
       "body {\n",
       "  counter-reset: question_num task_num;                     /* Set a counter named 'section', and it`s initial value is 0. */\n",
       "}\n",
       "\n",
       "/* Question Box */\n",
       ".Q {\n",
       "    background-color:#ff9900;\n",
       "    color:black;\n",
       "    font-size:17px;\n",
       "    width:100%;\n",
       "    border:1px solid #666666;\n",
       "    padding:5px;\n",
       "}\n",
       "\n",
       ".Q::before {\n",
       "    counter-increment: question_num;\n",
       "    content: \"QUESTION \" counter(question_num) '\\A';\n",
       "    white-space: pre;\n",
       "}\n",
       "\n",
       "\n",
       "/* Question Box */\n",
       ".T {\n",
       "    background-color:#00F7FF;\n",
       "    color:black;\n",
       "    font-size:17px;\n",
       "    font-weight: bold;\n",
       "    width:10%;\n",
       "    padding:5px;\n",
       "}\n",
       "\n",
       "\n",
       ".T::before {\n",
       "    counter-increment: task_num;\n",
       "    content: \"Task \" counter(task_num) ': ';\n",
       "}\n",
       "\n",
       "\n",
       "/* TASK BOX */\n",
       ".startTASK {\n",
       "    background: linear-gradient(to bottom, #b3ffb3 0%, #ffffff 100%);\n",
       "    border-top-left-radius: 10px;\n",
       "    border-top-right-radius: 10px;\n",
       "    border:1px solid #009900;\n",
       "    border-bottom:1px solid #ffffff;\n",
       "    color:#006600;\n",
       "    font-size:17px;\n",
       "    width:100%;\n",
       "    font-weight: bold;\n",
       "    padding:5px;\n",
       "    padding-top:15px;\n",
       "}\n",
       ".startTASK::before {\n",
       "    counter-increment: task_num;\n",
       "    content: \"Task \" counter(task_num) ': ';\n",
       "}\n",
       "\n",
       ".endTASK {\n",
       "    background: linear-gradient(to bottom, #ffffff 0%, #b3ffb3 100%);\n",
       "    border-bottom-left-radius: 10px;\n",
       "    border-bottom-right-radius: 10px;\n",
       "    border:1px solid #009900;\n",
       "    border-top:1px solid #ffffff;\n",
       "    color:#006600;\n",
       "    font-size:13px;\n",
       "    width:100%;\n",
       "    font-weight: bold;\n",
       "    padding:5px;\n",
       "    padding-top:15px;\n",
       "}\n",
       ".endTASK::before {\n",
       "    content: \"End of Task\";\n",
       "}\n",
       "\n",
       "\n",
       "\n",
       "\n",
       "\n",
       "\n",
       "\n",
       "\n",
       "\n",
       "\n",
       "\n",
       "\n",
       "\n",
       "\n",
       ".qhelp {\n",
       "    color:#666666;\n",
       "    font-size:15px\n",
       "}\n",
       "\n",
       ".qhelp::after {\n",
       "    content: \"Answer this question in the corresponding Brightspace LAB quiz\"\n",
       "}\n",
       "\n",
       "\n",
       "\n",
       "/* code */\n",
       ".rendered_html pre, .rendered_html code {\n",
       "    font-family: \"SF Mono\",\"Segoe UI Mono\",\"Roboto Mono\",\"Ubuntu Mono\",Menlo,Courier,monospace;\n",
       "    padding: 1px 3px;\n",
       "    border: 1px solid #e6e6e6;\n",
       "    border-radius: 3px;\n",
       "    background: #e6e6e6;\n",
       "    font-size: 12px;\n",
       "    line-height: 1.4;\n",
       "    color: #0052CC;\n",
       "}\n",
       "\n",
       "\n",
       "/* Console */\n",
       ".console {\n",
       "    background-color:#d2d2d2;\n",
       "    color:#FF2525;\n",
       "    font-size:12px;\n",
       "    line-height: 1.4;\n",
       "    border:1px solid #bfbfbf;\n",
       "    padding-top:0px;\n",
       "    padding-bottom:0px;\n",
       "    padding-left:3px;\n",
       "    padding-right:3px;\n",
       "    border-radius: 3px;\n",
       "    display: inline-block;\n",
       "}\n",
       ".console::before {\n",
       "    content: \"Console\";\n",
       "}\n",
       "\n",
       "/* Editor */\n",
       ".editor {\n",
       "    background-color:#d2d2d2;\n",
       "    color:#00b300;\n",
       "    font-size: 12px;\n",
       "    line-height: 1.4;\n",
       "    border:1px solid #bfbfbf;\n",
       "    padding-top:0px;\n",
       "    padding-bottom:0px;\n",
       "    padding-left:3px;\n",
       "    padding-right:3px;\n",
       "    border-radius: 3px;\n",
       "    display: inline-block;\n",
       "}\n",
       ".editor::before {\n",
       "    content: \"Editor\";\n",
       "}\n",
       "\n",
       "\n",
       "\n",
       "/* Copy to CONSOLE */\n",
       ".c2c {\n",
       "    background-color:#d2d2d2;\n",
       "    color:#ff8080;\n",
       "    font-size:13px;\n",
       "    border:1px solid #bfbfbf;\n",
       "    padding:1px 3px;\n",
       "    border-radius: 3px;\n",
       "    margin-top: 10px;\n",
       "    display: inline-block;\n",
       "}\n",
       "\n",
       ".c2c::before {\n",
       "    content: \"Copy-paste into Spyder's Console\";\n",
       "}\n",
       "\n",
       "/* Type in CONSOLE */\n",
       ".t2c {\n",
       "    background-color:#d2d2d2;\n",
       "    color:#ff8080;\n",
       "    font-size:13px;\n",
       "    border:1px solid #bfbfbf;\n",
       "    padding:1px 3px;\n",
       "    border-radius: 3px;\n",
       "    margin-top: 10px;\n",
       "    display: inline-block;\n",
       "}\n",
       "\n",
       ".t2c::before {\n",
       "    content: \"Type into Spyder's Console\";\n",
       "}\n",
       "\n",
       "\n",
       "/* SOURCE */\n",
       ".source {\n",
       "    background-color:#d2d2d2;\n",
       "    color:#009EBF;\n",
       "    font-size: 12px;\n",
       "    line-height: 1.4;\n",
       "    font-weight: bold;\n",
       "    border:1px solid #bfbfbf;\n",
       "    padding-top:0px;\n",
       "    padding-bottom:0px;\n",
       "    padding-left:3px;\n",
       "    padding-right:3px;\n",
       "    border-radius: 3px;\n",
       "    display: inline-block;\n",
       "}\n",
       ".source::before {\n",
       "    content: \"\\25B6\" \"Source\";\n",
       "}\n",
       "\n",
       "\n",
       "/* RUN */\n",
       ".run {\n",
       "    background-color:#d2d2d2;\n",
       "    color:#00b300;\n",
       "    font-size: 12px;\n",
       "    line-height: 1.4;\n",
       "    font-weight: bold;\n",
       "    border:1px solid #bfbfbf;\n",
       "    padding-top:0px;\n",
       "    padding-bottom:0px;\n",
       "    padding-left:3px;\n",
       "    padding-right:3px;\n",
       "    border-radius: 3px;\n",
       "    display: inline-block;\n",
       "}\n",
       ".run::before {\n",
       "    content: \"\\25B6\" \"RUN\";\n",
       "}\n",
       "\n",
       "/* RUN */\n",
       ".runcell {\n",
       "    background-color:#d2d2d2;\n",
       "    color:#00b300;\n",
       "    font-size: 12px;\n",
       "    line-height: 1.4;\n",
       "    border:1px solid #bfbfbf;\n",
       "    padding-top:0px;\n",
       "    padding-bottom:0px;\n",
       "    padding-left:3px;\n",
       "    padding-right:3px;\n",
       "    border-radius: 3px;\n",
       "    display: inline-block;\n",
       "}\n",
       ".runcell::before {\n",
       "    content: \"RUN current cell\";\n",
       "}\n",
       "\n",
       "/* FILE */\n",
       ".file {\n",
       "    color:#b36b00;\n",
       "    font-size: 12px;\n",
       "    line-height: 1.4;\n",
       "    font-weight: bold;\n",
       "    border:1px solid #bfbfbf;\n",
       "    padding-top:0px;\n",
       "    padding-bottom:0px;\n",
       "    padding-left:3px;\n",
       "    padding-right:3px;\n",
       "    border-radius: 3px;\n",
       "    display: inline-block;\n",
       "}\n",
       "\n",
       "/* important */\n",
       ".important {\n",
       "    background-color:#ff4d4d;\n",
       "    color:black;\n",
       "    font-size: 13px;\n",
       "    line-height: 1.4;\n",
       "    font-weight: bold;\n",
       "    border:1px solid #bfbfbf;\n",
       "    padding-top:0px;\n",
       "    padding-bottom:0px;\n",
       "    padding-left:3px;\n",
       "    padding-right:3px;\n",
       "    border-radius: 3px;\n",
       "    display: inline-block;\n",
       "    margin-top:5px;\n",
       "}\n",
       ".important::before {\n",
       "    content: \"Important:\";\n",
       "}\n",
       "\n",
       "/* important */\n",
       ".note {\n",
       "    background-color:#ffff80;\n",
       "    color:black;\n",
       "    font-size: 13px;\n",
       "    line-height: 1.4;\n",
       "    font-weight: bold;\n",
       "    border:1px solid #bfbfbf;\n",
       "    padding-top:0px;\n",
       "    padding-bottom:0px;\n",
       "    padding-left:3px;\n",
       "    padding-right:3px;\n",
       "    border-radius: 3px;\n",
       "    display: inline-block;\n",
       "    margin-top:5px;\n",
       "}\n",
       ".note::before {\n",
       "    content: \"Note:\";\n",
       "}\n",
       "\n",
       "\n",
       "/* TODO */\n",
       ".TODO {\n",
       "    background-color:red;\n",
       "    color:black;\n",
       "    font-size:18px;\n",
       "    width:100%;\n",
       "    padding:10px;\n",
       "}\n",
       ".TODO::before {\n",
       "    content: \"TO-DO:\";\n",
       "}\n",
       "\n",
       "\n",
       ".nomenclature {\n",
       "    font-family: \"SF Mono\",\"Segoe UI Mono\",\"Roboto Mono\",\"Ubuntu Mono\",Menlo,Courier,monospace;\n",
       "    background-color:#ffff80;\n",
       "    color:black;\n",
       "    font-size: 15px;\n",
       "    line-height: 1.4;\n",
       "    font-weight: bold;\n",
       "    border:1px solid #bfbfbf;\n",
       "    padding-left:3px;\n",
       "    padding-right:3px;\n",
       "    display: inline-block;\n",
       "    margin-top:3px;\n",
       "}\n",
       "\n",
       "\n",
       "\n",
       ".big_title {\n",
       "    background-color:#cccccc;\n",
       "    color:black;\n",
       "    font-size: 35px;\n",
       "    line-height: 1.4;\n",
       "    font-weight: bold;\n",
       "    border:1px solid #8c8c8c;\n",
       "    padding:20px;\n",
       "    margin-top:5px;\n",
       "    width:100%;\n",
       "}\n",
       "\n",
       "\n",
       ".codeX {\n",
       "    font-family: \"SF Mono\",\"Segoe UI Mono\",\"Roboto Mono\",\"Ubuntu Mono\",Menlo,Courier,monospace;\n",
       "    background-color:#e6e6e6;\n",
       "    color:#0066ff;\n",
       "    font-size: 13px;\n",
       "    line-height: 1.4;\n",
       "    padding:2px;\n",
       "    display: inline-block;\n",
       "}\n",
       "\n",
       "\n",
       "\n",
       "/* checkpoint */\n",
       ".checkpoint {\n",
       "    background-color:yellow;\n",
       "    color:blue;\n",
       "    font-size: 15px;\n",
       "    line-height: 1.4;\n",
       "    font-weight: bold;\n",
       "    border:1px solid #bfbfbf;\n",
       "    padding-top:0px;\n",
       "    padding-bottom:0px;\n",
       "    padding-left:3px;\n",
       "    padding-right:3px;\n",
       "    border-radius: 3px;\n",
       "    display: inline-block;\n",
       "    margin-top:5px;\n",
       "}\n",
       "\n",
       ".codeerror {\n",
       "    background-color:#ffb3b3;\n",
       "    color:red;\n",
       "    font-size: 13px;\n",
       "    line-height: 1.4;\n",
       "    border:1px solid #ffb3b3;\n",
       "    padding-top:7px;\n",
       "    padding-bottom:7px;\n",
       "    padding-left:7px;\n",
       "    padding-right:7px;\n",
       "    border-radius: 3px;\n",
       "    margin:20px;\n",
       "    margin-left:25px;\n",
       "}\n",
       "\n",
       ".lineerror {\n",
       "    background-color:#ffb3b3;\n",
       "    color:red;\n",
       "    font-size: 13px;\n",
       "    line-height: 1.4;\n",
       "    border:1px solid #ffb3b3;\n",
       "    padding-top:0px;\n",
       "    padding-bottom:0px;\n",
       "    padding-left:3px;\n",
       "    padding-right:3px;\n",
       "    border-radius: 3px;\n",
       "    display: inline-block;\n",
       "    margin-top:5px;\n",
       "}\n",
       "\n",
       "\n",
       "/* terminal */\n",
       ".terminal {\n",
       "    background-color:black;\n",
       "    color:white;\n",
       "    font-size: 12px;\n",
       "    line-height: 1.4;\n",
       "    border:1px solid black;\n",
       "    padding-top:0px;\n",
       "    padding-bottom:0px;\n",
       "    padding-left:3px;\n",
       "    padding-right:3px;\n",
       "    border-radius: 3px;\n",
       "    display: inline-block;\n",
       "}\n",
       ".terminal::before {\n",
       "    content: \"Terminal (i.e. Anaconda Promt)\";\n",
       "}\n",
       ".terminalBlock {\n",
       "    font-family: \"SF Mono\",\"Segoe UI Mono\",\"Roboto Mono\",\"Ubuntu Mono\",Menlo,Courier,monospace;\n",
       "    background-color:black;\n",
       "    color:white;\n",
       "    font-size: 14px;\n",
       "    line-height: 1.4;\n",
       "    border:1px solid black;\n",
       "    padding-top:7px;\n",
       "    padding-bottom:7px;\n",
       "    padding-left:7px;\n",
       "    padding-right:7px;\n",
       "    border-radius: 3px;\n",
       "    margin:20px;\n",
       "    margin-left:25px;\n",
       "}\n",
       "\n",
       ".fakeheader {\n",
       "    font-size: 20px;\n",
       "    font-weight:bold;\n",
       "    background-color:#ccebff;\n",
       "    border:1px solid #b3e0ff;\n",
       "    padding:2px;\n",
       "    margin-top:30px;\n",
       "    margin-bottom:15px;\n",
       "}\n",
       "\n",
       "\n",
       "/* Headers */\n",
       "h1 {\n",
       "    background-color:#99d6ff;\n",
       "    border:1px solid #007acc;\n",
       "    padding:8px;\n",
       "}\n",
       "\n",
       "h2 {\n",
       "    background-color:#ccebff;\n",
       "    border:1px solid #b3e0ff;\n",
       "    padding:2px;\n",
       "}\n",
       "\n",
       "h3 {\n",
       "    background-color:#e6f5ff;\n",
       "    padding:1px;\n",
       "}\n",
       "\n",
       "</style>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<style>.Q::before {counter-increment: question_num;\n",
       "    content: 'QUESTION ' counter(question_num) ': '; white-space: pre; }.T::before {counter-increment: task_num;\n",
       "    content: 'Task ' counter(task_num) ': ';</style>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "cssFile <- '../css/custom.css'\n",
    "IRdisplay::display_html(readChar(cssFile, file.info(cssFile)$size))\n",
    "\n",
    "IRdisplay::display_html(\"<style>.Q::before {counter-increment: question_num;\n",
    "    content: 'QUESTION ' counter(question_num) ': '; white-space: pre; }.T::before {counter-increment: task_num;\n",
    "    content: 'Task ' counter(task_num) ': ';</style>\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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