teach/Lab-2.html at master · kendavidn/teach · GitHub

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<title>Which college graduates make the most money? Introduction to data manipulation and plotting with R</title>

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JtTNAj6jJKAAZEs8WgqihJRgX9wJHOkYoXkf8iwR2RiKKqRRiitWw3lYdnr30cDzNae/8Tw/1L3sS5gFALINXpKDQgmp1pQxW86M3O8aoqMTlNtTGnSjATM2tjXEgCYfS3hKyuCkFHkzBeScI6WKhFVxLuD+EQLt4TkOo6CU5f1drrhvrrVly/dspDayfe+8EtQx7fuJG0HcbZLyyc1r+5qXbojtE1xa0dt4x/5c31r9hA6MYtP5DrVgijoiV5Po6KKs3MBOCVStFlgez8bG57v8/vq4tZ/Gilfr8pX7VqJm1EzJQGeg3j5/xX8ruWMbrG4oduFyXxMEFyQlkpkMeJTvhKbCMY1j/o2ykPlEmSr335KxvYPvbZydev29P65KNrX58+c92zfxv6+Kil76PnU1Sl6fe+l694//zIweMjUO1ZPnH2TU3fxqa09+l/6OHXAQgEAaSZuhddMDiaZ1epkRAzpTKAxyVzrnGh7JLreGi7qF1VqO5WvoGQ0DwF584uo3cpz4sCBzc9T9SAQPKgoqI082X2QfxhshCzXmZ5Jmoo6MvOYAk7gCWH6cudN5+98oSroZZNBoRWbuEw1ygDmqI9OZ36aJrbbTPYqIFmZrldRpdFA27ONADF4/HXxjyKYhkRU9LgYsIJ6e+pgHAkGUjkgUhLSBg2N9w3IMwpylMaKScT/n6efcC+PLN8xActmMGOhu+4bH6EpsV/yAgOoO0n9/+HnR2B5h7hr455LAPJ1+wc+1i1AYGhXOs6eQf4IR+uigYUp8WSlweZTnAWFNpz6mJ2u4d60kbEPGnUwENEvUTbVJbqTCjIAQJlPo8IXEUNdQEJcCAhMvd/gvy8Q3E6TmsbErv++Z2tRuuN/7f1X+zsNyv/vYhoN066sbVlcRuZiq/iWvuP7rEb/7LuhyPfsFPLMffdxfMnz7+1fu5qEc0RPdM6QIHLo14FgCDKRFYNMiWU1MaoAsLfupYpQwobhpDby4OfkoJ4iZQWPyy9jNLm8wLSdEtUyzvBB3lwOVwbLXYqnl6U+o3+Qo/Hnp1ttBtL+ihOZyBQXGwBS0Z9zJIGwfoYXGwTYYlLnVeWdKFwoCSqAj0/LqoW8qk7kShFiku3kK9cfCPVHyDedt/qpeyLL06zk4uXtU1DyfXfE2fPmrng0Ccjbhg+flxtq7zz3ZUzXhrU/O6sjqN73mrbXD2iY/Kzm89vbBp7Y/3VcwaOI3vqq674XdnlYysH1Ym8GajvcgekQQFURnOzZJfFEgyCCwqLtNy6mKZRrzd9RMyrUkMdR+Nfdbfu7DIBzCIaw0J5kS16edcXuNOdBXwbyU1J1ewxtvTOqxtHP/3+JIOl3xOz3v0nmr9Y+f2d8VNjp4xrbbm7jQ5mdazJdtYzasufW2r+83/H0fEE+3DTXbdNum1+Hfd4stOSZuvMURh1OXnyAPjtnsaYXeumMPAnaOwXTOb4NVYT72PqU+xG7xcf6mPNQAQX6/IUcHKmcllV1UUlBRXFZdIaYyZNUjgzJ6Rpm8u6mKrApzM0vUgYbrTrbF2SFHbS18Xa5GhSmF5P7JYqZODSiqKajIK/VYNEqQIEZRigFxShVFwJURhGD6JU0ZlDP443kvW7ccNSPH2abWFfCns140peoYDeNeZHHSqlRgkMcp00ViJSV30QKhkjagSue7JMQH4304/FkrTgKC9Tjh69VLueUScBrhFPNVAUJJTKEur6Ce0u1dCFuorNZH28UayJb2IaDjjNtKWsWmioXPicrpB365FYFc3LTU9PA+B2dlqdhUV2QCMFCAazGmNBl900ImaXkg7mVCR4KJVkyfpRJFR5F86oRckaXOFoe0m/7W6YevPVY5uWvzf1w3P7vm99YGyIHU4139VjH6ob1tLvqqpxR9u2r5m2onVI9RVXsHUX9eMTLkxQdnCc6AuVEIv2VCsq3G5XOGzt77rMZaWBtEDvNOgN0au8hkhEMg3QTPzqkVUq5feAklS7rOucMleiPU7ivc6kQtuiYCqrfNTdlVF8fxLxCKgtj3iUQC44+jrzOa06UfyDSESH3x2j106vnpWmTXnhlT1o+UfT/qt9NdGau79/Zhf73+exCP2T2Pz/ZefZXez6I/gIyv/EkRs7Yf3IFpM1FG27n5x++NQ9Q/otPPTGQSQBH/Pd/9Yf/vjjne1sx152gh0p6f3eKHwYW3/EZZ93sA627uCCpcfMzwj7AIC8WN4IKljh6miAWKkBQZHNZgqip6CSZLOSmpjVSs0yBZocIpTouZRiZWGortKL8gsDiITjI5Uik+LHJ7FXiYTziRJnywoMgWdwNFstbzxXRcbikdvy72CqiPvXAaQznI/t4Idczsm9VLdbktKzzeY83vfZ7QGDlqalDY9ZNLRSTbODPb0mZneCvyYG9BLcSxY9KQVDSTe5ArmSp7voCQYwWfE4HPqnwOu4AyOYNn/C/fPZh2fjx7C84/aZ8xev2nXHraxT3vDKpkVrHaacdQ++/xGdXTuy8Zr4NrZo3PgNgDCXI/UBnh9eKI36VZeLN+NWnxscUBNzSKpskmtiJleyNBOvSfVEKuQRD2+0Iw4l2BUdoTI+ZiikBS+9h9OfOtrxL7aJvdiOkQOHDrc2tEs72U/HmW846xyGi3DSZ3j9azd1FvUDImwoz+E2NIBd1OtGAIdVkjTZUhOTqWTlLbMzaamUcEELnGVzAbVA0BHKleew8ew2Ng534wR8gL3Dxq5ZjO/xGuQP7A55A7ubrcHDnUMBdY8RLs0Mg6L5BgnAqphMiBbFWBOzKNxLAnII3zehaKqJofOXXkp5iCsitPAkbol0bqDV8RN4ijmIm4tl7zK2BLqkUsalGqFvNN1AqVkBQDQJoSl5QlZS0MVSLhaCX7P9dHD8OHKMEwKWxLu8KBdxL6ZDTbQo3e8nNquVEFemy2DIsGlmjQdbOr9BNkt+r+zlsmTu1FB3wd0z5VlnstgW8BBwKLpv9YJL5RlPdMKNOALkU1L14E93sr+yVfg43vTxgZtW/GXnd1vevKGVHafhuOnyAlyMU3AcPjDybB377rOT591Y2mUHeYJu/Ug004jIzW+QJFm2GGhNrMaABoNsUijK3QmbMnfKFN2XPIHtjr/NdmE5uRrDZG78Xj5t2EIGAOCFiawBT+ozgRw+bSAGXiPLwM0MRsr79e4NCw4Rxa5IJL6kRnJurq0bOKEZy79hDV4k7gVL5JHn1l4AdgYS+tfxVS0wMJpjIcRkNiOAzUBl2cq/UrNZoXwP3VtwpgBXF1eWAOXEQAdVfSMRDKBcx1awhYvEZm7FB7CZETKxJf4D39CN6/Hf8XkJ6VIlly6LPUkqBVCQArccJKJUl6GXoPq6r3PD1MsbzldfSPxvRcyR3dAvmukGo9nI1bbxUPHKisdJjEQxq9QGilBcN36X0mUp6hA6Y9DpEYujXuXykscVRBpkK4wudhzbcaSC07GdfUgtRrZEms9Wzok3cw1WSi3nqklH6R3oPr8kYcedOm6WR9NMYETFagVwUFlRVM1MVW5RVLtHv11adI/EnAKwL1KEcM/JO9nv43fpSiwh81U7+qQGdrQtXseFv4FZvycdQPQ8+VKfDHgE0jgAfBZF8RpdNTGjRO01Mer6daQROSBexQQy16Hxpkj+kj3BXubXE3gz1vNr/PlDb76Bs9nSNzaSY+xxdivejVP5tZCj0mP/OYvf4smfoAvtpHU62rkEFkhGowdsNrvdbQXBV3ZNM9TENGr/TSzoRn/ZLXHoEyAo4ckJSx+au+BBspEdYacX8yA6iCb0UGXmlKkTd504Fz8rb/gchAXYat0CdkjjEZynUFmSCDVIJg9AhmYypVOVEwBXRFK5UWSV22N7Ev4uHU92T9OQe+LX7PPaKziWzWZnfL9pJMZW1bO5OPS3LSUP1S3lg9poocvnk0ySppm8njQw8cTzu4wWMA6PAZgtFm40C/WaRcikzJbSWfPzuXKqQ0sxKLdfgl3BF0A82brsgaXLW7gB12EPzH7oTqxuZWvZKtp73M0Tm+Pz4vvlDUeOLdxZwVwPk1KRVS2cQX0ce4s4n+RlpKcHICC7LeCGy4rdAbAELNlGX3ZNzCdRYyq+uhvwVHHWrRpn+IvGGoVFl/MhDadWMcJP9LZen9cr+din7JuOx/ZeN2FqnzFL7767DtWvZu2f2TrnyermlsJrn977BC7f/lkz5g4srx3e8+orqypveeqmzf8qL/13n8KGgcUDKqrHbRP6FwNIYiqrimdLCgBFNBhVKlHOuxSdv3y2lARgcoLtYrOlOn53IGEMEF7k+dXC13JCQdThQHSbDQaX08hRhsdSYuuXVBAOtyLx4BHI6+6CYLnlEXbyLfYFex/D9zz7BAf0ztqVZ+7EwHn6YufCPz33/DraBqjXfyHBI2K+RonRKAOiVZYkC3BDJ+q9VNpUJOaj+sXtVx6h57CC2dmLTMMKdPlKFXO0a4DY+dTwvZeN/qJLhrqRy8gSsx+T0e52yQh+v2ynlszMrKwci9mcnemSzdRvt6NJiOSi+EtCbgo1UyM3WkiKOMKJUtMlGvCIi78nPihD2fPbzWFJ6WPdxqngfix9q9Sr9HQdwoJDth5mUy/nm1hKoRixV/mpUJxwVT85trLi1EAa6twb+aS+9uuhNBsStmnSbVMVzTXLnPpUo6oYTYpJ0C2VLGYDkWXJqFCUkhDL9evG+ooUZ3VpjZj8Izex59h6fnXg56wfNmF/DGMtC5Pi+GHyHdka/47Y4j27dJCYyF2B7wZVlZEQEERvNFFF4QqiSgVDdslOjEH5Z65AarLLowIDZAGWchEZbA/LwDo6mozsXBTfQUqoXleVJiZ0RugfzTJISFUVEExmlYuSRP1I0IAGUcZdOgxNpl1qFqqPbALSzPPvkbfjTVJ6vIrs30m/RXi/0ykkLWUbyWw9T7KjVgXRIIFRJlTBfN2EuvH0BNZX4iUpmc0y8bOPPmIblXMHz60Xa1gA6MDkVFt/ZIKYnGpfnBa6sUmAHY9/mJhqI4S4fJ+QL55xoKIY+VYNoOZTiaaCvQtCfCFHMMy1CH34IX7GMmfKjQd/UoR8AzFIA+R3QIHeUTdBWVYkSTznFd6SVJko0DW+xLKLeyTRZYcwiGjADQ/jqVO8uP6KGOiGzmqyKN4maq1OtpHWXhja9SRIRonoRhEaJZ5K0NrOFyl//vMAAGKNdIQ+qATAwK1gBjVKRVTIdwCUpB/rioP0XWLww7EvHPD6PGRL5ZkqbKpcLx3ptW2gZ/z7GYIdmjju9pfm6E8Zq6OFTovBQvLy/P78LIMhaEkbFrNYZLfbPjjm5jWdnDM4JnvBk0Az/y+ZVYSeXlcUJWdMvMcN9+1u8h0omny9N6YT+huGr1r0xzd+Or/5xbv/On7T8Y9PswO/X3znY5MWPHHDsNfXvfono1K6rn7f+K3vx32E27h55MJbxwOBFVznDsUNTsjh7BvIojRg1Mw2n89szrWA2WPUFFDSh8QUL7iGxEC7mCz83SHi7H5mUeZ0aISzRVANCgTlw1AfH9d2D8WobftHX+7YNsMT+hpLLZbJM2ZOJJNvaZk+Q5rNdrPv2XH2t6XzFTdbPuiJ9jP3rwh0PPOXNWvWAMLoCyfoMWk2eDi6esRYymclxCubh8RkDexcM++lZZJuOTk32SdwmnJoYkjgUBQyIf4DZqJx81Mjh9525cmTzcuHVf/BTQZgFvauOZFVwBH49ZIydr4kH4iQK81M2CcaDRi9Gi+obTZhqFy7xwIOIyi6fTTdPt5ft4+oT4Q+ecShOXlPGioU/BLkji3iOnVPiAnZ9vHnOw9ON/mw7Jv+1omT5kyVp7dNmDnLjWVoRx7zq9vG4YSfTjyy5vt7ViWNk9BynD61y+DMEKROSUpzOLKcJlOm3+OkzuoYFVUUVMesmuoZHFNTel5aloiry3bI3RbgrbNeR4XKwOMJ6AVAxMMtOP2GaQZcT2aVs+/Y3zDt7LdoiJfID985vmNc3Qb61PyZM+d3NmAPdGAahth3Jx+789Eel5+4rCjB7nSOkgMeuCKa7SZElSn1+qwAPhndyHVz283akJgZqJ4bgp8v7QVDiRwWFgxH9KfOeieocBWpiZ1l+9eu3bj/ufm1o2uv6ocGOq9zCZ23rKHh3ZdLPsoafsVgoKAwtzSV26sYyiEKd0SrzFlZAwZIfRwOUqzmSkGUpIHpPXr4fJFg8Kp0K1jRqlj7qv2GxYy5Eke5wr7FpDpWXFxYWDksVqi5e1fH3BkXz+n4pxIOWz79gRHv0LneqJs2FQ76ewKfPao+pSsqEvmsj+ykQFfCF6ZeRcGFyUQK8v26El/4WGzqS33OfxjpXbL2ndc3sTfYvm9+vP3WksHVg5tvOnmsZKGTFc2buvrNabOfa5w5/drrmura10otT/ceNqZjJ5Xzew187smt/1i1bPw9We5Roeh1xYVrZ732vkM6L1UOHVlb2WcEHT5q0qRRuwBhBYC0lmeDB8LRdATw2Y0Wg8Fo9Nolp1MaEnNqJkCjR6D/JfU5336yUOPaKqJJEuCQeFQirWX7O+6YxfZjqapqE/61bQ958LsXt8S/40CwpeDekav/vh0ILAPAD7lsA1jEZFcyGsFksprtJg9Rr4kR6DJ/ZWoO7uobKtNnnyJUlrW3X3ttO14phMgLHn98yIjzPqkFgFxoY259XSt4oSTqd/L0JgaDT/NcE9PAaBctOk/sjOTEKYEwCRGJxwB6tajQpMDBcxoHXzN8CJbum6GLZe60066mRmnd+eJXN6mThXRIWPMH/Un+NdGgxLmTUKrIsmYzWa0Gg8lkN4P41WCzUcXkofbu2oTf3cjSZdpuokXRuGOyi1dx22KswGZWhYd5AffOIrF9jYxdh40sI74Et93MVivueDXr0gYPcG0ouF4DRIkAevQioLvExgPivyvuhO7qQJ5BQRgeLXS7XPrsKDMzI6PAajSaTPkuq9WRKzu46XwOzWzPRJNH7+G7krl7+OC8ePqbjJDCRIiEfKFykdziVfBd8q+ke9n++uvnTGL7vy529F437Xwso/dL097ZwvbVXz9jOnlw3rz12+LfSS1Lh1+/urZpy+F4kfhtxYuQjGCut1tMFxHAq6vrscoOoatQFU0Xx29SyV/XLRG8TS0ierkyof+ZtWWXEPbn7boC9dce3JHE5yf0pzhpostXLJYMcLnSvcYhMa9mp0Nidu8vu/xUrvPeVQMOCCQs6MzrxGVT5986ecr8W6dQmX3ELvzxh7swGyl/I6Xt6/70Qnv7mhfYKbbnQTS8jE7s8wA7B4LrOep1cC1ckMMn1Hl+RVFNlKpZmqrlcuQEq9U9hBOEwa5mQEaKzBKmSBWoSQVlTvPepDFCnPndRKFJtuemosq2GZrG9p/taZv8wfaPbt58TGf7vePdSx/wsv5K9SPtbB87/T/s7H10mU722JDgM67pTN1euaIq8dIsyh+TpOUZ+fg6PcNnz/ZanE5V4I0FhsQsv8m6iSfIBUmS5S2dL8HBXl8ook+LIkFBaLdMkafPPzxZ2v7R5zsmPXeFIQMJ22e1lq48uri9oOMZ9uLa9lNYiho3Z9+6xqU/bcBDAybXN3ZFFJ3LddVEh0mcejw5BCxZZVnUS7wGFxqlMrTMRy+JIqpdWewrCD+6iu3/sre97yvSbCP7xLR8SXyH1LKxZTYkqp/1XIZ4dpmjpLktAEU5bnchWNw5lhxTli9rcMynUdPgGPX+vJ2/2BgiqPTHK2HB5clePsGgXCkPt082oetPnbx1/bDrDtW395oycuG8yJd/3/Xu6MZHa5Zcv2zRrf2wZn1HILfzsvKx+b0rCstHz73+8VXN/8y//JriK/qHR/+30LeE6xuRa8AjToRYDHa7y2UyEIfB4fWZnHbn4JjVYrfL3HVyQt3QpktOVnRhgnBcxKOXvoLpIyFPwCO6cjK3bsas9tdeeHRt8xasYDuu+TD4aeiNN0jGwgknTn4e//yqK4UOT/Gc4zM+cENZ1E8cDrfby3t/j9NoJ7JNtumyPcmJ1sVDgItr7tQYgH+grxdrpR2zt72PpSLjsXRp7XUHt5Mj8dki4Ynt/EpI9JkPcrlm6BV1m0GWiYgIK0G0GNEuC5llKWndDU1X/x0SbTfiOtaElf/INyryZYexkjVJLfFF86aMXUzaumS4AZRtXEaWOMsoSyaOIVng81ETVTMyMjNzVEXJ9plMVLbbMxQ7yDqidR3RdPz2LIDSIO1WQ8wBsin/pGskRZpuUfew19lm7LMwJ1eRcrT7sG6R5NCsqBgvN92NPdk7uARPdt4vtTDH4m9q1lxH/PGvvE03jMkcer4XnuKKI5gApOW6bWqi+YoMaKSUSAQlGWWzQVWtfIZmMSoUAA1mj4T2S2cBqaROkYZeq3KlhdkClOu/mD2BI48cxZHsMWxja46fYO2kPwmyZ7A1fiy+DRewhcJLzK17ycs1KTC73ZrXK0koahm/Jgob/pNT8no0p9XJMTHDAFyVskQJkKKvhBlTUzxHyokifvTqgNsSaw9mmBRz7n4cwoqu+vcfR9RErqqfl+fkfr2/YcZNo8ic866XXnR8Z72xNZI450HXce2MIn+oKqkIYDYgmvQhAm8c7YR/MwyOoefSIULSSMJGySlCWEwR6LrOB4nC0uhAZiCmDrLp6+3xekDI4T38Id7D54ipCHUbcnIcfn+uNTMzIFGXy8qjKd9qSbTzYosp2hbbF7bnuBrm+REWRw08Coc18VTQ4xFQ6+EJhDmL2m6/c/OZG4cpn31T3XpmM9quH32qucGAVz7Z9jEdXMUObcyzBF8xskNVg+knbU8BIO5gJWSlYgMK7tcIpZJMAaCyhONDYlbqCOKOo0cV29lA1ylOauB7yBN7yOHlOmgGQ75bkoI52TabW3Z7qCzl/3/2IIuHzuFynuSi2BZnlftyiBSnzxyCyzwcrImh4e0Xbhz2+9mfKtWtL7xTP39x26LeM2aFPyFVQ7CnuWmyw5K3EXsOrqIfh2dPY5tNjY2nGm7QTxGQIqmCtoEHIlG/Ag4zmKnd7qNeu82mSJSaHQ5QoCRU1lYi9ElBdqqp5pwa1sv/RAMmELwQB0baym968pqFwxaOC99ePv7pgf89chFZcXX5l1NzcyPRii+nphf8lzhBwpbiQanl0rP6Dg26zurbad4v56mukCugE0Wi7Vh7JsTasSV5lIO0dJbKBcljHAhLOdJqfN6cwad7QYchPV3OyCA+n4mYMrPSXCNiBtuIGMiGNH4pGWmKygXqpwH4S8+ePzvOII575nOCTh4R15lS69q26gmSEBt94OCr7YtF6z7vlm8b7mpdcN+rL/fHcyhjZk77c8arjmflv/Bn9kZObzbAuFFEB4A0ST+d2BztZXeaidFqTfd6iV/zO51ado7Fn+avjxnT0sDFqcleG3P6QR7xs+NNXUfUIJTSVqjbjT+pBpRfbpXXFSKawsFwiBuQbNyyZcyzs2sbcS679w9k3/mvbhr+6qufy7sbvojGrt10dOm6WtZ5ttes1keObtl5BAjMBCYFpHXcnkW8R87TLC6j7EsnBrDZ8jIhM/OyYp9LSycWo2xQPZ4ctYBHz/YyHc11H2qb9S+iA4oURXyC3SM+0WGqPrVIoJJaFCmMXFRdbixfuGzBqEk3j1qwfGE43Pbogt+Nn93Y9siC8v1T6+qnzxxRO50cnPC7BcsWhCMLly6MTZs8uu2RtlBo/iNtYyYOnz6ttm7aDBHpCoDEp+PghZnR/7I53U6Plce2UaYyMYkJqxeRED/HBp/idDkbYkCRuuwmm93WEFPtdgt6FMsl5xX9mtiW3kNfypcpEhAfkgPKkCfoEXdAGF7cGCBD0YAVbOGWH374gX38448/vsOW4BViZBv3vHrfq8eO8RdyHMhFiKNCMGoniiKGmUaJSlTVsUcEbCpFdAhyJGBIAFHnAbag8wAAgUm89lnw/0o5D7g2jvTvPzOzu9KCJNSFaAKEBMYHAokSuQpiY04OODjYsWxCcjbkNaluuPdyiXuaS0jHpPfeE0N68fVO/ObSe+8uy39mVlqEzr76oeyi+bG7U3bK83yfkUZBGZwCMyKlaRaXRRTLC6E4JyfkAld4DKmpsbkrK0ttpSafxzc15nHqTVNjepQycUvmivi5NiuyMYtA0qyNo3NOVr9OFfZJmt75WUW7VMhOWtE4fsubj9zRP33SzuaW6LxFB3rWTJj4xSuvXdHyYsOAb/bpj257c+OS5s4tvmrim7appHXPputbn8kPlVdURssit194/xklXdGr7p3261Hh7uKKUGH0uu2nzi8Pxya1V5qmAUYu4UfygiRwVi0/YrQaWIvIdGcQ4pBB7dzU9snCdpLZJF/SOXJNjdRPPa0uMhVd2TKurqk5Mq5FXFPXEB0/7ucNExvqGieOb6wDIIw7lSbR99oBPqhmvm9ikm0mm7/c7yzPc+bV1IrpYEmnX1mlhbZglpActKMVbEo36zBrHWyifBGnSASrw44ZvIhr6bwgFCxiuH4R45HIul+c91p4c3j55tf/fvilPddGFx5b8zJqf5X9DCi9v/m10vvcrj6U09uHsg/0Ke/29invHSBfX7VJ+TAv99nwkcNvfNd82xjlI/4/Su+rLyi3/ObXaPaLTJb0b6xlBfCX+DHKMLqgAOoieZk65HLlmXXU56PLK/RmGI2e9HQbys4GEGweShSEA0F1mAtak3BQbR1SPGxVVo3K6irbp3YM1ToJV3pGr452r7n58XnrWi6tr79h3tY9yqTy/KbYvMvxsYvGRLrPu/BCWegef0l+cNcmpeGP/qIz6oqkNPas06Fd6BEEkMAIbZHRaUaDTKd2RMKCgERqGDdkGNkrBpBGCE4XBIMoIpOMsR4lWko4kLBqJI+K5j8Faab66Q897w8yR4ALIR3yqYfpaPGg8hFyDSo70RG06A12/oayC49HL1E/s9K3DL2QNXzKGb8fhTCZCCJkRZgzSkcQkogAAdYJoQTf6LXQWZQQHjx2hLz1I7pgEIaGErEHWAIzAAhaezTEW+S5kUqBYFHUgcViJEbamxB9uT/ROLFE8QLBIegdsp5+naSN8spKbara53ErgY4FlFnoIwadmhP5X7VaYcvuz5QHAu8h/cO3K+s89eFTJuceP+dft9utd0xUFqDpyj3kqh3K1+H6uhrlzX/ZctHQEckuSNLhJG8MjPTGCNLRbwWDZH+Fr/6Jm7D5hAmyIDMiQ0ZGTrbVkMkqRQ3FUq17vL06HSowmDyctbXd2N5201ln3XjW5a88G6uvnz2nLjJHWMg+7W0766bZL10emd02YWJ7G+NFAYSwiCGdcx+ZGTqdRB35BoSomd9sMRrSZYQkAYOKeoYC8S5MM5WnxriwyfZwnAs9I2/h3kG0RVlFY12UNylYiiCAo/gZTriVRKwOA5LAgiyuTNnkwQ4Hyucer4lJXb96j39EPHUF+JnjK/5+briipGXeqiuf3np9+4YudA6O3jbYEQv6S2bt37Cle8be7rMBwVgcxo+Ir4APJkRy7enY7QbIl/LTzVK65C8mdrvDIed4PSa5IIE5pbQ8dlABTRX6S6xu1DgHrezj3QjuuaN9/n1P7N541ards5oXtJ3REgwFWsOdE/b9v3W9wlu7a432i6at2N7wzOzzq6tvrAr76ePuDExYn+qLI0JEDyCnCdwXdyjui3uFjR/VNMjMIUk6ao6YiGZWHZ0i/DX75U5H1aEgAOK2LmrkhkxmMUmXJFnOsjrBQR/drXNlOGl7yiCq4Y2Z+zTTkbYwT8qwtv73xo0CxS6XhZtDZ7WvpVaAD0ZnlC6fNWF+vigy+yj67YoVdz/PrAF7Z8wo/9mM65SDUhQQLFSOCbslO2RAIOJINwsiAoTMFr0emUykKWYSWc8XiHtk4gMlbe5qgAb7UsMIa0IFwu6bbumd0PqX1/72IW5Tjkmn/3QfCVmPHEWCwiKd8Cj0e7KGEUURmUU6Ebk1RiCQCHSypSLhfEr/+2Eqe2hQsaNeALBCVcRlNjI7Fh1Y7Gaz0W60ySYW9pXNXt9QQI0EXB1/3PjAIiZPQYprQ3RWgnr3Xd88KXuOu/GW5v7s6Kwj6xc5btOZJpzh7hmf2cktXDiKGxPRSYI8MjopD+WfMDoJeePRSb4QbvyciNkVzReismdxFD2z4Oyi0vHr6MwOwnTUfEt8ic9KPBFjIvYqgzhkDw/xTGK3kxc9YlKPgt969IarH3/wwP4nFG9dY+PEiY2NdULbnf0v3Hr7wAu3dHR2dnTMm5cy6s2OlKZTy49OL2AW1Ib01FNiGh70BD7YIdHEB79/Oej1B9UBL+6NL0aoFonqQehRdg4ip/LxIFqsSMPn2KuMXYbaUNsyJZw1fMrGrnIA6Qpa2n5Y+TuAYvg1fgUA6eAP5Nrjj4L8IMFW+uJUVye0D51Au5h8T7W6B7CZSZlyNlXeJ75ClUs8XEnM8as+Eb9qmXpVwDBeWUH+LLTzNU5DpKiQug4YJk0jh0pMoyDbnI1lQp0JPk9rzJdhoRy8xZvKwaN4g9Cm5HHsnddbrUub3bCVWHLF4ldiF1wYPjM27aFzzp37w3lvHP3F7rOrUcnw6jY6d1dT86yJ4eiY0sOnTO6//YLru+j0cyyamXhHhoZU2lu3GPuhiOexHiQ0HfQPYqfoh9HVJ1B0w2//heIgzFQV2SMV52iKgYTCOlIxU1N0cUXaQwR7uWRYkxbXSNDfPYvXhpfEa4MpdD7OPtrg4sg4yUbMNmIRLCjNZEJsvgbgEETRbiYUvqb4syENGQkj/JFkkzkxTAQrMmlscsKiQLvUAAeUNb8G7yQ062PCs0QKkEYsI9rR6nzH9imOvcoLeLew9/ghbKIUT+hoLlq5jiPvcYqZDnXNrC6WKXZGjNP8+VlGYAXOBfY556p5+ZaodTT0KC89ZE+UXqqiG9pSFPdShT1JcXDoO1XhHnmNmZqia+gnXgMYFag1wGbucZ7cAJnQGCmivUCW3ep0GlBamtthAIqVWwGovcRJi9eKLYy8TgmP0+BgddahWmkscQqUlpiPo4MhBwPPA1tV5FzFz7cKwm9+d+CzzzahATIdd1Du/G5GoOPWnR9+ofQoyl1qHsRXeDuriLez36eUA+dUeTlUxtt7N1fgvJMpulHDv1AchOdUhXek4hxNMZBQZI1UzNQUXVzB2vvoeGkj2IAMglnogXTIjaRLBGTZYORGZXcgqMUn8260FqnLBlSM7lL+uB+Vocqr6Rhetkf5tfL7vfj3qKxH+SMavZf++VuaSiUAhD7DLeIHkgA2yIZCCEdyXJ4cuz0tB9LAW+TMK3Ab3QxXJQWpdOWImbyK8arGGFaJqpEG2V2IO/yqihEFV1Wm94Xts3tnv8iA1RevaL1x1sDRP56CjrR2UWL1/ZBiOG0+WqzyvXWXXHDpANrEwNWGNfM3DSi/fHYJ/rbsp+8e6j5uKR4aUmlIXgO18Vocrdaz1uOkKrqR6V8oDkKPqsgfqZipKbq4gr0RJcl9kqDwq4yNv3kb1KtYuCSJSmbrqZpIDiOjjbIoSpJTMDbFZEdTTJAFWdIRyZowKGrdjOZBjePIDroW0tZGwh2UUz1yNcPaH1CQ4fikjst3rbt0NcHv/agMUij5c2Vc18rz5/NZJM3JfMkD1dAaGU3tegXFxQDlWSZTbXkgUGPKKtBBcbEui2SWhkqnxEIQcFgyozFLwnGq7ZUx0g03TH/aTYLqcnOkuuX8iaFL8zhXsVAn4a3SSDRSWl1/RVfoo3fmXTau+ubIbfnTo2vnNjQ0TVjXsWQjbb4+hL9FfuGvkV+cNqai1JldVTJn7srmu+7JLfy6KLhqVGhcaeOylsh5lbWnl49r6TrnKPVMv/LO/azH5ASbVEBr5VQ+UtQfAPb2jbbEazY1vfvCE6Xna+kHfxhi6RUj001a+kAasPTikemClt4lAX+3T+GCYcUDmqJ/lKrwqwogTCEpQjeUQBBOgS2RydU1JDM/P2g3GoNBuabG7/GMKZPlsC/fW50fjVVXsyDp7OxQNJZtNo6aSoF3p+S0NFDHPHgbYiBJgQZGv/ERLZmZ0t5q6wkJKnqMhzBz8MufZG0ZXsZRzHYYrWJk1TDShwoZfiVWbn2rce4L19/03NdfPRtr2nHzvKc/emdx/d3LDyM4XkaJq+cfm/bY8bqFq1fv6FyOvX+1oHvwefbOru7Y0zcz5q91cn3Tq52bInXKZx9RCGvWp8UlOEsQzpxD6T/05acLVrNap952xtZhP0xWx0+0iY+fnCrjtT1FbQ2389oqStRWanr34n+eflDP00eNTBe09C6rWpeVidoeugYAvcGv8LTaXynTgF0DGRLXuBwA/y5J0T00eaRi6JdU8UmS4qDyuqqwJBTvUMXlkqApuriC9Vdu9UkSBIfk5fPVpZGx4MYuV46oJ+kEY0tOTnr6qEKLpcQNmZh+SJ2ImdjppB56CnnSKS02+RpiJifBU2MEnYC8izsQ2clwI9I+1YYLf3Gtkw8SVgdtm4XAwyNdtX46hDAvXCL2GCmnN3ZetuitjjuuvUr5/0PfKX9DwuFDDfpT17zfga0rz19x8fIFq84TXdXF99Wdtr1n/m5lz4fKh8pLyPrJR8gyV+hdtuva4/Mv2Lj1ih27+lg74MwMf2tPV9/aEPAZUHI97ucl3KK2k5t4PReeOJ319ZfAyRW8pRiS+gUt3aSlD6jpeSPTBS29y6C2pIDWK8yCw0JYeIl7wbKhNGJ1pqWZBQEIyYUcNwVKAXHz0vPBYdBQiw8WTxJRTWOGj2+K1tf/PFpXNzVaf2ojO+KOwcEvTpva/POG6c1EmNrUMqWhpRkIfcaHKAN0OZ81eEfOGnzxWQOjb0jBFAZx/C+zhmCNsJ9hQWsvOLVn0n5GBm1eUrt/zK5jR21o/OiJKy9AhwzKa/6alefjSoYJlXV2dVyL7IwUqpp+Qes1ytH2RjTouvnWlnFKMOP2oSGVpeD1c2ZST4ByefGmpvMavgVOruA1XMnTC0emC1p6V0B9A0u1np977P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<span class="author_name"><em>Which college graduates make the most money?</em> Introduction to data manipulation and plotting with R</span>
<span class="author_bio mbm">R Crash Course for Ceci</span>
<span class="author_bio mbm">Lesson 2</span>
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<li><a href="#introducing-the-data"><span class="toc-section-number">1</span> Introducing the data</a></li>
<li><a href="#cleaning-the-data"><span class="toc-section-number">2</span> Cleaning the data</a>
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<li><a href="#summary-functions"><span class="toc-section-number">2.1</span> Summary functions</a></li>
<li><a href="#deleting-columns"><span class="toc-section-number">2.2</span> Deleting columns</a></li>
<li><a href="#reordering-columns-intro-to-subsetting"><span class="toc-section-number">2.3</span> Reordering columns (Intro to subsetting)</a></li>
<li><a href="#separating-columns"><span class="toc-section-number">2.4</span> Separating columns</a></li>
<li><a href="#processing-text"><span class="toc-section-number">2.5</span> Processing Text</a></li>
<li><a href="#converting-variable-types"><span class="toc-section-number">2.6</span> Converting Variable Types</a></li>
<li><a href="#logical-subsetting"><span class="toc-section-number">2.7</span> Logical subsetting</a></li>
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<li><a href="#plotting"><span class="toc-section-number">3</span> Plotting</a></li>
<li><a href="#coefficient-of-determination"><span class="toc-section-number">4</span> Coefficient of determination</a></li>
<li><a href="#rinse-and-repeat"><span class="toc-section-number">5</span> Rinse and Repeat</a></li>
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<h1 number="1"><span class="header-section-number">1</span> Introducing the data</h1>
<p>Our primary research question is this: why do graduates of some schools earn more than graduates of others? We consider graduates of the top liberal arts colleges (LACs) and universities in the US, according to US News &amp; World Report rankings. The sample consists of 48 schools–25 universities and 23 LACs.</p>
<p>We will focus on the universities in our exercise. For homework, you’ll work with the LACs.</p>
<p>We’ll use graphs to visually test three hypotheses:</p>
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<li>Graduates of smaller schools earn more.</li>
<li>Graduates of higher-ranked schools earn more.</li>
<li>Graduates of STEM-focused schools earn more.</li>
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<p>We will create bubble plots, with the explanatory variable on the x axis and median salaries on the y axis. The size of the bubbles will correspond to the size of each school, and the colors will correspond to the type of school. Here is the aesthetic we will be aiming for:</p>
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" style="width:70.0%" alt />
<p class="caption">An example of a bubble plot from the Economist magazine</p>
</div>
<p>To begin, download the “Uni Salaries” Excel file from <strong>tinyurl.com/collegesal</strong>. Open it in Excel and explore it.</p>
<p>The first nine columns are:</p>
<ul>
<li><strong>Number:</strong> Meaningless. An artifact from data processing.</li>
<li><strong>School Name Long:</strong> Full name of school</li>
<li><strong>Location:</strong> Location of each school (from Google)</li>
<li><strong>Student Population:</strong> Total student enrollment (from Google)</li>
<li><strong>University or LAC:</strong> Type of school (redundant)</li>
<li><strong>Uni Ranking:</strong> US News Ranking</li>
<li><strong>Tags:</strong> Description tags</li>
<li><strong>Labels:</strong> Description tags (duplicate)</li>
<li><strong>School Name Short:</strong> Shortened name</li>
</ul>
<p>The last three columns columns are all sourced from the website PayScale.com, which surveys US graduates about their jobs. These columns are:</p>
<ul>
<li><strong>% High Meaning:</strong> Proportion of alumni who say they derive high meaning from their jobs</li>
<li><strong>% STEM Degrees:</strong> Proportion of alumni whose degree was in a STEM field</li>
<li><strong>Pay:</strong> This is a compound variable, composed of two figures, separated by a semicolon:</li>
</ul>
<ol style="list-style-type: decimal">
<li>Median salary of alumni 0 - 5 years after graduation</li>
<li>Median salary of alumni 10 or more years after graduation</li>
</ol>
<p>(I used the ‘merge’ function in R to combine data from several different sources to create this dataset.)</p>
<p>Next, let’s import the dataset into Rstudio.</p>
<div class="sourceCode" id="cb1"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb1-1"><a href="#cb1-1"></a><span class="co"># we use the openxlsx package to read Excel files into RStudio</span></span>
<span id="cb1-2"><a href="#cb1-2"></a><span class="kw">library</span>(openxlsx) </span>
<span id="cb1-3"><a href="#cb1-3"></a><span class="co"># you may need to install this package first!</span></span>
<span id="cb1-4"><a href="#cb1-4"></a></span>
<span id="cb1-5"><a href="#cb1-5"></a><span class="co"># insert your file path here</span></span>
<span id="cb1-6"><a href="#cb1-6"></a>unis &lt;-<span class="st">  </span><span class="kw">read.xlsx</span>(<span class="st">&quot;insert_file_path_here&quot;</span>, <span class="dt">sheet =</span> <span class="dv">1</span>) </span></code></pre></div>
<p>How do you get your file path? On a Macbook, two-finger click on the downloaded file in Finder. When a menu pops up, press the <strong>Option</strong> key. See the option to <strong>Copy “your_file_name” as pathname</strong>? Clicking on it copies the file path to the clipboard. You can now paste the path into the function above.</p>
</div>
<div id="cleaning-the-data" class="section level1" number="2">
<h1 number="2"><span class="header-section-number">2</span> Cleaning the data</h1>
<p>Before we clean the data, let’s first view it in RStudio’s native viewer. You should recall the command for this. If you have forgotten, Google it.</p>
<p><strong>Questions:</strong></p>
<ol style="list-style-type: decimal">
<li>What did the spaces in the column names get replaced by after the import? What does this tell you about naming columns in R?</li>
<li>Is the ‘Number’ column useful?</li>
<li>Would it be helpful to change the position of the ‘School.Name.Short’ column?</li>
</ol>
<div id="summary-functions" class="section level2" number="2.1">
<h2 number="2.1"><span class="header-section-number">2.1</span> Summary functions</h2>
<p>Later on in your R career, you’ll work with datasets too large to view in RStudio’s viewer without stressing out your computer. In such cases, using <em>summary</em> functions will allow you to peek at tiny bits of your data. Try these now.</p>
<div class="sourceCode" id="cb2"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb2-1"><a href="#cb2-1"></a><span class="co"># What information does each of these functions provide?</span></span>
<span id="cb2-2"><a href="#cb2-2"></a><span class="kw">str</span>(unis)</span>
<span id="cb2-3"><a href="#cb2-3"></a><span class="kw">summary</span>(unis)</span>
<span id="cb2-4"><a href="#cb2-4"></a><span class="kw">head</span>(unis)</span>
<span id="cb2-5"><a href="#cb2-5"></a><span class="kw">tail</span>(unis)</span>
<span id="cb2-6"><a href="#cb2-6"></a><span class="kw">names</span>(unis)</span>
<span id="cb2-7"><a href="#cb2-7"></a><span class="kw">colnames</span>(unis)</span>
<span id="cb2-8"><a href="#cb2-8"></a><span class="kw">nrow</span>(unis)</span>
<span id="cb2-9"><a href="#cb2-9"></a><span class="kw">ncol</span>(unis)</span>
<span id="cb2-10"><a href="#cb2-10"></a><span class="kw">dim</span>(unis)</span>
<span id="cb2-11"><a href="#cb2-11"></a></span>
<span id="cb2-12"><a href="#cb2-12"></a><span class="co"># My personal favorite summary function is in the &#39;dplyr&#39; package</span></span>
<span id="cb2-13"><a href="#cb2-13"></a><span class="kw">library</span>(dplyr)</span>
<span id="cb2-14"><a href="#cb2-14"></a><span class="kw">glimpse</span>(unis)</span></code></pre></div>
<p>The output of that last command shows the name of each column, the data types in each column (e.g. &lt;int&gt;) and the list of the first few elements in the column.</p>
<p>The data types in our dataset are:</p>
<ol style="list-style-type: decimal">
<li><strong>&lt;chr&gt;, Character:</strong> Strings of text.</li>
<li><strong>&lt;dbl&gt;, Double:</strong> A numeric data type. Does not have to be a whole number.</li>
</ol>
<p><strong>Questions:</strong></p>
<ol style="list-style-type: decimal">
<li><p>What is the mean student population for these schools?</p></li>
<li><p>What data type are the “%.High.Meaning” and “%.STEM.Degrees” columns? Is this appropriate?</p></li>
</ol>
</div>
<div id="deleting-columns" class="section level2" number="2.2">
<h2 number="2.2"><span class="header-section-number">2.2</span> Deleting columns</h2>
<p>Let’s get rid of the “Number” column, as we do not need it. The dollar sign, $, allow you to specify a column in a data frame.</p>
<div class="sourceCode" id="cb3"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb3-1"><a href="#cb3-1"></a><span class="co"># run this line of code to see what gets returned</span></span>
<span id="cb3-2"><a href="#cb3-2"></a>unis<span class="op">$</span>Number</span>
<span id="cb3-3"><a href="#cb3-3"></a></span>
<span id="cb3-4"><a href="#cb3-4"></a><span class="co"># now delete that column</span></span>
<span id="cb3-5"><a href="#cb3-5"></a>unis<span class="op">$</span>Number &lt;-<span class="st"> </span><span class="ot">NULL</span></span>
<span id="cb3-6"><a href="#cb3-6"></a><span class="co"># (we tell R, please replace the column called &quot;Number&quot; with NOTHING)</span></span>
<span id="cb3-7"><a href="#cb3-7"></a></span>
<span id="cb3-8"><a href="#cb3-8"></a><span class="co"># we can check to make sure it is gone</span></span>
<span id="cb3-9"><a href="#cb3-9"></a>unis<span class="op">$</span>Number</span>
<span id="cb3-10"><a href="#cb3-10"></a></span>
<span id="cb3-11"><a href="#cb3-11"></a><span class="co"># or use:</span></span>
<span id="cb3-12"><a href="#cb3-12"></a><span class="kw">colnames</span>(unis)</span>
<span id="cb3-13"><a href="#cb3-13"></a><span class="co"># to show that it is no longer one of the columns</span></span></code></pre></div>
<p><strong>Your turn:</strong></p>
<ol style="list-style-type: decimal">
<li><p>Delete the “Labels” column, since it is a duplicate of the “Tags” column.</p></li>
<li><p>Also delete the “University.or.LAC” column, as it is superfluous.</p></li>
<li><p>Use one of the summary functions discussed above to count the number of columns that remain.</p></li>
</ol>
</div>
<div id="reordering-columns-intro-to-subsetting" class="section level2" number="2.3">
<h2 number="2.3"><span class="header-section-number">2.3</span> Reordering columns (Intro to subsetting)</h2>
<p>We notice that the ‘School.Name.Short’ column is in a weird position. Let’s move that data to the first column, since it is a nice identifier.</p>
<p>To do this, you need to understand the principles of <em>subsetting</em> in R. To subset of a data frame, we use square brackets. Let’s look at some examples. Run the code lines below.</p>
<div class="sourceCode" id="cb4"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb4-1"><a href="#cb4-1"></a><span class="co"># retrieve row 1 and column 1 from data frame </span></span>
<span id="cb4-2"><a href="#cb4-2"></a>unis[<span class="dv">1</span>,<span class="dv">1</span>]</span>
<span id="cb4-3"><a href="#cb4-3"></a></span>
<span id="cb4-4"><a href="#cb4-4"></a><span class="co"># retrieve row 1, column 2</span></span>
<span id="cb4-5"><a href="#cb4-5"></a>unis[<span class="dv">1</span>,<span class="dv">2</span>]</span>
<span id="cb4-6"><a href="#cb4-6"></a></span>
<span id="cb4-7"><a href="#cb4-7"></a><span class="co"># row 1, and all columns</span></span>
<span id="cb4-8"><a href="#cb4-8"></a>unis[<span class="dv">1</span>,]</span>
<span id="cb4-9"><a href="#cb4-9"></a></span>
<span id="cb4-10"><a href="#cb4-10"></a><span class="co"># all rows, column 7</span></span>
<span id="cb4-11"><a href="#cb4-11"></a>unis[,<span class="dv">7</span>]</span>
<span id="cb4-12"><a href="#cb4-12"></a></span>
<span id="cb4-13"><a href="#cb4-13"></a><span class="co"># all rows, all columns</span></span>
<span id="cb4-14"><a href="#cb4-14"></a>unis[ , ]</span>
<span id="cb4-15"><a href="#cb4-15"></a></span>
<span id="cb4-16"><a href="#cb4-16"></a><span class="co"># all rows and all columns, except column 1</span></span>
<span id="cb4-17"><a href="#cb4-17"></a>unis[ ,<span class="op">-</span><span class="dv">1</span>]</span>
<span id="cb4-18"><a href="#cb4-18"></a></span>
<span id="cb4-19"><a href="#cb4-19"></a><span class="co"># rows 3 to 5, and columns 1 to 6</span></span>
<span id="cb4-20"><a href="#cb4-20"></a>unis[<span class="dv">3</span><span class="op">:</span><span class="dv">5</span>,<span class="dv">1</span><span class="op">:</span><span class="dv">6</span>]</span>
<span id="cb4-21"><a href="#cb4-21"></a></span>
<span id="cb4-22"><a href="#cb4-22"></a><span class="co"># rows 1,3 and 5, and column 2</span></span>
<span id="cb4-23"><a href="#cb4-23"></a>unis[<span class="kw">c</span>(<span class="dv">1</span>,<span class="dv">3</span>,<span class="dv">5</span>), <span class="dv">2</span>] </span>
<span id="cb4-24"><a href="#cb4-24"></a></span>
<span id="cb4-25"><a href="#cb4-25"></a><span class="co"># rows 1, 3, 5 and 7 to 9, all columns</span></span>
<span id="cb4-26"><a href="#cb4-26"></a>unis[<span class="kw">c</span>(<span class="dv">1</span>,<span class="dv">3</span>,<span class="dv">5</span>, <span class="dv">7</span><span class="op">:</span><span class="dv">9</span>), ]</span></code></pre></div>
<p>What we are doing above is selecting rows and columns based on their positions or ‘indices’. But how do we figure out their positions in the first place? For column indices, here’s one option:</p>
<div class="sourceCode" id="cb5"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb5-1"><a href="#cb5-1"></a><span class="co"># create a data frame with names of columns</span></span>
<span id="cb5-2"><a href="#cb5-2"></a><span class="kw">data.frame</span>(<span class="kw">colnames</span>(unis))</span>
<span id="cb5-3"><a href="#cb5-3"></a><span class="co"># the rownumbers match the column indices. There you go!</span></span></code></pre></div>
<p>And for row indices:</p>
<div class="sourceCode" id="cb6"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb6-1"><a href="#cb6-1"></a><span class="kw">data.frame</span>(unis<span class="op">$</span>School.Name.Long)</span>
<span id="cb6-2"><a href="#cb6-2"></a><span class="co"># and now you have each school&#39;s row number!</span></span></code></pre></div>
<p>Now we can change the position of the ‘School.Name.Short’ column, our original mission.</p>
<div class="sourceCode" id="cb7"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb7-1"><a href="#cb7-1"></a>unis &lt;-<span class="st"> </span>unis[ , <span class="kw">c</span>(<span class="dv">6</span>,<span class="dv">1</span><span class="op">:</span><span class="dv">5</span>,<span class="dv">7</span><span class="op">:</span><span class="dv">9</span>) ]</span>
<span id="cb7-2"><a href="#cb7-2"></a><span class="co"># replace the old &#39;uni&#39; with the new uni, </span></span>
<span id="cb7-3"><a href="#cb7-3"></a><span class="co"># which is arranged as follows: </span></span>
<span id="cb7-4"><a href="#cb7-4"></a><span class="co"># all rows, column 6, then columns 1 to 5, then columns 7 to 9</span></span></code></pre></div>
<p>Phew! Done. I’ll admit–that was quite cumbersome. It would have been much simpler to have done that using copy and paste commands in Excel. The benefits of R only become evident when analyzing large datasets or performing repetitive tasks.</p>
<p><strong>Your turn:</strong> Move the ‘Tags’ from its current position to position 3, beside the ‘School.Name.Long’ column.</p>
</div>
<div id="separating-columns" class="section level2" number="2.4">
<h2 number="2.4"><span class="header-section-number">2.4</span> Separating columns</h2>
<p>Next, we would like to separate out the ‘Pay’ column into two separate columns, each containing its own variable. We employ the ‘separate’ function in the tidyr package. The function takes five key arguments:</p>
<div class="sourceCode" id="cb8"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb8-1"><a href="#cb8-1"></a><span class="kw">library</span>(tidyr)</span>
<span id="cb8-2"><a href="#cb8-2"></a>unis &lt;-<span class="st"> </span><span class="kw">separate</span>(<span class="dt">data =</span> unis, <span class="co">#take the data frame &#39;unis&#39;</span></span>
<span id="cb8-3"><a href="#cb8-3"></a>                <span class="dt">col =</span> Pay, <span class="co"># the column called &#39;Pay&#39;</span></span>
<span id="cb8-4"><a href="#cb8-4"></a>                <span class="dt">sep =</span> <span class="st">&quot;[;]&quot;</span>, <span class="co"># separate based on semicolons</span></span>
<span id="cb8-5"><a href="#cb8-5"></a>                <span class="dt">into =</span> <span class="kw">c</span>(<span class="st">&#39;Early.Career.Pay&#39;</span>,<span class="st">&#39;Mid.Career.Pay&#39;</span>),  <span class="co">#into these two columns</span></span>
<span id="cb8-6"><a href="#cb8-6"></a>                <span class="dt">remove =</span> <span class="ot">TRUE</span>) <span class="co"># and remove the original column</span></span></code></pre></div>
<p>Done!</p>
<p>There is one problem though. Because there was a space after the semicolon, the values in the ‘Mid.Career.Pay’ now have a space before the dollar sign. Run the code below to see this.</p>
<div class="sourceCode" id="cb9"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb9-1"><a href="#cb9-1"></a>unis[ , <span class="st">&quot;Mid.Career.Pay&quot;</span>]</span></code></pre></div>
<p>This could be problematic. To remove this, we use the ‘trim white space’ function in base R.</p>
<div class="sourceCode" id="cb10"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb10-1"><a href="#cb10-1"></a>unis<span class="op">$</span>Mid.Career.Pay &lt;-<span class="st"> </span><span class="kw">trimws</span>(unis<span class="op">$</span>Mid.Career.Pay)</span></code></pre></div>
<p><strong>Your turn:</strong></p>
<ol style="list-style-type: decimal">
<li>Several pieces of information are contained in the ‘Tags’ column. Separate them out into four different columns, labelled “Tag1”, “Tag2” and so on. (Ignore the warning message.) Trim the white space created.</li>
<li>Separate the ‘Location’ column into three columns, called ‘County’, ‘State’ and ‘State.Initials’. Trim the white space generated.</li>
</ol>
</div>
<div id="processing-text" class="section level2" number="2.5">
<h2 number="2.5"><span class="header-section-number">2.5</span> Processing Text</h2>
<p>Now let’s do some text processing. The dollar sign and the the commas in the ‘Pay’ columns prevent R from understanding that those columns are actually numeric variables. We will need to get rid of those. We use the ‘gsub’ command, which we learned about in the last lesson.</p>
<div class="sourceCode" id="cb11"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb11-1"><a href="#cb11-1"></a>unis<span class="op">$</span>Early.Career.Pay &lt;-<span class="st"> </span><span class="kw">gsub</span>(<span class="dt">x =</span> unis<span class="op">$</span>Early.Career.Pay , <span class="co"># within this column</span></span>
<span id="cb11-2"><a href="#cb11-2"></a>                              <span class="dt">pattern =</span> <span class="st">&quot;[,]&quot;</span>, <span class="co"># find all commas</span></span>
<span id="cb11-3"><a href="#cb11-3"></a>                              <span class="dt">replacement =</span> <span class="st">&quot;&quot;</span> ) <span class="co">#replace with nothing </span></span></code></pre></div>
<p><strong>Your turn:</strong></p>
<ol style="list-style-type: decimal">
<li>Remove the commas from the “Mid.Career.Pay” column.</li>
<li>Remove the dollar signs from both the “Early.Career.Pay” and the “Mid.Career.Pay” columns.</li>
</ol>
</div>
<div id="converting-variable-types" class="section level2" number="2.6">
<h2 number="2.6"><span class="header-section-number">2.6</span> Converting Variable Types</h2>
<p>If we call the following command on our dataset,</p>
<div class="sourceCode" id="cb12"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb12-1"><a href="#cb12-1"></a><span class="kw">glimpse</span>(unis)</span></code></pre></div>
<p>we see that most of our numeric variables are still coded as characters (‘chr’). This will be a problem when trying to plot the data. We need to change the ‘classes’ of these variables. There is a family of functions for this. Type <strong>as.</strong> into Rstudio then pause. A list of variable-converting functions should show up. Let’s use some of these them on our variables now.</p>
<div class="sourceCode" id="cb13"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb13-1"><a href="#cb13-1"></a><span class="co"># &#39;% High Meaning&#39; should be numeric</span></span>
<span id="cb13-2"><a href="#cb13-2"></a>unis<span class="op">$</span><span class="st">`</span><span class="dt">%.High.Meaning</span><span class="st">`</span> &lt;-<span class="st"> </span><span class="kw">as.numeric</span>(unis<span class="op">$</span><span class="st">`</span><span class="dt">%.High.Meaning</span><span class="st">`</span>)</span>
<span id="cb13-3"><a href="#cb13-3"></a><span class="co"># </span><span class="al">NOTE</span><span class="co">: we need the backticks here because of the % sign in the column name</span></span>
<span id="cb13-4"><a href="#cb13-4"></a></span>
<span id="cb13-5"><a href="#cb13-5"></a><span class="co"># &#39;Early Career Pay&#39; should also be numeric</span></span>
<span id="cb13-6"><a href="#cb13-6"></a>unis<span class="op">$</span>Early.Career.Pay &lt;-<span class="st"> </span><span class="kw">as.numeric</span>(unis<span class="op">$</span>Early.Career.Pay)</span></code></pre></div>
<p><strong>Your turn:</strong></p>
<ol style="list-style-type: decimal">
<li>Convert the ‘Mid.Career.Pay’ column into a numeric variable.</li>
<li>Use the <em>glimpse</em> function again on the data frame. Are all data types appropriate?</li>
</ol>
</div>
<div id="logical-subsetting" class="section level2" number="2.7">
<h2 number="2.7"><span class="header-section-number">2.7</span> Logical subsetting</h2>
<p>Further above, we looked at subsetting based on column and row indices.</p>
<p>Another form of subsetting—subsetting based on a condition—is often needed. This is called logical subsetting. For logical subsetting, we use square bracket notation (which you already learnt) along with the following logical operators:</p>
<ol style="list-style-type: decimal">
<li><strong>&lt; </strong> ‘less than’</li>
<li><strong>&lt;= </strong> ‘less than or equal to’</li>
<li><strong>&gt; </strong> ‘greater than’</li>
<li><strong>&gt;= </strong> ‘greater than or equal to’</li>
<li><strong>== </strong> ‘exactly equal to’</li>
<li><strong>%in% :</strong> ‘found in’. Works similar to ’==’but sometimes preferred.</li>
<li><strong>!= </strong> ‘not equal to’</li>
<li><strong>| :</strong> ‘or’</li>
<li><strong>&amp; :</strong> ‘and’</li>
</ol>
<p>Let’s use these in some examples. First, the “==”, “&lt;” ,“&lt;=”, “&gt;”, and “=&gt;” operators.</p>
<div class="sourceCode" id="cb14"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb14-1"><a href="#cb14-1"></a><span class="co"># retrieve rows with schools in California, all columns</span></span>
<span id="cb14-2"><a href="#cb14-2"></a>unis[ unis<span class="op">$</span>State <span class="op">==</span><span class="st"> &quot;California&quot;</span> , ]</span>
<span id="cb14-3"><a href="#cb14-3"></a><span class="co"># another way of writing this is: </span></span>
<span id="cb14-4"><a href="#cb14-4"></a>unis[ unis<span class="op">$</span>State <span class="op">%in%</span><span class="st"> &quot;California&quot;</span> , ]</span>
<span id="cb14-5"><a href="#cb14-5"></a></span>
<span id="cb14-6"><a href="#cb14-6"></a><span class="co"># Retrieve the top 10 schools, and show only first column</span></span>
<span id="cb14-7"><a href="#cb14-7"></a>unis[ unis<span class="op">$</span>Uni.Ranking <span class="op">&lt;=</span><span class="st"> </span><span class="dv">10</span> , <span class="dv">1</span>]</span>
<span id="cb14-8"><a href="#cb14-8"></a></span>
<span id="cb14-9"><a href="#cb14-9"></a><span class="co"># Top 10 schools, and show only the School Name Short column</span></span>
<span id="cb14-10"><a href="#cb14-10"></a>unis[ unis<span class="op">$</span>Uni.Ranking <span class="op">&lt;=</span><span class="st"> </span><span class="dv">10</span> , <span class="st">&quot;School.Name.Short&quot;</span>]</span>
<span id="cb14-11"><a href="#cb14-11"></a></span>
<span id="cb14-12"><a href="#cb14-12"></a><span class="co"># Non top 10 schools </span></span>
<span id="cb14-13"><a href="#cb14-13"></a>unis[ unis<span class="op">$</span>Uni.Ranking <span class="op">&gt;</span><span class="st"> </span><span class="dv">10</span>, <span class="st">&quot;School.Name.Short&quot;</span>]</span></code></pre></div>
<p><strong>Your turn:</strong></p>
<ol style="list-style-type: decimal">
<li>Retrieve all school in Cambridge, first column.</li>
<li>Retrieve the bottom 15 schools, first column.</li>
<li>Retrieve schools where more than half of students graduate with STEM degrees, all columns.</li>
</ol>
<p>Now, let’s use the “|” and “&amp;” operators to combine conditions.</p>
<div class="sourceCode" id="cb15"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb15-1"><a href="#cb15-1"></a><span class="co"># Schools ranked 5 to 15, all columns</span></span>
<span id="cb15-2"><a href="#cb15-2"></a>unis[ unis<span class="op">$</span>Uni.Ranking <span class="op">&gt;=</span><span class="dv">5</span> <span class="op">&amp;</span><span class="st"> </span>unis<span class="op">$</span>Uni.Ranking <span class="op">&lt;=</span><span class="dv">15</span> , ]</span>
<span id="cb15-3"><a href="#cb15-3"></a></span>
<span id="cb15-4"><a href="#cb15-4"></a><span class="co"># The mid-career pay of schools ranked 5 to 15</span></span>
<span id="cb15-5"><a href="#cb15-5"></a>unis[ unis<span class="op">$</span>Uni.Ranking <span class="op">&gt;=</span><span class="dv">5</span> <span class="op">&amp;</span><span class="st"> </span>unis<span class="op">$</span>Uni.Ranking <span class="op">&lt;=</span><span class="dv">15</span> , <span class="st">&quot;Mid.Career.Pay&quot;</span> ]</span>
<span id="cb15-6"><a href="#cb15-6"></a></span>
<span id="cb15-7"><a href="#cb15-7"></a><span class="co"># Schools in California or Texas</span></span>
<span id="cb15-8"><a href="#cb15-8"></a>unis[ unis<span class="op">$</span>State <span class="op">==</span><span class="st"> &#39;California&#39;</span>  <span class="op">|</span><span class="st"> </span>unis<span class="op">$</span>State <span class="op">==</span><span class="st"> &#39;Texas&#39;</span> , ]</span>
<span id="cb15-9"><a href="#cb15-9"></a><span class="co"># Or:</span></span>
<span id="cb15-10"><a href="#cb15-10"></a>unis[ unis<span class="op">$</span>State <span class="op">%in%</span><span class="st"> &#39;California&#39;</span>  <span class="op">|</span><span class="st"> </span>unis<span class="op">$</span>State <span class="op">%in%</span><span class="st"> &#39;Texas&#39;</span> , ]</span>
<span id="cb15-11"><a href="#cb15-11"></a></span>
<span id="cb15-12"><a href="#cb15-12"></a><span class="co"># Schools in California with more than 10000 students</span></span>
<span id="cb15-13"><a href="#cb15-13"></a>unis[ unis<span class="op">$</span>State <span class="op">==</span><span class="st"> &#39;California&#39;</span>  <span class="op">&amp;</span><span class="st"> </span>unis<span class="op">$</span>Student.Population <span class="op">&gt;</span><span class="st"> </span><span class="dv">10000</span>, ]</span></code></pre></div>
<p><strong>Your turn:</strong></p>
<ol style="list-style-type: decimal">
<li>Retrieve the names of schools in California where the Mid-career pay is above 150000</li>
<li>Retrieve the names AND the populations of these schools</li>
</ol>
<p>Next, let’s use the negation operator, <strong>!=</strong>.</p>
<div class="sourceCode" id="cb16"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb16-1"><a href="#cb16-1"></a><span class="co"># Schools outside California</span></span>
<span id="cb16-2"><a href="#cb16-2"></a>unis[ unis<span class="op">$</span>State <span class="op">!=</span><span class="st"> &#39;California&#39;</span> , ]</span></code></pre></div>
<p><strong>Your turn:</strong></p>
<ol style="list-style-type: decimal">
<li>Retrieve the names of schools outside California and New York.</li>
</ol>
<p>Now, what if we want to test the condition across multiple columns?</p>
<div class="sourceCode" id="cb17"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb17-1"><a href="#cb17-1"></a><span class="co"># Schools which are tagged as &#39;Private School&#39; in any of the four tags</span></span>
<span id="cb17-2"><a href="#cb17-2"></a>unis[  unis<span class="op">$</span>Tag1 <span class="op">==</span><span class="st"> &#39;Private School&#39;</span> <span class="op">|</span><span class="st"> </span></span>
<span id="cb17-3"><a href="#cb17-3"></a><span class="st">       </span>unis<span class="op">$</span>Tag2 <span class="op">==</span><span class="st"> &#39;Private School&#39;</span> <span class="op">|</span></span>
<span id="cb17-4"><a href="#cb17-4"></a><span class="st">       </span>unis<span class="op">$</span>Tag3 <span class="op">==</span><span class="st"> &#39;Private School&#39;</span> <span class="op">|</span></span>
<span id="cb17-5"><a href="#cb17-5"></a><span class="st">       </span>unis<span class="op">$</span>Tag4 <span class="op">==</span><span class="st"> &#39;Private School&#39;</span> , ]</span>
<span id="cb17-6"><a href="#cb17-6"></a><span class="co"># OR</span></span>
<span id="cb17-7"><a href="#cb17-7"></a>unis[  unis<span class="op">$</span>Tag1 <span class="op">%in%</span><span class="st"> &#39;Private School&#39;</span> <span class="op">|</span><span class="st"> </span></span>
<span id="cb17-8"><a href="#cb17-8"></a><span class="st">       </span>unis<span class="op">$</span>Tag2 <span class="op">%in%</span><span class="st"> &#39;Private School&#39;</span> <span class="op">|</span></span>
<span id="cb17-9"><a href="#cb17-9"></a><span class="st">       </span>unis<span class="op">$</span>Tag3 <span class="op">%in%</span><span class="st"> &#39;Private School&#39;</span> <span class="op">|</span></span>
<span id="cb17-10"><a href="#cb17-10"></a><span class="st">       </span>unis<span class="op">$</span>Tag4 <span class="op">%in%</span><span class="st"> &#39;Private School&#39;</span> , ]</span></code></pre></div>
<p><strong>Your turn:</strong></p>
<ol style="list-style-type: decimal">
<li>Retrieve the names of all engineering schools.</li>
<li>Retrieve the names AND student populations of all engineering schools.</li>
<li>Retrieve the names of all engineering schools located in California.</li>
</ol>
<p>Okay, let’s finally put our newly-gained power to good use!</p>
<p>We would like to create a new column, called ‘Sports’ that tells us whether or not a school is sports-focused. We will use the ‘%in%’ operator, instead of the usual ‘==’ operator as this latter operator is problematic when trying to make a new variable.</p>
<div class="sourceCode" id="cb18"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb18-1"><a href="#cb18-1"></a><span class="co"># Create an empty column called &#39;Sports&#39;</span></span>
<span id="cb18-2"><a href="#cb18-2"></a>unis<span class="op">$</span>Sports &lt;-<span class="st"> </span><span class="ot">NA</span></span>
<span id="cb18-3"><a href="#cb18-3"></a></span>
<span id="cb18-4"><a href="#cb18-4"></a><span class="co"># Take the subset of schools tagged as &#39;For Sports Fans&#39;</span></span>
<span id="cb18-5"><a href="#cb18-5"></a>unis[  unis<span class="op">$</span>Tag1 <span class="op">%in%</span><span class="st"> &#39;For Sports Fans&#39;</span> <span class="op">|</span><span class="st"> </span></span>
<span id="cb18-6"><a href="#cb18-6"></a><span class="st">       </span>unis<span class="op">$</span>Tag2 <span class="op">%in%</span><span class="st"> &#39;For Sports Fans&#39;</span> <span class="op">|</span></span>
<span id="cb18-7"><a href="#cb18-7"></a><span class="st">       </span>unis<span class="op">$</span>Tag3 <span class="op">%in%</span><span class="st"> &#39;For Sports Fans&#39;</span> <span class="op">|</span></span>
<span id="cb18-8"><a href="#cb18-8"></a><span class="st">       </span>unis<span class="op">$</span>Tag4 <span class="op">%in%</span><span class="st"> &#39;For Sports Fans&#39;</span>, <span class="st">&#39;Sports&#39;</span>] &lt;-<span class="st"> &quot;Sports School&quot;</span> </span>
<span id="cb18-9"><a href="#cb18-9"></a><span class="co"># and assign the value &#39;Sports School&#39; to their Sports column</span></span></code></pre></div>
<p>Next, let’s indicate that all the other schools are “Non-Sports schools”. We will require a negation of the ‘%in%’ operator, which can be found in the ‘Hmisc’ package.</p>
<div class="sourceCode" id="cb19"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb19-1"><a href="#cb19-1"></a><span class="kw">library</span>(Hmisc)</span>
<span id="cb19-2"><a href="#cb19-2"></a><span class="co"># subset of all unis for which the &#39;Sports&#39; does not say &#39;Sports School.</span></span>
<span id="cb19-3"><a href="#cb19-3"></a>unis[unis<span class="op">$</span>Sports <span class="op">%nin%</span><span class="st"> &#39;Sports School&#39;</span>, <span class="st">&#39;Sports&#39;</span>] &lt;-<span class="st"> &#39;Non-Sports School&#39;</span></span></code></pre></div>
<p><strong>Your turn:</strong></p>
<ol style="list-style-type: decimal">
<li>Create a new variable called ‘Ivy’ that contains the values “Ivy League” and “Non-Ivy League”.</li>
<li>Create another variable called ‘Private’ that contains the values “Private” and “Public”.</li>
</ol>
<p>Most of the necessary data processing is now done! We can move on to plotting the data.</p>
</div>
</div>
<div id="plotting" class="section level1" number="3">
<h1 number="3"><span class="header-section-number">3</span> Plotting</h1>
<p>The ‘ggplot2’ (Grammar of Graphics Plot 2) package is the unrivaled choice for visualization in R. Let’s take a look at its syntax.</p>
<p>We first pass our data frame into the ‘ggplot()’ function. Then we add in specific geometric elements. For each element, we must provide the ‘aesthetics’ to be mapped.</p>
<p>Let’s plot a simple scatter plot of university rankings and early career pay.</p>
<div class="sourceCode" id="cb20"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb20-1"><a href="#cb20-1"></a><span class="kw">library</span>(ggplot2)</span>
<span id="cb20-2"><a href="#cb20-2"></a></span>
<span id="cb20-3"><a href="#cb20-3"></a><span class="kw">ggplot</span>(<span class="dt">data =</span> unis) <span class="op">+</span><span class="st"> </span><span class="co"># using the data in the &quot;unis&quot; data frame</span></span>
<span id="cb20-4"><a href="#cb20-4"></a><span class="st">  </span><span class="kw">geom_point</span>(<span class="dt">mapping =</span> <span class="kw">aes</span>( <span class="co"># create a scatterplot with the following aesthetic mappings</span></span>
<span id="cb20-5"><a href="#cb20-5"></a>    <span class="dt">x =</span> Uni.Ranking,       <span class="co"># on the x axis, place the university&#39;s ranking </span></span>
<span id="cb20-6"><a href="#cb20-6"></a>    <span class="dt">y =</span> Early.Career.Pay ) ) <span class="co"># on the y axis, place the early career pay</span></span></code></pre></div>
<div class="sourceCode" id="cb21"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb21-1"><a href="#cb21-1"></a><span class="co"># </span><span class="al">NOTE</span><span class="co">: The syntax above is separated over multiple lines, but it does not need to be.</span></span>
<span id="cb21-2"><a href="#cb21-2"></a></span>
<span id="cb21-3"><a href="#cb21-3"></a></span>
<span id="cb21-4"><a href="#cb21-4"></a><span class="co"># We can also assign the plot to an object, then call the object</span></span>
<span id="cb21-5"><a href="#cb21-5"></a>plot1&lt;-<span class="st"> </span><span class="kw">ggplot</span>(<span class="dt">data =</span> unis) <span class="op">+</span></span>
<span id="cb21-6"><a href="#cb21-6"></a><span class="st">  </span><span class="kw">geom_point</span>(<span class="dt">mapping =</span> <span class="kw">aes</span>( <span class="dt">x =</span> Uni.Ranking, <span class="dt">y =</span> Early.Career.Pay ) )</span>
<span id="cb21-7"><a href="#cb21-7"></a>plot1</span></code></pre></div>
<p>Instead of just seeing dots, what if we’d like to see the name of each university? We use the geom_text object for this. To the geom_text object, we add our usual <strong>x</strong> and <strong>y</strong> mappings, but we also include an extra aesthetic mapping: ‘labels’.</p>
<div class="sourceCode" id="cb22"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb22-1"><a href="#cb22-1"></a><span class="kw">ggplot</span>(<span class="dt">data =</span> unis) <span class="op">+</span></span>
<span id="cb22-2"><a href="#cb22-2"></a><span class="st">  </span><span class="kw">geom_text</span>(<span class="dt">mapping =</span> <span class="kw">aes</span>(<span class="dt">x =</span> Uni.Ranking, </span>
<span id="cb22-3"><a href="#cb22-3"></a>                          <span class="dt">y =</span> Early.Career.Pay, </span>
<span id="cb22-4"><a href="#cb22-4"></a>                          <span class="dt">label =</span> School.Name.Short ) )</span></code></pre></div>
<p>Nice!</p>
<p>But what if we want to show <em>both</em> the dot and the text labels? We can simply add another ‘+’ sign and hook on a second geometric element.</p>
<div class="sourceCode" id="cb23"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb23-1"><a href="#cb23-1"></a><span class="kw">ggplot</span>(<span class="dt">data =</span> unis) <span class="op">+</span></span>
<span id="cb23-2"><a href="#cb23-2"></a><span class="st">  </span><span class="kw">geom_point</span>(<span class="dt">mapping =</span> <span class="kw">aes</span>( <span class="dt">x =</span> Uni.Ranking, </span>
<span id="cb23-3"><a href="#cb23-3"></a>                            <span class="dt">y =</span> Early.Career.Pay ) ) <span class="op">+</span></span>
<span id="cb23-4"><a href="#cb23-4"></a><span class="st">  </span><span class="kw">geom_text</span>(<span class="dt">mapping =</span> <span class="kw">aes</span>(<span class="dt">x =</span> Uni.Ranking, </span>
<span id="cb23-5"><a href="#cb23-5"></a>                          <span class="dt">y =</span> Early.Career.Pay, </span>
<span id="cb23-6"><a href="#cb23-6"></a>                          <span class="dt">label =</span> School.Name.Short ) )</span></code></pre></div>
<p>Getting there!</p>
<p>The overlap of the geom_text elements is quite annoying though. Can we fix it? Yes! There’s a package for that. The ‘ggrepel’ package allows us to replace the usual ‘geom_text’ element with a ‘geom_text_repel’ element which tries avoid overlap.</p>
<div class="sourceCode" id="cb24"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb24-1"><a href="#cb24-1"></a><span class="kw">library</span>(ggrepel)</span>
<span id="cb24-2"><a href="#cb24-2"></a><span class="kw">ggplot</span>(<span class="dt">data =</span> unis) <span class="op">+</span></span>
<span id="cb24-3"><a href="#cb24-3"></a><span class="st">  </span><span class="kw">geom_point</span>(<span class="dt">mapping =</span> <span class="kw">aes</span>( <span class="dt">x =</span> Uni.Ranking, </span>
<span id="cb24-4"><a href="#cb24-4"></a>                            <span class="dt">y =</span> Early.Career.Pay ) ) <span class="op">+</span></span>
<span id="cb24-5"><a href="#cb24-5"></a><span class="st">  </span><span class="kw">geom_text_repel</span>(<span class="dt">mapping =</span> <span class="kw">aes</span>(<span class="dt">x =</span> Uni.Ranking, </span>
<span id="cb24-6"><a href="#cb24-6"></a>                          <span class="dt">y =</span> Early.Career.Pay, </span>
<span id="cb24-7"><a href="#cb24-7"></a>                          <span class="dt">label =</span> School.Name.Short ) )</span></code></pre></div>
<p>Much better!</p>
<p>Next, the graph would be easier to read if the rankings on the x axis were flipped. How do we do this? A quick Google search reveals that “scale_x_reverse” is the function we’re looking for. Google will be a close friend when working with ggplot. This ggplot cheatsheet at <strong>tinyurl.com/rcheat1</strong> might come in handy as well. The official reference for functions in ggplot is at <strong>tinyurl.com/rrefe1</strong>.</p>
<div class="sourceCode" id="cb25"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb25-1"><a href="#cb25-1"></a><span class="kw">ggplot</span>(<span class="dt">data =</span> unis) <span class="op">+</span></span>
<span id="cb25-2"><a href="#cb25-2"></a><span class="st">  </span><span class="kw">geom_point</span>(<span class="dt">mapping =</span> <span class="kw">aes</span>( <span class="dt">x =</span> Uni.Ranking, </span>
<span id="cb25-3"><a href="#cb25-3"></a>                            <span class="dt">y =</span> Early.Career.Pay ) ) <span class="op">+</span></span>
<span id="cb25-4"><a href="#cb25-4"></a><span class="st">  </span><span class="kw">geom_text_repel</span>(<span class="dt">mapping =</span> <span class="kw">aes</span>(<span class="dt">x =</span> Uni.Ranking, </span>
<span id="cb25-5"><a href="#cb25-5"></a>                          <span class="dt">y =</span> Early.Career.Pay, </span>
<span id="cb25-6"><a href="#cb25-6"></a>                          <span class="dt">label =</span> School.Name.Short ) ) <span class="op">+</span></span>
<span id="cb25-7"><a href="#cb25-7"></a><span class="st">  </span><span class="kw">scale_x_reverse</span>()</span></code></pre></div>
<p>Our plot looks good so far, but we could add some more information to it. Let’s add in two more geometric elements. We map the <strong>size</strong> of the geom_point dots onto the populations of each school. And we map the <strong>color</strong> of these dots onto the “Ivy” column.</p>
<div class="sourceCode" id="cb26"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb26-1"><a href="#cb26-1"></a><span class="kw">ggplot</span>(<span class="dt">data =</span> unis) <span class="op">+</span></span>
<span id="cb26-2"><a href="#cb26-2"></a><span class="st">  </span><span class="kw">geom_point</span>(<span class="dt">mapping =</span> <span class="kw">aes</span>( <span class="dt">x =</span> Uni.Ranking, </span>
<span id="cb26-3"><a href="#cb26-3"></a>                            <span class="dt">y =</span> Early.Career.Pay,</span>
<span id="cb26-4"><a href="#cb26-4"></a>                            <span class="dt">size =</span> Student.Population,  <span class="co"># map size to Population</span></span>
<span id="cb26-5"><a href="#cb26-5"></a>                            <span class="dt">color =</span> Ivy ) ) <span class="op">+</span><span class="st">    </span><span class="co"># map color to &#39;Ivy&#39;</span></span>
<span id="cb26-6"><a href="#cb26-6"></a><span class="st">  </span><span class="kw">geom_text_repel</span>(<span class="dt">mapping =</span> <span class="kw">aes</span>(<span class="dt">x =</span> Uni.Ranking, </span>
<span id="cb26-7"><a href="#cb26-7"></a>                          <span class="dt">y =</span> Early.Career.Pay, </span>
<span id="cb26-8"><a href="#cb26-8"></a>                          <span class="dt">label =</span> School.Name.Short ) ) <span class="op">+</span></span>
<span id="cb26-9"><a href="#cb26-9"></a><span class="st">  </span><span class="kw">scale_x_reverse</span>()</span></code></pre></div>
<p>Sweet!</p>
<p>Now let’s make the graph prettier. We can improve the visual appeal of the plot by using predesigned themes. The ggplot2 package has some default themes, but the package ‘ggthemes’ has nicer ones. Let’s use a theme remniscient of plots from The Economist magazine, since this was our goal aesthetic.</p>
<p>We’ll also assign the plot to an object, <em>p1</em>, so we don’t need to keep pasting the entire command to tweak the plot.</p>
<div class="sourceCode" id="cb27"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb27-1"><a href="#cb27-1"></a><span class="kw">library</span>(ggthemes)</span>
<span id="cb27-2"><a href="#cb27-2"></a></span>
<span id="cb27-3"><a href="#cb27-3"></a>p1 &lt;-<span class="st"> </span><span class="kw">ggplot</span>(<span class="dt">data =</span> unis) <span class="op">+</span></span>
<span id="cb27-4"><a href="#cb27-4"></a><span class="st">  </span><span class="kw">geom_point</span>(<span class="dt">mapping =</span> <span class="kw">aes</span>( <span class="dt">x =</span> Uni.Ranking, </span>
<span id="cb27-5"><a href="#cb27-5"></a>                            <span class="dt">y =</span> Early.Career.Pay,</span>
<span id="cb27-6"><a href="#cb27-6"></a>                            <span class="dt">size =</span> Student.Population,  <span class="co"># map size to Population</span></span>
<span id="cb27-7"><a href="#cb27-7"></a>                            <span class="dt">color =</span> Ivy ) ) <span class="op">+</span><span class="st">    </span><span class="co"># map color to &#39;Ivy&#39;</span></span>
<span id="cb27-8"><a href="#cb27-8"></a><span class="st">  </span><span class="kw">geom_text_repel</span>(<span class="dt">mapping =</span> <span class="kw">aes</span>(<span class="dt">x =</span> Uni.Ranking, </span>
<span id="cb27-9"><a href="#cb27-9"></a>                          <span class="dt">y =</span> Early.Career.Pay, </span>
<span id="cb27-10"><a href="#cb27-10"></a>                          <span class="dt">label =</span> School.Name.Short ) ) <span class="op">+</span></span>
<span id="cb27-11"><a href="#cb27-11"></a><span class="st">  </span><span class="kw">scale_x_reverse</span>() <span class="op">+</span></span>
<span id="cb27-12"><a href="#cb27-12"></a><span class="st">  </span><span class="kw">theme_economist</span>()</span></code></pre></div>
<p>Finally, let’s change all the font elements. To do this we use the ‘theme()’ function in ggplot, which can take many arguments. A full list can be found at <strong>tinyurl.com/ggthemeref1</strong>. Within the <em>theme</em> function, we pass the <em>element_text()</em> argument to the piece of text we want to change (e.g ‘axis.text’ or ‘axis.title.y’). Then, within element_text(), we can specify font family, size, face and justification.</p>
<p>We’ll also add a title and caption. And we’ll modify all text labels.</p>
<div class="sourceCode" id="cb28"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb28-1"><a href="#cb28-1"></a><span class="kw">library</span>(ggthemes)</span>
<span id="cb28-2"><a href="#cb28-2"></a></span>
<span id="cb28-3"><a href="#cb28-3"></a>p1 &lt;-<span class="st"> </span>p1 <span class="op">+</span><span class="st"> </span><span class="kw">labs</span>(<span class="dt">title =</span> <span class="st">&quot;Rankings and Median Salary for Graduates of Top 25 US Universities&quot;</span>,</span>
<span id="cb28-4"><a href="#cb28-4"></a>       <span class="dt">caption =</span> <span class="st">&quot;Source: PayScale.com and US News and World Report&quot;</span>,</span>
<span id="cb28-5"><a href="#cb28-5"></a>    <span class="co"># change the x and y axis labels</span></span>
<span id="cb28-6"><a href="#cb28-6"></a>       <span class="dt">x =</span> <span class="st">&quot;2019 Ranking&quot;</span>,</span>
<span id="cb28-7"><a href="#cb28-7"></a>       <span class="dt">y =</span> <span class="st">&quot;Median Salary 0-5 years after graduation, $&quot;</span>,</span>
<span id="cb28-8"><a href="#cb28-8"></a>    <span class="co"># change the legend labels</span></span>
<span id="cb28-9"><a href="#cb28-9"></a>       <span class="dt">color =</span> <span class="st">&quot;Ivy Status&quot;</span>,</span>
<span id="cb28-10"><a href="#cb28-10"></a>       <span class="dt">size =</span> <span class="st">&quot;Student Population&quot;</span>) <span class="op">+</span></span>
<span id="cb28-11"><a href="#cb28-11"></a><span class="st">    </span><span class="co"># change fonts</span></span>
<span id="cb28-12"><a href="#cb28-12"></a><span class="st">  </span><span class="kw">theme</span>(<span class="dt">axis.text =</span> <span class="kw">element_text</span>( <span class="dt">size =</span> <span class="dv">10</span>, <span class="dt">face =</span> <span class="st">&quot;bold&quot;</span>),</span>
<span id="cb28-13"><a href="#cb28-13"></a>        <span class="dt">axis.title.x =</span> <span class="kw">element_text</span>( <span class="dt">size =</span> <span class="dv">14</span>, <span class="dt">face =</span> <span class="st">&#39;bold&#39;</span>),</span>
<span id="cb28-14"><a href="#cb28-14"></a>        <span class="dt">axis.title.y =</span> <span class="kw">element_text</span>( <span class="dt">size =</span> <span class="dv">14</span>, <span class="dt">face =</span> <span class="st">&#39;bold&#39;</span>),</span>
<span id="cb28-15"><a href="#cb28-15"></a>        <span class="dt">legend.title =</span> <span class="kw">element_text</span>(<span class="dt">size =</span> <span class="dv">11</span>, <span class="dt">face =</span> <span class="st">&quot;bold&quot;</span>),</span>
<span id="cb28-16"><a href="#cb28-16"></a>        <span class="dt">legend.text =</span> <span class="kw">element_text</span>( <span class="dt">size=</span> <span class="dv">11</span>),</span>
<span id="cb28-17"><a href="#cb28-17"></a>        <span class="dt">plot.title =</span> <span class="kw">element_text</span>( <span class="dt">size=</span><span class="dv">16</span>, <span class="dt">face =</span> <span class="st">&quot;bold&quot;</span>, <span class="dt">hjust =</span> <span class="fl">0.5</span>),</span>
<span id="cb28-18"><a href="#cb28-18"></a>        <span class="dt">plot.caption =</span> <span class="kw">element_text</span>( <span class="dt">size=</span><span class="dv">8</span>, <span class="dt">colour =</span> <span class="st">&quot;dark gray&quot;</span>))</span></code></pre></div>
<p>And that’s as good as we’ll get for now Save your plot using the ‘Export’ button above the plotting pane, then proceed to the next assignment.</p>
<p><strong>Your turn:</strong></p>
<ol style="list-style-type: decimal">
<li>Create a new plot in which your x axis is the percentage of STEM degrees</li>
<li>Which of the two plots so far seems to better explain the differences in earnings?</li>
</ol>
</div>
<div id="coefficient-of-determination" class="section level1" number="4">
<h1 number="4"><span class="header-section-number">4</span> Coefficient of determination</h1>
<p>To better understand which of our independent variables better explains graduate earnings, we can calculate the Pearson’s correlation coefficient, r, for the relevant variables.</p>
<div class="sourceCode" id="cb29"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb29-1"><a href="#cb29-1"></a><span class="kw">cor</span>(unis<span class="op">$</span>Early.Career.Pay, unis<span class="op">$</span>Uni.Ranking)</span>
<span id="cb29-2"><a href="#cb29-2"></a><span class="co"># we notice a negative correlation. </span></span>
<span id="cb29-3"><a href="#cb29-3"></a><span class="co"># This is because a bigger number for the ranking denotes a worse school.</span></span></code></pre></div>
<p>The square of the Pearson’s correlation coefficient, R, gives us ‘R-squared’, also known as the coefficient of determination. This value represents the proportion of variance in the dependent variable (Salary) that is explained by the independent variable (Ranking in our case).</p>
<p>Let’s compute that value in the present case.</p>
<div class="sourceCode" id="cb30"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb30-1"><a href="#cb30-1"></a><span class="kw">cor</span>(unis<span class="op">$</span>Early.Career.Pay, unis<span class="op">$</span>Uni.Ranking)<span class="op">^</span><span class="dv">2</span></span></code></pre></div>
<p>Let’s now add this value as a subtitle in our plot. We use the ‘expression’ function to type superscript.And we add further styling elements for the subtitle.</p>
<div class="sourceCode" id="cb31"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb31-1"><a href="#cb31-1"></a>p1 <span class="op">+</span><span class="st"> </span><span class="kw">labs</span>(<span class="dt">subtitle =</span> <span class="kw">expression</span>(R<span class="op">^</span><span class="dv">2</span> <span class="op">~</span><span class="st"> &#39;=&#39;</span> <span class="op">~</span><span class="st"> &#39;0.18&#39;</span>)) <span class="op">+</span></span>
<span id="cb31-2"><a href="#cb31-2"></a><span class="st">  </span><span class="kw">theme</span>(<span class="dt">plot.subtitle =</span> <span class="kw">element_text</span>( <span class="dt">size =</span> <span class="dv">14</span>, <span class="dt">face =</span> <span class="st">&#39;bold&#39;</span>, <span class="dt">hjust =</span> <span class="fl">0.5</span>))</span></code></pre></div>
<p><strong>Your turn:</strong></p>
<ol style="list-style-type: decimal">
<li>Repeat the above steps for the second plot, in which the independent variable is the percentage of graduates with STEM degrees.</li>
<li>Which of the two plots better explains the differences in earnings?</li>
</ol>
</div>
<div id="rinse-and-repeat" class="section level1" number="5">
<h1 number="5"><span class="header-section-number">5</span> Rinse and Repeat</h1>
<p>We have come to the end of this lesson. Your job now is to replicate our analysis on the liberal arts colleges dataset (LAC salaries). You should import the data from the same folder(<a href="&#39;tinyurl.com/collegesal&#39;">tinyurl.com/collegesal</a>, clean it, then produce plots similar to those we have created in this exercise.</p>
<p>The datasets are more or less identical, so as long as you follow the steps we have followed here, you should emerge victorious.</p>
<p>Best of luck.</p>
</div>
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