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<meta name="viewport" content="width=device-width, initial-scale=1" />
<meta name="author" content="Bryan Murphy" />
<meta name="date" content="2023-04-04" />
<title>Analysis of Which American Weather Events Cause the Most Damages to Human Life and Private Property, 1950 - 2011</title>
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88wiJ34K4tdAIQjAOQkx8YArcM2PaAOjSZBL8uolzAJFFvGDXd8ej67P2AvKpUkOYghcnK7zl300RBcsExwzJ/hbrd7GuYBwhgAIYtbTx/3+d4klJ3gtKCQnGIz9InYZEzqG8EkjSzNavCB/cXYlcQshhyMsZrI6PYLWc3lOG/vlA4rHr/3uTFD3r38/r+3fMKOke9W4oJ9G566u7au84CpOz/ct5R99wF7W6dIYjjnawrHIAh3hlungFOWgXoyzVKbHOr1eD19Il6vISsrrU8kSzbY+0QMGpdjgYh60zDTHJKHoyP4404pw27zB4o1o62gq+BLL299am8j+zv774zj995/dgTOZsOfWr3rnTWPj2h8qGbo1/M//kYYvmxfms7TtPrM54E7ns4vwBw0rFy/aNJjRRVTet31OgCBPABhongUDOCAzuE0h6gnxChToCJ1ulB0iH0jeqvscFBZotflk+hMQ5oJDqhrC/l//FxmAUlGYeK5Z6Jl5MDec2yJQdc+l5ViNduL1avoZ805eGll04jy6COKheT8S+U6kQwdw+lW6nPpXF4qtEoBziwAye3mMnRLkqlPRLqZdQlsKxTcLghkqhzjrLL5M+WgUwldSkjbL1HPLrCf51d8MHbv66zu/mcGl5Kz0YNZ0+mcf759kbEB29qGGrZiYWop2b2R9fYqnKnlWOVzqXqgNfQIB5LtRr8fQLLT7CyT0ZLaL2K0WFzU5e0TcfmojkckcgvcyhJ4pNlr8Bd63VyEhIbiGhfIBFGTq8R9lqcWB2Dl1G79Rn/9i8n08OU3L/760UX2E369YuvqVUPrI9VryFR8CXc5V/rYefbW7svv/YNdxUHv/OnFVQ1V8yse2Dde0UcAIY/zU4L0sA1FEQg3jJT0jVAJFBlqbOOrALk1dCOmkuHNF+mpaKOYunHhldNAlZhEyFGpz4R20C+c47Vmu+6gqXo9lewuq5TfXrLnZORk9Ink5JjAlNwvYvJBoF8E5N8qd9nN3jrmj7mOx8OPLDXqolpgwv0zZkpuzaeTynf+vWjNvnr22b+bsfDJR7+e+cL6dQ1bXlu3CDvOWfHIMytnrhJPHt7x4L7eg/48+8C5U0euLuu/f8ozr1xteHTRssdGru8V3kwfeHTMsN937/zksLEzFdlO5NQpNsMLWdAtnJlizzQYAAQu26AljUvWZbEQlyuJi1Ymcr8Iaal2jjKNg5qJ9Ctqx02jMyDFKHJw8TpUIvjHKhXZQlZ0/Iwe1eO++6/RVHpg2mv/uPbBuguPMtfKLU+tuXfjkIFraEVzg2tlMuZg6O57/vXBP1C3kZ3H9od2PPV81RMVE/aNAy3HEcaokRS34Ta+LAA8XotzQMRiizkRDVfN87X0JXae6NzkVR6Znehb6J8XL+Y3IKovXMjn0oEDMrkmmc2iXu9yGm0DIkab6hgTZklwj/T6FDccpXsmn6Rjlxv+knyrTFMR8+U/cF9+DiRwh/UCiChwdeXD58cDhSwsRjeikNNcTo83/0AtP2DDKLywji1nhxSezMTjgo9eVHOy3LBbJgIQ0OsEsToiIFRHrIjI4wHOlfxEz6a4ZOTXTLq9eTjdTofW1bEH6up+g5GIBDhGEr2BkRNVlMZTa/P3HKVyrMMKrF3H/KPYUAWjlGsXaRnXrxTIhrJwqp/bMtnphFYWIdgGoLWtddqASGuPzdA7YhNaqFZLvVJSEa48LZwUd4YSN4mJ+aq/ctSSXgtmD6gf2emV91/9KNj38bHd9l3PX0tq19dMnzFw3OSsgsWjj+zqPXn0w4On3e9nZ+NJLYFZ1yqkQ2ITFEM5zzwyA+1KLJ1kVwpAjsvSTgx3S+rQQeiisxv5Ky+9kGbnqUmllmSFEhOP6/G4ug6C2nJQUPdSt0td36R1IFMgbsUalrqlQAbw4KK1v1BwIH/udKqm8NCQbeMHP2LUtVk3rv7Fb4712N3Tt/DeaWvZt3+8wA7swe6Y/5cvjv3I1rHJn+AyhLM44ODVn14/7bBUDpq/hpxb8c388XfdM+rU3veu+Tws17Pv7O79aFvzMnvxc3aaHRq8sAZX4jgUsP7CfvYntoNhGYquJiAAAKJNPAIyWLjk0ojFqENR0SwqyILNaiG9I0bRYhFECoKD518xh6iplZYz+5W8H0OIlBsz/tURB6IHmnaT7itJORvb6A94cnbjGZYvHrnSg0zENwfPGTGddQIKJwCEo9xyW8ALGdA7nO0UUg1Wn89iEGQLjwd01iRrUlXEarWAxVcVsTjAWxUBevt4QnM9/gxBMbluwe4SAjxpj/mcgN0ef3cCt2IAhVVLsR/7+TIjjZjU9PTeY1ew4I9/Ovhn8cCeI/Nf9BnK2Pk3/kZ7TF00+6HoquhndauXPAGAMIdb09Oqr8gOu6jFpbdQb5IDekccglHi/HK2DL+4emRymUNIE3+Ro3WokKfbtNP37Cs0/7rxjQ0X2Cvs2Rex/NNLuysbxBB7lX3FPmdvl64rwyU44QusOVSzuj8AUTgmDuEc04FdsYcWQQ8COJyiuSoiUsFSFREct4ppwc9rSBlA+ZuAPZTBx2Az2Uo2CY/hIHysic/1z59PI/dU5CtWz+aJB9gi9gKmYebVKZgHgMq89Bc+r1GJWSSDAQXQoWAyS/reEUlCQsTeEUKRr3B03DZmUZBwxy/6S/MZmh+dTYZHt5OF4oH1LKc+eilhJj0UhpMlAKQ6pAbjTRPxSW45Q0CbAac3asPzwaNfrY9LTuyi2ilOhUvnI8SSohNapUJK7wiAaDLZe0dMgujtHRGdt4+8/HaphRyV9+rq5lT1xe9nfPc0a2IrDuKQL//9bve3DrL/so/Qj0kbVrGXCYuWZWXjUhzzD7xn/+D6GvYau8Q+Ze8H8LUY7WK6yuVQ2KdHBJ0giCCaTTraO6LTiQaJoshJV81RgnG/Qbydi5f/DYnpjc2ssZGSRrI3Ws1z7dXkYQC8NoLNxfFqVpwaNht1OotVT4GzFDJj9GrpGI15+JJiPpxLMg0v6dVv9AONx9jclFWuR6fyFGvI0TNxvRC+UjHmnkjBViRGg4Ix0Yn6RGzLWkgJZRVRDKHw1TvRrzc2NpL1J6JN5M0l0dc5snnk4+jCBF0QIT1soQCCJCMFzgtw3EBXxTekkO0+0aio0pV/bIp9V+KIgpPrUZJOFCUev/JSmsuNBjuVjDK1gKQgp2DnLbuZlRjwuJUAn2MY4nce4COtZjadZSsCntbhh6zRomMm0bbpo+bh4oGrVQLPOume7Uev/BCXo1IDsUG7sFsvcaytVpDB7jBS2aqjKCdypaUI4xPzabNJKZdj+WvNn+tsW4/RVB2xkGeEk582NR/nE3ZMwaxy2guAqFp99FZ5bu+IXqDW3hHqvLVNiOltBiTmueJRtpW9oZgjHIE9sBOOujo9+v1/fvn5h/9Eeb77LHuYa+94HIt1bArbxs6yU1iIuRjEAnYqZp+E8erqdUBRONnA+c75DE6XQaiKGAySLDuqIjKVEtavhpXmSgW/mlplYChutYXx7Ay7tLsRZ5PWUePGL949euKoYPr7t1HOh2jK6mdXrVC5wHaoXLBCCp+Zp8MeAIEa+OqmZtns6x0xC7KTL2yZM+MtlRs3J6I2pViG8q258sX7OOxndrH0tpz5ki3rzuqxivyf/DnN+WMCN1SGs8yIxKS3y0aDQdYTwePVm8EMVRGzmVDK5UepkSi6cntnp2Ku8ktw20SOf5bGNm4BcRXyGdhfcfkJ9jQ7/VXTzl2vfEZGRLeJB94/zf4+LjqZjFi9cuWqJwDVHIFw29ha4V6a0wSQ5BSFrGxTGvV4uH30CFSfoEoJiY4mt0CGlozy8D+o5jgx+6jmBbwy4BEI+9d3rHnZ0I/GN+7usnL1ey+xM389WLx/1+INHRbWXfoDLjz+6Z07su+YN73vyIFFvd959sV3qtf2nfFA35F3FQw8AoDgABCGcv7JvJ7iABSRUp1epgK3CYLmFeJ5qGYSi7k3IEsbWYFQyQrE9PWqJzjM14yPj2OHrLDdhgYZZafDrqOCmQ8UpzGUuFzsLkUnVHMYs4uij/2F/cJfFxrfee3ld8QDzf2vsC8wo5nuaa44+Mabh+ghQAAA4XW1/pMcNqJgMuooCJQqiPLlrxWvQhjgF8//SgXTwej3O6M/NmF1x8zWHdVaFh/5uU3bnwXkmg1yXz6aT6km+QwpyW6LRdQn2Q0U9TGTotqUGOKqNclWAjJldKcyenwSZ0h8cyc75y5CT3v2xU42u+nL9p6UYpSa0Nne7yy+1EQ/7PaW6/dbm0N88llHNx18ic5qnrv59RXv0YUK93QAQr1q9QNhhyCJ3ORLiskXFJMvtDT5KhocAz63Yu7rj/PIY0oTXmKdjuAkfHg/60QWROeQZnI4+gq5M9oX4lybrUY5GWGrIBJRpnoDiChTUeOcJmE+qKL+GCJdcNEhlrSb+Q6T8+R887zoCZJPFyv1ZQBBscZ6pWKmQyqDLKBgMIoCNwcUdUrMcuuKmVot8AvlzU6qi9roq82/0LSFwoaNC69OAIQGdoRMVnSRY2mRUFAYoxcJlTDIOdBSfeJRD5nMSvEEu4B+dkS6svyKX6HWC0A+i1c2Kd5c2XRy3h0mgYbo/4spg/KNEDuCzdrMFFACSacHOUgFevPMXj5rMb9CfMoLfOrSA+KF5b9KyigFJCgExOMgQVJYD1TWiQQEwrO+G5rpVFUTC3DfaPxsA1vG9pEg3dQ8jnwV9QJea2Zv0k3XKtUKsJLHIlEqwBgjmU/LQUfRp9mbCwCxTjhHHZIf9OA8AILRID2BkJ+s1ZoxwDW1OMStBHU83G1fm5MZ0+4QzhUdK3f33F8MRKk50lPCUEXzoVc4K1NnTEvz+Rw6yqMpYkzrFSFGI7jd1ooIt4LJFRHRA24o/98LVH4tX7NllapJZ7zS6LZn8QVeLKsVKjrQrxv43GPPvUychyc/VveH0F3HR77xCrNs/mPDWy89tOWB3js3Y1+b1GPe7Jq5dxTuORZ11TZuHC3LD00fOhwI7OVWtVZygRPSeVUt0+D1Wq2mVGqiGX4zmNwOu8HOhccRljzgqoiArYV5DSXF1SDB1sddEk825YBijeRQiVcrvHAqyJ5Pv/3+k0l/7GwKzGzQ6Wa811i/qXFjfb0wlJ1jP/DXxwMGLpdcbNHcsTuWvv7ll29fOPPJXwAQpnMOLxWGxbIaK6VuPU3ySmaOmQ0cHDPPzVmNGM9qlJ1DHgNzu6hmOGTcZXYV9f8d8HTbUOn8QrbvuW11Tz3swiw0oRPvyPQu96Sywe9+2mlNGRBlVqGU88fB+dM97E+VvGCx2CV7ht/htgIgmqhez9mjt1FnRYR6bscerSYTkLTqvTcUDPLPA6osi+JOiG7ST//n2W+/++TCTLMsNCxmTzdu3Ny4evOmNS9gNlr5647tA/rh0V+/mfny+4Gv3r54+i+fxLF0cN44IRk6hdOTDF4jpdzqtkrxGit4uRskyaUyyqIw6paZQyiRZQ632++JsUuivNbh53Kb+x/2JYp/e/+7qFl8eecf/zBk65bfb7WQLstc2AZl1GMH9v3fJxx/p2pttp/+c/eGrS8oUksFoBYpHVxK3cVlMjkJ4UaSuj0GvhQMgKIsVkScspUqq0GtY98IAxWmOZS1p2QNgeJSXkPW3DX3mE+zrxreeANH3lObN6LH8KHopW83l9G3+3TugmsDC9PnPNkLgEKQuYQCzplcKIVu8HC4a56vQ5YpvYtY4ESnSHIzW6Vn+Qzd72xlLbYWV0R0nXpFDJm6XKvOqvPk5pJekVxrm/JekTY2T7teEU9KnHUa+zj/8pXd+rzbxD1uragaVBdAqDC+jaAUkrJv/OXKcGMXmJOnbhQXF/F3QsHJVnf87VhB3sSqoa/te5X9jf3r7FdPzMgtC/ccNOnTtwb3ZPb6ZWdOPLzh7amPD50/4z8/1T4uVE5ICkzt9ewxXYdBbfPqVx54ddvqMauTndXFnYfmBnY+2PS66ypEhs2ZFOn5IO08/ZFvfn4cEPYCCD24nnuUzM5i0nFz7dF7vEkWvcMhVEQcNgOA3q0Y7xjlCatesVT2mALbtRUfM1P06cfm/+GZhgadoWD/jBMnyJuLfn/kk+jrfHXnDOow4N5XP4gWAxDYDoDjxAtAwcr9tZ3PJCDa7Ga5MmImVlQ04/3EwqZSIqAJJVQc3NDQ1CG3TceObXI7CJWYU1Zc0qFDaSkAubaKudSxTZAEd4Q9TqPRrNP5kj22yognrLcC1z6ISzW5xSTOhATTljhb3v2det7Zv/eNGZnLt9g16B6h+aqNHZHv0yaP8TSV89QGJTzetxgMRqNOEkSdYHeYAGw2nY7KRje1xiKGfD5zeUyFyuJsRTUiQi0bdclYkzcER73JeuD5E2zOnB07dKSgy2icydpGlxLpQTZOcjW/XTo9NjcO5nNT4GQCoiASQHfca2tMVBjHYVRo6SRfJQGoCAfcdruDiz+gdwRo66xWHrfb4RPMPm5p0302p1UPDkUPuCLEt534Igi1bHVIVIgEzfAqepHh1bRDypryyOa1DVNmblnVsDhFl79rIuIAXcHhmYdfJicWLNj3cnSLcv/zx9HjQmV99dDDg8e8+heuMZq2cnxdUBBOApeiri69x23S22xcWW02g/V2ytpSV72Jmrp7m4JG6NDUt95RNPXwJ+q8d0XUSWM2dhSfU9EknsU6wSyDnOwzeLgds1GbYvxvmcVylSHFilGFxE4PYRT74fKaf/wOTZcvobX5lZ3PPffii88/10Cy2I/swyeR/AFNmMfeZ1f/8rfzH545p1j5vdyW1apU+6E8nOEzCrKsS3foHJkBwQhWq7siYrXprboUaHXDzMdZ0GLBqpaeO2hPAhMUr62Y+gRHrThpU8Niry7c+PBf/+f7yzvryabGFc8+6xowcMRg1kUqqh9azT5h/1GcNr14+GTWl29fevfUeYVXHNNSlVexqMKW6qHJyT6bL8OfnOK1pqalecxOp8wtv80MFRHz/+Y2VT5yJ1l63Ul6r3vQ0njtQyL9GzaIW15cvXnjnI8uf/fJ57P0SQsajObpM/d9mHXp3YunT59birloRDO2a6z/9T38eEzFCzE9okGOpw1ywy6zXm8wEF4DsZrB4FYtg03rc2nRkaE5IY15ZEfvjt4eRQtfaahz6rrsFoaZNlk/fTbaJFSenDQjlrnS6XyW1twOtIplrqLzeuZaEfHYJKq/rj/5t8pdueG5kbsG25Hfpq50+j/e/+tjA/bXzF82+dmN88r/evSPL3Z6ftEjj7Yds+J13jSzsaHnpjbt7h4Uvrdr2aAH+yzaXLm4R1W3O7p2KO71FCCkX/uG7BQrwKPWJlwu3jPioEKS1+C0OXtFLGGbVeaCkj1xU3kqIVjV5ONWqo52xVGXhtxKNuHyEMcdA5NSJuSy17ZurRiBXdlrw2vN8lyzHQeQZdU9/83mRWePngiAsIOvrjKhElx8fh86ZZPJ4DS4PSaz2aZzWdVV7TFqEbMS/4daVmW0rJcrhBY127EvX9TPNNQl6UP7Z7zztlAZLeMO6GMSvnpozV2Dj54hp7RcjgiVau+HAQ0ms6hHK6jhiJZl+NX0NFTicIYQt7ER+76ptuiMte/tYyP4oI/8o0cx9iPtrx6K5UpSgI/Winsblz4lNc3rsZipYBZ0yQ7ubnTuxCyYK7c2A1U2Z2Rlk8LhUHSq1BmbsoRPKeSfcBbp2qSdPsY+3jNxsk5nLHCcaHqjg0snBF7dzc6QBZ3OvHR/dK5QyUaz6j5l+4tJbXTp7trW9eRvHClACAIIOpXGzLBdFiVAUWlxQZ3RLaD1pnQ4ngmjmhUfYgteQT9m/JktwFVH2Cn27hFSQLxsGO6IfhU9jUdYD0AgfL1LfHw3z/sVMqnHK5jB7OBLO0UHfIJCVam1GRJo46KKOdrSUrLvuwFOnfnuS/tYTsWfl/StKu2xq3cXzuCVn9wf+pn87mrGy5vtC03HtkAsZ6YPCZW3yJl7RUQr6npF0P2/5cz0oeZ/ksHR0+TL6D5y31Q6eN685sPxrixetlPl5/YlJxu9AFbZRbmnpqlpTq09K3F7TdV/bpXcPJZTfEtxCddDvj7d3EK4ZLfHjedrpx794PFH58/49MClCxdM44aRZaRxE+aPjywnw0Zg4ebdS6Xj7NzZoCl4FhAvMxuZrfluorSo0RSABN+tlHzx8nKeJv3cDAiV7Ijaw5Oq4OwWDQ4H8UFqqsXiE2laujso0QScEzYFFXSDxYr7U7DPVNCV5Dj2pcRw4eKhDx+Z/9jjp45OnvHwVFIePIvB49LSPRvZ+yPvJcsjvOq5cRenZNg4zJn2qEvdpyXVQg6tAS/XAzu1JvkcpuoIdVglCaojEuTngS3pjfw38rSkOlOZT8nQVNOmbD9lKoU5HFg8t2TMUz2mRrqPyi95omTcisrHK/sMJSfuLFn/UKvsVinhsvqH/RkZSeoOPFuKdcJwrcuYCALV8343AGpSu4xtNPOWXcZcCQNO1/Xt0PNKk/Gszp3Ly0IVZPfVC2Lfxb3C5ZVhQDjK7fd5dVemazjNozNTahCARxo62irVJxKnwUz4SzDKgg+07k9ljt9sw2apra1KOJCldLR6NAOuqD89OWHNwpPHcdniPisKChY+tHv7My8sX/FdifTO+xlov4LNXXfvoH7vstCH5z462QkQypUYSDzBpV4Zzk5y6s3mZI+dGD1OMS3dlORL6h/R+3xOcNr6RpxJIPa5uRWkRdPQzZ6Nm29lf5Lfinl2ypuduEqQxqONXTatnD0HG9jQblU05erVU2+99f/EEzUL+/1uGTs397MxS+7YtDz/xwtzsfO+U4psZqMkeIVtnHNByAibW0GmBSxtctLd7iwZeNSYn1gJchaVBku9il8r9co82Ja9clCxDnKwNLs0IXQ6VLV4+OLx8+eOq7t/UVXVgmF14+YuGrN42MKqeVtnzHh627QZW8mHj01aNmxh794Lhz059ZEFD/CHvfj7JZN+N2XbM1Onbd8BiscDEJT9Fw8MDrdzWGSj0WYS9URPTS6LW/YmGSwW2So5HBScbqsz3UmsTqvThG7JlATlWg+33RHrzL7lpjuGUOGj1uaovjBEKnH2HjYCJfY6dmGv72BvYGd+ARu7j1wgZ5vZ3Ma57Ec08RslQBKsgaxUVYkkUR726QUqUDlmFjgmiYqtbgjFLYRiI5p/YebmnxVpXPuF1kupUABdeGdcdiE4pdy0Dj5fmkmCgNS13E07lbRqK/n1/mCviN+tt/WK6OGGznh/s4t9I39VVFmLztSUlwuwZdCiRC2l/Kk33lG0dHD/qprTbw5/ZmTxqMV9Z8yYvelw/cCqjf/+6K9P9H9t4KLl7R+cvmJR99W/f6Ggbs3LPQbRnMF1WW0mD5q1NDW4IJjSKdy5prTH+klDl+fctXrZxm5rs9r27dWuY8e8oqHTRvWb0MVZPfnuKWXOMUCwWLTQ8eKH6u5TWpiTanKAI8lnpW495N90QCAhzctKeI/FxVnZpaXZWcU4pzgrq7Q0K6tYnFrUrl1RYUFBYfwOQGEM7xzvEdt5hxKeSwWDXmrNT0936a1esbSDZAKH1ZRuIuCwOYjJYXKk5AWcoRQByhNPBdhblgFRMxHuG90bnN2obu8KDjc3eYHM1py5DiFU2NqhNXTQOXMWz10weE77sRWvffDZq0880vHB5vXv4PB3les1tv2D02z76xP2YNvdezD3pT3s7N497JOXhMCeTTu3t/2dq9X3n575qfMjIXZI/Q7b/u6brOGD0zj0rT+wD/+wB3P2xr8GQKCCushU8W1OdzqUhlt5pRQDokeJazP8rQwGh88D1EYJNTvSOakf3feGku9qVGpqG4xTV8ojfbXWGSt18iYUtdZJXEnDlt0/edPztWvHjM+btnB+HauecmLUlAeov2bk6HHjJkhCcGFoRIcJs1jnI2OaCgRBqd8NhFraSI+CBGbICTupxI21YNTrBbMkWKwmUYegHGS5WbPRiyhjVuw2EAfPVEriM1kjLsUhtexzTK9lO0kQ1/dk29mzvXB9yo23qh9EHfeDXhAhJWwiKKAki0J1RCSQr20nattixUJOXfM71Bv9Hhc+CdeuaV3LRAIbAAjXdUoX16r7wqGgF3iOLui5Zpn1JodXKu1gsnFoi9Pi0DmtjnQHAR63E4fT4bythikCCP22ZKVVoUS+hp0Bqm51Fnr+L2UjHz5YPXLwfRNx36B+l3eeXrwWxYbNVy/8n+pGrtwd7tNtSfXsNFaLo9jTdPZ89ub/pXB47YrkEiRpzW3r+oJ09UfBJLnmAoG5dBi5LJ5U83Z/2GIGp7L7nGwzHPNQhS3J7yWaAKe27LkytvA6c/fPn39g4Oqa+fun195VPX3qwLunC2vmH9i/oGZlTdOCgdOm3l0zdZoiv/GASic8yQYLAMhwBiA6Q93NqCLLub9OUmpcstOLaHGCwAsItnQvZqjyadHEUVx6cz+0JMt+sjy645vIQH91edGont0XbPj9msiaPXiIVI2/NHhk35IePbMLh0yeP6V6/ZPPA4KflKlzBqAsnGkVRaCONIPUOstxn/MhJ+nrRKMzxUmcTl2yP92s88eVhKvIfTe2KDHRmKtlyd/2PpPpA3vsPbRzw4w1sz/8snbmA6Or7+w+pUPP8mXDl2wVvqx+wJu//YmVHWb32L5q0oAeXXrkBYa2LZl5056LnkfvwhP6xD0X5YAIN3pyAOvaT85494494cnCD133dnN3O1oEqNZDegiV4IHicLJoMOhs4HS6dC6+LeC2ulLMRKks6LWkMWHX6XqfaELKyMnTOhsGs13PNCxJNkz+Z/0Qg6GhAeewK698pKaNLwyr2caOScrsU1mzMEJygRWCYYcgIoBopDa7TidSq4jaQa/8RJkG7MortqVTEvILI6Z9PL1rzacn//ov0pY1S3t/raYhx5WrKDBA2ED6Yh0dqvitsEECMJuofkCEQsyAJOqq2jzatUOseZR82L1nz+7xMwlZzIVNAOBQIge7xQhgUfrILXa7jtog/71CzQq3qDNoZYbSkOzBpo31obZtOw24a8BDQx4ubWIXRk7UT9S1Kckrtu+bHgSEvqQKP1d3kPleHwFKDSZuX2mGBGlK3sc5EGO7FpnEzw8MXLlQ8pQsvpNv4K4ld9471NP2/hFAoDt1kaPi26q3zgo7lONnEnBvHfMfbr3iP964r4XTTjgzJSYsWHJ0V/3qF3eu3/B8lN07fsKwYRMeGCZM3nHw8LPP7T+w/TH+b/YjjwCBau4hdsY9BF+ZRr1AgMrEoJdu5R/4fBhELEUxdqM72c5aTGef1+IQVnvjPTGxCb3wfhzek01IufGW24c+AOIZzq8gnCYLACAbHrsGKMNHNDV6EPR/osTBA8ziYuCw7Tjs+ThseQz2CwV2Ou3PYeV9xMZBVchkAMkvnuAQM34FFf4CxEZ9KD5qXmxUIBBiM2mNMBxSoY3Sba1zpQWwlbVVwCXk5EIqmmhqKj93lzEgkm2zG3tH7IEWecP9w+9rGZ4ohslCYnXDUm9MGF2J0ihbnJBfkf59Rs7q4vv9Y9X1ozq9+dbRTwPhSMnYbk2zOnXtXqqkXKHH1tZM7NOvw5ip2e0XjzjcWDEhMjB/yIz70jFvcU/eGRvmVKrdoPJ0bltbq9R1v/YaDgTdn4hNzIa84ltA1MLCGETS7SCOQSAGkdoSIv86xGsg3HKMrOsQE6CUQxiaKGmtgtyAkWIwIMNxKIN5QK4xAIk3MIIVnNA/fAdPM+wIOhPaRNEtuvROycm7kHm7iMHM7wabASUqOtByowkglmHm5an5G8bOiYau9y/SAF7vYVQ2zqR5UUeUXdxLDtMT0SMkNXqR9Lhag0cfURpetbZG/AvZr2jRHOZSOkc5ztkqzrMIAf55rM9N5VmbON8PqhxBs8aRmyFqoTwG4b4dxLFrV2MQyS0hsq5DTACHylWC/hhXgUA+gFip9id54Z5wod3t1glmAKcgCUk+rogS11erXC6/JJ+WL8jcIsuyoNfbqiJ6Kri17tNEXW55EDWhHZV7uVhLarxnM5QhVqpNqbM3bcJ9eBf+bn/07S9xNlt4lIyKtaWSunqyntWxHSQcba5nhhhNYrmqS+3jurSmJdWx7jiVLwUx3sKsmLb5bgdRi4YYhP92EMegKQaR3RIiX4PgeGy65RhZ1yEmwMdxnW4b5z7CQrQJJmEDGMEX1st6ino0mXXgy0+0x2rMHLeOu0ewbTh8BHua7RiLw9m2MThS2DCa/3fbaLyfPTsaR+CIsWwrAOXzv877434CJ6RAQFkZnnRvmsAPExtcAA6rqFMCF0+a32f2945YHTpRoDazQHnjnES1lrm3+Fq4+YgL/ygm0lglwc7fxSoM1BZEj3qKzovZ1zsLv1479tEH9ykddGe2jnx04rGmh6Mjpu/9zy/NwbFk68SdWpPhmOUDNr2FDyl9dMMXV699l61D26bmvgOVZjp2ZRN9qTc7xVdOrI9LlUxpXLoVMfk7Nb7fDFELp2MQKbeDOAZzYhAZLSGyrkNMgA3xlRNMtEfCbHWUTvF5CmKjOFSQeO/frHjvH9+pMOtFUbKDBB6vWeALiC8fs96sl2LdkZoVarkRrHVH8v9lCDcaJGexM+zzQ42NZ9GHnuYrO3mL5LvvUdvFy4zXWq/B6ei/V+5Y9yQAqv0oW6R0aK94ppxcMTUAXpMJUu25YkGhw5Hbrl12RaQd5LrV3S5tj+vm0xpaZCBL2vZIQjWCo6Q2/2lnOTKUqE/1UYJv5ZAOKb36Lxv32p+OTCrfUnn27ofnjujZq094yVz2TcPf/v7+58IPi6dX3OnPyC0L3b917LZdPTcF8w/0mVQxcHZN+cTisqHF1YMuXO0r7Nv3562c52pXkOTnPL8TACXovgLUVWlXOH6L57V56vN2t3t+7FP1eajFc/Gz689fe+UW3xc/vP58whegruiOKsCNGRZehzj+cwyiTQwCqAIhKbtXOVDENWdkOJQLre3tedlIaF+WlJTe3ghi5y4pbYNtKyK+AqGgV6RD66BdECyZQU+xzqKriLgsNtBaO9R97viBxZsNL1corarUot3Jy/+qHSkOv7bLFExMz5TiAMaaVIb/wg7NmPnUc0VVb4+a/3xO8a6Hj/0reqcOO967tWbwurHswpy73lz03Mt7Jg1ZtfPpwzvoK7OWGon8BOY/+yddrEUqp/ie+4eMYP/9+yRWGwjyVpav5k5sXH9/5MVNo2XdQ6Sw4ektO5V1zXc4lW4kzreeMU+JFaqnVDtxVIn1ikl8vyqRVppEbn5e21993vp2z4/9rD7PafGcS1R7PsEQk1d7TaLX/gqAo9URXolZHHYXKGOgqI3xIgApTICovZYRgzDHIa79iUMMSoA4xl6IQTg0iG84RDrHQ4OYwA4CqBbHZ9d89VRlx1zyq6euqsJ5fsnUqhXwYN5jsTttkj7YRp9eETFSj91nsfLIR0+9LqSttY3QmLJw6/3b430QyITiIlAqxdlBMcj/lHpUk+6gRVqnV4kwil39+e/sK5T/9sUYXdkp9n3vr4YN77ll3OW+pzc8v7NpC3vppe0vPUtC7Ev2FzR/cQmlWcInr25+cGHXgtrefZ6cNHMlm8b+taaRbXjh4Aku21jXgbraqmOrzaLyJC1RNqNUrt0Vk/1HquySb/e8drD6PPN2z4+p45Ngi+d8fu35a9/f4vtcJtrzCSkx3Wh3fS2Ph2YhR9gJVO1CD4WTPAaDTSACKjsZTifKZjMqJ/QQ8tX1yhOfG8nPjUN6iccXE96Pp8ejezqVFHXsFCrqot3J8iefZP/q3KW8Y1m4nPwYfwOUY3tEGCUsjvv7PvxEa3orl8vQ6iZn76u47uxt1M+b2Kjnf3P2ZWVxBdGcfXw7QXSpTl4Si1SnX6L2X2yaUjNt+Dw0Xd40o6Z25NzmV4rxTJ9pvAljfYjl95r63Iuxboyetf0XbEBQGjL6zuy7cMOvu8aRRcWffLRjTHRO6DzXjNjutSq5e2KSf0PVDI8mmZuf107VNOfWz4851OeBFs+5ZLXnE/yxtZarrfrYDqw6wr2xGWIjpKsAWu+I2t+VyXex0jOkFJfNZpfsrQMOsKeYPHqqT+NdjB7q5euvRZPnb3oYUWsXUUomXo/W9JUVbx7J4HugOKR748Sz333/yd8fMwk63mSElTs38OYRzF9LmyID2Efsvwpjn83sV86KdcDaFQ1NOXQi58u3ce/ZMxo1nF6Nmgn7Y/TmxejV+puEyuv9TaJArLfsb+Iw6gkU6UvxFLggHe4Ot0uSrE5nKpjtqZKY4bc6eDxpBaOR51hGGj+Vwg8UUAc4b5zk4det2ia1fWVJO2TlvZF9aafq7NnSl1EYN4y9zJ7BYRgeN5RaonxdR8+Rfs09fmXXEH+ecs89LqzDiTgeF3ljSZmwlZ1m55QTGn6hNi32qy1yujAU0iAXCmBQuG26zkI8nqx8t7tVlk4oDOW1Mbbh0RHvSCKixdiunWg32pIyxcyKCIieFj7YoVjVRAeseV9R9a0q5rdyvYktTFkxnyvWs/Nzup6pu8B+ROnrBae6djz2+InL0aAOq4Y/e8+QDVf9G154buPm5xvWCb3mrjKRjN+7vp4xEwtQh3q8Y+a0KbPYz19MYDO5tw1mkLIPz3985rOPP/10x9NP7wBEE68Q7pH8YFF6wGWwWXmN0KJs3CSfKkwsE/Igzx1QzhIE0DR3nLfB89CcmUMWLuFF2u+WPJGTu3C+t3TBoiIAgpP5iG2lhdp+kEMyxSpMejflw753u9KSrHUfcfpp29njxj46a8zY3z3YPRTq3rmsqJu4b9TM2lGjps8c3qFLlw78AkQdn+k78TN1N5wPn+Szg2gC/nKrZc73En4mKLYb3o4vKU6BwvQ0olRTQpJEXXkDB/TOLAxZRpmn39tucP/KjIL21tHmqcL5rLZZnbvMquO3Tl1n1aldEci5Ff/FEyCCePMvngykw+K/eMIh5f8VUtYgffQ49lB7+R0HUNTpQenhP6WBBkscHEs5y+QZ1WF29yx63DMUTVyicNM3RdTpRZly061Rq55Od5RisXIk/bGKDPGARzmLjqmfcouq/e4LkcAKAEQZizSpY1khOWwS0KwXbHbQUZP2M1+x3pUgbyrhA/vjeGG9tcNjs9M6maNnb2B4FnXTeR1Tw7TF6DZldL0ZRcHuMIs2WRn9LW10DWe/ei9JQJ4ELUkjOsxJ7m6+QYbnXvbTY2Ow6D6FHh/7lTTBZZSVLOtqB8g4iCCHzeZK+dC1Y38ymWJ3vb5SBnteXszG7cAfyXB6EYzgPBD/URrIP3Wr6u+OqQ9OmDF94qRp5JtZj/9u9sx5C/icym8TiHvgB8gGOwAEwU4c/M4nELJA1RaoJelK5ZPTbBAIlYikk0WuCInpvPM3e2CJ+16ASv2UpGqjUBAIkMRRWhRNSeqtK6QAyGYBkJXxUyYgEkE7ZYLxAQJIVjbPWkkXx4+ZIJRzr1gnnuT0TQ2Xp3rTPZ5kI5Hl5NZ2wZDslYJtjN4kb/+ILklMTUvtHyFp1rT0tPw0qqdJaUlpzsxM6BvJlJ0W3iDhg5ZN3bwwdMsfKruRW2ZQbuRlt9evdcorVpPyolGwuJT/dUDsCHUKOz4AWfRHQvA065Z1snHLxtW7/oddaNewgZANO4LY+n9OPN+rQSxmD80rC7ed1/Rm9/puaEacl3tH9TwUsfXIpYPVzprl6o4iBXdYT0AUtDAtYc3y+EuJtrjkUwGEVlI650ylKvE+5ABA/HNTwuf9lc+BgItUcf0/AgZwQedwuks0ypTyaYjSqY+iqLe60l3E5aIWOZ1mxPuV70toergeGwR4g0v8V2eKi0otVJZJ05xV7GHcsHQO+0ESk9LSjDup6913x/KzVKdeX9THFGzb1v5TDDfpQ45bECoJ9+43cBcf0nCXXr/F8/43notvxJ6rVEnqc1TWG05X9cp+AAQRKWiHl2Knck80KgqljCAC4Aq1QvJpPHP6XaxCImp1FiUv6pwAUXstt2Ud9NrbHGJCAsQx9ufEKktsFtJBzroOMYF9EK/V+GK1mv8PflNJUQAAAAABAAAAARmahXJJOF8PPPUACQgAAAAAAMk1MYsAAAAAyehMTPua/dUJoghiAAAACQACAAAAAAAAeAFjYGRg4Oj9u4KBgXPN71n/qjkXAUVQwU0Ap6sHhAB4AW2SA6wYQRRF786+2d3atm3b9ldQ27atsG6D2mFt2zaC2ra2d/YbSU7u6C3OG7mIowAgGQFlKIBldiXM1CVQQRZiurMEffRtDLVOYqbqhBBSS/ohgnt9rG+ooxYiTOXDMvUBGbnWixwgPUgnUoLMJCOj5n1IP3Oe1ImajzZpD0YOtxzG6rSALoOzOiUm6ps4K8NJPs6vc/4cZ1UBv4u85FoRnHWr4azjkRqYKFej8hP3eqCfDER61uyT44DbBzlkBTwZD8h8/sMabOD3ZmFWkAiUs5f4f2SFNZfv6iTPscW+jOHynEzEcLULuaQbivCdW5SDNcrx50uFYLzFHYotZl1umvNM1tgNWX+V/3gdebi3ThTgVEMWKYci4kHZhxBie3TYx3rHbGr+Pdo7x4dIHTKe5DFn+O/j+W2VnE3ooW6isf0LIUENvZs1gf/LHojJwdpplCP5gn/5gi26FoYa19ZVFOJ6Sxuoz/q2Ti20IKVJdnqvYJwnhfPH/2f6YHoQF30aZaK9J8T026RxH5fA/WPW/8IW4zkpnIfoFLifGB86v0ffm5nbyRs5iaHR3hNBD0HSfTzoPugRM+hdN0x052KoHLBS0tdgpidAiEesDsgWYO73RWQz2LWIwjqnMe/uYISQtlbyf2NlT9Q9PoBcBnrO6I5ELoMeyHkNnIXGdv809H/DXNOTeAEc0jWMJFcQxvFnto/5LjEvHrdbmh2Kji9aPL4839TcKPNAa6mlZUyOmZk6lzbPJ3bo56//Cz+Vaqqrat5rY8x7xnzxl3nvo+27jFnz8c/mI9Nmh2XBdMsilrBitsnD9rI8aiN5DI/jSftC9mIf9pMfIB4kHiI+hWfQY5aPAYYYYYwpcyfpMMX0aZzBWZzDeVygchGXcBlX8ApexWt4HW/gLbzNbnfwLt7DJ/p0TX4+Uucji1hCnY/U+cijVB7D46jzkb3Yh/3kB4gHiYeIT+EZ9JjlY4AhRhhjytxJOkwxfRpncBbncB4XqFzEJVzGFbyCV/EaXscbeAtvs9sdvIv3cjmftWavuWs2mg6byt3ooIsFOyx77Kos2kiWsIK/UVPDOjawiQmO4CgdxnAcJzClz2PVbNKsy2ZzvoncjQ66qE2kNpHaRJawgr9RU8M6NrCJCY6gNpFjOI4TmNIn36TNfGSH5RrssKtyN+59b410iF0sUFO0l2UJtY/8jU9rWMcGNjHBEUypf0z8mm7vZLvZaC/LzdhmV2XBvpBF25IlLJOvEFfRI+NjgCFGGGNK5Rs6Z7Ij/45yNzro4m9Ywzo2sIkJjuBj2ZnvLDdjGxntLLWzLGGZfIW4ih4ZHwMMMcIYUyq1s8xkl97bH0y3JkZyM36j/+58rvTQxwBDjDDGNzyVyX35Ccjd6KCLv2EN69jAJiY4go/lfr05F+Ua7CCzGx10sYA9tiWLxCWs2BfyN+Ia1rGBTUxwBEfpMIbjOIEpfdjHvGaTd9LJb0duRp2S1O1I3Y4sYZl8hbiKHhkfAwwxwhhTKt/QOZPfmY3//Ss3Y5tNpTpL9ZQeGR8DDDHCGN/wbCbdfHO5GbW51OZSm8sSlslXiKvokfExwBAjjDGlUpvLTBY0K5KbiDcT672SbXZY6k7lbnTQxQI1h+1FeZTKY3gcT2KvTWUf9pMZIB4kHiI+xcQzxGfpfA7P4wW8yG4eT/kYYIgRxvgb9TWsYwObmOAITlI/xf7TOIOzOIfzuEDlIi7hMq7gFbyK1/A63sBbeJtvdwfv4j28zyaP8QmVL/imL/ENJ5PJHt3RqtyMbbYlPfQxwBAjjPEN9ZksqkMqN6PuV7bZy7LDtuRudNDFwzx1FI/hcTzJp73Yh/3kB4gHiYeIT+EZ9JjlY4AhRhjjb1TWsI4NbGKCIzjJlCmcxhmcxTmcxwVcxCVcxhW8glfxGl7HG3gLbzPxDt7Fe/gY/+egvq0YCAEoCNa1n+KVyTUl3Q0uIhoe+3DnRfV7nXGOc5zjHOc4xznOcY5znOMc5zjHOc5xjnOc4xznOMc5znGOc5zjHOc4xznOcY5znOMc5zjHOc5xjnOc4xznOMc5znGOc5zjHOc4xznOcY5znOM8XZouTZemS1OAKcAUYAowBZgCTAHm3x31O7p3vNf5c1iXeBkEAQDFcbsJX0IqFBwK7tyEgkPC3R0K7hrXzsIhePPK/7c77jPM1yxSPua0WmuDzNcuNmuLtmq7sbyfsUu7De/xu9fvvvDNfN3ioN9j5pq0ximd1hmd1TmlX7iky7qiq7qmG3pgXYd6pMd6oqd6pud6oZd6pdd6p/f6oI/6pC/KSxvf9F0/1LFl1naRcwwzrAu7AHNarbW6oEu6rCu6qmu6ob9Y7xu+kbfHH1ZopCk25RVrhXKn4LCO6KiOGfvpd+R3is15xXmVWKGRptgaysQKpUwc1hEdVcpEysTI7xTbKHMcKzTSFDtCmVihkab4z0FdI0QQBAEUbRz6XLh3Lc7VcI/WN54IuxXFS97oH58+MBoclE1usbHHW77wlW985wcHHHLEMSecsUuPXMNRqfzib3pcllj5xd+0lSVW5nNIL3nF6389h+Y5NG3Thja0oQ1taEMb2tCGNrQn+QwjrcwxM93gJre4Y89mvsdb3vGeD3zkE5/5wle+8Z0fHHDIEceccMaOX67wNz3747gObCQAQhCKdjlRzBVD5be7rwAmfOMQsUvPLj279OzSYBks49Ibl97In/HCuNDGO+NOW6qlWqqlWqqlWqqlWqqYUkwpphTzifnEfII92IM92IM92IM92IM92IM92I/D4/A4PA6Pw+PwODwOj8M/f7kaaDXQyt7K3mqglcCVwNVAq4FWA60GWglZCVkJWQlZCVkJWQlZDbQyqhpoNdAPh3NAwCAAwwDM+7b2sg8kCjIO4zAO4zAO4zAO4zAO4zAO4zAO4zAO4zAO4zAO4zAO47AO67AO67AO67AO67AO67AO67AO67AO67AO67AO67AO63AO53AO53AO53AO53AO53AO53AO53AO53AO53AO53AO5xCHOMQhDnGIQxziEIc4xCEOcYhDHOIQhzjEIQ5xiEMd6lCHOtShDnWoQx3qUIc61KEOdahDHepQhzrUoQ6/h+P6RpIjiKEoyOPvCARUoK9LctP5ZqXTop7q/6H/0H+4P9yfPz82bdm2Y9ee/T355bS3/divDW9reFtDb4beDL0ZejP0ZujN0JuhN0Nvht4MvRl6M/Rm6M3w1of3PVnJSlaykpWsZCUrWclKVrKSlaxkJStZySpWsYpVrGIVq1jFKlaxilWsYhWrWMUqVrGa1axmNatZzWpWs5rVrGY1q1nNalazmtWsYQ1rWMMa1rCGNaxhDWtYwxrWsIY1rGENa1nLWtaylrWsZS1rWcta1rKWtaxlLWtZyzrWsY51rGMd61jHOtaxjnWsYx3rWMc61rEeTf1o6kdTP/84rpMqCKAYhmH8Cfy2JjuLCPiYPDH1Y+rH1I+pH1M/pn5M/Zh6FEZhFEZhFEZhFEZhFEZhFFZhFVZhFVZhFVZhFVZhFVbhFE7hFE7hFE7hFE7hFE7hFCKgCChPHQFlc7I52ZxsTgQUAUVAEVAEFAFFQBFQBBQBRUARUAQUAUVAEVAEFAFFQBFQti5bl63L1mXrsnXZuggoAoqAIqAIKAKKgCKgCCgCioAioAgoAoqAIqAIKAKKgCKgCCgCyt5GQBFQBPTlwD7OEIaBKAxSOrmJVZa2TsJcwJ6r0/+9sBOGnTDshOF+DndyXG7k7vfh9+n35fft978Thp2wKuqqqKtarmq58cYbb7zzzjvvf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<h1 class="title toc-ignore entry-title">Analysis of Which American
Weather Events Cause the Most Damages to Human Life and Private
Property, 1950 - 2011</h1>
<h3 class="author">Bryan Murphy</h3>
<h3 class="date">2023-04-04</h3>
</div><!-- /.header-title-wrap -->
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<div class="entry-content">
<div class="sourceCode" id="cb1"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb1-1"><a href="#cb1-1" aria-hidden="true" tabindex="-1"></a><span class="fu">library</span>(tidyverse)</span>
<span id="cb1-2"><a href="#cb1-2" aria-hidden="true" tabindex="-1"></a><span class="fu">library</span>(R.utils)</span>
<span id="cb1-3"><a href="#cb1-3" aria-hidden="true" tabindex="-1"></a><span class="fu">library</span>(lubridate)</span>
<span id="cb1-4"><a href="#cb1-4" aria-hidden="true" tabindex="-1"></a><span class="fu">library</span>(forcats)</span>
<span id="cb1-5"><a href="#cb1-5" aria-hidden="true" tabindex="-1"></a><span class="fu">library</span>(stringdist)</span></code></pre></div>
<div class="sourceCode" id="cb2"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb2-1"><a href="#cb2-1" aria-hidden="true" tabindex="-1"></a><span class="co"># This is our initial setup block, named setup, with include = TRUE so it will show up. </span></span>
<span id="cb2-2"><a href="#cb2-2" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb2-3"><a href="#cb2-3" aria-hidden="true" tabindex="-1"></a><span class="co"># First we'll set any global variables we care about...</span></span>
<span id="cb2-4"><a href="#cb2-4" aria-hidden="true" tabindex="-1"></a><span class="fu">options</span>(<span class="at">scipen=</span><span class="dv">999</span>) <span class="co"># This stops knitr from displaying 5 digit numbers as Scientific Notation.</span></span>
<span id="cb2-5"><a href="#cb2-5" aria-hidden="true" tabindex="-1"></a><span class="fu">options</span>(<span class="at">cache =</span> <span class="cn">TRUE</span>)</span>
<span id="cb2-6"><a href="#cb2-6" aria-hidden="true" tabindex="-1"></a><span class="co"># And then we'll use a seperate code chunk to load our data.</span></span></code></pre></div>
<div class="sourceCode" id="cb3"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb3-1"><a href="#cb3-1" aria-hidden="true" tabindex="-1"></a><span class="co">#Here we download our dataset, if it's not already downloaded, and read it as a tibble into RStudio.</span></span>
<span id="cb3-2"><a href="#cb3-2" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb3-3"><a href="#cb3-3" aria-hidden="true" tabindex="-1"></a>fileUrl <span class="ot"><-</span> <span class="st">"https://d396qusza40orc.cloudfront.net/repdata%2Fdata%2FStormData.csv.bz2"</span> <span class="co"># Set the DL link url.</span></span>
<span id="cb3-4"><a href="#cb3-4" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb3-5"><a href="#cb3-5" aria-hidden="true" tabindex="-1"></a><span class="cf">if</span>(<span class="sc">!</span><span class="fu">file.exists</span>(<span class="st">"./Proj2data/Stormdata.csv.bs2"</span>)) {<span class="fu">download.file</span>(fileUrl,<span class="at">destfile=</span><span class="st">"./Proj2data/StormData.csv.bz2"</span>,<span class="at">method=</span><span class="st">"curl"</span>)} <span class="co"># Actually download the file.</span></span>
<span id="cb3-6"><a href="#cb3-6" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb3-7"><a href="#cb3-7" aria-hidden="true" tabindex="-1"></a><span class="fu">gunzip</span>(<span class="at">filename =</span> <span class="st">"./Proj2data/StormData.csv.bz2"</span>, <span class="at">destname =</span> <span class="st">"./Proj2data/StormData.csv"</span>,</span>
<span id="cb3-8"><a href="#cb3-8" aria-hidden="true" tabindex="-1"></a> <span class="at">skip =</span> <span class="cn">TRUE</span>, <span class="at">ext =</span> <span class="st">"bz2"</span>)</span></code></pre></div>
<pre><code>## [1] "./Proj2data/StormData.csv"
## attr(,"temporary")
## [1] FALSE</code></pre>
<div class="sourceCode" id="cb5"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb5-1"><a href="#cb5-1" aria-hidden="true" tabindex="-1"></a>stormdata <span class="ot"><-</span> <span class="fu">as_tibble</span>(<span class="fu">read.csv</span>(<span class="at">file =</span> <span class="st">"./Proj2data/StormData.csv"</span>, <span class="at">stringsAsFactors =</span> <span class="cn">FALSE</span>, <span class="at">strip.white =</span> <span class="cn">TRUE</span>))</span></code></pre></div>
<h1 align="Center">
<strong>Part 1: Synopsis</strong>
</h1>
<p>Adverse weather events are a significant cause of damage to human
health as well as private and public property in the United States. This
report analyzes the effects of weather events on the United States, with
a focus on the health and economic consequences of adverse weather. The
data used in this analysis is derived from the U.S. National Oceanic and
Atmospheric Administration’s (NOAA) storm database from the years 1950
to 2011.</p>
<p>This report concludes that adverse weather events have significant
health and economic consequences in the United States, but that the
weather patterns which cause the most damage to human life are not the
same as those which cause the most damage to property. In particular,
Tornadoes, Heat and Lightning cause the most human injuries and
fatalities, while in general, water-related weather events, particularly
Floods and Hurricanes/Typhoons, cause by far the most economic damage to
the United States. Tornadoes, however,despite not being dependent on
water, tend to cause a great deal of damage to both private property as
well as human lives.</p>
<p>This report concludes by noting that further study is necessary, and
that it would be particularly worthwhile expanding the analysis to the
present day. For example, it is highly probable heat-related weather
events have been more consequential in the last decade, with wildfires
becoming more common as global climate change leads to drier weather in
some U.S. states, particularly California.</p>
<h1 align="Center">
<strong>Part 2: Data Processing and Cleaning</strong>
</h1>
<p>After loading our data, we look at some general descriptive
statistics of the data set. We see it has <span class="math inline">\(902,297\)</span> observations across <span class="math inline">\(37\)</span> variables. Converting the data in the
column named BGN_DATE to a format readable by R as an actual date and
not a character string, we can then also see that the data begins on
Jan. 3rd, 1950, and ends on Nov. 30th, 2011.</p>
<div class="sourceCode" id="cb6"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb6-1"><a href="#cb6-1" aria-hidden="true" tabindex="-1"></a>stormdata<span class="sc">$</span>BGN_DATE <span class="ot"><-</span> <span class="fu">mdy_hms</span>(stormdata<span class="sc">$</span>BGN_DATE)</span>
<span id="cb6-2"><a href="#cb6-2" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb6-3"><a href="#cb6-3" aria-hidden="true" tabindex="-1"></a>stormdata<span class="sc">$</span>BGN_DATE <span class="ot"><-</span> <span class="fu">as.Date</span>(stormdata<span class="sc">$</span>BGN_DATE)</span>
<span id="cb6-4"><a href="#cb6-4" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb6-5"><a href="#cb6-5" aria-hidden="true" tabindex="-1"></a><span class="fu">range</span>(stormdata<span class="sc">$</span>BGN_DATE)</span></code></pre></div>
<pre><code>## [1] "1950-01-03" "2011-11-30"</code></pre>
<p>Out of curiosity, we decide to look up the most fatal weather
incident in this dataset. A quick subsetting of the dataset by the row
which has the maximum number of fatalities tells us that the event with
the most fatalities recorded in this dataset is the 1995 Chicago
heatwave, with <span class="math inline">\(583\)</span> deaths. While a
tragedy, this dataset covers the year in which Hurricane Katarina
happens, which should handily exceed the death toll of the 1995 Chicago
Heatwave.</p>
<div class="sourceCode" id="cb8"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb8-1"><a href="#cb8-1" aria-hidden="true" tabindex="-1"></a>maxfatal <span class="ot"><-</span> stormdata[stormdata<span class="sc">$</span>FATALITIES <span class="sc">==</span> <span class="fu">max</span>(stormdata<span class="sc">$</span>FATALITIES), ]</span>
<span id="cb8-2"><a href="#cb8-2" aria-hidden="true" tabindex="-1"></a><span class="fu">glimpse</span>(maxfatal[<span class="fu">c</span>(<span class="dv">2</span>,<span class="dv">7</span>,<span class="dv">8</span>,<span class="dv">23</span>)])</span></code></pre></div>
<pre><code>## Rows: 1
## Columns: 4
## $ BGN_DATE <date> 1995-07-12
## $ STATE <chr> "IL"
## $ EVTYPE <chr> "HEAT"
## $ FATALITIES <dbl> 583</code></pre>
<p>However, a quick look at the Hurricane Katarina data shows us that
the incident is set to <span class="math inline">\(0\)</span>
fatalities, while Katrina’s own <em>REMARKS</em> column indicates the
event caused <span class="math inline">\(1097\)</span> fatalities!</p>
<div class="sourceCode" id="cb10"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb10-1"><a href="#cb10-1" aria-hidden="true" tabindex="-1"></a>stormdata[stormdata<span class="sc">$</span>REFNUM <span class="sc">==</span> <span class="dv">577615</span>,<span class="dv">23</span>]</span></code></pre></div>
<pre><code>## # A tibble: 1 × 1
## FATALITIES
## <dbl>
## 1 0</code></pre>
<div class="sourceCode" id="cb12"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb12-1"><a href="#cb12-1" aria-hidden="true" tabindex="-1"></a>Katrina <span class="ot"><-</span> stormdata[stormdata<span class="sc">$</span>REFNUM <span class="sc">==</span> <span class="dv">577615</span>,]</span>
<span id="cb12-2"><a href="#cb12-2" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb12-3"><a href="#cb12-3" aria-hidden="true" tabindex="-1"></a>Katrina<span class="sc">$</span>REMARKS</span></code></pre></div>
<pre><code>## [1] "Hurricane Katrina was one of the strongest and most destructive hurricanes on record to impact the coast of the United States. It will likely be recorded as one the worst natural disaster in the history of the United States to date resulting in catastrophic damage and numerous casualties in southeast Louisiana and along the Mississippi coast. Damage and casualties resulting from Hurricane Katrina extended as far east as Alabama and the panhandle of Florida. Katrina developed from a tropical depression southeast of the Bahamas on August 24th. After moving through the Bahamas as a tropical storm, Katrina strengthened to a category 1 hurricane prior to landfall in south Florida around the Miami area on the 25th of August. Katrina crossed south Florida and entered the Gulf of Mexico and began to strengthen. Hurricane Katrina strengthened to a category 5 storm on August 28th about 250 miles south southeast of the mouth of the Mississippi River with winds reaching their peak intensity of 175 mph and a central pressure of 902 mb. Post event analysis by the National Hurricane Center indicates that Katrina weakened slightly before making landfall as a strong category 3 storm in initial landfall in lower Plaquemines Parish. Maximum sustained winds were estimated at 110 knots or 127 mph and a central pressure of 920 mb around 610 AM CDT on August 29th in southeast Louisiana just south of Buras in Plaquemines Parish. The storm continued on a north northeast track with the center passing about 40 miles southeast of New Orleans with a second landfall occurring near the Louisiana and Mississippi border around 945 AM CDT as a Category 3 hurricane on the Saffir Simpson scale with maximum sustained winds estimated around 105 knots or 121 mph. Katrina continued to weaken as it moved north northeast across Mississippi during the day, but remained at hurricane strength 100 miles inland near Laurel, Mississippi. Katrina weakened to a tropical depression near Clarksville, Tennessee on August 30th.\r\n\r\nDamage in southeast Louisiana, especially in the New Orleans area and the coastal parishes, was catastrophic. Hurricane protection levees and floodwalls were overtopped and/or breached resulting in widespread and deep flooding of homes and businesses. Much of Orleans and Plaquemines Parishes and nearly all of St. Bernard Parish were flooded by storm surge. Approximately 80 percent of the city of New Orleans was flooded. Thousands of people were stranded by the flood waters in homes and buildings and on rooftops for several days and had to be rescued by boat and helicopter. In Jefferson Parish, levees were not compromised, however many homes were flooded by either heavy rain overwhelming limited pumping capacity or storm surge water moving through in-operable pumps into the parish. Severe storm surge damage also occurred along the north shore of Lake Pontchartrain from Mandeville to Slidell with storm surge water moving inland as far as Old Towne Slidell with water up to 6 feet deep in some locations. Hurricane force winds also caused damage to roofs, power lines, and downed trees. Windows were broken in large buildings in the metro New Orleans area from wind and wind driven debris. In areas away from storm surge flooding, wind damage was widespread with fallen trees taking a heavy toll on houses and power lines, especially over St. Tammany and Washington Parishes. Excluding losses covered by the Federal Flood Insurance Program, insured property losses in Louisiana were estimated at 22.6 billion dollars. Overall uninsured and insured losses combined were estimated to exceed 100 billion dollars along the entire Gulf Coast.\r\n\r\nFatalities occurring in Louisiana as a result of Hurricane Katrina numbered approximately 1097 people as of late June 2006. The majority of the victims were in the New Orleans area. 480 other Louisiana residents died in other states after evacuating..\r\n Detailed information on the deaths, locations, and indirect or direct fatalities will be described in updates to Storm Data. \r\n\r\nDue to the failure of power and equipment prior to the peak of the storm, data for wind, storm surge, pressure, and rainfall are incomplete. A university portable weather unit measured the lowest pressure of 920.2 mb near Buras around 0616 AM AM CDT on Aug 29th. with 934 mb being measured at the National Weather Service Office in Slidell at 938 AM CDT. \r\n\r\nThe highest wind gust recorded in Louisiana and the adjacent coastal waters was 99 knots (114 mph) at the Grand Isle CMAN station (338 AM CDT on August 29th) before the gage failed, though higher wind gusts certainly occurred. While most of the metro New Orleans escaped the extreme winds, the extreme eastern portions of the metro area from St Bernard Parish into extreme east New Orleans experienced the western portion of the hurricane eyewall. Wind gusts between 120 to 125 mph were recorded at a couple of locations in East New Orleans. Wind gust to hurricane force (64 kt or 74 mph) were also recorded at New Orleans Louis Armstrong Intl Airport by an FAA wind instrument. In eastern St. Tammany Parish, wind gusts to 87 knots (100) mph were measured at Slidell by a wind tower deployed by a university. An estimated wind gust of 105 kt (120 mph) was taken at a hospital in Slidell. \r\n\r\nPost storm high water surveys of the area conducted by FEMA indicated the following storm surge estimates: Orleans Parish - 12-15 feet in east New Orleans to 9 to 12 feet along the Lakefront; St. Bernard Parish - 14 to 17 feet; Jefferson Parish - 6 to 9 feet along the lakefront to 5 to 8 feet from Lafitte to Grand Isle; Plaquemines Parish - 15 to 17 feet; St. Tammany Parish - 11 to 16 feet in southeast portion to 7 to 10 feet in western portion. All storm surge heights are still water elevations referenced to NAVD88 datum. \r\n\r\nStorm total rainfall amounts generally ranged from 7 to 14 inches with lower amounts observed farther west toward the Atchafalaya River. A rainfall total of 11.63 inches was measured at the National Weather Service Office in Slidell."</code></pre>
<p>This is a baffling omission, so we’ll fix that.</p>
<p>There might be other errors in the dataset, but it seemed very
relevant to this analysis to ensure that the single most fatal weather
event in the time period that dataset comprises was recorded
accurately.</p>
<div class="sourceCode" id="cb14"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb14-1"><a href="#cb14-1" aria-hidden="true" tabindex="-1"></a>stormdata[stormdata<span class="sc">$</span>REFNUM <span class="sc">==</span> <span class="dv">577615</span>,<span class="dv">23</span>] <span class="ot"><-</span> <span class="dv">1097</span></span>
<span id="cb14-2"><a href="#cb14-2" aria-hidden="true" tabindex="-1"></a><span class="fu">max</span>(stormdata<span class="sc">$</span>FATALITIES)</span></code></pre></div>
<pre><code>## [1] 1097</code></pre>
<p>That accomplished, we can now begin to look at the dataset to answer
the following two questions:</p>
<blockquote>
<center>
<strong>1: Across the United States, which types of events kill and
injure the most people??</strong>
</center>
</blockquote>
<blockquote>
<center>
<strong>2: Across the United States, which types of events have the
greatest economic consequences?</strong>
</center>
</blockquote>
<p>In order to look at these questions in more detail, and to avoid the
unwieldyness of dealing with an enormous dataset, we are going to subset
our initial data, named <em>stormdata</em>, into two smaller datasets,
<em>human_events</em>, comprising only those weather-related incidents
which resulted in at least <span class="math inline">\(1\)</span> human
injury and/or fatality, and <em>property_events</em>, comprising only
those weather-related incidents which resulted in some measurable damage
to property (including crop damage from 1993 onward).</p>
<div class="sourceCode" id="cb16"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb16-1"><a href="#cb16-1" aria-hidden="true" tabindex="-1"></a>human_events <span class="ot"><-</span> <span class="fu">filter</span>(stormdata, FATALITIES <span class="sc">></span> <span class="dv">0</span> <span class="sc">|</span> INJURIES <span class="sc">></span> <span class="dv">0</span>)</span>
<span id="cb16-2"><a href="#cb16-2" aria-hidden="true" tabindex="-1"></a>property_events <span class="ot"><-</span> <span class="fu">filter</span>(stormdata, PROPDMG <span class="sc">></span> <span class="dv">0</span> <span class="sc">|</span> CROPDMG <span class="sc">></span> <span class="dv">0</span>)</span>
<span id="cb16-3"><a href="#cb16-3" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb16-4"><a href="#cb16-4" aria-hidden="true" tabindex="-1"></a>crops <span class="ot"><-</span> <span class="fu">filter</span>(property_events, CROPDMG <span class="sc">></span> <span class="dv">0</span>)</span>
<span id="cb16-5"><a href="#cb16-5" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb16-6"><a href="#cb16-6" aria-hidden="true" tabindex="-1"></a><span class="fu">range</span>(crops<span class="sc">$</span>BGN_DATE)</span></code></pre></div>
<pre><code>## [1] "1993-01-04" "2011-11-28"</code></pre>
<h1 align="center">
<strong>Results</strong>
</h1>
<h3 align="center">
<strong>Weather Events Impacting Human Health</strong>
</h3>
<p>We’ll start by focusing on weather events in the United States from
1950 until 2011 that had a direct impact on human health - in other
words, the data contained within our subset <em>human_events</em>.</p>
<p>Before we go any further, we are going to want to try to group
<em>human_events</em> by the column <em>EVTYPE</em>, so we can try and
look at the cumulative effect on human health for specific types of
weather incidents.</p>
<div class="sourceCode" id="cb18"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb18-1"><a href="#cb18-1" aria-hidden="true" tabindex="-1"></a>human_events <span class="sc">%>%</span> <span class="fu">group_by</span>(EVTYPE) <span class="sc">%>%</span></span>
<span id="cb18-2"><a href="#cb18-2" aria-hidden="true" tabindex="-1"></a> <span class="fu">summarise</span>(<span class="at">FATALITIES =</span> <span class="fu">sum</span>(FATALITIES), <span class="at">INJURIES =</span> <span class="fu">sum</span>(INJURIES))<span class="sc">%>%</span></span>
<span id="cb18-3"><a href="#cb18-3" aria-hidden="true" tabindex="-1"></a> <span class="fu">arrange</span>(<span class="fu">desc</span>(FATALITIES <span class="sc">+</span> INJURIES)) <span class="ot">-></span> summed_human_events</span></code></pre></div>
<p>However, when we do this, we notice our dataset covers
<strong><em><span class="math inline">\(220\)</span></em></strong>
different supposed event types. This is a lot less than the apparently
<strong><em><span class="math inline">\(985\)</span></em></strong>
different type of weather events in the original data set, but according
to <a href="https://www.ncdc.noaa.gov/stormevents/details.jsp">the NOAA
itself</a>, the <strong>Storm Events Database</strong> should only
include <span class="math inline">\(48\)</span> different types of
weather events! Looking at the column <em>EVTYPE</em> manually, we
notice the reason there are <span class="math inline">\(985\)</span>
event types instead of <span class="math inline">\(48\)</span> is… a
massive number of typos collected over the course of over <span class="math inline">\(50\)</span> years of data entry, much of which was
probably done manually for decades.</p>
<div class="sourceCode" id="cb19"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb19-1"><a href="#cb19-1" aria-hidden="true" tabindex="-1"></a><span class="fu">length</span>(<span class="fu">unique</span>(summed_human_events<span class="sc">$</span>EVTYPE))</span></code></pre></div>
<pre><code>## [1] 220</code></pre>
<div class="sourceCode" id="cb21"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb21-1"><a href="#cb21-1" aria-hidden="true" tabindex="-1"></a><span class="fu">length</span>(<span class="fu">unique</span>(stormdata<span class="sc">$</span>EVTYPE))</span></code></pre></div>
<pre><code>## [1] 985</code></pre>
<div class="sourceCode" id="cb23"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb23-1"><a href="#cb23-1" aria-hidden="true" tabindex="-1"></a><span class="fu">head</span>(<span class="fu">unique</span>(stormdata<span class="sc">$</span>EVTYPE),<span class="dv">40</span>)</span></code></pre></div>
<pre><code>## [1] "TORNADO" "TSTM WIND"
## [3] "HAIL" "FREEZING RAIN"
## [5] "SNOW" "ICE STORM/FLASH FLOOD"
## [7] "SNOW/ICE" "WINTER STORM"
## [9] "HURRICANE OPAL/HIGH WINDS" "THUNDERSTORM WINDS"
## [11] "RECORD COLD" "HURRICANE ERIN"
## [13] "HURRICANE OPAL" "HEAVY RAIN"
## [15] "LIGHTNING" "THUNDERSTORM WIND"
## [17] "DENSE FOG" "RIP CURRENT"
## [19] "THUNDERSTORM WINS" "FLASH FLOOD"
## [21] "FLASH FLOODING" "HIGH WINDS"
## [23] "FUNNEL CLOUD" "TORNADO F0"
## [25] "THUNDERSTORM WINDS LIGHTNING" "THUNDERSTORM WINDS/HAIL"
## [27] "HEAT" "WIND"
## [29] "LIGHTING" "HEAVY RAINS"
## [31] "LIGHTNING AND HEAVY RAIN" "FUNNEL"
## [33] "WALL CLOUD" "FLOODING"
## [35] "THUNDERSTORM WINDS HAIL" "FLOOD"
## [37] "COLD" "HEAVY RAIN/LIGHTNING"
## [39] "FLASH FLOODING/THUNDERSTORM WI" "WALL CLOUD/FUNNEL CLOUD"</code></pre>
<p>For example, in only the <span class="math inline">\(40\)</span>
entries above, we see <strong>THUNDERSTORM WINDS</strong> written as
everything from <strong>THUNDERSTORM WINDS</strong> to
<strong>THUNDERSTORM WINS</strong> to <strong>TSTM WIND</strong>, all of
which are currently considered separate event types, even though they
obviously are not.</p>
<p>Before we start, we’re also going to correct two particularly
problematically misentered event type observations - one event type was
listed as “cold” when the official weather formatting should be
<strong>“Extreme Cold/Wind Chill”</strong>, which will cause that
observation to be mismatched as “flood” simply because flood has the
letters O,L and D and is a short word, and one observation is listed as
the unnecessarily loquacious “unseasonably warm and dry,” which we’ll
amend to the official event type, <strong>“Heat”</strong>.</p>
<p>Having corrected these outliers, we’ll create a vector,
<em>dictionary</em>, of the NOAA’s <span class="math inline">\(48\)</span> official storm event types, and then
use approximate string matching using the amatch() command to condense
all of the <span class="math inline">\(220\)</span> event types we
currently have into the official <span class="math inline">\(48\)</span>.We are going to use the
<strong>longest common substring</strong> method, as this will help
ensure events like “Ex. Cold” get matched to the event type “Extreme
Cold/Wind Chill” even though the difference in the number of characters
is fairly large, rather than to something like “Funnel Cloud” or
“Flood”, which is closer in character count to “Ex. Cold” but don’t even
contain the word “cold”.</p>
<div class="sourceCode" id="cb25"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb25-1"><a href="#cb25-1" aria-hidden="true" tabindex="-1"></a>summed_human_events[<span class="dv">47</span>,<span class="dv">1</span>] <span class="ot"><-</span> <span class="st">"extreme cold"</span></span>
<span id="cb25-2"><a href="#cb25-2" aria-hidden="true" tabindex="-1"></a>summed_human_events[<span class="dv">67</span>,<span class="dv">1</span>] <span class="ot"><-</span> <span class="st">"heat"</span></span>
<span id="cb25-3"><a href="#cb25-3" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb25-4"><a href="#cb25-4" aria-hidden="true" tabindex="-1"></a>dictionary <span class="ot"><-</span> <span class="fu">c</span>(<span class="st">"Astronomical Low Tide"</span>,<span class="st">"Avalanche"</span>,<span class="st">"Blizzard"</span>,<span class="st">"Coastal Flood"</span>,<span class="st">"Cold/Wind Chill"</span>,<span class="st">"Debris Flow"</span>,<span class="st">"Dense Fog"</span>,<span class="st">"Dense Smoke"</span>,<span class="st">"Drought"</span>,<span class="st">"Dust Devil"</span>,<span class="st">"Dust Storm"</span>,<span class="st">"Excessive Heat"</span>,<span class="st">"Extreme Cold/Wind Chill"</span>,<span class="st">"Flash Flood"</span>,<span class="st">"Flood"</span>,<span class="st">"Freezing Fog"</span>,<span class="st">"Frost/Freeze"</span>,<span class="st">"Funnel Cloud"</span>,<span class="st">"Hail"</span>,<span class="st">"Heat"</span>,<span class="st">"Heavy Rain"</span>,<span class="st">"Heavy Snow"</span>,<span class="st">"High Surf"</span>,<span class="st">"High Wind"</span>,<span class="st">"Hurricane"</span>,<span class="st">"Typhoon"</span>,<span class="st">"Ice Storm"</span>,<span class="st">"Lakeshore Flood"</span>,<span class="st">"Lake-Effect Snow"</span>,<span class="st">"Lightning"</span>,<span class="st">"Marine Hail"</span>,<span class="st">"Marine High Wind"</span>,<span class="st">"Marine Strong Wind"</span>,<span class="st">"Marine Thunderstorm Wind"</span>,<span class="st">"Rip Current"</span>,<span class="st">"Sleet"</span>,<span class="st">"Storm Tide"</span>,<span class="st">"Strong Wind"</span>,<span class="st">"Thunderstorm Wind"</span>,<span class="st">"Tornado"</span>,<span class="st">"Tropical Depression"</span>,<span class="st">"Tropical Storm"</span>,<span class="st">"Tsunami"</span>,<span class="st">"Volcanic Ash"</span>,<span class="st">"Waterspout"</span>,<span class="st">"Wildfire"</span>,<span class="st">"Winter Storm"</span>,<span class="st">"Winter Weather"</span>)</span>
<span id="cb25-5"><a href="#cb25-5" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb25-6"><a href="#cb25-6" aria-hidden="true" tabindex="-1"></a>dictionary <span class="ot"><-</span> <span class="fu">tolower</span>(dictionary)</span>
<span id="cb25-7"><a href="#cb25-7" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb25-8"><a href="#cb25-8" aria-hidden="true" tabindex="-1"></a>summed_human_events<span class="sc">$</span>EVTYPE <span class="ot"><-</span> <span class="fu">tolower</span>(summed_human_events<span class="sc">$</span>EVTYPE)</span>
<span id="cb25-9"><a href="#cb25-9" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb25-10"><a href="#cb25-10" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb25-11"><a href="#cb25-11" aria-hidden="true" tabindex="-1"></a>summed_human_events<span class="sc">$</span>EVTYPE_MATCHED <span class="ot"><-</span> dictionary[<span class="fu">amatch</span>(summed_human_events<span class="sc">$</span>EVTYPE,dictionary,<span class="at">method=</span><span class="st">"lcs"</span>, <span class="at">maxDist=</span><span class="dv">40</span>)]</span>
<span id="cb25-12"><a href="#cb25-12" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb25-13"><a href="#cb25-13" aria-hidden="true" tabindex="-1"></a><span class="fu">length</span>(<span class="fu">unique</span>(summed_human_events<span class="sc">$</span>EVTYPE_MATCHED))</span></code></pre></div>
<pre><code>## [1] 41</code></pre>
<p>You can see we’re now down to just <span class="math inline">\(41\)</span> event types (because <span class="math inline">\(7\)</span> of the official <span class="math inline">\(48\)</span> event types never actually caused any
human injuries or fatalities). Now we can look at just the top <span class="math inline">\(10\)</span> entries in our refined dataset
<em>summed_human_events</em> to see the <span class="math inline">\(10\)</span> storm types in the United States from
1950 to 2011 which caused the most human injuries and fatalities.(We’ll
also delete the column containing the <span class="math inline">\(220\)</span> unmatched event types)</p>
<div class="sourceCode" id="cb27"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb27-1"><a href="#cb27-1" aria-hidden="true" tabindex="-1"></a>summed_human_events<span class="sc">$</span>EVTYPE <span class="ot"><-</span> <span class="cn">NULL</span></span>
<span id="cb27-2"><a href="#cb27-2" aria-hidden="true" tabindex="-1"></a><span class="fu">head</span>(summed_human_events,<span class="dv">10</span>) <span class="ot">-></span> human10</span>
<span id="cb27-3"><a href="#cb27-3" aria-hidden="true" tabindex="-1"></a>human10</span></code></pre></div>
<pre><code>## # A tibble: 10 × 3
## FATALITIES INJURIES EVTYPE_MATCHED
## <dbl> <dbl> <chr>
## 1 5633 91346 tornado
## 2 1903 6525 excessive heat
## 3 504 6957 strong wind
## 4 470 6789 flood
## 5 816 5230 lightning
## 6 937 2100 heat
## 7 978 1777 flash flood
## 8 1161 1275 hurricane
## 9 89 1975 ice storm
## 10 133 1488 thunderstorm wind</code></pre>
<p>Before we do anything else, we’re going to recapitalize our event
types, as we put them all into lower case for the sake of making our
approximate matching work better.</p>
<div class="sourceCode" id="cb29"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb29-1"><a href="#cb29-1" aria-hidden="true" tabindex="-1"></a>capwords <span class="ot"><-</span> <span class="cf">function</span>(s, <span class="at">strict =</span> <span class="cn">FALSE</span>) {</span>
<span id="cb29-2"><a href="#cb29-2" aria-hidden="true" tabindex="-1"></a> cap <span class="ot"><-</span> <span class="cf">function</span>(s) <span class="fu">paste</span>(<span class="fu">toupper</span>(<span class="fu">substring</span>(s, <span class="dv">1</span>, <span class="dv">1</span>)),</span>
<span id="cb29-3"><a href="#cb29-3" aria-hidden="true" tabindex="-1"></a> {s <span class="ot"><-</span> <span class="fu">substring</span>(s, <span class="dv">2</span>); <span class="cf">if</span>(strict) <span class="fu">tolower</span>(s) <span class="cf">else</span> s},</span>
<span id="cb29-4"><a href="#cb29-4" aria-hidden="true" tabindex="-1"></a> <span class="at">sep =</span> <span class="st">""</span>, <span class="at">collapse =</span> <span class="st">" "</span> )</span>
<span id="cb29-5"><a href="#cb29-5" aria-hidden="true" tabindex="-1"></a> <span class="fu">sapply</span>(<span class="fu">strsplit</span>(s, <span class="at">split =</span> <span class="st">" "</span>), cap, <span class="at">USE.NAMES =</span> <span class="sc">!</span><span class="fu">is.null</span>(<span class="fu">names</span>(s)))</span>
<span id="cb29-6"><a href="#cb29-6" aria-hidden="true" tabindex="-1"></a>}</span>
<span id="cb29-7"><a href="#cb29-7" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb29-8"><a href="#cb29-8" aria-hidden="true" tabindex="-1"></a>summed_human_events<span class="sc">$</span>EVTYPE_MATCHED <span class="ot"><-</span> <span class="fu">capwords</span>(summed_human_events<span class="sc">$</span>EVTYPE_MATCHED)</span>
<span id="cb29-9"><a href="#cb29-9" aria-hidden="true" tabindex="-1"></a><span class="fu">head</span>(summed_human_events,<span class="dv">10</span>) <span class="ot">-></span> human10</span></code></pre></div>
<p>Now we can try actually plotting the <span class="math inline">\(10\)</span> events which are most destructive to
human life in the United States.</p>
<div class="sourceCode" id="cb30"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb30-1"><a href="#cb30-1" aria-hidden="true" tabindex="-1"></a><span class="fu">ggplot</span>(<span class="at">data =</span> human10) <span class="sc">+</span></span>
<span id="cb30-2"><a href="#cb30-2" aria-hidden="true" tabindex="-1"></a> <span class="fu">geom_col</span>(<span class="at">mapping =</span> <span class="fu">aes</span>(<span class="at">x =</span> <span class="fu">fct_reorder</span>(EVTYPE_MATCHED, FATALITIES <span class="sc">+</span> INJURIES, <span class="at">.desc =</span> <span class="cn">TRUE</span>), <span class="at">y =</span> FATALITIES, <span class="at">fill =</span> <span class="st">"Fatalities"</span>)) <span class="sc">+</span></span>
<span id="cb30-3"><a href="#cb30-3" aria-hidden="true" tabindex="-1"></a> <span class="fu">geom_col</span>(<span class="at">mapping =</span> <span class="fu">aes</span>(<span class="at">x =</span> <span class="fu">fct_reorder</span>(EVTYPE_MATCHED, FATALITIES <span class="sc">+</span> INJURIES, <span class="at">.desc =</span> <span class="cn">TRUE</span>), <span class="at">y =</span> INJURIES, <span class="at">fill =</span> <span class="st">"Injuries"</span>, <span class="at">alpha =</span> <span class="fl">0.5</span>), <span class="at">show.legend =</span> <span class="cn">FALSE</span>) <span class="sc">+</span></span>
<span id="cb30-4"><a href="#cb30-4" aria-hidden="true" tabindex="-1"></a> <span class="fu">scale_fill_manual</span>(<span class="at">values =</span> <span class="fu">c</span>(<span class="st">"Fatalities"</span> <span class="ot">=</span> <span class="st">"red"</span>, <span class="st">"Injuries"</span> <span class="ot">=</span> <span class="st">"yellow"</span>)) <span class="sc">+</span></span>
<span id="cb30-5"><a href="#cb30-5" aria-hidden="true" tabindex="-1"></a> <span class="fu">theme</span>(<span class="at">plot.title =</span> <span class="fu">element_text</span>(<span class="at">hjust =</span> <span class="fl">0.5</span>), <span class="at">plot.caption =</span> <span class="fu">element_text</span>(<span class="at">hjust =</span> <span class="fl">0.5</span>))<span class="sc">+</span></span>
<span id="cb30-6"><a href="#cb30-6" aria-hidden="true" tabindex="-1"></a> <span class="fu">theme</span>(<span class="at">axis.text.x =</span> <span class="fu">element_text</span>(<span class="at">angle =</span> <span class="dv">45</span>, <span class="at">size =</span> <span class="dv">6</span>, <span class="at">margin =</span> <span class="fu">margin</span>(<span class="dv">10</span>)))<span class="sc">+</span></span>
<span id="cb30-7"><a href="#cb30-7" aria-hidden="true" tabindex="-1"></a> <span class="fu">labs</span>(<span class="at">x =</span> <span class="st">"Event Type"</span>, <span class="at">y =</span> <span class="st">"Weighted Human Suffering Index"</span>, <span class="at">title =</span> <span class="st">"10 U.S. Weather Events Which Most Affect </span><span class="sc">\n</span><span class="st">Human Health"</span>, <span class="at">fill =</span> <span class="st">"Human Health </span><span class="sc">\n</span><span class="st">Impact Type"</span>)</span></code></pre></div>
<p><img 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" /><!-- --></p>
<p>We see that the event which causes the most injuries by far is
Tornado (the original event type which the NOAA began tracking in 1950).
Next is Excessive Heat, a perennially overlooked source of human misery,
suffering and death, followed by Strong Wind, Flood, and Lightning. Of
particular interest are the next three event types, Heat, Flash Flood
and Hurricane, because we notice their bars are almost entirely orange;
this is because the graph was “stacked” by placing the Injuries bar on
top of the Fatalities bar (which was invariably smaller for all event
types in the top <span class="math inline">\(10\)</span>), and making
the Injuries bar transparent.Thus, the red Fatalities bar underneath
these three event types is almost the same size as the Injuries bar. In
other words, Heat, Flash Flood and Hurricane don’t hurt as many people
as, say, Strong Wind or Flood, but they kill far more people.</p>
<p>It hardly seems fair to weight Fatalities and Injuries equally, so we
can create what we might call a <em>“Weighted Index of Human
Suffering”</em> by assigning injuries a weight of <span class="math inline">\(1\)</span> and Fatalities a higher weight. As far
as I know, most of the literature on the topic of determining an
equivalency ratio between injury and death presumes you know the actual
nature of the injury, i.e. for the sake of calculating the actuarial
payout for an insurance claim,but since the NOAA doesn’t track that
data, we can assign Fatality a generic weight of <span class="math inline">\(10\)</span>, ensuring that a fatality is
considered one order of magnitude more important than an injury.</p>
<p>Now, if we plot our data again, we see the following:</p>
<div class="sourceCode" id="cb31"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb31-1"><a href="#cb31-1" aria-hidden="true" tabindex="-1"></a>human10_adj <span class="ot"><-</span> human10</span>
<span id="cb31-2"><a href="#cb31-2" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb31-3"><a href="#cb31-3" aria-hidden="true" tabindex="-1"></a>human10_adj<span class="sc">$</span>FATALITIES <span class="ot"><-</span> human10<span class="sc">$</span>FATALITIES<span class="sc">*</span><span class="dv">10</span></span>
<span id="cb31-4"><a href="#cb31-4" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb31-5"><a href="#cb31-5" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb31-6"><a href="#cb31-6" aria-hidden="true" tabindex="-1"></a>human10_long_adj <span class="ot"><-</span> human10_adj <span class="sc">%>%</span></span>
<span id="cb31-7"><a href="#cb31-7" aria-hidden="true" tabindex="-1"></a> <span class="fu">pivot_longer</span> (<span class="at">cols =</span> <span class="fu">c</span>(INJURIES, FATALITIES), <span class="at">names_to =</span> <span class="st">"type"</span>, <span class="at">values_to =</span> <span class="st">"value"</span>)</span>
<span id="cb31-8"><a href="#cb31-8" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb31-9"><a href="#cb31-9" aria-hidden="true" tabindex="-1"></a><span class="fu">ggplot</span>(<span class="at">data =</span> human10_long_adj) <span class="sc">+</span></span>
<span id="cb31-10"><a href="#cb31-10" aria-hidden="true" tabindex="-1"></a> <span class="fu">geom_col</span> (<span class="at">mapping =</span> <span class="fu">aes</span> (<span class="at">x =</span> <span class="fu">fct_reorder</span>(EVTYPE_MATCHED, value, <span class="at">.desc =</span> <span class="cn">TRUE</span>), <span class="at">y =</span> value, <span class="at">fill =</span> type)) <span class="sc">+</span></span>
<span id="cb31-11"><a href="#cb31-11" aria-hidden="true" tabindex="-1"></a> <span class="fu">theme</span>(<span class="at">plot.title =</span> <span class="fu">element_text</span> (<span class="at">hjust =</span> <span class="fl">0.5</span>), <span class="at">plot.caption =</span> <span class="fu">element_text</span> (<span class="at">hjust =</span> <span class="fl">0.5</span>))<span class="sc">+</span></span>
<span id="cb31-12"><a href="#cb31-12" aria-hidden="true" tabindex="-1"></a> <span class="fu">theme</span>(<span class="at">axis.text.x =</span> <span class="fu">element_text</span>(<span class="at">angle =</span> <span class="dv">45</span>, <span class="at">size =</span> <span class="dv">6</span>, <span class="at">margin =</span> <span class="fu">margin</span>(<span class="dv">10</span>))) <span class="sc">+</span></span>
<span id="cb31-13"><a href="#cb31-13" aria-hidden="true" tabindex="-1"></a> <span class="fu">labs</span>(<span class="at">x =</span> <span class="st">"Event Type"</span>, <span class="at">y =</span> <span class="st">"Weighted Index of </span><span class="sc">\n</span><span class="st">Human Suffering"</span>, <span class="at">title =</span> <span class="st">"10 U.S. Weather Events Which Most Affect </span><span class="sc">\n</span><span class="st">Human Health, Weighted"</span>, <span class="at">fill =</span> <span class="st">"Human Health </span><span class="sc">\n</span><span class="st">Impact Type"</span>) <span class="sc">+</span></span>
<span id="cb31-14"><a href="#cb31-14" aria-hidden="true" tabindex="-1"></a> <span class="fu">theme</span>(<span class="at">axis.text.y =</span> <span class="fu">element_blank</span>())</span></code></pre></div>
<p><img 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+04oMMO1ciRVK3BnicdL/E4QFXuhqnmI6EA0MoYzAjQpkKmp7npLa6bX6uf9CaVmUEWMznZW9r0U9kSNJR4DKA7y3pZ3c6yn7DnswpAK2FQDqBezyQA1BScbXPwfxoZ/02quoMsfVqO1PZTeYAGiccB+iv08+9im+YSnSQAKtRgVlX8msXL/KMXES+E3gxnsTszW6FUfht0MqCTZSMHoEINBABqquHImxFAbZ0cSsUCVAHeTtTrALQyBgIA7Wt8+lklaN/uzQil4gDaXXp7uR1eZBcH1kYOQIUalAuontgZdhZigOqisZkF6LCjR9oXwqn0L3MCah8uEgxXJYC1kQNQoQYlj4PqsaJPPhxrg3ZV2/Lp5XY6oGaYSZeioVRdp7FLQBPAAlDhBmUAWrKie5J0iRnsSQKgVTOoO6C0zTiYSooBC0ClG9Qe0LhiwAJQ4QZzB2hcNnIAKtSgzoAGz1oMLbfDXHy1DMoAdMzKo6huntX/6m67/wjPXSjtwQ0GSwBaQQNJgIbS5WY6RdmPvkk+0BaASjeYK0Cx3K56BiUBGlkAatZy6tlGu8TTVOoNc9QKsqh/u7qS7zX89N7S0PyARp7NhCq+ggZlARpeADpo0jJPb4mn9zpRgtJEUttL7y0N5QDqPW4RgFbUoDRAQwtAEwtBLZgJQA21e9e99P1Qhyc3oGGhDVo9g9Kq+LVg8t38NKDREk+vhZhsg/Ya+j8vfbA0dJeAog1aPYNZAuot8cwGdLD35U47AHRi7Z4ENPG4xbhs5ABUqMFMADVV9t71yELQVECHnUf2rrt++oW1xLknABp/3CK2fFTNYDaAUqfHW+LpdYBC9a/Hak+P1gedJFoaygA0vcW683BwDhs5ABVqMJsq/kmaJrJLPO0wkz5uBFnoJ/WPbHpvaWh+QJP74I36QWPBRg5AhRqUAWgKsPkSmoqdPWofffRNausT60ErYyAc0F6Ll14rWsXH25xGXZSgVTEQDagZ3r+zEjQmC2yonWAjB6BCDWYB6JSF9aB1MgCgJAAq1KDOgMYXLLvmDyDqd7HlozIGdQY0IT2GSgOu2DRXFYN5AnTYadmR1R568VUxKAPQH6ereDRJmYDS+hTdg0+OjnIAnVbg0Iz049QHac8IUFN4YqC+MgallKBCANXznrR6BAP1lTGYJ0B16WmaoP3Qs8Nt5ABUqEGdAU0OM3X1CJN+vF6Q2kYOQIUa1BlQrd6kbSI2cgAq1KDmgE7+Czg2cgAq1ACAkgCoUIPZA0q7hlq0zt0+9kZ1ss2qd7MclLYm0fv/ofd42vVIuomZBl7mvngAWkEDAYDutZD1P9oKHZsjH9D14B8vfTCqOQ7Q8L54AFpBAzmADr/49Ec2/WM6Ggeo+RneXZQO6CTZyAGoUAM5gA4+uNlt+8d0NA5Qs4UuTWMBTWxDtpEDUKEGAgBt0pMWey233/ABpaNUQJu2lapr77QtdOP+TlJ8GzIAlW4gAFCC0HSPFj0Q7dG4EpQyp+wGGfd3kvBkkaoZiAFU1ekKp7YHIh2NA9QUtybHGEDjf9UDf467agZiAO21LXTm2B5N7MVP6iQl/uxMYkTURg5AhRpIAXT4JfMUecWbPvaOjpv2ZiOlDeq0zTjoxDZo4u8k4dE31TKYPaCFa+zfSUrIRg5AhRrUHtBJspEDUKEGNQc0MRePcdCKGcwZoMNOS3Xkw4NNNnIAKtSg1oAGj/wOj4N2W3i6XXUMSgF0drs64wPz+rjXwKa56hjUHNCEuoZO7IuvjEEpgKbe5nIAjXeK9MMbuni6XXUMag5ocnFIXDZyACrUoOaATv6rSjZyACrUoOaAJhaHYBy0YgY1BzS+OATrQatmUGdAkw9uwHrQyhnUGdAUYT1o1QxmD6j312K9zUjeik9/ZZ0q/QY5dhvnAhTrQatmIARQf69HFFDanrS0mWe3cSqgQS1v257oJFXMQAage/+hkQ0ovZi42zgVUP/BDbZ3ZB6yHJGNHIAKNRAC6Lq/x3hSCZq52zgV0GA1E9Xt6CRVzUAKoDsf2cxqgy5t5tptnAtQdJKqZiAFULfXSi9BzTPl8+w2TgU0eDaTbbZGTgNA5RuIAXT4xeMel9TCHHyQytSe7cVP2m2cDqj3bKYeFbrYNFc1AzGAqv714j8pMvVa4m6DujP2GQ6NXLuNMwCdJBs5ABVqIAdQXVR6zcsu7cS0dX2zlWe3MQCtpcHsAS1coanOdrxKRxVfNYM6A5qpcAvBRg5AhRrMJaDYNFcdg9oDGn38ohU2zVXGoO6Axh6/6L+LErQiBqUAOtNtx5Gn29lOEjbNVcagDEBL1tjHLyZkIwegQg1qDmj88YtJ2cgBqFCDugMafvxiyhYQACrdoM6A7iyn1OzxHr2NHIAKNagzoGbGNL49BIBWy6DWgGqZOfwQpGFA71GyLzmAlvEhoPoqZSapFxCKErRaBrUvQc2fXAp14gFotQzqDmiXtokEAqDVMqg1oL2UlaMAtFoGdQY0+XhljINWzqDOgOrm59hnkGjZyAGoUINaA+qaUabxj7C1kQNQoQZ1B9TVLdFxW5hs5ABUqMEcAKqreqxmqqpB7QGNzyTFZSMHoEIN6gyoHqKf8KhGACrdoM6Apq5mistGDkCFGtQZ0FyykQNQoQYAlARAhRoAUBIAFWoAQEkAVKgBACUBUKEGAJQEQIUaAFASABVqAEBJAFSoAQAlAVChBgCUBECFGgBQEgAVagBASQBUqAEAJQFQoQYAlARAhRoAUBIAFWoAQEkAVKgBACUBUKEGAJQEQIUaAFASABVqAEBJAFSoAQAlAVChBgCUBECFGgBQEgAVagBASQBUqAEAJQFQoQYAlARAhRoAUBIAFWoAQEkAVKgBACUBUKEGAJQEQIUaAFASABVqAEBJAFSoAQAlAVChBgCUBECFGgBQEgAVagBASQBUqAEAJQFQoQYAlMQG9Cf5VeDlF8cPAOWLB6gVB1CTgQFooZ8OqrxQgtbJYO5LUBs5ABVqAEBJAFSoAQAlAVChBgCUBECFGgBQEgAVagBASQBUqAEAJQFQoQYAlARAhRoAUBIAFWoAQEkAVKgBACUBUKEGAJQEQIUaAFASABVqAEBJAFSoAQAlAVChBgCUBECFGgBQEgAVagBASQBUqAEAJQFQoQYAlARAhRoAUBIAFWoAQEkAVKgBACVNHVBG+jFEi+MHgPJVE0ALARqAxn4pQQD0Du4vAJ2+5hRQtkEJPMwgPQCtC6DiAgKgqQKgUgICoKkCoFICAqCpAqBSAgKgqQKgUgICoKkCoFICAqCpAqBSAgKgqQKgUgICoKkCoFICAqCpqg2g3E/ADmiKVwiAZmu3gE79dk2RHwA6UwHQXBGxA5r6JwagaSoLULYBAAWgWgAUgJYsAJrLAIDOSgA0lwEAnZUAaC4DADorAdBcBvIAnWJ67xswJeR4AqC5DNi3d4ofAIBma24BZQc0xfQlDS0D0GLSA9ApGQDQIq/mtA3EBQRAUwVApQQEQBO6R2mKgUBQmnZVgqar8s/lqL4BxkGLvDribm/1DQBokVdH3O2tvgEALfLqiLu91TcAoEVeHXG3t/oGALTIqyPu9lbfAIAWeXXE3d7qGwDQIq+OuNtbfQMAWuTVEXd7q28AQIu8OuJub/UNCgzovW/91pSQ4wmA1smgyIDe+9PVKTHHEgCtk0GRJeif/8UpCYQC0DoZFBXQe//8V//7Gz9yNwQQCkDrZFBUQN/+y2/95p/9i3v5b6dEHUM8QMeKuxiPvXgPBkWnz8iw9ZA72jhy6NeOcE83BUkD9MZjr0/XoMgM4gyKCOjWc4rOVXf0Bvdc05EwQEevfvrxqRoUmkGcQREBbXzqjCFUiGQBOjr95omVTEIrz08FAH3/h+4rv+CKKT8LBbQAbR/d/tzJT68cnXUc86uNlYP/cyK7EitfsgAdXfvr6//14qyj8MSu6ATVjLvV7c//4NDvPiDoY0gCdHT1jdHJZx+bdRiBRie5hHIzyNKtJ864W6vuu7/90KwjCSQIUNUA3Xh8dG3WYQQanX6eN5nCziBNo42D//0712cdRURCAB2dPKMboJ+/Iub+js6f2VZtYValzc4gT7cOHZb1AYQA6t4+dGZ04qB7I//V2TrGc1ANCE7ycy9uHPvKBd5kyoibYbr64a5yXZHVRRUB6Pvq/62VM7cOreRugOralEWoaUDkTXzruV1MpqhvwC0Zsy9Go9OfPcirrXcH9JQ1JUB5xdXWQffyUVWGMrKY2jQ3oaa+ZjQgNj71InsyRX0DLh/kZJiuRq8ddW9wCOUDXYqmAyj3Zm3poc/bz+U+/fkzt5+46F4+nBdpXV/nb0CYwWr2kJH+ymxJab+NTh346hMX3Bv5q2s20CVpCoCqW8u+WVusskfxtrp9aOXods7rb+vrvA0IGqxmlZ+qxtBfmZzfgOmPl2498JB7WxGaX+eYQJelaZSgo5NfyH+zrDiEGt5UDfz6Zy/mi+fVFVZ9zRusvvWCrTEYDdCpj5eOXnlwlVMlnTrMBbosTaMEVf0XTveC2qu5xz/PXbCLbUav56uQ9AT/g7mB4A5Wbz9xeJVbvXPHS8ct8cqyeCX/R9ZfeX1N8wNdogoHdHT+q7zRQFb3Wp//8CqvitQT/Cfyz43wBqu3n7g4OrHKqN4pIt4VGrfEKzXDVf31ZTRR1AWVOk1bNKC6uPo5xmjg5YvM8flzL6qygdc+5E7wcwarVdPkyqOnVjnjS6Orr7PGS8cv8UrPwBtPUBFt8K5peSoaUFNcPZD/bm0/zhuf1+1PVu01unqBP8HPGKzeWjnI6/RofA5wxkuZS7xuPPYqs4uqI3pUJp4FAzo6f4ZXXJ1/c3T6Imd83vaO8l/N0el/u+AyJvi581PUveOUPuwRDt4lvT565teZXVRRA2RxFQqoHv5hFFequ3P+wLOckQ3VW+CUVucumIt/OfcMAHt+Sos1RDa6+hoPH94Sr+0Dh1bdrUcZBbS6pMwWdLkqElAa/uEUV6us2nR0/vlnHmeUVqNTqj+lm3u5AeXNT6mATEOSsQCLXb/zlnjdVr270RvXtxhLvkfqkkqaoY2rOEBHp9jTgz/gtN02jun5oC1Gb8EMnuS/+Nz5KQoodzR6vJRXm/KXeG0fHW38/LOPcuaDrrmcS1q+igPUDp/n1+j03/w7Z7BlQxscy106mC8Mq0HAnJ+yAeU8uxkvdXm1KX+J18bKMXf0lXzzF1rqHlx0OQVu6SqwBOWWnzdW3z20wmjxbT3AMrANjtwZuPNTvE9M46UuozbdxRIv0pW8JeLo1Bl9Dw7k53kGKgxQM5bGyXDt9qGD11n9l2c4e2UuH2N+YZjzU0rXGOWzHS/NH85ulni5ulmQvzhUX0nePZiFigFUtX2YY2mmbrn8bP4OsK54X2HslVGVL6/Bwf2C6U/AKdC546XcJV7cFdl2zQrnHsxChQCq6i/uWBrVLXmXyJ674PLW111WpeHJY6wvDPMLdv5ZZu3IHS/lLvFiThn7X0mRy5QDFQHo9qHPXOe1/kdXX+PVLYr9/P0X1fvVhQNjMvHUGfZyjzdPPMisHXm8sXNwt3RpoB+TOn8UqABAFZtXPnOdNRutv+y56xbdWzC1Ue7+i+79bqzkv7u6McZd7rH9uZMrzNqRv2GVM8L67q8y+vtmza6kPYqZKqiTpAhlpKZrk7duMRUXdbDzmNjeLyMcc/LcX7BbL+hQzPSjkNrx/etmSvd11pTxScYWg1mqqF48g9DR1TeZg3ua0HfzVtjs3i9v/lSPF2lCBT1h4oa69rwpXbtmlzuANQsVNsyUuzbSyzeYg3uKUMZ0B7P3e33EmT9VfLrfN4QKesKEKh04U7q72fI/M5W97fhfrzO/60YbrPWQnL4Fra5g0H/QvXLwurTVvbw+wG62/M9MZQN67iJv+YbV91mp8xPKX12xZTpf0lb3cvoAkp6fPFllArp95NUDK0eulbB2Jnf1u4vVFfzRojKUv8HBnmKbrUoEVDff3BtHWAtApy7u6gpXKqG5xZ1im7HKA1TzefmFvEuFylTu1RVWgrpHbLGn2Gat8gA1wz/ytrZyVldUXqPzz7Om2ASoxCre9ERkr46tt8wK6y9wptgEqMxOkipDwefstH2EtcJaiEodZtpiDWdChcqbAqtQ+1Or3HFQ3nAmVKD0krMq/pUHEQ+whaYuWnJWufITgM6TeEvOhAiAzpGqSCgAnSdVcIoBgEKiBUAh0QKgkGgBUEi0ACgkWgAUEi0ACokWAIVEC4BCogVAIdECoJBoAVBItAAoJFoAFBItAAqJFgCFRAuAQqIFQCHRAqCQaAFQSLQAKCRaABQSLQAKiRYAhUQLgEKiBUAh0QKgkGgBUEi0ACgkWgAUEq15B3TYcYwW17NS3DwbTuc0yosNcgHosNMan2CwZy3lJVSWACgAFS0A2gq/6C2u68pcVfiDPcebjtMeqH88hAnQrq7ke43BnicdZ2mTKv/sBgJ0hwKgHn49RZs6GnYa5vWgqY4Vr4kStK9oHHYUuZrlhuuln0Xw8yAASn2flsFP/a/5c3eWFYBtg2QCUPU7d7B33fu9l35mn6DmAqCt0CtVEvYssIbGNEBV9a7/MwcKTC996ZHPiQCoj1Z/6R1i1BxlAzrY+3KnHQCK2n2qAqA+oDsPH9+77vYX1sxRNqDDziMqHVXxe9e99NB0BECDyrmrR+GHHVUkKuo8QEPNS4/Vnk4XdJIofclxz40AKLUhNWF9p23fWVjzS1DC1sgD1PaPnqRpJZsemo7mHdDdSFXsGLUvSwCUr3n7imcAAAAwSURBVJ5uFQDQcgRAuTIj+AC0LAFQSLQAKCRaABQSLQAKiRYAhUQLgEKiBUAh0fp/7N7SWNVl80wAAAAASUVORK5CYII=" /><!-- --></p>
<p>Now we see that while Tornado still leads the pack (being a storm
type that tends to cause a fair number of fatalities, even though it
causes far more injuries), we see that Hurricane jumps from <span class="math inline">\(8th\)</span> to a more plausible <span class="math inline">\(4th\)</span> place, Lightning and Strong Wind
swapped places, with Lightning now being ranked higher, and Flash Flood
is now ranked higher than Flood. All these changes make intuitive sense;
for example, naturally more people would die in an unexpected flood than
an expected one. This gives us reassurance that our weighting system was
a good, if rudimentary idea.</p>
<p>In conclusion, we can see that in the United States from 1950 to
2011, the weather events which most impacted human health were
Tornadoes, Heat waves, Lightning storms (probably correlated somewhat
with tornadoes), and then Hurricanes, Strong Winds and Flash Floods. Ice
Storms, as annoying as they might be to drivers in northern states, tend
not to cause many fatalities, as well as comparatively few injuries
compared to the higher ranked event types.</p>
<p>One final thing to note is that splitting up Excessive Heat from Heat
seems to end up undercounting the effects of both events, but the NOAA
itself explicitly draws this distinction, so we maintained it in our
analysis.</p>
<h3 align="center">
<strong>Weather Events with Economic Consequences</strong>
</h3>
<p>From here, we can go on to analyze weather events with economic
consequences, defined as those weather events in the NOAA Storm Data set
which contained an explicitly recorded value for either the variable
PROPDMG or CROPDMG. PROPDMG is typically defined as damage inflicted to
private property as well as public infrastructure and facilities;
beginning in 1993, the NOAA began including estimates of damage to crops
as well, hence the inclusion of the variable CROPDMG.</p>
<p>A quick calculation shows us that there are actually <span class="math inline">\(5,857\)</span> observations spanning the full
duration of the period in which CROPDMG was being recorded that caused
no property damage but some crop damage, which seems to imply the value
of this variable; it’s uncertain whether these instances would simply
have been rolled into property damage prior to 1993, however.</p>
<div class="sourceCode" id="cb32"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb32-1"><a href="#cb32-1" aria-hidden="true" tabindex="-1"></a>stormdata <span class="sc">%>%</span> </span>
<span id="cb32-2"><a href="#cb32-2" aria-hidden="true" tabindex="-1"></a> <span class="fu">filter</span>(CROPDMG <span class="sc">></span> <span class="dv">0</span> <span class="sc">&</span> PROPDMG <span class="sc">==</span> <span class="dv">0</span>) <span class="ot">-></span> cropsonly</span>
<span id="cb32-3"><a href="#cb32-3" aria-hidden="true" tabindex="-1"></a><span class="fu">nrow</span>(cropsonly)</span></code></pre></div>
<pre><code>## [1] 5857</code></pre>
<div class="sourceCode" id="cb34"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb34-1"><a href="#cb34-1" aria-hidden="true" tabindex="-1"></a><span class="fu">range</span>(cropsonly<span class="sc">$</span>BGN_DATE)</span></code></pre></div>
<pre><code>## [1] "1993-01-19" "2011-11-27"</code></pre>
<center>
<strong><em>Calculating dollar values for PROPDMG and
CROPDMG</em></strong>
</center>
<p>One quirk of the NOAA dataset we have is that the values for property
and crop damage are recorded as relatively short numbers, with the unit
of measurement of that number recorded in a seperate column called
PROPDMGEXP and CROPDMGEXP. Thus, an event that causes <span class="math inline">\(25\)</span> <em>thousand</em> dollars worth of
damage to property would have the same PROPDMG value (<span class="math inline">\(25\)</span>) as an event that caused <span class="math inline">\(25\)</span> <strong><em>billion</em></strong>
dollars worth of damage to property. However, for the first event, the
PROPDMGEXP would be “K” for thousand, while for the second it would be
“B” for billion. (The dataset also includes “M” for million.)</p>
<p>Before proceeding further in our analysis, it would help to create
variables called <em>PROPDMG_TOTAL</em> and <em>CROPDMG_TOTAL</em> by
actually multiplying those PROPDMG values by the appropriate number
(<span class="math inline">\(1\)</span> thousand, <span class="math inline">\(1\)</span> million or <span class="math inline">\(1\)</span> billion). We can then create a final
value for each observation called TOTALDMG by summing PROPDMG_TOTAL and
CROPDMG_TOTAL.</p>
<p>We have accomplished this neatly and quickly using case_when() from
the <strong><em>dplyr</em></strong> package, as seen below.</p>
<div class="sourceCode" id="cb36"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb36-1"><a href="#cb36-1" aria-hidden="true" tabindex="-1"></a>property_events<span class="sc">$</span>PROPDMG_TOTAL <span class="ot"><-</span> <span class="fu">case_when</span> (</span>
<span id="cb36-2"><a href="#cb36-2" aria-hidden="true" tabindex="-1"></a> property_events<span class="sc">$</span>PROPDMGEXP <span class="sc">==</span> <span class="st">"K"</span> <span class="sc">~</span> property_events<span class="sc">$</span>PROPDMG <span class="sc">*</span> <span class="dv">10</span><span class="sc">^</span><span class="dv">3</span>,</span>
<span id="cb36-3"><a href="#cb36-3" aria-hidden="true" tabindex="-1"></a> property_events<span class="sc">$</span>PROPDMGEXP <span class="sc">==</span> <span class="st">"M"</span> <span class="sc">~</span> property_events<span class="sc">$</span>PROPDMG <span class="sc">*</span> <span class="dv">10</span><span class="sc">^</span><span class="dv">6</span>,</span>
<span id="cb36-4"><a href="#cb36-4" aria-hidden="true" tabindex="-1"></a> property_events<span class="sc">$</span>PROPDMGEXP <span class="sc">==</span> <span class="st">"B"</span> <span class="sc">~</span> property_events<span class="sc">$</span>PROPDMG <span class="sc">*</span> <span class="dv">10</span><span class="sc">^</span><span class="dv">9</span>,</span>
<span id="cb36-5"><a href="#cb36-5" aria-hidden="true" tabindex="-1"></a> <span class="cn">TRUE</span> <span class="sc">~</span> <span class="dv">0</span></span>
<span id="cb36-6"><a href="#cb36-6" aria-hidden="true" tabindex="-1"></a>)</span>
<span id="cb36-7"><a href="#cb36-7" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb36-8"><a href="#cb36-8" aria-hidden="true" tabindex="-1"></a>property_events<span class="sc">$</span>CROPDMG_TOTAL <span class="ot"><-</span> <span class="fu">case_when</span> (</span>
<span id="cb36-9"><a href="#cb36-9" aria-hidden="true" tabindex="-1"></a> property_events<span class="sc">$</span>CROPDMGEXP <span class="sc">==</span> <span class="st">"K"</span> <span class="sc">~</span> property_events<span class="sc">$</span>CROPDMG <span class="sc">*</span> <span class="dv">10</span><span class="sc">^</span><span class="dv">3</span>,</span>
<span id="cb36-10"><a href="#cb36-10" aria-hidden="true" tabindex="-1"></a> property_events<span class="sc">$</span>CROPDMGEXP <span class="sc">==</span> <span class="st">"M"</span> <span class="sc">~</span> property_events<span class="sc">$</span>CROPDMG <span class="sc">*</span> <span class="dv">10</span><span class="sc">^</span><span class="dv">6</span>,</span>
<span id="cb36-11"><a href="#cb36-11" aria-hidden="true" tabindex="-1"></a> property_events<span class="sc">$</span>CROPDMGEXP <span class="sc">==</span> <span class="st">"B"</span> <span class="sc">~</span> property_events<span class="sc">$</span>CROPDMG <span class="sc">*</span> <span class="dv">10</span><span class="sc">^</span><span class="dv">9</span>,</span>
<span id="cb36-12"><a href="#cb36-12" aria-hidden="true" tabindex="-1"></a> <span class="cn">TRUE</span> <span class="sc">~</span> <span class="dv">0</span></span>
<span id="cb36-13"><a href="#cb36-13" aria-hidden="true" tabindex="-1"></a>)</span>
<span id="cb36-14"><a href="#cb36-14" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb36-15"><a href="#cb36-15" aria-hidden="true" tabindex="-1"></a>property_events<span class="sc">$</span>TOTALDMG <span class="ot"><-</span> (property_events<span class="sc">$</span>PROPDMG_TOTAL <span class="sc">+</span> property_events<span class="sc">$</span>CROPDMG_TOTAL)</span></code></pre></div>
<p>This accomplished, we now group by EVTYPE again so we can see the
cumulative economic consequences of particular weather events.</p>
<div class="sourceCode" id="cb37"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb37-1"><a href="#cb37-1" aria-hidden="true" tabindex="-1"></a>property_events <span class="sc">%>%</span> <span class="fu">group_by</span>(EVTYPE) <span class="sc">%>%</span></span>
<span id="cb37-2"><a href="#cb37-2" aria-hidden="true" tabindex="-1"></a> <span class="fu">summarise</span>(<span class="at">TotalDamage =</span> <span class="fu">sum</span>(TOTALDMG)) <span class="sc">%>%</span></span>
<span id="cb37-3"><a href="#cb37-3" aria-hidden="true" tabindex="-1"></a> <span class="fu">arrange</span>(<span class="fu">desc</span>(TotalDamage)) <span class="ot">-></span> summed_property_events</span>
<span id="cb37-4"><a href="#cb37-4" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb37-5"><a href="#cb37-5" aria-hidden="true" tabindex="-1"></a><span class="fu">length</span>(<span class="fu">unique</span>(summed_property_events<span class="sc">$</span>EVTYPE))</span></code></pre></div>
<pre><code>## [1] 431</code></pre>
<p>Unfortunately, we see we have <span class="math inline">\(431\)</span> event types within our newly grouped
subset, when really we want at most <span class="math inline">\(48\)</span>. We follow the same approximate
matching procedure as we did when looking at weather events impacting
human health. Before we begin, we are going to split “Hurricane/Typhoon”
into three entries in our dictionary, “Hurricane” “Typhoon” and
“Hurricane/Typhoon”, and will re-collate these after the matching is
done; this is done as including only “Hurricane/Typhoon” in our
dictionary can sometimes cause events listed as “Hurricane” or “Typhoon”
only to be matched to another category, which we don’t want.</p>
<p>We will also re-capitalize our Event Type names again, and delete the
now unnecessary EVTYPE column.</p>
<div class="sourceCode" id="cb39"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb39-1"><a href="#cb39-1" aria-hidden="true" tabindex="-1"></a>dictionary <span class="ot"><-</span> <span class="fu">c</span>(<span class="st">"Astronomical Low Tide"</span>,<span class="st">"Avalanche"</span>,<span class="st">"Blizzard"</span>,<span class="st">"Coastal Flood"</span>,<span class="st">"Cold/Wind Chill"</span>,<span class="st">"Debris Flow"</span>,<span class="st">"Dense Fog"</span>,<span class="st">"Dense Smoke"</span>,<span class="st">"Drought"</span>,<span class="st">"Dust Devil"</span>,<span class="st">"Dust Storm"</span>,<span class="st">"Excessive Heat"</span>,<span class="st">"Extreme Cold/Wind Chill"</span>,<span class="st">"Flash Flood"</span>,<span class="st">"Flood"</span>,<span class="st">"Freezing Fog"</span>,<span class="st">"Frost/Freeze"</span>,<span class="st">"Funnel Cloud"</span>,<span class="st">"Hail"</span>,<span class="st">"Heat"</span>,<span class="st">"Heavy Rain"</span>,<span class="st">"Heavy Snow"</span>,<span class="st">"High Surf"</span>,<span class="st">"High Wind"</span>,<span class="st">"Hurricane"</span>,<span class="st">"Typhoon"</span>,<span class="st">"Hurricane/Typhoon"</span>,<span class="st">"Ice Storm"</span>,<span class="st">"Lakeshore Flood"</span>,<span class="st">"Lake-Effect Snow"</span>,<span class="st">"Lightning"</span>,<span class="st">"Marine Hail"</span>,<span class="st">"Marine High Wind"</span>,<span class="st">"Marine Strong Wind"</span>,<span class="st">"Marine Thunderstorm Wind"</span>,<span class="st">"Rip Current"</span>,<span class="st">"Sleet"</span>,<span class="st">"Storm Tide"</span>,<span class="st">"Strong Wind"</span>,<span class="st">"Thunderstorm Wind"</span>,<span class="st">"Tornado"</span>,<span class="st">"Tropical Depression"</span>,<span class="st">"Tropical Storm"</span>,<span class="st">"Tsunami"</span>,<span class="st">"Volcanic Ash"</span>,<span class="st">"Waterspout"</span>,<span class="st">"Wildfire"</span>,<span class="st">"Winter Storm"</span>,<span class="st">"Winter Weather"</span>)</span>
<span id="cb39-2"><a href="#cb39-2" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb39-3"><a href="#cb39-3" aria-hidden="true" tabindex="-1"></a>dictionary <span class="ot"><-</span> <span class="fu">tolower</span>(dictionary)</span>
<span id="cb39-4"><a href="#cb39-4" aria-hidden="true" tabindex="-1"></a>summed_property_events<span class="sc">$</span>EVTYPE <span class="ot"><-</span> <span class="fu">tolower</span>(summed_property_events<span class="sc">$</span>EVTYPE)</span>
<span id="cb39-5"><a href="#cb39-5" aria-hidden="true" tabindex="-1"></a>summed_property_events<span class="sc">$</span>EVTYPE_MATCHED <span class="ot"><-</span> dictionary[<span class="fu">amatch</span>(summed_property_events<span class="sc">$</span>EVTYPE,dictionary,<span class="at">method=</span><span class="st">"lcs"</span>, <span class="at">maxDist=</span><span class="dv">60</span>)]</span>
<span id="cb39-6"><a href="#cb39-6" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb39-7"><a href="#cb39-7" aria-hidden="true" tabindex="-1"></a>summed_property_events<span class="sc">$</span>EVTYPE_MATCHED <span class="ot"><-</span> <span class="fu">case_when</span>(</span>
<span id="cb39-8"><a href="#cb39-8" aria-hidden="true" tabindex="-1"></a> summed_property_events<span class="sc">$</span>EVTYPE_MATCHED <span class="sc">==</span> <span class="st">"hurricane"</span> <span class="sc">~</span> <span class="st">"Hurricane/Typhoon"</span>,</span>
<span id="cb39-9"><a href="#cb39-9" aria-hidden="true" tabindex="-1"></a> summed_property_events<span class="sc">$</span>EVTYPE_MATCHED <span class="sc">==</span> <span class="st">"hurricane/typhoon"</span> <span class="sc">~</span> <span class="st">"Hurricane/Typhoon"</span>,</span>
<span id="cb39-10"><a href="#cb39-10" aria-hidden="true" tabindex="-1"></a> summed_property_events<span class="sc">$</span>EVTYPE_MATCHED <span class="sc">==</span> <span class="st">"typhoon"</span> <span class="sc">~</span> <span class="st">"Hurricane/Typhoon"</span>,</span>
<span id="cb39-11"><a href="#cb39-11" aria-hidden="true" tabindex="-1"></a> <span class="cn">TRUE</span> <span class="sc">~</span> summed_property_events<span class="sc">$</span>EVTYPE_MATCHED</span>
<span id="cb39-12"><a href="#cb39-12" aria-hidden="true" tabindex="-1"></a> )</span>
<span id="cb39-13"><a href="#cb39-13" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb39-14"><a href="#cb39-14" aria-hidden="true" tabindex="-1"></a>summed_property_events<span class="sc">$</span>EVTYPE_MATCHED <span class="ot"><-</span> <span class="fu">capwords</span>(summed_property_events<span class="sc">$</span>EVTYPE_MATCHED)</span>
<span id="cb39-15"><a href="#cb39-15" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb39-16"><a href="#cb39-16" aria-hidden="true" tabindex="-1"></a>summed_property_events<span class="sc">$</span>EVTYPE <span class="ot"><-</span> <span class="cn">NULL</span></span>
<span id="cb39-17"><a href="#cb39-17" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb39-18"><a href="#cb39-18" aria-hidden="true" tabindex="-1"></a><span class="fu">head</span>(summed_property_events, <span class="dv">10</span>)</span></code></pre></div>
<pre><code>## # A tibble: 10 × 2
## TotalDamage EVTYPE_MATCHED
## <dbl> <chr>
## 1 150319678250 Flood
## 2 71913712800 Hurricane/Typhoon
## 3 57340613590 Tornado
## 4 43323541000 Storm Tide
## 5 18752904170 Hail
## 6 17562128610 Flash Flood
## 7 15018672000 Drought
## 8 14610229010 Hurricane/Typhoon
## 9 10148404500 Flood
## 10 8967041310 Ice Storm</code></pre>
<p>Looking at our <span class="math inline">\(10\)</span> most
economically consequential weather events, we notice that we need to
re-collate the data so all the various Hurricane/Typhoon observations
get grouped back together, which we do below.</p>
<div class="sourceCode" id="cb41"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb41-1"><a href="#cb41-1" aria-hidden="true" tabindex="-1"></a>summed_property_events <span class="sc">%>%</span> <span class="fu">group_by</span>(EVTYPE_MATCHED) <span class="sc">%>%</span></span>
<span id="cb41-2"><a href="#cb41-2" aria-hidden="true" tabindex="-1"></a> <span class="fu">summarise</span>(<span class="at">TotalDamage =</span> <span class="fu">sum</span>(TotalDamage)) <span class="sc">%>%</span></span>
<span id="cb41-3"><a href="#cb41-3" aria-hidden="true" tabindex="-1"></a> <span class="fu">arrange</span>(<span class="fu">desc</span>(TotalDamage)) <span class="ot">-></span> summed_property_events_refined</span>
<span id="cb41-4"><a href="#cb41-4" aria-hidden="true" tabindex="-1"></a><span class="fu">head</span>(summed_property_events_refined,<span class="dv">10</span>)</span></code></pre></div>
<pre><code>## # A tibble: 10 × 2
## EVTYPE_MATCHED TotalDamage
## <chr> <dbl>
## 1 Flood 161071361250
## 2 Hurricane/Typhoon 90732527810
## 3 Tornado 57345502190
## 4 Storm Tide 47965589000
## 5 Hail 19145902670
## 6 Flash Flood 18439139760
## 7 Drought 15091766600
## 8 Ice Storm 8968208310
## 9 Wildfire 8895482130
## 10 Thunderstorm Wind 8685898490</code></pre>
<p>Finally, our data looks like it’s in a format where we can analyze it
using a plot!</p>
<div class="sourceCode" id="cb43"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb43-1"><a href="#cb43-1" aria-hidden="true" tabindex="-1"></a>neat10 <span class="ot"><-</span> summed_property_events_refined[<span class="dv">1</span><span class="sc">:</span><span class="dv">10</span>,]</span>
<span id="cb43-2"><a href="#cb43-2" aria-hidden="true" tabindex="-1"></a></span>
<span id="cb43-3"><a href="#cb43-3" aria-hidden="true" tabindex="-1"></a><span class="fu">ggplot</span>(<span class="at">data =</span> neat10) <span class="sc">+</span></span>
<span id="cb43-4"><a href="#cb43-4" aria-hidden="true" tabindex="-1"></a> <span class="fu">geom_col</span>(<span class="at">mapping =</span> <span class="fu">aes</span>(<span class="at">x =</span> <span class="fu">fct_reorder</span>(EVTYPE_MATCHED, TotalDamage, <span class="at">.desc =</span> <span class="cn">TRUE</span>), <span class="at">y =</span> TotalDamage<span class="sc">/</span><span class="dv">1000000000</span>, <span class="at">fill =</span> TotalDamage)) <span class="sc">+</span></span>
<span id="cb43-5"><a href="#cb43-5" aria-hidden="true" tabindex="-1"></a> <span class="fu">theme</span>(<span class="at">axis.text.x =</span> <span class="fu">element_text</span>(<span class="at">angle =</span> <span class="dv">45</span>, <span class="at">size =</span> <span class="dv">8</span>, <span class="at">margin =</span> <span class="fu">margin</span>(<span class="dv">12</span>))) <span class="sc">+</span></span>
<span id="cb43-6"><a href="#cb43-6" aria-hidden="true" tabindex="-1"></a> <span class="fu">theme</span>(<span class="at">plot.title =</span> <span class="fu">element_text</span>(<span class="at">hjust =</span> <span class="fl">0.5</span>), <span class="at">plot.caption =</span> <span class="fu">element_text</span>(<span class="at">hjust =</span> <span class="fl">0.5</span>))<span class="sc">+</span></span>
<span id="cb43-7"><a href="#cb43-7" aria-hidden="true" tabindex="-1"></a> <span class="fu">scale_fill_gradient</span>(<span class="at">low =</span> <span class="st">"sienna"</span>, <span class="at">high =</span> <span class="st">"red2"</span>)<span class="sc">+</span></span>
<span id="cb43-8"><a href="#cb43-8" aria-hidden="true" tabindex="-1"></a> <span class="fu">theme</span>(<span class="at">legend.position =</span> <span class="st">"none"</span>)<span class="sc">+</span></span>
<span id="cb43-9"><a href="#cb43-9" aria-hidden="true" tabindex="-1"></a> <span class="fu">labs</span>(<span class="at">title =</span> <span class="st">"The Most Economically Consequential Weather Events,</span><span class="sc">\n</span><span class="st"> Ordered"</span>, <span class="at">y =</span> <span class="st">"Cumulative Damage in Billions of Dollars,</span><span class="sc">\n</span><span class="st"> from 1950 to 2011"</span>,</span>
<span id="cb43-10"><a href="#cb43-10" aria-hidden="true" tabindex="-1"></a> <span class="at">x =</span> <span class="st">"Type of Weather Event"</span>)</span></code></pre></div>
<p><img src="data:image/png;base64,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" /><!-- --></p>
<p>We can see clearly that the weather events that cause the most
economic damage in the United States share some similarities with the
weather events that cause the most impact on human health, but there is
not a <span class="math inline">\(1:1\)</span> correspondence. In
particular, we see that water and wind related events cause by far the
most economic damage in the United States, while heat related events,
which ranked highly in the causes of weather-related injuries and
fatalities, rank fairly low in terms of economic consequence.</p>
<p>It is important to note, however, that being as this data set ends in
2011, it misses the effect of recent global-warming mediated wildfires
that have caused so much havoc in the United States in the last <span class="math inline">\(12\)</span> years, particularly in California. It
would be interesting to re-run this analysis using more recent data to
see if Wildfires jumped up the rankings of economically-consequential
weather events as a result.</p>
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