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<!DOCTYPE html>
<html xmlns="http://www.w3.org/1999/xhtml">
<head>
<meta charset="utf-8">
<meta http-equiv="Content-Type" content="text/html; charset=utf-8" />
<meta http-equiv="Content-Style-Type" content="text/css" />
<meta name="generator" content="pandoc" />
<meta name="author" content="Elliot Cohen, Sustainable Engineering Lab, Columbia University" />
<meta name="date" content="2014-06-25" />
<title>R Basic Training</title>
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xaXpygKB5dWVlFrb5mQOydwB7i0J6ubFjh7npdh1XXdKUdEAmoj3W5Db4m4QE1LVCapO1XgH2D%2FkB08JF83SNM9ZhZf5I9%2B7Rz8J%2FI3IIfrMb5LHcjByu%2BUvGUKxQ%2FkcaLYdZ40f8JWOkw%2BA4iEBtlYSmddP1HFw%2FdKtShd1YQrncW61ZeqsIAcmYleEIEKAhx1ry8ND6aFh%2F8V9VHtiPdBPGObFcRVTzQgcz40RO7WEpW0ba3k2z3i6zBvlL9xO4ka%2F1EVnLTSa%2Buo%2BmkNzrwWDyPcz4lWOAxAp2%2BTrKORXoPGvm6d5aKpzt50u4573rAOfhP1CUXfpbjl36l4XWpysmXBCr6PbGqqsTnSRvReJFj4Xl9rvYfHFko%2FqPrb9YYEpV1CsKxTiSrXhisap%2F%2BC%2B6uu%2F8CZVz1iWlY7fGdnKxd2eM7WS2lcof2ANfjrBHRsoomvoE%2BuiF%2FpXpsCFnJYVZIVk7UsM85PYeLXYofPYf9H42YsMdtUq%2FMTd79Ho4bf2S7Gq9PVRxDC6lKG1VpVBV9cvFXGr6oVycSFZ%2FauXWquCnKooTpFvcfy0YiWVX%2BI%2FD8UOPXvp%2BE5%2Bom3JHt0IXP6MxS5U%2FAqovYd3l62%2BltAxkkq6q7T7LR3xT%2Bcz8gICtekEZU28Hq3d9JkEXhcfclufMmmZusgqh%2B5ww%2B7k3Vwcd9XbHh1JbiELt3Fu67QYbvmRs%2FCg5by%2Fr3TnRlZXhu7ijl5o6qlZ%2BrQrpkGp3PTVTZFKpwyqWabnH%2Fce5T6T%2Ba%2BNhiV5b7vOiQWG1XNvGNzngqewJWX%2BO7%2BPupIVbDas6n8HPju1ZMXmrvkcJ%2FhQ1Ya89RaqlV9P%2Bu0DacBdfrcx5rlPXMqW5%2B%2FVv8cfLHuP4t%2F6ZgDdhPFR8DGRmRoMk6R6Yc9MgU65TdN%2BKH32y1gyJruJn9x3%2BQ4%2B1zUvkZZYoyqq52HfJR1T3OUca8Ss9VrVhgoR1nngEaUXl%2BEwnEj%2B5Lg7fIsNx%2FsEWwXdT%2BI%2FD8UOO75zqXdKad2WS2XFS9L3XHH%2FH3m9r4nePnO%2F7Ys%2Fk6qErLYy0lJ6agZeeN%2FwwccYGT1QoEPwJqvCIveWiGhaw0jqw4olppFhqa8b%2FC%2BdTSMfJ8SlX%2Fp%2B918A8%2Bor5fxP%2FNTuvM3TtoeHe%2FThlVC9NKslFVNwNc6blq86mEHcyuYlSiOqOvmLPKAkLhPzbxCP6j62%2B9f0qwwjHOiiWI80kKVVktH0mD79lpNHuEPZ9yx6Q3iKpAKUiIS%2BdKK4ZmEpg0z1gZ%2FjhD12TDPydq2A6DMfTF%2FCCSlRZO0fLXPJsLM9o5%2FJ%2BS1UU8jHffrIaAVo2H93qXRX3g%2FV5fZeaqtAxwpeeqH3Trtr4z%2Boo5qyzjqus%2FuvixZ5JszVn3T9bAuDz2jM54qngC1XyGupJhFtiO%2BapVZZeFqvBG9bLT64cJ8zEW2juBk3sMRUbTMA9ggQX%2FxuNExVKRpvwrklXWFA5ZaUTl2d%2FRNOseP4pZYB18QoGHvnMyyNx5aXCbSf9x47s%2BV9Vv%2FQ2nBFacueFU%2BfxH298aemcj%2BAollosRz0QwI9ygM56qmBNbdKZpA5nYorobKAtV1yzEXc4xMjWQ2Xzu%2BE7FzK0BZnfYN3ezIOaa7cYL%2BL91Kw6%2BwooX2cJdqES1Yl3fwkumIJzZeGJX6gvVmzcdfOOV0JL6h1jjlZuVAa78e1Xd1scYiWP5mRhHlc9%2F9PFYhwhrwqz%2BfBOLKP8AaClJVcxhKRb%2FbHAgM5D5bBD67tR1UFX8XUSS8JcpqS8QN340V4kWuHQHNRnlvBhKfbH7Vr8H6fkKWPUwq6eGeuYgy1ZNqcPGOz6%2BR9SZ1V7of6CvfPi4Z0Y6zuSjamFa6bv2XhUpNzRDI2ruGXdyLNC1U%2FVs9fxHH4912DsxkOnMds9tPgd%2FPNEpvw99qvKrPL3r8FZaC3Vl26BE558ratV5h5brrxqrSjZWe2oL%2FbGSS62BPrSGWgP31eFR1ZYP7%2F23Jv5tWjqelgG%2BMe9VoU1C5X6i5fIfCh7GYRjtwIM65C%2FhnJc6S%2F9XnldPHdKgJoWtn6q6WQa0qq4yZuwG%2BY%2FxN2PGjBkzZsyYMWPGjBkzZsyYMWPGjBkzZsyYMWPGjBkzZsyYseVnpcgqGjNm7EYTNd1MEGK0kaUITo7BMiNUZj1D6RBgIRmWfxAO%2FFpHr1Vl6qOHh2WCxTJWwxWpzzycMW1rU6TJ5QMexTRpz5cqLpp7Tvw4WJlhBGp%2BBo7p79qA4m5Bmngs%2F5fXJWZFOsN86VmYRFb3agsKnuuswqLabGc2wig6q2t%2BjKX3TfZN4tc1P6Y0CV1XT7c%2Buvjor4tXpcgXo5daH5QVaUf8fJhRy0f8ShZltOdLW1lj1bW5cG1qRVxmL77HjjgmvE9R%2FzPNIBmL4pz2%2FS4zGXS56nQyJ70tVuA43%2FnhhXCDWLVTsHLHWSNKIav78VHwuOp%2FIIMipCx1YtdARq2zGvoUS986vnXcXu35aXmpqlsfXTwuQ3eviwCdg68rVx9cjSm%2F72K8qp0Ka69yLaEXBOWD7i5NogZ0pBfagNRNqJFxxsqEFfWPwLL%2B09uwHqe3jaYjJJ1eWCSe0SKcjaeJvbvxVmCVVIo0mRsZbeHSfvd3JVC1cDG3mnyFKyZVeNRZBYmKOKvFKsJysjg6o%2BzmrAAX9OBrN1HQg9KAVKq66wPik8r6uPCT1iQBH4jbAtCO9c7Gmb8KgVM%2BfqdTH%2Fyu9bWEasxz4a06Nd5xLnewJl9aLgT0WD2Vqon8Ot6IYncG62CUwRrSIF%2FgCGtkgnsnosow2zrYZMcO3HPU%2FiPwqxhxq40cvmkhMjI00%2FaV%2FG5xiVySOYQtkapLVRdU5HNTVb3sG3VWD28VlUA7vFWus3rbPl76oT28l06y2%2FaVj6ru%2BnC8vD4Cb%2FWFUMG%2FT4XH2hcKkKBASfsT6vpA4Buj1ydHk5BqNakbb68qVa4%2BFe7kXqsqkWWbd4sS5D7Nq8qnh9fhq6ASUbCGlDUMzYSvyueGPCTnI541tkrS2bvxqGrS9jZl7inw218N%2Fe75%2B2RdsdMygrCWQjrAh6pe8igqsjo4SsrB0VkV1VPprIau8tLvS92Xk%2BgMXVU0X8ZyaqQIe9z14TWS14fjYceRL%2B1585dWnQL%2FP5PsR790%2F%2BRHvwQRrA%2FU9YliQHhQVZ%2FkNwJPM6d8qumNqgkP%2F0mw8lE1zh7dUfyzR3fECSoZKDuzd6IZZrcwKtdQ8FVVjzwQWaTGlIj%2F8bZ2hrK8%2FmKhznTC9jV7MiHrLH2pir3s0qaWj5Q6KQe3zmoyT2uZzqoVE5InEAzWijBJLnFcKMQlb2pRH8sWpuLEltWH42E86rbr1I2LlGV43EFndIv7J6NbZDvXuNsHHvmrqvpgcshjZLvsG56mlmoIoIpQuUZV3SSUF1VlSr1JNtlU%2FLPJJlp9qqruehS%2F3vUolXr3DHZmdah6zyC%2Fm3sGfdu%2F353R5d2ebHiTzlX1GloX7%2Bisiscu11nlsp9izM7nFX8lu4aOEJeoD253wLdAkNfHqb%2B4Uzne1nzFunMl2j7LFl%2F1byOn%2FJ452FykRVV%2B%2FwUv3dqB93QSKHKdW71RtRSqWnUDmQP7T287sJ%2FL6B3bIU1yVbtd284wVNPqYwVwRMWR1T8ALqz12mMbzupQde2xzeesGEaAsoCZ%2B7I7D5y8HqqKTF%2B5qaqrs4r5U97TYn24Ui%2BMSl%2FL5w4rGd%2FUaKUyJBf1YbWjaZALrVXVx6m%2FuFM5Pr%2BN0IR9NxOqNnLKB6m1enX53u354H7lm700rf1vxKgKMUqcrYAU0Qq1jF6xzJjjwH6jGB6jaR4AQ964gTXA%2FzUUPFDv2r6dcu8vxEe%2BHv9Z62vgq7WUlzWibUsKgIsTMuWnqp7OqjUswt%2FeP%2FExJx8CD9Nqw5NRlPrAcBdU67668PbGCiq8837RST9I1ded8qspOrT6urVcqfn5%2B%2Bj4So%2BqOjPn0bQXVUcVbylY8MQuzMOLvLHqKgJ%2Faotc974YP33f44mOxUd%2BQXtZI8ZXERIvM6rq6ay2vyHKvOOP%2BD3f70P2ZwTFdQkq1P71dV%2F18N6uK6tRZeuDNcJ3rzr4ys9V6Ybqy0upij9VnFeLGzFqXMfGUzUvHTyrAXo3UF7W8PeqPBMsU6QUvxVqiFVVOPzcEKrq6Kx2LIoyh1%2FG74dfFt%2F7adrru4qu7qseXp%2BqpdYHE1IDGTUeXnak9k5sPkfXuV36Z4X%2B9Xf2vHMO%2BW4LelR1Tz2KPy8Pk9Pb%2FXrGsmM%2B2v4Gzp8YYrYjN9a7Y2eS5mu%2F%2Ft6bVJ1VlwJqkPepKi1UPRVd3fro43V1fa%2BjPt2o2k7QrQ3Z%2BA4dnVsNXeKUBzolL1%2BbDK7rU%2Fd6XTZELtRtroeDrMTsRDdVxowZM2bMmDFjxowZM2bMmDFjxowZM2bMmDFjxowZM2bMmDFjxowZM2bMmDFjBeb6o%2By0Pt5KEzRo61BQha4rq6dBmzsnnUdfrlhLodptTKuFmDVJLn0sd0ZGdafebSRTli2lRY0tO8OVCkMzSS0VYAfPQuvfUp3VeDG%2BiNKTVLFK3ZU7aM6KnDijC0TqWfiqSo2CGxdvO7TnwP7e2RBZwpo1jB9Nss3n5Eu3rMmwTX%2BdtTuaqr4lULqUjgBkOUvuaJT4gG7ntEQYW3qvxWdWaOgsrn8SpSdDAxm6CnAhXk3WKLNXSzKqWKU%2BVVGCxCXi9rBegxCbcDgJy73WXiOQzpbYhBUUKy5Al9TxZ%2BpD6nolyZQaBC1QcgsKloK4S79Y%2F4gKvIWCpm7TwRa3Pr271z2L1ZTe0ajwxWVTFnZSl9cVo5JMl3qq8r3WNSU5VVFTIKKhAlyMV5HVq9nKS9X2J9xomXApF8O0Ckq3lHKYaKHfDZ7unU0wykYMos4oZh1fpFI18QcvyS8vpwJH7%2BQrGPOqi%2FX%2Bo7EOtngJHDEi6L%2F%2Bs6hlU%2FD6CzWFkAqt1jpU9fZmf77wPSuKNxTJURXJp6cCXIyXk7WyVEWioaCnc%2FzgvKzvcsbf3lmuczj6G5UcJoZr7ez5%2B7b%2FraojcFPVijWdBE3gs1SqYquqRNyKW05noTVR0lxzxxrtADVQQkBb8QA4yaiRVjGqFKqipK%2BsJvamNFdzeR5bmse%2BCp930lWAvfEyslaWqrbu7DcFwcI3lIBq78TU0NTQ3olkXn5ZZrftQ6mrX4LAaDthLszH6iaGTa4KaZ0gHqcWFMJVlqq6Gha6AapXcHpzA2A%2B9y%2FWIqTUJ0%2FySZ324drbsueLHBt5BYmKm4AEFqw6G89CemT1w%2FuTtfJUjRf0n3HCo%2B%2BdPdC5iq1iBzqFgrAi%2FL26%2BW2rL74B0S0vUNwd91fBRFHzx7TNkOL%2FNcnu%2Fh83n6q6Ghb6qhf6AW3lAmD0F0j9pR0lQZ3a81a10iHJSLzUm9e%2FpRIXtfdACCJRo7YAeuPFHN6LfOvfklTWB7%2F%2BLe%2BzKk9V%2FV66%2B9KmNvy6qa37EiHI7otDd4QCa9F8aKImBUhkNY5MRVngeRKRjifZxqM3n6olvSrTPEMnQK1sAIyEG5liIXcATLtXVxooBCX40ttDEN%2FmjzQUr4MXg4Hm%2B6O5M1y7%2BhSTD4SzpPvQeOGt9Pq3vM8qjaq9s6e3UUa80qgahTzx6W2nt4UfjhK6g9DfgxxnP9%2BJzB7D%2B6ikuB8SXrEPKUSKfYZ731WOqtaYHXyNUQitm8%2FVPUMnQK18AIwZdXcATLvXglGwRa89kT%2F%2BeCvWeKUNIr7b%2Fpdn%2Fd3kUxHVC%2B9P1NKoauvk14gNLtTBZnFqW%2F3om1ngo8BHzaT9Q8Ps4eeEk20%2BB3vo%2FD0prRSwxlqvJdmdv6dQCbuMB%2FoqR9Xm3F3rB8CVyAAvlwC4OANMFwHUzQB3gbokPzh%2F%2FPFNuSlcxK%2BrEeSjELUYLyOqN1WHZmQ3Jza0EBtcEGgx7wp2AkmSGGaURUlymJjHPZB%2FvTH4d%2FbcXPJXS9YenlZaBcRIEBNL%2FJxn%2FlqJg82hnSQGbaTkeLeLydIgJQWcyw1%2F3Rlgan3UVHVUHIGB9lFKWq%2FoKkg%2BKlHdeGtMRlS%2FEMAf7955hpNV1RTFfwKhLYYpkcO0DmK%2BrmlBqNnzfHFgwb9GrQUbJ8GfH9QrCZjh5zReUbZ5C7R4i85IKZIe7iPEdF6tlT9AXV4ZYEHVUupTiqpxGahqZ55COsVyPGjMhsoZUhVuEYUC1cp61BaU31DOpsspEafyWxUrlW4L6tJN2VgB%2Fkghh6a0uX8w5o3nSQ%2F3gYmUcgacyw2v73NLz6HVpxRV41Iy8MtUCVj9ZwDGtNqzRf0TY8aMGTNmzJgxY8aMGTNmzJgxY8aMGTNmzJgxY8aMGTNmzJgxY8ZujsFqucCyqMe0eRbGjEmI2nhR%2FHG6FDdmTYPuyzRFFsxGn7Euw%2FqXMzQ8LucGaZThkjub4WXQkmmasrIxYyU5%2Bbrm7ksJ6coR7obNoDIUgX%2FNSo1DlOxuxtV32QT8D%2FgMRcKk%2BWOQRvmKgrSGoRNgjuA1djarJcu%2FXfgzuKpUq%2Fx57MRgjX6dmqhhorKyW1sW20ofqz5LDw%2F327dsPDLvLTS%2FWYrnS%2BJoZ1GwrvZM08R4nFand95Qtux%2B0ckDH00N9c6mvpCPkbgwbCAzmsaF1mHFiklUiBnIgGh3SqwTbbyorOjBqK2ZFHhJTaTVuaW4fJ2rVTd4y4ZTvbN%2BMp0C35UdP4prB1cxaw%2Bl%2FL0TQzNd2eM7Wa2VblPeAZe60pFBF%2B0fZbpYyll6%2BDgsJ4J4iTwNEms3SxV4kZ2D3sJd1vkks6V4viSOdhYF67RnmFQjd%2FuTO2%2FwH8n98sJACwjIeqFfVtDa923hbnyiHUjWte%2FLaB3l2FpnLV5UQW6rry2HDCmD4JavQB40nVsq1Dl4S%2BPFxovn1sGysmoFPsUaISwHAaxWdMs6Zfmw1A%2BXwnESbjglb2gUssrpU4QoMuhcUYfLXulhaWfp4fEa7bhGl7gzgLN2s1RVRBlWuGySUcjqhaesV6Vj3e0ZJZJV4OWqZY5xqTSf0p3Cui%2FtvlW%2BEA2lJIZf5p9RFSHC5LQ%2BvLX4EcnVdNd86SDXLMibAkc8FnTfQ%2FelwVvUeFRYbWZ87AO3PKnET9qhr01U2QJ8KHc6zNzjX1jaFQik0%2F5qN3ewtLN08bzlEyC7CkFY382mqnDZJIkaXnhaq1Kx7vakkdXB62qseJTuFObWYPAzFD0R4l6xD%2FEcf2xssSvLl2W7H1HiG9k4HGc4VguHkSv%2BOU3L7wHrL6OqwAeejwCWb%2BgxNHNunWIUmwyhHut0WNncjRfbc4r9yfzXdkXAbCtAXIySZbgcLOUst2PRruI8pyiGwmn1SFZZqnKXpVHDC08VN6Nh3e1JqZE%2BUSVk1SNqVdWGs3w7DEiv2FtiyMLB7rnOrMcjysrG4aEZHKvzAixX6XM3TlQZMQR%2B3dTpbRAmx7uyeycgXK2W45%2B%2BtwuSYu1CS0oSvm845SW3IQ%2BYhYAklapRl5uoz2o6qYcvplJYKSRTeaoW3rN%2FDOSNp%2BsQUrCFRFVnX0T7g%2Bi7psZKz1zR%2FeoStapq306uqNaWU1U7frc%2FtvdPibxomHNz%2FRdk4fXGo32TDjbOKFQNPBbN1V%2BeNMnv3BJiNfj11BbWIKNeTguq9vhOjCU4UWVqRrw3dG%2FnpO5NedvTqepgKWedW6eHL6bS0MzH99x8qrrvwT8G8sbTqUrB5uV13Eenuv3jTO%2FlnZWOF94vfz2jQ1TcoGhkymlmSLrU%2BGPHnoH55tniRzT2jCy87vjz4fWOsmCSRNWew3sneP2jykdfcC81%2BOKJQG0gK9ckalbUSKi8ijPVYQ8ihZAqxakcLOUsoftIv0rSta8PyLiFVF5ReaqibyZJw4kXnkpVGlZ3Vx%2Bn%2FcNM52VNuPh%2BA4%2Fx1zN0oqK9kxCPHLK7QanjNvTO2sFyzN13yaTKNpyNstW%2F7b8g0PKdXDBIwEwlTKoaWDXqkmOoocbnk0CgSEjDgwRai7p8Tlbcyys3V02rwx57%2FK2mqB4XY2ln6eKF4Cq8zuqkyLhVmqrcN2nDiReeGqvQsXqKiPpk9SAqzAw%2FFa9ndOTHWPWR7Tju9cydT6mwWM0I7rA67%2FRdMvzJjV3ZcF6wuCsr8sc%2BwfgTuEGU6NtW2ptF7Xvi5uELkwK0REIuyCaoHi%2FF0s7Sw%2BM1Np%2BDqUHjcnhZI9yVFvd54Sn0o2NLURR0yKrzsqbgfq0AzvC2%2F63q9YzHxSEg7Jk7tkN9HsqD7p0YyHRme%2BY2n4MkTqf8HFZzagv%2BuQF3dXCYWil6xYH9fPotlHcP7Gcrbh7eTVZqxi8fZCtVj72wlLMEyn7Nr8Tb6sUdsmmNl%2FM6X2l4mqMLd7XqaHGfg3I%2Bqemngy3NeKvrvKwput%2Fdt9p50MZSBD0hIOyW08gdBsOsOgX4DopKL2zMFMpJa4fUDsNWQLlu5d0VNxfvNDc14%2Bc4q1r12AtL0UrmKP6cVXiaevGNMXedKV7qoFyf1KOeBvZ6yKrzsqaoY4IGqKkyZszYjeh2qiuHNmbMmDFjxowZM2bMmDFjxowZM2bMmDFjxowZM2bMmDFjxowZM2bMmLEym65O7%2FcNb59Dlp1cnvWvuA8FoEYxnda02A2oVVr3SlbMmlyuNM3p9MYZTaf3%2B4YXZ3EZMXp7JrOdWZ36VAp%2FoyzwEUhYfkStC3XBXMGdZ%2FT3fXj%2BPr0rWbHAAuhkTt7Mrobvb5ETbA24WrRYK0al01sani9qw%2F8rU34yL2pW7vK5OPNKG7tyXq3b7%2Bges9SJXQMZSn1Kxe%2BdoOkq2488o%2B0yGtSwJqNK3aPrpWoUBU%2B19k1A4tGvBCMw4BPgpU%2FfW4n6U4nK97ewh4ZY00JeBg11evllhR6QXKfXwbudXYXvnf1kE67f%2FGQTLrClly%2BEoNR4WEUKomYndtHrT7tftKaTHYtIiiR76CUQO5VrGOd0jzfegd9tvIPFkXzy%2BgidZHthoQbeirVeo9QfhcebFqLajkWnhjUcyoum0UJyXVe3%2Bqw9qF%2B5mtHDU0G8E7uGZij4MAggIB6F2W8eVdc1xxdxfwssH%2BsfX1zXbP8CdXqdy6p1eh28%2B5Dju7K49g7Xb%2BKaPRxZaeX3zn58D6eICg8SMCusPVaM1fK19pTyhQCoSpfYqguz3S%2ByBpvejQ%2B91HpN1tBc9xh6QxTlTMP%2FscNbVfUROhd8awUqXjgitpBcV9maXA0uqO9YVGpYgTULzlNrvUYZi3VcHZQx54MgrZ6XcSeO9m2%2FEsSjL%2FEHUb%2BdlFXYlaMqq0HJvUROWteuP1%2BoGlsUI4wNy400%2Fjq9Dl4canzCFRY1nUwQykf5rYEMEnx8AKmtwsO4wkJXezZbdVFGqb%2B4A7UuMYq0JHFGyA7tObTHAodJSGcpXPeY1Q7N4J41sOofPkPDfyPHw3X2WAHcWqHpJBGfIyo8yAYZHgPfkKtzUsl2uTFUagQ%2BSri6bRgRPiqnq%2BPGI4miKK7pCsnpW0amaMRz16n1NfUc1SpqUcq8liaahugm1%2F0mUOqI47vniptMrtNbGt5RlOGqMSr81BCrhyCvmjukGp%2B0FY9YfdNJlc6wwFsFUYQ%2F3nEQqFG9Wg1I6B7z%2B%2BT33ZmV1Qfx1p5WtopFc0KSBLxD1Fo5vulKVEu2ywurogaX23R1gZ2KGXChq8u3uuoLL6lP7%2ByxHbQsLmuhEFVkiXODT0hNZx0RND0tKe8r2L%2Fo%2FZNoMsF%2FuU6vg3f3L3J8V9ZRbj23DkdJVfkBO5OYJ6oSjweEaiys1Bl28O5m8cez%2Fs8G7Zlwv2WPrKj6JBP14LrHVgxHVBxZ8XNCWh%2FET4cxcOdqOgT8mJuocvyxHY4ibqlUVVNDZ5ycGnILx3Zlp4akQezbmLEYmcKYRugSQyvVliOLW0qWuPJUZf0ndjkt1DMHCUTubajTu9R1%2FXV6Hbz7kOMTtpg%2Fzt0wu5UglG%2FL%2F%2FcJotLq4w5o1XgR8Kt1iVn10AwmkxC37qcdi7tflDU01z1mtdDAMDeH%2F2vXnFXVB%2FAwh7ddEEQ61PjmguSHHM9qsWR30C8XHiue2lCooUdVVvPkU47nPPmUXDKoHXWmu2F0tGMa1V4IelncUrLEhW1D1CvU0je0Arz%2BvCOL4wYsfPqBOr3FaRaZTq%2BDd%2Fe6KnwUXnPg3G3lfJRYPu6WktCqj1793TGBXJcYI4HB0825lAkmmKSPJad7LDLAuFWIqj6ID92OLghjMAGPNYF62COqCm%2FrZ4X2TuCj108rqalRyuyTrRCS7yCCrhCVA6IG8xHFLM7My5fFLSVLXPm00u5bU19g%2FXmKqyub%2BmL3rblfOYrrNJ3e0vAio1Wp8iuH586OYXCSfbJJrbjo6B5b800sQqqPLt5OdBHxnODHd6oFSJcE2wRqlOa8rIUH%2FKxFiQy675z6EoWeTNLNEt%2BADDBEWCipa4%2Fatae2uJQLhU5vF1Wn93uGF2dxdXoKUuged2vVp1J4JxDWdRpa4Fua8x7Z3jMnn6V63Xl5srjXkyWuPFWFjmhOW7mm6K71dHq%2Ff3gx4yBjl2P9q%2FVdRgvdr9VCqCJdAWFbSha3mKz6RNW9W2PGjBkzZsyYMWPGjBkzZsyYMWPGjBkzZsyYMWPGjBkzZsyYMWPGjBkzVgaDZXN7TCsYM1ZICw1dWVC6QQmPPvtzn72m%2F6DGlfrg%2FDNqWUZc2dpzuOL3PW0xqhwm1PqMVtkZlcJBHsm8P2tcK13GNnkOD%2B%2FvvpO%2BHbsxisPqZ3TdT0lXFzf2Ye9sfDEIq%2BytySDoqfXOxj4kXWUMnTfI2tidv1evSQi8FGU%2F3qbRAUwjleD%2FPh18x%2F%2B98wrtj9KtsQi0Dl3a0gpwsU01Ta0CVY1kSfqyoDYx7f3UoPtlHse8f1m9R%2FDw%2Fu67aO1PoG6H1DPn1a1SOl4QFddrXydZdXVxI%2Bzh53D1IwoQ4kq%2Fh5%2BLqB0y1nilmfHrbD7X%2F09qqq4F2cz1D9hjWZ2y6aaDrJ0hldqhK%2FBzWi%2F87eze1wtEkX3P4TKeoAZBHINZDW2ZlK4AiHASrmMPI8Y8J3i7z1NLeJafYOWmKldbLGV8kY824rdC%2F0gp47aka2qVyNlwmbtEvlUoMnF6eIeoOenV0skqdHFhmS8s6hEKDBIdWlu%2Frwkwzz797NOop9acFxXzf4yBhXhOKWbvxIP7w0Bx1aiHzdH%2BRlXVnb%2BXdxwYKLcXuGE7y4scE%2FAPPtJ8VIYXY3wC1HcHMqDG9xK1ZWlUXSrmIV9clVcFBBkPR3sKjw2n6F2BrF7Q2wWdBd2F38laNczU7bh0fJGPNs5vWUjIzsjr339h6b0Ov6zb%2Fv5PQL897auEuMQLbXleYWvkvxO6uPFF%2FC6%2BqNLFdfSL7nrgrgccXSPZpbmgaFd2%2FCjr7jrULJUd4zYeyfVBk%2BvfiUrnwo3vRmGcFuLeXAsI4oJ3qfhX1%2FSmZXg%2BdqE85%2Bs%2FfP2HttQmaV8WvnGGeusMz%2FCUyV0LZbJSX8Q3CKkRfCYgYxoqj2sV1oA0ZtTxyCxKIKtVl%2FjcGV%2FEaJP4XDEWQaSE7u7oGvmVv%2Fm1pfe6dVy3%2FS2JTFzPnF7n6n4S1FC5aHLDySp0cWEwr4NmVOriOg%2FfTVX5xTsWcdQ%2BtWV0i3W5TdnPWcPWQRE%2BhhiOgOGr%2FikuFCz5oDvJpu9DAtpiHXfbakUHKfjUV7DivsUfzw2FxkbTq0D%2BczSNomUUojZe5LVROW9JfTTIeDz7k9Uu1UI%2FopZC1cLfq%2BsjiJokkRXlSHjsBi54OSyVzGEhrlIIVOhwZxRkNbr%2FiaX3%2BnhCt%2F1lio6P%2FAKyLkw3A0ClKu%2BeChVBc2R1dHQDjwUeU%2BviVlVh4IsHUlV8lo9JCXgYR7YH34MkVK78w%2Bv98a2frJvCG%2BudFSNfnPlJRvdNwoz2X1j1QGb1b%2FfZwlGwSUTN%2Bn9Jsr5JP3z3JY6%2F96Uk23Icf%2BqPFwmlnrkZe5yfifTMRdTbTsS43Bt33pXzsnEYHXHpP9UD7eniRJ3YvX2tfQdvlS9g06OqQ1SxaQmdrPE8Uf1Se1x5WcfpxweK7xRGwZryURVKa2j7yvuc7a9eP1VzOtlFh%2F18HV3c1vxWA%2FIAFSlafEjdanNX9uHnAguRfOlIJ3981O5vkYL78j1kfIPfvGoggyPi%2BVTn%2Fw7aJZ9P4cgJVwj64SGgTiMex3fMMVtpf7xIKL3wU1a9%2Bdzmc6z6hZ%2FaWz3UyYiKgakY4zefs%2BeVMf8HyLO%2Bhf9UXUEgR9Tmj5vssv0zwJWlqkNUGNe7%2BWxSvd2VQ1Y5Uf0cXFYj3Jug8E4H3tOPavzLhzG1L%2B5zjmwAolKVz8kLJV5zMZOujm5V1WrGD6So%2BCy9eH3%2FhWaXtH9X9nxKdUvYGHf%2Bnq1wlO390OdTvbNtLPA8nII3Fg881gaP3%2F8KiIcEyLuv%2FxCdhdVbB8NSPCaUgKJw%2FQf3P7gf7wbJJ9vvLLDQmQWJ745czTsO7O%2FMBhbKNapiSCTkM8cjuBFRkzQDXOy44hn4X4HvpwcZ1z7IL6d5d%2BNPug2nckQNOakf7wSXF1lVRF3q4JgJzonD%2BqSiQHPK5ehd2cG%2FKydVYUx92%2FsMaIOacsxVsRXd3%2BcnN7o6urpzVXxrGylI%2BoBAp%2FThiPFo306%2BcR2eI3%2Fom8%2B1wTiM73mty63gWLLHz8WxI%2Fa2E3f92Qq0MSGV7Z2JxhdSJzfi52bIdePXkxu7sjCK%2Bb6%2F3XAWpCFXiLrDllcrTm3ZcLZco6qgmi2HaW9EJM8Ao%2BPyZ%2FrZoMuFJZ0l77zDkPhtyu1WgF2UzLGcmXLhdyqyylrefb%2F5eZsrE0y7Ck6%2B5G%2FndalqBdp9znh0h%2FSVk6KDKWxTn%2B90dXH1qNp4sfvS6W2gTgd5S8xVqgWgT25EYoNoZn3ra7w%2BzjYa3g%2BddcBmVO8l%2FpD4w8B7h%2FYUpiA88ZCuuAeS%2BtsObWoDvKRGzR9jwm1VvhvDr6vsCKH5Y9%2FyO7B3FYrtOJ6CQGRHuUZVQbX8Jo%2BpgiDJ5xrq0cs9bvBx1TVdaVC8iAj5fad6Duo6uWvivLaR3S28xMqTuwnOmmwqJ1Wbj3rjYXirV4W11A5G1mRaurh6VO2%2BBE5Sk3Mrkogjq%2Fn4HiB3A87AemdpOrewTUIHhMBx%2BL%2BedI36TdAN%2FGwjkKhe1idHmd%2BjjCpe2nTbe42c2NU9p3JF%2FbnqUpO6bp0OUfkkYWRKbMQwMiWfrlTail6KhNTdATzTfmenP9ZP82YqVdde88Y%2F%2FJw6rL1OooqelK4rm2%2B2W%2BBQvlXSc5J851GTu1InXedW8xotTClIDZ1Lv%2BSQng1tWcvVbrVcsb80fVmp61bTRL0LzmjJzfhS8Km66jtnogXVLan7JxCFM2HXoeZNqAxErWijfQcftDFjxowZM2bMmDFjxowZM2bMmDFjxowZM2bMmDFjxowZM2bMmDFjxoz9%2F2u4KsiaNO1gTOYkgWLZCarynNZV6uyy6zRcFwRkdJzX1iee%2F64%2BhRBIyIR81S6W3CtZKy%2BHR53neZrO8zLFT0NXdhn%2BH9PyuTN2Gx0sqydraWHlz8rYyAyt9KVXs7%2BJjBQLTzbfr0X0PUh2BQokTLBsuXhJvoED6LqNFxsvhph9BYoOXl3o2vjRBNN4jJlcMxzU6Azmya41r%2BcgsAakBv9R0VQdJkfnOUHUeV6O%2BLav%2Bv9p5JV9T9zzWzXescZ3caU0LHMMSkrX1kkubXlF1F7rEyWIzRaWJ1YI8UJ%2BvWSB16%2BJzg594yoW%2Fmr3rSqHCX6ZyHUDwY9U5a5rhvWhGVxmhkvQrMwqtq5ZXZvA853ZX3YnSRLZ1suo5rrhFOt%2F9umRqTbSWAaaCJdv%2F%2Bcnn2omdQbN7Mmnbv9nGAX66FQFXZ1v7QXyhNqM%2FsY5lA6rqfNcKr53VkjTlr98EMwJxT6M%2FjryNUUQRvgnSgQNv4nLHP3JnWDutsQDqSHr8kGmr5jYw%2BquIMkKxNnnZX7Ax187qszwETXn17HPiqka%2B4zguOmVi21fJv5P97%2FGGatWadrF3es8FX0iq%2BmdjbrwsGhXOdZYsTb20EsYH1DGow1HhS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2Fy8vL6%2Bvqfn5%2Ft7e339%2Ff29vbo6Ojz8%2FP6%2Bvr19fX19fWmpqbLy8v6%2Bvr4%2BPjT09Pr6%2Bv6%2Bvrr6%2Buqqqrz8%2FPt7e2ioqLPz8%2Fa2trW1taioqLr6%2Bvi4uL5%2BflVVVXNzc3%2F%2F%2F%2FW1tbj4%2BPh4eHq6ur8%2FPz%2F%2F%2F%2F29vb7%2B%2Fvz8%2FP09PTMzMz%2F%2F%2F%2F%2F%2F%2F%2F5%2Bfn19fX%2F%2F%2F%2Fy8vL9%2Ff0AAADZ2dn8%2FPz7%2B%2Fv8%2FPzp6em%2Fv7%2F7%2B%2Fvq6urp6en%2B%2Fv7%2F%2F%2F%2F4ck%2FmAAAA8nRSTlMAGgDUzwIP8SMQ759fCgUvqfDGFeIYA78fbxNTt98%2FhsV%2FBhdD4Q1rRI%2Bvwo3ATxJTD18IoKWasozTETbQ4D40IX5hC6dAMR7RXydvEsRuotKLkZCATYahkzOxQlFqmbZwJiUhFWy1wyJYcXI7gB2XIEFbgjxgiWFtfTSFMy8wSYgEqFBDTSE2KCpnSyZZUaZHRFAsDuWBYJJ7AVZQpC0Z6njBKWjdN4dlMV30iN8bV7%2BzJJeHMRiDYsR6U9yVYxdP2c1dj8CKFZZVFjtaaTxOI9cMKQk4NnBW4PKUOmiNI%2FkwWoQYUdQOSk6GvkUURFSM3n71h14AAB4tSURBVHhe7J2HfyPHmaa%2FYicCDTQCQRAkQWgABpMcDSkOw3CGM5o8Gk2QRjlZOVjBsizbcs5pndb22r7d23ybc7zbdDnnnHPO%2Bd6%2F4FjdIGu6vmp280CtbF%2B%2Fkkn%2Fnip%2BaPSDDqA%2BFOm7J3ncIoDi0NAQkY3d2IaJSws2NNZZnCouduhAsrgf7o4BYy59O4fvn3ROJQKoREkBGKqYytAJCCEQWh220I81TPFIozLaNiCMH4PJr47WIooL1C1isUUsFSwpkMqPAMARDUIFjLcYpj5tGbmqw%2BP6bhz49jZwbZ9S9k8Kd20QQLDdzFbdIz%2FJJ0OFyJFaI6mOcZ6HGOzAGxdi0tN8JL063CmJwi9FviX34m4FUrlxl0PgaBgIbrXJJfVp08yTrbq2tt99bINtCj9p%2F2TiZMMigCzYGa0WxwBgrEjxCBUici56obyLjsF%2B%2Fez8KGLwUdxVJuq9HZcpljX16lgjlaeg8hTpmUVNqubcUjzNKnBbGIBbJS6pT8nMo5ilAms3kxsAbElvsP0DqP2Ttt9KsEYIoBELpWxWDyHMocSDdUiCYMYDvJmAl5sUE%2BcDgiaiLMd6FrTJUmskFaTyEah8JPZsiru8WGL8ouLpx2rfqshkVQix%2Bz%2FOoyRIte64GalXsb5%2FJFX7J4UfxsVI3EUcMyrVrbqAtbVVB1x96q39f4Yo0goYpBJ6EmhWa31nx3WrUmskFaTyJah8iVSofNrqY2umHOeNsyIQ457ksUTnlP0dq4NfVyuLrpTKLlHy0sUp1UASq%2F2Txr2ASAiiwJvNYrVzBLjUbJ4BjlS0qbdE%2F%2FStUgAExAMyWG2jI2EFDd3qujNsWcPOesxquY6d1OOSmiMbId4YCTT%2BBEq0xDjRKACM8mO1HwF2mYm%2BDnRdrRRht5hUmRPHAeD4CdL3jwBEBQ3OheC84VE%2FXi2L1Q9fB14kOgFc%2F%2BzeVgmgJKuVTnzsPSh2iFoncY82uVrw14aH1%2FxCNVbszO4heYa0vDPk30uc%2F%2BOxv2LgBAAGdjSM8R548OvqoxHiUl0bYWyX7R8h1P5J22%2BHUIlAxXSl5FYDeZS67hHgTO%2F%2F2aoPbWy12r9JqFVifFsqsLYGbGtlJyq%2BV2TuBRrA3WTgs%2FAY70S30519XFebg19X3520%2BbakctBO2j%2BZ%2BNt23qsdwduyWCWnjjB1h%2FZvdWkqhPxKVhi3gMamh2Ilhn304xeIaTVJpVlsTp9FLRt3F9DPgvtmX1czbf4FSeXghcT9k4WXLYxtg8oYrLJRKZNTC7XWa7R%2Fq1RDCDfq7RltpDwixPTcjIdii1R87MYnptUktdKYn6Pz89ZSJj6l6k8NcA%2Bc9bqavvmF6jbdHqwW0vYP5%2Fq9dLGo7qVTrY5OXKnXr0yM7m3V%2BFzIAs4S0crEckCmBDOedY1UCmI3%2BtN0LjUucan0yDOycjD8PZk4VJDCB3%2B%2FqmmtSqlc68g2dUYKlLJ%2FUrgzMl73Gu3xESfFqorzwO0OsXiI4kmrCe%2FSRoQ4T3slOK2ea0qa001Tgci0E2TiQkVk5tHooO9XnYJDWcP3TzovT4xOL5dJixDqXz29gHhGRZTRIRogzT2l5mk6b8F%2BG5KhtyB5%2Fj%2B2mie3mie3mlvNk1vNk1vNk1vNk1vNrZbouy65VR8%2BsST3UJbGpopjJYZdoNsFXGOptDrpfAn9LGWvU5xaLJFvmr9Ycu3kzstYuiHrUuaUFsfGFg%2FyQOFa%2BHaCIAeHMNTnPgA%2FwSrnrg3UPPD%2B1R8AHnwQ%2BIEUq7xONv4w%2Brk7ex2vBhRhng9ks%2BoyGAXU6XYrROTzXnTg4PrRW3EAIQQI8tueVn3I%2BGarnNuoz4%2BOztdZ%2F%2Bpl4KWXgMspVnmdbHwWIgxm91XHA7JxiJ1A64jHvJg0WS0CmBqzWf3NLSG2NmGTnopCOm8l8RZKpNsDSbG0l1UfUXyjVcZLqE8EoGCirj1cC%2F20Eq3yOgjrML6wwHgLFoWxUGHzicx1iB4CQJy7Iee7S4biAw4QUM9k1XhodzE%2B1yRqzo2zk3YXkPZaZODoEJl48avVU5pVQQjFJltVUgHfZJX9N%2FDDuJ3Cerdr%2FasvA5icBPByolVeB6yO5B2go%2FOXdzpJLuAVVseuGOv0%2F2M%2Bce6EnO0uIRO3elPbciars9UyhSlXZ%2FkJvoVSCS3OaeNRCTi%2F59j7UGFc0J7XVSWVad2hpv5VEO9fPQXgwx8GcMr4CRybTHWg6ijungJOuRo%2Fhp%2BgMD%2BBw4Y6Xb3OrOSG1BTPdKxCJZNVfJn6%2BbKhTnMcGG9yTv%2B5Zrb6CQT4LLtQEAkBIRKtFgR2IgpZrDa8aEzvX60AQBAAQIVi6bdzEa9jA7aso3FnGph2NA58ncJ8HTBsz5%2FQ69QkD1OM9TmNju7yYsqxmtFqI46fo36eg8HSzwM%2Fb7L3fR6RkYPwfTdzQVBtOklWuT1ulfevCiH0%2FtV7sZt7zd1ovM5F4KKqozgBpHMA6BB1AIDNb0yuGOvIVPAk8YT1BztW3fq5rXMb9fZjcexEEwEHpjPw%2BLjxDPwH2xHgvIIPMy5EBqtCiBSr6f2rswAaQjQAzCbsFVYn2NwMVB3FCWD1AeC9RO8FADb%2FmZ6az7fzcf15%2BWr7BzhWnYnV5irr1gPtWCX9swSbQHOrXN5qck61ByTgfPY9DzUC%2Fs6GICfsbVXCFKtp%2FatL%2FY9pHQKAJUOZyUnwOnNzYR3GhWAcKmDzHbU9GfpsvfcpPsh11ZhNZXVTG5qbt6hJ8l%2FOT%2FeITPyjX6l9kG8mqe0cwGp6%2F%2BrdAHCd6Lr%2BewKopNbh3Gx1kDoET%2FHBrqvGzCmrc6QlGFFA480k3jxdZpsJEoCgeE8lhfdQQ2JocjKj1fT%2B1RoAvJ%2Fo%2FQBQS7fK63CeZjW9TsOrM14dHW%2Br%2Ban6vF3qZbJKyurBpGnQQpicNDY0E4aAXnQC73%2BNh3XZsv5V1ow6RzQnv4%2FyMjJZ6nDO64jMdaZHJxgvUHnZND%2Bh%2FuguHdXmU0KEUF8PPpES0esZG5pJDAkxdDPOk%2FdC5Mmt5smt5smt5smt5lbz5Fbz5Fbz5Fbz5FZzq%2BYGw13usyHXNjcHKKrHZwuvpKWLinmDsECGlAwdND5R2vIg39VWq0atfV5Y14fs2ryIktVqjbUepmUW9zI2CUyes9AlnvJZtEfiaAEL%2B7OabBqJQ619rSnT4jwKiCHaRaD08MdFvVDnWhVXWpm9jFYrEf5AwoYQz1INNQZ7QG91GJfJkH%2BGBzSyghWyJoUAhJi0SIXRAaw292W1eSBWE4uI3YAIGAOYVsWl1sGsvhLhY3FahN6SqXLs0xZKxud5QurmeQee0xGIRnrRD%2FVGFE6iJKIQj0gdYsjI1RCzKhErnWrVj3Hy0Y3OwCDaqx2Ya92%2FVeBYhK8DbLplAbcC7MT2vh%2FEMZPVyhraZAjgMGRe9S2RQiWJg519D%2BoMnPi4e1n1oReZJXLHKtJqNUFrNaZ1EKuzIswstzp%2B6dK44Vm%2B3Ah%2BCmiZ9%2FsDxFNBnX6%2FrTYVHuwMvJBmdcFs1YdmtYp7yLVRdEFUSNBaiGsdwKqKxq0vAF%2BwuNVTn%2B68buH7uQqxdRYnXWL5XXxtYKtChfHEgcHPwKEeSixPJO3BZHVdt1oQc64NwEZM3zqpxPn6%2Bpth9fiLwIvHmdUOwswaVDTPb%2BB%2BYnkYP3dwx2rmu6XWQZyBhdgogHj5XYTChhCm%2BoWqZtXttn4EYW7Wpy%2Beqbi%2FXshk1dqy9mMVH9ja%2BgCY1YcsIcQW0DGp%2BGkcJpYbeGlfVktAaXCrzYM5A6%2FQ3lZpJWE7C9UYr0wBP4kwSh%2BTKrmSmsWqNdwctrhV0Q%2B3ipMnway6eF7usjYe4lZbpRpeIBbge%2FZlVZnI0nyXOjD4PTCHu8hk26tvh6gQ5ypKH5MacSWVW63G9VnDTriYLnNRhEyLdWSaWzLX8AU5%2Fkn98zpXwW6X1Mj%2F0NkCFhKOym0emVgYbKXag74HNtchGGyPTmyH2VbZ1adLVbykpGpW41xKJalV34qd30KwjkxjS2YX4WLXFfY%2BFmEakz3SYpt%2BH7lSXWFHJVud%2BvfXanNAqzxpj1vQpSpeLmQ7VUmpUivrTw9EmDJlyty25SD6oVHD404zqTQiMaOF3R%2FS%2BIboZ6Mw4PrDB3UGFpRchxTkSX3cwePQd0ZW2P8ZNHkvRJ7cap7cap7cam41T241T241T241T241t%2Bq2aOAs0rdVcquuXawYFrpdTF6%2Fl6eDJUqOu0SDxvdpH8mtus8eR9HUnQ3ftK4vANslPSdxisOlKcjZ8uc6jI9Ryzy%2FWKEuq%2BUn9KOHW5VA%2BVASn%2BrQAcR2wy%2FJvAWwoYjyuD4vFDnwTdi33ZhV1z55ybJYx0B9sw2UDOvrRqK0dAHcZ16C2xp2T1ywntO4d3aCcJXPH696M4EPm0s1aQWkISRQPpTEgcUWGQKAkLknBtIRlFbGm6yUokyeHDJyGLT6QKRVTcPJS8P6Wq%2F1ibnlNg4b1tcFwHR3jOunn8KmEGLkhG3fMeJofPR8yQcWXX1%2BuTAa%2BMAFTWpBAKLAtALSEBIoH0riAIrdwa1KEVA2OAfMQyZ5csjM14llHX2tahqOX2PLSr0%2F6kkwrK9r6ts%2BFcKaq1eZix7h%2BAnG%2B4uZr%2Bl8oUK%2B3p3hLiJURVhkjyANgQzUOJTEIWN3BrUqRSgbBs5KKcrlySHOE1tXIq1qGl8V1MPh0KJlWF%2FX0AVYQjuk9xuvb7CGTzB%2BP6w6UL1D4wsoLrLttrEbW7cRjpGR8iFXcaMl3x3UKmxlw8BZKUWZPF6IS%2BVauVSVNjDWHQMu8PWBI6u1%2BEFc%2FaTBNQE7Um3Gf3RrZMIbLzgaf6cHKTV%2Bgr%2BgrF4w2iAj5UNrGmeWpga2GmXNzHkpjbKXsc22v5HUutIAsDbEpao8gCg%2FxtcHJsn19XV%2F7kGE4VafkvVtMF5oEp0ul3QezHhSKvTXYfSpJ%2FY6BSylSKd86A7FjZaqzwxsNXqEOxI4K2WmI2InI3z7fTLGDx93KHxLY5ZKvQ3IbDYN6wMDgLa%2Brnf5i16C1Y%2BMb9cHGJdp%2BpwHMxsFAkjTGg3qUgkYlo7MlA85ihssWZMFZ1Cr1rA6TgycWzVTFcP2%2B4lSh50hoqfkWxopleddFoD6nGl9YAD6%2BrptrB2SuE6xNNClubLjdtFgfDtmHqworrRG72x0qQSEokzUOJTEw7daI71B75akTyXVwFkpTrlVrjVZ6hDRZZy8ZJZKzkUPjTNkWh%2FYsJL%2BxzzIeLfH8Z3YyZ0D8eS2ZSAUlUD5UBIH2qfPD%2F7ORvpUUg2clTJTocK33%2FzOpi91iFqwfvCaQ%2BYEM43H1FjKerzN01UPqJ4O4nj1XAMyjXOrA%2FHktmUgFJVE%2BZBELueNyeVmQk8mZW7dJ2nI5VIljwaZP0Z5uFZzU34kdYiubY2cdygpwbRylLoeb7MwKkSB7ZjVaSEzvToYT25bFkK1IXPKh0LkcD7dowNIVNkx8ugLO%2FY4TddqbsqPpA6R06TvvORxEnDeCzFo8l6IPLnVPLnVPLnV3Gqe3Gqe3Gqe3Gqe3Gpu1dzf%2B5Zx1ShxcPUHT2uqktR9uN%2F4ST9UWThIq65taI95S7gaAsyddTWPzc%2FCB85JnHT3ZdVuUULel2C1UsTCYK8at0XAUMsNrdqm9pi9uQd4%2B5kPq569Prk%2BgOISkaEPeXS%2BDns%2FfJzXJ2oplMnGAoC1fVlV28%2FkGVX529x750BW5YcvgEox7EYrAYAQrL835ICJW09Xq09b5vkNGPi5kYl5jbPHVVmrjQMfqpWI9SE%2FQvTIRB0lnQdEgZlXarw%2BLRVBaTZ4o3opu9XOVQALCVK9%2BaqxkjcTDGT12RqKQBG1Z4eIDgMAEevvlVyGc2%2FYKRScYc8033pmHIxbq1crgZWxPrm4qwyc%2F1CN9D7kCnwfldtxTOPRRxc4j3biuOLqyKOEmGy0apCptTJa7RYRxnTc3yvlFYxWpdRBrDZnPIQvjuYQUQ0QgkgIredTchnOo5PRGufWZH3Y%2BVmPceDUZ12Ac1ld5zt%2FBF1G60MOqkA1CLxZjdPVlo01zkOpM%2BU4b9kpa5oxGyf7%2FGQ2qz5UCyrLyoaSp1V6KKVtKfvirkNEDWCH1khlL74u8Trnl3oTTqXIOXBnbw0mTgSdQ6bXg4zeh7wePrZX0%2FjVsF%2BS8UhqoPEppFhlNkaEAKA3cJJtvi3oYCfWY4a73BUu1Q%2Bri8JBWj0UjbP%2BXsllOPfCRtc7PJ3L%2B8RKMXsd8%2BOKKzgngMmqJ4TWh1xBtSq%2FHtL4j1uyC4vxUiRV449ZKVa5DfNBecmDjHcpjh9FYyM81VSHg5S7XHZXPJBVMe9J2JgXQ0Rvi8ZZf2%2FIARO3Xd93bc5hd4rmOkIYuSASOienWisBf7J2F%2Bl9yMF8oTAfHNHrHHGGq8MOMY5Qqs6D4SqApHUDlY1Uq803IPNGM46xOb3S60F9JIHd5Wa7K%2B5vjcj8B%2Bdbxei9SbE1FPb3yrD%2B3r14ESgmzR%2BUE93xaQC1u8q8D1neA4%2FxOmO%2FUHAqBo67AmKcnMK4B0qIspFqlVY3AGysanRzTgJrppx2l8vvige7W7pmeTPAjGddG%2Br394L19751nJzCFeDpsrEPucjmZ%2BdK%2BIxFhjAbKVZpxYK1osO56MGDlLtcdlc8sFVn%2BHQABKdl735Cf%2B9bwtUQwOBB1g%2BGiYfZSLVKFxuXyBwn5S6X3RUPbpWcJgkx1JS9%2Bwn9vW8lZ82xB1%2BfiU4ZEOYNCqablDXqLlfXGuz1M3kvRJ7vZKt5cqu51Ty51Ty51Ty51Ty51Ty51dxqntyqe5W%2BrZP3A3dhd4g6NrpkSqc7BibV%2FgjtM4twDXRsjIyxYXMI9dUcn7Lk1VdfVd%2FexFSAA18nOXs%2F8GGrhoUF1KzDxOyVbAAvMKv34RNkSGdxbGyxY%2Bb%2F%2Bs84xFKCefnoFoCUngEebT3LoppfjM265ZZb1Lc3Ma94IgbcItsUxlPWSWZWk%2Fty8fyMBXgzzzN5lSkAaH%2BNDdTwx2jM1aC7iDCLrpE%2F0XLZ86kBNZd4mvuwiv5f%2FAwt2sb5yGrV9d3kA8b3GfUN9Yus0YdvTzrn6ySbrZYAWEJYAEqs6tGjkDG8iBrjj8Mj1oh1N71g8xZ6dTLg%2FHvvk1w75Ot13JexOUCoMNvFViQVFzJYnZubU9%2F44svmAyb6ptNCFWHM%2FVvvT9p%2BkdFqtE4y36DIqi%2BtHo6a%2FGp6X64AgNtuAwBBsawB3tnpf6e%2F6Ih%2BE8DCC9p1%2BAjaaABAFUdM%2FE%2F9Zcm1C8%2FHPw5UiMUF4GY8VoWY96zXZfuIQLWQwSoA9S198WX3BqRAH8AN7TBadAv8L36%2Fhp28lO1YbTDbKuaO1cgq%2FKFIJ1yXrwGrrLLzrHX661PsRbfUbQKsTBfWu%2FHRNoAfs9Dl%2FI87KxZ7HWwCm8o1270Zr6vB6T8ddRkqqZmtmhdf5qsJTmktOoVJQCw7hl3%2F09jJV7JZ%2FWAJKsUUq%2FrfgG4AwFNP8fV%2Bj27nttvkV%2F1Qsi4egcwXY%2FzyjxKssFqLVB7Edae%2B9gBQb17Hgzfz49v8d%2FAXiUKuUkLjV4FfbaBkasYDipUYhdj9h7T8QhHe2%2F820TrtzyqX2kjSyhYX7QHmXf%2Bz6KfRzGS1UT4FlXtSra6ryevJ%2Fbp0224ols96zxchU9c3bwrX7wSA10llro7umXcA9TNd1Odi3Jf8E%2BTHOLk1fMZpt53PoObqUtsA2kpryrFakVJPFivqHjijVSa1Ol2VWolpZVKJkqwGHqI8SZmsfrADFXzRZJWvnyyDaoH166afgXuHEMY7wzfvhVUA6N2Mz1i4f0KIiadgndH4kY9b6HU1fh%2FaPXr8ceq1tcWwi965ZQDL57xilmM1%2BszJb9b023sPO9Hu9uqA36n4QDum7gLkbipUgQvEtTKpBCTcqI6KMN4ns1ktPwqVapNb5Vqj62q1wPqB0626dv%2Fm52mHLeiOBypyRHvqbUwtLEyhPezEeRXAn22hGuMdeB%2BLtvpjHjqx%2BqdXZfXK6ul20rHK%2B2%2FDjxTFhIwKwLoiZEbJ2Nb%2BOFvnVH3TtUYbzy35ScveVvCJbFZbUMGXs7yzKURWC0OsHzjdqn18a1rMzwvWDP2xBsZ7D7GVyclZnnzyHe94cnLZ0Xhh8vfwR1%2FU1s4%2BiSj880rLTYrelzeXsxyr0lpAFIyyNd1hXuS6XEeYepmvc6q%2B6c%2BBVYLSKqVyTc9ls3ofVKxeyjrJrBeC9c2mWD0%2B3CQKAmJpPrNVJlisDlFveWJiuUfE%2BGv4B804ryCWCsXSmBfzjf3%2Fbiku1Y2k8pzZ8ABv4wwNFBGlwF4HTYFtDuFks1qDyvNp6yRzq6pvlm%2Fe6ip7uzTs7NFlTGKkTNmzIgKNBCIWbXg6oGA6bclrngJbG9gYZ2VUiNEVh96sCCmdwYQnMCpUmJtC4YB7IRz67kneC5Ent5ont5ont5ont5pbzZNbzZNbzZNbzZNbXZhaiIPcagtRii5ljQsYJy%2B4rnn3dlGkNzH4KFomHIbz0liR9T8fKF%2BcmlqUnMcdA7qUKXt0xNqAbZgeWu3%2FwFfRz4%2BQKa2rLUOTAYoV0%2FK0tg1qceFu7SzMi58i4Ul2i6a9VUzYIWiimdnqm7%2Fu8amv3XPna5LzHEFjck6C9P7kvZrRhICBhVZFNPYw%2BnmYWNzSGH7lGjR6yrhW47OwJbfxLOm53%2FspZhX4w8AFcXTDcJh1pn7jDRAL3viNqY7RK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<div id="header">
<h1 class="title">R Basic Training</h1>
<h4 class="author"><em>Elliot Cohen, Sustainable Engineering Lab, Columbia University</em></h4>
<h4 class="date"><em>June 25, 2014</em></h4>
</div>
<div id="TOC">
<ul>
<li><a href="#getting-started"><span class="toc-section-number">1</span> Getting Started</a><ul>
<li><a href="#interface-of-rstudio"><span class="toc-section-number">1.1</span> Interface of Rstudio</a></li>
<li><a href="#working-directory"><span class="toc-section-number">1.2</span> Working directory</a></li>
<li><a href="#libraries"><span class="toc-section-number">1.3</span> Libraries</a></li>
<li><a href="#help"><span class="toc-section-number">1.4</span> help!</a></li>
<li><a href="#importing-data"><span class="toc-section-number">1.5</span> Importing data</a><ul>
<li><a href="#csv"><span class="toc-section-number">1.5.1</span> .csv</a></li>
<li><a href="#txt"><span class="toc-section-number">1.5.2</span> .txt</a></li>
<li><a href="#xlsx"><span class="toc-section-number">1.5.3</span> .xlsx</a></li>
<li><a href="#scan-directly-from-a-website"><span class="toc-section-number">1.5.4</span> Scan directly from a website</a></li>
<li><a href="#fixed-width-data-formats"><span class="toc-section-number">1.5.5</span> Fixed width data formats</a></li>
</ul></li>
</ul></li>
<li><a href="#data-structures"><span class="toc-section-number">2</span> Data Structures</a><ul>
<li><a href="#structure"><span class="toc-section-number">2.1</span> structure</a></li>
<li><a href="#class"><span class="toc-section-number">2.2</span> class</a></li>
<li><a href="#data-frames"><span class="toc-section-number">2.3</span> data frames</a></li>
<li><a href="#dimensions"><span class="toc-section-number">2.4</span> dimensions</a></li>
<li><a href="#time-series"><span class="toc-section-number">2.5</span> time series</a></li>
<li><a href="#names"><span class="toc-section-number">2.6</span> names</a></li>
<li><a href="#records"><span class="toc-section-number">2.7</span> records</a></li>
<li><a href="#dissecting-a-data-frame"><span class="toc-section-number">2.8</span> dissecting a data frame</a></li>
</ul></li>
<li><a href="#handling-real-world-data"><span class="toc-section-number">3</span> Handling Real-World Data</a><ul>
<li><a href="#missing-values"><span class="toc-section-number">3.1</span> missing values</a></li>
<li><a href="#summary-statistics"><span class="toc-section-number">3.2</span> summary statistics</a></li>
<li><a href="#mean-standard-deviation"><span class="toc-section-number">3.3</span> mean, standard deviation</a></li>
<li><a href="#subset"><span class="toc-section-number">3.4</span> subset</a></li>
<li><a href="#merge"><span class="toc-section-number">3.5</span> merge</a></li>
<li><a href="#writing-data"><span class="toc-section-number">3.6</span> writing data</a></li>
</ul></li>
<li><a href="#assignment"><span class="toc-section-number">4</span> Assignment</a></li>
</ul>
</div>
<p><link 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rel="stylesheet" /></p>
<hr />
<p>Let’s get started!</p>
<pre class="r"><code>## The following function will load packages required for this tutorial.
## If a package cannot be found in your instance of Rstudio...
## ... it will be installed automatically.
## Don't worry about how to write functions quite yet, we will introduce that later
## But do run the following code:
load_install<-function(lib){
if(! require(lib, character.only=TRUE)) install.packages(lib, character.only=TRUE)
library(lib, character.only=TRUE)
}
## the required libraries (e.g. packages)
Thelib<-c("knitr", "xlsx", "plyr", "ggplot2", "scales", "gdata", "chron", "reshape2", "grid")
## apply the function
lapply(Thelib, load_install)
## and load any custom functions that you defined elsewhere:
source("multiplot.R")</code></pre>
<p>This is an R Markdown document. Markdown is a simple formatting syntax for authoring HTML, PDF, and MS Word documents. For more details on using R Markdown see <a href="http://rmarkdown.rstudio.com">http://rmarkdown.rstudio.com</a>.</p>
<p>When you click the <strong>Knit</strong> button a document will be generated that includes both content as well as the output of any embedded R code chunks within the document.</p>
<p>Alternatively, you can run this: <code>knit('ECREEE_R_Training_1.Rmd')</code></p>
<p>R is a programming language and environment for statistical computing and graphics. R is free, open-source, and supported by a large community of active researchers and analysts. R (with R Markdown) is quickly becoming the standard for reproducible research in academia and industry.</p>
<p>There are many, excellent R tutorials available online, and we recommend you refer to these often for supplimentary information:<br />* <a href="http://cran.r-project.org/doc/manuals/R-intro.html#The-scan_0028_0029-function">R Project for Statistical Computing</a><br />* <a href="http://www.r-tutor.com/">R Tutor</a><br />* <a href="http://www.statmethods.net/">Quick R</a><br />* <a href="https://www.codeschool.com/courses/try-r">Code School</a></p>
<hr />
<div id="getting-started" class="section level1">
<h1><span class="header-section-number">1</span> Getting Started</h1>
<div id="interface-of-rstudio" class="section level2">
<h2><span class="header-section-number">1.1</span> Interface of Rstudio</h2>
<p><img 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alt="alt text" /></p>
<p>Interface components:<br />* Console<br />* Script<br />* Environment (will make sense later)<br />* Help, Plots</p>
</div>
<div id="working-directory" class="section level2">
<h2><span class="header-section-number">1.2</span> Working directory</h2>
<p>The “working directory” is where R will look to read/write files. You must set the working directory to the directory (e.g. folder) containing the data files that you want to analyze, and where you want your work to be saved. To run this tutorial, you must set the working directory to wherever you save the following data files:<br />- <strong>sample_health_facilities.csv</strong><br />- <strong>Daily_Temperature_1995-2013_Delhi.txt</strong><br />- <strong>UN_2011_Population_Cities_Over_750k.xlsx</strong></p>
<p>Check the current working directory</p>
<pre class="r"><code>getwd()</code></pre>
<pre><code>## [1] "/Users/elliotcohen/github/ECREEE"</code></pre>
<p>Set the working directory</p>
<pre><code>setwd("~/github/ECREEE")</code></pre>
</div>
<div id="libraries" class="section level2">
<h2><span class="header-section-number">1.3</span> Libraries</h2>
<p>R is a programming languages, so it allows you to write “modules” or “libraries” that can be distributed to others. These are called packages in R. To install packges in R, use <code>install.packages</code> with quoted package name:</p>
<pre><code>install.packages("plyr")</code></pre>
<p>To load the library (similar to <code>import</code> in other languages), you use the <code>library</code> function with no quotes: <code>library(plyr)</code> Any library you wish to use, you will need to load at the start of each new R session. You will only need to install the package once, hence the <code>if</code> command below. Let’s install and load two of our favorite libraries: <code>plyr</code> and <code>ggplot2</code>.</p>
<pre class="r"><code>if (!require(plyr)) install.packages("plyr")
library(plyr)
if (!require(ggplot2)) install.packages("ggplot2")
library(ggplot2)</code></pre>
<p><code>plyr</code> contains very useful functions for aggregating data. <code>ggplot2</code> creates professional-looking figures with easy-to-interpret (if not verbose) function calls. We will learn how to use both of these libraries in-depth in the next tutorial.</p>
</div>
<div id="help" class="section level2">
<h2><span class="header-section-number">1.4</span> help!</h2>
<p>Before we get any further, lets see how to get help. You can go to the “Help” tab in R-studio (right-hand-side bottom), or if you know the function to get help on, just use a question mark followed by the function name.</p>
<pre><code>?getwd</code></pre>
<p>Use two question marks to search for functions if you don’t know the name:</p>
<pre><code>??workingdirectory</code></pre>
</div>
<div id="importing-data" class="section level2">
<h2><span class="header-section-number">1.5</span> Importing data</h2>
<p>There are many different data formats available, each with its own purpose, virtues and limitations. A few of the most common to R include:<br />* .csv<br />* .xlsx<br />* .txt<br />* .ncdf (we will cover this in Advanced R)</p>
<p>.csv is the prefered data format for importing to R. Although there are functions in R to read other data formats (a few examples, below), we recommend that you convert to csv prior to loading. Motivation for using csv is found <a href="http://dataprotocols.org/simple-data-format/#why-csv">here</a>.</p>
<p>You may also load data directly from other statistical packages including EpiInfo, Minitab, S-PLUS, SAS, SPSS, Stata and Systat. For a more complete description of data formats and their compatability with R, refer <a href="http://cran.r-project.org/doc/manuals/r-release/R-data.html#Importing-from-other-statistical-systems">here</a>.</p>
<div id="csv" class="section level3">
<h3><span class="header-section-number">1.5.1</span> .csv</h3>
<p>First, let’s read (e.g. import) a csv file. The most general and flexible way to read tabular data (e.g. comma seperated or tab seperated) is with the <code>read.table()</code> function.</p>
<p>As with any R function, I suggest reading the help file, which shows exactly how to use it, the required syntax, and a few reproducible examples. This is usually all you need to get started using a new function.</p>
<pre><code>?read.table</code></pre>
<p>As you will see from the <em>help</em> file, there is a special case of <code>read.table()</code> tailored specifically to csv files. The only difference between the two functions is the default values. <code>read.csv()</code> expects a header row and a comma (“,”) as the character seperator whereas <code>read.table()</code> expects no header and a blank space (" “) as the character seperator. Default values can easily be changed to meet your specific needs by supplying a different value when calling the function.</p>
<pre class="r"><code># Nigeria facility inventory
file <- "sample_health_facilities.csv"
sample_data <- read.csv(file) # read the .csv file</code></pre>
</div>
<div id="txt" class="section level3">
<h3><span class="header-section-number">1.5.2</span> .txt</h3>
<p>Text files, like .csv, are quick to read but provide no structure to the data. Limited information can be added if you have prior knowledge of the data. In the example below, we know that the first three columns are character strings that we wish to interpret as factors (factors are useful when we have a finite set of recuring character stings within a dataframe. More on this later). We also know the fourth column is numeric. Finally, let’s assign descriptive column names using the <code>names()</code> function.</p>
<pre class="r"><code># Daily mean temperature for Delhi, India 1995-2013 in degrees Farenheit
file <- "Daily_Temperature_1995-2013_Delhi.txt"
temps <- read.table(file, header = FALSE, colClasses = c("factor", "factor",
"factor", "numeric"))
names(temps) <- c("Month", "Day", "Year", "Temp") # assign column names</code></pre>
</div>
<div id="xlsx" class="section level3">
<h3><span class="header-section-number">1.5.3</span> .xlsx</h3>
<p>Microsoft Excel (.xls and .xlsx) files are common throughout the world, and you will invariably have to work with one eventually. If you have access to the microsoft office suite, you can convert .xls or .xlsx directly to .csv, otherwise, you can import .xls(x) directly with the ‘xlsx’ package in R. It is important to note that after you install a library, you must still <code>load()</code> or <code>require()</code> the library in each new R session. You only need to install the library once, hence the <code>if()</code> statement below.</p>
<pre class="r"><code># reading .xlsx is not native to R, so we need a special package to do it.
# load the library
library(xlsx)
# Population of cities with pop. > 750,000, 1950-2025 (UN 2011)
file <- "UN_2011_Population_Cities_Over_750k.xlsx"
pop <- read.xlsx(file, sheetName = "CITIES-OVER-750K", as.data.frame = TRUE,
header = TRUE, check.names = TRUE, startRow = 13, endRow = 646, colIndex = c(1:23))</code></pre>
</div>
<div id="scan-directly-from-a-website" class="section level3">
<h3><span class="header-section-number">1.5.4</span> Scan directly from a website</h3>
<p>The <code>scan()</code> function allows us to read data directly from the R console, a website or text file. In the example below, we supply a few additional arguments to get it right. Notably, the <code>scan()</code> function must know what type(s) of data to expect. The options are logical, integer, numeric, complex, character, raw and list. Here we specify a generic list since we are not sure what data types to expect.</p>
<pre class="r"><code># list of countries
file <- "http://download.geonames.org/export/dump/countryInfo.txt"
countries <- scan(file, what = list("", "", "", "", ""), flush = TRUE, comment.char = "#",
sep = "\t", strip.white = TRUE, allowEscapes = TRUE)</code></pre>
</div>
<div id="fixed-width-data-formats" class="section level3">
<h3><span class="header-section-number">1.5.5</span> Fixed width data formats</h3>
<p>Finally, we may have fixed width data to work with. No problem–there’s a funciton for that.</p>
<pre class="r"><code># list of cities from Hadley Urban Analysis
file <- "http://www.metoffice.gov.uk/hadobs/urban/data/Station_list1.txt"
stns <- read.fwf(file, widths = c(5, 18, 7, 7), header = FALSE, sep = "\t",
skip = 5, strip.white = TRUE)
names(stns) <- c("WMONo", "Stn.name", "Lat", "Long") # assign column names</code></pre>
<hr />
</div>
</div>
</div>
<div id="data-structures" class="section level1">
<h1><span class="header-section-number">2</span> Data Structures</h1>
<p>Now that we learned how to import data, let’s get familiar with it.</p>
<p>The first step of any analysis is to formulate a general impression of the data, including:<br />1. How is the data <em>organized</em>? (structure)<br />2. What <em>type</em> of data is it? (class)<br />3. How <em>big</em> is the data? (dimensions) 4. How <em>complete</em> is the data? (missing values)<br />5. How long is the <em>period</em> of record? (timeseries data)<br />6. Do the <em>values</em> make sense? (benchmarking)<br />7. What does the data <em>look</em> like? (vizualization)</p>
<p>As a working example, let’s use the temperature data (‘temps’) imported previously. Let’s assign <code>temps</code> to a new object simply called <code>data</code> to convey that these principles are general and can apply to any dataset. The preferred syntax for assigning a value or object is a left arrow:</p>
<pre class="r"><code>data <- temps</code></pre>
<div id="structure" class="section level2">
<h2><span class="header-section-number">2.1</span> structure</h2>
<p>First, we use the <code>head</code> and <code>str</code> functions to see what the data looks like. <code>head()</code> returns the first 6 records. Similarly, <code>tail()</code> returns the last 6. Right away we see this is tabular data with 4 columns (Month, Day, Year and Temp). The “Temp” column (<code>data$Temp</code>) contains the observed (or calculated) data of interest – average daily temperature for each Month-Day-Year combination.</p>
<p>[Later when we introduce the <code>plyr</code> package for split-apply-combine data analysis, we will learn how to summarize and manipulate data by combinations of identifying variables (in this case, Month, Day, Year).]</p>
<pre class="r"><code>head(data)</code></pre>
<pre><code>## Month Day Year Temp
## 1 1 1 1995 50.7
## 2 1 2 1995 52.1
## 3 1 3 1995 53.8
## 4 1 4 1995 53.7
## 5 1 5 1995 54.5
## 6 1 6 1995 54.3</code></pre>
<p>Similar to <code>head</code>, you can click on the name <code>data</code> in the Environment panel on the top-left in R-studio, and you will see the data rendered in spreadsheet format.</p>
<p><code>str()</code> is another useful way to get a feel for the data. While <code>head</code> is great for visual inspection, <code>str</code> provides a more programming-oriented view of the data. <code>str</code> returns the object structure (in this case, a <code>data.frame</code>–more on that soon), the number of observations (rows), the number of variables (columns), and the class of each variable (e.g. numeric, integer, complex, character, logical, list, raw or expression).</p>
<pre class="r"><code>str(data)</code></pre>
<pre><code>## 'data.frame': 6701 obs. of 4 variables:
## $ Month: Factor w/ 12 levels "1","10","11",..: 1 1 1 1 1 1 1 1 1 1 ...
## $ Day : Factor w/ 31 levels "1","10","11",..: 1 12 23 26 27 28 29 30 31 2 ...
## $ Year : Factor w/ 19 levels "1995","1996",..: 1 1 1 1 1 1 1 1 1 1 ...
## $ Temp : num 50.7 52.1 53.8 53.7 54.5 54.3 57.1 55.6 54 52.4 ...</code></pre>
</div>
<div id="class" class="section level2">
<h2><span class="header-section-number">2.2</span> class</h2>
<p>Every element is R has a data type, also known as a <code>class</code>. Let’s look at a few simple examples:</p>
<pre class="r"><code>class(1)</code></pre>
<pre><code>## [1] "numeric"</code></pre>
<pre class="r"><code>class(TRUE)</code></pre>
<pre><code>## [1] "logical"</code></pre>
<pre class="r"><code>class("Suya")</code></pre>
<pre><code>## [1] "character"</code></pre>
<p>Class definitions extend to vectors, not only individual elements (e.g. values). For example:</p>
<pre class="r"><code>class(data$Year)</code></pre>
<pre><code>## [1] "factor"</code></pre>
<pre class="r"><code>class(data$Temp)</code></pre>
<pre><code>## [1] "numeric"</code></pre>
<p>The core class definitions in R include: 1. numeric<br /> 2. integer<br /> 3. complex<br /> 4. character<br /> 5. logical<br /> 6. list<br /> 7. raw<br /> 6. expression<br /> 8. factor<br /> - factor is a generic data type used as an alternative to any of the above. Useful when keeping track of a limited set of recurring values within a vector of a <code>data.frame</code>.<br /> - Beware of challenges with factor => integer/numeric conversions.<br /> - For additional information on working with factors in your data: <a href="http://www.statmethods.net/input/datatypes.html">More information on Factors</a></p>
<p>A note: <code>NA</code> (<em>Not Available</em>) is a internal value in R, and can be of any type. <code>NA</code> indicates a missing value. More on this later.</p>
<p>Before proceeding, let’s delve more into dataframes, as they are a central concept in R.</p>
</div>
<div id="data-frames" class="section level2">
<h2><span class="header-section-number">2.3</span> data frames</h2>
<p>A <code>data.frame</code> is like a rectangular <code>matrix</code> containing rows and columns, but with the flexibility to include non-numeric values (e.g. character strings and factors). For example, a <code>data.frame</code> may contain information for a set of individuals (e.g. the people in this room), with each row (record) representing a different individual. Each column would contain a piece of information about that individual, such as name, age, nationality, occupation, favorite ice cream, etc…</p>
<p>Our sample <code>data.frame</code> contains temperature measurements over time for a single location. Each row represents a different time-stamp, identified by a unique day-month-year combination (first three columns). The fourth column contains the observed or calculated temperature measurement itself.</p>
</div>
<div id="dimensions" class="section level2">
<h2><span class="header-section-number">2.4</span> dimensions</h2>
<p>You can check the dimensions of a dataframe, matrix or list using the <code>dim</code> function:</p>
<pre class="r"><code>dim(data)</code></pre>
<pre><code>## [1] 6701 4</code></pre>
<p>We see <code>data</code> has 6701 rows and 4 columns. Alternatively, the functions <code>nrow</code> and <code>ncol</code> return these values individually:</p>
<pre class="r"><code>nrow(data)</code></pre>
<pre><code>## [1] 6701</code></pre>
<pre class="r"><code>ncol(data)</code></pre>
<pre><code>## [1] 4</code></pre>
<p>As a convention in many larger geophysical datasets (e.g. global weather station networks), it is common practice to have locations represented as columns and time represented as rows.</p>
</div>
<div id="time-series" class="section level2">
<h2><span class="header-section-number">2.5</span> time series</h2>
<p>For time series data (whenever observations are associated with a timestamp), it is helpful to create a ‘Date’ or <code>POSIXct</code> variable to keep track of time. <code>Date</code> class objects are excellent for going back and forth between Date formats, e.g., Year-Month-Day to Day-Month-Year or Week-Day-Year, etc… <code>POSIXct</code> are for sub-daily timekeeping, down to a fraction of a second. <code>POSIXct</code> is the ultimate class for nuanced timekeeping, including proper handling of time-zones, leap-years, and much more. <code>?POSIXct</code></p>
<p>In this tutorial, our data is daily (not sub-daily), so a <code>Date</code> object is sufficient rather than <code>POSIXct</code>. Let’s create a <code>Date</code> object from the Year-Month-Day variables already contained in <code>data</code>.</p>
<pre class="r"><code>data$Date <- as.Date(as.character(paste(data$Year, data$Month, data$Day, sep = "-")),
"%Y-%m-%d")
range(data$Date) # '1995-01-01' '2013-05-06'</code></pre>
<pre><code>## [1] "1995-01-01" "2013-05-06"</code></pre>
</div>
<div id="names" class="section level2">
<h2><span class="header-section-number">2.6</span> names</h2>
<p>Names are critical in R. It is always good practice to call/retrieve/manipulate data by its name (rather than column number) to be sure you know what you’re working with. To check the names of columns in a <code>data.frame</code>, we use the <code>colnames</code> function, or simply, the <code>names</code> function:</p>
<pre class="r"><code>colnames(data)</code></pre>
<pre><code>## [1] "Month" "Day" "Year" "Temp" "Date"</code></pre>
<pre class="r"><code>names(data)</code></pre>
<pre><code>## [1] "Month" "Day" "Year" "Temp" "Date"</code></pre>
<p>To retrieve a particular column within a data.frame, we use the syntax: data.frame$column.<br />Alternatively, we use the syntax: data.frame[row number, column number]. Again we can use the <code>head</code> function to show the first 6 records and supress the rest.</p>
<pre class="r"><code>head(data$Temp) # use head() to look at the first 6 records.</code></pre>
<pre><code>## [1] 50.7 52.1 53.8 53.7 54.5 54.3</code></pre>
<pre class="r"><code>head(data[, "Temp"])</code></pre>
<pre><code>## [1] 50.7 52.1 53.8 53.7 54.5 54.3</code></pre>
<p>Review Questions: * What are the dimensions of <code>data</code>?<br /> * Did you count to get your answer? If so, how could you get your answer from R?<br /> * How many columns of data did we get out? How would you check in R?<br /> * Can you change the number of rows that <code>head</code> outputs? How would you find out?<br /> * Can you create a new data.frame, called <code>small_sample</code>, which is just the first 10 rows of <code>data</code>?</p>
</div>
<div id="records" class="section level2">
<h2><span class="header-section-number">2.7</span> records</h2>
<p>So far we have focused on the columns of a <code>data.frame</code>. Now let’s explore rows. A row in our data set represents one record. Records are also referred to as cases, depending on context and convention. Several of the most useful libraries and functions in R expect data of this format: Rows are observations and columns are variables. For example, <code>plyr</code> and <code>ggplot</code>, which we will introduce later for <a href="http://www.jstatsoft.org/v40/i01/">split-apply-combine</a> and <a href="http://docs.ggplot2.org/current/">vizualization</a>, respectively.</p>
<p>NOTE: Indexing starts at 1 in R, not 0. There is no 0th item.</p>
<p>First, let’s create a small sample of our data to work with. Let’s grab the first 10 rows and all the columns.</p>
<pre class="r"><code>small_sample <- data[1:10, ]
small_sample[1, ] # the first row</code></pre>
<pre><code>## Month Day Year Temp Date
## 1 1 1 1995 50.7 1995-01-01</code></pre>
<pre class="r"><code>small_sample[5, ] # the fifth row</code></pre>
<pre><code>## Month Day Year Temp Date
## 5 1 5 1995 54.5 1995-01-05</code></pre>
<p>Question: what do you think <code>class(small_sample[1,])</code> is?</p>
</div>
<div id="dissecting-a-data-frame" class="section level2">
<h2><span class="header-section-number">2.8</span> dissecting a data frame</h2>
<p>For a <code>data.frame</code>, the [,] operator selects one or more rows or columns. The syntax is <code>data.frame[row, col]</code>.</p>
<p>The simplest example: Retrieve the 4th row and 5th column:</p>
<pre class="r"><code>small_sample[4, 5]</code></pre>
<pre><code>## [1] "1995-01-04"</code></pre>
<p>In R (like in python), the <code>:</code> operator is an operator for making a list of numbers.</p>
<pre class="r"><code>1:5</code></pre>
<pre><code>## [1] 1 2 3 4 5</code></pre>
<pre class="r"><code>small_sample[4:7, 1:4]</code></pre>
<pre><code>## Month Day Year Temp
## 4 1 4 1995 53.7
## 5 1 5 1995 54.5
## 6 1 6 1995 54.3
## 7 1 7 1995 57.1</code></pre>
<p>Note that the selectors for our [,] operator don’t need to be integers. What do the following do?</p>
<pre><code>small_sample[4:6, 'Temp']
small_sample[4:6, c('Day','Month',"Year")]</code></pre>
<p>Let’s see if your right…</p>
<pre class="r"><code>small_sample[4:7, "Temp"]</code></pre>
<pre><code>## [1] 53.7 54.5 54.3 57.1</code></pre>
<pre class="r"><code>small_sample[4:7, c("Day", "Month", "Year")]</code></pre>
<pre><code>## Day Month Year
## 4 4 1 1995
## 5 5 1 1995
## 6 6 1 1995
## 7 7 1 1995</code></pre>
<p>We haven’t seen <code>c</code> before. What does <code>c</code> do?</p>
<hr />
</div>
</div>
<div id="handling-real-world-data" class="section level1">
<h1><span class="header-section-number">3</span> Handling Real-World Data</h1>
<div id="missing-values" class="section level2">
<h2><span class="header-section-number">3.1</span> missing values</h2>
<p>Most large data sets will invariably have some missing data. This can happen for many reasons. For example, malfunctioning equipment can lead to missing values in observational records. This may be recorded as “NA”, “-999”, “10e-30” or simply a blank " “, depending on the specific data protocal. It is <strong>good practice to use <code>NA</code></strong> rather than blanks or numeric substitutes for missing data to avoid ambiguity.</p>
<p><code>NAs</code> can also be introduced into a dataset by attempting a non-sensical operation. For example, trying to coerse the word “ice cream” into a numeric value will result in <code>NA</code>:</p>
<pre class="r"><code>as.numeric("ice cream")</code></pre>
<pre><code>## Warning: NAs introduced by coercion</code></pre>
<pre><code>## [1] NA</code></pre>
<p>R distinguishes between <code>NA</code> (‘not available’) and <code>NaN</code> (‘not a number’). <code>NaN</code> is reserved for numeric and complex data types only, whereas <code>NA</code> can apply to any class of variables. A numeric operation that is correct in syntax but has no mathematical meaning will return <code>NaN</code>, for example, dividing 0 by 0:</p>
<pre class="r"><code>0/0</code></pre>
<pre><code>## [1] NaN</code></pre>
<p>Because <code>NAs</code> may exist in the raw data <em>or</em> may be introduced by accident, it is good practice to check for them often. In particular, immediately after importing a data set and then again after any major data manipulations. The function <code>is.na</code> indicates which elements are missing. The function <code>is.na</code> will return a logical value for each element of the object passed into the function. Therefore, we recommend taking the <code>sum</code> of <code>is.na</code> to count <em>how many NAs</em> you’re dealing with instead of returning the entire (and potentially very long) logical vector.</p>
<pre class="r"><code>is.na(c(1, 2, 3, NA, 5))</code></pre>
<pre><code>## [1] FALSE FALSE FALSE TRUE FALSE</code></pre>
<pre class="r"><code>sum(is.na(c(1, 2, 3, NA, 5)))</code></pre>
<pre><code>## [1] 1</code></pre>
<p>In the simple example above, we see there are exactly 3 NA, which makes sense because we put them there. Now let’s apply the same concept to the temperature dataset.</p>
<pre class="r"><code># count the number of NAs in the temperature dataset
sum(is.na(data))</code></pre>
<pre><code>## [1] 0</code></pre>
<pre class="r"><code># show the NAs in the temperature dataset, if any...
data[which(is.na(data)), ]</code></pre>
<pre><code>## [1] Month Day Year Temp Date
## <0 rows> (or 0-length row.names)</code></pre>
<pre class="r"><code># combining these two lines of code: Are there any NA? If so, where?
if (sum(is.na(data)) > 0) data[which(is.na(data)), ] else print("no missing values")</code></pre>
<pre><code>## [1] "no missing values"</code></pre>
<p>We didn’t find any <code>NA</code>, so does that mean there are no missing values?<br />Let’s look at a summary of the data to see if the numbers make sense. Recall, the temperature measurements are in degrees Fahrenheit. As a reference, room temperature is 68-72 F., a hot summer day in Delhi can reach 115 F., and a cold winter day can drop below freezing (32 F.)</p>
<pre class="r"><code>summary(data$Temp)</code></pre>
<pre><code>## Min. 1st Qu. Median Mean 3rd Qu. Max.
## -99.0 64.8 79.9 75.6 87.0 104.0</code></pre>
<pre class="r"><code>range(data$Temp)</code></pre>
<pre><code>## [1] -99.0 103.7</code></pre>
<p>Does that look right based on what we would expect for Delhi?</p>
<p>The -99 values could not possibly be real unless these temepratures were recorded at the top of Mt. Everest or in Antarctica in the dead of winter, which they were not. Let’s remove them as erroenous.</p>
<pre class="r"><code># assign NA to elements in the 'Temp' column with values equal to -99
data$Temp[data$Temp == -99] <- NA
# count how many NA there are now...
sum(is.na(data$Temp))</code></pre>
<pre><code>## [1] 34</code></pre>
<pre class="r"><code># remove them from the data...
data <- na.omit(data)
# re-check for NA...
sum(is.na(data))</code></pre>
<pre><code>## [1] 0</code></pre>
<pre class="r"><code># re-check the summary statistics
summary(data$Temp)</code></pre>
<pre><code>## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 43.9 64.9 80.1 76.5 87.1 104.0</code></pre>
<pre class="r"><code>range(data$Temp)</code></pre>
<pre><code>## [1] 43.9 103.7</code></pre>
<p>Now the temperatures values make intuitive sense!</p>
<p><strong>Note</strong>: In general, we recommend being judicious with the <code>na.omit</code> function as you may lose more information than necessary. <code>na.omit</code> will omit the entire row (e.g. record) if there are any <code>NA</code> elements in that row. Depending on the situation, you may want to first try resolving any <code>NA</code> in your data before simply omitting them. That said, if you have <em>BIG</em> data and just a few <code>NA</code>, it’s probably fine to simply use <code>na.omit</code>.</p>
<p>Similar to <code>is.na</code>, you can check for <code>complete.cases</code>. <code>is.na</code> returns a logical for each <em>element</em> whereas <code>complete.cases</code> returns a logical for each <em>case</em> (row). <code>complete.cases(data)</code></p>
<p>Putting it all together, you may find it useful to write a short function to do all your NA checks and complete record checks without having to repeat lines of code. We’ll see this again later when we introduce writing functions in R.</p>
<pre class="r"><code>check <- function(data) {
NAs <- sum(is.na(data))
print(paste("NAs:", NAs)) # count NA's
if (NAs > 0)
data1[which(is.na(data)), ] # Show NA's, if any.
cc <- complete.cases(data) # logical for each case (row)
print(paste("Complete Cases:", all(cc))) # Given a set of logical values, are all TRUE?
}</code></pre>
<p>Then when you want to use your <code>check</code> function, simply call it and pass the object you wish to check.</p>
<pre class="r"><code>check(data)</code></pre>
<pre><code>## [1] "NAs: 0"
## [1] "Complete Cases: TRUE"</code></pre>
</div>
<div id="summary-statistics" class="section level2">
<h2><span class="header-section-number">3.2</span> summary statistics</h2>
<p>R is also called the “the R project for stastical computing. The power of R is in data analysis and statistics, which is why we are working with it. Let’s start exploring some of R’s very basic statistic functionalities.</p>
<p>The first set of functions will just give you a simple <code>summary</code> of the values in a certain column. There are two useful functions for this: * <code>table</code> should be used for character (string) or categorical (factor) variables<br />* <code>summary</code> should be used for numerical or boolean variables</p>
<pre class="r"><code>table(data$Month)</code></pre>
<pre><code>##
## 1 10 11 12 2 3 4 5 6 7 8 9
## 588 555 540 553 537 585 562 563 531 558 556 539</code></pre>
<pre class="r"><code>table(data$Year)</code></pre>
<pre><code>##
## 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009
## 364 366 365 359 362 366 364 360 365 366 365 365 359 365 363
## 2010 2011 2012 2013
## 361 360 366 126</code></pre>
<pre class="r"><code>summary(data$Temp)</code></pre>
<pre><code>## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 43.9 64.9 80.1 76.5 87.1 104.0</code></pre>
<p>Questions: * What is different between table and summary for numerical variables? * What is different between table and summary for boolean (‘logical’) variables?</p>
</div>
<div id="mean-standard-deviation" class="section level2">
<h2><span class="header-section-number">3.3</span> mean, standard deviation</h2>
<p>Calculating the mean is easy, but it does require some care. There are many numerical functions that return <code>NA</code> unless <code>na.rm</code> is passed as true, if there are any NAs in your data:</p>
<pre class="r"><code>mean(data$Temp) # 5-yr average daily temperature</code></pre>
<pre><code>## [1] 76.54</code></pre>
<pre class="r"><code>mean(data$Temp, na.rm = TRUE) # same as above but remove missing values first.</code></pre>
<pre><code>## [1] 76.54</code></pre>
<p>What do you think the function for calculating standard deviation is? How would you find out?</p>
</div>
<div id="subset" class="section level2">
<h2><span class="header-section-number">3.4</span> subset</h2>
<p>Subset data to a specific period of interest:</p>
<pre class="r"><code>hot <- subset(data, Temp > 90) # days with avg. temp > 90 deg.F.
y95 <- subset(data, Year == "1995") # days in the year 1995 only
winter <- subset(data, Month %in% c("12", "1", "2")) # days contained in winter months
# For more precise subsetting with respect to time, create a date attribute.
# To do so, we combine the year, month, day to create a unique date.
data$Date <- as.Date(as.character(paste(data$Year, data$Month, data$Day, sep = "-")),
"%Y-%m-%d")
# subset data to a one-year period of interest, say, 2012-04-01 to
# 2013-03-31
data <- subset(data, Date > as.Date("2012-03-31") & Date < as.Date("2013-04-01"))</code></pre>
</div>
<div id="merge" class="section level2">
<h2><span class="header-section-number">3.5</span> merge</h2>
<p>R supports SQL-like join functionality with <code>merge</code>. The <code>merge</code> function can merge two data frames by common columns or row names, or do other versions of database join operations.</p>
<p>To illustrate join operations, let’s create a simple example. Let’s say we have two data.frames, the first, called ‘icecream’, contains each person’s favorite flavor of icecream. The second, called ‘color’, contains each person’s favorite color. Here’s what they look like:</p>
<pre class="r"><code>icecream <- data.frame(name = c("Joe", "Jonathan", "Elliot", "Vijay", "Candace"),
icecream = c("vanilla", "chocalate", "coffee", "mint", "cherry"))
color <- data.frame(name = c("Joe", "Jonathan", "Elliot", "Vijay", "Candace"),
color = c("red", "blue", "green", "orange", "purple"))
icecream</code></pre>
<pre><code>## name icecream
## 1 Joe vanilla
## 2 Jonathan chocalate
## 3 Elliot coffee
## 4 Vijay mint
## 5 Candace cherry</code></pre>
<pre class="r"><code>color</code></pre>
<pre><code>## name color
## 1 Joe red
## 2 Jonathan blue
## 3 Elliot green
## 4 Vijay orange
## 5 Candace purple</code></pre>
<p>Now let’s say we want to combine these two pieces of information (favorite ice cream and favorite color) and save it as a new data frame called ‘merged’. We <code>merge</code> the two data frames by a common column, in this case, the ‘name’ column. That is, we combine information associated with each person’s name in a new data frame.</p>
<pre class="r"><code>merge(icecream, color, by = "name")</code></pre>
<pre><code>## name icecream color
## 1 Candace cherry purple
## 2 Elliot coffee green
## 3 Joe vanilla red
## 4 Jonathan chocalate blue
## 5 Vijay mint orange</code></pre>
<p>We can also concatenate two data.frames side-by-side. This is called a ‘column-bind’, abbreviated to <code>cbind</code> in R. Note: the number of rows in each data.frame must be equal to combine side-by-side.</p>
<pre class="r"><code>cbind(icecream, color)</code></pre>
<pre><code>## name icecream name color
## 1 Joe vanilla Joe red
## 2 Jonathan chocalate Jonathan blue
## 3 Elliot coffee Elliot green
## 4 Vijay mint Vijay orange
## 5 Candace cherry Candace purple</code></pre>
<p>Question: How is this different than <code>merge</code>?</p>
</div>
<div id="writing-data" class="section level2">
<h2><span class="header-section-number">3.6</span> writing data</h2>
<p>Notice that the original data files that we imported at the beginning of this tutprial have not been changed, only manipulated in the R environment. If you open <code>Daily_Temperature_1995-2013_Delhi.txt</code>, it is the same as it was. If you want to save the changes we have made so far (e.g. removed missing data, added a Date attribute, etc..) you can write the current R object (’data) to an external file. This is like hitting the “save” button in Excel, but it is not done automatically in R; you have to do it expicitly.</p>
<p>To write a .csv, call the <code>write.csv</code> function and supply the name of the R object you wish to save (‘data’) and the desired filepath (“data.csv”).</p>
<pre class="r"><code>write.csv(data, file = "./temperature_data.csv", row.names = FALSE)</code></pre>
<p>Note the row.names argument. Try to see what the csv looks like if you omit the argument, or change row.names=TRUE. We generally prefer to output csv files without the row.names.</p>
<p>Similarly, you can save your work as an R object, which is very efficient but means you can only re-open the object in R. This is an excellent choice for “works in progress” that you are developing in R.</p>
<p>Call the <code>save</code> function and pass the R ojbect you wish to save (‘data’), the name of the file you wish to save it as (‘temperature_data’), and a valid file extension (‘rsav’). Later, you can retrieve the R object using the <code>load</code> function.</p>
<pre class="r"><code>save(data, file = "temperature_data.rsav")
load("temperature_data.rsav")</code></pre>
<p>After loading the file, the object name will be the same as it was when you saved it. That is, the file is named “temperature_data.rsav”, but the object is named “data”. Recall, the object name was the first argument passed to the <code>save</code> function.</p>
<pre class="r"><code>str(data)</code></pre>
<pre><code>## 'data.frame': 365 obs. of 5 variables:
## $ Month: Factor w/ 12 levels "1","10","11",..: 7 7 7 7 7 7 7 7 7 7 ...
## $ Day : Factor w/ 31 levels "1","10","11",..: 1 12 23 26 27 28 29 30 31 2 ...
## $ Year : Factor w/ 19 levels "1995","1996",..: 18 18 18 18 18 18 18 18 18 18 ...
## $ Temp : num 84 85.3 87.7 85.1 84.4 86.8 85.3 84.7 86.9 81.3 ...
## $ Date : Date, format: "2012-04-01" "2012-04-02" ...</code></pre>
<hr />
</div>
</div>
<div id="assignment" class="section level1">
<h1><span class="header-section-number">4</span> Assignment</h1>
<p>Until tomorrow, please do the following activity:<br />1. Go to this <a href="http://bit.ly/1fj3sjD">link</a> and download the file into the working directory.<br />2. Produce a new <code>data.frame</code>, which has the following properties:<br /> - From the Nigeria Facilities Inventory data (<code>sample_data</code> from section 1.5.1), select all facilities located in the Southern zones of Nigeria.<br /> - Incorporate the pop2006 column from the <code>lgas.csv</code> file into the new data.frame containing only those facilities located in Southern Nigeria. (Hint: your id column is <code>lga_id</code>).<br /> - Calculate the total number of full time nurses and doctors for all health facilities in each state.<br /> - Sort the resulting dataset by state population, in descending order.<br /> - Show the results!</p>
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