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en:learning:schools:s01:code-examples:ba-ce-09-01 [2015/09/22 16:20] (current)
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 +====== ​ C09-1 - Generic plotting functions ======
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mRqKxaXpygKB5dWVlFrb5mQOydwB7i0J6ubFjh7npdh1XXdKUdEAmoj3W5Db4m4QE1LVCapO1XgH2D%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%2BmdjbrwsGhXOdZYsTb20EsYH1DGow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wjkxjS2YX4WLXFfY%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%2FspZhX4w8AFcXTDcJh1pn7jDRAL3viNq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 +
 +<!---
 +Version: "​2013-10-23"​
 +Copyright (C) 2013 Thomas Nauss, GNU GPL (>=3)
 +-->
 +
 +<​p>​The following plotting examples will show R’s generic plotting way.</​p>​
 +<​p>​The underlaying example data is taken from our combined natural disaster and Ebola data set which is loaded in the background into the data frame called df. We will generally not interpret the plots but just look at formal aspects.</​p>​
 +<div id="​scatter-plot"​ class="​section level5">​
 +<​h5>​Scatter plot</​h5>​
 +<​p>​Scatter plots are probably the most often produced plots since they visualize the dependence between two variables in a straight forward manner.</​p>​
 +<p>In the following example, we will visualize the number of totally affected people against the time line. The aggregation looks like this:</​p>​
 +<pre class="​r"><​code>​dfa &lt;- aggregate(df$total_affected,​ by = list(df$year),​ FUN = sum)
 +colnames(dfa) &lt;- c(&​quot;​year&​quot;,​ &​quot;​total_affected&​quot;​)</​code></​pre>​
 +<​p>​The lattice way to produce scatter plots is using the <​code>​xyplot</​code>​ function:</​p>​
 +<pre class="​r"><​code>​plot(dfa$year,​ dfa$total_affected)</​code></​pre>​
 +<​p><​img 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title="​plot of chunk unnamed-chunk-3"​ alt="​plot of chunk unnamed-chunk-3"​ width="​672"​ /> The x- and y-axis still show the variable names which is a good thing if you are not sure what information is plotted on which of the axis. For presenting this plot to someone else, it might be better to add custom labels and a title.</​p>​
 +<p>To add custom lables, use the <​code>​xlab</​code>​ and <​code>​ylab</​code>​ arguments. For the title, use the <​code>​main</​code>​ argument:</​p>​
 +<pre class="​r"><​code>​plot(dfa$year,​ dfa$total_affected,​
 +     xlab = &​quot;​Year&​quot;,​ ylab = &​quot;​Affected persons&​quot;, ​
 +     main = &​quot;​Persons affected by natural disasters from 1900 to 2013&​quot;​)</​code></​pre>​
 +<​p><​img 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title="​plot of chunk unnamed-chunk-4"​ alt="​plot of chunk unnamed-chunk-4"​ width="​672"​ /></​p>​
 +<p>If you do not like blue dots, one can of course change the color and symbols using the <​code>​col</​code>​ and <​code>​pch</​code>​ argument:</​p>​
 +<pre class="​r"><​code>​plot(dfa$year,​ dfa$total_affected,​
 +     xlab = &​quot;​Year&​quot;,​ ylab = &​quot;​Affected persons&​quot;, ​
 +     main = &​quot;​Persons affected by natural disasters from 1900 to 2013&​quot;,​
 +     col = &​quot;​red&​quot;,​ pch = 13)</​code></​pre>​
 +<​p><​img 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QVKTCdwOZVEKDAHmQ1ItjGSNuvEr91FQQo8JKc1muVEW8qhXvm57ZVEKDAS3KarlVeZCgwncDmVRCgwGtyWq8V5meJobSbV0GAAi/​Jis0687PEzVybV0GAAi/​JNNwrrYCWmE5g8yoIUOAlDa3Xalvw1EDrQ4ACHZMdtd6szzXQ6hCgQMduvtcaoOMYq12HMW1ZBQEKvKT6a6AlphMYVuHcjpSwNgIUeEn1XwMtMZ3APWz1+wlQ4CWZpFCQn3uVMZCbaWH91AF6Cji6TEAl7AZylQlaYDqBcanSPycEKPCahI3W5yS9p/​OpA9RHgAKdSbfJiyeodDwTAQq8JDcmCNCbaEQ9AQrg5Unv6SRAAbw8aqCrEKAAfFwDXYUABbKof0L7JNYnYRhTHAEKZOEMgXqCTnx7VGzC2whQAAJj0DzHMFLhoFgCFEA6K2ieLD+TPg8BCiBd/​RPaJ5HeNkqAAkinYDKnEghQAOkUTCdaAgEKIF39E9oXQYACz2u/​0ZrUQFsEKPC89hutyTXQFgEKPLHdRmuaAZNmBLrz64zrqhgBCjyvHUdr2rfueN/​L9jKVUgIUeF47jtZ0bt2ZJujiupyLs4rTlgAFntd+VyqdrLyPXUrD7xLervoaKgEKPK/​9+sr9Ce2tzv4Vq3IuzuYsWFkEKPC8So7WTJpS09Q7VecnAQo8sZKjNZMmdZd+C2ZtCFDgea25BpprsH3S1wpJZ4CvDQEKPC+ni3z2NdvHHyUHqOQ7iGpDgAJPzL7UOJOg5sXyMEttwt9Wp23FCFDg6ZhO8RWjhazfbLocmdiJdKMGGkSAAgcLh2ckHnMNtk8axsQ10BgCFDiadeXTSrNogOYZbJ8yMikpbStGgALPJ6VVnm2o0/​LVglAJCVAXAQocze58XwqoXIPt7fRcE9sJaVsvAhR4Fnbf0dgqXwzFXDVQ573LK01J23oRoMCziPUdLb1rrwlHFlYa/​kEXAhR4GvaFRaubZvlNS4PtEUGAAk/​DD9B1g9qXB9sjjAAFnobbd7TyGuik5V+gmE+EAAWehtV3ZCXp4puc6ioJmoIABZ6Gewtn/​9S629LDP2EBAQoczRl+tCXCrBFJtMqLIECBo2ULPCuJJwNCsQ8CFDicSc1ofq6qpYbHzxOg+9EXoOfT4C39zQQoauQEaPwli7XUlL4j5KAsQC8nV2qGEqCokTv8KP6a6aPQiyYPsCtVAfr5/​TT19Z+kJRCgqNGaW9dX1FIZ0VmcpgD9+9NNzD8/​Hj9/​+52yCAIUNbLHbs624W9uSHr3lNN3VJimAL16cdlE6kfKIghQ1MieWjgeoPYg+WA9kxGdxWkK0LPfYH9UQpMugxKgqNG6GmhokDwheSxFAfqobr57T17S2vAEKGq07hrozZ1liZ6iCigK0Edt02+uX9O6kQhQ1GjN8CO/​BkpP0fEIUOB4q3rY7Vrq2pmWsC9FAUoTHs9qzfAjt+K5cME08G7rPqaltyW9+LUpClA6kfCk1g0/​crqOUsPNCeilln/​Si1+bpgAND2Pya6UzCFBUaNXwo7svpXZoXyRYM0Xo+he/​NE0BGhxIn3YrEgEKpfxaatI1UCsJV+Unt4SuoylAu8R0ffmVtAQCFErZkyuN7ffhoujyRE2mLT53ldWJ6Pvsi9FSFaD2VExMJgKtnD4aQUBNq4d+yz64TutGpuhyJ5c+acEvURagN6azg3or8m7924dnzO8ibxrfGc/​s4c1ukKaW76XoC9BNCFBUYDnvFt7stLQnURp7l1nfuoFSCy9GiwAFCjP1TkkDeXxLoOUer9GuqYGO10kJ0NX0BShNeCg3CaoNy3Ga3LP98uuugdqvoQm/​hrIAZUZ66GffkJkxQGdrjCta+XYt1SQy+TlLVYAyI33YmmEsqIdd+9waoG43/​C2+QCdtZ8rlLIxhoEs0BSgz0kcELoaZX92iP+EgdjZtC1B7RP1CIkcPkUC5/​M4pRGgKUGakjzGH+fSAnzbIjigdJjIG6BicVpJGXrt8IEyb8GbBiNEUoEwmEuMEqP8rKhN1ydeEv1k7+HZbbp6HfzLPWuPnnWcRoyhAmc4uyjmB7t6v3Ac42hBUW/​+muR3m46I3FWwMZXc1iFAUoEyoHBVvwjmVk2MKB084qASL8UYdbV4irZU0BOgzMGdRqK1mTrKjyvfSQp16GePuNom9LYvMtJhXoihAacJH2dWZwMWu8Zw4pHCvLpBud9/​wt246IG12iJq9OOvJDSWN/​IAYRQFKJ1LUfA10yNa9z4iZ8VMl3TfOdbRDgSYVziGbQiG6/​ExkwRk+KGPe0mkKUGakj5m7Btr9t0yAelWtI8wkznEFGh70/​1q/​cbvPTYnjz1jLNX8qqvikL0hTgDIjfYx3Hlq/​uY3n6e6nWCXBFU2cMiuf1n/​HzeEXxwnH1c9Yq4r+hEI0BSgz0kfNnmSmKbh/​KXIMpslQiuOK4dd/​TdMgnK231AeoiKoAZUb6CO+knfymf1SkFLejh5zeM811JFipswvMvzcvV0223k2+3lY+g4ooC9Ab09kFOHWbQCVneFGBTiRT1dp3XbUVww/​HyRXPYdeMr7aKavbWqmewJ2cbL29wfQG6iZoATbq+NemUmFRyYpXT7SV061JOq/​TgAI0Uw//​7knGtds+6KYHdj2cHqFvUmx2XS89gT/​bZsuKMIUDrNK1UOr+6RWPAhKSfn5lb8U4uOzWm/​Kd6Uq1gphj+35fUYsxteDso3Tb4mKuBbHWXRw30cGmHCAFaKe9Un6tmOu8aH93cd00rjBlKaBXoNptc21flZvX8i02Zxo0w3WDeyXGfuwribXN7YdZrnKJOEtA04u/​3lCue/​jPY1XhorMlPpQF6Se19H6z4uMEz6YgHw0lo/​TTzKyc3Ar+6B2QooLvkcc27bAzX3LucwPLf6SVykltkY1ordf6KOO+aJvt9kudrnsGe3N21iACdSj2fdhUvTqCkN6e6Enj1Hp8v0JbPvAZrVQmL9l98m5wckXfd7H8Dbx82YnRjTgPUWeDN+9Ut/​QF2NPlTuIQA9dgH7H3fB/​eFB9blr9l3Wb9yG5HmV/​5rF9c+/​2Bch1dk6293zu0kWnLgg44Rv2Kjht4+8xp76WM1003ZQOV0/​NXKZ7AnewcToFNra6AFH9ziDwJnkvnVNFv7f+232r+yTt2MH8Jb1Y4bzAm+mQeTwLI22JqNGkjJ6dtvw/​+ne9JedrA4k2y133Prn15+Brsa9+f4eBYBOuWeHAUe3OIPzJlnvWt6qrrvst9p/​2p4Z+ayj/​8NljnvA3tdghfbFyhjy/​G6ekK/​us+/​xlrgcEANR5a1DOc34+vWPINdEaBz1gWoc6jv/​+AWf2Cd697pO/​1VX/​jQcm5O3bQ/​t8dzfdMDf4HmmbwPbilLDu3J+4rl3L0HM293XtNtA3sjeKef/​RrR4Yv9mb34jAHazidimZ+Kybtz/​rQyQO2kUfHA+sNpPbB/​Zf8+58qnuTlswryfL5ZcwQc3qxi3pLff4i9efOZ+j1xAcI6t2G9QC/​fP7uLLCdApN2kKPJg5oecqnvaDsdg3+zS+2b/​a4WONix8C00mu/​bbTwotvc8WwFhYo4cwqrHdO94nz87BV7J1hDi3/​NajM5Lhe8tQB6tNUA71bAeD/​alL1u3kvnvxqh48VLOCem3DdX5NIDdQptPXc6lWYxd+sp/​1NbuWmewberR/​vdxK0Uvdh36zaR8oCtPcS10Dvdj6t+NUtfuX05r096zVQ+5mdNmG/​aPuzzT6YFONuL8cq8/​wq7qFf2Rt+Uh77EIocWeEfUJHJUb2AAJ2anBz7P7jFHzj7ceFX3b/​BT3Gz3573QzjlcFa133Za8eLZYkyKPFlFZENZSzTvDqwUytl7eMVOJUA9zumy/​4Nb8MHkVL/​br/​F/​E9jTwSV7b9rwYBoru2+wlMLf4m+3NkZ8Ff66ZkT2AFRKbCcQoJ6F06WkmeL4v5r5EPt9rJLrSjGzJ7fs5D4n51aKl0KATglPLaHb/​IOuZrPyV+6HCLw48+e7jUuOP5NtXfe55Fr1YrNlwssxv7pNX2Nt3f5X/​sFCHfQlEaBTzqkyni67PJj5tVuCsVzhX7nl9n6ILGfjg5liZN+EM58/​6cUbf+Wb2eh4CToDVEzPfKAA6keAAoAQAQoAQjoDtPmC+C+/​bpdvvxPfSIACyEdjgDbx2QXo6fSR9lYCFEA+CgO0y88mQJuviE9LUAIUQD76ArSZT+Tb78/​vzUCmR4J+/​SflzQQogHz0Bei1nYOpC9CmEZ80IRMBCiAffQF6PjVdR32APqqjSR1JBCiAfNQF6CMymzpnH6CPKmhSG54ABZCPugD986PtNxoC9EqAAjgKAQoAQuoCdNKEP3MNFMBR1AXoIzKbOmcfoI/​66FvKmwlQAPnoC9DrqcnMLkCbMaFJI+lD39MJAFK5A273O5Ha0fNtgF5Op7QWPAEKIKvc+VbqVs5O2o1I62ls6ysss8IiayyzwiKrLHMW+08mYn05/​F75qXL3KSyzwiJrLLPCIqsscxYlprPrI3S3+NS5+xSWWWGRNZZZYZFVljmLqidUXk3j7lNYZoVF1lhmhUVWWeYsCNCjKCyzwiJrLLPCIqsscxb7jwMdJI0ATaRx9ykss8IiayyzwiKrLHMWuwboZTKEYLcM1bj7FJZZYZE1lllhkVWWOYsdA/​TzuzcIi2FMhsIyKyyyxjIrLLLKMmexX4C2fe9WYrYDQpO/​V24djbtPYZkVFlljmRUWWWWZs9gvQK9eXCbfyrmaxt2nsMwKi6yxzAqLrLLMWewXoGe/​wZ46mchqGnefwjIrLLLGMissssoyZ7FbgPYT2bkuO7XhNe4+hWVWWGSNZVZYZJVlzmK3AO2nUnYlTqi8msbdp7DMCousscwKi6yyzFkQoEdRWGaFRdZYZoVFVlnmLGjCH0VhmRUWWWOZFRZZZZmzoBPpKArLrLDIGsussMgqy5xF6WFMfq00B427T2GZFRZZY5kVFlllmbMoPJB+p1uRNO4+hWVWWGSNZVZYZJVlzmLHWzmdueg73Xdz5qdx9ykss8IiayyzwiKrLHMWu04mcp7k554TMgFAac8xnR0AHOA5JlQGgAMQoAAgRIACgBABCgBCBCgACBGgACBEgAKAEAEKAEIEKAAIEaAAIESAAoAQAQoAQgQoAAgRoAAgRIACgBABCgBCBCgACBGgACBEgAKAEAEKAEIEKAAIEaAAIESAAoAQAQoAQgQoAAhpCtA/​P77+Y376/​H56eHd+v/​xMaUtl7p758st+x8FlXiry8PSH9Y7aN/​Pt2jxjFfn4Mq87Mt6cdxxZ5DVHao1n4M4UBejfnydzyHU7xjkpzv0TZh/​7z5S2UGbzzPiqw8u8tJnH52c2fGlLZe5Ofk2b+fHr5eO7oFVHao1n4N4UBejZ2nlmf45H2OU03cX+M8XNl9l+pn/​Z8WVe2Mzji8Ynji/​yUpnH/​Dydvv1unzm+zAtFPnvPHFrkVUdqlWfg3tQEaPsn2fnr15wLzbPdk81J0jzTtNW6do//​TG1lbo6v9+FXb1WUeanInesknerezM3Z3wbnuL0PL/​NSkYeCNUWPHd8lrTlSazwD96clQLtKxHDINT/​111Uu/​b45W3utazD4zxS2VGarHXzpX3d0mRc3c6urj5i6Ut2bufm3r3he+0dHl3nN0dw9Mx4jhxZ51ZFa4RlYgI4Abc/​Y//​bTbhq8mV81z/​b/​9L98v4WeKWu5zFfzTFP3+Di8zMtFHgr79X9blyEq38yP4g5nb/​+w/​s18HotcxdG85kit7wwsQkeANi2ID+uy+9m5tt4ca4+/​ccMufjxsqhn+M2Utl9k+qrrfHlzm5SIPL/​u4DL+qfzNbJezVv5mdAP24HV3kNUdqfWdgEVoC9M3pt7TO5v4Is3Zx/​zr/​mbKWy2zzIvWIMq8r8uNkeDc/​1r+Zr17tp/​7NPJZwqD4fvZmN2JFa3xlYhI4AbUUOue5ykRVJ/​THnP3OA2TJbHq2d5i90DWVeKvLj94+i2gF6eJFXHBrdsCBzHe/​wMs9v5qaV/​2b9W0WRW9EjtdIzcG86A9T929Y89ptBkfZnWbNltvS/​rKHMS0U+uydHDUVeOjS+/​BrG03SvqqHMC5vZjBt664t5fJFb0SO10jNwbzoD1OrVu3THmPtXvNt902cOMFtmY7hEVEOZF4rcN4itAD2+yIuHxr+74xFrKPPikXGejAs9vsiN+JFa6Rm4N50B2jbI3ocHfoA2h53/​zAFmyzx6HJVD1ej4Ms8XuW/​BxQK0ws3c/​dsWehiPWEOZF44MM5B+GBF0fJFvs0dqpWfg3nQGqHVvyZf/​+D6004YXVvT3b7bMg+s4JLCGMs8X+WzuAaizBuqVeYyn21jXq6HMS5vZROqkW/​7I2tzckVrpGbg3pQE6HnNffl1PSgLUK3PPHJVVlHm2yH7fUQ1FXjo0zBCaUNWows3sNuqb0tdQ5PkjtdIzcG9aA7S/​z3Y8pyvtA5wtc8c+v2so81yRzci+anvh+zJZm9ke8nCdbv4qN/​OkW6mWo3n+SK30DNyb3gAddH/​brs4h19/​nc/​wotNky9y+wLofWUOa5IpvJITrvdRR5fjNfvACtocyzRZ4dzHpQkZeO1ErPwL2pD9D+frFK74OYLfPNuQ26//​HwMs8VORCgNRR5fjNb9yHertWUebbIfoAeXuTFI7XSM3BvOgP003TCmIk5arwTd7bM/​iQLNZR5rsiBAK2hyPObeew5vg3XQGso82yRJyNDKyjy8pFa6Rm4N50B6lbhIrPV1DAXzHyZH09NClZBmeeLPDDXtyoo8tpDY8zSCso8W+SrmYK4jnm61hypdZ6Be9MZoM2u+dLfIty3DeqcjXC2zH3lwlZBmec388C5Nf7oIq88NNoi1jEf6Iojw4wMPb7Iq47UOs/​AvSkNUGuGbKuVsPhMcbNlvrrt4Y86yrywmXtWD+vxRV5/​aNQzI/​18ka059Gso8rojtcozcG9KAzTwLTfjzRvO7Q+TZ0qbK/​Pf8WtvrMPy+DIvbebOZCBWxZu5MQaAPSC06s1sMrWCIq89Ums8A/​emNUD7v27TC9unyr4TcK7M9vfMmMPy8DIvbubh6Yq+lXO5zOfpX4Gjy7xYZHfW+sZhRV59pFZ4Bu5NUYACQF0IUAAQIkABQIgABQAhAhQAhAhQABAiQAFAiAAFACECFACECFAAECJAAUCIAAUAIQIUAIQIUAAQIkABQIgABQAhAhQAhAhQABAiQAFAiAAFACECFACECFAAECJAAUCIAAUAIQIUAIQIUAAQIkABQIgABQAhAhQAhAhQABAiQAFAiAAFACECFACECFAAECJAAUCIAAUAIQIUAIQIUAAQIkABQIgABQAhAhQAhAhQABAiQAFAiAAFACECFACECFAAECJAAUCIAAUAIQIUAIQIUAAQIkABQIgABQAhAhQAhAhQABAiQFG/​y+n09R/​r58/​vp9Ojk6ssAAACrElEQVTHYaUBRgQo6vfnx+n0Zn78+9P5ETgMAQoFrie7yjmtkAJHIUChwdnKTBrwqAYBCg2sRnzTgH8/​tjRAjwCFCo9G/​Jdf7aNHA/​7b74NLA3QIUOhw7nPzURftk7TRXBx1GvTn9onhFefHe67Wz0BeBCh0aBrxTcv9bDXgm6uhreH66OU06CqpjwD9zxMBit0QoFCia8RfrQb8mJ9Dgpr87FP2fPqX9jUMesI+CFBo8ah7vv39aWqTTW9S+8O1j8ihkto+aGP2fOKCKfZEgEKLJhb/​1WrAm8pof130OlY1H3XTtk56Jj+xKwIUalxPToXybCqj18nIpkeiDgHKiCfsiACFHme7O+gRkvbV0MmtnmOAMuQeOyJAoYeTk02L3tKF6dXtVjrT/​45dEaDQwwlQqw9+CFD7KQIUBRCg0GMaoJMOoqFO+vUfcw2UAMWeCFDoMW3CT+ZkMrM0EaAogwCFHk6ANl1FTg+RNUb0ShMeRRCg0MPtbLemBW2HMZkAbdryBCgKIEChhxug7SXPtg7aD28aJg29jLe/​E6DYFwEKPdwAtcYsdePl7Z8JUJRAgEKPSYBaw5a6+43Ow4//​o78+SoBiXwQo9JgG6DD/​0pv1gi5N+9viCVDsiwAFACECFACECFAAECJAAUCIAAUAIQIUAIQIUAAQIkABQIgABQAhAhQAhAhQABAiQAFAiAAFACECFACECFAAECJAAUCIAAUAIQIUAIQIUAAQIkABQIgABQAhAhQAhAhQABAiQAFAiAAFACECFACECFAAECJAAUCIAAUAIQIUAIQIUAAQIkABQIgABQAhAhQAhAhQABAiQAFAiAAFACECFACECFAAECJAAUCIAAUAIQIUAIQIUAAQIkABQIgABQAhAhQAhAhQABAiQAFAiAAFAKH/​D+uOcuCz1J2RAAAAAElFTkSuQmCC"​ title="​plot of chunk unnamed-chunk-5"​ alt="​plot of chunk unnamed-chunk-5"​ width="​672"​ /></​p>​
 +<​p>​And if you want filled symbols, add the <​code>​bg</​code>​ argument (and change to a symbol which actually can be filled like the one with the number 21). To see the fill a little better, we also increase the symbol size using <​code>​cex</​code>:</​p>​
 +<pre class="​r"><​code>​plot(dfa$year,​ dfa$total_affected,​
 +     xlab = &​quot;​Year&​quot;,​ ylab = &​quot;​Affected persons&​quot;, ​
 +     main = &​quot;​Persons affected by natural disasters from 1900 to 2013&​quot;,​
 +     col = &​quot;​red&​quot;,​ bg = &​quot;​blue&​quot;,​ pch = 21, cex = 2)</​code></​pre>​
 +<​p><​img 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UAfrhfyp34yuU1Tt0mUor3qhj/​na3Zs9r3KUbnpk5UytA3WUyZqBd5tTAtJMx5hHsIpCgiwueAJ2oT1D/​U3sUWCxBBU1QsyG4UHO+JuisDWq1Ta0UnBdJINbFf5yw2AAlQA31BOhCaymQktOTxzVBvcl1QBPU24bc3AT9CL3GTs3wEXcwW1GLwG619BYC1FBPgsY/​Za1pf4I6O3+OBP3wV7HDUtmUoHa0rc7CnZcbrr414I9QZ/​PibGblAidaFugMUDEN44Ga+/​r6U87a9jy1S4Ef3iryL4y0wuZlfA7XvNBcjjkHYM80LhxJz8p9zDel1XVFgJrqaYKGWkvuU/​PV7WwDn92df3sTtK+jQBM0qXi3DDf2F2bhzst66iPwmvF5wlE9a/​eJWJsEqKWmNmigubTY+vvwX/​F1dv7tBU6B4VaR/​Yf44r1lWIvOqNk7C3deH5+tH93XWMukzIaMvXzY1l6uM0CbL4j/​9ONx+/​Iz8Y06vpXT2tdXn3L3Xu9Tzs6/​scBAe9dT8uZ5JRTvluFfdoFZuPMyf/​wwhGYq2p5Rk2klR7xYY4A28dkF6OXyPe2tSr4Xfra7rj3l7rzuU7OdP0uEjhMOPJJtXinFewr7sPlW8vw15tL1PjV7jbMG8BoUBmiXn02ANl8Rn5agER93eXcpbKEe96mFT7HbB/​POapc5JQqVsXElL28lSdsizkBfgDbjiXz5+etr05HpmaB//​ZPy5qiPK967Mluox31q4VPs9vnCs8o2C9mUkxZPzGtiNpbwa3Be+gL03o7B1AVocxCfNCBT/​o8L4HXpC9Drpbl01AfoszmadCGJAAWQj7oAfUZm0+bsA/​TZBE06hidAAeSjLkB/​f2uvGw0BeidAARyFAAUAIXUBOjuEv3IOFMBR1AXoMzKbNmcfoM/​26FvKmwlQAPnoC9D7pcnMLkCbPqFJPel939MJAFK5A273O5Ha3vNtgN4ul7QjeAIUQFa5863UrZydtBuR4mk81ldYs8KSNdassGSVNWex/​2AixpfD75WfKlefwpoVlqyxZoUlq6w5ixLD2fURult86lx9CmtWWLLGmhWWrLLmLKoeUDmaxtWnsGaFJWusWWHJKmvOggA9isKaFZassWaFJausOYv9+4EOknqAJtK4+hTWrLBkjTUrLFllzVnsGqC3WReC3TJU4+pTWLPCkjXWrLBklTVnsWOA/​vrqdMKiG9NEYc0KS9ZYs8KSVdacxX4B2l57NxKz7RCa/​L1ycTSuPoU1KyxZY80KS1ZZcxb7BejdicvkWzmjaVx9CmtWWLLGmhWWrLLmLPYL0Kt7wJ46mEg0jatPYc0KS9ZYs8KSVdacxW4B2g9kZ7vtdAyvcfUprFlhyRprVliyypqz2C1A+6GUbYkDKkfTuPoU1qywZI01KyxZZc1ZEKBHUVizwpI11qywZJU1Z8Eh/​FEU1qywZI01KyxZZc1ZcBHpKAprVliyxpoVlqyy5ixKd2NyW6U5aFx9CmtWWLLGmhWWrLLmLAp3pN/​pViSNq09hzQpL1lizwpJV1pzFjrdyWmPRd7rv5sxP4+pTWLPCkjXWrLBklTVnsetgItdZfu45IBMAlHaO4ewA4ADnGFAZAA5AgAKAEAEKAEIEKAAIEaAAIESAAoAQAQoAQgQoAAgRoAAgRIACgBABCgBCBCgACBGgACBEgAKAEAEKAEIEKAAIEaAAIESAAoAQAQoAQgQoAAgRoAAgRIACgBABCgBCBCgACBGgACCkKUB/​f/​vrn+m3X18vT+/​W8+uPlLZWc/​fIpx/​mOw6uea3k4eHvxjtqX8yPe/​OIUfLxNcdtGW/​WO44sOWZLrXEP3JmiAP3z92Xa5LoVY+0U1/​6BaR27j5S2UvP0yPiqw2teW8zj4wsLvrS1mrudX9Nifj69vn0XFLWl1rgH7k1RgF6NlTetz3ELu13mq9h9pLjlms1H+pcdX/​PKYh5fND5wfMlrNY/​5ebl8+dk+cnzNKyVfnUcOLTlqS61yD9ybmgBt/​yRbf/​2afaF5tHuw2UmaR+6X4bjHfaS2mpvt63146q2KmtdK7txn6VT3Ym72/​jY4x+V9eM1rJQ+FNaWHtu+SYrbUGvfA/​WkJ0K4RMWxyzW/​9eZVbv26uxlrrDhjcRwpbq9k4Dr71rzu65tXF3OraI1Nbqe7F3PzbNzzv/​U9H1xyzNXePjNvIoSVHbakV7oEF6AjQdo/​9b3+bhwZv01PNo/​0//​ZPvD98jZa3XfJ8eadoe3w+veb3kodi//​rdxGqLyxfwsd9h7+x/​rX8zXseQqtuaYLbW+PbAIHQHaHEF8N067X61z68229vwbN6zi549NM8N9pKz1ms2tqnv24JrXSx5e9v02PFX/​YjYq7NW/​mK0A/​f44uuSYLbW+PbAILQH6Zl23NPbmfgszVnH/​OveRstZrNjmRekTNcSU/​d4b36df6F/​Pdaf3Uv5jHCofm89GLeRLaUuvbA4vQEaCtwCbXnS4yIqnf5txHDrBYs+F5tNP8ha6h5rWSn88/​SzUD9PCSIzaNrlvQdB7v8JqXF3NzlP9mDLcTswAABtRJREFU/​FtFya3gllrpHrg3nQFq/​21rfnYPgwLHn2Ut1mzon6yh5rWSr/​bOUUPJa5vGpx9Df5ruVTXUvLKYp35Db32Zx5fcCm6ple6Be9MZoMZVvVu3jdl/​xbvVN3/​kAIs1T4ZTRDXUvFJyf0BsBOjxJa9uGv9u90esoebVLaPvCTr2dTi+5EZ4S610D9ybzgBtD8jehx/​cAG02O/​eRAyzWPHpulUPT6Pial0vuj+BCAVrhYu7+bYse+iPWUPPKljF1pB96BB1f8mNxS610D9ybzgA17i359B9fh+O04YUV/​f1brHlwH7sE1lDzcsnX6R6AOlugTs1jPD3Gtl4NNa8t5ilSZ5flj2zNLW2ple6Be1MaoOM29+nH/​aIkQJ2ae9NWWUXNiyW7145qKHlt05i60PiaRhUuZvugvqm+hpKXt9RK98C9aQ3Q/​j7bcZ+u9BrgYs0dc/​+uoealkqeefdVehe9rMhaz2eXhPl/​8VS7m2WWlWrbm5S210j1wb3oDdND9bbtbm1x/​n8/​xvdAWa+5fYJwOraHmpZKnwSE673WUvLyYb06A1lDzYsmLnVkPKnltS610D9yb+gDt7xer9D6IxZof1m3Q/​a+H17xUsidAayh5eTHfjR3/​Xk3NiyW7AXp4yatbaqV74N50Buiv6SLMNDBHjXfiLtbsDrJQQ81LJXsCtIaSlxfzeOX4MZwDraHmxZJnPUMrKHl9S610D9ybzgC1m3CB0WpqGAtmuebnQ7PCKqh5ueTBdH6rgpJjN40xSyuoebHk+zQEcR3jdMVsqXXugXvTGaDNqvnU3yLcHxvUORrhYs1948JUQc3Li3lg3Rp/​dMmRm0ZbYh3jgUZsGVPP0ONLjtpS69wD96Y0QI0Rso2jhNVHilus+W4fD3+vo+aVxdwzrrAeX3L8plHPiPTLJRtj6NdQctyWWuUeuDelAer5lpvx5g3r9ofZI6Ut1fxn/​NobY7M8vua1xdyZdcSqeDE3xgAwO4RWvZinTK2g5NgttcY9cG9aA7T/​6zY/​sX2p7DsBl2o2v2dm2iwPr3l1MQ8PV/​StnOs1X+d/​BY6uebVke9T6xmElR2+pFe6Be1MUoABQFwIUAIQIUAAQIkABQIgABQAhAhQAhAhQABAiQAFAiAAFACECFACECFAAECJAAUCIAAUAIQIUAIQIUAAQIkABQIgABQAhAhQAhAhQABAiQAFAiAAFACECFACECFAAECJAAUCIAAUAIQIUAIQIUAAQIkABQIgABQAhAhQAhAhQABAiQAFAiAAFACECFACECFAAECJAAUCIAAUAIQIUAIQIUAAQIkABQIgABQAhAhQAhAhQABAiQAFAiAAFACECFACECFAAECJAAUCIAAUAIQIUAIQIUAAQIkABQIgABQAhAhQAhAhQ1O92ufz1j/​H7r6+Xy/​fDqgFGBCjq9/​vb5fI2/​frnb+tX4DAEKBS4X8wm57xBChyFAIUGVyMzOYBHNQhQaGAcxDcH8O/​HVgP0CFCo8DyI//​Sj/​el5AP/​l58HVAB0CFDpc+9x8tkX7JG00J0etA/​pr+8DwiuvzPXfjdyAvAhQ6NAfxzZH71TiAb86Gtobzo7fLoGukPgP0Py8EKHZDgEKJ7iD+bhzAj/​k5JOiUn33KXi//​0r6GTk/​YBwEKLZ5tz7c/​f0+tyeZqUvvLvY/​IoZHa/​tDG7PXCCVPsiQCFFk0s/​qtxAD81Rvvzovexqflsm7Zt0iv5iV0RoFDjfrEalNepMXqf9Wx6JuoQoPR4wo4IUOhxNS8HPUPSPBs6u9VzDFC63GNHBCj0sHKyOaI3dGF6ty8rXbn+jl0RoNDDClDjGvwQoOZDBCgKIEChxzxAZxeIhjbpX/​9M50AJUOyJAIUe80P42ZhM0yhNBCjKIEChhxWgzaUi6wqR0Uf0ziE8iiBAoYd9sd0YFrTtxjQFaHMsT4CiAAIUetgB2p7ybNugffemYdDQ23j7OwGKfRGg0MMOUKPPUtdf3vydAEUJBCj0mAWo0W2pu9/​oOvz6P/​rzowQo9kWAQo95gA7jL70ZL+jStL8tngDFvghQABAiQAFAiAAFACECFACECFAAECJAAUCIAAUAIQIUAIQIUAAQIkABQIgABQAhAhQAhAhQABAiQAFAiAAFACECFACECFAAECJAAUCIAAUAIQIUAIQIUAAQIkABQIgABQAhAhQAhAhQABAiQAFAiAAFACECFACECFAAECJAAUCIAAUAIQIUAIQIUAAQIkABQIgABQAhAhQAhAhQABAiQAFAiAAFACECFACECFAAECJAAUCIAAUAIQIUAIQIUAAQIkABQIgABQAhAhQAhAhQABD6//​hUAaA+vZnrAAAAAElFTkSuQmCC"​ title="​plot of chunk unnamed-chunk-6"​ alt="​plot of chunk unnamed-chunk-6"​ width="​672"​ /></​p>​
 +<​p>​Finally,​ a logarithmic scale on the y-axis might be of advantage to visually change the exponential increase to a more linear increase in the number of affected persons:</​p>​
 +<pre class="​r"><​code>​plot(dfa$year,​ dfa$total_affected,​
 +     xlab = &​quot;​Year&​quot;,​ ylab = &​quot;​Affected persons&​quot;, ​
 +     main = &​quot;​Log of persons affected by natural disasters from 1900 to 2013&​quot;,​
 +     col = &​quot;​red&​quot;,​ bg = &​quot;​blue&​quot;,​ pch = 21, cex = 1,
 +     log = &​quot;​y&​quot;​)</​code></​pre>​
 +<​pre><​code>##​ Warning: 8 y values &lt;= 0 omitted from logarithmic plot</​code></​pre>​
 +<​p><​img 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H02egO0Gy6OBANAQrE8GczQBlfKhABCkTQp2PQ0iFQjwAF9jNb5+4WPAFaHAIU2M+MTffSIQRocQhQYD8jHJ1D7QRogQjQ0jBR5gzTcLR/​AgRogQjQwjDV8BQ+4UiAFogALQtTDc/​hF6BMYyoOAVoUvqMn8apd8utWHgK0KLQST+L3y0X/​SnEI0KIQoGfxq12SnxqEfEoEaFEI0NNQuyxF0CdJgBaFAD0P+VmGsJ5qArQoBCiwS+A4rLIA/​fXtr7+bPzxulS8/​Ql9PgAJYEfgV0hWg99utCdDnrdPFqbfyAzTlNCYarShOyQF672qdQ34GV0JLD9CkUw0ZNkF5Cg7Qj9fb7eWzbsd3Vc/​f30ProMUHaMJUY1o4ClRwgFYV0Pf6v482R2tVXfQtZBPlB2iydrXSi56oNGNVuQFa1Tdfuv+O1c777evPgG1cIEBT0TleRbcD1pUboFXL/​b377xiaj7A2vKoAzfurrjJA6XbAhnKnMRkB+jI8+Cw3QDOvLGkM0P3dDll/​JIih2In0VdP9rfvvFQI098qSzgDdWebMf9QQQ7GXct674LxPmvCF9oFmP0ZzxQDN/​UcNUZS6mMizm/​X58dq25T/​HWqkvTQGaeT5lX0CLnWXO/​kcNyWkK0HraZ1MHHUeO7oEz6QnQaDSmye4AzfwzQXKaArSZSd802bvcrK9ICqqAEqARKWzPEqCITFWAtgk68bL9IhMBGpG+ERUCFJHpCtD2cnjD+/​YrJgjQmLTl595uBw2fCdLSFqDtlfCtoPH3FgF6bfu6HfhMMKcvQHfRFKD6xmgU2NXtQIBijgDN1WplSV3jORt7jhw/​apjTF6B36QBSTVGArlWW9A3flEHhxAMcK22A1qPogp7L0WPfILyqAHVXlq74Pc7jF4NfLkwlCtBf3+rcbGchhd/​JqLecxRR6Tw9VAepyxZZkLsmVRymQjTQB+mySrr6SSHIjo17zeuPFzXh8WIW2kAC93FjGFevc0CBJgNY1x6reWf2nir9H6NVDg+ciLutIDZoKSoCqdMU6N1RIEqDdPTi66LxLRn8+mxcu6q6TtUE9EKAqXW+PoUSKAK3qiXXNsfpPdwW7rA1vXXmp2OXsVlwvTq63x1AiRYB2S8lX/​+lu6i4L0H5F+olyF1R2u16cnL7HjB3BLmGAPrtZRwToTqfHSXJn73EucwCQnYQBOt6VWDYVlCZ853pDKicHKHMA4JKoD/​Rt7AINHfcZMYjUudwX+twAvd4PFrwlGYVv1j9+tJOQ6ilNoYvQdezTmAq9pceqqzUpz40wa3xf6wOAS7J5oN3qnXfZMnQN60T6sP7UQgL0cl/​fU+vctgC92k8YHNJcifQY1u98yC9EMpcCHQReF1pKgF7OmYFlCdDLdaLAIdG18M/​bMAQvbL+3ZgvSl72YCAwnVviWAUqvKDosZwesswXo1SaSwUFfgO5CgCIYAeGarKkAACAASURBVAonAhRouXoJlg12AhSdRAE6XclTPoy0FwEKF/​c41WLIiABFJ+UofLwAfUhXZb50gF554s32vq8NrM+zlQBFJ0mAPvctI79EgApceeri9r6vD6zPXk2AopPoSqTIrXYCNNyVpy567Ps0Ezd+aJjGhE6ia+Ej93oSoMGu/​J332XcjQD2q6lf+NYIp0WpMwpt4uBCgwa7c6vTZdzNjPcLxyv0hMBCgF0GAegaoZ1Wd/​EQt3XJ2UfQ39uytb3dx5fyNACVAN55z5QOFYIluKiddgGmOAJW6ci4QoDhKkgCN14YPC9AlAvSKueAXoH8IUARLM5H+4zVaHbRBH2iwK+eC1773CXrlA4VgiQaRmEh/​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​X/​YvZ7UKAlkYaoH8IUESQbBCpvYA90oLKYgRoaSadmP7D6f3LCFDskiZAn01zu++7PPGmHgRoccxh9LAEbRGg2CFJgNY1z6reWf2nys7QFeiiIkDLYwRnyIQkAhQRJAnQRzP3vY/​O+74FlXchQAu0qEwGJCHTmLBLotWY6h7Q6j9N9+fO9UB3IUCLJaxLMpEeeyRcD7T6T5OcBCgOIG2McykndkgYoO1F7PIAnVyBJLwWiQAtlrg3k/​yEXMIAvXcLf0gXVCZAsYLhIJwg3Yr0fReofEHldhopAQobAhQnSLQifbt0Uj+ZXrqgcl0H3TcFigAtFgGKEySbB3pr11S+71pQuUrQfdcxEaDFYkISTpDmSqRHk591cD72XYhURfGua+kJ0HIxIQnpJboWvr6TRzcEv+eGSE0A79kAAVowJiQhOW3L2VWN+D1VUAK0ZOQnUksziBRx5vy+KigBqgyZiKwlvJQzBwSoLrTKkbdEE+nPWz5kigBVhXEhZI4aKLLFzCTkLkkf6HPf0HlEBKgmzI1H7tKMwu+dvhkNAaoJAYrcJeoD3XH5elRpA/​TCAyBRdp0ARe4I0ONceAg5zq6rCNDLfsaoEaCHufAQcqRd1xCgF/​6VxKe+K5F2ShigFx5CjrXrCgL0wr+SqBGgR1Hw7T9KrF3P/​zco/​xLiWAToUQjQGFXQzOt3F/​6Q0UgWoHVHaL2s8rnTmQjQFOLteu49jBf+kNFIFKDtOFK7Lv2Zc+oJ0BQi7nre+XnlDxmNNAHajcNXAXq/​nZqgBGgK19n16+wp7JIE6O/​v9XL0H6/​17TjuZ85iIkCTuM6uX2dPYZfqWvi3+nrO5n5Gj703htuDaUwpXGfXCdCrS3RXzvZ+nE2Anro2U34T6TPv5JPJfvQ8luv8VMAu3X3h+wCtqqDXuBLJawg592FmoUJ3a+kyPxWwS3QpZz1u1Afo8yoB6lG7LPb7d5H8vNBPBawI0Pj8v1G0APUjPy/​thCb8vfA+0IA6CWMQgGqJBpHqOmcXoKfeISlBgIa0yglQQLVU05he+gCt54SeN5P++AANapUToIBqaa5EambPNwH6uN3OvLtHigANyEQCFFAt6aWcZy+nTIACiCnRYiJ1wz2D/​CRAAUSUbDm7LkJPjc8MA5RpTIBiLKgcWVilstiJ9MAlEKCRBbbKuZAFUIwAjSy0VU5+AnolC9CP16YP9Nw7euQ2kf6S+MlAORIF6DOPWUy5Xcp5RRweFCRNgD6MaaC39or4c2S2mMgFUUFHSZIEaN187yqexh/​PkHY5OywwcQtFSbSYiHH5+1Vu6QEbz0kKVOKhQ6L1QM3ILH05O6zwC1D6SaFEmgCddHsWv6Ay3LwClH5SaEGAIiWfAKWfFGok6gN9cf4tLQL0ZH4ByhIrUCLVKPzYCfose0FlrCJAUZQ080CrBO1a8fXKoOcNwhOgZ/​NpnhOgUCPRKLzNGT2hBOjZPAaICFCoQYDiM+m8y+0pSgQo1CBAr+zPQsI3XXkCAQolWM7uwozgzGneJdOYoIbeAJ1d3+SHADX0QeUZWOkqqZkFOuBEgF6WEVM+TeaUzXwu5YQSBOhlDbmZ4eWV5Cd0UBSgMcaiCNBRUIDSLwlYEKBJ5VSzCgxQRsaBBUUBat4XRGmAZtW3R4ACe2kK0KYOOqwlqrAPNK/​RZQIU2EtVgDZr2/​dL4+kL0Mz6EY0A5fp0QERZgDb3VGpzU2OAZhVCf5YJyvXpQBBtAfr5+3vXjCdA95dnzE2uTwcE1AVoe4/​kdwI0goBL4TPrfkgkoyE/​5ElhgDbN+BcCNIKAgMhrACyNrCZNIEsaA7Rpxv/​1fwjQpK6XJlf8zUCgYwP0sIXsHs12CNCULpufKj8tpKE0QJtmvL4A1fiNvFpuDpT/​3iEJrQHaTAlVFqAq24TXa7n3CFBsS9MH+ryZN5U776acXMoZSmPmR0KAYtsZtzXuryU6AYuJhNHZ6xAHAYptSQL0fntx/​i3YfegJEGzm7ADV5sohcuV9h69EN5UzW+3PHX2gj1lvamiGEqBhrhwiV953+FIVoM3Q+67xKAI0zJVD5MrdF/​CVIkB/​f5814Ycl6cLU8+fNxGzG+MO2RYCGuXKAXnkADb4S9YEaVdCHqPOy9lzEZR2pQWP6BGiYQwM0+xE1fZMmkFqqUfi+5lj/​UToKf1822KtKaFAaE6BhjgxQBfGUfQFxtjTzQKdjP8J5oFV1czlz/​hHWhidAwxzYD0gDGQVItJiIcTsj8SzQ2VhUv2FN90TS57CYY4gGJUi2GlN3VeeOqzgJ0DMc1dC+9PAUiqFoOTua8Kc4qB+QAEUJFAUog0glIUBRgqRN+C8/​Ph/​COaA1+zSmoCWZCNBcEKAoQaIAbXtA6wDdsRiTdSJ9WKcqAZoLAhQlSBOg3QhSFaB3+TQm6/​KigWP6BGguCFCUIEmAtrci/​nit0+6+ayT+PstPFhPRimlMKEGSAH02PZVtgNaN+PCV5A0sZ1cGJtKjAImuha8Hf7oAraqjOwaSdiJA86HgUk5gQ6LVmOo6ZxegVRU0yj2RRK4UoNnHU/​YFBLYkXA+0D9A9CyrvdaEApYIHHE5ngD6kV9RfJ0DpYgSOd0ITXryg8ogA3cIgN5BAokGkus7ZBWjo1Zc2BOgWplkCCaSaxvTSB2jwIvI2BOgWAhRIIM2VSM3s+SZAH8G3MbIhQLcQoEACSS/​lFN1I04YA3UKAAgkkWkykWQckQn4a22msX9O0uHL+RoBuvo6pT4C3ZMvZddG3s/​p5zQAVpJowQJk8CoTQtKDyZ2iALqkMUEmqyaYxHTB5lEBGyZQFaOdSfaCyVJO86oDJo1RpUbQ0VyJN4o6J9EGkqSaIrvgjT1wPhbKdEqBcyhlAnGqSdn/​kAOV6KBQufYD++kaAhkg4IemAAGUyFYp2cIA+bAPhNOFDEKBAtg4OUMtdjARj5wsEqI63IkBRuKOb8M8j8lOOAE36VgQoCnfCINKZCND1t4o85jMtO9OZUBwCNHcpR7L9Zh35R6ERoEwIRYl0TqSvu1brpZ2CR6MUBmjSuZQ+MRcQhWP4MyEURUoVoI92EdBf3yKsxdSOTLVr4wUuLaoxQJNezeOVn/​5R2D+ZCaEoU7rl7Jqo+3gV3c59sbE2QOtbxIclqMoAzep68sAoHLKf0SSUKEmANkuANGPvzaD8vgStN/​b1Z7u8/​T10eSedAZqT0CgkQFGyJAH6uI3zNh/​BtcaZZ5PF3Q2WHoGTogjQvWRRSICiTInuymnUE6tW/​K4qaLsWSReg1aaDBpII0L0IUGCU6L7wZjXxsetSztk9kh9hbXgCdC8CFBglClCz0f7ctZhIt7E+QAM3RoDuRYACIwIUQaQByjQmlChRH6jZaN+3oPKsCR+4MQJ0L2EUMpEeRUo1Cj92gj53ribSrsfcBWhVHw0akSJAdxNGIZdyokRJArS78rL/​476LkZ7NRNI2QOs5oUFzogjQ/​YRRSH6iQGmuRJouardrGmg3e74J0Efw4swEaAREIdBJdC18fQlnZ/​fKTJNFmgNrswQogHiSrcY0XsG+m3Fz+NDeAAJUPeq/​yIjO5ey6CA3vTCVAtWMwCjnRGaBiBKhyTIdCVpI24UVrIEdFgOrGhHzkJVGA7lgDOSoCVDcuCUVe0i2oLF0DOSoCVDcCFHlJtqCyeA3kqAhQ3QhQ5CVJgO5aAzkqAlQ3AhR5SRKgu9ZAjooA1Y0ARV4Srca0Yw3kweQKJOG1SASobgQo8pJwPVDhEp7mZgjQi2MaE/​KiKEDNC+oJ0ItiIj2yckITXr6gcl0H3TcARYBqx6WcyEmiQaQdayCbqtfuW42EAFWP/​ERGUk1jkq+BPFG14ncN4ROgAOJJcyXSnjWQp3ZeCkqAAogn6aWcsjU851vak78EKIB4Ei0msmMN5Jl9VVACFEA8yZazE6+BHBUBCiCeYwP01Ms2bQhQAPEcG6DtlKVf32LcCSkKAhRAPAQoAAgd3oSvspMABVCkgweR7vvX/​4iKAAUQz8EBGmH9j6gIUADxHD2NyZqgBCiAEqRZzo4+UAAFIkABQIiJ9AAgxDxQWAUtu8kanbgoAhQ2QQu/​s0o8roqJ9LAIuvVQvPsUkcNQhon0WAq6+WW8O2VSk4U2TKTHUtDt16Pdq507bkIdJtJj6ZQA5Z7v0Id5oFg6KUAj1WSBZAhQLBGggJdkt/​TIAwHqhQAFvJwQoE/​6QHNHgAJeUgdoc285AjRzp0xjIkChT9oAfTIKr8MZE+kJUOiTMECHe8O/​xH5LfwSopxMu5WQaE/​RJFqDP0+eA1ghQXycsJsJEeqiTJkCHyud77HcLRIDmjEs5oU2KAH0OlyCdvjgoAZo18hPKHB6gfeXz688cVlcmQAHEc3CAdpXP5kokAhRAWQ5fULny1v6FAAVQlsMD9G34CwEKoCwpaqDdzCUCFEBZDu4D7YeQ3j4JUAClOX4a03D5JgEKoCwp5oEO0+gJUAAlSXQpZz+XniuRAJQj2bXwfTWUa+EBlCLlcnZ9NZTVmAAUIe16oF01lPVAAZQg+S09ngQogEKccE+k398JUAAl4K6cACBEgAKAEAF6DpYOBgpAgJ6Cm1cAJSBAz8Dt04AiKAvQj9fpFfW/​v4fNicojQLmBL1AGXQF6n1/​LpDVA//​EPEhTQT1WA9vk5VkIJUADn0RSgdfu9Xs7pMSYoAQrgPJoC9N7e3bO9pL5NUAIUwHkUBWgVlkPf571LUAIUwHkUBeivb8Z6zF2CEqAAzqM1QOsEfdEboExjAkqgNkDrftA3pQHKRHqgDIoC1OwDbf96e1caoFzKCRRBUYDW05fMm9J9vN6+/​G+lAcpiIkAJNAVoPQ/​0zfj7M/​z+INkEKIACaArQZga92Yx/​EqAATqQqQJsENeugdZ2UAL0oekFwPl0BWo8cvU0eeBCgF8U4HDKgLED3IkBLwUww5IAAhUZci4As6AvQcU27l+0nzxGgheBqWGRBWYA+blOhGUqAFoIARRZUBWhzQ4+poCEkArQYBCiyoClA64s3zcT89W16gyQPBGghCFBkQVOAPhdx2VwPH7IJArQQBCiyoClA78sGe1UJDeoGJUALQYAiC4oCdDGJvvYIa8MToIVgGhOyoChAp+uBdp5ciXRNTKRHDghQ6MSlnMiAogClCQ8T+YnzKQpQBpEA5EVTgNqnMS1rpSsIUADxaApQ60T6sEuRCFAA8WgK0DYxp778CNoCAQogHlUBai7FxGIiAM6mLEA/​Wc4OQDb0BeguBCiAeAhQABAiQAFASGeAPkJH33sEKIB4CFAAECJAAUCIAAUAIQIUAIQIUAAQUhagzXoihvWlmBZXzt8IUADxEKAAIFR0gC4RoADiURagHfpAAWSAAAUAIQIUAIQIUAAQIkABQIgABQAhnQEqRoACiIcABQAhAhQAhHQGaH2D+C8/​Ph9ffwa+kAAFEI/​GAK3jsw3Q2+097KUEKIB4FAZom591gNa3iA9LUAIUQDz6ArReT+Trz4/​XeiJTlaB//​R3yYgIUQDz6AvTZrMHUBmjdiA9akIkABRCPvgC93+qhoy5Aq+po0EASAQogHnUBWkVmXefsArSqgga14QlQAPGoC9Bf35pxoz5AnwQogLMQoAAgpC5AZ034O32gAM6iLkCryKzrnF2AVvXRl5AXE6AA4tEXoM9bnZltgNZzQoNm0hOgAOLRF6Dt7PkmQB+3W1gLngAFEJHCAO0u5WyFXYhEgAKISGGAmjeHD8zPQwP0T+e4dwCQF40BOkRoaHweGqB//​pCgwMXoDFCx4wK0Cs5/​NEhQ4DII0DiG/​KwT9KD3AJAZAjSOMT9JUOAyCNA4CFDgggjQOAhQ4III0DgIUOCCCNA4CFDgghQF6OQKJOG1SAQogHgI0DCuyfJMYwIuSFGAfn68nh6g7suNmEgPXI+mAG3qoEE34VzYubtrKcmlnMDlqArQOkHbheil9u3uejud/​ASuRleA1q34sAVAZ/​YGaIqRIoIY0EJZgNZrKActQT+jIEDpCgDU0BagVSN+TxU0/​wBlMArQQ1uA7qyCHhCgceuLTIcCFFEXoPvED9DILW4m5AOKEKABLOkWu8VNgAKKEKABlu3r6C1uAhRQhAANsahvRs87AhRQhAANMu/​xJECBKyNAw8xGjAhQ4MoI0F0OCFCmMQFqEKC7xK8wMpEe0IMA3eWAFjeXcgJqEKC7HNHiJj8BLQjQfWhxAxdGgO5Eixu4LgJ0L/​ITuCwCFACECFAAECJAAUCIAAUAIQIUAIQIUAAQIkABQIgABQAhAhQAhAhQABAiQAFAiAAFACECFACECFAAECJAAUCIAAUAIQIUAIQIUAAQIkABQIgABQAhAhQAhAhQABAiQAFAiAAFACECFACECFAAECJAAUCIAAUAIQIUAIQIUAAQIkABQIgABQAhfQF6v/​Vewl9MgAKIR1mAPm5ToRlKgAKIR1WAfrze5v76O2gLBCiAeDQF6O/​v08T89a36+9efIZsgQAHEoylAn4u4rCP1PWQTBCiAeDQF6H3ZYK8qoUHdoAQogHgUBWhV3XxbPPgIa8MToADiURSgVW1z2Vx/​hg0jEaAA4iFAAUBIUYDShAeQF0UByiASgLxoClD7NKZlrXQFAQogHk0Bap1IH3YpEgEKIB5NAdom5tSXH0FbIEABxKMqQM2lmFhMBMDZlAXoJ8vZAciGvgDdhQAFEA8BKvSnE2t7APTRF6B5NOH//​CFBgctTFqC5rEhfBec/​GiQocGGqAjSbFemH/​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​6wCtbxF/​UIJq/​PgUlllhkTWWWWGRVZY5iqMDtF5P5OvPj9d6IlOVoH/​9fci7aPz4FJZZYZE1lllhkVWWOYqjA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title="​plot of chunk unnamed-chunk-7"​ alt="​plot of chunk unnamed-chunk-7"​ width="​672"​ /></​p>​
 +</​div>​
 +<div id="​box-whisker-plots"​ class="​section level5">​
 +<​h5>​Box-whisker plots</​h5>​
 +<​p>​Box-whisker plots visualize some descriptive statistic values of a data set.</​p>​
 +<p>In the following example, we will visualize the distribution of the totally affected people across the regions. The aggregation looks like this:</​p>​
 +<pre class="​r"><​code>​dfa &lt;- aggregate(as.numeric(df$total_affected),​ by = list(df$region),​ FUN = sum)
 +colnames(dfa) &lt;- c(&​quot;​region&​quot;,​ &​quot;​total_affected&​quot;​)</​code></​pre>​
 +<p>To produce a box whisker plot for the variablity of the number of affected persons over all regions, use the <​code>​boxplot</​code>​function:</​p>​
 +<pre class="​r"><​code>​boxplot(dfa$total_affected, ​
 +        ylab = &​quot;​Affected persons&​quot;,​
 +        main = &​quot;​Persons affected by natural disasters from 1900 to 2013&​quot;​)</​code></​pre>​
 +<​p><​img 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6h8m40u+fSDBWgQD2rjQPdHOdU3j4yofK5HcOYgFewzpVIxx3HLjh3D1yIdOh7H95dyUB6oJ2VroX/​2Fy64MuP3z8mcelSTqChSNPZbac7ryYTAdpZpxOpysDP0e3ljxaOiRKgQD0rXsr5qNNw0iETKgPNrDSQ/​rHpQy7NCFDghQTaA3UID7yWVc6Bfjwydv5KJxLwUtbphf/​1rcY+aH4Y06LZmQUoUM9K50AfunzoLDuQfllbAhSoJ1KAThtafIc6AQrUEypA+1MxmUwEaC3SlUhHprMDXkS8AH2IAAXqEaAAhVYL0O5EaDetcrvbeXQEKFDPSgF67Ec6zktfY0x9KQEK1LNOgJ764fcBut1UTdBf31wLD7SySoB2I+C//​Pz1rRu0uX1kFNOEAAXaWeta+O9d2B1Gve8WXn15kwAF2lnprpzH+3EeArTS3ExlBChQz3r3hT8H6H4XtOIx/​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​OMEKFDPSr3wOS3OhApQoB4BClBIgAIUCjSdXQ0CFKhHgAIUEqAAhQQoQCEBClBIgAIUEqAAhQQoQCEBClBIgAIUEqAAhQQoQCEBClBIgAIUem6AvtBEdkcCFKhHgAIUEqAAhdY5B/​qx6d9Urt1NOQUoUFGL2xqfsrQFAQrUs0qAbjdfZ39blwAF6lnppnL9o/​YP50CBt/​DWAZrrwFrnnYF/​gjUC9L//​Hh3Cf/​lZ+03zBCjwTCudA+3tgu42zoECb2GtXvjzYXv3o1544C2sMw50NziKNg4UeAsrTSbycY3PhvufAhSoabXZmE5XdbYbwXQgQIF6TGcHUEiAAhRa9RD+j79+79YaA5onQIF6VgrQ4xnQLkCbdsILUKCidQL01IO0D9CtYUzAu1glQP/​7783my89f37oRTNumPfECFKhnlQD92HTzgR4DtDuI/​z59ykoEKFDPStfCd11HpwDd746260gSoN+TQZwAABClSURBVEA9K83G1O1zngJ0vwtqPlDgHaw4H+g5QE2oDLwHAQpQqMEh/​HoTKk8JUKCelTqRun3OU4Du90dNqAy8g7WGMX09B2g3JrTdSHoBCtSzzpVIh9HzhwDtplZueDm8AAXqWfVSzk3zKUEFKFDPSpOJdAfuL5CfAhSoaLXp7E4RakZ64G2YUBmgkAAFKLTOlUiDO3EaSA+8hyYB6lJO4B2sH6B//​ylAgbfw5ADdbXIcwgPv4MkBOhhBf2FGeuAdPPsQ/​qN2fl4jueREgAAF6mnQifSIUR4vPhcgQIF6YgXodrwzu7RhAQrUE2og/​W5znEr017fu38PFoQsTVIAC9awVoLvjJKB///​nAEKZ9bp5mEj3Ncd89suwoXoAC9aw3nd0h+/​aJVz4f/​e760vPtQa6Zeh8BCtSzSoAeDrYPu4yHTqDCBD3vdp5+PrayXdaaAAXqWSVAd71zld3Q+rJbepxu7nl0vqB+t+wYXoAC9ax0V87emc9jD1CBUYAem1x4j2QBCtSz0n3h+2PnF+40Zpu5ZLIABZpZKUD7B+0LM++qNw/​epfvdITzQTKQA3V2vAt1uLuOYdCIBjax0DrS/​m1g8oXI3GOqQvZcfPpb2SAlQoJ61euGvJ0E/​ymcT6UaRnv04jY5a1iElQIF6VgnQbo/​xNI7psvNY5Jqg3X5nF6ALd2YFKFDPOlciDSdRKhsG2m/​ptAu7XTxJiQAF6lnpWvjewXe1qe1KCFCgntVmYzpNhNw0PgUoUFOo6ewOrnOCFlzQJECBeoIF6PgmdUszVIAC9ax6CL8/​ft89ckfO/​jCmshsjCVCgnpUC9HgGtAvQBzrhD+M+e4l5aNSEykAr602ofAzQ7QPDmD4mcdlFqiuRgEZWm1D5y8/​jLPLb8oH02+kr98nsWnigkVUC9Hj15uk2HLvSSzn7M9JfmI0JaGaVAD1OH3IK0NHUIvcbTep0ZD5QoJmVZmPqdh3PN4LbFR7DC1Dgtaw4H+g5QEvnA3UID7yWQAGqEwl4LQ0O4YsnVM4PY1rUIyVAgXpW6kTqdh1PAbp0p/​EqO5B+2d6sAAXqWWsY09dzgC4e+95zGo/​ft3B2JwEK1LPOlUiH0fOHAN0tvvpy3JDJRIAXseqlnEXzf4yZzg54EStNJnI4fVklPx8jQIF6VpvO7hShTeNTgAI1PTdAiy/​bfBYBCtTz3AA9Dln6+8/​ad0Lald5bSYAC9QhQgEJPP4TfJ50ABd7SkzuRxuM2K3XEC1DgBTw5QDO3gROgwJt49jCmbIIKUOAdrDOdXbVzoL0B+QdLbw4iQIF6BChAoWAD6QUo8DqMAwUoJEABChlID1DIQHqAQgbSAxQykB6gULBxoI8SoEA9AhSg0Gq39HgNAhSop0GAfjx+DrS7y2d3j+TFlzkJUKCetQP0cC3mgwF6vEny8SbzP5a9VIAC9awboB81euFPN5nfB2g3yvRWguaGUD3yzgB9KwboZSKQrw+13zXz5eevb13H1PZ2GAtQ4JlWC9CPWmNAPw5zMB0DtDuIXzQhkwAF6lknQC87nwtPWeZsDzPknQJ06Xx5AhSoZ40A/​bgcP1eYHHQfmd0+5ylA97ugi3ZpBShQz9MD9Lzz+eVnndmV//​7zsBt7DtCFY6IEKFDPkwP0tPNZdLidJ0CBV/​H0CZU3l26eOgE6OoTfOgcKtPL0AL12kle6QdL2sM95CtDjlPf3E6BAPWvsgZ4OsisF6MdhIOkxQLsTrIs69gUoUM+Tz4Geu5C+/​653i87D6PlDgO4Wd+wLUKCe5w9july+WStAT5dyFo3LF6BAPWuMA73ezL3OTeJ7N4dfel2TAAXqWelSzvNY+gpXInVOEbr8slABCtSz2rXw593Gh6+Ff4gABepZczq7827oY7MxPUSAAvWsOx9o8aF3z/​VW8wVBLECBela/​pcfHQwG6G83uuTRDBShQT4N7Iv3336UBmrnJvGFMQDOR7so5vp/​SYUCogfRAK5EC9GMSly7lBBqKFKDb6QG7yUSAdgIF6Gkiu6Gd6eyAVgIF6Gkq5SETKgPNCFCAQoEC1CE88FoCBahOJOC1RArQ/​DCm6V7pDQIUqCdSgGYH0i+7FEmAAvVECtDhXPS9GybfT4AC9YQK0P5UTCYTAVoLFqC/​TWcHvIx4AfoQAQrUI0ABCglQgEICFKBQ8AD99c218EArAhSgkAAFKBQ8QJcSoEA9AhSgkAAFKCRAAQoJUIBCAhSgkAAFKCRAAQoJUIBCgQI0c0OPzdKbIglQoB4BClAoUIB2F74LUOB1RArQwz7ootvATwhQoJ5QAdol6ML7GI8IUKCeWAHaHcV/​+fnA6wUoUE+wAP2922x+PPByAQrUEy1A9wfxj+yCClCgnmgB+uAuqAAF6gkXoI8RoEA9AhSgkAAFKCRAAQoJUIBCAhSgkAAFKCRAAQoJUIBCAhSgkAAFKCRAAQoJUIBCAhSgkAAFKCRAAQoJUIBCAhSgkAAFKCRAAQoJUIBCAhSgkAAFKCRAAQoJUIBCAhSgkAAFKCRAAQoJUIBCAhSgkAAFKCRAAQq9dYBuMlrXBLwPAQpQ6K0DdEqAAvUIUIBCAhSgkAAFKCRAYUSHI/​cSoDBkyAZ3E6AwcAlOCcqnBCj09WLT1sJnBCj09TcRmwufEKDQJ0BZQIBCnwBlAQEKfQKUBQQo9AlQFhCg0CdAWUCAQp9hTCwgQGHAQHruJ0BhyKWc3E2Awoj85F4CFKCQAAUoJEABCglQgEICFKCQAAUoJEABCglQgEICFKCQAAUoJEABCglQgELxAnR7mSvn6/​IXC1CgnmAButsMLc1QAcrnzMbEvUIF6K9vm7F//​WdRC74UfMp8oNwtUoD+99/​DxPz7z/​3vX34uacJ3gs+YkZ77RQrQj0lcdpH6Y0kTvhJ8wj2RWCBSgG6nB+z7ndBFp0F9JfiEu3KyQKAA3e9ufp88uFt2DO8bwScEKAsECtD93ub0cP1jWTeSbwSfEKAsIEChT4CyQKAAdQjPCgQoCwQKUJ1IrECAskCkAM0PY5ruld7gG8EnDGNigUgBmh1Iv+xSJF8JPmMgPfeLFKDHxBz6469FLfhK8CmXcnK3UAHan4rJZCI8i/​zkXsEC9Lfp7ICXES9AHyJAgXoEKECheAHqEB54EcEC1Iz0wOsIFaBmpAdeSaQANSM98FIiBagZ6YGXEilATSYCvJRAAWo6O+C1BApQEyoDr0WAAhQKFKAO4YHXEihAdSIBryVSgJqRHngpkQLUjPT87s93/​AZaL0weFClAzUjPm+WnzTG6UAFqRnpWYkPhLsEC9Lfp7FiDDYW7xAvQh/​hecBcbCncRoDBlQ+EuAhSmbCjcJWaA7pb2vp/​5XnAXGwp3EaAwZUPhLgIUpmwo3EWAAhQSoACFBChAoWABephPpGfRVEy/​BShQkwClludMt8HjWm8Zb0yAUkmbbOAerbeN9xUsQE+cA31Blu2rsmaeR4BSiWX7qqyZ5xGgVGLZvipr5nkEKJVYtq/​KmnkeAUolrTtKmNd623hfApRKWocE81pvG+8rZoAWsyk9T+uQYF7rbeN9CVAqaR0SzGu9bbwvAUolrUOCea23jfcVM0C7G8T/​8dfv3ZefC19oU3qe1iHBvNbbxvuKGKBdfB4DdLP5seylNqXnaR0SzGu9bbyvgAF6zM8uQLtbxN9KUJvSO1ozeJ6u9cLkQfECtJtP5MvPX9+6gUz7BP3Xf+afaot9Q2tH3HO1Xpo8KF6AfhzmYDoGaHcQv2hCJlssUE+8AN1uuq6jU4Dud0cXdSQJUKCecAG6j8xun/​MUoPtd0FvH8BMCFKgnXID+/​eeh3+gcoB8CFGhFgAIUChego0P4rXOgQCvhAnQfmd0+5ylA9/​ujX5e8WIAC9cQL0I9Nl5nHAO3GhC66FkmAAvXEC9Dj6PlDgO42m2VH8AIUqChggJ4u5Txa1IUkQIGaAgZo/​+bwC/​NTgAIVRQzQS4QujU8BCtQUM0CLCVCgHgEKUChegG4vJ0AXjQA9EqBAPcECdDeaTnFphgpQoJ5QAfrr22RCWsOYgGYiBeih772XmIcBoQbSA61ECtCPSVy6lBNoKFKAbqcH7CYTAdoJFKCnieyGdqazA1oJFKCnqZSHFk+oDFBPvYQ7SrUbPBOgwKupl3BHqXaDZzUO4QFeWXpayxU6kQBeWXpay/​lhTNO9UoCg0tNazg6kXz6lHcCrSs9rejAX/​dHx3pwAbyE9s/​HtKD+dAAXeSXpy+w9NZwfwylLrAgCiSq0LAIgqtS4AIKrUugCAqNKab/​brm4GgwPtIa76ZAAXeSVrzzQQo8E5S6wIAokqtCwCIKrUuACCq1LoAgKjSk9t3LTzwttIzG9+ZjQl4Y+l5Tf/​6NpkP1CAm4I2kp7WcnZHePeWA95Ge1nL+nkjTWx0DBJWe1rK7cgJvLj2rYfeFB95delbD+73N6eH6h24k4H2kZzUsQIF3l57VsEN44N2lp7WsEwl4c+lpLeeHMU33SgGCSk9rOTuQ3ilQ4H2k5zV9SMyhP/​563tsBrCw9s/​HtKD+dAAXeSXpy+6azA95Wal0AQFSpdQEAUaXWBQBElVoXABBVal0AQFSpdQEAUaXWBQBElVoXABBVal0AQFSpdQEAUaXWBQBElVoXABBVal0AQFSpdQEAUaXWBQBElVoXABBVal0AQFSpdQEAUaXWBQBElVoXABBVal0AQFSpdQEAUaXWBQBElVoXABBVal0AQFSpdQEAUaXWBQBElVoXABBVal0AQFSpdQEAUaXWBQBElVoXABBVal0AQFSpdQEAUaXWBQBElVoXABBVal0AQFSpdQEAUaXWBQBElVoXABBVal0AQFSpdQEAUaXWBQBElVoXABBVal0AQFSpdQEAUaXWBQBElVoXABBVal0AQFSpdQEAUaXWBQBElVoXABBVal0AQFSpdQEAUaXWBQBElVoXABBVal0AQFSpdQEAUaXWBQBElVoXABBVal0AQFSpdQEAUaXWBQBElVoXABBVal0AQFSpdQEAUaXWBQBElVoXABBVal0AQFSpdQEAUaXWBQBElVoXABBVal0AQFSpdQEAUaXWBQBElVoXABBVal0AQFSpdQEAUaXWBQBElVoXABBVal0AQFSpdQEAUaXWBQBElVoXABBVal0AQFSpdQEAUaXWBQBElVoXABBVal0AQFSpdQEAUaXWBQBElVoXABBVal0AQFSpdQEAUaXWBQBElVoXABBVal0AQFSpdQEAUaXWBQBElVoXABBVal0AQFSpdQEAUaXWBQBElVoXABBVal0AQFSpdQEAUaXWBQBElVoXABBVal0AQFSpdQEAUaXWBQBElVoXABBVal0AQFSpdQEAUaXWBQBElVoXABBVal0AQFSpdQEAUaXWBQBElVoXABBVal0AQFSpdQEAUaXWBQBElVoXABBVal0AQFSpdQEAUaXWBQBElVoXABBVal0AQFSpdQEAUaXWBQBElVoXABBVal0AQFSpdQEAUaXWBQBElVoXABBVal0AQFSpdQEAUaXWBQBElVoXABBVal0AQFSpdQEAUaXWBQBElVoXABBVal0AQFSpdQEAUaXWBQBElVoXABBVal0AQFSpdQEAUaXWBQBElVoXABBVal0AQFSpdQEAUaXWBQBElVoXABBVal0AQFSpdQEAUaXWBQBElVoXABBVal0AQFT/​P5vmUKkANYyTAAAAAElFTkSuQmCC"​ title="​plot of chunk unnamed-chunk-9"​ alt="​plot of chunk unnamed-chunk-9"​ width="​672"​ /></​p>​
 +<p>As you see, you see almost nothing because of the distribution of the data values. We could use the log = “y” statement again, but a square root transformation would also be an option. Why we perform it to the third power of the root is a storyline we come back for later.</​p>​
 +<pre class="​r"><​code>​boxplot((dfa$total_affected^(0.5)^3),​
 +        ylab = &​quot;​Third power of the square root of affected persons&​quot;,​
 +        main = &​quot;​Persons affected by natural disasters from 1900 to 2013&​quot;​)</​code></​pre>​
 +<​p><​img 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t4icT+a3o6GQpNJydt05Zx+gz5403m7meil/​IkBZgoZCkkpTObtTx0uA3s28e3Z9cO5Gl/​DugVKehkKSVwrQQz/​q8+vj2pF/​OStN5bggiYZCkgaX8E92nI+2cz4HHW4C7J4cSe+4AMqp1InUBV4foN94QlI3kv6Uvn1udjOSnuvRF6BAObWGMb1fArQ7jfzWXKSxJ7NYgALl1JmJdBo9fwrQfX8OmWs3zs9no1iAAuVUncpZZDnlYVsZQSxAgXIqLSbSXbg3X47+lwAFSqq2nF0foU3jU4ACJb3WgsrfJkBJoqGQpNJA+tyBS6U5LkiioZCk3lM5s8culeS4IImGQpIql/​D98M3G9z87jguSaCgkqXUPtM/​Q1pfyjguSaCgkqdiJdMh5ClxhjguSaCgkqdsLf8gdAF+K44IkGgpJqg9jev5JmiU5LkiioZCkboDuWk9GclyQREMhSb0A3ZvKCWxLnQA9rGIi/​C8BCpRUbyB9086jCwEKlFMrQFufevYEKFBOxTPQtiNAzwQoUE6lTqTLQvKtJ8QLUKCcer3wlzuhzz0FrjABCpTTYiZSwxnxApQkGgpJzESCKQ2FJHUDtF+Tqd1VvOOCJBoKSaqvxtR2NKjjgiQaCklqBejeTCReiIZCkioBupZBTI4LEmkoJKk3kL7p8KULxwVJNBSSVArQFUyDP3FcvL63LWldmHyT58LzWlpHXlmtS5Nvqhag/​USkxr1IWixQTqUAvczjbB2hAhQop06ABvnZdlUmAQqUUyVAu+mb/​RimbjS9p3ICm1AlQA9BaHZh2m48qAAFyqk0kD648Xm8nLcaE7AFNQL0eNIZjqLfN7yGF6BAOZUG0ocX7QfL2QGbIEABMrmEB8ikEwkgk2FMAJkMpAfI1GAqZ8vJ8AIUKMdiIgCZLGcHkMmCygCZBChAJgEKkKlagH59nO6BNn66nAAFyqkUoId1jGISoEBBdQJ0Hz6I0CM9gG2oEqDd5Xt/​4hn8tQUBCpRTaTGRYPr78Wz0c/​qSSgQoUE6l9UDDyNxZzg7YhDoBOrrtaUFlYBsEKECmSvdA32f/​VZcABcqp1Qs/​3AQ9WFAZ2IY640CPCdpfxXeLMrXrhBegQEGVeuFjWtwJFaBAOQIUIJMABchkOTu4cfkV33o/​WD8BCmPDRVLrPWH1BCiMXINTgvKQAIVQEJtaC48IUAiFTURz4QEBCiEByhMEKIQEKE8QoBASoDxBgEJIgPIEAQohAcoTBCiEDGPiCQIURgykJ50AhTFTOUkmQOGG/​CTVsgG6ooXszhwUQDkCFCCTAAXIVOce6OEtfKhcu4dyClCgoBaPNe6ztAUBCpRTJUB3b++z/​6pLgALlVHqoXHjVfnAPFNgEAQqQqUaA/​v3XzSX8H/​8q/​aGpBChQTqV7oMEp6P7NPVBgE2r1wl8u27u/​6oUHNqHOOND9aBy9caDAJlRaTOQwxGfD808BCpRUbTWmflZnuw74EwEKlGM5O4BMdXrhG593DqJrmwBkKp1R0XGg7UZ+jrUubGBbSmdUdCZSu5GfAK9j1WegAGsWuQd6aDr2E+BVxHrhvz6cgwI8FL0HOraaTnmAVRGgAJkEKECmVc9EAlgzAQqQSYACZBKgAJnCAO26j37oRAJII0ABMglQgEzugQJkEqAAmQQoQCYBCpApGqDjfiSdSAAxsQDd64UHeCwSoF8fAhTgsdhjjWUmQILoQ+XkJ8Bj0QWVPxvsCMCrEaAAmaKX8AIU4LFIJ9LeQ40BEkQC1DU8QIrYQPqvD+egAA/​drgcaYVATQIwABcgkQAEyWc4OIJMABcgkQAEy3dwDfW+2IwCvRoACZBKgAJkEKEAmAQqQSYACZBKgAJkEKEAmc+EBMglQgEwCFCCTe6AAmQQoQCYBCpBJgAJkEqAAmQQoQCYBCpBJgAJk8kwkgEwCFCCTAAXIJEABMglQgEwCFCCTAAXIJEABMglQgEwCFCCTAAXIJEABMglQgEwCFCCTAAXIJEABMglQgEwCFCCTAAXIJEABMglQgEwCFCCTAAXIJEABMglQgEwCFCCTAAXIJEABMglQgEwCFCCTAAXIJEABMglQgEwCFCCTAAXIJEABMglQgEwCFCCTAAXIJEABMglQgEwCFCCTAAXIJEABMglQgEwCFCCTAAXIJEABMglQgEwCFCCTAAXIJEABMglQgEwCFCCTAAXIJEABMglQgEwCFCCTAAXIJEABMglQgEwCFCCTAAXIJEABMglQgEwCFCCTAAXIJEABMglQgEwCFCCTAAXIJEABMglQgEwCFCCTAAXIJEABMglQgEwCFCCTAAXIJEABMglQgEwCFCCTAAXIJEABMglQgEwCFCCTAAXIJEABMglQgEwCFCCTAAXIJEABMglQgEwCFCCTAAXIJEABMglQgEwCFCCTAAXIJEABMglQgEwCFCCTAAXIJEABMglQgEwCFCCTAAXIJEABMglQgEwCFCCTAAXIJEABMglQgEwCFCCTAAXIJEABMglQgEwCFCCTAAXIJEABMglQgEwCFCCTAAXIJEABMglQgEwCFCCTAAXIJEABMglQgEwCFCCTAAXIJEABMglQgEwCFCCTAAXIJEABMglQgEwCFCCTAAXIJEABMglQgEz/​HxJmCuQfhLFGAAAAAElFTkSuQmCC"​ title="​plot of chunk unnamed-chunk-10"​ alt="​plot of chunk unnamed-chunk-10"​ width="​672"​ /> The individual parts of the plot show the following:</​p>​
 +<​p>​The box includes the distribution of the values located in the second and third quartil, thus of the 50% of values which are closest to the mean value.</​p>​
 +<​p>​The median is depicted by the line in the box. The whiskers and representation of outliers represent the spread of the values.</​p>​
 +<​p>​The Whiskers mark the remaining values which don’t fall into the second and third quartile. The length of the whiskers is not standardizized. Often they are expanded to 1.5*the interquartile range (IQR).</​p>​
 +<​p>​The interquartile range is the range between the lowest value falling into the second quartile and the highest value falling into the third quartile.</​p>​
 +<​p>​All values which are higher than 1.5*IQR are considered as outliers and are usually marked by points over or under the whiskers, respectively.</​p>​
 +<​p>​One nice feature of the <​code>​boxplot</​code>​ function is that it can also handel the aggregation of a variable. To do that, supply the aggregation variable in the function. The following example uses the original data frame and computes the distribution of the totally affected persons across all years for each region:</​p>​
 +<pre class="​r"><​code>​boxplot((total_affected^(0.5)^3) ~ region, data = df,
 +        ylab = &​quot;​Third power of the square root of affected persons&​quot;,​
 +        main = &​quot;​Persons affected by natural disasters from 1900 to 2013&​quot;,​
 +        las=2)</​code></​pre>​
 +<​p><​img 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S5HUoE14GSBwWUnGTdSsrtUSIooORBASWnaTSDkr3SwwYtVS0CBZScpc0MooCSBwWURKDJBMohoN//​4iSSMSig5Dwt5k8iAb3PHlDOwluDAkqi0FzypBHQxXp6Cqg1KKAkCs0lTxIBXS4G/​RX7mmKaq18hFFASheaSJ4mAPhugH4/​Hrfu/​7ud8+tlY/​coF0SlEASXhNJc8KQR0OBX03nfdbxl78G3VL9CkbPQkCEJOkmYh/​eXHo+vIv9bTP+WUXfgUvIVToKDswhMSQiIB/​TX9Z+jK56EhcZj1ygWFKaCEwCQU0GfTs2uIPlui+V7z0ZA4uLd6eNsUUEJCSCigj2vf9Pz65DKmBFBACVEn0STSq+l5698wd+c60BSAAgoUJoQMJFnGNEy83y+vWaRbxjd1NiQObIESok6qhfSddD7/​85TO+4WTSCmggJL0NJc86bZy/​ph2dHIZUwIooCQ9zSVPGgH98/​ulmt8/​X89nvgZoS/​XLZUwkPc0lT6rj7O79ofRDUzQbLdUvupB++RMhMM0lT9EHKveHlCAN2qbqF2hSsgVqDqgyzNScARfSUrCAduMC6JhqW/​Urf6wooNaAakNe+LIkgqtz+5ENWqdgAXUOGRUraHP1K4QCagxk/​AUovNJPCuhJyhXQ+9B772amxCvzm6tfIRRQWziVIGpVygtj5WCaS55yBfQ6Tkd1CiptgjZXv0K4E8kW2tvIKKCxKFlAx7fN3+Qz+83Vr5BZI4Yxyk6xAtoclQgoW6DnmDru7MJbgAJaCuUK6Lvd+ef3W0oPYeL44RioLSigpVCugHZDnx/​Of0UwcfxQQG1BAS0FXQEd3oakw7Ax9LKtn+tFG0wcP4yRLcwIKFPiAF0BfWrcR/​f/​4i42xvVgFSgFVApDZAsry5iYFEcULKDTQnq5eSaCF37PWONdBZK6gAqredEm6l34p7jpCOh4Lkm3o5OTSKdgQ90eUFUo1Zti07YalCeRrqsnsyPGKz2GQ5o7bhfxSGtz9SuH0mkLcX3offtxduoYZQHtz0vSEFBn9Tywjqm5+oVgdApEsftAAT1GexmTV0FjCOjVEU35Svrm6heC0SkYhcqjgB6TYh2oyhgoBTQ+jE7BUECzUKyAzrvwFFAfaJeurehUBgU0C8XuRLpf3Ekk6aBAU/​ULD4o1FZ3agCpPVtiMgBqe4CxWQF/​n0f9wf5Bgsg6UeCecOPNaik51qAiojWVM8afH4pFMQIeNlzHmj3rc6SnxflGLVaCElewnaVAQUCPfwbgXCUkkoNO+9XgSOtmU77c3WANaBPS/​SMFoCKiJtl9ASyAhaQTU0U9k4+URfSMUUWSDNaAFBbQtVATUwuij7UROIqCvYcp+nvwONRijY68C1LCdd6YxoBrKlHRvthM5iYDeHdEE1hwpYK8C1LCdd5ax0G9VpqRbs53ISQT06naz+xOaMmGvAtSwnXeGMT1nEYmS7sx2tAUlfwAAIABJREFUIqcQ0Gej011mdMvYh7dXAWrYzju72J6ziERJd2Y7kdPsRJp12u8R1zKh2KsANZoQAgVsP68NYrtCKKDVYmMRX3HYfl6to7Kh1HJLgF34euFWzhAooGfgQvrTcBLJDDxMJIBSBdSGr9zKeRouYyoWRudBAT0HDxM5DRfSFwuj86CAnsOGFwnJsJUz3xRSg/​ULweg8rM9ZbGPDWRteJKTgw0RCaK5+IRidDtNzFtvo7IVX9aIGEgmownF2QTRXvxAVRwcZRbM8Z7ENBTQLyQTUBs3VL0S90cE0sUT9pIDmgQJK6scZga82AWwIaHNQQCumds2QMsWg4mBQQLNAAa2X+ltdQmYT660H4wWjEAsKaLW00G+VUerSTj0YhVhQQGuliX6rDAroEkYhFhTQWmG/​9Q0FdAmjEAsKaK1QNd4wFEQLCijw2aLGE5+Ovj0WOl3MvYFQQJPRXHgpoPKPlqWglxnCjyj7lAmOZiQjfngvS2Jf4BwUUPEnC5uSmU0iNS+ghVVeADbuLLoXK/​00cZsTKQT0++df/​3b+eS3xRPpZIyaSM7qwBTph9/​mLh407a26PaBYBLfCdSOUNo1FAHerXTyOVRwE9zZGAfv+kgKaAAupSvX4aqTwK6GlmAnpbD2Fcch5JTwHd/​4iyT0QPG3vhyzN8EmUBnR1F/​+bH1mfVoYDuf0TZJ6JHYQKKdgms5qZ2F/​5uSj8poAcfUfaJ6FGWgMKD0lZzM8MkUk4aE9DlT6RebAio+ProurLcHm9BAZV+sMBlTJ4fSa2UJKABT5PVFOZCevEnC1uLzRYo2SS7gPp/​LpBkAlr8S+XgUZvMBIyBklaggMYikYDW8FpjsRatp83CLxpOfg+IWSigsUgjoLPVTDkHRFPUlmfdgf5Fj9zI4UGxmAiaqhPZ783/​c4EkEdA/​v5+J8Ov1Y7esKd86+qS1lT1JKaBhmIiaCSe0oIDu4BHQuyOanZj+in1NMRTQ/​Y8o+1QE72jlFC8TTgQg85YCuoNHQGfHhzy78x+xrymmVQGVfkTXpSJwopUvHiacCEEqoIXe3poUAvpsdLqbj24l7oU3fi3v5dkCDcFE6yjACRuVJ3aWC+m38C2kn3Xa7yWexmT8Wt7LU0BDoICegVs5T0MBzXAt39WXSD6j75d5KKBnAAQRmyKzcXtr2IXPe60QnZNdnALqEhYECiiKmhc2bm8NJ5GyXitI55DLu/​8v/​EiNAOFtQkDL0zmrucllTCZQ8IsCOoHMWVBAz1Ce4ZNwIb0JVAT0MhoufabzLNCqGRNLbAKcoIBmIYmAzrdy5twM3041DA9h/​z+jd50KrD2HL7FRAHfChoCqYdXjNAJaxWEimqj4FX9ctVDADrGJuMFOUECzkEhAKzjOThUdvwzogAnQEUUTcVN1gjkRi2QCagOridPc0FFSTMwLmYJRiAUF1AQUUE0ooEsqjkLi7gO78CauVXFCG+AZ3fdjxUh31BsFbOz4vNpyEqmKa5E9Li65nSGaYKsXImRFhmVMtZ9In+NacSjPYyEmViY1Qf6Zt+lHeekTacGF9FVcKw7leSyDLdBkAKuuNKoDGu3G1FZwxThwK2eGa8WhPI9lXFpQUBs3Jl/​2D1aHdEwT+ESUucUkAsrDRGJa1ZMBG89gfKZ4ySJXpNTacFfoBd55bllAeZxdTKOKT7eNZzA+s75aITuRYGx4K578xj7QtoDyQOWINsFv7so3+MnAnpRCZ5xsOKugc2qGKaA4VgUU7flEzruQwgWRfmohAzZqmgJ6GnbhAwjIIjyj4hYuiPSPVQZs1HR2AQW+/​YoR0EYnkRAooJpAY6AUUH0v1AQUGn/​hMiac0Nu9rMjil7KAiilskgWahaeA6qMnoNAMIBfSwwTe7lo/​mxbQ0qapoZqjgOqjKKDQl3uERE4ioNWcSJ+552NEQIubpoa++UoVUIjsAmpkou58QyCNgNZymAgF9GEo++Ugkk8BTXL9wr6DN0kkoJUcZ5dfQFHtUhFQVfMqAH21Ar8fcLLfWWmjQJskE1AbpBRQpDAwRL74IT0lCijSV9Mb6bZD/​jurJcJpljFlbndOWBVQwGj2zCtOQC8rpOXT+EcKJoWA/​vmdc+J9RvECqvrNrbaILytr/​dx3uoUWqBrNhSyFgGZdOj+nfAHVpE4BHVEbZyYTzYWMLVC1j5eXSwUJKNotfyiudLCBDV9zT7MmJ8kY6D3n5qMZKauh3i58/​iYa2i3vPyM0PcXY6lPrw4av+QU08ehLmln4r08jbdDiBVR1eA7t5OYfJFRY6QDKshls+JpdQFNXXaIx0Dk8zi7cJqhdKoMOdvRFSUCXP5WADV9zC2jyL/​eyBfT+MgcMD1gVUBsL6a12k/​YciV52FuP8dyjGxvh8ZsPph5dKFtCvT9ie1SdCbYLDxmOlhoqAhpnPjY2azi6gCVzZvGIcku1EeusncL6T1SeCAhoGW6BvbNQ0BfQ0qQS0a9e+hPP2zPMfh8V7rD4RNgRUTJFdeLHJBsZAS7qxHgpobG7vhudd3gQ9ebu5v7iNCGiZk0hikzZm4VEPKKCPHGvQihXQP78vf/​179eMRFNAIJJ/​p3EZFQFM/​gxtuaGp49opTIv23X7EC+vUZsj+UAnqe9DOdSbExiaT8JVVjxXVQQMXc5QOfDsULqOoyJhk2FEYNE98P2k5UWG8vLspfPL4rxraYSEBv3frP18uWEq4DVRtnkpa10HuuXECbiHGN9daRfgawWAG9Xv769+1gHejFw6mL5h+oNzC9UbuAMsblwi68mKeA/​tc7UhsKWqGAqq4gsrpUJA7IhHZm/​Sw2xrmdpYBK6Tvvr9VL3X5O6XxS+QKqCQXUCqXGOLez7MJLeQnoMIv09WlyGVNRif+CAmqFgBjbuL3M06yzyTcK6B5XZ/​X8VTyRlDLLbGQ0gjhJDYwSjr7olM1N/​QKKJpD4y/​1tuDIB7XZePtuJt1jngl6dZUzyJU0U0D3ki6kooLrMWlLSj2g5gwAsJ8EyKODLvSYB7Q9k6gQ01uH0NwqoBI1BBwqoPu5YnvQTet7IQRc0R769WgV0ONDuKaDPhmMcBb07M0flC2jmoSOoMLvwKVBqooUURggYqoxsuEYB7WZ8/​v7n67Ob6rlGOg7063OyY3MMFKEsAQ00r0C9Aqo0SBhSGEEthZoW0L6F2AvorO99huu7Cepq6QFWHyIKaBgVCyhK/​Jq+rIjlhaKAVjmJdH1NmA8CGuslx915yu91oI2cB2rCcKkCWjnRBXStn9GCrdkC9fyoSQoBfUpmJ3CDgD6boHFe6XGbKjbVifQl6ZyaYQqoSdS68DZSSN6FX/​6kSwoB/​f75GqIcBfQe651Id1g/​KaCRTeYWsNzXL5XyBFRquMYxUC0BfY2DYnNSFNC4JusUMCNLDBSpV0BrHANddOGvccZAg6CAxjAJr0Epi7RNmCxkF1ClFKpzDPQpmV0rcRDQZ3s04Cj5SKQU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D47eggCOH0VDE3i4bIKDgwcBQ5SHr3YG4aU3JgLWnlceJqFlAH6Ma7NbLdVZg5+Q7ZQEFe4H9AVMqBwkraaj8yGEwFNG3ywYIKHYwMFR58tsD46Y0JYN5oZnHKahcQB+HanBbVFunebHe7YN1OqBe4NCIe/​7n+aGD+daQgd3+qY07Dyc/​chgKBbJdVhaKEAEFTgGEKg9Z7y6MW0AiQymE1B4UitgzFDGoX0AffZ9qp1k5r7RhW2d6oF7gMIg4pNzuqsrQWewdDQ1ddC89XRMKBbBQVBiKEAFFDgYGKg9bByuLG157ei9bQkIR/​8WrEahfQK+76eGZzYi67hAB6AUOuTRulDl6Z3loS3LrIGr180GwUIivK1+UFHJ70lMAkcoDdwOL4obfHphC8EsNZKEwsjRxTt0CejvMjeu6ufmspzxfdEAv8Pt9hv5wjFS05oP7sU0/​1AUUC4X4+07eAtW8PaTywnYDx64OMIXkXkChYAs0KXdJDfrqBPvO//​6XpN8qOx4HOgu+S7zx4KjDfT3ogthx4G1vPk11oAMKBbTbSxCKNAIqqzxwrjzkZUsCwBQSewGFIiSR1alVQMdn4Hhp2bpXcHRA4x15rLBjhaS9wCHxxrWVB0v6wOnWsd2+9xn9ZSdAh1hpS6KIgH4lVHnwEURBL1s6BI2b0Assj7mVMx3fsnf0hAjodaafx1+xg9JEPVaof6zGoaP9hxjdRtJzrArSRffa/​X2tBeFik6iAIpWndwQRchi82hJ2KBRcSJ+QSbiOiqFd+OWykl1RVDtW6Do0asdFyPuvbxfm3fvWpG0e0YJRbQEFOrmqAio8d+QBVZ7menfopQZKlYeEggKaFsnpyD6xPJhEur5aZ/​2rj69Hjcrwg2kO1h+PWverv8+DUyMkeTcWA7vlxxqqPuMEbZe1IKBA5T3U+q3gSw3kcYMSGQkFBTQ5Y8i3E9szMrn/​So9h2mkY7j46KSH8YJqjgdjbW7hucTLpFasgQ8JF9wEn0sue16yPVYiAyiov6ItHFDfdc/​GxRI6ex4mpW0A7himfjed7/​fV+sPR36FFNmgjsSdyVW/​RNOeNrJ54/​RBlaPdVQFGgodCI9sDuzRAEVVV5Ihcji5rcca6AATOTYeZyY+gX0sbsTqfvTPHNuhx3SX9N/​hq78QeGjcxXU35Qj4WxPe2vR/​QhyIj2yfbFIARVaRitEGjetc/​FNJHJa6hfQIam28qP78/​zJPhzJ7v48ri75+hSo7ZGA6rYI3pfYX4x6RkAFi1a+sQOVFWVQvi5XaE1JQANA4gYf6yyIm4lETkvlAno//​g68OQX6BDh6wdBLCIYm1NfnwaIg0cE0p1sEB8v5scWoIAeL7icXxAIKvswUInYoIgjo8V4MIeBrjiABlcUtKJEBTVRN5CBqFtCbrA21rPT9ihmbnsPyvIO9E8DBNGiLAFjOr7QYtUOy6P4FciI9tnIHOqMneijCx0DBVr7MF602MBA3FWWGvUhFtQIKvOJ91vE4VILbuG13WJp0dNqh9GAaMO+A5fxai1GBRfcP9ER66B2Q8u5c/​FAECShQeeB6dyUBReKGJTKiiWqrqk9Qq4AKOuMuQ0NKUvPDcXf99P3+kqcHeM4uALKcX+Udl/​Cie+BEemj7IrQVKH4ozqwDlVQesigIjZtcxtVekwppot7LWsOpWEC1hpmHV8fLmrghx+NI3pSDLOeP/​47LoEX38hPpoe2L0A2phCJgJ5K88qBFQUjcIAENi5sgkSFN1HpZ6xlqFVBFhq74kIBHrR/​keBz562GQ5fwKb0kMW3QvP5Fe4YSlF1qhWCM4Auyw8vBFQUjcEAFF4yZPZEQT1V73eYaqBVRrycN9nFQX9SLEx+MAr4dBlvML11IhhC55+haeSI9tX1R/​8+mBSTQU0soLWBQUuO3zqDUHxk2eyJAmKtTeeSoWUHtLHvZBXg+DLOe3oRpqhpGWlI1QiCtPvijodIXsd/​6xuAGJDBmmgCZFa8mD2putkNfDIMv5wXc1SihVQBVCEQC4F0My0Hy6QvY7MVjcgESGNNFG7S2oVkAV1+6E1Nux7KKvh5Ev54fecZkXZQG1EQqo8hIJ6EETFIkbksiYJpqovQXVCqjWkgek3v78/​nj/​JDk2E3o9jHw5P/​SOy/​j0Zy/​ZOIsscyh6sL0YidgffkTiBiUy+gLr/​LW3oFoB1VryAJhyjiV5TUjuNyW+odfDQMv5tRajyrAkoJlDMQBVXiIO5rSBuEGJjGmiidqbU6uA6i15EK+auc4083qkGd/​oa47ky/​lDFqPGw5SA5g3FAFJ5ieJ2tG4aiBuWyJAmmqi9ObUKqOKMnXCpyG1Rx13lHwwdAW/​KwZbzK72rUQvscP7+E/​HffKoIUHnYIgM0bu5V9vNNHjcskTFNNFF7M/​5/​5B/​nvMk5v2kAAAAASUVORK5CYII="​ title="​plot of chunk unnamed-chunk-11"​ alt="​plot of chunk unnamed-chunk-11"​ width="​672"​ /> The x labes are rotated by 90 degree using the las argument.</​p>​
 +</​div>​
 +<div id="​histogramm-plots"​ class="​section level5">​
 +<​h5>​Histogramm plots</​h5>​
 +<​p>​Histogramm plots show the frequency distribution of the data.</​p>​
 +<p>In the following example we will use the same aggregated data frame as above and have a look at the distribution of the affected persons.</​p>​
 +<p>A simple histogram can be compute using the <​code>​hist</​code>​ function:</​p>​
 +<pre class="​r"><​code>​hist(dfa$total_affected,​
 +     xlab = &​quot;​Affected persons&​quot;,​
 +     main = &​quot;​Persons affected by natural disasters from 1900 to 2013&​quot;​)</​code></​pre>​
 +<​p><​img 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title="​plot of chunk unnamed-chunk-12"​ alt="​plot of chunk unnamed-chunk-12"​ width="​672"​ /> As we know already from the plots above, the frequency distribution of the values is far from normaly distributed. Let’s have a look at the log transformation we used for the scatter plot:</​p>​
 +<pre class="​r"><​code>​hist(log(dfa$total_affected),​
 +     xlab = &​quot;​Logarithm of affected persons&​quot;,​
 +     main = &​quot;​Persons affected by natural disasters from 1900 to 2013&​quot;​)</​code></​pre>​
 +<​p><​img 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URoHWTikLYYpFgdoo0NopZSFMsShQGwVaO6UshCkWBWqjQGunlIUwxaJAbRRo7ZSyEKZYFKiNAq2dUhbCFIsCtVGgtVPKQphiUaA2CrR2SlkIUywK1EaB1k4pC2GKRYHaKNDaKWUhTLEoUBsFWjulLIQpFgVqo0Brp5SFMMWiQG0UaO2UshCmWBSojQKtnVIWwhSLArVRoLVTykKYYlGgNgq0dkpZCFMsCtRGgdZOKQthikWB2ijQ2illIUyxKFAbBVo7pSyEKRYFaqNAa6eUhTDFokBtFGjtlLIQplgUqI0CrZ1SFsIUiwK1UaC1U8pCmGJRoDYKtHZKWQhTLArURoHWTikLYYpFgdoo0NopZSFMsShQGwVaO6UshCkWBWqjQGunlIUwxaJAbRRo7ZSyEKZYFKiNAq2dUhbCFIsCtVGgtVPKQphiUaA2CrR2SlkIUywK1EaB1k4pC2GKRYHaKNDaKWUhTLEoUBsFWjulLIQpFgVqo0Brp5SFMMWiQG0UaO2UshCmWBSojQKtnVIWwhSLArVRoLVTykKYYlGgNgq0dkpZCFMsCtRGgdZOKQthikWB2ijQ2illIUyxKFAbBVo7pSyEKRYFaqNAa6eUhTDFokBtFGjtlLIQplgUqI0CrZ1SFsIUiwK1UaC1U8pCmGJRoDYKtHZKWQhTLArURoHWTikLYYpFgdoo0NopZSFMsShQGwVaO6UshCkWBWqjQGunlIUwxaJAbRRo7ZSyEKZYFKiNAq2dUhbCFIsCtVGgtVPKQphiUaA2CrR2SlkIUywK1EaB1k4pC2GKRYHaKNDaKWUhTLEoUBsFWjulLIQpFgVqo0Brp5SFMMWiQG0UaO2UshCmWBSojQKtnVIWwhSLArVRoLVTykKYYgkX6PEweLn/​xhRo7ZSyEKZYqgV6Oszd26EUaO2UshCmWJoF+uvrYenzX3fdAwVaO6UshCmWZIH++T5vzN/​fLj9/​+XnPXVCgtVPKQphiSRboOarLplLf7rkLCrR2SlkIUyzJAj3GL9gvO6F3vQ1KgdZOKQthiqVYoJfdzdfowtN9r+Ep0NopZSFMsRQL9LK3Gb9cP299jBR95HS4o0ABPK/​ElnocBQqgdokt9biiX8IDQMmK/​hAJAEr20YcxxXulAFCpDz6Q/​s6vIgFAwTJ+lbNtzLlPP/​I9HAB8sKwnEzku+pM3QAEoKfp0dgBQsqJPqAwAJaNAASARBQoAiShQAEhEgQJAIgoUABJRoACQiAIFgEQUKAAkokABIBEFCgCJKFAASESBAkAiChQAEqkUaNY/​+QegaLsVj0aB7j0+AHvarXpUCnTvJXCkFEYpC2GKRYE+iK2hUEpZCFMsCvRBbA2FUspCmGJRoA9iayiUUhbCFIsCfRBbQ6GUshCmWBTog9gaCqWUhTDFokAfxNZQKKUshCkWBfogtoZCKWUhTLEo0AexNRRKKQthikWBPoitoVBKWQhTLAr0QWwNhVLKQphiUaAPYmsolFIWwhSLAn0QW0OhlLIQplgU6IPYGgqllIUwxaJAH8TWUCilLIQpFgX6ILaGQillIUyxKNAHsTUUSikLYYpFgT6IraFQSlkIUywK9EFsDYVSykKYYlGgAFAfChQAElGgAJCIAgWARBQoACSiQAEgEQUKAIkoUABIRIECQCIKFAASUaAAkIgCBYBEFCgAJKJAASARBQoAiShQAEhEgQJAIgoUABJRoACQiAIFgEQUKAAkokABIBEFCgCJKFAASESBAkAiChQAElGgAJCo7gL9/​e3zX9NPv74eLj792G95HjIP07okettlWR61yHJuBlNpFHsre9lvcdLFT5Df35pLXvdbpHRxmF2e/​1UX6J/​vh2nT7raFRtRDVZiF6TSRqmydeZZuw9YYzOWHXnWDMZ4gx/​6C+nY64jA7Pf+rLtBjsLKm9VfpE/​UYL/​exxudpY5Zl7M/​D4cvPPZcq0SzMcdrKKpuM8QQ5VfuMicPs9fyvuEDbnYFxXZ2GlyLHKl9gzcN0zhU+TRvzLM2W3RbnOKGqzMOc+42rCVVZ6cRPkOY3WzOZc33PmDjMXs//​egu027EJd+D7rjlVt20vw3S636n1Fegiy2nc8TxXuAu6CHMcfgdU9+6K8QQ5Dk1zCVnXi/​g4zG7P/​1oLtG2Xf5renTpPv3aafYaqtu0oTKt56+1/​1JYkznKJMTw5g39WIhrMcUxwqmx3On6CXMINueoPs9vzv9YCbfbY34K398NN4Fhb7SzDDBe+nWpLEme57N1U9vIwEA1mVqBVjSZ+ggSTufyzqtcGcZjdnv/​1FuiL+cF141jbjo4V5rJNv1b3LH2Ps5wr27mZiQYzPk/​r25sOdE+QoHNWnkhViJ/​tH/​r8r7VAW/​bcLy9Nqvp12puHufx0CVFhgbbmLw3e+oN/​qowyH0zzmv4l+N8q9U+QYOOq+NdB/​Gz/​2Oe/​YIHW9oZObx5m3EmosnWCLE2Q4XCZOvdyVo42rrY/​hydI+Eq3uhdto/​jZ/​rHPf70Cre39nMEsTP/​CV6NA/​63aww1bi63sWOVRoIHhCRKWZrUFGj/​bP/​j5L1egl/​VX5dN0+UpxOHSyyifqlKV78d6GqfBww9ZsK5sOpK+0caYnyLxA69zQ4mf7Rz//​1Qr0XOluzjzMsf+nSIH2r6iqO9ywMx9Ml6aJVWOW8AkisAcaP9s//​PkvVqD19mf0wcvsH5WZvYSfXlHVuZ8zPyYrOIypxneKgidI/​QVaQH+KFeixzq26NYWZDtDTKNDxLf06D2myjzau84Pr8AlS/​afw8bN9h+e/​UoE2r6tqfJOtM4U5HebqK52VbzhUX6DhjlqFv9zmT5Bz3ceBxs/​2XZ7/​QgXafG25wufnQLRAg+/​YUaD7WjxBKv4m0rv1bN/​n+a9ToJV+RDESLdDwU9Hq3wOdv4SvLMzyCVLxd+GtZ/​tOz3+ZAr1sDlX3p/​kyqr69nM7ig+t+R6fSI8zmu9PBh0h1hYmfIPWejckIs9fzX6VA69shWFIt0OGsk+1xoJXt57QWW9l4GFNdYYwnSL3nA43D7Pb8VynQ8/​xVb4W1o1qg4TsStb3R1lr+NqgzjPUEmSZT1860EWa3579IgU5/​qoYCLcA8y7hx11U5g5XvwtcVxn6CVPo3keIw+z3/​RQo0/​IsoFOjullmOFe7ljOLzDFYYZuUJUudf5YzD7Pf8r7pAAWBPFCgAJKJAASARBQoAiShQAEhEgQJAIgoUABJRoACQiAIFgEQUKAAkokABIBEFCgCJKFAASESBAkAiChQAElGgAJCIAgWARBQoACSiQAEgEQUKAIkoUABIRIECQCIKFAASUaAAkIgCBYBEFCgAJKJAASARBQoAiShQAEhEgQJAIgoUABJRoACQiAIFgEQUKAAkokABIBEFCgCJKFAASESBAkAiChQAElGgAJCIAgWARBQoACSiQAEgEQUKAIkoUABIRIECQCIKFAASUaAAkIgCBYBEFCgAJKJAASARBQoAiShQAEhEgQJAIgr0KZ0Ph1fHu/​vz/​fD5r/​Zfv/​9n//​OnHz53fTpcfPl543VuuXanW9CtK3y77Y7w1CjQp5SrQI/​d3boV6OWOGi+3XeeWa/​eOV/​NToLgBBfqUMhXocLduBXpuG3F7Wafr3HLt8TYUKBxQoE/​JuUCXd+tWoKcbXpBP17nl2h0KFD4o0KdUUYG+3X6dW67doUDhgwJ9ShQoBQoPFOhTMgukewtxqqBLhRwOn/​8KmuTYXmPoxuPl8nP7c/​ceaP8W5OUeugI9Bx+Jd3dyGm99mj+UuQzt4xtvaoZLMV1nce1lmHBppgVdjf0SFeivr92Fsw+p5redVsj4n9au262xxUED0U1QPAr0KRkFOhZQf0BS9+y++NehSYYLhuf8pS/​+47BeoMewppo6+n/​fh4YbHmvZFfNlsAt0vhQrBXppu3mY8YK393mBRtfs7//​L/​zUKtL/​y8BtkedtphUz/​aVj4xept1tj/​DpfpPb4JKkCBPqW4QMcn+PAUPx8m0zGW4VP8ePjb164GrQL959ndNQX63/​sLPv39+2G86sYymAW6WAq7QKcu6sNM99yUX1Cg0TXH+//​HuED/​6XtwJ8ZtpxUSJBl/​gcyu++f7dIXu7qKboAYU6FOKC/​TYF9Xp0O0YNs/​n5qk+vvJtLmhv0/​yjrZbjYdgXNQ5j6mvg1N9t/​3bA0BPNv5r+edlcBuNdzXgpjPdAm4cfX0a/​DPf81t3oJcwfXXO42/​Dth05bl8MaeVl9lHGZxp4c11UYLV5D0U1QAwr0KUUFeqmH/​oVp/​6/​T8DRuiqOvlJfxuu1T/​Tg+060C7V/​WnubF1NXN+K9lRc2XwSjQeCmMAj2Hb702d9T/​z/​SPYUGja0axw6Wb/​lO/​Hzu/​bbBCjsMy9WsmihavoeVNUAUK9ClFBXqa9gZP7X87hrU03yO69MVQoP2dWAXa/​6dLYQz7r+NHQ32XDPezugybn6uPt44L9DgdA3AejrB/​mV3lPL4LMb9mcPjAMvbUgX3w6FGCFbK8cRQtXkPRakYNKNCnFBXo1Jfjx83B5y/​hM7t98TkU6OwzZfMwpqlAh2/​LG/​+yl+F9o0CnpYgLNPz8vLsja0f21brmeuzw5/​PyU/​p+cacA58V7mVG0eA0tb4IqUKBPaVmg4YGb7RM6qIfpn9PnSn2BDrdxKdBoGd7tAl0shVmgocsdBTuLs/​zRNc3Y4yK9hP+MHyV8mOEQhPi42C5avIYWN0EdKNCnZBTo2GVtcwT7W0OTBB865yrQ+TK8WwUaLUVcoOFVtgs0uqYRO3jgoEDbq24UaHC0wKsVzSjQ+U1QBwr0KSXsgQ57XO2R9bvtgcZLYRbo4t3EjQKN3t69dQ80fpTFwwTHVd20Bzq/​CepAgT6lrfdAh3f4Fm8Gng7L4nMu0GgZ3o0CjZfCfAm/​+Bg7upvpJfz8mve9B7r8sDzq6a4R213gRTS7QMOboAoU6FPa+hT+2O8zzT+FD57x5zwv4aNleI+bz1iKuECDZZ/​iTgc/​vUwLGl0zij0ZD5vqmz5+lGmFBC/​Zu5BRtGgNRTdBFSjQp3THcaD9EZzTM344xN69QG84DtRYCuMwpmk3tV+i6Z77Zj5PBT2/​5jL2bOn6EuyrNLptsEKmA5q6kNZxoIs1tLwJqkCBPqXtbyINb3kO3xcanuDt07r9qKM/​btwo0LZ00go0WgbjtXe8FEaBtq+C+yNCx0Pc2wuGmw8LGl0zij0aLwm/​tLR8lGD3uGvb4btPy2j2YUyzm6AGFOhTCr/​ovvji+dp34Wc3WSnQ7pPpt9QCjZbBfPNysRTW6ezCa70u7vn1PVjQ+JrjBf8af4j0t/​nJQ6LbWocxLU+MMn1hc/​UwpgM7oPWgQJ9SXKDXz8Y0PsH/​8/​dhd25ZoN03vF9TC9Rchuh9xsVSmOcDXT8Z0uuwwIflkUyvs1Vjno3pPF+65W2DAp1OFhK85RDc2CjQ6CaoAAX6lIwCXT0f6Hj8TtcX0zcj4wLtSuAluUCjZYgLNFqKlRMqd/​W/​PB3n+Hj9glrX7P+TeRhT++DBEs1vO/​sUvm/​M6crL84HGn8JHN0HxKFBcERwA+cxYDTBQoDBNRy4eow+cnhIFCgMFCtPwyfL0jydHgcJAgcIUftWbHdB3ChQmChS2M/​05Q4HCQIFizXH5QfZTo0BhoEABIBEFCgCJKFAASESBAkAiChQAElGgAJCIAgWARBQoACSiQAEgEQUKAIkoUABIRIECQCIKFAASUaAAkIgCBYBEFCgAJKJAASARBQoAiShQAEhEgQJAIgoUABJRoACQiAIFgEQUKAAkokABIBEFCgCJKFAASESBAkAiChQAElGgAJCIAgWARBQoACSiQAEgEQUKAIkoUABIRIECQCIKFAASUaAAkIgCBYBEFCgAJKJAASARBQoAiShQAEj0/​wEcucz5LRlJ/​gAAAABJRU5ErkJggg=="​ title="​plot of chunk unnamed-chunk-13"​ alt="​plot of chunk unnamed-chunk-13"​ width="​672"​ /> It obisouly results in something quite similar to a normal distribution.</​p>​
 +<​p>​Finally,​ let’s check the square root transoformation used for the box whisker plots:</​p>​
 +<pre class="​r"><​code>​hist(dfa$total_affected^0.5,​
 +     xlab = &​quot;​Square root of affected persons&​quot;,​
 +     main = &​quot;​Persons affected by natural disasters from 1900 to 2013&​quot;​)</​code></​pre>​
 +<​p><​img 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title="​plot of chunk unnamed-chunk-14"​ alt="​plot of chunk unnamed-chunk-14"​ width="​672"​ /> That does not change much, but if we increase the power of the root, the resulting value distribution becomes more and more normaly distributed (except for the values:</​p>​
 +<pre class="​r"><​code>​hist(dfa$total_affected^(0.5)^3,​
 +     xlab = &​quot;​Fourth power of the square root of affected persons&​quot;,​
 +     main = &​quot;​Persons affected by natural disasters from 1900 to 2013&​quot;​)</​code></​pre>​
 +<​p><​img 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en/learning/schools/s01/code-examples/ba-ce-09-01.txt · Last modified: 2015/09/22 16:20 (external edit)