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03_basic_data_analysis.html
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03_basic_data_analysis.html
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<meta name="author" content="Gregor Pirs, Jure Demsar and Erik Strumbelj" />
<title>Basic data analysis</title>
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<h1 class="title toc-ignore">Basic data analysis</h1>
<h3 class="author">Gregor Pirs, Jure Demsar and Erik Strumbelj</h3>
<h3 class="date">25/7/2019</h3>
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1Bzudw+x1dXWyfIzn5FY0l8OgvC0gNHEiygNwMYEEiQ3/b68HScpmk4diJPCoKrvDfPUq0Q3vyN4rbbb+tXb7mr0r5R+ZrKkrg2C8KnBETzL0ZwA0dmH4EEAYBIkfcOwRxtjhyL+gPjw09XfyW77fqrtDExMf16gIqLLwrWP/eKqjl2jQIiZSO/ThIkGPlp9DFIEAAQCoRevyssPCdppCfUeRu42A7dBkTVJ6vvWf7nfveW60v0gvXrn//ckvDYS1j8PSN7wTnOqGvEQoK4CA4JzgkNDfX6/S8Hj0+HyGuYK+KNB9NmpQZDhjSj38WlysqqqLVr1n1QbJ+1Exu/aiuwRuCIDiwIICgU5SCjlYSxCV4f7D3f7FHQMX/spe+Cbt8sM+vRFcqo6P6/acvLKwSrn1zz+ZcHLVGOtLdlWMSckfUw8saSgBHG4U7GUIME+Y3x48d5fSAbGhpg/zG9AISzmHMHe50gvO6DrJdfeWGpWCweUIX2k48/WbJq9Quac/RSFTb5eSVw4kZGxThk/MgSdohAgvxGSmoK4wOw8/OdWXT8X5S93jLymFRY/97G9RueVva2sIM3qiqrBOueeVa9ZdsxWVPiawps3KNqYIsGFJavwPhXBPw6XwOBUCoHtF3fZYdAICA1u3Y/6O37lpYWbmhEAjkxKZKEll+Z90C3lEWFs1tiUube++qhQ4evdzq8r6HFRKm+VPLddz/cZabHkIkzH8sKEWccAMqRBpZKgatI53eIUMBiFmuI5FXPYRFXD8nKliMNrC+7w44Wnn7qmabz5897HaAYEhIMW996RRF2cY26L/ugY9E36Qptf1A/n/2Cyma1DSptOI7D1GlT8+ddM2/n7KtTdAR5UEY3HJSDKV8CMDABPUcUDCC8WoeJrlEbnOPJn34+ICNJ440Prnwg2neRBA4oB+lCo8EwNe9sXpq379va2qCNIgRXXXPnm3T93kW9BthSFCMWibjjp96mPvDLgUFNWXW9yKqqqqKOHjkq+2Fvzl02dpJ17NX3ZQVLlqiBE9OCYawx0GbiD2gD0aAowCJn5eNjl7+DJ2cpzzXElKs/2//gO1vfvz8vL196i/zm9xMliXsHk/5ABeUgXaioqLjjkYce7bUvYt36tX+/KuZ8PF3+8dK+hItF36QtZd2y9dln1u/sunKKL3DlLFdOvbJu6rQrc5InJO9MTk7OJ1w5iqU6jbZVAm03LgHKDuC0AgSFAoax6oAdmYNx4wG4Ym2LxSorLCiMP3++aNbxo8eWlJaWuYXP5/Pho48/TMBxvMKnCQ8QkCDdWPXYE00lJSWMs5pCQ0Ng06YXFVG1L6nAou/TDChXcaucdZPqzS3vvl5cXOy3HjeXMHHxcRAWFqaPFEXqBXw+4F3mzttsNmiobwCzySSrrauDpsae+yx2ZeH1C7UrH35ovr/SO9JBgnRj7w97N7/z9ru9rkebNC6JfDX7ESWr8AkVOL1vheBG6GQSG//ks9/uz5vw723/zmpfWmiEo3pzs0KSJBm1m3miZt5uyObLVK5iRW+UXCwRbP3nbgU2aYOSabSvG+ZzAjr3/q2L06ukW7a8rpiSMbIHKU6dNlU/muUAVEnvCUEQRgwDie50bq8LFJSWlsZgvDH69GnX6qDxSN8XNDAVSEIs/5PNv3HZjsTJs7/X6/VSU7Op7+Pl/QyHw4Fld/9J/dcH/k+O4/jgmt8CHFTE8oDdbk9c9fgTxysrKvtUV3jgofvVi6ZYgda/0+8NZzD+VXoq4S+qAydrBN9//8PK8+fOD9uIQDabDX9Y8AftHbctVAuoc3J8zAIFAD7qhpd0BQnihbKy8iVPrXrq877UEzAMg5WPPKS+Lt0O9MUtigF14glm6PGYGzddaBTBLzkHF/yS84vcaPT/s+lK+7jx4+pmz5m9Z8H86dpQs1ZGV32pICZvyALhlVv8noARDhKEgQ//9dH+rzRf9an/wvWgrXlmtXLG2FoZrd868D4PThxgcUu2UqJrdx4+8j/Z3h/2rsw7m+fzXEUoFMI1mfO01994vXJMBNdGVe16ha7WyICyuGStI9JfHJUdg91BgjDQ3GzKeOzhx3Qk2bfxgmx2EKx6cpVyxgQH0MVblOAYRA5AhAEWOUuHCWeqjZBEFv5aLiguvjinqrJSXF5eIaupqYE2e++LKAQHB4NYLIZIUaQ2Ni4WJImJ2gkTksj4CDtgxlwp3XhUBuYCSWeuh7GBkL4jh5DE3QNP/OUDEqQXTp86/fjzyhdUfb1Prpxk0Q2LtPfceb2aW7ZZBeZ8H6wUhwHwEgELGacFXrweghP1wBHrW+08idkG0EYRWpPJDARBQHAwD3DKLgvmsSCEDSQbSD1lb5KCrRbAWiOjWy5KoaVYALTnujeW9Kgaj7tlxeDTfHmABOkD//nk0107P9sp7881sbGx5JOrs7KSWEfldOWncq+7VY0kIufriJR1ruLhqK6YdwX1g/SBPy27S5F+RXq/+iyqqqoET69ep/7iGBfotC0KCJk0MuZ1eCNkIklMfEKO5HAHCdIHcBw3Tpo8qd/zIRwOhyv3kT+54R3VefZKJZa8WgnceP8kcjAEJ9cRqS/IgOCWDndSRhqjb6GjARLEYg2417tUXypY98yzqtlzZutvv+PZh8cJDWKq/D9ZYC4c3pWscR5gY27R4mP/vBQIbq9byo1GkCBDyOFDhyWHDx3eOnPmjPz77t/wVxFWnEaV/lMJlqF+cROARd+QjycuXwpsQcEQRx5QIEGGgaNHj6WdPHlq57xr5moXXf9U1oSkVoCGnxR00ykptPXcisE3YJcGS4qu0VhDZmqqGqzSZCRHryBBhom2tjb4+af9Mte/2LhYcu7cORrplYs/mJQQBJipcAI050lp83kZ2KoGFgHOAwgZr8fCUnQ4X6ol6fi23LwLQUf2HFaEhNZI73/gr/1qlRutoGbePrLj0x0bd2z/zO8jO3nBPJg4cYI+JSVFmzwhWScZGwMRPDOJ2eoltL0JwNEoAKddCk5L+1JD7etVEWwAIlgLbBHg7AjSikVBRYON1JeUSc6dPy8ryCuQVVVVtXdkLl+xXL34psVZqLWqb6AcZIRhabVAru6MJFd3pnPgI5vDhujoaAgNDc3n8/n1oaGhgOPszmusNjOYTTUykylX2mhoFBgMPefLS6+U1q24T3F/YiLqIe8PSJB+QhA4RERGglAoJKOjo3UisYiMiYnRiUQi0mBoEHz6n+1Kssm3XR52mx3Ky8pd/+t1vrw34uPjyfvuv0955ZXSUT/wcCCgIlYfMTQYEmmgJUKhUE8QhNdmJ4vFkrj/5/2rv/1mz92VFZXD1owbGRkJS+9corpuwXVKFouFilMDBAniJygnxT9y5EjWt9/sURTkFzCvo+VDkpOTycU336iad808FRJj8CBBhoBfi35d+cV/v1h57OjxNH/c76CgIJg+4+ryGxbf+K/09LRsn0cwikGCDCGNjY0Z+Xn58vy8AmlhQaGsrKxMMND7Hx8fDympk7VTMqZopk2bpg0OCc71eYIRSJDhxGq1ZpSVlkmqq6ulNTW1ApPJJG1tbYWuqzByOGwICQkBgVCojYoSg0gs1kkSE/W8YB4SYghAgiAQDKDRvAgEA0gQBIIBJAgCwQASBIFgAAmCQDCABEEgGECCIBAMIEEQCAaQIAgEA0gQBIIBJAgCwYBPZxSuX7t+f15efufK5nf96U7lXcvuQsOvEQELykEQCAYu/znpjtZEaPn10ow+bpweOKKhXaXNWpcBtupLU29DJ+mA4KJZfgHEZS+Is2iTBhoPtu8fiI17TInH3jyERT6K79St1HXsE0Jc+S8ZhIzNGbr4EYPl8i9imc71fXNNX9NaLR3UJjqIYefyFsTWuMR/S3n2DtVyceBbsSFGBJe1IJRRlzqsCWgpHrLVTBD+IXDqIE57KrRWpAFlB8A5ACEJ+YCzmBdfNhX1e6E1X0JbypEgAc6QCKLX65f88N0Py06f0slramraj8XGxcK8eXPVt952qzI4ONhLyxLFpxqO3E3XfLcajKclQHfZkhnnAgiu1uHRiz7AIq/+T+d+3nZjBrRclFKtZRK68fCSrqHRTcdklKPJLQY8aqEauDGl3eMFY+HdVOORBTSpk4GlXABU66UV0jmxgPGv0OIxi9UQPnlbjyRbajLBUp5GWarE0JznVsSiqncrgM13O4aP/YvHRgODwZC574d9ilMnT8lLS8sENtulhRwEQgFMmDBBlym7RjNr9iwVQRBeKzlnz5xd/vPP+xXnCs/JamtqgaIowHEcYmJiYHLqZO21181Xp6en9/wNv/Hdnu83HzpwqYEjiM2GBx96QBkdE51z7Oixjd9+s0dRWFgoabO3QVh4GMyePUt7z1/uyQoNC21fTMJms7UvoHf0yLHFRUVFktaWVghiB0FSUhI5/1qZZuGihVme0t7W1pb4xc4vOpddTUlN0WdIM9rTWFxcvPy7Pd+3/x5TswkwHIPYMWP08zKv0SxctEDJdC8Gik8XbejeUXjHkttVzc0m2d4f9nqtKEdHR5PrNqyTJyZ2a91x2vnOwmwtkMd7r2SHXaEnUjbIgS3MpYpU++m6b/tc9ieueEMG/Izf4zaefdxZ/LYSWi/2sioiBljCcjWeeLfbhpfOEytosFX0NXog5u7Duh0Sf73769f//fEnit72aE9OHk8+sfoJeWxsrNu9MzQYMt7c8qYmV3em1xzsyiul+pWPrFSIo8Q9Wtc++lC9f/eu3Z338u4/L1PX1NRIf/rxZ49/k6QkCfnqplelJEkKsp97XltVVeX1Hqalp5Lr1q+bExIS4lYKsFgsmX9auqxzN69wfjh8qP6X5N8ff6L+SvOVzNvzOmnyJHL9hmdlYeFhPl3txa91kN2ar7KY5HBRW1sreHr109qK8opbux6nSt7X9EkOF6Y8CVhrBt1aRdX+vNl59ilV73JA+8rqdLlaQVV/v3mw8XbltVdez/vXPz/sVQ4XFy4UCza9uknT9Vh9fX3GM2vWavsih4vTp3WSRx95TOvKbXo7d8f2HQpvcrgoKdELPtv+meqlF15mlMNFfl6B4MXsF/c7nU4+03nNxmbY8OwGnUtUppf5+XPnBW9sekND0zRjeP3Fr0Wstra29sWe58ydmz9z1owPomOidQ31DVKXOF2X47RarKD+SK1a/9z6SyuPt5kz6Nrv3HOBiLk6IvFeJXBEJDitQDUdl9NVuxTQWizAxtymhfCU9mwYj5OrKG6kFqg2CV2xXeEWhnCGFgub6L7XICe6c2s1PGyyxokRWUBTlw4QYYCNuVWN86/QAitMT5mL5HTpR1ldm27p0n9mQdS1KiDY7cU0bNxDWdByTgCUo0f8WMwtamDzvW7l9uO+nz4/cvhIVNdjUVFi8tbb5OrxyeO1BEGQlRWVsh/3/ajIO5sncRWX7vvrfZ1bMlAUxd/02hva+vr6zoeTIAi4YfENWqk0Y2dEZEQ+2URKjh09rvhx3z6Z00l13v9Nr7+hfvudt3Th4eFe38Cu8zEMg4WLFmjnzJujZrFYZEF+4YIv//vlw62tre3n7PpSI+94kBMSEurkf5R/migZq6murpF+9ukOZUWX9YoLC89FnTh+QjFz1kzGhbULC861XzNn3hzdzJkzNVweF2prasR7v997d1lZeWd4rpfCwQMHs+ZdM89nfV1+FYTD4cCzG9ZlTcmY0nkDxo8fnzPtqmnqtU+v0/1a9GunJCf/d0rievuJxeJcMJ+XAd1lk3yMDcSktfKOhxBYPMBjFuVAzKJV0NaSCUEhus5zQyS78RDJbnBYMp3dH1DhDC1jR2FwbA6W/KSKrv1WjoVnnMDj5C9BEL/zgcHDJuQAL0HvzHtS1XmNSxbytBwiZ7T/Rjxy+haInA6e4sfH3Kr21lFoaDBkvv/u+251poSxCeQrr74s6yjXu5gwITlHNj8zO1eX+zhgGKRfkd55bw8dPJRVdL7I7c399No1WdNnTO/6AOZMnTZ1W0rq5I9Uf9/SmT7Xm3rXlxrlcsVfbvN2e1xCPrXmSdXsObNXdRxLTU3dLRaL6jf/TdUuaoccEyYkky+/9nJ6UFBQ+96HycnJOampKbqH7l+p7Zo7nvzfSXlvgrhesk+tfko5a84st7/ddddd98Gqx1dpa2pqO3/znm+/U/hSEL8WsRbfvFjVVY4OWCyW0VU/6XrMdWN1p3WXdj2iHO7ZM+0AsDd4LjIEheT4cjMYPPq6VcSUvyfhkuVLu8rRiWDKFmCPcTtEmc8Punin1eYoXDluVx5a+WBWVzm6kiHN2JLR7d7u2/ujm5DTZ0zXd5OjE9l82YruW1v/tO8nOVORJywsDLrK0SUeTfdjNyy+QdUhRweRkZE5k1Mmu8VZVtZ7S98dS+7QdJcDLm02lHvb7X9Udz12rvCcxEgaM3sLs6/4VRB2UJDXjTLS0tN03Y+VlZVfKlZxorttuUyB8+xTWropd6M/0tlveLHun53WQTfnnisovLrrZ4FQAKlpqV5bmLrjdDrjCwsK3dIxfcbVaqZrpk+/+tuun5ubm6GioqLfsvN4PZdBFYlE+Z7OFQgEboI4HI5e751AKOjxrHSQkTHFTU7Xi7agoGBqr4nuI8PWURgaGpoTHBzsdqxz45nQsTrgjnWXy14PVP5TSuepB5so/ccfQUvprUOZXn9TWVXl1meTJJF4fSg8UVVVNat7DpSQkMAYRnxC/Inux6qrqn3S+4/juMdtpblcri+C70QcJda76kVdqaurm+Wr8Ie1o9D1luyo3Lkwkh1O4EZi4uosZ/4zanC2uF/UWiygW4sVzop/KyDsCj0W+0c1Lp6r8vmee05bKpCnF1BNJ2V0ywUpWOsk4Gi8VNzzAy1m998ZKRL1a5uqqsqem31GiiIZwxCJRD0aDIzGwBo7RhBEaXh4uFu6mxqbfBb+sApCUZTbZwzv8iYIn7yNkL6rp8q3b6UNv6T1EMWF6ayEPn9W6Wy6QU5MfOJKnyTK1pBJ6T9U0g1a94aCIcZVIe4PDkdPcXsLo/ubFzz8TQIRX/btDacg/O6mC4UR7mfwxuTgE59IB+fKRMpwRE4bDiug6YQUqG6y1H0npSPnPo5Feq6Q9hlT0XJn/lo1OJrdjwdFAIRO1GGcGBLwIKAbfpGBvXZQUXWHzWG7fSbJ/r0Fo2OiexwzNZsgIiLC6zXG5uYefRWuingg4XQ6+Wazye2YQOC7ne+GTZCqqipZx/CJDuLiYz33ERDcUjxq/hZw/XNaE6nKXSq6bJscwNl5Ct14VD4oQai2RGfh8yo3OUImksS4h7OAn6rpWoRztlykfS1IbGysvr6uvrPCWnyhfSQwv69Fx4SEhHwWi+WWk5SWlsoSJYle559UdDSKuKejX3Wf4cZVZ+roz+kgUZKo9VX4w1ZJP3jgYI+N7NOvSO/9h7lkGfun2yBqgfsf0lMRrD80nZaD/ff2dMCDgUh/RQb89pYkvxfMp2RMcfvtBoMBdLrcPm/2z+FwCq6Y4t5se+jgYQXTNUeOHHELn8/ngyRJ4rOHayg4duyY22/g8rh9e476iF8FOXfuvNzhcPRoV6+qrLpVs2u32x8vJiaGnDx58u9Ndq3lt1L1BzeC0yb2GLjTPfcBjti9QorhPSuojmavTYqUvcE9X3YVq4I89CrbDBlg6cNYq37GL5NlqoOCgtyOffDeBypTsymj+7muYsWO7Z999Pqrr++3Wq2JHccXLlzo1qx7/NhxyYnjJx73FN+hg4c2dx+Oct2C6zQ4jo+4WnpVZZXHpmeDoTFD86X7czRz5gwdm81mHuXdD/wqiO60Tqp8Lruo6HzRKspJiSmKEp8+dXrjs2vXa1pbWt3OXXzzjf/BMOzSH8dpEzvPPqWhz2crnSdX1NEuUQA6RaENR98Gg9btpmH8K/e6BUhwcoEIdztE1+xRQGvlSjCXrKQKXy6jDUd/71dhuZ/bPuDQdOEOt2OW6jucZ5/c26filSt+PMTtkLN8++tgI5dciv/F2vaRv78RKYrMkf9RfqTr+ZWVlYK1T6/de67w3MqO31+qL125cYOyaMenOxSHDx2RffLxJ51SzJg14524+Li6zt9L0/D6q5tUe77d81lra+tMuLTtW+puzVefbf7b5qyucbHZbLj5lptUvf+woefrr76Rf77j88+cTmfnM1BRUbHqhewX9ppM7vWP62+4/h++jNvvdZC8s3lRa556+u8EQfwdJ3Bos/dsGUpJTalbdP2iTR2fqcov/gFtjZc+2OuBOp+thBKxEoKEAE4LgLXcPQBeEokJpJ/2CDhiVj7U//B7/4K9DpynFFs7PtKm/CyCn6EGFq8UD0/RODGWsmszrjNvzU5MlKkFgge0tVoA5AkpULa+//iwVD0YT/z+liaPRzlPLPm8M3zaoSJSlZ2tb3fetfTW/T/vr2uo/30WZEVFZdQza9ZuZbPZW10PfPe+jl9yDsj+fM+fM7g8bi6O4/UPP7JyzcYNSnXHea7//uO9D5a6/gUHB7s1q3dFce9ytVAoHLHz5T/9z/alX3/1zdKo6CiX5FBZUdnjnJmzZugmp0x+15fx+jUHuUV+s66jKdHpdHqUY/ac2brnX8xOZ7PZv8/J4MbqgXB/+7pEgZYiT3LUEanPywDvOReASLpvKQRFek+gvU5AlW+/9NbkROZisXe4l12dJqBrv5HRVTtl7Qs/uOTgJZJ42iZl+3yUXiDG3q0EILyf0HhISjee7CwCsVis+tffeE2WfkVaj8YKu93eQ47o6ChS+fxGhUuOjmOpaanbNmxcrwjnh/eIzpMcQqEA1j77jPLGxTeu6PUHDRMEcekeunKL4gvFHuVISU0hH896nLHONRD8JohIJIJ7/nKPfP1zzyonTJxQ1/37uPg48rGsx5Rrnlkt6z5mB4+SrSKmqWVY3DI18BI9RxA8jsTGP6kkpr6bDrwYzyNQ2cIC4oo35BCa2rM+ECQGLHapBo9f2jkaFk9aIccS/6ruUdxqD0sMWOL/qQnpO1JMKM3Gohdr2ydQMcFP24anZCuBHdXzO248iY17TI0JpW71hoiIiJzsF7KlD658QOW6R56CjY+PJ1fcq1C/ufVN6bjx43oMR5mSMWXbO+9tldx511K1OMpzFc71Jr5r2V3qt995WzJj5owRvbjfsrv/pLn2uvlaT/06XC4X7lhyuyb7hWyJP3b+HbJdbkmSzHSZ74ovOjoaPE3Q8UqbOaN9Vh9tB8CCALhxAGx+/4oD1vpMsFX9NiswDoAb6f16yskHS5W0vefcBTsagBejG3hrFsWHlnIpOMi+xd8Fg8GQWVNd037fOFyuK9eA8PDwfv12g6Exs7a2Fiins/1tHB0T3S7iwH6Lf+k+YcrFAw/dr7zhxhuy6+vrMwoLCuUtLS3tnaBisZhMSU3R8Hg8v611hraBRowomAQZjvRc1quaIBCDBQmCQDCABEEgGECCIBAMoEo6AsEAykEQCAaQIAgEA0gQBIIBJAgCwQASBIFgAAmCQDCABEEgGECCIBAMIEEQCAaQIAgEA0gQBIIBJAgCwQASBIFgAAmCQDCABEEgGECCIBAMIEEQCAaQIAgEA0gQBIIBJAgCwQASBIFgAAmCQDCABEEgGECCIBAMIEEQCAaQIAgEA0gQBIIBJAgCwQASBIFgAAmCQDDw/wEAAP//fK6m5bsFMuMAAAAASUVORK5CYII=" alt="drawing" width="128" /></p>
</div>
<div id="basic-data-analysis" class="section level1">
<h1>Basic data analysis</h1>
<div id="descriptive-summary-statistics" class="section level2">
<h2>Descriptive (summary) statistics</h2>
<p>Descriptive statistics are numerical summarizations of a particular data set, which can be either a representation of the entire or a sample of a population. Descriptive statistics can be broken down into measures of central tendency and measures of variability (spread).</p>
<p>Mean, also called mathematical expectation or average, is probably the most basic descriptive statistic, calculated as the sum of all values divided by the number of values. To calculate mean in R we can use the <code>mean()</code> function.</p>
<div class="sourceCode" id="cb1"><pre class="sourceCode r"><code class="sourceCode r"><a class="sourceLine" id="cb1-1" title="1"><span class="co"># load data</span></a>
<a class="sourceLine" id="cb1-2" title="2">data <-<span class="st"> </span><span class="kw">read.csv</span>(<span class="st">"./data/temperature.csv"</span>, <span class="dt">sep=</span><span class="st">";"</span>)</a>
<a class="sourceLine" id="cb1-3" title="3"></a>
<a class="sourceLine" id="cb1-4" title="4"><span class="co"># mean temperature</span></a>
<a class="sourceLine" id="cb1-5" title="5"><span class="kw">mean</span>(data<span class="op">$</span>temperature)</a></code></pre></div>
<pre><code>## [1] 12.55811</code></pre>
<p>The trimmed mean (also truncated mean) involves the calculation of the mean after discarding given parts of a sample at the high and low end. So for example the 5% trimmed mean would first discard the 5% of the lowest and the 5% of the highest values and then calculate mean of the remaining values.</p>
<div class="sourceCode" id="cb3"><pre class="sourceCode r"><code class="sourceCode r"><a class="sourceLine" id="cb3-1" title="1"><span class="co"># 5% trimmed mean</span></a>
<a class="sourceLine" id="cb3-2" title="2"><span class="kw">mean</span>(data<span class="op">$</span>temperature, <span class="dt">trim=</span><span class="fl">0.05</span>)</a></code></pre></div>
<pre><code>## [1] 12.81281</code></pre>
<p>Median represents the value that separtes the higher half of the data sample from the lower half of the data sample (50% of values in the sample are lower than the median and 50% of values in the sample are higher than the median).</p>
<div class="sourceCode" id="cb5"><pre class="sourceCode r"><code class="sourceCode r"><a class="sourceLine" id="cb5-1" title="1"><span class="co"># some data</span></a>
<a class="sourceLine" id="cb5-2" title="2">some_data <-<span class="st"> </span><span class="kw">c</span>(<span class="dv">1</span>, <span class="dv">2</span>, <span class="dv">2</span>, <span class="dv">4</span>, <span class="dv">5</span>, <span class="dv">7</span>, <span class="dv">7</span>, <span class="dv">9</span>, <span class="dv">10</span>)</a>
<a class="sourceLine" id="cb5-3" title="3"><span class="kw">median</span>(some_data)</a></code></pre></div>
<pre><code>## [1] 5</code></pre>
<p>The standard deviation is a measure that is used to quantify the amount of variation or dispersion of a set of data values. A low standard deviation indicates that the data points tend to be close to the mean of the set, while a high standard deviation indicates that the data points are spread out over a wider range of values. If data are normally distributed then we can use the <em>68–95–99.7 rule</em> to quickly assess the spread of the data – approximately 68% of the data lies in a band of <span class="math inline">\(\pm SD\)</span> from the mean, 95% lies in a band of <span class="math inline">\(\pm 2SD\)</span> from the mean and 99.7% of the data lies in a band of <span class="math inline">\(\pm 3SD\)</span> from the mean. To calculate the standard deviation in R we can use the <code>sd()</code> function.</p>
<div class="sourceCode" id="cb7"><pre class="sourceCode r"><code class="sourceCode r"><a class="sourceLine" id="cb7-1" title="1"><span class="co"># standard deviation</span></a>
<a class="sourceLine" id="cb7-2" title="2"><span class="kw">sd</span>(data<span class="op">$</span>temperature)</a></code></pre></div>
<pre><code>## [1] 13.13446</code></pre>
<p>Confidence interval (CI) represents a range of values for which we are fairly sure the true value of a certain population parameter lies in. When calculating the interval we can specify the desired degree of confidence. The example below calculates the 95% CI for the temperature. We can calculate the interval’s lower and upper bounds by using the <code>quantile()</code> function. In the example below we merge the two bounds in a proper mathematical representation of an interval by using the <code>paste0()</code> function.</p>
<div class="sourceCode" id="cb9"><pre class="sourceCode r"><code class="sourceCode r"><a class="sourceLine" id="cb9-1" title="1"><span class="co"># lower bound - discard bottom 2.5% of the data</span></a>
<a class="sourceLine" id="cb9-2" title="2">lower <-<span class="st"> </span><span class="kw">quantile</span>(data<span class="op">$</span>temperature, <span class="fl">0.025</span>)</a>
<a class="sourceLine" id="cb9-3" title="3"></a>
<a class="sourceLine" id="cb9-4" title="4"><span class="co"># upper bound - discard top 2.5% od the data</span></a>
<a class="sourceLine" id="cb9-5" title="5">upper <-<span class="st"> </span><span class="kw">quantile</span>(data<span class="op">$</span>temperature, <span class="dv">1</span> <span class="op">-</span><span class="st"> </span><span class="fl">0.025</span>)</a>
<a class="sourceLine" id="cb9-6" title="6"></a>
<a class="sourceLine" id="cb9-7" title="7"><span class="co"># merge</span></a>
<a class="sourceLine" id="cb9-8" title="8"><span class="kw">paste0</span>(<span class="st">"["</span>, lower, <span class="st">", "</span>, upper, <span class="st">"]"</span>)</a></code></pre></div>
<pre><code>## [1] "[-12.180925, 32.8248625]"</code></pre>
<p>We can use all of the functions above inside the <code>dplyr</code> framework, for example we can easily calculate mean temperature for each country independently.</p>
<div class="sourceCode" id="cb11"><pre class="sourceCode r"><code class="sourceCode r"><a class="sourceLine" id="cb11-1" title="1"><span class="co"># load the library</span></a>
<a class="sourceLine" id="cb11-2" title="2"><span class="kw">library</span>(dplyr)</a>
<a class="sourceLine" id="cb11-3" title="3"></a>
<a class="sourceLine" id="cb11-4" title="4"><span class="co"># calculate</span></a>
<a class="sourceLine" id="cb11-5" title="5">countries_t <-<span class="st"> </span>data <span class="op">%>%</span></a>
<a class="sourceLine" id="cb11-6" title="6"><span class="st"> </span><span class="kw">group_by</span>(country) <span class="op">%>%</span></a>
<a class="sourceLine" id="cb11-7" title="7"><span class="st"> </span><span class="kw">summarize</span>(<span class="dt">mean_t =</span> <span class="kw">mean</span>(temperature))</a>
<a class="sourceLine" id="cb11-8" title="8"></a>
<a class="sourceLine" id="cb11-9" title="9">countries_t</a></code></pre></div>
<pre><code>## # A tibble: 3 x 2
## country mean_t
## <fct> <dbl>
## 1 Finland 1.55
## 2 Niger 27.4
## 3 Slovenia 8.77</code></pre>
</div>
<div id="linear-regression-model" class="section level2">
<h2>Linear regression model</h2>
<p>The linear regression model (sometimes also called just the linear model) is a linear approach towards modelling the relationship between the dependent (or response) variable and one or more independent (or explanatory) variables. In this workshop we will take a look at the simplest case with only one explanatory variable, which is called simple linear regression.</p>
<p>The dependent (<span class="math inline">\(y\)</span>) and the independent variable (<span class="math inline">\(x\)</span>) are thus linked via the <span class="math inline">\(y = kx + n\)</span> equation, where <span class="math inline">\(n\)</span> represents the value of the intercept and <span class="math inline">\(k\)</span> the slope. Intercept defines the value of the dependent variable when the independent variable equals 0. The slope determines the correlation between the two variables, if slope is positive, <span class="math inline">\(x\)</span> and <span class="math inline">\(y\)</span> are positively correlated (larger <span class="math inline">\(x\)</span> results in a larger <span class="math inline">\(y\)</span>) and if slope is negative they are negatively correlated (larger <span class="math inline">\(x\)</span> results in a smaller <span class="math inline">\(y\)</span>). Usually the regression line is calculated in way that minimizes squared values of errors. On the image below data points are marked as red circles, errors as green lines and the blue line represents the calculated regression line.</p>
<div style="text-align:center">
<p><img 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" alt="drawing" width="256" /></p>
</div>
<p>In R we can use the <code>lm()</code> function to calculate the linear regression model. The example below calculates how temperature in Slovenia is correlated with the first 6 months (January to June).</p>
<div class="sourceCode" id="cb13"><pre class="sourceCode r"><code class="sourceCode r"><a class="sourceLine" id="cb13-1" title="1"><span class="co"># filter the data</span></a>
<a class="sourceLine" id="cb13-2" title="2">t_<span class="dv">6</span> <-<span class="st"> </span>data <span class="op">%>%</span></a>
<a class="sourceLine" id="cb13-3" title="3"><span class="st"> </span><span class="kw">filter</span> (country <span class="op">==</span><span class="st"> "Slovenia"</span> <span class="op">&</span><span class="st"> </span>month <span class="op"><=</span><span class="st"> </span><span class="dv">6</span>)</a>
<a class="sourceLine" id="cb13-4" title="4"></a>
<a class="sourceLine" id="cb13-5" title="5"><span class="co"># set January as month 0</span></a>
<a class="sourceLine" id="cb13-6" title="6">t_<span class="dv">6</span><span class="op">$</span>month_minusone <-<span class="st"> </span>t_<span class="dv">6</span><span class="op">$</span>month <span class="op">-</span><span class="st"> </span><span class="dv">1</span></a>
<a class="sourceLine" id="cb13-7" title="7"></a>
<a class="sourceLine" id="cb13-8" title="8">result <-<span class="st"> </span><span class="kw">lm</span>(<span class="dt">formula =</span> temperature <span class="op">~</span><span class="st"> </span>month_minusone, <span class="dt">data =</span> t_<span class="dv">6</span>)</a>
<a class="sourceLine" id="cb13-9" title="9">result</a></code></pre></div>
<pre><code>##
## Call:
## lm(formula = temperature ~ month_minusone, data = t_6)
##
## Coefficients:
## (Intercept) month_minusone
## -2.546 3.720</code></pre>
<p>The output states that the average temperature at month 0 (January) is -2.54 °C and the temperature grows by 3.72 °C each month from January to June. We can also visualize this by using some basic R programming and <code>ggplot</code>.</p>
<div class="sourceCode" id="cb15"><pre class="sourceCode r"><code class="sourceCode r"><a class="sourceLine" id="cb15-1" title="1"><span class="co"># load ggplot library</span></a>
<a class="sourceLine" id="cb15-2" title="2"><span class="kw">library</span>(ggplot2)</a>
<a class="sourceLine" id="cb15-3" title="3"></a>
<a class="sourceLine" id="cb15-4" title="4"><span class="co"># intercept is stored in</span></a>
<a class="sourceLine" id="cb15-5" title="5">intercept <-<span class="st"> </span>result<span class="op">$</span>coefficients[<span class="dv">1</span>]</a>
<a class="sourceLine" id="cb15-6" title="6"></a>
<a class="sourceLine" id="cb15-7" title="7"><span class="co"># slope is stored in </span></a>
<a class="sourceLine" id="cb15-8" title="8">slope <-<span class="st"> </span>result<span class="op">$</span>coefficients[<span class="dv">2</span>]</a>
<a class="sourceLine" id="cb15-9" title="9"></a>
<a class="sourceLine" id="cb15-10" title="10"><span class="co"># x - months</span></a>
<a class="sourceLine" id="cb15-11" title="11">x <-<span class="st"> </span><span class="kw">seq</span>(<span class="dv">1</span><span class="op">:</span><span class="dv">6</span>)</a>
<a class="sourceLine" id="cb15-12" title="12"></a>
<a class="sourceLine" id="cb15-13" title="13"><span class="co"># y - temperature</span></a>
<a class="sourceLine" id="cb15-14" title="14">y <-<span class="st"> </span>slope <span class="op">*</span><span class="st"> </span>(x <span class="op">-</span><span class="st"> </span><span class="dv">1</span>) <span class="op">+</span><span class="st"> </span>intercept</a>
<a class="sourceLine" id="cb15-15" title="15"></a>
<a class="sourceLine" id="cb15-16" title="16"><span class="co"># create data frame</span></a>
<a class="sourceLine" id="cb15-17" title="17">reg_line <-<span class="st"> </span><span class="kw">data.frame</span>(<span class="dt">x=</span>x, <span class="dt">y=</span>y)</a>
<a class="sourceLine" id="cb15-18" title="18"></a>
<a class="sourceLine" id="cb15-19" title="19"><span class="co"># plot</span></a>
<a class="sourceLine" id="cb15-20" title="20"><span class="kw">ggplot</span>() <span class="op">+</span></a>
<a class="sourceLine" id="cb15-21" title="21"><span class="st"> </span><span class="kw">geom_point</span>(<span class="dt">data=</span>t_<span class="dv">6</span>, <span class="kw">aes</span>(<span class="dt">x=</span>month, <span class="dt">y=</span>temperature), <span class="dt">alpha=</span><span class="fl">0.1</span>, <span class="dt">shape=</span><span class="dv">16</span>) <span class="op">+</span></a>
<a class="sourceLine" id="cb15-22" title="22"><span class="st"> </span><span class="kw">geom_line</span>(<span class="dt">data=</span>reg_line, <span class="kw">aes</span>(<span class="dt">x=</span>x, <span class="dt">y=</span>y), <span class="dt">size=</span><span class="dv">1</span>)</a></code></pre></div>
<p><img 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" 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