diff --git a/data/life_expectancy.csv b/data/life_expectancy.csv new file mode 100644 index 0000000..a4c053f --- /dev/null +++ b/data/life_expectancy.csv @@ -0,0 +1,11321 @@ +Country,Year,GDP,BMI,Alcohol,LifeExpectancy +Afghanistan,1970,946.5275010270169,,, +Afghanistan,1971,919.0928945463115,,, +Afghanistan,1972,747.7739455563161,,, +Afghanistan,1973,749.2379723517879,,, +Afghanistan,1974,770.7450572327659,,, +Afghanistan,1975,800.7535428302443,18.9,, +Afghanistan,1976,827.9708741724843,19.0,, +Afghanistan,1977,772.5970478840444,19.2,, +Afghanistan,1978,819.2379965935437,19.3,, +Afghanistan,1979,793.972051483133,19.4,, +Afghanistan,1980,767.5824672062388,19.5,, +Afghanistan,1981,795.1113815275083,19.6,, +Afghanistan,1982,829.5001890925149,19.8,, +Afghanistan,1983,894.1992688054698,19.9,, +Afghanistan,1984,935.4684516736528,20.0,, +Afghanistan,1985,958.8687644518586,20.1,, +Afghanistan,1986,1004.5583540404588,20.2,, +Afghanistan,1987,911.6698063018819,20.3,, 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Korea,1974,800.331119737273,,, +Democratic People's Republic of Korea,1975,866.0480411855381,21.7,, +Democratic People's Republic of Korea,1976,886.3071938881421,21.8,, +Democratic People's Republic of Korea,1977,909.3618060132404,21.9,, +Democratic People's Republic of Korea,1978,934.4395055733718,21.9,, +Democratic People's Republic of Korea,1979,960.3530329078692,22.0,, +Democratic People's Republic of Korea,1980,986.1968342414829,22.0,, +Democratic People's Republic of Korea,1981,1007.7425253468898,22.1,, +Democratic People's Republic of Korea,1982,1028.9354598777393,22.1,, +Democratic People's Republic of Korea,1983,1050.1159611353366,22.2,, +Democratic People's Republic of Korea,1984,1071.869998443225,22.2,, +Democratic People's Republic of Korea,1985,1094.6241877783095,22.3,, +Democratic People's Republic of Korea,1986,1093.7513420803111,22.4,, +Democratic People's Republic of Korea,1987,1093.3575471075903,22.4,, +Democratic People's Republic of 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Republic,1979,425.9422499784184,19.1,, +Lao People's Democratic Republic,1980,461.24509590195015,19.2,, +Lao People's Democratic Republic,1981,521.5223992033436,19.3,, +Lao People's Democratic Republic,1982,545.6870904519081,19.4,, +Lao People's Democratic Republic,1983,556.7316840827841,19.5,, +Lao People's Democratic Republic,1984,580.5947598357413,19.6,, +Lao People's Democratic Republic,1985,593.227599368373,19.6,, +Lao People's Democratic Republic,1986,604.7755043733843,19.7,, +Lao People's Democratic Republic,1987,579.1585867552344,19.8,, +Lao People's Democratic Republic,1988,551.2450276420565,19.9,, +Lao People's Democratic Republic,1989,611.5186073093612,20.0,, +Lao People's Democratic Republic,1990,634.1881137129944,20.1,, +Lao People's Democratic Republic,1991,643.1963087076989,20.2,, +Lao People's Democratic Republic,1992,660.6847805029099,20.3,, +Lao People's Democratic Republic,1993,681.3824076064432,20.4,, +Lao People's Democratic Republic,1994,716.162182116965,20.5,, 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Republic,2009,1488.9418650975717,22.1,5.25,63.3 +Lao People's Democratic Republic,2010,1584.1051227736846,22.2,,63.7 +Lao People's Democratic Republic,2011,1684.9430829556954,22.3,,64.1 +Lao People's Democratic Republic,2012,1790.6731550092568,22.4,,64.5 +Lao People's Democratic Republic,2013,1905.7798988580848,22.6,,64.8 +Lao People's Democratic Republic,2014,2020.4379172290191,22.7,,65.1 +Lao People's Democratic Republic,2015,2134.7123633341184,22.8,,65.5 +Lao People's Democratic Republic,2016,2249.700093626476,22.9,,65.8 +Lao People's Democratic Republic,2017,2367.685958766889,,, +Lao People's Democratic Republic,2018,2476.976482608449,,, +Latin America and the Caribbean,1970,4636.832177645705,,, +Latin America and the Caribbean,1971,4810.551269859975,,, +Latin America and the Caribbean,1972,5016.390025357037,,, +Latin America and the Caribbean,1973,5295.143381667934,,, +Latin America and the Caribbean,1974,5491.461094283336,,, +Latin America and the Caribbean,1975,5558.973705725337,,, +Latin America and the Caribbean,1976,5754.199118045373,,, +Latin America and the Caribbean,1977,5908.233353226769,,, +Latin America and the Caribbean,1978,6017.590114176324,,, +Latin America and the Caribbean,1979,6251.582132485423,,, +Latin America and the Caribbean,1980,6463.065595642196,,, +Latin America and the Caribbean,1981,6362.4871969049145,,, +Latin America and the Caribbean,1982,6185.712072278151,,, +Latin America and the Caribbean,1983,5918.642652847981,,, +Latin America and the Caribbean,1984,6004.291114085671,,, +Latin America and the Caribbean,1985,6045.85395042495,,, +Latin America and the Caribbean,1986,6167.085087837806,,, +Latin America and the Caribbean,1987,6244.032874365409,,, +Latin America and the Caribbean,1988,6181.276726220911,,, +Latin America and the Caribbean,1989,6125.947808737388,,, +Latin America and the Caribbean,1990,6028.831789502863,,, +Latin America and the Caribbean,1991,6138.729332502185,,, +Latin America and the Caribbean,1992,6215.172369774534,,, +Latin America and the Caribbean,1993,6354.02528625284,,, +Latin America and the Caribbean,1994,6544.536505625774,,, +Latin America and the Caribbean,1995,6507.446166520058,,, +Latin America and the Caribbean,1996,6642.106107292057,,, +Latin America and the Caribbean,1997,6884.5115709914435,,, +Latin America and the Caribbean,1998,6941.18169843093,,, +Latin America and the Caribbean,1999,6839.478945080315,,, +Latin America and the Caribbean,2000,6975.80708597696,,, +Latin America and the Caribbean,2001,6923.379202559795,,, +Latin America and the Caribbean,2002,6807.774252275732,,, +Latin America and the Caribbean,2003,6846.012836100932,,, +Latin America and the Caribbean,2004,7175.219012518687,,, +Latin America and the Caribbean,2005,7403.27318174522,,, +Latin America and the Caribbean,2006,7711.686465035394,,, +Latin America and the Caribbean,2007,8044.712941437815,,, +Latin America and the Caribbean,2008,8255.182794170416,,, +Latin America and the Caribbean,2009,7984.463498458677,,, +Latin America and the Caribbean,2010,8357.693580633977,,, +Latin America and the Caribbean,2011,8633.402789413314,,, +Latin America and the Caribbean,2012,8769.766148898862,,, +Latin America and the Caribbean,2013,8913.296634329135,,, +Latin America and the Caribbean,2014,8904.677869049574,,, +Latin America and the Caribbean,2015,8823.508962646532,,, +Latin America and the Caribbean,2016,8632.811043026028,,, +Latin America and the Caribbean,2017,8622.797852407848,,, +Latin America and the Caribbean,2018,8583.896959660533,,, +Latvia,1990,9231.041215333124,25.1,, +Latvia,1991,8317.54609773516,25.1,, +Latvia,1992,5477.9218387726105,25.2,, +Latvia,1993,4731.307736002602,25.2,, +Latvia,1994,4834.941421680098,25.2,, +Latvia,1995,4862.942233977698,25.3,, +Latvia,1996,5046.398984161058,25.3,, +Latvia,1997,5554.706662795265,25.4,, +Latvia,1998,5963.5939911821115,25.4,, +Latvia,1999,6189.419161054323,25.5,, +Latvia,2000,6601.323568189484,25.6,7.13,70.3 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and Nevis,1992,10820.820958887374,26.5,, +Saint Kitts and Nevis,1993,11433.791951914303,26.6,, +Saint Kitts and Nevis,1994,11900.53477741513,26.7,, +Saint Kitts and Nevis,1995,12404.937809615018,26.8,, +Saint Kitts and Nevis,1996,13005.782375912953,26.9,, +Saint Kitts and Nevis,1997,13770.420615647907,27.0,, +Saint Kitts and Nevis,1998,13587.034833254735,27.2,, +Saint Kitts and Nevis,1999,13898.148806186746,27.3,, +Saint Kitts and Nevis,2000,15126.209951568902,27.4,8.82, +Saint Kitts and Nevis,2001,15741.745948234135,27.6,9.28, +Saint Kitts and Nevis,2002,15757.18101299201,27.7,8.95, +Saint Kitts and Nevis,2003,14945.135489068898,27.8,9.27, +Saint Kitts and Nevis,2004,15348.46726650951,27.9,9.87, +Saint Kitts and Nevis,2005,16650.724250620387,28.1,11.1, +Saint Kitts and Nevis,2006,17000.311139011672,28.2,11.81, +Saint Kitts and Nevis,2007,16945.370169376492,28.3,10.97, +Saint Kitts and Nevis,2008,17830.606853335612,28.4,10.53, +Saint Kitts and Nevis,2009,16963.189458660712,28.6,9.92, 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Lucia,1982,4253.203015840767,23.9,, +Saint Lucia,1983,4432.782305374424,24.1,, +Saint Lucia,1984,4671.45037782132,24.2,, +Saint Lucia,1985,4944.548070933017,24.4,, +Saint Lucia,1986,5676.352024782411,24.5,, +Saint Lucia,1987,5723.0472932151115,24.7,, +Saint Lucia,1988,6415.496554641224,24.8,, +Saint Lucia,1989,6765.1229599572525,25.0,, +Saint Lucia,1990,6852.045420273118,25.1,, +Saint Lucia,1991,6795.124491345505,25.3,, +Saint Lucia,1992,7200.171849685441,25.4,, +Saint Lucia,1993,7136.574212670303,25.6,, +Saint Lucia,1994,7177.1179416042505,25.8,, +Saint Lucia,1995,7170.032520698602,25.9,, +Saint Lucia,1996,7573.128638385826,26.1,, +Saint Lucia,1997,7442.918661127521,26.3,, +Saint Lucia,1998,7807.535324826357,26.5,, +Saint Lucia,1999,7883.954919811001,26.7,, +Saint Lucia,2000,7768.478375465639,26.9,12.8,72.2 +Saint Lucia,2001,7412.315953752165,27.1,12.92,72.3 +Saint Lucia,2002,7354.938664204771,27.2,13.15,72.3 +Saint Lucia,2003,7653.78705775844,27.4,13.78,72.1 +Saint Lucia,2004,8209.707982112199,27.6,12.58,72.2 +Saint Lucia,2005,7992.705666814985,27.8,12.87,72.5 +Saint Lucia,2006,9161.138817188094,28.0,13.02,73.2 +Saint Lucia,2007,9232.360123478316,28.2,12.59,73.8 +Saint Lucia,2008,9548.525378639237,28.4,12.05,74.2 +Saint Lucia,2009,9288.130063958544,28.6,11.06,74.6 +Saint Lucia,2010,9225.721526540907,28.7,,74.7 +Saint Lucia,2011,9527.345695709746,28.9,,75.0 +Saint Lucia,2012,9432.79254727561,29.1,,74.9 +Saint Lucia,2013,9201.383646173834,29.4,,75.0 +Saint Lucia,2014,9283.626719574353,29.6,,75.1 +Saint Lucia,2015,9262.256146201245,29.8,,75.4 +Saint Lucia,2016,9506.021438988266,30.0,,75.6 +Saint Lucia,2017,9698.325381236296,,, +Saint Lucia,2018,9731.171800478158,,, +Saint Vincent and the Grenadines,1970,2117.037508837838,,, +Saint Vincent and the Grenadines,1971,2156.4750362563004,,, +Saint Vincent and the Grenadines,1972,2683.548083590038,,, +Saint Vincent and the Grenadines,1973,2358.664188504046,,, +Saint Vincent and the Grenadines,1974,2126.908227170693,,, +Saint Vincent and the Grenadines,1975,1943.5500651345392,22.7,, +Saint Vincent and the Grenadines,1976,2122.3769678837593,22.8,, +Saint Vincent and the Grenadines,1977,2160.0301327119173,22.9,, +Saint Vincent and the Grenadines,1978,2347.288157782225,23.1,, +Saint Vincent and the Grenadines,1979,2403.5100684436647,23.2,, +Saint Vincent and the Grenadines,1980,2439.781080041129,23.3,, +Saint Vincent and the Grenadines,1981,2530.4365079005734,23.4,, +Saint Vincent and the Grenadines,1982,2622.535430331848,23.5,, +Saint Vincent and the Grenadines,1983,2659.2086081195125,23.6,, +Saint Vincent and the Grenadines,1984,2813.9860715818563,23.7,, +Saint Vincent and the Grenadines,1985,2967.0465680141547,23.8,, +Saint Vincent and the Grenadines,1986,3112.0503248115297,23.9,, +Saint Vincent and the Grenadines,1987,3105.348467481967,24.0,, +Saint Vincent and the Grenadines,1988,3521.5860674603255,24.1,, +Saint Vincent and the Grenadines,1989,3553.0901329263847,24.2,, +Saint Vincent and the Grenadines,1990,3686.321193142058,24.4,, +Saint Vincent and the Grenadines,1991,3718.087342225556,24.5,, +Saint Vincent and the Grenadines,1992,3948.818331274408,24.6,, +Saint Vincent and the Grenadines,1993,4034.9479205608372,24.7,, +Saint Vincent and the Grenadines,1994,3952.010218042844,24.8,, +Saint Vincent and the Grenadines,1995,4247.234970838125,24.9,, +Saint Vincent and the Grenadines,1996,4318.096955005849,25.0,, +Saint Vincent and the Grenadines,1997,4479.101838661097,25.1,, +Saint Vincent and the Grenadines,1998,4709.560724763046,25.2,, +Saint Vincent and the Grenadines,1999,4910.611704603753,25.4,, +Saint Vincent and the Grenadines,2000,4998.689461857488,25.5,5.13,70.9 +Saint Vincent and the Grenadines,2001,5081.846752442492,25.6,5.07,70.9 +Saint Vincent and the Grenadines,2002,5390.013779692407,25.7,5.21,70.9 +Saint Vincent and the Grenadines,2003,5791.996014504302,25.8,5.47,70.9 +Saint Vincent and the Grenadines,2004,6022.069069737354,25.9,5.77,70.9 +Saint Vincent and the Grenadines,2005,6166.817009490531,26.0,6.37,71.2 +Saint Vincent and the Grenadines,2006,6642.532274588309,26.1,6.22,71.5 +Saint Vincent and the Grenadines,2007,6868.341909729996,26.3,6.2,71.9 +Saint Vincent and the Grenadines,2008,6985.220688604824,26.4,7.17,72.0 +Saint Vincent and the Grenadines,2009,6845.036948342962,26.5,6.98,72.2 +Saint Vincent and the Grenadines,2010,6617.960610854251,26.6,,72.1 +Saint Vincent and the Grenadines,2011,6586.177416811704,26.7,,72.1 +Saint Vincent and the Grenadines,2012,6670.364720807873,26.8,,71.9 +Saint Vincent and the Grenadines,2013,6780.813266581518,26.9,,71.8 +Saint Vincent and the Grenadines,2014,6848.026584482275,27.0,,71.9 +Saint Vincent and the Grenadines,2015,6920.87807380804,27.1,,72.3 +Saint Vincent and the Grenadines,2016,7032.160616203604,27.2,,72.0 +Saint Vincent and the Grenadines,2017,7078.708844765205,,, +Saint Vincent and the Grenadines,2018,7206.633115069976,,, +Samoa,1970,2291.183785093338,,, +Samoa,1971,2560.310147175664,,, +Samoa,1972,2567.7000142580687,,, +Samoa,1973,2725.910194516304,,, +Samoa,1974,2413.16868618679,,, +Samoa,1975,2303.5573251427722,26.4,, +Samoa,1976,2501.6356693455136,26.6,, +Samoa,1977,2468.3596649360184,26.8,, +Samoa,1978,2609.637096346617,27.0,, +Samoa,1979,2940.6693950916656,27.2,, +Samoa,1980,2748.4860159509453,27.4,, +Samoa,1981,2485.498164491777,27.6,, +Samoa,1982,2445.211874293899,27.8,, +Samoa,1983,2440.55359764044,28.0,, +Samoa,1984,2458.0687025510847,28.2,, +Samoa,1985,2542.5657845380333,28.3,, +Samoa,1986,2673.2665481507265,28.5,, +Samoa,1987,2679.1553521723636,28.7,, +Samoa,1988,2633.7889171480615,28.8,, +Samoa,1989,2721.9187811053675,29.0,, +Samoa,1990,2587.7798512221084,29.2,, +Samoa,1991,2509.8534250541074,29.3,, +Samoa,1992,2482.236117402391,29.5,, +Samoa,1993,2558.819762776363,29.6,, +Samoa,1994,2470.489164713717,29.7,, +Samoa,1995,2598.639514399241,29.9,, 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Marino,1997,55234.44123569942,,, +San Marino,1998,58772.94885295167,,, +San Marino,1999,63387.67880240777,,, +San Marino,2000,64023.64193291091,,, +San Marino,2001,66762.86766112011,,, +San Marino,2002,66069.34216568431,,, +San Marino,2003,67721.92846994221,,, +San Marino,2004,69919.68434716684,,, +San Marino,2005,70605.10354940005,,, +San Marino,2006,72398.52934348026,,, +San Marino,2007,74023.00993305529,,, +San Marino,2008,74327.58950499097,,, +San Marino,2009,64021.34287652751,,, +San Marino,2010,60238.04361365058,,, +San Marino,2011,53793.17027893303,,, +San Marino,2012,49060.0509535973,,, +San Marino,2013,46920.69388817794,,, +San Marino,2014,45920.75063861407,,, +San Marino,2015,42643.354537855026,,, +San Marino,2016,43389.56682941154,,, +San Marino,2017,43434.43937267879,,, +San Marino,2018,43777.08143615153,,, +Sao Tome and Principe,1970,1169.4612398263075,,, +Sao Tome and Principe,1971,1203.5440810265757,,, +Sao Tome and Principe,1972,1206.0479476742303,,, +Sao Tome and Principe,1973,1201.6608593221367,,, +Sao Tome and Principe,1974,1243.5447834865997,,, +Sao Tome and Principe,1975,1279.120425399611,20.5,, +Sao Tome and Principe,1976,1264.4546768040154,20.6,, +Sao Tome and Principe,1977,1513.9952287177332,20.7,, +Sao Tome and Principe,1978,1510.9921875663144,20.8,, +Sao Tome and Principe,1979,1708.6932857902054,20.9,, +Sao Tome and Principe,1980,1649.0520645768602,21.0,, +Sao Tome and Principe,1981,1449.0794207299525,21.1,, +Sao Tome and Principe,1982,1468.3204728497308,21.2,, +Sao Tome and Principe,1983,1389.191100046105,21.4,, +Sao Tome and Principe,1984,1283.0351612397592,21.5,, +Sao Tome and Principe,1985,1374.3553616932575,21.6,, +Sao Tome and Principe,1986,1264.023490899255,21.7,, +Sao Tome and Principe,1987,1195.5475271610426,21.9,, +Sao Tome and Principe,1988,1186.8164444408296,22.0,, +Sao Tome and Principe,1989,1192.0898021051953,22.1,, +Sao Tome and Principe,1990,1138.1602357322504,22.2,, +Sao Tome and Principe,1991,1125.8851663361902,22.4,, 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Principe,2009,1357.151997899511,24.4,4.34,66.6 +Sao Tome and Principe,2010,1384.1694027329625,24.4,,67.0 +Sao Tome and Principe,2011,1410.6174251914056,24.5,,67.3 +Sao Tome and Principe,2012,1421.8096980236005,24.6,,67.7 +Sao Tome and Principe,2013,1460.8762836212782,24.6,,68.1 +Sao Tome and Principe,2014,1519.443558194301,24.7,,68.3 +Sao Tome and Principe,2015,1544.940211282106,24.7,,68.5 +Sao Tome and Principe,2016,1586.0949412789762,24.8,,68.7 +Sao Tome and Principe,2017,1628.5521890335863,,, +Sao Tome and Principe,2018,1632.5768287837273,,, +Saudi Arabia,1970,21336.253276901472,,, +Saudi Arabia,1971,24599.09117371173,,, +Saudi Arabia,1972,28856.984125794108,,, +Saudi Arabia,1973,34129.12634622877,,, +Saudi Arabia,1974,37741.13333426414,,, +Saudi Arabia,1975,32680.66314302318,24.8,, +Saudi Arabia,1976,36612.56966213215,25.0,, +Saudi Arabia,1977,37275.96780857122,25.1,, +Saudi Arabia,1978,33535.943471598235,25.3,, +Saudi Arabia,1979,35516.16086297907,25.4,, +Saudi Arabia,1980,35380.452069937506,25.5,, +Saudi Arabia,1981,33898.25193200959,25.7,, +Saudi Arabia,1982,25215.514712324697,25.8,, +Saudi Arabia,1983,19879.524873899347,25.9,, +Saudi Arabia,1984,17857.532216643005,26.0,, +Saudi Arabia,1985,15248.768498658166,26.2,, +Saudi Arabia,1986,16969.655554648296,26.3,, +Saudi Arabia,1987,15129.566097876306,26.4,, +Saudi Arabia,1988,16403.893519755944,26.5,, +Saudi Arabia,1989,15700.233747577578,26.6,, +Saudi Arabia,1990,17453.4135236814,26.7,, +Saudi Arabia,1991,19427.85902894048,26.8,, +Saudi Arabia,1992,19606.39316154691,26.9,, +Saudi Arabia,1993,18815.772834302545,26.9,, +Saudi Arabia,1994,18452.230092452322,27.0,, +Saudi Arabia,1995,18070.40476154518,27.1,, +Saudi Arabia,1996,18162.044771942114,27.2,, +Saudi Arabia,1997,18009.310612488567,27.3,, +Saudi Arabia,1998,18178.046396008584,27.4,, +Saudi Arabia,1999,17137.723664496058,27.5,, +Saudi Arabia,2000,17690.665093215688,27.6,0.05,72.7 +Saudi Arabia,2001,17032.366323144437,27.6,0.05,72.9 +Saudi Arabia,2002,16094.712705243293,27.6,0.06,73.0 +Saudi Arabia,2003,17384.797282795684,27.7,0.06,73.2 +Saudi Arabia,2004,18219.866950026124,27.7,0.06,73.3 +Saudi Arabia,2005,18683.37755630233,27.8,0.05,73.3 +Saudi Arabia,2006,18669.62257332223,27.8,0.08,73.3 +Saudi Arabia,2007,18496.325299000087,27.9,0.09,73.4 +Saudi Arabia,2008,19117.93122020503,27.9,0.08,73.5 +Saudi Arabia,2009,18202.699162902325,28.0,0.09,73.6 +Saudi Arabia,2010,18568.374604780056,28.1,,73.8 +Saudi Arabia,2011,19813.26032408828,28.1,,74.0 +Saudi Arabia,2012,20249.80866980482,28.2,,74.2 +Saudi Arabia,2013,20175.554670624806,28.3,,74.3 +Saudi Arabia,2014,20327.67890396653,28.3,,74.4 +Saudi Arabia,2015,20627.932782344928,28.4,,74.6 +Saudi Arabia,2016,20503.379245186567,28.5,,74.8 +Saudi Arabia,2017,19946.95778103058,,, +Saudi Arabia,2018,20024.84767764794,,, +Senegal,1970,1055.3106628932621,,, +Senegal,1971,1022.7726291224561,,, +Senegal,1972,1055.551886364377,,, +Senegal,1973,967.0985502655444,,, 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Africa,1978,4845.727084261014,24.4,, +South Africa,1979,4904.995840625809,24.4,, +South Africa,1980,5096.148068896385,24.5,, +South Africa,1981,5227.233660519088,24.6,, +South Africa,1982,5066.033610718998,24.7,, +South Africa,1983,4837.190291323636,24.7,, +South Africa,1984,4948.476579270126,24.8,, +South Africa,1985,4763.2774123832705,24.9,, +South Africa,1986,4647.911620895276,25.0,, +South Africa,1987,4634.617650439948,25.1,, +South Africa,1988,4718.769718475221,25.1,, +South Africa,1989,4720.27695993762,25.2,, +South Africa,1990,4593.980685113472,25.3,, +South Africa,1991,4436.477356631064,25.4,, +South Africa,1992,4234.6035175730485,25.5,, +South Africa,1993,4182.883575079807,25.5,, +South Africa,1994,4219.128079658419,25.6,, +South Africa,1995,4259.0584816959,25.7,, +South Africa,1996,4357.79480893796,25.8,, +South Africa,1997,4395.462208018984,25.9,, +South Africa,1998,4347.928740333241,25.9,, +South Africa,1999,4384.584422209648,26.0,, +South Africa,2000,4502.850308306421,26.1,7.7,59.0 +South Africa,2001,4564.753243439148,26.2,7.56,57.9 +South Africa,2002,4672.746475157827,26.3,7.47,56.8 +South Africa,2003,4752.0346829646005,26.4,7.32,55.8 +South Africa,2004,4908.330984429931,26.5,7.2,54.9 +South Africa,2005,5103.781370810767,26.6,7.17,54.4 +South Africa,2006,5322.110523351218,26.6,7.3,54.4 +South Africa,2007,5535.4472510216165,26.7,7.5,54.8 +South Africa,2008,5636.3852878996,26.8,7.46,55.6 +South Africa,2009,5473.001654917043,26.9,7.25,56.8 +South Africa,2010,5557.89208557988,26.9,,58.0 +South Africa,2011,5653.572693081563,27.0,,59.1 +South Africa,2012,5688.042878355575,27.1,,60.7 +South Africa,2013,5736.622866902293,27.1,,61.9 +South Africa,2014,5750.7730276640805,27.2,,62.8 +South Africa,2015,5730.934174366495,27.2,,63.3 +South Africa,2016,5669.734570520812,27.3,,63.6 +South Africa,2017,5669.033884722371,,, +South Africa,2018,5636.264139618829,,, +South America,1970,4756.49615264951,,, +South America,1971,4970.984192971997,,, +South America,1972,5171.949164030553,,, +South America,1973,5481.442095315808,,, +South America,1974,5723.755756839486,,, +South America,1975,5778.543264513554,,, +South America,1976,6020.9558517868145,,, +South America,1977,6198.585929858947,,, +South America,1978,6230.444572033549,,, +South America,1979,6447.037793424165,,, +South America,1980,6648.794203942909,,, +South America,1981,6344.581247214265,,, +South America,1982,6159.1465120185185,,, +South America,1983,5905.5233424910675,,, +South America,1984,5982.896589259062,,, +South America,1985,6040.220260161394,,, +South America,1986,6333.441734213174,,, +South America,1987,6442.884977554333,,, +South America,1988,6352.713396542085,,, +South America,1989,6211.723346058816,,, +South America,1990,6006.243792646524,,, +South America,1991,6139.36846650513,,, +South America,1992,6211.540895351608,,, +South America,1993,6383.300306948361,,, +South America,1994,6587.131542335547,,, +South America,1995,6687.536989997544,,, +South America,1996,6763.22824476774,,, +South America,1997,6981.47733325144,,, +South America,1998,6951.685944737656,,, +South America,1999,6737.646747418116,,, +South America,2000,6832.812848301177,,, +South America,2001,6779.320819071063,,, +South America,2002,6630.586784503039,,, +South America,2003,6675.1397105742735,,, +South America,2004,7080.404100807899,,, +South America,2005,7370.763616377722,,, +South America,2006,7708.006184777591,,, +South America,2007,8145.25874880161,,, +South America,2008,8456.180563383818,,, +South America,2009,8255.674067340256,,, +South America,2010,8710.809514634037,,, +South America,2011,9047.735772490494,,, +South America,2012,9173.418473192176,,, +South America,2013,9372.376601612139,,, +South America,2014,9302.478566527248,,, +South America,2015,9095.839418181964,,, +South America,2016,8744.786321371816,,, +South America,2017,8690.841732022047,,, +South America,2018,8596.245649140366,,, +South Sudan,2008,1802.5348284287436,,,54.3 +South Sudan,2009,1775.609728581388,,,55.0 +South Sudan,2010,1692.1843688578888,,,55.9 +South Sudan,2011,1638.2615411271593,,,56.4 +South Sudan,2012,798.922719500462,,,57.0 +South Sudan,2013,1013.8128762396769,,,57.1 +South Sudan,2014,1210.5531011839914,,,57.3 +South Sudan,2015,1233.3344974947522,,,58.2 +South Sudan,2016,1224.111866124964,,,58.6 +South Sudan,2017,1206.9347677954759,,, +South Sudan,2018,1240.5613576825237,,, +South-Eastern Asia,1970,745.3473059337838,,, +South-Eastern Asia,1971,780.378041510398,,, +South-Eastern Asia,1972,813.6291893806207,,, +South-Eastern Asia,1973,865.2548395223231,,, +South-Eastern Asia,1974,892.4356732026779,,, +South-Eastern Asia,1975,909.0175487277872,,, +South-Eastern Asia,1976,964.9899263890218,,, +South-Eastern Asia,1977,1019.4982766778139,,, +South-Eastern Asia,1978,1068.108645914027,,, +South-Eastern Asia,1979,1115.9563896283396,,, +South-Eastern Asia,1980,1160.781336584885,,, +South-Eastern Asia,1981,1200.7028604506143,,, +South-Eastern Asia,1982,1222.9159933056123,,, +South-Eastern Asia,1983,1268.9465570147665,,, +South-Eastern Asia,1984,1291.3286494908941,,, +South-Eastern Asia,1985,1269.8609898926584,,, +South-Eastern Asia,1986,1290.8363636434867,,, +South-Eastern Asia,1987,1337.9957707615224,,, +South-Eastern Asia,1988,1411.9595140611334,,, +South-Eastern Asia,1989,1506.9999006058986,,, +South-Eastern Asia,1990,1598.7647186198121,,, +South-Eastern Asia,1991,1677.6868456011084,,, +South-Eastern Asia,1992,1761.4127386172931,,, +South-Eastern Asia,1993,1861.0612427860087,,, +South-Eastern Asia,1994,1972.1862514773134,,, +South-Eastern Asia,1995,2091.4831574432537,,, +South-Eastern Asia,1996,2208.796876237027,,, +South-Eastern Asia,1997,2261.4721025152776,,, +South-Eastern Asia,1998,2064.7316646086383,,, +South-Eastern Asia,1999,2107.0326741645035,,, +South-Eastern Asia,2000,2200.2233997108942,,, +South-Eastern Asia,2001,2232.1935104910294,,, +South-Eastern Asia,2002,2313.7431388374857,,, +South-Eastern Asia,2003,2412.7568449990476,,, +South-Eastern Asia,2004,2537.9045230018232,,, +South-Eastern Asia,2005,2649.814265113059,,, +South-Eastern Asia,2006,2775.429086038497,,, +South-Eastern Asia,2007,2925.4250570547074,,, +South-Eastern Asia,2008,3014.630241985922,,, +South-Eastern Asia,2009,3035.2512306949193,,, +South-Eastern Asia,2010,3235.2811641206945,,, +South-Eastern Asia,2011,3351.000198974969,,, +South-Eastern Asia,2012,3507.366517807416,,, +South-Eastern Asia,2013,3639.456165290535,,, +South-Eastern Asia,2014,3759.226862071229,,, +South-Eastern Asia,2015,3888.9465228428744,,, +South-Eastern Asia,2016,4027.4518246158186,,, +South-Eastern Asia,2017,4191.6435722758815,,, +South-Eastern Asia,2018,4356.514761830967,,, +Southern Africa,1970,4328.570110873426,,, +Southern Africa,1971,4397.159686396183,,, +Southern Africa,1972,4359.727080131519,,, +Southern Africa,1973,4443.462983786669,,, +Southern Africa,1974,4594.902084264851,,, +Southern Africa,1975,4559.3836646535165,,, +Southern Africa,1976,4550.801469625354,,, +Southern Africa,1977,4440.274371761114,,, +Southern Africa,1978,4466.128369014246,,, +Southern Africa,1979,4518.325245450188,,, +Southern Africa,1980,4691.629392447461,,, +Southern Africa,1981,4809.028321697299,,, +Southern Africa,1982,4665.817888456896,,, +Southern Africa,1983,4460.803197880259,,, +Southern Africa,1984,4560.664052634262,,, +Southern Africa,1985,4398.502696236404,,, +Southern Africa,1986,4303.586635467381,,, +Southern Africa,1987,4295.831285266096,,, +Southern Africa,1988,4374.3650234965835,,, +Southern Africa,1989,4400.153619106053,,, +Southern Africa,1990,4295.34325417458,,, +Southern Africa,1991,4166.8018565168295,,, +Southern Africa,1992,3995.680025287777,,, +Southern Africa,1993,3946.466242224514,,, +Southern Africa,1994,3979.48908260067,,, +Southern Africa,1995,4023.337115838254,,, +Southern Africa,1996,4117.5933434526805,,, +Southern Africa,1997,4161.973243771624,,, +Southern Africa,1998,4118.354333576211,,, +Southern Africa,1999,4163.715591708479,,, +Southern Africa,2000,4270.109698434243,,, +Southern Africa,2001,4324.352695364538,,, +Southern Africa,2002,4433.012297864904,,, +Southern Africa,2003,4515.852530403074,,, +Southern Africa,2004,4671.930267346427,,, +Southern Africa,2005,4855.1375587898865,,, +Southern Africa,2006,5070.673659256275,,, +Southern Africa,2007,5279.382875458557,,, +Southern Africa,2008,5381.144446916822,,, +Southern Africa,2009,5219.812574841167,,, +Southern Africa,2010,5318.230921345924,,, +Southern Africa,2011,5421.048299297548,,, +Southern Africa,2012,5469.452730129336,,, +Southern Africa,2013,5543.353353567213,,, +Southern Africa,2014,5572.259155578844,,, +Southern Africa,2015,5557.267376594871,,, +Southern Africa,2016,5510.202923609192,,, +Southern Africa,2017,5507.716906725923,,, +Southern Africa,2018,5482.8523334456095,,, +Southern Asia,1970,596.3865174159155,,, +Southern Asia,1971,612.1187537331945,,, +Southern Asia,1972,621.259147504078,,, +Southern Asia,1973,638.3258885161467,,, +Southern Asia,1974,648.4915304925784,,, +Southern Asia,1975,657.7026434196947,,, +Southern Asia,1976,698.6559491105531,,, +Southern Asia,1977,696.9562055612308,,, +Southern Asia,1978,669.9001553143002,,, +Southern Asia,1979,614.8164737069253,,, +Southern Asia,1980,580.166488079151,,, +Southern Asia,1981,581.2623135055647,,, +Southern Asia,1982,616.177030474809,,, +Southern Asia,1983,649.4144591204425,,, +Southern Asia,1984,639.4590219064745,,, +Southern Asia,1985,651.584094635134,,, +Southern Asia,1986,642.3598527350459,,, +Southern Asia,1987,646.1650037638973,,, +Southern Asia,1988,662.3834837578323,,, +Southern Asia,1989,682.2581374786873,,, +Southern Asia,1990,714.2662537841502,,, +Southern Asia,1991,728.342953305315,,, +Southern Asia,1992,748.9533677872213,,, +Southern Asia,1993,753.2391154658043,,, +Southern Asia,1994,767.9417302617519,,, +Southern Asia,1995,798.4540937310962,,, +Southern Asia,1996,837.2242547655204,,, +Southern Asia,1997,848.8249756557835,,, +Southern Asia,1998,872.0968077220416,,, +Southern Asia,1999,913.0430673239391,,, +Southern Asia,2000,932.9963698605961,,, +Southern Asia,2001,951.0182716888332,,, +Southern Asia,2002,978.7683099944898,,, +Southern Asia,2003,1033.296311353321,,, +Southern Asia,2004,1084.5608729013718,,, +Southern Asia,2005,1139.3218649509024,,, +Southern Asia,2006,1202.0993776466335,,, +Southern Asia,2007,1272.3221949385356,,, +Southern Asia,2008,1286.5170300450668,,, +Southern Asia,2009,1342.694069189114,,, +Southern Asia,2010,1418.3966853069521,,, +Southern Asia,2011,1465.597492109549,,, +Southern Asia,2012,1495.104502410493,,, +Southern Asia,2013,1551.6405387337554,,, +Southern Asia,2014,1633.0272816460226,,, +Southern Asia,2015,1712.33041427765,,, +Southern Asia,2016,1832.6540752490946,,, +Southern Asia,2017,1928.4317699768462,,, +Southern Asia,2018,2003.1622245419312,,, +Southern Europe,1970,11640.28711807634,,, +Southern Europe,1971,11955.682863771468,,, +Southern Europe,1972,12490.04858574937,,, +Southern Europe,1973,13281.881759010954,,, +Southern Europe,1974,13819.172972998458,,, +Southern Europe,1975,13591.109773108448,,, +Southern Europe,1976,14272.712392909774,,, +Southern Europe,1977,14602.249226012425,,, +Southern Europe,1978,14976.546813353812,,, +Southern Europe,1979,15478.130096662228,,, +Southern Europe,1980,15821.540143727374,,, +Southern Europe,1981,15808.473643554396,,, +Southern Europe,1982,15821.39725535305,,, +Southern Europe,1983,15894.512883813315,,, +Southern Europe,1984,16210.319767382716,,, +Southern Europe,1985,16562.664549436788,,, +Southern Europe,1986,17000.00107183622,,, +Southern Europe,1987,17551.20601283779,,, +Southern Europe,1988,18249.41283404433,,, +Southern Europe,1989,18916.096571002367,,, +Southern Europe,1990,19148.01828226461,,, +Southern Europe,1991,19358.441234966016,,, +Southern Europe,1992,19354.03608726031,,, +Southern Europe,1993,19057.826925651003,,, 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Europe,2016,24153.12304591516,,, +Southern Europe,2017,24729.132198413623,,, +Southern Europe,2018,25156.317795693507,,, +Spain,1970,11408.242016835513,,, +Spain,1971,11804.97954154877,,, +Spain,1972,12620.923225319113,,, +Spain,1973,13446.718134859359,,, +Spain,1974,14039.329495970678,,, +Spain,1975,13956.988788656314,24.8,, +Spain,1976,14260.096193530406,24.8,, +Spain,1977,14508.893053759575,24.9,, +Spain,1978,14572.665319059892,25.0,, +Spain,1979,14443.638085720657,25.0,, +Spain,1980,14640.719649248958,25.1,, +Spain,1981,14515.906274444644,25.1,, +Spain,1982,14605.405323778206,25.2,, +Spain,1983,14785.146103117098,25.3,, +Spain,1984,14981.391378011765,25.3,, +Spain,1985,15270.932108430161,25.4,, +Spain,1986,15718.338098149838,25.4,, +Spain,1987,16548.145610012718,25.5,, +Spain,1988,17353.654169466485,25.5,, +Spain,1989,18154.17880030149,25.6,, +Spain,1990,18800.306789358307,25.6,, +Spain,1991,19231.595200965916,25.7,, +Spain,1992,19355.247191784958,25.7,, 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Nam,1989,517.4372337939054,19.1,, +Viet Nam,1990,532.3239272743598,19.2,, +Viet Nam,1991,552.291709200058,19.2,, +Viet Nam,1992,587.7979249957448,19.3,, +Viet Nam,1993,622.8006267612343,19.4,, +Viet Nam,1994,665.4167451546335,19.5,, +Viet Nam,1995,716.6478918636726,19.5,, +Viet Nam,1996,771.6515144161586,19.6,, +Viet Nam,1997,823.0369415381551,19.7,, +Viet Nam,1998,859.5317313407008,19.8,, +Viet Nam,1999,890.0708010780403,19.9,, +Viet Nam,2000,940.0909524024496,19.9,0.71,73.3 +Viet Nam,2001,994.5541613529829,20.0,0.76,73.6 +Viet Nam,2002,1054.623248175693,20.1,0.82,73.9 +Viet Nam,2003,1121.4918196477397,20.2,0.93,74.3 +Viet Nam,2004,1194.960483211334,20.3,1.13,74.5 +Viet Nam,2005,1273.3455335477795,20.4,1.19,74.7 +Viet Nam,2006,1349.5637875556479,20.6,1.35,74.9 +Viet Nam,2007,1432.2057741434924,20.7,1.51,75.0 +Viet Nam,2008,1498.8385668787619,20.8,1.78,75.2 +Viet Nam,2009,1564.3474610651315,20.9,1.97,75.3 +Viet Nam,2010,1648.262268600804,21.0,,75.4 +Viet Nam,2011,1733.3116777952007,21.2,,75.5 +Viet Nam,2012,1805.361482179083,21.3,,75.7 +Viet Nam,2013,1883.308718941013,21.5,,75.8 +Viet Nam,2014,1975.0788947843253,21.6,,76.0 +Viet Nam,2015,2085.101637740856,21.7,,76.2 +Viet Nam,2016,2191.820175620006,21.9,,76.3 +Viet Nam,2017,2317.368962683102,,, +Viet Nam,2018,2456.791164107548,,, +Western Africa,1970,1194.2632771209614,,, +Western Africa,1971,1297.7870764041832,,, +Western Africa,1972,1338.9955846790592,,, +Western Africa,1973,1366.4139474369174,,, +Western Africa,1974,1456.1032712021251,,, +Western Africa,1975,1425.3103217709242,,, +Western Africa,1976,1477.0862546956405,,, +Western Africa,1977,1535.15442164662,,, +Western Africa,1978,1484.83764655826,,, +Western Africa,1979,1426.2004934047595,,, +Western Africa,1980,1395.4049399539433,,, +Western Africa,1981,1351.8584266002197,,, +Western Africa,1982,1253.3371241919008,,, +Western Africa,1983,1115.4845465914493,,, +Western Africa,1984,1076.9021337064382,,, +Western Africa,1985,1107.1890743031186,,, +Western Africa,1986,1090.1920165466045,,, +Western Africa,1987,1089.9998467150494,,, +Western Africa,1988,1127.8695475273428,,, +Western Africa,1989,1129.700406253608,,, +Western Africa,1990,1191.6154650761698,,, +Western Africa,1991,1169.6316896816504,,, +Western Africa,1992,1178.6397706067662,,, +Western Africa,1993,1135.6774742693772,,, +Western Africa,1994,1102.0760247672215,,, +Western Africa,1995,1089.9726588553906,,, +Western Africa,1996,1110.1141808841849,,, +Western Africa,1997,1117.3138906375916,,, +Western Africa,1998,1124.8131873792402,,, +Western Africa,1999,1116.8027077386055,,, +Western Africa,2000,1129.1431998409062,,, +Western Africa,2001,1158.7266302357064,,, +Western Africa,2002,1257.8881406706466,,, +Western Africa,2003,1301.8708780865222,,, +Western Africa,2004,1362.7939244813422,,, +Western Africa,2005,1406.162403358306,,, +Western Africa,2006,1445.8712368496267,,, +Western Africa,2007,1491.5445525413559,,, +Western 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+Zimbabwe,2017,1479.2993751831827,,, +Zimbabwe,2018,1529.0274304770946,,, diff --git a/src/lecture1.ipynb b/src/lecture1.ipynb new file mode 100644 index 0000000..1c18b31 --- /dev/null +++ b/src/lecture1.ipynb @@ -0,0 +1,1003 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Lecture 1\n", + "## Machine Learning Examples" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "lines_to_next_cell": 2 + }, + "outputs": [], + "source": [ + "IRdisplay::display_html('')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Organization of this Course" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "IRdisplay::display_html('')" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "lines_to_next_cell": 0 + }, + "source": [ + "## The Life Expectancy Dataset\n", + "Run the following cell with `Shift + Enter` to watch the video." + ] + }, + { + "cell_type": "code", + "execution_count": 90, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "IRdisplay::display_html('')" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "lines_to_next_cell": 0 + }, + "source": [ + "In this section you will load the life expectancy dataset,\n", + "look at it and produce some plots.\n", + "You will learn about the `R` functions `file.path`, `read.csv`, `str`, `?`,\n", + "`<-`, `$`, `:`, `c`, `pdf`, `par`, `plot`.\n", + "\n", + "Let us load the life expectancy dataset from the csv file.\n", + "You can run the following cell by clicking it and pressing `Shift + Enter`." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "data <- read.csv(file.path(\"..\", \"data\", \"life_expectancy.csv\"))\n", + "str(data)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "- We used the function `file.path`, to generate a valid path\n", + " in an operating system independent way.\n", + "- We used the function `read.csv` and assign the output to the variable `data`.\n", + "- In `R` the it is common to use `<-` for (left-)assignment,\n", + " but `=` can also be used.\n", + "- On the second line we used the function `str`\n", + " to look at the names and data types of the columns of `data`;\n", + " if you just want to extract the names, you can use the function `names`.\n", + "- In `R` the dot `.` does not have any special meaning\n", + " and can be used in any variable or function name.\n", + "- `R` has usually excellent documentation. You can access it with `?`, e.g." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "lines_to_next_cell": 0 + }, + "outputs": [], + "source": [ + "?read.csv" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "?\"<-\"" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "lines_to_next_cell": 0 + }, + "source": [ + "Let us actually look at the data." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "lines_to_next_cell": 0 + }, + "outputs": [], + "source": [ + "data$LifeExpectancy" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "- The output consists of a list with the life expectancies\n", + " measured in different countries and in different years.\n", + "- We can access the data in the columns of `data` by using the extraction\n", + " operator `$`. Type `?\"$\"` in the empty field below and have a look at the\n", + " examples at the bottom of the documentation.\n", + "- We could have also accessed this data with `data[,6]`. Try it out." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Let us continue to explore the data.\n", + "First we look at rows 30 to 40." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "lines_to_next_cell": 0 + }, + "outputs": [], + "source": [ + "data[30:40,]" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "lines_to_next_cell": 0 + }, + "source": [ + "Do you wonder what `NA` means? Look it up with `?NA`.\n", + "\n", + "With the following command we look at rows 13, 33, 41 and 72." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "data[c(13, 33, 41, 72),]" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "lines_to_next_cell": 0 + }, + "source": [ + "The combine function `c` is important to know.\n", + "You may want to have a look at its documentation\n", + "or play with some examples, like" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "x <- c(1, 2, 3)\n", + "y <- c(4, 5, 6)\n", + "x + y\n", + "c(x, y)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Before you move on to the next video, you find in the cell below\n", + "the code to generate the figures used in the slides.\n", + "The first and the last line are commented out,\n", + "such that the plots are shown in this notebook\n", + "instead of being printed to the pdf.\n", + "Use the documentation, if you want to know more\n", + "about the usage of the functions `pdf`, `par` and `plot`." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# pdf(\"life_expectancy_example_plots.pdf\", width = 5.8, height = 2.8)\n", + "par(mfcol = c(1, 3), cex = .7)\n", + "plot(data$GDP, data$LifeExpectancy, xlab = \"GDP per capita [USD]\",\n", + " ylab = \"Life Expectancy [Years]\",\n", + " xlim = c(0, 100000))\n", + "plot(data$BMI, data$LifeExpectancy, xlab = \"BMI [kg/m^2]\",\n", + " ylab = \"Life Expectancy [Years]\")\n", + "plot(data$Year, data$LifeExpectancy, ylab = \"Life Expectancy [Years]\",\n", + " xlab = \"Year\", xlim = c(1999, 2016))\n", + "# dev.off()" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "lines_to_next_cell": 0 + }, + "source": [ + "## Error Decomposition and Parametric versus Non-parametric Methods" + ] + }, + { + "cell_type": "code", + "execution_count": 91, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "IRdisplay::display_html('')" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "lines_to_next_cell": 0 + }, + "source": [ + "### Artificial Data Generation Process\n", + "\n", + "With the following code we define the custom function `f`." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "lines_to_next_cell": 0 + }, + "outputs": [], + "source": [ + "f <- function(x) {x^2}" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "lines_to_next_cell": 0 + }, + "source": [ + "You may want to look at the documentation `?\"function\"`,\n", + "evaluate `f` at different points or create your own function." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "f(3)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "lines_to_next_cell": 0 + }, + "source": [ + "In the slides you saw data generated with the following function `myfunc`." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "myfunc <- function(x) {sin(2*x) + 2*(x - 0.5)^3 - 0.5*x}" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "lines_to_next_cell": 0 + }, + "source": [ + "We will generate `N = 60` data points." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "lines_to_next_cell": 0 + }, + "outputs": [], + "source": [ + "set.seed(12)\n", + "N <- 60\n", + "x <- sort(runif(N))\n", + "error <- .06*rnorm(N)\n", + "y <- myfunc(x) + error\n", + "par(mfcol = c(1, 1))\n", + "plot(x, y)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "In the first line we set the pseudo-random number generator seed to 12.\n", + "This means that we will obtain the same pseudo-random numbers,\n", + "every time we run the code above.\n", + "The functions `runif` and `rnorm` generate uniformly and normally distributed\n", + "pseudo-random numbers, respectively. And the function `sort`, well, does the\n", + "obvious :)\n", + "\n", + "You may want to convince yourself that\n", + "running the cell above always gives the same data.\n", + "What happens, if you remove the first line or replace it by `set.seed(123)`?\n", + "\n", + "If you feel you don't understand something in the code above,\n", + "it would be a good idea to insert a cell below\n", + "(you can use e.g. the + button above) and experiment a bit." + ] + }, + { + "cell_type": "markdown", + "metadata": { + "lines_to_next_cell": 0 + }, + "source": [ + "### Parametric Method\n", + "\n", + "As an example of a parametric method to estimate the function `myfunc`,\n", + "we will fit a linear function to the data." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "lines_to_next_cell": 0 + }, + "outputs": [], + "source": [ + "linear.fit <- lm(y ~ x)\n", + "plot(x, y)\n", + "curve(myfunc, 0, 1, col = 'blue', add = TRUE, lw = 2)\n", + "abline(linear.fit, col = \"dark green\", lw = 2)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "lines_to_next_cell": 0 + }, + "source": [ + "The cryptically looking `linear.fit <- lm(y ~ x)` means simply:\n", + "\"fit a linear model with response `y` and predictor `x`\n", + "and assign the result to variable `linear.fit`\".\n", + "The functions `curve` and `abline` plot the true function\n", + "and the fitted line.\n", + "\n", + "### Non-Parametric Method\n", + "\n", + "As an example of a non-parametric method to estimate the function `myfunc`,\n", + "we define the `kNN` method with three mandatory arguments and one optional\n", + "argument with default value `k = 2`." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "lines_to_next_cell": 0 + }, + "outputs": [], + "source": [ + "kNN <- function(x0, x, y, k = 2) { # test point x0, predictor values x, response values y, default k = 2 nearest neighbours\n", + " d = abs(x - x0) # compute all distances between the test point and the data\n", + " o = order(d) # compute the order of the distances (smallest to largest)\n", + " mean(y[o[1:k]]) # take the average response of the k nearest neighbours\n", + "}" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "If you feel comfortable with the `kNN` function you can skip this paragraph and\n", + "move to the next cell. Otherwise, create a new cell below and experiment a bit,\n", + "e.g. `tmp.x <- c(5, 2, 3, 1)`, `tmp.x0 <- 1.4`, `abs(tmp.x - tmp.x0)`,\n", + "`order(tmp.x)`, `kNN(tmp.x0, tmp.x, c(1, 2, 3, 4), k = 1)`." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "lines_to_next_cell": 0 + }, + "outputs": [], + "source": [ + "grid <- seq(0, 1, length.out = 1000)\n", + "y.hat <- sapply(grid, kNN, x, y, k = 1)\n", + "plot(x, y)\n", + "curve(myfunc, 0, 1, col = 'blue', add = TRUE, lw = 2)\n", + "lines(grid, y.hat, col = 'red', lw = 2)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "lines_to_next_cell": 0 + }, + "source": [ + "With the `seq` function we generated 100 evenly spaced points in the interval\n", + "[0, 1]. The `sapply` function lets us apply the function kNN to all values of\n", + "grid; from the third argument onward, the `sapply` function passes the arguments\n", + "to the function `kNN`, i.e. you can change the `k` to 50, for example, if you\n", + "want to see the figure of the slide on the curse of dimensionality.\n", + "It is highly recommendable to execute the cell above for different values of `k`\n", + "between 1 and 50 and observe how well the curve fits the data.\n", + "We call kNN with `k = 1` a flexible method, because it can fit very rough\n", + "data with many jumps and kNN with `k = 50` an inflexible method, because it can\n", + "fit only rather smooth data.\n", + "We will later assess more formally the kNN method with different values of `k`.\n", + "If you want to better understand the `sapply` function, it may be worthwile to\n", + "experiment a bit, e.g. `tmp.f <- function(x, y) { x + y }; tmp.x <- c(1, 2, 3);\n", + "sapply(tmp.x, tmp.f, 2)` or look at its documentation.\n", + "\n", + "Take a little moment to think about the definitions of the reducible and the\n", + "irreducible error as well as parametric and non-parametric methods.\n", + "When you are ready, move over to [the quiz](https://moodle.epfl.ch).\n", + "\n", + "## Assessing Model Accuracy" + ] + }, + { + "cell_type": "code", + "execution_count": 92, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + " " + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "IRdisplay::display_html(' ')" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "lines_to_next_cell": 0 + }, + "source": [ + "### Assessing Model Accuracy with Artificial Data\n", + "Let us generate a test set with the same generative process as above." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "set.seed(42)\n", + "N.test <- 10^3\n", + "x.test <- sort(runif(N.test))\n", + "error.test <- 0.06 * rnorm(N.test)\n", + "y.test <- myfunc(x.test) + error.test" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "lines_to_next_cell": 0 + }, + "source": [ + "Now we compute the training error for the linear model. To do so we first\n", + "compute the predicted responses `y.pred`." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "lines_to_next_cell": 0 + }, + "outputs": [], + "source": [ + "y.pred <- predict(linear.fit, data.frame(x = x))\n", + "1/length(y) * sum((y - y.pred)^2)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "lines_to_next_cell": 0 + }, + "source": [ + "and the test error for the linear model" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "lines_to_next_cell": 0 + }, + "outputs": [], + "source": [ + "y.test.pred <- predict(linear.fit, data.frame(x = x.test))\n", + "1/length(y.test) * sum((y.test - y.test.pred)^2)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "lines_to_next_cell": 0 + }, + "source": [ + "The predict function takes as first argument some fitted model, like the\n", + "`linear.fit` we obtained above. As a second argument it expects a `data.frame`\n", + "with some values in a column called `x`.\n", + "\n", + "Let us do the same with the kNN method." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "lines_to_next_cell": 0 + }, + "outputs": [], + "source": [ + "y.pred.kNN <- sapply(x, kNN, x, y, k = 5)\n", + "1/length(y) * sum((y - y.pred.kNN)^2)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "y.test.pred.kNN <- sapply(x.test, kNN, x, y, k = 5)\n", + "1/length(y.test) * sum((y.test - y.test.pred.kNN)^2)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "lines_to_next_cell": 0 + }, + "source": [ + "As expected, the kNN method has a much lower training and test error than the\n", + "linear method.\n", + "\n", + "Let us now investigate how the training and test errors depend on the choice of\n", + "`k`. To do so, we will define the following `assess.kNN` function, and evaluate\n", + "it with different `k`." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "lines_to_next_cell": 0 + }, + "outputs": [], + "source": [ + "assess.kNN <- function(k, data.train, data.test) {\n", + " x <- data.train$x\n", + " y <- data.train$y\n", + " x.test <- data.test$x\n", + " y.test <- data.test$y\n", + " y.pred <- sapply(x, kNN, x, y, k = k)\n", + " error.train <- 1/length(y) * sum((y - y.pred)^2)\n", + " y.test.pred <- sapply(x.test, kNN, x, y, k = k)\n", + " error.test <- 1/length(y.test) * sum((y.test - y.test.pred)^2)\n", + " c(error.train, error.test)\n", + "}" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "lines_to_next_cell": 0 + }, + "source": [ + "This function takes `k`, a training set and a test set as input and returns both\n", + "the training and the test error.\n", + "\n", + "We will evaluate this function now for different values of `k`." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "lines_to_next_cell": 0 + }, + "outputs": [], + "source": [ + "ks <- seq(1, 20)\n", + "errors.kNN <- sapply(ks, assess.kNN, data.frame(x, y), data.frame(x = x.test, y = y.test))\n", + "plot(ks, errors.kNN[1,], col = \"red\", ylab = \"MSE\", xlab = \"k\", type = \"b\")\n", + "points(ks, errors.kNN[2,], col = \"blue\", type = \"b\")\n", + "abline(h = 0.0036, lty = 2)\n", + "legend(\"bottomright\", legend = c(\"training error\", \"test error\", \"irreducible error\"),\n", + " col = c(\"red\", \"blue\", \"black\"), lty = c(1, 1, 2))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We repeat here three important observations we made saw already in the video:\n", + "1. The test error is always above the irreducible error.\n", + "2. The very flexible method with `k=1` can perfectly fit the training data\n", + "(zero training error) but its test error is higher than the one of a less\n", + "flexible method, with `k = 5` for example.\n", + "3. Training and test error increase with decreasing flexibility of the method." + ] + }, + { + "cell_type": "markdown", + "metadata": { + "lines_to_next_cell": 0 + }, + "source": [ + "### Assessing Model Accuracy with Real Data\n", + "\n", + "For real dataset we cannot easily generate an additional test set, typically.\n", + "Common practice is therefore to split the dataset into two parts.\n", + "We will do this for the life expectancy dataset.\n", + "For now, we will only look at the GDP as input and the life expectancy as\n", + "output." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "lines_to_next_cell": 0 + }, + "outputs": [], + "source": [ + "data1 <- na.omit(data[, c(\"GDP\", \"LifeExpectancy\")])" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "lines_to_next_cell": 0 + }, + "source": [ + "The function `na.omit` removes all rows where either the BMI or the life\n", + "expectancy is not available (na).\n", + "\n", + "Now we split into training and test set." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "lines_to_next_cell": 0 + }, + "outputs": [], + "source": [ + "set.seed(199)\n", + "idx.train <- sample(nrow(data1), nrow(data1)/2)\n", + "data1.train <- data1[idx.train,]\n", + "data1.test <- data1[-idx.train,]" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "lines_to_next_cell": 0 + }, + "source": [ + "There are `n = nrow(data1)` data points in total. The first line above samples\n", + "`n/2` indices from the indices 1 to n (without replacement). In the second and\n", + "third line we extract every row with index occurring in `idx.train` or its\n", + "complement `-idx.train` to form the training set `data.train` and the test set\n", + "`data1.test`.\n", + "\n", + "Next we fit a linear model to the training set, define a function to compute\n", + "the MSE and compute the training and test error." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "fit <- lm(LifeExpectancy ~ GDP, data1.train)\n", + "mse <- function(fit, data) {\n", + " 1/nrow(data) * sum((data$LifeExpectancy - predict(fit, data))^2)\n", + "}\n", + "c(mse(fit, data1.train), mse(fit, data1.test))" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "lines_to_next_cell": 0 + }, + "source": [ + "Interestingly, the training error is higher than the test error. This is an\n", + "indication that the model is not flexible enough. This we can also see in the\n", + "plot." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "plot(data1)\n", + "abline(fit, col = 'dark green', lw = 2)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "lines_to_next_cell": 0 + }, + "source": [ + "Instead of linear fits, we could use a quadratic fit." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "lines_to_next_cell": 0 + }, + "outputs": [], + "source": [ + "q.fit <- lm(LifeExpectancy ~ poly(GDP, 2), data1.train)\n", + "c(mse(q.fit, data1.train), mse(q.fit, data1.test))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We used the function `poly(GDP, 2)` to form a polynomial of degree 2, i.e.\n", + "$$\\beta_0 + \\beta_1 \\mathrm{GDP} + \\beta_2 \\mathrm{GDP}^2$$.\n", + "Still the training error is higher than the test error, but they both decreased.\n", + "How does the plot look like?" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "lines_to_next_cell": 0 + }, + "outputs": [], + "source": [ + "plot(data1)\n", + "grid <- seq(min(data1$GDP), max(data1$GDP), length.out = 1000)\n", + "lines(grid, predict(q.fit, data.frame(GDP = grid)), col = 'orange', lw = 2)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "lines_to_next_cell": 0 + }, + "source": [ + "Hm, also a quadratic model does not seem ideal.\n", + "Let us move on to polynomials of arbitrary degree.\n", + "To do so we create the function `poly.fit` that takes the degree `d` and\n", + "training and test sets as input, and returns the training error, the test error\n", + "and the fit object." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "lines_to_next_cell": 0 + }, + "outputs": [], + "source": [ + "poly.fit <- function(d, data.train, data.test) {\n", + " fit <- lm(LifeExpectancy ~ poly(GDP, d), data.train)\n", + " c(mse(fit, data.train), mse(fit, data.test), fit)\n", + "}\n", + "ds <- 1:14\n", + "results.poly <- sapply(ds, poly.fit, data1.train, data1.test)\n", + "plot(ds, results.poly[1,], col = \"red\", ylab = \"MSE\", xlab = \"d\", type = \"b\")\n", + "points(ds, results.poly[2,], col = \"blue\", type = \"b\")\n", + "legend(\"topright\", legend = c(\"training error\", \"test error\"),\n", + " col = c(\"red\", \"blue\"), lty = c(1, 1))" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "lines_to_next_cell": 0 + }, + "source": [ + "Here, a polynomial of degree `d = 1` is the least flexible method and it\n", + "underfits the data, while the polynomial with degree `d = 14` is the most\n", + "flexible method we considered and it overfits the data. We see again the\n", + "typical U-shaped curve of the test error: first it decreases with increasing\n", + "flexibility, but at some point it starts to increase again. In contrast, the\n", + "training error decreases continually.\n", + "\n", + "Let us look at how well the best performing polynomial (with degree `d = 11`)\n", + "fits the data:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "lines_to_next_cell": 0 + }, + "outputs": [], + "source": [ + "plot(data1)\n", + "grid <- seq(min(data1$GDP), max(data1$GDP), length.out = 1000)\n", + "lines(grid, predict(lm(LifeExpectancy ~ poly(GDP, 11), data1.train),\n", + " data.frame(GDP = grid)), col = 'orange', lw = 2)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "lines_to_next_cell": 0 + }, + "source": [ + "In my opinion, this does not look like coming very close to the true generating\n", + "function. The downswing for very high GDPs looks wrong and also the wiggles for\n", + "the high GDPs where there is only little data does not look convincing. Maybe we\n", + "have to try our luck with other methods.\n", + "\n", + "Take a little moment to think about the definitions of the test and training\n", + "error and the flexibility of methods.\n", + "When you are ready, move over to [the quiz](https://moodle.epfl.ch).\n", + "\n", + "## Exercises\n", + "\n", + "**Q1.** For each of parts (a) through (d), indicate whether we would generally\n", + "expect the performance of a flexible statistical learning method to be better or\n", + "worse than an inflexible method. Justify your answer.\n", + "\n", + "a) The sample size $n$ is extremely large, and the number of predictors $p$ is small?\n", + "\n", + "b) The number of predictors $p$ is extremely large, and the number of\n", + "observations $n$ is small ?\n", + "\n", + "c) The relationship between the predictors and response is highly non-linear?\n", + "\n", + "d) The variance of the error terms, i.e. $\\sigma^2 = \\mathrm{var}(\\epsilon)$ is extremely high?\n", + "\n", + "**Q2.** Describe the differences between a parametric and a non-parametric\n", + "machine learning approach. What are the advantages of a parametric approach (as\n", + "opposed to a nonparametric approach)? What are its disadvantages?\n", + "\n", + "**Q3.** In this exercise you will look at a [Parkinsons Telemonitoring Data\n", + "Set](https://archive.ics.uci.edu/ml/datasets/Parkinsons+Telemonitoring).\n", + "Navigate to that page to find some information about the dataset (if you click\n", + "on the link in the previous sentence it will typically open a new browser tab).\n", + "You can load the data with the following command." + ] + }, + { + "cell_type": "code", + "execution_count": 93, + "metadata": { + "lines_to_next_cell": 0 + }, + "outputs": [], + "source": [ + "data.parkinsons <- read.csv(\"https://archive.ics.uci.edu/ml/machine-learning-databases/parkinsons/telemonitoring/parkinsons_updrs.data\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "In order to make progress in progamming in R I recommend you to refrain from\n", + "copy-pasting as much as possible.\n", + "\n", + "We will only focus on the PPE feature and the total UPDRS as a response here.\n", + "\n", + "a) Plot the PPE feature against the total UPDRS response.\n", + "\n", + "b) Create a training and a test set.\n", + "\n", + "c) Fit kNN to the training data for different values of k and compute the\n", + "training and the test error. For which value of k do you neither see\n", + "underfitting nor overfitting?\n", + "\n", + "d) Estimate an upper bound for the irreducible error of this dataset." + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "R", + "language": "R", + "name": "ir" + }, + "language_info": { + "codemirror_mode": "r", + "file_extension": ".r", + "mimetype": "text/x-r-source", + "name": "R", + "pygments_lexer": "r", + "version": "4.0.2" + } + }, + "nbformat": 4, + "nbformat_minor": 4 +}