diff --git a/live-demo-olga/data/brozek.csv b/live-demo-olga/data/brozek.csv deleted file mode 100644 index 5c0b4a21..00000000 --- a/live-demo-olga/data/brozek.csv +++ /dev/null @@ -1,253 +0,0 @@ -brozek,siri,density,age,weight,height,adipos,free,neck,chest,abdom,hip,thigh,knee,ankle,biceps,forearm,wrist -12.6,12.3,1.0708,23,154.25,67.75,23.7,134.9,36.2,93.1,85.2,94.5,59,37.3,21.9,32,27.4,17.1 -6.9,6.1,1.0853,22,173.25,72.25,23.4,161.3,38.5,93.6,83,98.7,58.7,37.3,23.4,30.5,28.9,18.2 -24.6,25.3,1.0414,22,154,66.25,24.7,116,34,95.8,87.9,99.2,59.6,38.9,24,28.8,25.2,16.6 -10.9,10.4,1.0751,26,184.75,72.25,24.9,164.7,37.4,101.8,86.4,101.2,60.1,37.3,22.8,32.4,29.4,18.2 -27.8,28.7,1.034,24,184.25,71.25,25.6,133.1,34.4,97.3,100,101.9,63.2,42.2,24,32.2,27.7,17.7 -20.6,20.9,1.0502,24,210.25,74.75,26.5,167,39,104.5,94.4,107.8,66,42,25.6,35.7,30.6,18.8 -19,19.2,1.0549,26,181,69.75,26.2,146.6,36.4,105.1,90.7,100.3,58.4,38.3,22.9,31.9,27.8,17.7 -12.8,12.4,1.0704,25,176,72.5,23.6,153.6,37.8,99.6,88.5,97.1,60,39.4,23.2,30.5,29,18.8 -5.1,4.1,1.09,25,191,74,24.6,181.3,38.1,100.9,82.5,99.9,62.9,38.3,23.8,35.9,31.1,18.2 -12,11.7,1.0722,23,198.25,73.5,25.8,174.4,42.1,99.6,88.6,104.1,63.1,41.7,25,35.6,30,19.2 -7.5,7.1,1.083,26,186.25,74.5,23.6,172.3,38.5,101.5,83.6,98.2,59.7,39.7,25.2,32.8,29.4,18.5 -8.5,7.8,1.0812,27,216,76,26.3,197.7,39.4,103.6,90.9,107.7,66.2,39.2,25.9,37.2,30.2,19 -20.5,20.8,1.0513,32,180.5,69.5,26.3,143.5,38.4,102,91.6,103.9,63.4,38.3,21.5,32.5,28.6,17.7 -20.8,21.2,1.0505,30,205.25,71.25,28.5,162.5,39.4,104.1,101.8,108.6,66,41.5,23.7,36.9,31.6,18.8 -21.7,22.1,1.0484,35,187.75,69.5,27.4,147,40.5,101.3,96.4,100.1,69,39,23.1,36.1,30.5,18.2 -20.5,20.9,1.0512,35,162.75,66,26.3,129.3,36.4,99.1,92.8,99.2,63.1,38.7,21.7,31.1,26.4,16.9 -28.1,29,1.0333,34,195.75,71,27.3,140.8,38.9,101.9,96.4,105.2,64.8,40.8,23.1,36.2,30.8,17.3 -22.4,22.9,1.0468,32,209.25,71,29.2,162.5,42.1,107.6,97.5,107,66.9,40,24.4,38.2,31.6,19.3 -16.1,16,1.0622,28,183.75,67.75,28.2,154.3,38,106.8,89.6,102.4,64.2,38.7,22.9,37.2,30.5,18.5 -16.5,16.5,1.061,33,211.75,73.5,27.6,176.8,40,106.2,100.5,109,65.8,40.6,24,37.1,30.1,18.2 -19,19.1,1.0551,28,179,68,27.3,145.1,39.1,103.3,95.9,104.9,63.5,38,22.1,32.5,30.3,18.4 -15.3,15.2,1.064,28,200.5,69.75,29.1,169.8,41.3,111.4,98.8,104.8,63.4,40.6,24.6,33,32.8,19.9 -15.7,15.6,1.0631,31,140.25,68.25,21.2,118.2,33.9,86,76.4,94.6,57.4,35.3,22.2,27.9,25.9,16.7 -17.6,17.7,1.0584,32,148.75,70,21.4,122.6,35.5,86.7,80,93.4,54.9,36.2,22.1,29.8,26.7,17.1 -14.2,14,1.0668,28,151.25,67.75,23.2,129.8,34.5,90.2,76.3,95.8,58.4,35.5,22.9,31.1,28,17.6 -4.6,3.7,1.0911,27,159.25,71.5,21.9,151.9,35.7,89.6,79.7,96.5,55,36.7,22.5,29.9,28.2,17.7 -8.5,7.9,1.0811,34,131.5,67.5,20.3,120.3,36.2,88.6,74.6,85.3,51.7,34.7,21.4,28.7,27,16.5 -22.4,22.9,1.0468,31,148,67.5,22.9,114.9,38.8,97.4,88.7,94.7,57.5,36,21,29.2,26.6,17 -4.7,3.7,1.091,27,133.25,64.75,22.4,127,36.4,93.5,73.9,88.5,50.1,34.5,21.3,30.5,27.9,17.2 -9.4,8.8,1.079,29,160.75,69,23.8,145.7,36.7,97.4,83.5,98.7,58.9,35.3,22.6,30.1,26.7,17.6 -12.3,11.9,1.0716,32,182,73.75,23.6,159.7,38.7,100.5,88.7,99.8,57.5,38.7,33.9,32.5,27.7,18.4 -6.5,5.7,1.0862,29,160.25,71.25,22.2,149.8,37.3,93.5,84.5,100.6,58.5,38.8,21.5,30.1,26.4,17.9 -13.4,11.8,1.0719,27,168,71.25,23.3,142.5,38.1,93,79.1,94.5,57.3,36.2,24.5,29,30,18.8 -20.9,21.3,1.0502,41,218.5,71,30.5,172.7,39.8,111.7,100.5,108.3,67.1,44.2,25.2,37.5,31.5,18.7 -31.1,32.3,1.0263,41,247.25,73.5,32.2,170.4,42.1,117,115.6,116.1,71.2,43.3,26.3,37.3,31.7,19.7 -38.2,40.1,1.0101,49,191.75,65,32,118.4,38.4,118.5,113.1,113.8,61.9,38.3,21.9,32,29.8,17 -23.6,24.2,1.0438,40,202.25,70,29.1,154.5,38.5,106.5,100.9,106.2,63.5,39.9,22.6,35.1,30.6,19 -27.5,28.4,1.0346,50,196.75,68.25,29.7,142.6,42.1,105.6,98.8,104.8,66,41.5,24.7,33.2,30.5,19.4 -33.8,35.2,1.0202,46,363.15,72.25,48.9,240.5,51.2,136.2,148.1,147.7,87.3,49.1,29.6,45,29,21.4 -31.3,32.6,1.0258,50,203,67,31.8,139.4,40.2,114.8,108.1,102.5,61.3,41.1,24.7,34.1,31,18.3 -33.1,34.5,1.0217,45,262.75,68.75,39.1,175.8,43.2,128.3,126.2,125.6,72.5,39.6,26.6,36.4,32.7,21.4 -31.7,32.9,1.025,44,205,29.5,29.9,140.1,36.6,106,104.3,115.5,70.6,42.5,23.7,33.6,28.7,17.4 -30.4,31.6,1.0279,48,217,70,31.2,151.1,37.3,113.3,111.2,114.1,67.7,40.9,25,36.7,29.8,18.4 -30.8,32,1.0269,41,212,71.5,29.2,146.7,41.5,106.6,104.3,106,65,40.2,23,35.8,31.5,18.8 -8.4,7.7,1.0814,39,125.25,68,19.1,114.7,31.5,85.1,76,88.2,50,34.7,21,26.1,23.1,16.1 -14.1,13.9,1.067,43,164.25,73.25,21.3,141.1,35.7,96.6,81.5,97.2,58.4,38.2,23.4,29.7,27.4,18.3 -11.2,10.8,1.0742,40,133.5,67.5,20.6,118.5,33.6,88.2,73.7,88.5,53.3,34.5,22.5,27.9,26.2,17.3 -6.4,5.6,1.0665,39,148.5,71.25,20.6,139,34.6,89.8,79.5,92.7,52.7,37.5,21.9,28.8,26.8,17.9 -13.4,13.6,1.0678,45,135.75,68.5,20.4,117.6,32.8,92.3,83.4,90.4,52,35.8,20.6,28.8,25.5,16.3 -5,4,1.0903,47,127.5,66.75,20.2,121.2,34,83.4,70.4,87.2,50.6,34.4,21.9,26.8,25.8,16.8 -10.7,10.2,1.0756,47,158.25,72.25,21.3,141.4,34.9,90.2,86.7,98.3,52.6,37.2,22.4,26,25.8,17.3 -7.4,6.6,1.084,40,139.25,69,20.6,129,34.3,89.2,77.9,91,51.4,34.9,21,26.7,26.1,17.2 -8.7,8,1.0807,51,137.25,67.75,21.1,125.3,36.5,89.7,82,89.1,49.3,33.7,21.4,29.6,26,16.9 -7.1,6.3,1.0848,49,152.75,73.5,19.9,142,35.1,93.3,79.6,91.6,52.6,37.6,22.6,38.5,27.4,18.5 -4.9,3.9,1.0906,42,136.25,67.5,21.1,129.6,37.8,87.6,77.6,88.6,51.9,34.9,22.5,27.7,27.5,18.5 -22.2,22.6,1.0473,54,198,72,26.9,154.1,39.9,107.6,100,99.6,57.2,38,22,35.9,30.2,18.9 -20.1,20.4,1.0524,58,181.5,68,27.6,145.1,39.1,100,99.8,102.5,62.1,39.6,22.5,33.1,28.3,18.5 -27.1,28,1.0356,62,201.25,69.5,29.3,146.7,40.5,111.5,104.2,105.8,61.8,39.8,22.7,37.7,30.9,19.2 -30.4,31.5,1.028,54,202.5,70.75,28.4,141,40.5,115.4,105.3,97,59.1,38,22.5,31.6,28.8,18.2 -24,24.6,1.043,61,179.75,65.75,29.2,136.7,38.4,104.8,98.3,99.6,60.6,37.7,22.9,34.5,29.6,18.5 -25.4,26.1,1.0396,62,216,73.25,28.2,161.2,41.4,112.3,104.8,103.1,61.6,40.9,23.1,36.2,31.8,20.2 -28.8,29.8,1.0317,56,178.75,68.5,26.8,127.4,35.6,102.9,94.7,100.8,60.9,38,22.1,32.5,29.8,18.3 -29.6,30.7,1.0298,54,193.25,70.25,27.6,136.1,38,107.6,102.4,99.4,61,39.4,23.6,32.7,29.9,19.1 -25.1,25.8,1.0403,61,178,67,27.9,133.3,37.4,105.3,99.7,99.7,60.8,40.1,22.7,33.6,29,18.8 -31,32.3,1.0264,57,205.5,70,29.5,141.7,40.1,105.3,105.5,108.3,65,41.2,24.7,35.3,31.1,18.4 -28.9,30,1.0313,55,183.5,67.5,28.3,130.4,40.9,103,100.3,104.2,64.8,40.2,22.7,34.8,30.1,18.7 -21.1,21.5,1.0499,54,151.5,70.75,21.3,119.6,35.6,90,83.9,93.9,55,36.1,21.7,29.6,27.4,17.4 -14,13.8,1.0673,55,154.75,71.5,21.3,133.1,36.9,95.4,86.6,91.8,54.3,35.4,21.5,32.8,27.4,18.7 -7.1,6.3,1.0847,54,155.25,69.25,22.8,144.2,37.5,89.3,78.4,96.1,56,37.4,22.4,32.6,28.1,18.1 -13.2,12.9,1.0693,55,156.75,71.5,21.6,136.1,36.3,94.4,84.6,94.3,51.2,37.4,21.6,27.3,27.1,17.3 -23.7,24.3,1.0439,62,167.5,71.5,23.1,127.8,35.5,97.6,91.5,98.5,56.6,38.6,22.4,31.5,27.3,18.6 -9.4,8.8,1.0788,55,146.75,68.75,21.9,132.9,38.7,88.5,82.8,95.5,58.9,37.6,21.6,30.3,27.3,18.3 -9.1,8.5,1.0796,56,160.75,73.75,20.8,146.1,36.4,93.6,82.9,96.3,52.9,37.5,23.1,29.7,27.3,18.2 -13.7,13.5,1.068,55,125,64,21.5,107.9,33.2,87.7,76,88.6,50.9,35.4,19.1,29.3,25.7,16.9 -12,11.8,1.072,61,143,65.75,23.3,125.9,36.5,93.4,83.3,93,55.5,35.2,20.9,29.4,27,16.8 -18.3,18.5,1.0666,61,148.25,67.5,22.9,121.1,36,91.6,81.8,94.8,54.5,37,21.4,29.3,27,18.3 -9.2,8.8,1.079,57,162.5,69.5,23.7,147.5,38.7,91.6,78.8,94.3,56.7,39.7,24.2,30.2,29.2,18.1 -21.7,22.2,1.0483,69,177.75,68.5,26.7,139.1,38.7,102,95,98.3,55,38.3,21.8,30.8,25.7,18.8 -21.1,21.5,1.0498,81,161.25,70.25,23,127.2,37.8,96.4,95.4,99.3,53.5,37.5,21.5,31.4,26.8,18.3 -18.6,18.8,1.056,66,171.25,69.25,25.1,139.5,37.4,102.7,98.6,100.2,56.5,39.3,22.7,30.3,28.7,19 -30.2,31.4,1.0283,67,163.75,67.75,25.1,114.3,38.4,97.7,95.8,97.1,54.8,38.2,23.7,29.4,27.2,19 -26,26.8,1.0382,64,150.25,67.25,23.4,111.2,38.1,97.1,89,96.9,54.8,38,22,29.9,25.2,17.7 -18.2,18.4,1.0568,64,190.25,72.75,25.3,155.6,39.3,103.1,97.8,99.6,58.9,39,23,34.3,29.6,19 -26.2,27,1.0377,70,170.75,70,24.5,126,38.7,101.8,94.9,95,56,36.5,24.1,31.2,27.3,19.2 -26.1,27,1.0378,72,168,69.25,24.7,124.1,38.5,101.4,99.8,96.2,56.3,36.6,22,29.7,26.3,18 -25.8,26.6,1.0386,67,167,67.5,26,123.9,36.5,98.9,89.7,96.2,54.7,37.8,33.7,32.4,27.7,18.2 -15,14.9,1.0648,72,157.75,67.25,24.6,134.1,37.7,97.5,88.1,96.9,57.2,37.7,21.8,32.6,28,18.8 -22.6,23.1,1.0462,64,160,65.75,26,123.8,36.5,104.3,90.9,93.8,57.8,39.5,23.3,29.2,28.4,18.1 -8.8,8.3,1.08,46,176.75,72.5,23.7,161.1,38,97.3,86,99.3,61,38.4,23.8,30.2,29.3,18.8 -14.3,14.1,1.0666,48,176,73,23.3,150.9,36.7,96.7,86.5,98.3,60.4,39.9,24.4,28.8,29.6,18.7 -20.2,20.5,1.052,46,177,70,25.4,141.3,37.2,99.7,95.6,102.2,58.3,38.2,22.5,29.1,27.7,17.7 -18.1,18.2,1.0573,44,179.75,69.5,26.2,147.3,39.2,101.9,93.2,100.6,58.9,39.7,23.1,31.4,28.4,18.8 -9.2,8.5,1.0795,47,165.25,70.5,23.4,150.1,37.5,97.2,83.1,95.4,56.9,38.3,22.1,30.1,28.2,18.4 -24.2,24.9,1.0424,46,192.5,71.75,26.3,145.9,38,106.6,97.5,100.6,58.9,40.5,24.5,33.3,29.6,19.1 -9.6,9,1.0785,47,184.25,74.5,23.4,166.6,37.3,99.6,88.8,101.4,57.4,39.6,24.6,30.3,27.9,17.8 -17.3,17.4,1.0991,53,224.5,77.75,26.1,185.7,41.1,113.2,99.2,107.5,61.7,42.3,23.2,32.9,30.8,20.4 -10.1,9.6,1.077,38,188.75,73.25,24.8,169.6,37.5,99.1,91.6,102.4,60.6,39.4,22.9,31.6,30.1,18.5 -11.1,11.3,1.073,50,162.5,66.5,25.9,143.5,38.7,99.4,86.7,96.2,62.1,39.3,23.3,30.6,27.8,18.2 -17.7,17.8,1.0582,46,156.5,68.25,23.7,128.8,35.9,95.1,88.2,92.8,54.7,37.3,21.9,31.6,27.5,18.2 -21.7,22.2,1.0484,47,197,72,26.7,154.2,40,107.5,94,103.7,62.7,39,22.3,35.3,30.9,18.3 -20.8,21.2,1.0506,49,198.5,73.5,25.9,157.2,40.1,106.5,95,101.7,59,39.4,22.3,32.2,31,18.6 -20.1,20.4,1.0524,48,173.75,72,23.6,138.9,37,99.1,92,98.3,59.3,38.4,22.4,27.9,26.2,17 -19.8,20.1,1.053,41,172.75,71.25,24,138.6,36.3,96.7,89.2,98.3,60,38.4,23.2,31,29.2,18.4 -21.9,22.3,1.048,49,196.75,73.75,25.5,153.7,40.7,103.5,95.5,101.6,59.1,39.8,25.4,31,30.3,19.7 -24.7,25.4,1.0412,43,177,69.25,26,133.2,39.6,104,98.6,99.5,59.5,36.1,22,30.1,27.2,17.7 -17.8,18,1.0578,43,165.5,68.5,24.8,136,31.1,93.1,87.3,96.6,54.7,39,24.8,31,29.4,18.8 -19.1,19.3,1.0547,43,200.25,73.5,26,162,38.6,105.2,102.8,103.6,61.2,39.3,23.5,30.5,28.5,18.1 -18.2,18.3,1.0569,52,203.25,74.25,26,166.3,42,110,101.6,100.7,55.8,38.7,23.4,35.1,29.6,19.1 -17.2,17.3,1.0593,43,194,75.5,24,160.6,38.5,110.1,88.7,102.1,57.5,40,24.8,35.1,30.7,19.2 -21,21.4,1.05,40,168.5,69.25,24.7,133.1,34.2,97.8,92.3,100.6,57.5,36.8,22.8,32.1,26,17.3 -19.5,19.7,1.0538,43,170.75,68.5,25.6,137.5,37.2,96.3,90.6,99.3,61.9,38,22.3,33.3,28.2,18.1 -27.1,28,1.0355,43,183.25,70,26.3,133.5,37.1,108,105,103,63.7,40,23.6,33.5,27.8,17.4 -21.6,22.1,1.0486,47,178.25,70,25.6,139.7,40.2,99.7,95,98.6,62.3,38.1,23.9,35.3,31.1,19.8 -20.9,21.3,1.0503,42,163,70.25,23.3,128.9,35.3,93.5,89.6,99.8,61.5,37.8,21.9,30.7,27.6,17.4 -25.9,26.7,1.0384,48,175.25,71.75,24,129.9,38,100.7,92.4,97.5,59.3,38.1,21.8,31.8,27.3,17.5 -16.7,16.7,1.0607,40,158,69.25,23.4,131.7,36.3,97,86.6,92.6,55.9,36.3,22.1,29.8,26.3,17.3 -19.8,20.1,1.0529,48,177.25,72.75,23.6,142.1,36.8,96,90,99.7,58.8,38.4,22.8,29.9,28,18.1 -14.1,13.9,1.0671,51,179,72,24.3,153.8,41,99.2,90,96.4,56.8,38.8,23.3,33.4,29.8,19.5 -25.1,25.8,1.0404,40,191,74,24.6,143.1,38.3,95.4,92.4,104.3,64.6,41.1,24.8,33.6,29.5,18.5 -17.9,18.1,1.0575,44,187.5,72.25,25.3,153.8,38,101.8,87.5,101,58.5,39.2,24.5,32.1,28.6,18 -27,27.9,1.0358,52,206.5,74.5,26.2,150.7,40.8,104.3,99.2,104.1,58.5,39.3,24.6,33.9,31.2,19.5 -24.6,25.3,1.0414,44,185.25,71.5,25.5,139.6,39.5,99.2,98.1,101.4,57.1,40.5,23.2,33,29.6,18.4 -14.8,14.7,1.0652,40,160.25,68.75,23.9,136.5,36.9,99.3,83.3,97.5,60.5,38.7,22.6,34.4,28,17.6 -16,16,1.0623,47,151.5,66.75,23.9,127.3,36.9,94,86.1,95.2,58.1,36.5,22.1,30.6,27.5,17.6 -14,13.8,1.0674,50,161,66.5,25.6,138.5,37.7,98.9,84.1,94,58.5,36.6,23.5,34.4,29.2,18 -17.4,17.5,1.0587,46,167,67,26.2,137.9,36.6,101,89.9,100,60.7,36,21.9,35.6,30.2,17.6 -26.4,27.2,1.0373000000000001,42,177.5,68.75,26.4,130.7,38.9,98.7,92.1,98.5,60.7,36.8,22.2,33.8,30.3,17.2 -17.4,17.4,1.059,43,152.25,67.75,23.4,125.8,37.5,95.9,78,93.2,53.5,35.8,20.8,33.9,28.2,17.4 -20.4,20.8,1.0515,40,192.25,73.25,25.2,153,39.8,103.9,93.5,99.5,61.7,39,21.8,33.3,29.6,18.1 -15,14.9,1.0648,42,165.25,69.75,23.9,140.5,38.3,96.2,87,97.8,57.4,36.9,22.2,31.6,27.8,17.7 -18,18.1,1.0575,49,171.75,71.5,23.7,140.9,35.5,97.8,90.1,95.8,57,38.7,23.2,27.5,26.5,17.6 -22.2,22.7,1.0472,40,171.25,70.5,24.3,133.3,36.3,94.6,90.3,99.1,60.3,38.5,23,31.2,28.4,17.1 -23.1,23.6,1.0452,47,197,73.25,25.8,151.2,37.8,103.6,99.8,103.2,61.2,38.1,22.6,33.5,28.6,17.9 -25.3,26.1,1.0398,50,157,66.75,24.8,117.2,37.8,100.4,89.4,92.3,56.1,35.6,20.5,33.6,29.3,17.3 -23.8,24.4,1.0435,41,168.25,69.5,24.5,128.3,36.5,98.4,87.2,98.4,56,36.9,23,34,29.8,18.1 -26.3,27.1,1.0374,44,186,69.75,26.8,137.1,37.8,104.6,101.1,102.1,58.9,37.9,22.7,30.9,28.8,17.6 -21.4,21.8,1.0491,39,166.75,70.75,23.5,131,37,92.9,86.1,95.6,58.8,36.1,22.4,32.7,28.3,17.1 -28.4,29.4,1.0325,43,187.75,74,24.1,134.4,37.7,97.8,98.6,100.6,63.6,39.2,23.8,34.3,28.4,17.7 -21.8,22.4,1.0481,40,168.25,71.25,23.3,131.6,34.3,98.3,88.5,98.3,58.1,38.4,22.5,31.7,27.4,17.6 -20.1,20.4,1.0522,49,212.75,75,26.6,169.9,40.8,104.7,106.6,107.7,66.5,42.5,24.5,35.5,29.8,18.7 -24.3,24.9,1.0422,40,176.75,71,24.6,133.8,37.4,98.6,93.1,101.6,59.1,39.6,21.6,30.8,27.9,16.6 -18.1,18.3,1.0571,40,173.25,69.5,25.3,141.8,36.5,99.5,93,99.3,60.4,38.2,22,32,28.5,17.8 -22.7,23.3,1.0459,52,167,67.75,25.6,129,37.5,102.7,91,98.9,57.1,36.7,22.3,31.6,27.5,17.9 -9.9,9.4,1.0775,23,159.75,72.25,21.6,143.9,35.5,92.1,77.1,93.9,56.1,36.1,22.7,30.5,27.2,18.2 -10.8,10.3,1.0754,23,188.15,77.5,22.1,168.4,38,96.6,85.3,102.5,59.1,37.6,23.2,31.8,29.7,18.3 -14.4,14.2,1.0664,24,156,70.75,21.9,133.6,35.7,92.7,81.9,95.3,56.4,36.5,22,33.5,28.3,17.3 -19,19.2,1.055,24,208.5,72.75,27.7,168.9,39.2,102,99.1,110.1,71.2,43.5,25.2,36.1,30.3,18.7 -28.6,29.6,1.0322,25,206.5,69.75,29.8,147.5,40.9,110.9,100.5,106.2,68.4,40.8,24.6,33.3,29.7,18.4 -6.1,5.3,1.0873,25,143.75,72.5,19.3,135,35.2,92.3,76.5,92.1,51.9,35.7,22,25.8,25.2,16.9 -24.5,25.2,1.0416,26,223,70.25,31.8,168.3,40.6,114.1,106.8,113.9,67.6,42.7,24.7,36,30.4,18.4 -9.9,9.4,1.0776,26,152.25,69,22.5,137.2,35.4,92.9,77.6,93.5,56.9,35.9,20.4,31.6,29,17.8 -19.1,19.6,1.0542,26,241.75,74.5,30.7,195.1,41.8,108.3,102.9,114.4,72.9,43.5,25.1,38.5,33.8,19.6 -10.6,10.1,1.0758,27,146,72.25,19.7,130.5,34.1,88.5,72.8,91.1,53.6,36.8,23.8,27.8,26.3,17.4 -16.5,16.5,1.061,27,156.75,67.25,24.4,130.9,37.9,94,88.2,95.2,56.8,37.4,22.8,30.6,28.3,17.9 -20.5,21,1.051,27,200.25,73.5,26.1,159.3,38.2,101.1,100.1,105,62.1,40,24.9,33.7,29.2,19.4 -17.2,17.3,1.0594,28,171.5,75.25,21.6,142,35.6,92.1,83.5,98.3,57.3,37.8,21.7,32.2,27.7,17.7 -30.1,31.2,1.0287,28,205.75,69,30.4,143.9,38.5,105.6,105,106.4,68.6,40,25.2,35.2,30.7,19.1 -10.5,10,1.0761,28,182.5,72.25,24.6,163.4,37,98.5,90.8,102.5,60.8,38.5,25,31.6,28,18.6 -12.8,12.5,1.0704,30,136.5,68.75,20.3,119.1,35.9,88.7,76.6,89.8,50.1,34.8,21.8,27,34.9,16.9 -22,22.5,1.0477,31,177.25,71.5,24.4,138.3,36.2,101.1,92.4,99.3,59.4,39,24.6,30.1,28.2,18.2 -9.9,9.4,1.0775,31,151.25,72.25,20.4,136.2,35,94,81.2,91.5,52.5,36.6,21,27,26.3,16.5 -14.8,14.6,1.0653,33,196,73,25.9,167,38.5,103.8,95.6,105.1,61.4,40.6,25,31.3,29.2,19.1 -13.3,13,1.069,33,184.25,68.75,24.4,159.8,40.7,98.9,92.1,103.5,64,37.3,23.5,33.5,30.6,19.7 -15.2,15.1,1.0644,34,140,70.5,19.8,118.8,36,89.2,83.4,89.6,52.4,35.6,20.4,28.3,26.2,16.5 -26.5,27.3,1.037,34,218.75,72,29.7,160.8,39.5,111.4,106,108.8,63.8,42,23.4,34,31.2,18.5 -19,19.2,1.0549,35,217,73.75,28.1,175.8,40.5,107.5,95.1,104.5,64.8,41.3,25.6,36.4,33.7,19.4 -21.4,21.8,1.0492,35,166.25,68,25.3,130.7,38.5,99.1,90.4,95.6,55.5,34.2,21.9,30.2,28.7,17.7 -20,20.3,1.0525,35,224.75,72.25,30.3,179.7,43.9,108.2,100.4,106.8,63.3,41.7,24.6,37.2,33.1,19.8 -34.7,34.3,1.018,35,228.25,69.5,33.3,149.3,40.4,114.9,115.9,111.9,74.4,40.6,24,36.1,31.8,18.8 -16.5,16.5,1.061,35,172.75,69.5,25.2,144.2,37.6,99.1,90.8,98.1,60.1,39.1,23.4,32.5,29.8,17.4 -4.1,3,1.0926,35,152.25,67.75,23.4,146.1,37,92.2,81.9,92.8,54.7,36.2,22.1,30.4,27.4,17.7 -1.9,0.7,1.0983,35,125.75,65.5,20.6,123.4,34,90.8,75,89.2,50,34.8,22,24.8,25.9,16.9 -20.2,20.5,1.0521,35,177.25,71,24.8,141.7,38.4,100.5,90.3,98.7,57.8,37.3,22.4,31,28.7,17.7 -16.8,16.9,1.0603,36,176.25,71.5,24.3,146.6,38.7,98.2,90.3,99.9,59.2,37.7,21.5,32.4,28.4,17.8 -24.6,25.3,1.0414,36,226.75,71.75,31,170.9,41.5,115.3,108.8,114.4,69.2,42.4,24,35.4,21,20.1 -10.4,9.9,1.0763,37,145.25,69.25,21.3,130.2,36,96.8,79.4,89.2,50.3,34.8,22.2,31,26.9,16.9 -13.4,13.1,1.0689,37,151,67,23.7,130.8,35.3,92.6,83.2,96.4,60,38.1,22,31.5,26.6,16.7 -28.8,29.9,1.0316,37,241.25,71.5,33.2,171.7,42.1,119.2,110.3,113.9,69.8,42.6,24.8,34.4,29.5,18.4 -22,22.5,1.0477,38,187.25,69.25,27.5,146.1,38,102.7,92.7,101.9,64.7,39.5,24.7,34.8,30.3,18.1 -16.8,16.9,1.0603,39,234.75,74.5,29.8,195.3,42.8,109.5,104.5,109.9,69.5,43.1,25.8,39.1,32.5,19.9 -25.8,26.6,1.0387,39,219.25,74.25,28,162.7,40,108.5,104.6,109.8,68.1,42.8,24.1,35.6,29,19 -0,0,1.1089,40,118.5,68,18.1,118.5,33.8,79.3,69.4,85,47.2,33.5,20.2,27.7,24.6,16.5 -11.9,11.5,1.0725,40,145.75,67.25,22.7,128.4,35.5,95.5,83.6,91.6,54.1,36.2,21.8,31.4,28.3,17.2 -12.4,12.1,1.0713,40,159.25,69.75,23,139.5,35.3,92.3,86.8,96.1,58,39.4,22.7,30,26.4,17.4 -17.4,17.5,1.0587,40,170.5,74.25,21.8,140.8,37.7,98.9,90.4,95.5,55.4,38.9,22.4,30.5,28.9,17.7 -9.2,8.6,1.0794,40,167.5,71.5,23.1,152.1,39.4,89.5,83.7,98.1,57.3,39.7,22.6,32.9,29.3,18.2 -23,23.6,1.0453,41,232.75,74.25,29.7,179.2,41.9,117.5,109.3,108.8,67.7,41.3,24.7,37.2,31.8,20 -20.1,20.4,1.0524,41,210.5,72,28.6,168.3,38.5,107.4,98.9,104.1,63.5,39.8,23.5,36.4,30.4,19.1 -20.2,20.5,1.052,41,202.25,72.5,27,161.4,40.8,109.2,98,101.8,62.8,41.3,24.8,36.6,32.4,18.8 -23.8,24.4,1.0434,41,185,68.25,28,141,38,103.4,101.2,103.1,61.5,40.4,22.9,33.4,29.2,18.5 -11.8,11.4,1.0728,41,153,69.25,22.5,135,36.4,91.4,80.6,92.3,54.3,36.3,21.8,29.6,27.3,17.9 -36.5,38.1,1.014,42,244.25,76,29.8,155.2,41.8,115.2,113.7,112.4,68.5,45,25.5,37.1,31.2,19.9 -16,15.9,1.0624,42,193.5,70.5,27.4,162.6,40.7,104.9,94.1,102.7,60.6,38.6,24.7,34,30.1,18.7 -24,24.7,1.0429,42,224.75,74.75,28.3,170.8,38.5,106.7,105.7,111.8,65.3,43.3,26,33.7,29.9,18.5 -22.3,22.8,1.047,42,162.75,72.75,21.6,126.5,35.4,92.2,85.6,96.5,60.2,38.9,22.4,31.7,27.1,17.1 -24.8,25.5,1.0411,42,180,68.25,27.2,135.4,38.5,101.6,96.6,100.6,61.1,38.4,24.1,32.9,29.8,18.8 -21.5,22,1.0488,42,156.25,69,23.1,122.6,35.5,97.8,86,96.2,57.7,38.6,24,31.2,27.3,17.4 -17.6,17.7,1.0583,42,168,71.5,23.1,138.4,36.5,92,89.7,101,62.3,38,22.3,30.8,27.8,16.9 -7.3,6.6,1.0841,42,167.25,72.75,22.3,155.1,37.6,94,78,99,57.5,40,22.5,30.6,30,18.5 -22.6,23.6,1.0462,43,170.75,67.5,26.4,132.1,37.4,103.7,89.7,94.2,58.5,39,24.1,33.8,28.8,18.8 -12.5,12.2,1.0709,43,178.25,70.25,25.4,155.9,37.8,102.7,89.2,99.2,60.2,39.2,23.8,31.7,28.4,18.6 -21.7,22.1,1.0484,43,150,69.25,22,117.5,35.2,91.1,85.7,96.9,55.5,35.7,22,29.4,26.6,17.4 -27.7,28.7,1.034,43,200.5,71.5,27.6,144.9,37.9,107.2,103.1,105.5,68.8,38.3,23.7,32.1,28.9,18.7 -6.8,6,1.0854,44,184,74,23.7,171.4,37.9,100.8,89.1,102.6,60.6,39,24,32.9,29.2,18.4 -33.4,34.8,1.0209,44,223,69.75,32.3,148.5,40.9,121.6,113.9,107.1,63.5,40.3,21.8,34.8,30.7,17.4 -16.6,16.6,1.061,44,208.75,73,27.6,174.2,41.9,105.6,96.3,102,63.3,39.8,24.1,37.3,23.1,19.4 -31.7,32.9,1.025,44,166,65.5,27.2,113.5,39.1,100.6,93.9,100.1,58.9,37.6,21.4,33.1,29.5,17.3 -31.5,32.8,1.0254,47,195,72.5,26.1,133.6,40.2,102.7,101.3,101.7,60.7,39.4,23.3,36.7,31.6,18.4 -10.1,9.6,1.0771,47,160.5,70.25,22.9,144.3,36,99.8,83.9,91.8,53,36.2,22.5,31.4,27.5,17.7 -11.3,10.8,1.0742,47,159.75,70.75,22.5,141.8,34.5,92.9,84.4,94,56,38.2,22.6,29,26.2,17.6 -7.8,7.1,1.0829,49,140.5,68,21.4,129.5,35.8,91.2,79.4,89,51.1,35,21.7,30.9,28.8,17.4 -26.4,27.2,1.0373000000000001,49,216.25,74.5,27.4,159.3,40.2,115.6,104,109,63.7,40.3,23.2,36.8,31,18.9 -19.3,19.5,1.0543,49,168.25,71.75,23,135.9,38.3,98.3,89.7,99.1,56.3,38.8,23,29.5,27.9,18.6 -18.5,18.7,1.0561,50,194.75,70.75,27.4,158.7,39,103.7,97.6,104.2,60,40.9,25.5,32.7,30,19 -19.3,19.5,1.0543,50,172.75,73,22.8,139.4,37.4,98.7,87.6,96.1,57.1,38.1,21.8,28.6,26.7,18 -45.1,47.5,0.995,51,219,64,37.6,120.2,41.2,119.8,122.1,112.8,62.5,36.9,23.6,34.7,29.1,18.4 -13.8,13.6,1.0678,51,149.25,69.75,21.6,128.7,34.8,92.8,81.1,96.3,53.8,36.5,21.5,31.3,26.3,17.8 -8.2,7.5,1.0819,51,154.5,70,22.2,141.9,36.9,93.3,81.5,94.4,54.7,39,22.6,27.5,25.9,18.6 -23.9,24.5,1.0433,52,199.25,71.75,27.2,151.7,39.4,106.8,100,105,63.9,39.2,22.9,35.7,30.4,19.2 -15.1,15,1.0646,53,154.5,69.25,22.7,131.2,37.6,93.9,88.7,94.5,53.7,36.2,22,28.5,25.7,17.1 -12.7,12.4,1.0706,54,153.25,70.5,24.5,151.3,38.5,99,91.8,96.2,57.7,38.1,23.9,31.4,29.9,18.9 -25.3,26,1.0399,54,230,72.25,31,171.9,42.5,119.9,110.4,105.5,64.2,42.7,27,38.4,32,19.6 -11.9,11.5,1.0726,54,161.75,67.5,25,142.6,37.4,94.2,87.6,95.6,59.7,40.2,23.4,27.9,27,17.8 -6.1,5.2,1.0874,55,142.25,67.25,22.2,133.6,35.2,92.7,82.8,91.9,54.4,35.2,22.5,29.4,26.8,17 -11.3,10.9,1.074,55,179.75,68.75,26.8,159.5,41.1,106.9,95.3,98.2,57.4,37.1,21.8,34.1,31.1,19.2 -12.8,12.5,1.0703,55,126.5,66.75,20,110.3,33.4,88.8,78.2,87.5,50.8,33,19.7,25.3,22,15.8 -14.9,14.8,1.065,55,169.5,68.25,25.6,144.2,37.2,101.7,91.1,97.1,56.6,38.5,22.6,33.4,29.3,18.8 -24.5,25.2,1.0418,55,198.5,74.25,25.3,149.9,38.3,105.3,96.7,106.6,64,42.6,23.4,33.2,30,18.4 -15,14.9,1.0647,56,174.5,69.5,25.4,148.3,38.1,104,89.4,98.4,58.4,37.4,22.5,34.6,30.1,18.8 -16.9,17,1.0601,56,167.75,68.5,25.2,139.4,37.4,98.6,93,97,55.4,38.8,23.2,32.4,29.7,19 -11.1,10.6,1.0745,57,147.75,65.75,24.1,131.4,35.2,99.6,86.4,90.1,53,35,21.3,31.7,27.3,16.9 -16.1,16.1,1.062,57,182.25,71.75,24.9,152.9,39.4,103.4,96.7,100.7,59.3,38.6,22.8,31.8,29.1,19 -15.5,15.4,1.0636,58,175.5,71.5,24.2,148.4,38,100.2,88.1,97.8,57.1,38.9,23.6,30.9,29.6,18 -25.9,26.7,1.0384,58,161.75,67.25,25.2,119.9,35.1,94.9,94.9,100.2,56.8,35.9,21,27.8,26.1,17.6 -25.5,25.8,1.0403,60,157.75,67.5,24.1,117.5,40.4,97.2,93.3,94,54.3,35.7,21,31.3,28.7,18.3 -18.4,18.6,1.0563,62,168.75,67.5,26.1,137.6,38.3,104.7,95.6,93.7,54.4,37.1,22.7,30.3,26.3,18.3 -24,24.8,1.0424,62,191.5,72.25,25.8,145.2,40.6,104,98.2,101.1,59.3,40.3,23,32.6,28.5,19 -26.4,27.3,1.0372,63,219.15,69.5,31.9,161.2,40.2,117.6,113.8,111.8,63.4,41.1,22.3,35.1,29.6,18.5 -12.7,12.4,1.0705,64,155.25,69.5,22.6,135.5,37.9,95.8,82.8,94.5,61.2,39.1,22.3,29.8,28.9,18.3 -28.8,29.9,1.0316,65,189.75,65.75,30.9,135.1,40.8,106.4,100.5,100.5,59.2,38.1,24,35.9,30.5,19.1 -17,17,1.0599,65,127.5,65.75,20.8,105.9,34.7,93,79.7,87.6,50.7,33.4,20.1,28.5,24.8,16.5 -33.6,35,1.0207,65,224.5,68.25,33.9,149.2,38.8,119.6,118,114.3,61.3,42.1,23.4,34.9,30.1,19.4 -29.3,30.4,1.0304,66,234.25,72,31.8,165.6,41.4,119.7,109,109.1,63.7,42.4,24.6,35.6,30.7,19.5 -31.4,32.6,1.0256,67,227.75,72.75,30.3,156.3,41.3,115.8,113.4,109.8,65.6,46,25.4,35.3,29.8,19.5 -28.1,29,1.0334,67,199.5,68.5,29.9,143.6,40.7,118.3,106.1,101.6,58.2,38.8,24.1,32.1,29.3,18.5 -15.3,15.2,1.0641,68,155.5,69.25,22.8,131.8,36.3,97.4,84.3,94.4,54.3,37.5,22.6,29.2,27.3,18.5 -29.1,30.2,1.0308,69,215.5,70.5,30.5,152.7,40.8,113.7,107.6,110,63.3,44,22.6,37.5,32.6,18.8 -11.5,11,1.0736,70,134.25,67,21.1,118.9,34.9,89.2,83.6,88.8,49.6,34.8,21.5,25.6,25.7,18.5 -32.3,33.6,1.0236,72,201,69.75,29.1,136.1,40.9,108.5,105,104.5,59.6,40.8,23.2,35.2,28.6,20.1 -28.3,29.3,1.0328,72,186.75,66,30.2,133.9,38.9,111.1,111.5,101.7,60.3,37.3,21.5,31.3,27.2,18 -25.3,26,1.0399,72,190.75,70.5,27,142.6,38.9,108.3,101.3,97.8,56,41.6,22.7,30.5,29.4,19.8 -30.7,31.9,1.0271,74,207.5,70,29.8,143.7,40.8,112.4,108.5,107.1,59.3,42.2,24.6,33.7,30,20.9 diff --git a/live-demo-olga/data/data-diabetes.csv b/live-demo-olga/data/data-diabetes.csv deleted file mode 100644 index 2ba3da54..00000000 --- a/live-demo-olga/data/data-diabetes.csv +++ /dev/null @@ -1,404 +0,0 @@ -id,chol,stab.glu,hdl,ratio,glyhb,location,age,gender,height,weight,frame,bp.1s,bp.1d,bp.2s,bp.2d,waist,hip,time.ppn -1000,203,82,56,3.5999999,4.30999994,Buckingham,46,female,62,121,medium,118,59,NA,NA,29,38,720 -1001,165,97,24,6.9000001,4.44000006,Buckingham,29,female,64,218,large,112,68,NA,NA,46,48,360 -1002,228,92,37,6.19999981,4.63999987,Buckingham,58,female,61,256,large,190,92,185,92,49,57,180 -1003,78,93,12,6.5,4.63000011,Buckingham,67,male,67,119,large,110,50,NA,NA,33,38,480 -1005,249,90,28,8.89999962,7.71999979,Buckingham,64,male,68,183,medium,138,80,NA,NA,44,41,300 -1008,248,94,69,3.5999999,4.80999994,Buckingham,34,male,71,190,large,132,86,NA,NA,36,42,195 -1011,195,92,41,4.80000019,4.84000015,Buckingham,30,male,69,191,medium,161,112,161,112,46,49,720 -1015,227,75,44,5.19999981,3.94000006,Buckingham,37,male,59,170,medium,NA,NA,NA,NA,34,39,1020 -1016,177,87,49,3.5999999,4.84000015,Buckingham,45,male,69,166,large,160,80,128,86,34,40,300 -1022,263,89,40,6.5999999,5.78000021,Buckingham,55,female,63,202,small,108,72,NA,NA,45,50,240 -1024,242,82,54,4.5,4.76999998,Louisa,60,female,65,156,medium,130,90,130,90,39,45,300 -1029,215,128,34,6.30000019,4.96999979,Louisa,38,female,58,195,medium,102,68,NA,NA,42,50,90 -1030,238,75,36,6.5999999,4.46999979,Louisa,27,female,60,170,medium,130,80,NA,NA,35,41,720 -1031,183,79,46,4,4.59000015,Louisa,40,female,59,165,medium,NA,NA,NA,NA,37,43,60 -1035,191,76,30,6.4000001,4.67000008,Louisa,36,male,69,183,medium,100,66,NA,NA,36,40,225 -1036,213,83,47,4.5,3.41000009,Louisa,33,female,65,157,medium,130,90,120,96,37,41,240 -1037,255,78,38,6.69999981,4.32999992,Louisa,50,female,65,183,medium,130,100,NA,NA,37,43,180 -1041,230,112,64,3.5999999,4.53000021,Louisa,20,male,67,159,medium,100,90,NA,NA,31,39,1440 -1045,194,81,36,5.4000001,5.28000021,Louisa,36,male,64,126,medium,110,76,NA,NA,30,34,120 -1250,196,206,41,4.80000019,11.2399998,Buckingham,62,female,65,196,large,178,90,NA,NA,46,51,540 -1252,186,97,50,3.70000005,6.48999977,Buckingham,70,male,67,178,large,148,88,148,84,42,41,1020 -1253,234,65,76,3.0999999,4.67000008,Buckingham,47,male,67,230,large,137,100,149,110,45,46,480 -1254,203,299,43,4.69999981,12.7399998,Buckingham,38,female,69,288,large,136,83,NA,NA,48,55,240 -1256,281,92,41,6.9000001,5.55999994,Buckingham,66,female,62,185,large,158,88,160,88,48,44,285 -1271,228,66,45,5.0999999,4.61000013,Buckingham,24,female,61,113,medium,100,70,110,70,33,38,210 -1277,179,80,92,1.89999998,4.17999983,Buckingham,41,female,72,118,small,144,112,NA,NA,28,36,780 -1280,232,87,30,7.69999981,5.0999999,Buckingham,37,male,68,252,large,140,95,NA,NA,43,47,420 -1281,NA,74,NA,NA,4.28000021,Buckingham,48,male,68,100,small,120,85,NA,NA,27,33,510 -1282,254,84,52,4.9000001,4.51999998,Buckingham,43,female,62,145,medium,125,70,NA,NA,31,38,720 -1285,215,72,42,5.0999999,4.36999989,Louisa,40,male,70,189,medium,180,122,170,112,37,39,450 -1301,177,101,36,4.9000001,5.11000013,Buckingham,42,female,65,174,medium,146,94,139,89,37,40,540 -1303,182,85,43,4.19999981,4.46999979,Buckingham,52,male,68,139,large,130,90,NA,NA,29,35,780 -1304,265,330,34,7.80000019,15.5200005,Buckingham,61,male,74,191,medium,170,88,168,80,39,41,225 -1305,182,85,37,4.9000001,5.65999985,Buckingham,61,female,69,174,medium,176,86,180,90,49,43,330 -1309,199,87,63,3.20000005,3.67000008,Buckingham,25,male,66,118,medium,120,78,NA,NA,32,34,720 -1312,183,81,60,3.0999999,4.03000021,Buckingham,47,female,66,186,medium,140,97,NA,NA,39,44,780 -1313,194,86,67,2.9000001,2.68000007,Buckingham,35,male,66,159,medium,115,64,NA,NA,31,35,720 -1314,190,107,32,5.9000001,3.55999994,Buckingham,46,male,72,205,medium,NA,NA,NA,NA,46,49,240 -1315,173,80,57,3,6.21000004,Buckingham,57,male,71,145,medium,124,64,NA,NA,31,36,30 -1316,182,206,43,4.19999981,7.90999985,Buckingham,70,male,69,214,large,158,90,160,96,45,48,840 -1317,136,81,51,2.70000005,4.57999992,Buckingham,22,female,66,160,large,105,85,NA,NA,35,40,720 -1321,218,68,46,4.69999981,3.8900001,Buckingham,52,female,62,170,medium,142,79,NA,NA,40,43,720 -1323,225,83,42,5.4000001,4.38000011,Buckingham,36,male,67,192,large,149,89,136,88,40,42,30 -1326,262,84,38,6.9000001,NA,Buckingham,43,male,75,253,large,124,80,NA,NA,43,49,300 -1500,213,76,40,5.30000019,5.96000004,Buckingham,72,female,59,137,large,130,60,NA,NA,40,40,90 -1501,243,52,59,4.0999999,4.40999985,Buckingham,37,female,64,233,medium,110,82,NA,NA,49,57,90 -1502,148,193,14,10.6000004,6.13999987,Buckingham,54,female,67,165,medium,140,65,NA,NA,42,42,150 -2004,128,223,24,5.30000019,10.8999996,Buckingham,60,male,67,196,medium,110,68,NA,NA,42,43,450 -2750,169,85,51,3.29999995,6.13999987,Buckingham,40,female,65,180,medium,106,82,NA,NA,40,44,780 -2753,157,74,47,3.29999995,5.57000017,Buckingham,55,female,66,219,medium,150,82,142,78,43,52,360 -2754,196,82,58,3.4000001,4.25,Buckingham,76,male,65,154,NA,158,78,140,84,37,41,120 -2756,237,87,41,5.80000019,5.3499999,Buckingham,43,female,64,181,medium,104,90,NA,NA,36,46,240 -2757,212,97,45,4.69999981,6.32999992,Buckingham,65,female,61,187,large,158,94,149,96,43,47,360 -2758,233,92,39,6,4.55999994,Buckingham,45,female,64,167,large,124,86,NA,NA,39,44,270 -2762,289,111,50,5.80000019,9.39000034,Buckingham,70,female,60,220,medium,126,80,NA,NA,51,54,780 -2763,193,106,63,3.0999999,6.3499999,Buckingham,20,female,68,274,small,165,110,153,100,49,58,60 -2765,204,128,61,3.29999995,5.19999981,Buckingham,62,male,68,180,large,141,81,NA,NA,38,41,540 -2770,165,94,69,2.4000001,4.98000002,Buckingham,92,female,62,217,large,160,82,NA,NA,51,51,180 -2773,237,233,58,4.0999999,13.6999998,Buckingham,49,female,62,189,large,130,90,NA,NA,43,47,195 -2774,218,88,39,5.5999999,NA,Buckingham,44,female,66,191,large,138,79,NA,NA,40,45,720 -2775,296,262,60,4.9000001,10.9300003,Buckingham,74,female,63,183,large,159,99,160,103,42,48,300 -2777,178,78,59,3,5.23000002,Buckingham,36,male,70,161,medium,130,79,NA,NA,34,40,720 -2778,443,185,23,19.2999992,14.3100004,Buckingham,51,female,70,235,medium,158,98,148,88,43,48,420 -2780,145,85,29,5,3.99000001,Buckingham,38,female,NA,125,NA,NA,NA,NA,NA,31,35,120 -2784,234,80,63,3.70000005,NA,Buckingham,31,male,70,165,medium,121,71,NA,NA,35,39,720 -2785,146,77,60,2.4000001,4.26999998,Buckingham,28,female,64,126,small,120,90,NA,NA,28,32,180 -2787,223,75,85,2.5999999,4.25,Buckingham,22,female,62,137,medium,120,70,NA,NA,28,35,960 -2791,213,203,75,2.79999995,11.4099998,Buckingham,71,female,63,165,medium,150,80,145,80,34,42,960 -2793,173,131,69,2.5,4.44000006,Buckingham,76,female,61,102,medium,160,60,160,60,31,33,1020 -2794,232,184,114,2,8.39999962,Buckingham,91,female,61,127,NA,170,82,NA,NA,35,38,120 -2795,171,92,54,3.20000005,4.59000015,Buckingham,40,male,71,214,medium,138,94,140,80,41,39,240 -3250,164,86,40,4.0999999,5.23000002,Buckingham,23,female,69,245,large,126,75,NA,NA,44,47,420 -3750,170,69,64,2.70000005,4.38999987,Buckingham,20,female,64,161,medium,108,70,NA,NA,37,40,120 -3751,180,84,69,2.5999999,5.19999981,Buckingham,40,female,68,264,medium,142,98,130,92,43,54,240 -3752,204,57,74,2.79999995,6.11000013,Buckingham,52,male,75,142,small,140,90,NA,NA,31,35,300 -4000,209,113,65,3.20000005,7.44000006,Buckingham,76,female,60,143,large,156,78,144,76,35,40,1200 -4500,242,108,53,4.5999999,5.46999979,Buckingham,46,female,62,183,medium,130,86,NA,NA,37,45,180 -4501,134,105,42,3.20000005,4.28999996,Buckingham,48,male,70,173,large,178,120,182,110,36,40,240 -4506,217,81,60,3.5999999,3.93000007,Buckingham,22,female,71,223,medium,120,75,NA,NA,46,50,210 -4515,251,94,36,7,6.96000004,Buckingham,58,female,63,154,large,174,75,NA,NA,38,41,180 -4517,217,88,40,5.4000001,4.84000015,Buckingham,34,male,73,219,medium,145,100,NA,NA,41,42,270 -4750,300,103,44,6.80000019,5.17999983,Louisa,61,female,67,169,small,138,78,NA,NA,40,44,10 -4751,218,87,38,5.69999981,5.51999998,Louisa,40,male,73,200,small,120,76,NA,NA,38,41,210 -4753,189,96,47,4,4.38000011,Louisa,28,female,64,200,medium,136,52,NA,NA,38,45,60 -4758,185,84,52,3.5999999,5.28000021,Louisa,53,female,61,145,medium,147,72,NA,NA,37,40,420 -4759,206,85,46,4.5,4.82000017,Louisa,67,male,67,178,large,119,68,NA,NA,37,41,780 -4760,218,182,54,4,10.5500002,Louisa,51,female,NA,215,large,139,69,NA,NA,42,53,720 -4761,189,75,72,2.5999999,4.86000013,Louisa,49,female,62,205,medium,120,80,NA,NA,40,49,840 -4763,229,95,74,3.0999999,4.86000013,Louisa,65,female,62,151,medium,125,64,NA,NA,37,42,660 -4767,228,76,53,4.30000019,4.11000013,Louisa,54,male,66,170,large,121,62,NA,NA,36,41,420 -4770,159,88,43,3.70000005,5.01999998,Louisa,38,male,68,169,large,138,79,NA,NA,34,40,690 -4771,249,197,44,5.69999981,9.17000008,Louisa,64,female,63,159,medium,151,85,148,79,33,41,1140 -4772,170,106,42,4,5.11000013,Louisa,41,female,61,110,small,103,64,NA,NA,29,30,120 -4776,174,125,44,4,5.07000017,Louisa,67,male,68,198,large,119,72,NA,NA,36,43,60 -4780,204,62,70,2.9000001,4.84000015,Louisa,27,female,67,185,medium,110,90,NA,NA,35,44,10 -4783,203,84,75,2.70000005,4.0999999,Louisa,21,female,63,142,medium,125,85,117,68,28,39,900 -4786,241,86,63,3.79999995,4.78999996,Louisa,41,female,59,139,medium,112,72,NA,NA,29,39,1560 -4787,245,120,39,6.30000019,7.78999996,Louisa,47,female,63,156,medium,142,102,156,106,35,39,120 -4789,143,91,37,3.9000001,5.1500001,Louisa,61,female,65,220,large,160,92,150,98,40,50,20 -4790,224,341,33,6.80000019,10.1499996,Louisa,65,male,67,197,medium,160,80,158,80,42,43,390 -4792,168,69,45,3.70000005,4.17000008,Louisa,28,female,63,200,large,111,65,NA,NA,42,46,780 -4793,184,79,39,4.69999981,4.05000019,Louisa,41,male,69,154,large,136,96,130,94,34,39,600 -4794,199,130,48,4.0999999,5.44000006,Louisa,37,female,61,203,large,136,84,NA,NA,42,51,10 -4795,158,91,48,3.29999995,4.30999994,Louisa,50,male,71,180,medium,136,90,126,84,36,40,45 -4796,209,176,55,3.79999995,9.77000046,Louisa,57,female,61,150,small,115,68,NA,NA,36,39,780 -4801,214,111,59,3.5999999,3.8900001,Louisa,28,male,68,204,medium,130,90,NA,NA,40,41,60 -4802,293,85,94,3.0999999,5.17000008,Louisa,31,female,67,200,medium,110,90,NA,NA,41,42,240 -4803,227,105,44,5.19999981,5.71000004,Louisa,83,female,59,125,medium,150,90,156,88,35,40,300 -4805,292,235,55,5.30000019,7.86999989,Buckingham,79,male,70,165,NA,170,90,170,100,39,41,240 -4808,218,80,71,3.0999999,NA,Buckingham,68,male,70,170,large,130,73,NA,NA,37,42,720 -4813,244,101,36,6.80000019,4.65999985,Buckingham,32,male,70,212,NA,132,90,NA,NA,39,44,NA -4818,283,83,74,3.79999995,4.21999979,Louisa,26,male,72,227,large,158,104,158,108,41,44,330 -4821,186,74,76,2.4000001,5.17000008,Louisa,36,male,69,150,small,138,82,NA,NA,31,38,60 -4822,273,94,49,5.5999999,3.75999999,Louisa,53,female,64,174,medium,160,96,162,96,34,43,30 -4823,193,77,49,3.9000001,4.30999994,Louisa,19,female,61,119,small,118,70,NA,NA,32,38,300 -4825,194,80,34,5.69999981,4.61000013,Buckingham,63,male,73,175,medium,131,88,NA,NA,34,39,30 -4826,231,105,61,3.79999995,NA,Buckingham,58,female,63,230,large,141,99,NA,NA,39,48,30 -4827,217,78,48,4.5,NA,Buckingham,53,female,63,158,medium,139,79,NA,NA,33,40,720 -4833,174,173,34,5.0999999,5.3499999,Buckingham,50,male,70,263,large,159,99,150,89,51,64,210 -4835,225,84,82,2.70000005,4.36000013,Buckingham,41,male,71,156,small,150,80,NA,NA,31,40,120 -4840,268,85,51,5.30000019,4.40999985,Louisa,48,male,70,120,small,150,105,150,100,32,35,120 -4841,195,108,46,4.19999981,8.44999981,Louisa,59,female,67,172,small,150,102,150,100,38,43,300 -4842,179,70,52,3.4000001,3.98000002,Louisa,34,male,72,170,medium,138,82,NA,NA,31,39,1170 -4843,215,119,44,3.9000001,9.76000023,Louisa,63,female,63,158,medium,160,68,158,74,34,42,240 -10000,185,76,58,3.20000005,4.82999992,Buckingham,23,male,76,164,small,124,78,NA,NA,32,40,720 -10001,132,99,34,3.9000001,4.01000023,Buckingham,21,female,65,169,large,112,62,NA,NA,39,43,180 -10012,175,91,42,4.19999981,3.83999991,Louisa,23,female,65,235,medium,110,80,NA,NA,44,50,10 -10014,179,81,35,5.0999999,4.94999981,Buckingham,36,female,63,125,medium,110,76,NA,NA,33,36,240 -10016,228,115,61,3.70000005,6.38999987,Buckingham,71,female,63,244,large,170,92,NA,NA,48,51,660 -10020,181,177,24,7.5,7.53000021,Buckingham,64,male,71,225,large,130,66,NA,NA,44,47,180 -12002,160,100,36,4.4000001,4.61999989,Louisa,43,female,64,140,small,180,110,210,110,37,40,225 -12004,188,77,45,4.19999981,4.78999996,Louisa,31,female,67,227,medium,122,70,NA,NA,47,53,140 -12005,168,101,59,2.79999995,5.09000015,Louisa,44,female,64,160,small,130,88,NA,NA,40,43,60 -12006,318,270,108,2.9000001,6.51000023,Louisa,60,female,65,167,medium,132,72,NA,NA,38,44,30 -12501,192,109,44,4.4000001,4.86000013,Buckingham,43,female,64,325,large,141,79,NA,NA,53,62,60 -12502,209,87,34,6.0999999,4.40999985,Buckingham,48,female,63,121,small,111,62,NA,NA,32,38,855 -12506,129,110,42,3.0999999,6.13000011,Buckingham,56,male,74,151,small,140,75,NA,NA,34,38,90 -12507,160,122,41,3.9000001,6.48999977,Buckingham,55,female,67,223,medium,136,83,NA,NA,43,48,960 -12509,160,196,33,4.80000019,7.51000023,Buckingham,49,male,71,266,large,150,98,NA,NA,49,45,90 -12751,211,48,34,6.19999981,6.96999979,Louisa,58,male,67,177,medium,162,78,156,82,38,43,315 -12754,262,93,43,6.0999999,4.9000001,Louisa,33,female,63,170,medium,110,68,NA,NA,33,46,210 -12760,201,81,87,2.29999995,4.80999994,Buckingham,48,female,68,146,small,145,95,NA,NA,32,41,600 -12761,263,82,92,2.9000001,4.57999992,Buckingham,66,female,66,121,small,104,64,NA,NA,31,33,30 -12763,219,112,73,3,9.18000031,Buckingham,59,male,66,170,medium,146,92,168,98,37,40,120 -12765,191,83,88,2.20000005,5.46000004,Buckingham,45,female,67,151,small,130,90,NA,NA,33,38,1320 -12766,171,97,69,2.5,4.03999996,Buckingham,52,male,71,159,small,125,72,NA,NA,33,39,750 -12768,219,112,73,3,5.23000002,Buckingham,76,male,64,105,medium,125,82,NA,NA,29,33,60 -12769,347,197,42,8.30000019,6.34000015,Buckingham,36,male,70,277,large,140,86,NA,NA,51,49,900 -12772,269,73,34,7.9000001,5.36999989,Buckingham,41,female,62,160,medium,126,90,NA,NA,39,41,390 -12778,164,71,63,2.5999999,4.51000023,Buckingham,20,male,72,145,small,108,78,NA,NA,29,36,1080 -13250,181,255,26,7,9.57999992,Buckingham,50,male,71,320,large,140,86,NA,NA,56,49,30 -13254,190,84,44,4.30000019,5.55000019,Buckingham,43,female,62,163,large,135,88,NA,NA,40,45,720 -13500,255,112,34,7.5,5.5999999,Louisa,82,male,66,163,NA,179,89,172,91,37,43,60 -13501,218,126,32,6.80000019,4.86999989,Louisa,35,male,69,169,medium,139,90,136,86,39,41,720 -13503,223,90,48,4.5999999,5.5999999,Buckingham,47,female,65,232,large,120,86,NA,NA,46,54,900 -13505,254,342,37,6.9000001,12.9700003,Buckingham,75,male,68,210,large,151,87,NA,NA,44,45,15 -14756,236,102,36,6.5999999,5.63000011,Buckingham,62,male,76,160,large,150,80,NA,NA,35,39,270 -14758,176,92,55,3.20000005,4.5,Buckingham,31,female,62,145,small,110,72,NA,NA,36,42,720 -15007,158,91,31,5.0999999,5.55999994,Louisa,50,male,70,215,large,138,89,137,79,40,45,720 -15008,181,83,44,4.0999999,4.03000021,Louisa,39,female,66,255,medium,140,98,NA,NA,46,54,210 -15010,151,85,48,3.0999999,4.38000011,Louisa,33,male,69,308,large,110,90,NA,NA,52,58,300 -15012,115,239,36,3.20000005,13.6000004,Louisa,58,male,69,NA,medium,125,69,NA,NA,30,37,10 -15013,271,121,40,6.80000019,4.57000017,Louisa,81,female,64,158,medium,146,76,NA,NA,36,43,10 -15016,190,92,44,4.30000019,4.65999985,Louisa,27,female,65,210,medium,150,106,160,116,39,47,60 -15017,118,95,39,3,4.71000004,Louisa,47,female,64,123,small,140,76,NA,NA,30,36,300 -15250,168,82,44,3.79999995,4.4000001,Buckingham,33,female,66,118,small,98,66,NA,NA,29,35,150 -15252,254,121,39,6.5,9.25,Buckingham,67,male,68,167,large,161,118,151,111,36,39,60 -15260,193,77,45,4.30000019,4.73999977,Buckingham,42,female,75,186,medium,125,90,NA,NA,37,46,60 -15264,187,84,64,2.9000001,4.4000001,Buckingham,21,female,63,158,small,138,88,NA,NA,39,43,180 -15271,212,79,49,4.30000019,5.48999977,Buckingham,51,female,65,145,small,230,120,235,120,38,42,60 -15274,170,76,60,2.79999995,3.44000006,Buckingham,27,female,63,119,small,122,86,NA,NA,28,37,270 -15276,215,110,36,6,9.81999969,Louisa,51,female,67,282,medium,142,78,136,84,52,59,420 -15277,199,85,59,3.4000001,4.96000004,Louisa,71,male,69,171,large,136,86,NA,NA,38,40,240 -15278,140,385,31,4.5,11.5900002,Louisa,50,male,69,172,large,138,66,NA,NA,37,41,210 -15279,216,79,46,4.69999981,4.40999985,Louisa,54,female,65,138,small,132,80,NA,NA,33,39,990 -15500,204,113,35,5.80000019,4.44000006,Buckingham,59,male,73,187,medium,148,76,148,78,38,37,90 -15501,193,248,24,8,7.13999987,Buckingham,59,female,66,189,medium,140,90,NA,NA,38,45,90 -15502,267,133,34,7.9000001,8.81000042,Louisa,40,female,59,204,small,118,69,NA,NA,40,47,780 -15512,201,106,53,3.79999995,5.3499999,Louisa,58,male,66,215,large,186,102,190,110,46,44,360 -15513,204,120,44,4.5999999,4.69000006,Louisa,72,male,65,167,large,140,72,NA,NA,45,46,480 -15514,246,104,62,4,7.4000001,Louisa,66,female,66,189,medium,200,94,208,90,45,46,195 -15515,229,91,43,5.30000019,4.73000002,Louisa,23,male,72,180,small,110,78,NA,NA,34,41,60 -15516,172,101,46,3.70000005,4.51999998,Louisa,42,female,65,165,small,118,68,NA,NA,33,45,150 -15517,197,120,37,5.30000019,4.94999981,Louisa,43,male,71,179,medium,146,98,136,96,37,44,30 -15518,205,79,32,6.4000001,4.21000004,Louisa,75,male,69,204,large,136,90,NA,NA,44,42,120 -15519,219,106,50,4.4000001,4.55999994,Louisa,65,female,63,233,large,140,90,136,86,40,53,45 -15520,174,90,36,4.80000019,5.3499999,Louisa,34,male,71,210,medium,142,92,148,98,37,43,90 -15521,192,89,30,6.4000001,4.03999996,Louisa,37,male,71,195,medium,136,96,130,98,36,43,630 -15522,206,94,44,4.69999981,5.48999977,Louisa,61,female,63,199,medium,180,96,176,94,41,47,720 -15527,160,71,44,3.5999999,4.63999987,Louisa,36,female,64,185,medium,110,80,NA,NA,39,45,300 -15529,216,109,86,2.5,4.4000001,Louisa,45,female,67,147,medium,140,102,148,102,32,38,80 -15540,236,111,82,2.9000001,5.23999977,Louisa,68,female,61,119,small,142,96,140,86,29,37,135 -15542,205,88,41,5,NA,Louisa,57,male,66,171,medium,132,82,NA,NA,37,40,210 -15545,206,112,33,6.19999981,4.03000021,Louisa,41,female,62,184,small,104,80,NA,NA,39,44,10 -15546,143,371,46,3.0999999,4.80999994,Louisa,68,male,67,158,small,138,82,NA,NA,37,43,90 -15757,173,83,37,4.69999981,4.30999994,Buckingham,40,female,NA,130,small,122,76,NA,NA,37,38,360 -15758,235,91,37,6.4000001,5.23000002,Buckingham,79,female,65,134,small,142,70,NA,NA,34,38,240 -15760,169,95,29,5.80000019,5.21999979,Buckingham,62,male,66,251,large,118,72,NA,NA,50,47,720 -15761,283,145,39,7.30000019,8.25,Buckingham,63,female,61,200,medium,190,110,170,90,44,48,720 -15762,174,93,77,2.29999995,4.94999981,Buckingham,55,male,70,140,medium,118,86,NA,NA,32,33,120 -15763,271,103,90,3,4.01000023,Buckingham,55,female,63,114,small,180,105,165,105,30,37,15 -15766,203,94,62,3.29999995,4.67000008,Buckingham,27,female,67,209,medium,140,80,NA,NA,34,43,780 -15773,188,174,24,7.80000019,6.17000008,Louisa,66,male,68,210,large,160,78,158,84,45,48,60 -15777,293,87,120,2.4000001,4.76000023,Louisa,63,female,64,179,medium,142,80,142,90,47,45,30 -15779,215,80,100,2.20000005,4.65999985,Louisa,78,male,65,109,small,170,88,180,100,33,34,435 -15782,207,77,46,4.5,4.82000017,Buckingham,68,male,55,130,small,199,115,190,99,29,33,120 -15787,179,77,72,2.5,4.96999979,Buckingham,31,male,66,145,medium,131,79,NA,NA,33,38,150 -15792,202,81,55,3.70000005,5.5,Buckingham,64,female,62,167,medium,190,118,NA,NA,44,47,120 -15795,211,98,40,5.30000019,3.54999995,Buckingham,40,female,68,179,small,110,76,NA,NA,37,43,60 -15797,211,225,29,7.30000019,10.0900002,Buckingham,61,female,63,144,medium,190,100,170,86,40,42,120 -15798,151,74,47,3.20000005,4.01000023,Buckingham,28,male,69,130,small,135,75,NA,NA,29,35,720 -15799,171,85,61,2.79999995,5.0999999,Buckingham,34,female,63,164,medium,120,80,NA,NA,34,43,60 -15800,342,251,48,7.0999999,12.6700001,Buckingham,63,female,65,201,medium,178,88,160,82,45,46,180 -15801,179,236,63,2.79999995,12.0699997,Buckingham,55,male,75,186,medium,122,74,NA,NA,38,38,180 -15802,155,58,69,2.20000005,4.17000008,Buckingham,26,male,73,174,small,110,76,NA,NA,30,35,180 -15805,197,92,46,4.30000019,4.75,Buckingham,36,female,64,136,small,NA,NA,NA,NA,32,37,NA -15812,200,56,51,3.9000001,3.54999995,Buckingham,40,female,62,105,small,125,64,NA,NA,26,33,720 -15813,237,96,52,4.5999999,NA,Buckingham,45,male,69,130,small,137,74,NA,NA,33,35,720 -15814,198,118,46,4.30000019,4.44000006,Buckingham,68,female,63,124,medium,130,70,NA,NA,32,38,60 -15815,240,88,49,4.9000001,4.92000008,Buckingham,82,female,63,170,medium,180,86,NA,NA,41,46,720 -15816,192,56,42,4.5999999,4.59000015,Buckingham,60,female,62,134,small,130,70,NA,NA,31,40,90 -15818,145,84,54,2.70000005,4.73000002,Buckingham,30,female,65,165,small,102,56,NA,NA,33,42,720 -15820,269,59,66,4.0999999,5.13999987,Buckingham,41,male,67,191,large,130,73,NA,NA,38,41,240 -15821,240,96,57,4.19999981,5.73999977,Buckingham,54,female,65,175,medium,152,100,140,100,37,43,60 -15827,205,83,42,4.9000001,4.86999989,Buckingham,72,female,61,180,NA,170,90,150,100,39,47,240 -15828,266,82,54,4.9000001,5.40999985,Buckingham,47,male,68,142,medium,118,78,NA,NA,35,39,120 -16000,188,88,51,3.70000005,5.13000011,Buckingham,50,female,61,147,large,160,66,150,80,34,41,720 -16001,222,82,87,2.5999999,4.63999987,Buckingham,51,female,66,110,small,150,110,150,90,28,37,270 -16003,142,155,25,5.69999981,6.96000004,Buckingham,45,male,69,204,large,165,115,160,96,40,43,720 -16004,268,90,48,5.5999999,5.36000013,Buckingham,38,female,63,181,medium,142,100,144,110,38,46,210 -16005,174,105,117,1.5,5.53000021,Buckingham,20,male,70,187,medium,132,86,NA,NA,37,41,210 -16016,214,87,35,6.0999999,5.38000011,Buckingham,44,female,NA,190,large,140,75,NA,NA,38,44,720 -17002,194,54,57,3.4000001,4.26000023,Louisa,63,male,70,181,large,184,76,180,84,37,42,60 -17751,196,115,62,3.20000005,4.34000015,Louisa,50,male,67,140,medium,176,110,150,102,35,37,60 -17752,207,187,46,4.5,8.56999969,Louisa,44,female,67,201,large,150,74,146,76,46,49,30 -17754,204,89,56,3.5999999,5.01999998,Louisa,48,male,68,196,medium,170,96,178,96,38,42,90 -17755,189,84,46,4.0999999,4.36000013,Louisa,41,female,63,153,medium,130,80,NA,NA,32,40,15 -17756,179,77,50,3.5999999,3.32999992,Buckingham,29,male,68,170,small,122,68,NA,NA,38,39,300 -17757,159,100,54,2.9000001,4.17999983,Buckingham,76,male,66,188,large,116,53,NA,NA,40,41,180 -17760,260,68,60,4.30000019,4.78000021,Buckingham,69,female,59,179,large,158,98,159,80,45,48,180 -17762,228,79,37,6.19999981,4.73999977,Buckingham,26,male,72,259,large,122,90,NA,NA,48,49,720 -17765,242,74,55,4.4000001,3.97000003,Buckingham,70,female,66,200,medium,140,65,NA,NA,41,47,180 -17766,227,98,66,3.4000001,6.42000008,Buckingham,25,male,71,162,medium,123,82,NA,NA,35,39,900 -17767,208,122,51,4.0999999,6.48000002,Buckingham,42,female,62,141,large,118,78,NA,NA,33,40,720 -17771,208,95,32,6.5,5.5999999,Buckingham,56,male,68,183,medium,131,75,NA,NA,36,39,20 -17772,209,89,43,4.9000001,4.8499999,Buckingham,31,female,67,160,medium,108,58,NA,NA,30,44,240 -17773,163,83,57,2.9000001,4.61000013,Buckingham,31,female,65,120,small,136,86,NA,NA,29,40,240 -17776,201,100,46,4.4000001,4.0999999,Buckingham,27,female,65,145,small,121,75,NA,NA,32,35,60 -17781,237,118,45,5.30000019,7.51000023,Buckingham,73,female,64,174,large,162,75,NA,NA,38,44,300 -17784,176,90,34,5.19999981,4.23999977,Buckingham,32,female,63,252,medium,100,72,NA,NA,45,58,180 -17790,146,79,41,3.5999999,4.76000023,Buckingham,19,female,60,135,medium,108,58,NA,NA,33,40,240 -17791,231,70,110,2.0999999,3.75,Buckingham,71,female,63,155,small,150,78,NA,NA,33,41,900 -17794,241,92,40,6,5.03999996,Buckingham,27,female,63,179,medium,120,75,NA,NA,40,42,720 -17795,305,91,44,6.9000001,5.34000015,Buckingham,31,male,71,211,large,100,60,NA,NA,40,45,540 -17800,149,77,49,3,4.5,Buckingham,20,female,62,115,small,105,82,NA,NA,31,37,720 -17802,183,69,51,3.5999999,4.36999989,Buckingham,31,female,66,190,medium,125,70,NA,NA,41,47,720 -17805,235,109,59,4,7.48000002,Buckingham,62,female,63,290,large,175,80,152,102,55,62,300 -17808,244,101,39,6.30000019,4.36000013,Buckingham,44,male,71,168,medium,140,89,NA,NA,36,39,720 -17813,199,153,77,2.5999999,4.73999977,Buckingham,36,female,66,255,large,118,66,NA,NA,47,52,360 -17814,224,85,30,7.5,5.26000023,Buckingham,36,male,69,205,medium,150,99,130,80,37,41,360 -17816,173,225,31,5.5999999,10.4700003,Buckingham,47,male,73,260,medium,150,98,142,90,42,47,60 -17817,192,124,31,5.5999999,5.17000008,Buckingham,30,male,72,250,medium,142,79,NA,NA,43,51,120 -17818,157,91,34,4.5999999,5.69999981,Buckingham,63,male,69,166,large,106,82,NA,NA,39,38,420 -17819,172,117,56,3.0999999,3.58999991,Buckingham,48,female,63,170,medium,130,82,NA,NA,35,42,240 -17828,170,67,33,5.19999981,6.42000008,Buckingham,65,male,69,182,large,140,65,NA,NA,42,39,270 -17829,215,97,46,4.69999981,5.03000021,Buckingham,59,female,63,176,large,140,70,NA,NA,34,44,60 -17830,214,67,47,4.5999999,4.40999985,Buckingham,37,female,64,145,medium,108,76,NA,NA,34,42,90 -17834,195,171,29,6.69999981,5.67999983,Buckingham,78,male,66,172,large,130,82,NA,NA,40,40,60 -17835,230,86,37,6.19999981,4.38999987,Buckingham,23,male,71,277,large,150,99,150,85,50,49,840 -17841,206,90,38,5.4000001,4.07000017,Buckingham,38,female,69,167,medium,138,90,NA,NA,36,47,90 -17846,147,86,34,4.30000019,4.61999989,Buckingham,38,male,69,205,small,130,96,130,90,39,41,480 -17849,234,78,54,4.30000019,3.70000005,Buckingham,41,male,67,183,medium,122,96,126,96,38,40,NA -20254,135,88,34,4,3.96000004,Buckingham,29,female,65,123,small,118,61,NA,NA,26,37,240 -20258,226,68,83,2.70000005,NA,Buckingham,49,female,63,128,small,121,75,NA,NA,31,36,720 -20260,179,75,36,5,4.75,Buckingham,23,female,65,183,medium,120,80,NA,NA,43,45,720 -20261,163,69,48,3.4000001,4.30999994,Buckingham,29,female,62,99,small,125,60,NA,NA,30,36,720 -20267,191,74,33,5.80000019,5.3499999,Louisa,40,male,72,270,large,136,70,NA,NA,45,49,150 -20271,138,95,40,3.5,4.80000019,Louisa,38,female,60,138,small,140,90,NA,NA,31,39,330 -20272,184,92,36,5.0999999,4.80999994,Louisa,40,female,63,285,large,142,98,142,96,50,60,690 -20274,181,101,44,4.0999999,4.88000011,Louisa,29,male,68,180,medium,130,78,NA,NA,38,42,720 -20275,224,98,44,5.0999999,5.05000019,Louisa,78,female,63,160,large,150,81,NA,NA,36,45,300 -20278,293,115,54,5.4000001,4.86999989,Buckingham,50,male,71,170,medium,131,75,NA,NA,34,39,120 -20279,147,78,42,3.5,4.67000008,Buckingham,23,female,61,185,NA,127,71,NA,NA,43,47,600 -20288,198,92,62,3.20000005,4.42999983,Louisa,60,male,70,163,medium,126,78,NA,NA,36,40,795 -20289,152,103,32,4.80000019,4.26999998,Louisa,40,female,52,187,medium,148,82,158,80,38,49,135 -20290,277,119,62,4.5,5.03000021,Louisa,60,female,61,128,small,140,86,128,74,33,39,240 -20292,219,105,63,3.5,4.4000001,Louisa,40,female,62,153,small,106,82,NA,NA,36,44,10 -20293,182,74,44,4.0999999,4.67000008,Louisa,30,female,62,125,medium,132,80,NA,NA,31,39,480 -20294,135,88,47,2.9000001,4.21000004,Louisa,21,male,69,155,small,110,68,NA,NA,31,39,10 -20298,277,88,45,6.19999981,5.23999977,Louisa,63,female,64,223,medium,220,100,202,98,45,54,375 -20306,212,82,68,3.0999999,4.61000013,Louisa,63,male,70,161,medium,180,110,190,114,37,40,30 -20308,162,76,40,4.0999999,4.4000001,Louisa,43,male,67,216,large,100,70,NA,NA,41,44,30 -20309,207,102,43,4.80000019,5.01000023,Louisa,46,female,63,179,medium,212,114,210,112,38,46,150 -20312,255,100,34,7.5,6.05999994,Louisa,64,male,68,227,medium,134,74,NA,NA,44,47,270 -20313,404,206,33,12.1999998,10.75,Louisa,56,male,69,159,medium,162,88,150,80,38,39,570 -20314,239,97,55,4.30000019,4.69000006,Louisa,35,male,74,170,small,122,62,NA,NA,32,38,720 -20315,220,95,58,3.79999995,5.63000011,Louisa,59,female,66,138,small,138,80,NA,NA,32,38,30 -20318,165,76,46,3.5999999,3.69000006,Louisa,22,female,63,114,small,112,78,NA,NA,28,35,120 -20325,243,74,42,5.80000019,3.8499999,Louisa,43,female,64,239,medium,128,90,138,90,48,53,330 -20329,149,138,50,3,4.09000015,Louisa,26,female,62,174,medium,148,92,138,84,38,46,10 -20332,178,64,52,3.4000001,4.0999999,Louisa,41,female,65,188,small,130,76,NA,NA,35,46,5 -20335,190,228,57,3.29999995,9.27999973,Louisa,43,female,65,198,small,110,64,NA,NA,40,49,60 -20337,226,97,70,3.20000005,3.88000011,Louisa,20,female,64,114,small,122,64,NA,NA,31,39,90 -20340,132,83,40,3.29999995,5.69999981,Louisa,28,female,68,225,medium,136,86,NA,NA,41,52,105 -20343,160,82,41,3.9000001,2.8499999,Louisa,30,female,63,143,medium,172,124,176,124,33,40,30 -20346,204,173,37,5.5,13.0600004,Louisa,66,male,67,146,medium,138,78,NA,NA,36,48,1260 -20350,164,91,67,2.4000001,3.97000003,Louisa,20,female,70,141,small,122,86,NA,NA,32,39,390 -20352,155,81,70,2.20000005,3.02999997,Louisa,32,female,65,151,small,120,68,NA,NA,33,40,420 -20355,251,118,38,6.5999999,5.51000023,Louisa,38,female,64,248,medium,110,80,NA,NA,49,58,15 -20361,198,86,66,3,5.67999983,Louisa,61,male,74,152,small,138,76,NA,NA,33,38,420 -20365,179,90,60,3,4.19999981,Louisa,26,female,60,130,small,138,84,NA,NA,32,40,270 -20367,223,88,42,5.30000019,6.44000006,Louisa,74,female,62,165,medium,250,100,NA,NA,41,46,60 -20368,207,71,41,5,9.61999989,Louisa,72,male,70,180,medium,138,88,NA,NA,39,40,45 -20369,244,89,92,2.70000005,4.53999996,Louisa,21,male,71,163,medium,116,76,NA,NA,34,39,180 -20750,245,119,26,9.39999962,7.51000023,Louisa,36,male,66,179,large,150,92,130,86,37,42,390 -20754,191,81,53,3.5999999,5.63000011,Louisa,42,female,61,156,medium,138,84,NA,NA,36,42,150 -20761,221,120,83,2.70000005,5.76999998,Louisa,66,female,64,130,small,110,64,NA,NA,31,38,15 -20762,300,65,59,5.0999999,4.55999994,Louisa,34,female,NA,160,small,120,60,NA,NA,40,47,300 -20765,173,85,58,3,4.4000001,Buckingham,43,female,69,210,medium,130,75,NA,NA,44,47,720 -20768,138,81,45,3.0999999,4.69999981,Buckingham,57,male,73,164,small,148,81,NA,NA,31,37,240 -20773,203,71,78,2.5999999,2.8499999,Louisa,45,male,66,115,small,135,88,NA,NA,30,34,15 -20774,260,67,46,5.69999981,5.34000015,Louisa,44,female,62,159,small,140,94,130,95,36,43,330 -20775,166,77,68,2.4000001,4.94999981,Louisa,27,male,72,141,small,110,58,NA,NA,33,38,120 -20782,180,92,34,5.30000019,3.58999991,Buckingham,63,male,69,169,small,145,72,142,70,35,39,30 -20783,159,172,28,5.69999981,8.22999954,Buckingham,65,male,70,181,large,142,81,NA,NA,43,49,480 -20784,207,75,44,4.69999981,5.05999994,Buckingham,30,male,72,180,small,118,62,NA,NA,35,41,180 -20787,298,84,50,6,NA,Buckingham,28,male,66,209,medium,131,111,130,80,42,46,300 -20790,203,104,36,5.5999999,NA,Buckingham,41,male,71,210,NA,140,112,138,89,37,42,30 -21254,191,155,58,3.29999995,8.06000042,Buckingham,31,female,62,237,large,140,87,NA,NA,53,56,240 -21255,231,84,91,2.5,4.9000001,Buckingham,33,male,69,163,small,140,70,NA,NA,35,38,150 -21257,184,76,42,4.4000001,4.71000004,Buckingham,66,male,74,185,medium,130,75,NA,NA,40,41,180 -21281,164,94,58,2.79999995,3.79999995,Buckingham,28,female,67,180,small,128,94,124,96,39,43,270 -21284,134,101,36,3.70000005,4.67000008,Buckingham,25,female,63,245,NA,142,78,141,80,47,58,10 -21298,220,60,66,3.29999995,10.9700003,Buckingham,26,male,70,150,small,136,88,NA,NA,33,39,300 -21318,180,76,46,3.9000001,4.42999983,Louisa,40,female,64,146,medium,128,82,NA,NA,37,43,240 -21320,216,155,30,7.19999981,5.90999985,Louisa,38,male,68,145,medium,110,60,NA,NA,34,37,20 -21321,158,74,64,2.5,2.73000002,Louisa,30,female,62,142,medium,108,68,NA,NA,NA,NA,330 -21322,261,101,83,3.0999999,5.11999989,Louisa,52,female,64,198,medium,152,92,162,92,42,49,20 -21323,172,70,36,4.80000019,3.77999997,Louisa,22,female,64,148,small,90,48,NA,NA,35,38,240 -21329,249,81,28,8.89999962,5.11999989,Louisa,51,female,65,200,medium,122,90,NA,NA,43,46,150 -21333,189,80,40,4.69999981,3.61999989,Louisa,45,male,69,190,large,140,75,NA,NA,39,44,300 -21334,225,74,36,6.30000019,4.65999985,Louisa,53,female,63,182,large,126,80,NA,NA,38,46,540 -21338,193,75,49,3.9000001,5.01000023,Louisa,21,female,61,220,small,130,82,NA,NA,40,52,240 -21341,219,78,67,3.29999995,3.75,Louisa,53,female,64,179,medium,135,100,170,98,39,47,150 -21343,156,86,34,4.5999999,4.55000019,Louisa,37,female,67,212,small,122,74,NA,NA,48,51,150 -21345,224,71,42,5.30000019,4.92000008,Louisa,34,female,60,165,medium,135,80,NA,NA,34,46,30 -21346,181,77,46,3.9000001,4.09000015,Louisa,30,female,66,257,medium,162,108,158,110,47,55,60 -21347,306,92,56,5.5,5.57999992,Louisa,74,male,69,184,large,140,72,NA,NA,39,41,195 -21357,122,82,43,2.79999995,3.98000002,Louisa,36,female,71,183,NA,110,80,NA,NA,41,45,90 -21359,219,130,44,5,7.21999979,Louisa,45,male,67,218,large,172,110,168,108,41,45,180 -40251,150,80,38,3.9000001,3.97000003,Louisa,35,male,73,179,medium,138,92,135,88,32,37,450 -40253,185,67,59,3.0999999,4.6500001,Louisa,50,female,64,228,medium,142,90,142,92,42,54,225 -40500,226,100,65,3.5,4.82999992,Louisa,27,male,69,289,large,130,100,170,114,48,51,75 -40501,206,83,68,3,4.88000011,Louisa,52,male,69,153,small,140,98,142,102,36,40,195 -40502,199,81,36,5.5,4.92999983,Louisa,42,female,67,235,large,178,100,170,96,47,52,210 -40751,239,85,63,3.79999995,5.15999985,Louisa,39,male,60,144,medium,162,90,152,90,33,42,180 -40754,235,106,37,6.4000001,6.78000021,Louisa,73,male,65,183,large,134,78,NA,NA,43,46,195 -40755,184,99,36,5.0999999,4.15999985,Louisa,28,male,67,154,small,124,94,110,74,35,38,330 -40762,242,297,34,7.0999999,12.1599998,Louisa,53,male,69,216,large,142,96,142,98,43,45,285 -40764,307,87,58,5.30000019,4.28000021,Louisa,49,male,67,181,small,120,80,NA,NA,41,42,240 -40772,204,94,54,3.79999995,4.15999985,Louisa,55,female,66,202,small,140,90,140,90,43,47,150 -40773,212,88,36,5.9000001,5.21999979,Louisa,37,female,64,160,small,124,82,NA,NA,37,45,15 -40774,203,90,51,4,14.9399996,Louisa,60,female,59,123,medium,130,72,NA,NA,36,41,60 -40775,219,173,31,7.0999999,10.1599998,Louisa,56,female,65,197,small,100,50,NA,NA,41,50,210 -40784,226,279,52,4.30000019,10.0699997,Louisa,84,female,60,192,small,144,88,146,82,41,48,210 -40785,217,75,54,4,3.66000009,Louisa,20,female,67,187,medium,110,72,NA,NA,40,45,1440 -40786,157,92,47,3.29999995,6.48000002,Louisa,80,male,71,212,medium,156,88,158,86,47,48,390 -40787,235,102,42,5.5999999,4.9000001,Louisa,60,male,69,186,medium,148,98,130,100,40,42,900 -40789,252,161,87,2.9000001,11.1800003,Louisa,80,female,62,162,small,160,100,160,100,44,41,1440 -40792,204,71,55,3.70000005,4.32999992,Louisa,29,female,64,120,small,110,70,NA,NA,33,38,90 -40797,188,84,46,4.0999999,3.75,Louisa,43,female,66,152,small,122,80,NA,NA,37,41,260 -40799,194,95,36,5.4000001,4.96999979,Louisa,63,female,58,210,medium,140,100,136,100,44,53,240 -40803,215,64,84,2.5999999,4.03999996,Louisa,37,female,59,148,medium,140,100,136,92,32,42,270 -40804,179,105,60,3,4.67999983,Louisa,20,female,58,170,medium,140,100,138,82,34,46,270 -40805,202,84,33,6.0999999,4.17000008,Louisa,44,male,68,157,small,125,80,NA,NA,33,37,180 -41000,194,87,65,3,4.13999987,Louisa,54,male,69,129,small,170,96,160,94,30,37,15 -41001,227,85,26,8.69999981,4.98000002,Louisa,58,male,70,211,large,144,82,144,80,38,43,480 -41003,337,85,62,5.4000001,4.65999985,Louisa,35,male,72,189,medium,124,84,NA,NA,36,44,240 -41004,255,83,90,2.79999995,4.28999996,Louisa,52,male,70,120,medium,170,110,166,108,30,33,780 -41021,162,90,46,3.5,5.55999994,Louisa,60,female,63,121,medium,110,64,NA,NA,32,34,300 -41023,322,87,92,3.5,4.44999981,Louisa,43,female,56,120,NA,120,98,122,100,32,41,60 -41029,289,267,38,7.5999999,11.4099998,Louisa,59,male,68,169,large,142,79,NA,NA,36,38,900 -41034,217,87,40,5.4000001,4.07000017,Louisa,33,female,62,186,small,140,90,138,84,42,46,40 -41035,209,91,36,5.80000019,5.01000023,Louisa,37,male,70,262,medium,130,94,130,88,42,48,450 -41036,214,77,48,4.5,4.48000002,Louisa,40,male,72,222,medium,120,84,NA,NA,40,44,1020 -41037,302,81,57,5.30000019,4.6500001,Louisa,38,female,67,222,medium,128,82,NA,NA,41,51,210 -41039,179,85,52,3.4000001,4.05000019,Louisa,32,female,62,179,medium,140,96,148,100,37,47,60 -41041,279,270,40,7,8.10999966,Louisa,60,female,68,224,large,174,90,174,84,48,50,180 -41055,144,81,28,5.0999999,4.13000011,Louisa,30,male,72,165,small,118,78,NA,NA,31,38,180 -41063,270,73,40,6.80000019,3.57999992,Louisa,42,male,66,185,large,146,94,149,94,39,41,30 -41065,196,120,67,2.9000001,9.36999989,Louisa,52,female,62,147,medium,144,94,142,92,34,42,480 -41075,221,126,48,4.5999999,5.53000021,Louisa,59,female,62,177,medium,130,78,NA,NA,39,45,60 -41078,210,81,81,2.5999999,4.96000004,Louisa,78,male,66,145,large,110,70,NA,NA,38,39,540 -41253,192,85,69,2.79999995,4.38000011,Louisa,51,male,65,146,large,130,110,170,118,NA,NA,60 -41254,169,104,58,2.9000001,4.82000017,Louisa,25,female,60,154,medium,140,95,130,94,40,42,60 -41500,179,85,50,3.5999999,4.98999977,Louisa,37,male,66,136,medium,190,94,172,100,33,39,480 -41501,216,84,64,3.4000001,NA,Louisa,54,female,66,168,medium,132,90,126,80,38,42,330 -41503,301,90,118,2.5999999,4.28000021,Louisa,89,female,61,115,medium,218,90,238,90,31,41,210 -41506,296,369,46,6.4000001,16.1100006,Louisa,53,male,69,173,medium,138,94,130,94,35,39,210 -41507,284,89,54,5.30000019,4.38999987,Louisa,51,female,63,154,medium,140,100,146,102,32,43,180 -41510,194,269,38,5.0999999,13.6300001,Louisa,29,female,69,167,small,120,70,NA,NA,33,40,20 -41752,199,76,52,3.79999995,4.48999977,Louisa,41,female,63,197,medium,120,78,NA,NA,41,48,255 -41756,159,88,79,2,NA,Louisa,68,female,64,220,medium,100,72,NA,NA,49,58,900 diff --git a/live-demo-olga/fs-demo.qmd b/live-demo-olga/fs-demo.qmd deleted file mode 100644 index 73dab0ab..00000000 --- a/live-demo-olga/fs-demo.qmd +++ /dev/null @@ -1,228 +0,0 @@ ---- -title: "live-demo-fs" -format: html -editor: visual -editor_options: - chunk_output_type: console ---- - - -EDA -```{r} -# load libraries -library(tidyverse) -library(tidymodels) -library(ggcorrplot) -library(reshape2) -library(vip) - -# import raw data -input_diabetes <- read_csv("data/data-diabetes.csv") - -# create BMI variable -conv_factor <- 703 # conversion factor to calculate BMI from inches and pounds BMI = weight (lb) / [height (in)]2 x 703 -data_diabetes <- input_diabetes %>% - mutate(BMI = weight / height^2 * 703, BMI = round(BMI, 2)) %>% - relocate(BMI, .after = id) - -# preview data -glimpse(data_diabetes) - -# run basic EDA -# note: we have seen descriptive statistics and plots during EDA session -# note: so here we only look at missing data and correlation - -# calculate number of missing data per variable -data_na <- data_diabetes %>% - summarise(across(everything(), ~ sum(is.na(.)))) - -# make a column plot to visualize the counts -data_na_long %>% - ggplot(aes(x = variable, y = count)) + - geom_col(fill = "blue4") + - xlab("") + - theme_bw() + - theme(axis.text.x = element_text(angle = 45, vjust = 1, hjust=1)) - - -# make a table with counts sorted from highest to lowest -data_na_long <- data_na %>% - pivot_longer(-id, names_to = "variable", values_to = "count") %>% - arrange(desc(count)) - -# calculate correlation between numeric variables -data_cor <- data_diabetes %>% - dplyr::select(-id) %>% - dplyr::select(where(is.numeric)) %>% - cor(use = "pairwise.complete.obs") - -# visualize correlation via heatmap -ggcorrplot(data_cor, hc.order = TRUE, lab = FALSE) - -# bass on the number of missing data, let's delete bp.2s, bp.2d -# and use complete-cases analysis -data_diabetes_narm <- data_diabetes %>% - dplyr::select(-bp.2s, -bp.2d) %>% - na.omit() -``` - -Data splitting -```{r} -# use tidymodels framework to fit Lasso regression model for predicting BMI -# using repeated cross-validation to tune lambda value in L1 penalty term - -# select random seed value -myseed <- 123 - -# split data into non-test (other) and test (80% s) -set.seed(myseed) -data_split <- initial_split(data_diabetes_narm, strata = BMI, prop = 0.8) # holds splitting info -data_other <- data_split %>% training() # creates non-test set (function is called training but it refers to non-test part) -data_test <- data_split %>% testing() # creates test set - -# prepare repeated cross-validation splits with 5 folds repeated 3 times -set.seed(myseed) -data_folds <- vfold_cv(data_other, - v = 5, - repeats = 3, - strata = BMI) - -# check the split -dim(data_diabetes) -dim(data_other) -dim(data_test) - -# check BMI distributions in data splits -par(mfrow=c(3,1)) -hist(data_diabetes$BMI, xlab = "", main = "BMI: all", 50) -hist(data_other$BMI, xlab = "", main = "BMI: non-test", 50) -hist(data_test$BMI, xlab = "", main = "BMI: test", 50) -``` - -Feature engineering -```{r} -# create data recipe (feature engineering) - -inch2m <- 2.54/100 -pound2kg <- 0.45 - -data_recipe <- recipe(BMI ~ ., data = data_other) %>% - update_role(id, new_role = "sampleID") %>% - step_mutate(height = height * inch2m, height = round(height, 2)) %>% # convert height to meters - step_mutate(weight = weight * pound2kg, weight = round(weight, 2)) %>% # convert weight to kg - step_rename(glu = stab.glu) %>% # rename stab.glu to glu - step_log(glu) %>% #ln transform glucose - step_zv(all_numeric()) %>% # removes variables that are highly sparse and unbalanced (if found) - step_corr(all_numeric(), -all_outcomes(), -has_role("sampleID"), threshold = 0.8) %>% # removes variables with large absolute correlations with other variables (if found) - step_dummy(location, gender, frame) %>% # convert categorical variables to dummy variables - step_normalize(all_numeric(), -all_outcomes(), -has_role("sampleID"), skip = FALSE) - - # you can implement more steps: see https://recipes.tidymodels.org/reference/index.html - -# print recipe -data_recipe - -# check if recipe is doing what it is supposed to do -# i.e. bake the data -data_other_prep <- data_recipe %>% - prep() %>% - bake(new_data = NULL) - -## bake test data -data_test_prep <- data_recipe %>% - prep() %>% - bake(new_data = data_test) - -# preview baked data -print(head(data_other_prep)) - -``` - -Lasso regression (tune) -```{r} -# define model -model <- linear_reg(penalty = tune(), mixture = 1) %>% - set_engine("glmnet") %>% - set_mode("regression") - -# create workflow with data recipe and model -wf <- workflow() %>% - add_model(model) %>% - add_recipe(data_recipe) - -# define parameters range for tuning -grid_lambda <- grid_regular(penalty(), levels = 25) - -# tune lambda -model_tune <- wf %>% - tune_grid(resamples = data_folds, - grid = grid_lambda) - -# show metrics average across folds -model_tune %>% - collect_metrics(summarize = TRUE) - -# plot k-folds results across lambda range -model_tune %>% - collect_metrics() %>% - dplyr::filter(.metric == "rmse") %>% - ggplot(aes(penalty, mean, color = .metric)) + - geom_errorbar(aes( ymin = mean - std_err, ymax = mean + std_err), alpha = 0.5) + - scale_x_log10() + - geom_line(linewidth = 1.5) + - theme_bw() + - theme(legend.position = "none") + - scale_color_brewer(palette = "Set1") - - -``` - -Lasso final model (predictions) -```{r} - -# best lambda value (min. RMSE) -model_best <- model_tune %>% - select_best("rmse") -print(model_best) - -# finalize workflow with tuned model -wf_final <- wf %>% - finalize_workflow(model_best) - -# last fit -fit_final <- wf_final %>% - last_fit(split = data_split) - -# final predictions -y_test_pred <- fit_final %>% collect_predictions() # predicted BMI - -# final predictions: performance on test (unseen data) -fit_final %>% collect_metrics() - -# plot predictions vs. actual for test data -plot(data_test$BMI, y_test_pred$.pred, xlab="BMI (actual)", ylab = "BMI (predicted)", las = 1, pch = 19) - -# correlation between predicted and actual BMI values for test data -cor(data_test$BMI, y_test_pred$.pred) - - -``` - -Lasso final model (feature selection) -```{r} - -# re-fit model on all non-test data -model_final <- wf_final %>% - fit(data_other) - -# show final model -tidy(model_final) - -# plot variables ordered by importance (highest abs(coeff)) -model_final %>% - extract_fit_parsnip() %>% - vip(geom = "point") + - theme_bw() - -``` - diff --git a/live-demo-olga/knn-demo.qmd b/live-demo-olga/knn-demo.qmd deleted file mode 100644 index 8fee4f82..00000000 --- a/live-demo-olga/knn-demo.qmd +++ /dev/null @@ -1,147 +0,0 @@ ---- -title: "knn-demo" -format: html -editor: visual -editor_options: - chunk_output_type: console ---- - -```{r} - -# load libraries -library(tidyverse) -library(splitTools) -library(kknn) -library(faraway) - -# input data -#input_diabetes <- read_csv("data/data-diabetes.csv") -input_diabets <- diabetes - -# clean data -inch2cm <- 2.54 -pound2kg <- 0.45 -data_diabetes <- input_diabetes %>% - mutate(height = height * inch2cm / 100, height = round(height, 2)) %>% - mutate(waist = waist * inch2cm) %>% - mutate(weight = weight * pound2kg, weight = round(weight, 2)) %>% - mutate(BMI = weight / height^2, BMI = round(BMI, 2)) %>% - mutate(obese= cut(BMI, breaks = c(0, 29.9, 100), labels = c("No", "Yes"))) %>% - mutate(diabetic = ifelse(glyhb > 7, "Yes", "No"), diabetic = factor(diabetic, levels = c("No", "Yes"))) %>% - mutate(location = factor(location)) %>% - mutate(frame = factor(frame)) %>% - mutate(gender = factor(gender)) - -``` - - -```{r} -# select data for KNN -data_input <- data_diabetes %>% - select(obese, waist, hdl) %>% - na.omit() - -# preview data -glimpse(data_input) - -# How many obese and non-obese in our data set? -data_input %>% - count(obese) - -# make a plot -data_input %>% - ggplot(aes(x = waist, y = hdl, fill = obese)) + - geom_point(shape=21, alpha = 0.7, size = 2) + - theme_bw() + - scale_fill_manual(values = c("blue3", "brown1")) + - theme(legend.position = "top") - -``` - - -```{r} -# split data into train (40%), validation (40%) and test (20%) -# stratify by obese -randseed <- 123 -set.seed(randseed) - -inds <- partition(data_input$obese, p = c(train = 0.4, valid = 0.4, test = 0.2), seed = randseed) -str(inds) - -data_train <- data_input[inds$train,] -data_valid <- data_input[inds$valid,] -data_test <- data_input[inds$test, ] - -# check dimensions of data -data_train %>% dim() -data_valid %>% dim() -data_test %>% dim() - -# check distribution of obese and non-obese -data_train %>% - group_by(obese) %>% - count() - -data_valid %>% - group_by(obese) %>% - count() - -data_test %>% - group_by(obese) %>% - count() - -``` - - -```{r} -# prepare parameters search space -n <- nrow(data_train) -k_values <- seq(1, n-1, 2) # check every odd value of k between 1 and number of samples-1 - -# allocate empty vector to collect overall classification rate for each k -cls_rate <- rep(0, length(k_values)) - -for (l in seq_along(k_values)) -{ - - # fit model given k value - model <- kknn(obese ~., data_train, data_valid, - k = k_values[l], - kernel = "rectangular") - - # extract predicted class (predicted obesity status) - cls_pred <- model$fitted.values - - # define actual class (actual obesity status) - cls_true <- data_valid$obese - - # calculate overall classification rate - cls_rate[l] <- sum((cls_pred == cls_true))/length(cls_pred) - -} -``` - - -```{r} -# plot classification rate as a function of k -plot(k_values, cls_rate, type="l", xlab="k", ylab="cls rate") -``` - - -```{r} - -k_best <- 22 - - -# How would our model perform on the future data using the optimal k? -model_final <- kknn(obese ~., data_train, data_test, k=k_best, kernel = "rectangular") -cls_pred <- model_final$fitted.values -cls_true <- data_test$obese - -cls_rate <- sum((cls_pred == cls_true))/length(cls_pred) -print(cls_rate) - -``` - - - diff --git a/notes-olga.md b/notes-olga.md new file mode 100644 index 00000000..8e5207f2 --- /dev/null +++ b/notes-olga.md @@ -0,0 +1,12 @@ +# ToDo + +## From 2024-04 + +- adjust/improve quizzes +- improve clustering presentation +- PCA? +- add some scRNA-seq examples? +- mention Python packages when talking about tidymodels? +- have some Python examples in parallel? +- for each section: add latest papers, further reading +- prepare list of R packages to install diff --git a/todo-and-notes.md b/notes-todo.md similarity index 93% rename from todo-and-notes.md rename to notes-todo.md index bc4a5ccd..ec0ae07f 100644 --- a/todo-and-notes.md +++ b/notes-todo.md @@ -3,8 +3,6 @@ layout: default title: 'To Do' --- -# Notes - ## 2023 notes ### Overall @@ -26,17 +24,8 @@ title: 'To Do' - future: list all libraries used throughout the course - future: double-check the R quiz & potentially add more questions -### Welcome slides - -- updated - ### Descriptive stats -- add summary descriptive in data science project context, can also include EDA and IDA; data cleaning, missing values, outliers etc. -- add some links to learning more about descriptive statistics and data visulizations -- update exercises -- update the slides given the writing -- future: add more plots examples with summary statistics to the box plot chapter - notes: potential group discussion points: - a) think of roboust measure of spread: MAD - b) mean vs. median -> transformations @@ -45,10 +34,8 @@ title: 'To Do' - future: add a note on geometric mean - future: move to distributions: add a note on mean vs. median on skewed distributions - ## Probability & Inference -- Eva improves - future: add transformations after distributions, explained skewed distributions ## Non-parametric tests @@ -59,7 +46,6 @@ title: 'To Do' - Read more about the CI estimates and functions in R - Improve exercises and add (next round) - Add more on sign test (next round) -- Fix “weight” spelling (next round) - Add examples on Kruskal-Wallis (next round) - Shiny app / Python app to demonstrate W distribution and associated p-values? - Visualisation of Kendal tau? (next round) @@ -75,18 +61,15 @@ title: 'To Do' - mention beyond: mixed effect models (maybe write a short intro one day) - fix typos as per Jonas's email -## Dimnesion reduction +## Dimnension reduction -- Payam is improving this round up -- Maybe add covariance matrix already in the precourse +- Maybe add covariance matrix already in the precourse? - Talk correlation and covariance before, maybe in linear models - Make it easier to understand eigenvectors and eigenvalues ## Clustering -- Payam is improving this round up - Better questions and examples for the quiz -- What are the teaching goals here? ## Supervised learning @@ -94,13 +77,9 @@ title: 'To Do' - Link to scikit-learn main website of main machine learning application and highlight where all these methods fall so it does not feel that biostats and ML are two different words and we focus only last two days on ML - Highlight that regression, logistic regression, GLM, feature selection are all part of it -## Feature engineering and selection - -- IMP: Adjust following Eva's feedback - ## Daily quizzes -- IMP: check questions and answers, especially clustering +- IMP: check questions and answers, especially clustering - check questions and answers but in principle redesign them to reiterate the message of the day - maybe even have more questions to practice, pool of questions? - prepare .pdfs with solutions diff --git a/olga-notes.md b/olga-notes.md deleted file mode 100644 index a39f90b9..00000000 --- a/olga-notes.md +++ /dev/null @@ -1,188 +0,0 @@ -# ToDo - -## Logistics - -- split students into groups -- adjust quizzes -- adjust feedback form - -## Welcome - -- (done) add explanation about chapters v.s presentations -- (done) add note about lunches - -## Descriptive statistics - -- (done) check if code is available -- (done) add gtsummary() examples and to exercises - -## Wednesday - -10:00 - 11:00 Introduction to supervised learning with KNN-classifier - -Objectives: - -- understand difference between supervised vs. unsupervised learning -- being able to run KNN classifier with data splitting and outcome evaluation - -11:00 - 12:00 Introduction to linear models - -- what linear models are -- simple linear regression demo -- slope / intercept -- hypothesis testing -- vector-matrix notations - -12:00 - 13:00 lunch - -13:00 - 14:30 Linear models: regression and classification - -- checking model assumptions -- assessing model fit -- linear model selection & regularization -- GLM: logistic regression, Poisson regression -- Logistics Lasso? - -14:30 - 15:00 break -15:00 - 16:00 Linear models exercises - -- common cases -- putting into ML context (regression, regularized regression) - -## Thursday - -10:00 - 12:00 Linear models: common cases (go through together) -12:00 - 13:00 lunch -13:00 - 14:30 PCA -14:30 - 15:00 break -15:00 - 16:30 Clustering - -## Friday - -10:00 - 12:00 Putting everything together with tidymodels -12:00 - 13:00 lunch -13:00 - 14:30 Random Forest - -ChatGPT - -Creating a structured and engaging 30-minute presentation on an introduction to supervised learning can provide a solid foundation for understanding this key concept in machine learning. Here’s an outline that covers the basics, introduces core concepts, and includes examples to help illustrate the principles: - -### Presentation Outline: Introduction to Supervised Learning - -#### Slide 2: Overview of Machine Learning -- Definition of machine learning -- Brief description of the types of machine learning: Supervised, Unsupervised, and Reinforcement Learning - -#### Slide 3: What is Supervised Learning? -- Definition of supervised learning -- Key characteristics of supervised learning -- Contrast with unsupervised learning - -#### Slide 4: How Supervised Learning Works -- Overview of the process (Training Data -> Learning Algorithm -> Model) -- Explanation of training data (features and labels) - -#### Slide 5: Types of Supervised Learning -- Classification - - Definition and when to use it - - Example: Email spam detection -- Regression - - Definition and when to use it - - Example: Housing price prediction - -#### Slide 6: Data Collection and Preparation -- Importance of data in machine learning -- Steps in data collection and cleaning -- Example of a simple dataset - -#### Slide 7: Splitting Data -- Explanation of training set and test set -- Importance of data splitting -- Cross-validation (brief mention) - -#### Slide 8: Choosing the Right Algorithm -- Factors influencing the choice of algorithm -- Brief overview of popular algorithms: - - Linear regression - - Logistic regression - - Decision trees - -#### Slide 9: Model Training -- Explanation of how models are trained -- Importance of feature selection -- Overfitting vs. underfitting - -#### Slide 10: Model Evaluation -- Metrics for performance evaluation - - Accuracy, precision, recall for classification - - MSE, RMSE for regression -- Importance of model evaluation - -#### Slide 11: Improving Model Performance -- Techniques for improving performance - - Feature engineering - - Hyperparameter tuning - -#### Slide 12: Practical Example: Building a Simple Classifier -- Step-by-step example using a simple dataset (e.g., Iris dataset) -- Tools and code snippets (e.g., Python with scikit-learn) - -#### Slide 13: Applications of Supervised Learning -- Real-world applications in various industries - - Healthcare, finance, marketing, etc. - -#### Slide 14: Challenges in Supervised Learning -- Data quality and quantity issues -- Ethical considerations (bias, privacy) - -#### Slide 15: Future Trends and Conclusion -- Brief discussion on the future of supervised learning -- Recap of key points -- Q&A - -#### Slide 16: Additional Resources -- Books, courses, and websites for further learning -- Contact information for follow-up questions - -### Tips for Presentation: -- Use clear, concise language and avoid jargon when possible. -- Include visual aids like diagrams and flowcharts to explain concepts. -- Provide real-world examples to help the audience relate to the material. -- Engage the audience with questions or quick activities related to the examples. - -This outline will help you craft a comprehensive introduction to supervised learning that is informative and engaging for your audience. - - - - -- Linear: remove what linear models are and are not? Missing plus, and indices; change term to least-squares models? -- Linear: checking assumptions plots from lm() method, maybe focus more on that, explain Cook’s distance; add more examples, correlated measurements. -- Splitting presentation: checking assumptions, logistics regressions into two entries under Schedule -- Linear: bring the same challenge from Tuesday but add age now - - - - - - -- Probability: add potential group exercises: log-normal, normal, binomial, negative binomial, omits: guess what is the distribution and what omics it comes from? -- Probability: add a note about transformations, scaling, normalisation -- Remove non-parametric session, move the correlation back to main on Tuesday main or Wednesday, add link to chapter. -- Tuesday competition: simulated data, find DEGs, log it, t-test, confidence intervals and/or demonstration -- Check quizzes for Tuesday after removing non-parametric session. - - -- Feature engineering & selection: check tidymodels -- Move intro to supervised to Wednesday morning, finish linear models with supervised example -- Thursday afternoon: feature selection, expand Lasso, logistic-Lasso; Friday more practical example with tidymodels, maybe not with tidymodels. - - -Wednesday -10 - 12: Introduction to supervised learning -12 - 16: Linear models I - -Thursday -10 - 12: Linear models II -13 - 15: PCA -15 - 16: Clustering - diff --git a/olga-working-notes/The-main-types-of-machine-learning-Main-approaches-include-classification-and.png b/olga-working-notes/The-main-types-of-machine-learning-Main-approaches-include-classification-and.png deleted file mode 100644 index 32ba2662..00000000 Binary files a/olga-working-notes/The-main-types-of-machine-learning-Main-approaches-include-classification-and.png and /dev/null differ diff --git a/olga-working-notes/notes b/olga-working-notes/notes deleted file mode 100644 index 70cb679d..00000000 --- a/olga-working-notes/notes +++ /dev/null @@ -1,71 +0,0 @@ -# Work in progress notes - -## Pre-course - -- fix typo in the title "Mathematical foundations" -- add sets visualizaiton e.g. from here https://people.tamu.edu/~j-epstein//eHW/C1a.pdf -- add matrix operations as visualizations -- go through quiz stats from V23 and see if any improvements to the quiz or materials are needed -- make sure inverse of matrix is in the math pre-course together with R functions - -## Overall - -- refresh mathematical foundation quiz -- refresh daily challenges -- add slides summaries / what's beyond recommendations - -## Descriptive statistics - -- add examples and/or more about formatting figures -- add literature and further information - -## Non-parametric tests - -- refresh & improve -- ask how it went / who they did it - -## Linear models - -- add code to get data again in exercises -- exercises 1: clarify that the goal is to fit one model (remove typo indicating otherwise) -- in the chapter change Poisson example to age, and compare predicted age values with Poisson glm vs. lm() to show that we can count data now -- add plot and proper explanation of predicting outside the range, perhaps a classical example of height vs. age in children and adults -- add edgeR explanation distributions to GLM -- complete all common cases with the same structure: coefficients interpretation, hypothesis testing, model fit, predictions, assumptions -- improve model assumptions section, better examples when assumptions are not met -- add exercises for common cases and/or additional practise quiz -- change examples in the daily challenge -- have more on design matrix -- add more on logistic regression, including odds ratios and risk etc. -- improve explanation of assumpionts: trend in increased variance, examples of RNA-seq data? - -## PCA - -- tiny example of eigendecompostion? - -## Clustering - -- check and make new exercises given new session -- dendorgram plots side by side to compare linkage? - -## Supervised learning - -- make overview plot: machine learning - - supervised: classification and regression - - unsupervised: clustering - - reinforcment learning -- minor: include ROC curve example -- make slides more attractive - -## Feature engineering and selection - -- keep engineerign shorter and regularization longer or start from regularization? (took too much time) -- add pictres of skewed distributions density plots -- exercises: add assumptions about prevelance of diabetes -- prepare live demo worksheet -- improve slides - -## Generic comments - -- add transformations to descriptive stats -- how to solve keeping the history of the materials?