#contoh data dan panggil data eksternal
datakesehatan = read.csv("data_kesehatan.csv")
datakesehatan
## X id umur jenis_kelamin tinggi_badan berat_badan gula_darah
## 1 1 1 33 Perempuan 159.5587 62.85027 80.08403
## 2 2 2 59 Perempuan 152.9881 54.73592 79.20090
## 3 3 3 39 Perempuan 156.1915 66.84162 99.64040
## 4 4 4 64 Laki-laki 173.8024 54.83932 97.35650
## 5 5 5 67 Perempuan 164.0242 62.21020 49.01314
## 6 6 6 20 Perempuan 150.0967 61.15284 120.81147
## 7 7 7 45 Perempuan 157.9015 59.39500 104.99451
## 8 8 8 64 Laki-laki 165.8377 55.87974 148.32415
## 9 9 9 47 Perempuan 156.7671 62.21052 113.70396
## 10 10 10 42 Perempuan 161.3638 58.73365 91.06081
## 11 11 11 68 Perempuan 162.9534 39.93666 155.94782
## 12 12 12 42 Laki-laki 171.7058 57.82400 156.64452
## 13 13 13 53 Laki-laki 178.6273 86.87589 75.62576
## 14 14 14 48 Perempuan 159.2637 61.65035 109.38064
## 15 15 15 23 Perempuan 157.5179 62.21498 95.77506
## 16 16 16 65 Perempuan 144.1411 66.57205 103.74102
## 17 17 17 31 Perempuan 159.4424 58.44678 104.55085
## 18 18 18 20 Laki-laki 164.9385 63.24518 74.76199
## 19 19 19 35 Perempuan 163.2124 52.62787 105.71179
## 20 20 20 68 Laki-laki 161.0070 85.46609 134.98495
## 21 21 21 64 Perempuan 170.6770 50.16811 96.71820
## 22 22 22 54 Laki-laki 174.3259 73.18390 96.74147
## 23 23 23 51 Laki-laki 177.7689 78.46436 127.97144
## 24 24 24 70 Laki-laki 174.9531 71.78190 117.96792
## 25 25 25 52 Perempuan 155.6892 57.85835 67.03010
## 26 26 26 55 Perempuan 157.2980 55.26486 104.57114
## 27 27 27 46 Laki-laki 165.0678 71.70588 133.07094
## 28 28 28 49 Laki-laki 164.9795 59.36502 128.30553
## 29 29 29 33 Perempuan 169.8014 50.64308 108.39903
## 30 30 30 26 Laki-laki 162.8909 90.86717 114.42442
## 31 31 31 68 Laki-laki 183.6871 63.21497 76.06130
## 32 32 32 65 Perempuan 162.2705 55.22136 106.00263
## 33 33 33 54 Perempuan 161.8002 66.31568 80.91102
## 34 34 34 59 Laki-laki 164.8303 73.10230 90.83964
## 35 35 35 19 Perempuan 160.1156 58.46580 118.71207
## 36 36 36 43 Perempuan 153.5355 62.27103 77.26214
## 37 37 37 57 Perempuan 164.2762 45.99146 105.33837
## 38 38 38 29 Perempuan 166.5087 53.45064 108.56664
## 39 39 39 35 Perempuan 146.6501 60.44972 101.09824
## 40 40 40 30 Laki-laki 164.5292 65.07063 136.44378
## 41 41 41 25 Laki-laki 164.4796 61.65812 79.55305
## 42 42 42 40 Perempuan 162.7286 81.47887 112.12261
## 43 43 43 40 Laki-laki 180.4724 71.57353 98.22139
## 44 44 44 37 Perempuan 158.8007 60.51395 94.78336
## 45 45 45 26 Laki-laki 168.7466 66.04202 109.28182
## 46 46 46 25 Laki-laki 183.3165 78.99354 79.59199
## 47 47 47 30 Laki-laki 169.2932 61.69188 73.73098
## 48 48 48 42 Perempuan 165.2998 68.36245 90.11038
## 49 49 49 32 Perempuan 147.6860 72.92436 135.03514
## 50 50 50 63 Perempuan 150.1817 59.76245 101.11530
## 51 51 51 20 Laki-laki 167.3708 50.61495 106.62869
## 52 52 52 41 Perempuan 166.2798 59.22070 96.20307
## 53 53 53 60 Perempuan 162.6117 68.13164 109.40985
## 54 54 54 24 Laki-laki 170.6335 55.49176 80.96641
## 55 55 55 47 Laki-laki 181.1896 74.80626 123.15821
## 56 56 56 29 Laki-laki 169.3800 61.71826 111.69411
## 57 57 57 25 Laki-laki 177.5656 80.20253 83.87094
## 58 58 58 57 Laki-laki 174.4153 75.38482 101.09106
## 59 59 59 65 Perempuan 161.3837 61.17367 114.32663
## 60 60 60 37 Laki-laki 159.2697 71.20719 111.15462
## 61 61 61 53 Perempuan 165.2318 67.28968 129.63868
## 62 62 62 23 Perempuan 157.9092 61.13967 87.74024
## 63 63 63 38 Perempuan 163.1110 48.88413 122.32273
## 64 64 64 32 Laki-laki 179.1014 69.71605 120.73096
## 65 65 65 60 Perempuan 153.4433 58.69372 96.75034
## 66 66 66 41 Laki-laki 180.8331 70.31526 80.48147
## 67 67 67 60 Perempuan 164.4454 45.11818 78.21710
## 68 68 68 60 Laki-laki 157.7043 68.44655 109.15574
## 69 69 69 59 Laki-laki 167.2785 75.68289 98.57747
## 70 70 70 41 Laki-laki 170.6245 80.10678 135.58205
## 71 71 71 57 Laki-laki 175.9151 64.82018 110.70276
## 72 72 72 51 Laki-laki 176.7377 67.05905 92.56110
## 73 73 73 55 Laki-laki 174.7902 73.97842 79.48916
## 74 74 74 18 Perempuan 158.5639 67.22813 88.35197
## 75 75 75 43 Laki-laki 175.9475 70.91267 106.85777
## 76 76 76 29 Perempuan 160.9101 75.60577 90.98131
## 77 77 77 38 Laki-laki 171.2236 58.80100 110.28460
## 78 78 78 50 Laki-laki 170.5219 56.72245 93.31324
## 79 79 79 36 Laki-laki 172.9972 61.46376 97.88880
## 80 80 80 24 Laki-laki 170.1727 63.06695 85.38981
## 81 81 81 31 Perempuan 160.1227 63.81630 138.10087
## 82 82 82 53 Perempuan 161.8843 52.22405 106.65243
## 83 83 83 40 Laki-laki 172.7022 62.72616 104.61267
## 84 84 84 59 Laki-laki 168.1404 60.03161 66.16275
## 85 85 85 23 Laki-laki 170.8270 59.58311 113.19584
## 86 86 86 41 Perempuan 164.6732 63.65886 79.52753
## 87 87 87 69 Perempuan 165.4886 51.69495 82.16957
## 88 88 88 64 Perempuan 156.5536 55.16389 118.36682
## 89 89 89 64 Laki-laki 168.4666 66.78675 90.94599
## 90 90 90 27 Perempuan 157.7143 63.16237 65.03255
## 91 91 91 25 Laki-laki 178.1787 69.08566 135.39808
## 92 92 92 52 Laki-laki 177.3793 81.87187 52.45186
## 93 93 93 36 Laki-laki 178.0168 81.91601 111.45623
## 94 94 94 52 Laki-laki 165.9577 62.11037 120.34498
## 95 95 95 35 Laki-laki 184.0174 54.52223 87.38064
## 96 96 96 28 Perempuan 151.8515 54.39197 108.88574
## 97 97 97 59 Perempuan 161.0911 61.92837 108.78261
## 98 98 98 23 Perempuan 160.9890 57.18037 120.81246
## 99 99 99 42 Laki-laki 170.1469 74.20574 109.68199
## 100 100 100 45 Laki-laki 178.7494 53.85961 95.10232
## 101 101 101 49 Perempuan 156.3886 51.88709 118.31984
## 102 102 102 35 Perempuan 154.0378 53.66949 116.01245
## 103 103 103 43 Laki-laki 163.4302 63.06905 81.26862
## 104 104 104 68 Perempuan 164.5064 73.11242 71.98425
## 105 105 105 43 Laki-laki 166.9399 56.35291 103.20555
## 106 106 106 64 Laki-laki 172.3183 75.89983 94.52075
## 107 107 107 66 Perempuan 154.6243 66.60738 80.28922
## 108 108 108 50 Laki-laki 171.4839 60.95785 101.67861
## 109 109 109 39 Laki-laki 178.6567 72.26325 73.60007
## 110 110 110 26 Laki-laki 184.2630 77.48081 103.22453
## 111 111 111 67 Perempuan 159.4158 80.60360 87.50143
## 112 112 112 34 Laki-laki 175.2974 67.87152 119.14329
## 113 113 113 21 Perempuan 165.2948 60.80736 148.48978
## 114 114 114 67 Laki-laki 165.7895 69.13286 81.68042
## 115 115 115 55 Perempuan 156.3014 64.71869 121.15328
## 116 116 116 25 Perempuan 155.5912 68.77287 116.50299
## 117 117 117 47 Perempuan 159.2092 71.56530 98.59612
## 118 118 118 68 Perempuan 161.8601 44.59884 90.92707
## 119 119 119 48 Laki-laki 181.7910 73.73241 131.50615
## 120 120 120 39 Laki-laki 176.3797 74.03290 59.89084
## 121 121 121 52 Laki-laki 171.6620 59.58327 87.13610
## 122 122 122 35 Perempuan 159.3503 58.30280 71.26313
## 123 123 123 34 Laki-laki 160.6286 76.41830 127.90627
## 124 124 124 29 Laki-laki 174.6257 54.70689 96.18593
## 125 125 125 37 Perempuan 166.6879 65.32128 89.50658
## 126 126 126 69 Laki-laki 174.7862 72.50248 163.68089
## 127 127 127 26 Laki-laki 169.5742 75.63867 98.99925
## 128 128 128 23 Perempuan 160.8346 61.57022 91.12501
## 129 129 129 25 Laki-laki 179.3486 62.67146 105.99731
## 130 130 130 54 Perempuan 156.6493 63.44879 68.63151
## 131 131 131 50 Perempuan 163.6322 61.51002 109.80605
## 132 132 132 64 Perempuan 156.9620 49.26205 98.07674
## 133 133 133 53 Laki-laki 164.9488 50.56349 109.37050
## 134 134 134 56 Laki-laki 180.6345 83.99576 80.35259
## 135 135 135 45 Laki-laki 172.6417 69.43944 79.54032
## 136 136 136 52 Laki-laki 155.6344 75.24914 86.13171
## 137 137 137 61 Perempuan 166.9915 54.50547 84.64021
## 138 138 138 59 Laki-laki 168.5945 69.03314 125.98100
## 139 139 139 69 Laki-laki 176.0605 69.24737 131.58291
## 140 140 140 41 Laki-laki 169.2868 80.19157 96.86216
## 141 141 141 34 Laki-laki 174.3693 77.11602 92.82127
## 142 142 142 39 Perempuan 157.1878 57.12020 93.41922
## 143 143 143 19 Perempuan 164.2250 46.51267 101.38473
## 144 144 144 28 Laki-laki 170.3921 76.64416 101.93808
## 145 145 145 62 Perempuan 165.1982 56.33792 105.80069
## 146 146 146 30 Laki-laki 157.7273 47.89367 85.06642
## 147 147 147 30 Laki-laki 170.6953 96.91714 83.06207
## 148 148 148 22 Laki-laki 165.9970 65.17323 123.94155
## 149 149 149 31 Perempuan 174.5814 57.82186 89.02745
## 150 150 150 56 Laki-laki 168.7407 73.74644 106.06091
## 151 151 151 62 Laki-laki 177.1046 85.38430 98.86059
## 152 152 152 44 Laki-laki 156.0508 68.90290 80.84301
## 153 153 153 38 Perempuan 166.6643 61.24925 111.82124
## 154 154 154 31 Laki-laki 170.8165 72.13958 103.46210
## 155 155 155 24 Laki-laki 163.7475 68.13879 127.99567
## 156 156 156 38 Perempuan 151.2462 47.02513 102.34919
## 157 157 157 48 Laki-laki 172.8800 80.12834 93.36908
## 158 158 158 29 Laki-laki 169.7687 67.98542 105.56590
## 159 159 159 41 Laki-laki 152.7387 49.62318 76.28817
## 160 160 160 29 Perempuan 156.2225 67.64293 83.28212
## 161 161 161 44 Perempuan 154.9969 66.27337 110.20547
## 162 162 162 36 Laki-laki 174.5584 76.16456 93.33758
## 163 163 163 52 Laki-laki 171.9164 76.16568 98.68078
## 164 164 164 37 Laki-laki 177.1727 53.07898 97.69557
## 165 165 165 36 Perempuan 152.8828 60.77269 86.98975
## 166 166 166 46 Laki-laki 168.5314 79.67859 59.62623
## 167 167 167 56 Perempuan 163.1979 45.21222 106.97670
## 168 168 168 29 Laki-laki 163.3821 67.75039 115.23279
## 169 169 169 39 Perempuan 158.1895 59.37969 74.22568
## 170 170 170 32 Laki-laki 166.7727 84.87838 129.64805
## 171 171 171 51 Laki-laki 186.9174 53.32072 107.70310
## 172 172 172 28 Laki-laki 158.4427 65.63170 126.83281
## 173 173 173 63 Perempuan 155.3096 48.01041 80.85659
## 174 174 174 57 Perempuan 161.9224 51.18813 103.33563
## 175 175 175 53 Laki-laki 173.5709 72.79628 97.99972
## 176 176 176 50 Laki-laki 165.8736 88.77864 115.37015
## 177 177 177 37 Perempuan 164.0395 53.60389 88.48281
## 178 178 178 46 Laki-laki 171.0113 67.21546 99.79805
## 179 179 179 63 Laki-laki 169.8998 74.74912 64.42682
## 180 180 180 48 Perempuan 160.1566 61.17134 84.44757
## 181 181 181 62 Laki-laki 170.2419 78.13400 102.50068
## 182 182 182 34 Perempuan 159.3859 51.00317 85.87357
## 183 183 183 55 Perempuan 152.9106 57.55624 99.12861
## 184 184 184 32 Perempuan 162.9919 55.86592 90.64148
## 185 185 185 49 Perempuan 153.7663 72.09916 112.13860
## 186 186 186 43 Perempuan 158.6427 53.84412 123.36977
## 187 187 187 32 Perempuan 162.2886 59.34330 83.54997
## 188 188 188 47 Perempuan 155.2989 66.29707 93.85927
## 189 189 189 65 Perempuan 163.4979 51.53128 128.79523
## 190 190 190 65 Laki-laki 172.4227 63.46220 56.02153
## 191 191 191 32 Perempuan 143.1414 65.40610 93.60324
## 192 192 192 35 Laki-laki 154.8965 60.86434 141.29409
## 193 193 193 69 Laki-laki 176.1898 78.86749 143.87180
## 194 194 194 50 Laki-laki 164.1937 73.33370 103.13191
## 195 195 195 67 Laki-laki 165.9851 68.29360 82.72782
## 196 196 196 42 Laki-laki 180.5273 78.18828 103.30915
## 197 197 197 39 Laki-laki 164.5810 73.88365 86.94451
## 198 198 198 52 Laki-laki 175.9201 65.54065 129.05635
## 199 199 199 26 Perempuan 167.0871 55.81907 83.87035
## 200 200 200 48 Perempuan 171.1595 58.00647 107.45823
## tekanan_sistolik tekanan_diastolik kolesterol skor_kesehatan
## 1 129.29775 69.13882 181.32930 100.00000
## 2 108.63735 73.34697 209.69540 100.00000
## 3 132.77287 87.14848 176.38007 88.52949
## 4 108.78105 75.68339 172.18406 100.00000
## 5 129.45360 82.27615 138.68861 100.00000
## 6 136.44992 92.94946 183.71655 75.39378
## 7 105.17336 85.78335 264.36237 80.21044
## 8 136.61993 93.64673 219.69107 59.51184
## 9 112.65701 62.98420 240.26716 76.40171
## 10 124.41530 77.19324 217.44896 95.02224
## 11 123.02756 80.65068 224.32788 48.51474
## 12 113.59205 85.78589 217.08629 62.26762
## 13 124.02154 68.30793 204.26177 96.82670
## 14 101.54354 88.06185 172.52414 100.00000
## 15 117.95795 83.07390 174.01196 100.00000
## 16 132.38686 82.63806 141.97480 85.92333
## 17 87.38813 85.08485 189.82547 100.00000
## 18 97.68111 78.83642 216.98067 100.00000
## 19 102.57094 89.25546 220.95768 90.72733
## 20 96.16365 86.48230 192.85398 67.80169
## 21 126.29375 78.49791 173.57117 94.09372
## 22 105.10607 90.40377 288.71552 77.75112
## 23 87.53179 82.92559 212.10057 85.65371
## 24 110.43647 86.68751 232.61726 80.64646
## 25 114.14047 74.05822 179.86958 100.00000
## 26 132.85178 95.80432 188.86127 93.04345
## 27 103.44372 79.96011 203.45821 76.98552
## 28 137.41934 88.47843 240.29567 67.08839
## 29 125.97544 78.99883 180.65576 87.27387
## 30 125.43528 77.20370 215.60847 60.08132
## 31 107.21211 87.84438 127.15556 100.00000
## 32 149.30502 64.15383 174.78237 88.97758
## 33 117.53594 84.78366 151.23973 100.00000
## 34 92.62654 83.93566 223.40088 95.54148
## 35 116.94215 53.04671 185.06641 91.28614
## 36 90.98334 83.68377 178.28878 100.00000
## 37 115.34235 58.31582 231.07300 78.69969
## 38 113.66659 86.59804 223.48819 84.01331
## 39 130.22745 75.46086 163.05936 90.56092
## 40 135.14244 73.05063 202.83599 69.44558
## 41 109.10843 79.93154 153.14666 100.00000
## 42 132.09163 93.73052 175.73155 75.93673
## 43 141.36485 73.64677 238.49733 81.59355
## 44 108.23784 85.58103 233.50384 91.65975
## 45 110.21393 83.41158 223.27056 87.91820
## 46 129.76168 68.20481 172.87288 100.00000
## 47 122.74572 62.58978 133.55706 100.00000
## 48 128.23162 60.07414 154.72905 100.00000
## 49 141.07026 85.51274 218.57670 45.44594
## 50 125.80625 79.65258 181.28296 90.69250
## 51 135.77552 98.50572 125.12176 99.78350
## 52 129.34358 85.73675 184.59438 98.97290
## 53 126.50431 88.49696 232.30718 75.01840
## 54 125.79127 93.34384 209.30406 96.87835
## 55 139.36985 74.99281 165.36224 86.55135
## 56 104.96610 85.10098 143.62250 100.00000
## 57 103.42226 88.68793 165.20358 100.00000
## 58 128.87919 93.69352 191.03583 90.67423
## 59 118.20466 87.62651 216.64221 83.79867
## 60 121.11008 84.21147 131.80180 96.46025
## 61 131.11916 71.31776 220.69605 68.50658
## 62 131.29943 87.29560 190.16373 97.28383
## 63 116.05994 85.00266 207.09337 78.62644
## 64 115.31184 86.34250 140.40233 100.00000
## 65 121.10398 84.23645 170.00037 100.00000
## 66 135.94527 77.98162 176.55297 100.00000
## 67 126.39031 79.23134 221.31674 90.30077
## 68 141.49511 86.87364 220.84522 69.50376
## 69 119.88545 81.71632 203.05271 87.39251
## 70 136.88501 71.69891 238.12637 53.51686
## 71 133.24503 77.09841 174.53766 91.99147
## 72 129.18125 66.80874 220.37612 92.00174
## 73 126.22051 70.32968 188.92180 100.00000
## 74 115.80176 78.55389 169.97974 100.00000
## 75 118.36444 62.01867 217.70140 88.37807
## 76 123.44093 63.11458 231.43301 78.60337
## 77 120.73333 91.02565 216.34751 84.13958
## 78 134.14837 74.23381 174.71790 97.46217
## 79 118.36024 61.48308 175.90637 100.00000
## 80 118.94435 78.87137 231.33028 96.88081
## 81 112.73521 93.21069 200.58479 74.94486
## 82 117.92496 86.62254 214.88722 86.99939
## 83 118.96852 84.41383 179.83894 98.63909
## 84 85.29396 91.83746 227.83105 100.00000
## 85 99.52752 72.28499 165.73735 100.00000
## 86 118.91270 87.29689 208.76055 100.00000
## 87 116.02074 74.12914 165.48475 100.00000
## 88 101.98696 80.00764 116.12275 100.00000
## 89 90.12693 102.14465 149.71127 100.00000
## 90 114.68446 89.69434 194.08886 100.00000
## 91 129.80244 87.68008 216.48768 71.93331
## 92 146.59858 68.91672 137.46094 100.00000
## 93 119.42315 72.13764 152.45727 96.15151
## 94 142.39777 102.84116 242.93638 68.02514
## 95 121.24533 69.06699 176.98302 97.35104
## 96 121.73298 82.14479 205.17100 88.01014
## 97 124.87238 88.92571 174.19194 94.20821
## 98 106.94134 90.18758 181.04251 96.06106
## 99 119.22423 90.89112 192.61733 87.49844
## 100 133.62672 78.36871 190.32886 87.53790
## 101 108.75411 71.79013 184.22319 94.46348
## 102 115.17591 76.92743 175.90611 95.88734
## 103 102.78344 70.97902 98.56417 100.00000
## 104 125.31528 86.27069 246.05967 88.18862
## 105 126.37200 91.20355 243.71273 79.28644
## 106 129.72521 101.27214 156.96755 100.00000
## 107 101.70285 83.66114 184.95677 100.00000
## 108 121.60853 71.25219 231.25826 85.76841
## 109 105.83913 90.24475 219.94870 100.00000
## 110 119.99423 89.04759 228.29805 87.17492
## 111 140.13936 77.61751 157.84759 88.14031
## 112 112.44712 64.42145 267.31804 73.44250
## 113 130.75025 87.61310 155.99620 80.16160
## 114 108.75497 91.29144 212.61749 100.00000
## 115 112.82208 77.04892 194.23828 81.77373
## 116 126.58083 85.36243 177.88869 79.76589
## 117 109.81332 77.24110 178.61753 94.47592
## 118 94.45553 86.82315 160.25810 100.00000
## 119 138.97753 78.82709 238.67979 65.12971
## 120 125.40536 76.55324 192.68540 100.00000
## 121 111.24541 81.11620 197.77654 100.00000
## 122 90.08882 77.16595 196.28898 100.00000
## 123 148.53315 74.08983 178.74488 64.11414
## 124 170.85556 76.84063 155.97936 85.34060
## 125 123.11221 79.91848 197.61179 99.28702
## 126 132.74710 82.07495 127.19082 76.14837
## 127 138.36840 95.32424 147.44299 96.40854
## 128 109.47293 66.42002 157.70810 100.00000
## 129 114.73206 78.00381 157.63963 100.00000
## 130 94.09318 86.31523 193.21566 100.00000
## 131 108.95133 97.62021 237.95463 84.84621
## 132 129.33615 84.26014 144.54028 100.00000
## 133 115.63926 79.86247 203.01028 86.68373
## 134 116.78683 76.92443 216.98634 96.66257
## 135 118.31161 84.14308 160.31403 100.00000
## 136 92.04500 89.89058 188.41602 98.51579
## 137 132.56445 78.16142 214.70833 92.40607
## 138 98.34761 81.63761 182.33473 91.56964
## 139 116.87145 82.16936 183.37947 86.22689
## 140 113.42155 87.29278 199.23180 89.48931
## 141 116.72109 91.11380 188.19960 98.40598
## 142 141.89949 82.79161 173.33041 97.92177
## 143 111.26910 79.23829 185.84155 93.74989
## 144 108.25354 93.94663 246.48519 80.02805
## 145 97.20519 81.64534 216.21006 95.43306
## 146 107.91453 95.77852 162.56209 100.00000
## 147 102.50723 79.38077 152.65247 100.00000
## 148 126.11919 86.13923 179.20053 85.38529
## 149 107.05494 64.53911 229.86324 93.77802
## 150 124.56063 78.87608 198.78037 85.45402
## 151 117.80359 79.78205 168.95484 96.11934
## 152 98.49657 72.41655 216.46704 97.54007
## 153 108.14088 69.64107 185.99889 98.94197
## 154 133.27669 89.48159 156.37965 96.80100
## 155 133.54614 89.14159 203.83577 71.20314
## 156 150.08360 67.01268 235.72428 74.48061
## 157 119.94630 84.24379 203.03405 90.45487
## 158 97.56260 68.87455 195.76001 100.00000
## 159 108.47374 69.48927 170.31271 100.00000
## 160 126.12733 85.25412 207.05196 90.62093
## 161 148.50205 73.13976 157.88288 82.25335
## 162 121.65014 89.93480 140.40553 100.00000
## 163 137.10580 80.38524 188.69942 87.74158
## 164 131.52122 85.36149 188.96219 87.39012
## 165 102.47863 74.76373 260.95167 86.60551
## 166 117.43331 68.48779 153.50958 100.00000
## 167 139.57892 89.14752 195.12719 78.65485
## 168 133.14144 82.38071 214.15159 75.20394
## 169 126.95694 77.60932 221.51779 99.23040
## 170 127.15671 80.69235 189.67827 65.83611
## 171 112.62892 93.25908 167.70232 90.70285
## 172 100.20922 73.01833 188.02648 85.38622
## 173 139.43139 72.50592 248.19268 84.35429
## 174 98.69671 73.80385 204.48217 96.85953
## 175 105.91656 64.15009 128.66569 100.00000
## 176 129.43447 88.19628 232.70377 57.96087
## 177 101.06708 81.92370 206.21508 100.00000
## 178 111.72194 82.07172 188.99285 100.00000
## 179 102.25801 79.56653 189.46409 100.00000
## 180 129.30995 74.89840 185.50631 100.00000
## 181 126.69470 71.76581 197.69678 84.67545
## 182 126.32827 88.51856 174.88399 100.00000
## 183 126.63697 65.73815 198.31034 90.91384
## 184 128.35869 84.40299 162.05932 100.00000
## 185 129.59035 72.07388 196.00441 72.24570
## 186 90.47008 82.82310 223.20512 88.64576
## 187 117.76775 72.59309 205.27618 100.00000
## 188 121.68696 74.76658 221.01249 83.77710
## 189 130.87014 97.69366 157.27394 84.71513
## 190 102.18771 86.68283 191.64378 100.00000
## 191 112.50600 58.55103 208.51751 81.22331
## 192 103.89536 81.26412 157.95985 85.99595
## 193 135.85860 75.48187 236.97443 54.22224
## 194 139.18609 68.63374 158.95576 89.39700
## 195 131.81515 82.09786 194.93556 97.88776
## 196 101.66395 81.29966 194.55497 99.33587
## 197 126.77928 76.71493 193.65011 92.80241
## 198 137.25674 99.72704 183.68726 79.65326
## 199 122.51911 57.51310 203.49810 99.83833
## 200 111.50836 88.38219 159.06507 100.00000
data("Titanic") #contoh data bawaan R
titanic = as.data.frame(Titanic)
titanic
## Class Sex Age Survived Freq
## 1 1st Male Child No 0
## 2 2nd Male Child No 0
## 3 3rd Male Child No 35
## 4 Crew Male Child No 0
## 5 1st Female Child No 0
## 6 2nd Female Child No 0
## 7 3rd Female Child No 17
## 8 Crew Female Child No 0
## 9 1st Male Adult No 118
## 10 2nd Male Adult No 154
## 11 3rd Male Adult No 387
## 12 Crew Male Adult No 670
## 13 1st Female Adult No 4
## 14 2nd Female Adult No 13
## 15 3rd Female Adult No 89
## 16 Crew Female Adult No 3
## 17 1st Male Child Yes 5
## 18 2nd Male Child Yes 11
## 19 3rd Male Child Yes 13
## 20 Crew Male Child Yes 0
## 21 1st Female Child Yes 1
## 22 2nd Female Child Yes 13
## 23 3rd Female Child Yes 14
## 24 Crew Female Child Yes 0
## 25 1st Male Adult Yes 57
## 26 2nd Male Adult Yes 14
## 27 3rd Male Adult Yes 75
## 28 Crew Male Adult Yes 192
## 29 1st Female Adult Yes 140
## 30 2nd Female Adult Yes 80
## 31 3rd Female Adult Yes 76
## 32 Crew Female Adult Yes 20
databaru = datasets:: Titanic #contoh data bawaan R
databaru = data.frame(Titanic)
databaru
## Class Sex Age Survived Freq
## 1 1st Male Child No 0
## 2 2nd Male Child No 0
## 3 3rd Male Child No 35
## 4 Crew Male Child No 0
## 5 1st Female Child No 0
## 6 2nd Female Child No 0
## 7 3rd Female Child No 17
## 8 Crew Female Child No 0
## 9 1st Male Adult No 118
## 10 2nd Male Adult No 154
## 11 3rd Male Adult No 387
## 12 Crew Male Adult No 670
## 13 1st Female Adult No 4
## 14 2nd Female Adult No 13
## 15 3rd Female Adult No 89
## 16 Crew Female Adult No 3
## 17 1st Male Child Yes 5
## 18 2nd Male Child Yes 11
## 19 3rd Male Child Yes 13
## 20 Crew Male Child Yes 0
## 21 1st Female Child Yes 1
## 22 2nd Female Child Yes 13
## 23 3rd Female Child Yes 14
## 24 Crew Female Child Yes 0
## 25 1st Male Adult Yes 57
## 26 2nd Male Adult Yes 14
## 27 3rd Male Adult Yes 75
## 28 Crew Male Adult Yes 192
## 29 1st Female Adult Yes 140
## 30 2nd Female Adult Yes 80
## 31 3rd Female Adult Yes 76
## 32 Crew Female Adult Yes 20
library(readxl) #habis install packages, library dulu
dataexcel = read_excel("df_mahasiswa.xlsx") #panggil datanya
dataexcel
## # A tibble: 55 × 6
## id_mahasiswa jenis_kelamin jam_belajar_per_hari frekuensi_login_lms
## <chr> <chr> <dbl> <dbl>
## 1 MHS001 L 4 1
## 2 MHS002 P 4 2
## 3 MHS003 P 2 6
## 4 MHS004 P 5 3
## 5 MHS005 L 3 2
## 6 MHS006 L 3 7
## 7 MHS007 P 1 7
## 8 MHS008 L 3 4
## 9 MHS009 P 2 6
## 10 MHS010 L 1 5
## # ℹ 45 more rows
## # ℹ 2 more variables: motivasi_belajar <dbl>, ipk <dbl>
#Alternatif lain panggil data bawaan R
databaru2 = data.frame(datasets::WorldPhones)
databaru2
## N.Amer Europe Asia S.Amer Oceania Africa Mid.Amer
## 1951 45939 21574 2876 1815 1646 89 555
## 1956 60423 29990 4708 2568 2366 1411 733
## 1957 64721 32510 5230 2695 2526 1546 773
## 1958 68484 35218 6662 2845 2691 1663 836
## 1959 71799 37598 6856 3000 2868 1769 911
## 1960 76036 40341 8220 3145 3054 1905 1008
## 1961 79831 43173 9053 3338 3224 2005 1076
#Lihat Struktur Data
str(databaru)
## 'data.frame': 32 obs. of 5 variables:
## $ Class : Factor w/ 4 levels "1st","2nd","3rd",..: 1 2 3 4 1 2 3 4 1 2 ...
## $ Sex : Factor w/ 2 levels "Male","Female": 1 1 1 1 2 2 2 2 1 1 ...
## $ Age : Factor w/ 2 levels "Child","Adult": 1 1 1 1 1 1 1 1 2 2 ...
## $ Survived: Factor w/ 2 levels "No","Yes": 1 1 1 1 1 1 1 1 1 1 ...
## $ Freq : num 0 0 35 0 0 0 17 0 118 154 ...
#Ringkasan statistik
summary(databaru)
## Class Sex Age Survived Freq
## 1st :8 Male :16 Child:16 No :16 Min. : 0.00
## 2nd :8 Female:16 Adult:16 Yes:16 1st Qu.: 0.75
## 3rd :8 Median : 13.50
## Crew:8 Mean : 68.78
## 3rd Qu.: 77.00
## Max. :670.00
#Cek Missing Value
colSums(is.na(databaru))
## Class Sex Age Survived Freq
## 0 0 0 0 0
#Tidak ada missing value
dataairqual = as.data.frame(airquality)
dataairqual
## Ozone Solar.R Wind Temp Month Day
## 1 41 190 7.4 67 5 1
## 2 36 118 8.0 72 5 2
## 3 12 149 12.6 74 5 3
## 4 18 313 11.5 62 5 4
## 5 NA NA 14.3 56 5 5
## 6 28 NA 14.9 66 5 6
## 7 23 299 8.6 65 5 7
## 8 19 99 13.8 59 5 8
## 9 8 19 20.1 61 5 9
## 10 NA 194 8.6 69 5 10
## 11 7 NA 6.9 74 5 11
## 12 16 256 9.7 69 5 12
## 13 11 290 9.2 66 5 13
## 14 14 274 10.9 68 5 14
## 15 18 65 13.2 58 5 15
## 16 14 334 11.5 64 5 16
## 17 34 307 12.0 66 5 17
## 18 6 78 18.4 57 5 18
## 19 30 322 11.5 68 5 19
## 20 11 44 9.7 62 5 20
## 21 1 8 9.7 59 5 21
## 22 11 320 16.6 73 5 22
## 23 4 25 9.7 61 5 23
## 24 32 92 12.0 61 5 24
## 25 NA 66 16.6 57 5 25
## 26 NA 266 14.9 58 5 26
## 27 NA NA 8.0 57 5 27
## 28 23 13 12.0 67 5 28
## 29 45 252 14.9 81 5 29
## 30 115 223 5.7 79 5 30
## 31 37 279 7.4 76 5 31
## 32 NA 286 8.6 78 6 1
## 33 NA 287 9.7 74 6 2
## 34 NA 242 16.1 67 6 3
## 35 NA 186 9.2 84 6 4
## 36 NA 220 8.6 85 6 5
## 37 NA 264 14.3 79 6 6
## 38 29 127 9.7 82 6 7
## 39 NA 273 6.9 87 6 8
## 40 71 291 13.8 90 6 9
## 41 39 323 11.5 87 6 10
## 42 NA 259 10.9 93 6 11
## 43 NA 250 9.2 92 6 12
## 44 23 148 8.0 82 6 13
## 45 NA 332 13.8 80 6 14
## 46 NA 322 11.5 79 6 15
## 47 21 191 14.9 77 6 16
## 48 37 284 20.7 72 6 17
## 49 20 37 9.2 65 6 18
## 50 12 120 11.5 73 6 19
## 51 13 137 10.3 76 6 20
## 52 NA 150 6.3 77 6 21
## 53 NA 59 1.7 76 6 22
## 54 NA 91 4.6 76 6 23
## 55 NA 250 6.3 76 6 24
## 56 NA 135 8.0 75 6 25
## 57 NA 127 8.0 78 6 26
## 58 NA 47 10.3 73 6 27
## 59 NA 98 11.5 80 6 28
## 60 NA 31 14.9 77 6 29
## 61 NA 138 8.0 83 6 30
## 62 135 269 4.1 84 7 1
## 63 49 248 9.2 85 7 2
## 64 32 236 9.2 81 7 3
## 65 NA 101 10.9 84 7 4
## 66 64 175 4.6 83 7 5
## 67 40 314 10.9 83 7 6
## 68 77 276 5.1 88 7 7
## 69 97 267 6.3 92 7 8
## 70 97 272 5.7 92 7 9
## 71 85 175 7.4 89 7 10
## 72 NA 139 8.6 82 7 11
## 73 10 264 14.3 73 7 12
## 74 27 175 14.9 81 7 13
## 75 NA 291 14.9 91 7 14
## 76 7 48 14.3 80 7 15
## 77 48 260 6.9 81 7 16
## 78 35 274 10.3 82 7 17
## 79 61 285 6.3 84 7 18
## 80 79 187 5.1 87 7 19
## 81 63 220 11.5 85 7 20
## 82 16 7 6.9 74 7 21
## 83 NA 258 9.7 81 7 22
## 84 NA 295 11.5 82 7 23
## 85 80 294 8.6 86 7 24
## 86 108 223 8.0 85 7 25
## 87 20 81 8.6 82 7 26
## 88 52 82 12.0 86 7 27
## 89 82 213 7.4 88 7 28
## 90 50 275 7.4 86 7 29
## 91 64 253 7.4 83 7 30
## 92 59 254 9.2 81 7 31
## 93 39 83 6.9 81 8 1
## 94 9 24 13.8 81 8 2
## 95 16 77 7.4 82 8 3
## 96 78 NA 6.9 86 8 4
## 97 35 NA 7.4 85 8 5
## 98 66 NA 4.6 87 8 6
## 99 122 255 4.0 89 8 7
## 100 89 229 10.3 90 8 8
## 101 110 207 8.0 90 8 9
## 102 NA 222 8.6 92 8 10
## 103 NA 137 11.5 86 8 11
## 104 44 192 11.5 86 8 12
## 105 28 273 11.5 82 8 13
## 106 65 157 9.7 80 8 14
## 107 NA 64 11.5 79 8 15
## 108 22 71 10.3 77 8 16
## 109 59 51 6.3 79 8 17
## 110 23 115 7.4 76 8 18
## 111 31 244 10.9 78 8 19
## 112 44 190 10.3 78 8 20
## 113 21 259 15.5 77 8 21
## 114 9 36 14.3 72 8 22
## 115 NA 255 12.6 75 8 23
## 116 45 212 9.7 79 8 24
## 117 168 238 3.4 81 8 25
## 118 73 215 8.0 86 8 26
## 119 NA 153 5.7 88 8 27
## 120 76 203 9.7 97 8 28
## 121 118 225 2.3 94 8 29
## 122 84 237 6.3 96 8 30
## 123 85 188 6.3 94 8 31
## 124 96 167 6.9 91 9 1
## 125 78 197 5.1 92 9 2
## 126 73 183 2.8 93 9 3
## 127 91 189 4.6 93 9 4
## 128 47 95 7.4 87 9 5
## 129 32 92 15.5 84 9 6
## 130 20 252 10.9 80 9 7
## 131 23 220 10.3 78 9 8
## 132 21 230 10.9 75 9 9
## 133 24 259 9.7 73 9 10
## 134 44 236 14.9 81 9 11
## 135 21 259 15.5 76 9 12
## 136 28 238 6.3 77 9 13
## 137 9 24 10.9 71 9 14
## 138 13 112 11.5 71 9 15
## 139 46 237 6.9 78 9 16
## 140 18 224 13.8 67 9 17
## 141 13 27 10.3 76 9 18
## 142 24 238 10.3 68 9 19
## 143 16 201 8.0 82 9 20
## 144 13 238 12.6 64 9 21
## 145 23 14 9.2 71 9 22
## 146 36 139 10.3 81 9 23
## 147 7 49 10.3 69 9 24
## 148 14 20 16.6 63 9 25
## 149 30 193 6.9 70 9 26
## 150 NA 145 13.2 77 9 27
## 151 14 191 14.3 75 9 28
## 152 18 131 8.0 76 9 29
## 153 20 223 11.5 68 9 30
colSums(is.na(dataairqual))
## Ozone Solar.R Wind Temp Month Day
## 37 7 0 0 0 0
#Ada missing value
#Replace Missing Value atau Imputasi
library(dplyr)
##
## Attaching package: 'dplyr'
## The following objects are masked from 'package:stats':
##
## filter, lag
## The following objects are masked from 'package:base':
##
## intersect, setdiff, setequal, union
#Pilih hanya kolom tertentu
titanic_selected = select(titanic, Class, Sex, Survived)
head(titanic_selected)
## Class Sex Survived
## 1 1st Male No
## 2 2nd Male No
## 3 3rd Male No
## 4 Crew Male No
## 5 1st Female No
## 6 2nd Female No
#head untuk memunculkan 6 baris data pertama
# Filter dan Sort Data
#Filter penumpang anak-anak
titanic_child = filter(titanic, Age == "Child")
#Urutkan berdasarkan frekuensi (ascending: kecil -> besar)
titanic_sorted_asc = arrange(titanic, Freq)
head(titanic_sorted_asc)
## Class Sex Age Survived Freq
## 1 1st Male Child No 0
## 2 2nd Male Child No 0
## 3 Crew Male Child No 0
## 4 1st Female Child No 0
## 5 2nd Female Child No 0
## 6 Crew Female Child No 0
#Urutkan berdasarkan frekuensi (descending: besar->kecil)
titanic_sorted_desc = arrange(titanic, desc(Freq))
head(titanic_sorted_desc)
## Class Sex Age Survived Freq
## 1 Crew Male Adult No 670
## 2 3rd Male Adult No 387
## 3 Crew Male Adult Yes 192
## 4 2nd Male Adult No 154
## 5 1st Female Adult Yes 140
## 6 1st Male Adult No 118
#Rename & Mutate
#Ganti nama kolom
titanic_rename = rename(titanic, Umur = Age, Kelas = Class, Jenis_Kelamin = Sex, Selamat = Survived, Frekuensi = Freq)
titanic_rename
## Kelas Jenis_Kelamin Umur Selamat Frekuensi
## 1 1st Male Child No 0
## 2 2nd Male Child No 0
## 3 3rd Male Child No 35
## 4 Crew Male Child No 0
## 5 1st Female Child No 0
## 6 2nd Female Child No 0
## 7 3rd Female Child No 17
## 8 Crew Female Child No 0
## 9 1st Male Adult No 118
## 10 2nd Male Adult No 154
## 11 3rd Male Adult No 387
## 12 Crew Male Adult No 670
## 13 1st Female Adult No 4
## 14 2nd Female Adult No 13
## 15 3rd Female Adult No 89
## 16 Crew Female Adult No 3
## 17 1st Male Child Yes 5
## 18 2nd Male Child Yes 11
## 19 3rd Male Child Yes 13
## 20 Crew Male Child Yes 0
## 21 1st Female Child Yes 1
## 22 2nd Female Child Yes 13
## 23 3rd Female Child Yes 14
## 24 Crew Female Child Yes 0
## 25 1st Male Adult Yes 57
## 26 2nd Male Adult Yes 14
## 27 3rd Male Adult Yes 75
## 28 Crew Male Adult Yes 192
## 29 1st Female Adult Yes 140
## 30 2nd Female Adult Yes 80
## 31 3rd Female Adult Yes 76
## 32 Crew Female Adult Yes 20
#Tambahkan kolom proporsi
titanic_mutate = mutate(titanic_rename, Proporsi = Frekuensi / sum(Frekuensi))
head(titanic_mutate)
## Kelas Jenis_Kelamin Umur Selamat Frekuensi Proporsi
## 1 1st Male Child No 0 0.00000000
## 2 2nd Male Child No 0 0.00000000
## 3 3rd Male Child No 35 0.01590186
## 4 Crew Male Child No 0 0.00000000
## 5 1st Female Child No 0 0.00000000
## 6 2nd Female Child No 0 0.00000000