Tugas Pertemuan Pertama
Lili
2025-08-10
Tugas Pertemuan Pertama Komputasi Statistika-E
1. Mengimport Data dalam Bentuk CSV
Mengimport data csv di R Studio dilakukan dengan menggunankan sintax
read.csv(). Dengan syarat kita mengetahui dimana kita
menyimpan file yang akan kita import. Berikut sintax import data
csv:
data2 <- read.csv("D:/komputasi.csv", sep = ",")
data2## Age Gender TotalBilirubin DirectBilirubin AlkalinePhosphotase
## 1 65 Female 0.7 0.1 187
## 2 62 Male 10.9 5.5 699
## 3 62 Male 7.3 4.1 490
## 4 58 Male 1.0 0.4 182
## 5 72 Male 3.9 2.0 195
## 6 46 Male 1.8 0.7 208
## 7 26 Female 0.9 0.2 154
## 8 29 Female 0.9 0.3 202
## 9 17 Male 0.9 0.3 202
## 10 55 Male 0.7 0.2 290
## 11 57 Male 0.6 0.1 210
## 12 72 Male 2.7 1.3 260
## 13 64 Male 0.9 0.3 310
## 14 74 Female 1.1 0.4 214
## 15 61 Male 0.7 0.2 145
## 16 25 Male 0.6 0.1 183
## 17 38 Male 1.8 0.8 342
## 18 33 Male 1.6 0.5 165
## 19 40 Female 0.9 0.3 293
## 20 40 Female 0.9 0.3 293
## 21 51 Male 2.2 1.0 610
## 22 51 Male 2.9 1.3 482
## 23 62 Male 6.8 3.0 542
## 24 40 Male 1.9 1.0 231
## 25 63 Male 0.9 0.2 194
## 26 34 Male 4.1 2.0 289
## 27 34 Male 4.1 2.0 289
## 28 34 Male 6.2 3.0 240
## 29 20 Male 1.1 0.5 128
## 30 84 Female 0.7 0.2 188
## 31 57 Male 4.0 1.9 190
## 32 52 Male 0.9 0.2 156
## 33 57 Male 1.0 0.3 187
## 34 38 Female 2.6 1.2 410
## 35 38 Female 2.6 1.2 410
## 36 30 Male 1.3 0.4 482
## 37 17 Female 0.7 0.2 145
## 38 46 Female 14.2 7.8 374
## 39 48 Male 1.4 0.6 263
## 40 47 Male 2.7 1.3 275
## 41 45 Male 2.4 1.1 168
## 42 62 Male 0.6 0.1 160
## 43 42 Male 6.8 3.2 630
## 44 50 Male 2.6 1.2 415
## 45 85 Female 1.0 0.3 208
## 46 35 Male 1.8 0.6 275
## 47 21 Male 3.9 1.8 150
## 48 40 Male 1.1 0.3 230
## 49 32 Female 0.6 0.1 176
## 50 55 Male 18.4 8.8 206
## 51 45 Female 0.7 0.2 170
## 52 34 Female 0.6 0.1 161
## 53 38 Male 3.1 1.6 253
## 54 38 Male 1.1 0.3 198
## 55 42 Male 8.9 4.5 272
## 56 42 Male 8.9 4.5 272
## 57 33 Male 0.8 0.2 198
## 58 48 Female 0.9 0.2 175
## 59 51 Male 0.8 0.2 367
## 60 64 Male 1.1 0.5 145
## 61 31 Female 0.8 0.2 158
## 62 58 Male 1.0 0.5 158
## 63 58 Male 1.0 0.5 158
## 64 57 Male 0.7 0.2 208
## 65 57 Male 1.3 0.4 259
## 66 57 Male 1.4 0.7 470
## 67 54 Male 2.2 1.2 195
## 68 37 Male 1.8 0.8 215
## 69 66 Male 0.7 0.2 239
## 70 60 Male 0.8 0.2 215
## 71 19 Female 0.7 0.2 186
## 72 75 Female 0.8 0.2 188
## 73 75 Female 0.8 0.2 205
## 74 52 Male 0.6 0.1 171
## 75 68 Male 0.7 0.1 145
## 76 29 Female 0.7 0.1 162
## 77 31 Male 0.9 0.2 518
## 78 68 Female 0.6 0.1 1620
## 79 70 Male 1.4 0.6 146
## 80 58 Female 2.8 1.3 670
## 81 58 Female 2.4 1.1 915
## 82 29 Male 1.0 0.3 75
## 83 49 Male 0.7 0.1 148
## 84 33 Male 2.0 1.0 258
## 85 32 Male 0.6 0.1 237
## 86 14 Male 1.4 0.5 269
## 87 13 Male 0.6 0.1 320
## 88 58 Male 0.8 0.2 298
## 89 18 Male 0.6 0.2 538
## 90 60 Male 4.0 1.9 238
## 91 60 Male 5.7 2.8 214
## 92 60 Male 6.8 3.2 308
## 93 60 Male 8.6 4.0 298
## 94 60 Male 5.8 2.7 204
## 95 60 Male 5.2 2.4 168
## 96 75 Male 0.9 0.2 282
## 97 39 Male 3.8 1.5 298
## 98 39 Male 6.6 3.0 215
## 99 18 Male 0.6 0.1 265
## 100 18 Male 0.7 0.1 312
## 101 27 Male 0.6 0.2 161
## 102 27 Male 0.7 0.2 243
## 103 17 Male 0.9 0.2 224
## 104 55 Female 0.8 0.2 225
## 105 63 Male 0.5 0.1 170
## 106 36 Male 5.3 2.3 145
## 107 36 Male 5.3 2.3 145
## 108 36 Male 0.8 0.2 158
## 109 36 Male 0.8 0.2 158
## 110 36 Male 0.9 0.1 486
## 111 24 Female 0.7 0.2 188
## 112 48 Male 3.2 1.6 257
## 113 27 Male 1.2 0.4 179
## 114 74 Male 0.6 0.1 272
## 115 50 Male 5.8 3.0 661
## 116 50 Male 7.3 3.6 1580
## 117 48 Male 0.7 0.1 1630
## 118 32 Male 12.7 6.2 194
## 119 32 Male 15.9 7.0 280
## 120 32 Male 18.0 8.2 298
## 121 32 Male 23.0 11.3 300
## 122 32 Male 22.7 10.2 290
## 123 58 Male 1.7 0.8 188
## 124 64 Female 0.8 0.2 178
## 125 28 Male 0.6 0.1 177
## 126 60 Male 1.8 0.5 201
## 127 48 Male 5.8 2.5 802
## 128 64 Male 3.0 1.4 248
## 129 58 Female 1.7 0.8 1896
## 130 45 Male 2.8 1.7 263
## 131 45 Male 3.2 1.4 512
## 132 70 Female 0.7 0.2 237
## 133 18 Female 0.8 0.2 199
## 134 53 Male 0.9 0.4 238
## 135 18 Male 1.8 0.7 178
## 136 66 Male 11.3 5.6 1110
## 137 46 Female 4.7 2.2 310
## 138 18 Male 0.8 0.2 282
## 139 18 Male 0.8 0.2 282
## 140 15 Male 0.8 0.2 380
## 141 60 Male 0.6 0.1 186
## 142 66 Female 4.2 2.1 159
## 143 30 Male 1.6 0.4 332
## 144 30 Male 1.6 0.4 332
## 145 45 Female 3.5 1.5 189
## 146 65 Male 0.8 0.2 201
## 147 66 Female 2.9 1.3 168
## 148 65 Male 0.7 0.1 392
## 149 50 Male 0.9 0.2 202
## 150 60 Male 0.8 0.2 286
## 151 56 Male 1.1 0.5 180
## 152 50 Male 1.6 0.8 218
## 153 46 Female 0.8 0.2 182
## 154 52 Male 0.6 0.1 178
## 155 34 Male 5.9 2.5 290
## 156 34 Male 8.7 4.0 298
## 157 32 Male 0.9 0.3 462
## 158 72 Male 0.7 0.1 196
## 159 72 Male 0.7 0.1 196
## 160 50 Male 1.2 0.4 282
## 161 60 Male 11.0 4.9 750
## 162 60 Male 11.5 5.0 1050
## 163 60 Male 5.8 2.7 599
## 164 39 Male 1.9 0.9 180
## 165 39 Male 1.9 0.9 180
## 166 48 Male 4.5 2.3 282
## 167 55 Male 75.0 3.6 332
## 168 47 Female 3.0 1.5 292
## 169 60 Male 22.8 12.6 962
## 170 60 Male 8.9 4.0 950
## 171 72 Male 1.7 0.8 200
## 172 44 Female 1.9 0.6 298
## 173 55 Male 14.1 7.6 750
## 174 31 Male 0.6 0.1 175
## 175 31 Male 0.6 0.1 175
## 176 31 Male 0.8 0.2 198
## 177 55 Male 0.8 0.2 482
## 178 75 Male 14.8 9.0 1020
## 179 75 Male 10.6 5.0 562
## 180 75 Male 8.0 4.6 386
## 181 75 Male 2.8 1.3 250
## 182 75 Male 2.9 1.3 218
## 183 65 Male 1.9 0.8 170
## 184 40 Male 0.6 0.1 171
## 185 64 Male 1.1 0.4 201
## 186 38 Male 1.5 0.4 298
## 187 60 Male 3.2 1.8 750
## 188 60 Male 2.1 1.0 191
## 189 60 Male 1.9 0.8 614
## 190 48 Female 0.8 0.2 218
## 191 60 Male 6.3 3.2 314
## 192 60 Male 5.8 3.0 257
## 193 60 Male 2.3 0.6 272
## 194 49 Male 1.3 0.4 206
## 195 49 Male 2.0 0.6 209
## 196 60 Male 2.4 1.0 1124
## 197 60 Male 2.0 1.1 664
## 198 26 Female 0.6 0.2 142
## 199 41 Male 0.9 0.2 169
## 200 7 Female 27.2 11.8 1420
## 201 49 Male 0.6 0.1 218
## 202 49 Male 0.6 0.1 218
## 203 38 Female 0.8 0.2 145
## 204 21 Male 1.0 0.3 142
## 205 21 Male 0.7 0.2 135
## 206 45 Male 2.5 1.2 163
## 207 40 Male 3.6 1.8 285
## 208 40 Male 3.9 1.7 350
## 209 70 Female 0.9 0.3 220
## 210 45 Female 0.9 0.3 189
## 211 28 Male 0.8 0.3 190
## 212 42 Male 2.7 1.3 219
## 213 22 Male 2.7 1.0 160
## 214 8 Female 0.9 0.2 401
## 215 38 Male 1.7 1.0 180
## 216 66 Male 0.6 0.2 100
## 217 55 Male 0.9 0.2 116
## 218 49 Male 1.1 0.5 159
## 219 6 Male 0.6 0.1 289
## 220 37 Male 0.8 0.2 125
## 221 37 Male 0.8 0.2 147
## 222 47 Male 0.9 0.2 192
## 223 47 Male 0.9 0.2 265
## 224 50 Male 1.1 0.3 175
## 225 70 Male 1.7 0.5 400
## 226 26 Male 0.6 0.2 120
## 227 26 Male 1.3 0.4 173
## 228 68 Female 0.7 0.2 186
## 229 65 Female 1.0 0.3 202
## 230 46 Male 0.6 0.2 290
## 231 61 Male 1.5 0.6 196
## 232 61 Male 0.8 0.1 282
## 233 50 Male 2.7 1.6 157
## 234 33 Male 2.0 1.4 2110
## 235 40 Female 0.9 0.2 285
## 236 60 Male 1.5 0.6 360
## 237 22 Male 0.8 0.2 300
## 238 35 Female 0.9 0.3 158
## 239 35 Female 0.9 0.2 190
## 240 40 Male 0.9 0.3 196
## 241 48 Male 0.7 0.2 165
## 242 51 Male 0.8 0.2 230
## 243 29 Female 0.8 0.2 205
## 244 28 Female 0.9 0.2 316
## 245 54 Male 0.8 0.2 218
## 246 54 Male 0.9 0.2 290
## 247 55 Male 1.8 9.0 272
## 248 55 Male 0.9 0.2 190
## 249 40 Male 0.7 0.1 202
## 250 33 Male 1.2 0.3 498
## 251 33 Male 2.1 1.3 480
## 252 33 Male 0.9 0.8 680
## 253 65 Male 1.1 0.3 258
## 254 35 Female 0.6 0.2 180
## 255 38 Female 0.7 0.1 152
## 256 38 Male 1.7 0.7 859
## 257 50 Male 0.9 0.3 901
## 258 44 Male 0.8 0.2 335
## 259 36 Male 0.8 0.2 182
## 260 42 Male 30.5 14.2 285
## 261 42 Male 16.4 8.9 245
## 262 33 Male 1.5 7.0 505
## 263 18 Male 0.8 0.2 228
## 264 38 Female 0.8 0.2 185
## 265 38 Male 0.8 0.2 247
## 266 4 Male 0.9 0.2 348
## 267 62 Male 1.2 0.4 195
## 268 43 Female 0.9 0.3 140
## 269 40 Male 14.5 6.4 358
## 270 26 Male 0.6 0.1 110
## 271 37 Male 0.7 0.2 235
## 272 4 Male 0.8 0.2 460
## 273 21 Male 18.5 9.5 380
## 274 30 Male 0.7 0.2 262
## 275 33 Male 1.8 0.8 196
## 276 26 Male 1.9 0.8 180
## 277 35 Male 0.9 0.2 190
## 278 60 Male 2.0 0.8 190
## 279 45 Male 2.2 0.8 209
## 280 48 Female 1.0 1.4 144
## 281 58 Male 0.8 0.2 123
## 282 50 Male 0.7 0.2 192
## 283 50 Male 0.7 0.2 188
## 284 18 Male 1.3 0.7 316
## 285 18 Male 0.9 0.3 300
## 286 13 Male 1.5 0.5 575
## 287 34 Female 0.8 0.2 192
## 288 43 Male 1.3 0.6 155
## 289 50 Female 1.0 0.5 239
## 290 57 Male 4.5 2.3 315
## 291 45 Female 1.0 0.3 250
## 292 60 Male 0.7 0.2 174
## 293 45 Male 0.6 0.2 245
## 294 23 Male 1.1 0.5 191
## 295 22 Male 2.4 1.0 340
## 296 22 Male 0.6 0.2 202
## 297 74 Female 0.9 0.3 234
## 298 25 Female 0.9 0.3 159
## 299 31 Female 1.1 0.3 190
## 300 24 Female 0.9 0.2 195
## 301 58 Male 0.8 0.2 180
## 302 51 Female 0.9 0.2 280
## 303 50 Female 1.7 0.6 430
## 304 50 Male 0.7 0.2 206
## 305 55 Female 0.8 0.2 155
## 306 54 Female 1.4 0.7 195
## 307 48 Male 1.6 1.0 588
## 308 30 Male 0.8 0.2 174
## 309 45 Female 0.8 0.2 165
## 310 48 Female 1.1 0.7 527
## 311 51 Male 0.8 0.2 175
## 312 54 Female 23.2 12.6 574
## 313 27 Male 1.3 0.6 106
## 314 30 Female 0.8 0.2 158
## 315 26 Male 2.0 0.9 195
## 316 22 Male 0.9 0.3 179
## 317 44 Male 0.9 0.2 182
## 318 35 Male 0.7 0.2 198
## 319 38 Male 3.7 2.2 216
## 320 14 Male 0.9 0.3 310
## 321 30 Female 0.7 0.2 63
## 322 30 Female 0.8 0.2 198
## 323 36 Male 1.7 0.5 205
## 324 12 Male 0.8 0.2 302
## 325 60 Male 2.6 1.2 171
## 326 42 Male 0.8 0.2 158
## 327 36 Female 1.2 0.4 358
## 328 24 Male 3.3 1.6 174
## 329 43 Male 0.8 0.2 192
## 330 21 Male 0.7 0.2 211
## 331 26 Male 2.0 0.9 157
## 332 26 Male 1.7 0.6 210
## 333 26 Male 7.1 3.3 258
## 334 36 Female 0.7 0.2 152
## 335 13 Female 0.7 0.2 350
## 336 13 Female 0.7 0.1 182
## 337 75 Male 6.7 3.6 458
## 338 75 Male 2.5 1.2 375
## 339 75 Male 1.8 0.8 405
## 340 75 Male 1.4 0.4 215
## 341 75 Male 0.9 0.2 206
## 342 36 Female 0.8 0.2 650
## 343 35 Male 0.8 0.2 198
## 344 70 Male 3.1 1.6 198
## 345 37 Male 0.8 0.2 195
## 346 60 Male 2.9 1.3 230
## 347 46 Male 0.6 0.2 115
## 348 38 Male 0.7 0.2 216
## 349 70 Male 1.3 0.4 358
## 350 49 Female 0.8 0.2 158
## 351 37 Male 1.8 0.8 145
## 352 37 Male 1.3 0.4 195
## 353 26 Female 0.7 0.2 144
## 354 48 Female 1.4 0.8 621
## 355 48 Female 0.8 0.2 150
## 356 19 Male 1.4 0.8 178
## 357 33 Male 0.7 0.2 256
## 358 33 Male 2.1 0.7 205
## 359 37 Male 0.7 0.2 176
## 360 69 Female 0.8 0.2 146
## 361 24 Male 0.7 0.2 218
## 362 65 Female 0.7 0.2 182
## 363 55 Male 1.1 0.3 215
## 364 42 Female 0.9 0.2 165
## 365 21 Male 0.8 0.2 183
## 366 40 Male 0.7 0.2 176
## 367 16 Male 0.7 0.2 418
## 368 60 Male 2.2 1.0 271
## 369 42 Female 0.8 0.2 182
## 370 58 Female 0.8 0.2 130
## 371 54 Female 22.6 11.4 558
## 372 33 Male 0.8 0.2 135
## 373 48 Male 0.7 0.2 326
## 374 25 Female 0.7 0.1 140
## 375 56 Female 0.7 0.1 145
## 376 47 Male 3.5 1.6 206
## 377 33 Male 0.7 0.1 168
## 378 20 Female 0.6 0.2 202
## 379 50 Female 0.7 0.1 192
## 380 72 Male 0.7 0.2 185
## 381 50 Male 1.7 0.8 331
## 382 39 Male 0.6 0.2 188
## 383 58 Female 0.7 0.1 172
## 384 60 Female 1.4 0.7 159
## 385 34 Male 3.7 2.1 490
## 386 50 Male 0.8 0.2 152
## 387 38 Male 2.7 1.4 105
## 388 51 Male 0.8 0.2 160
## 389 46 Male 0.8 0.2 160
## 390 72 Male 0.6 0.1 102
## 391 72 Male 0.8 0.2 148
## 392 75 Male 0.9 0.2 162
## 393 41 Male 7.5 4.3 149
## 394 41 Male 2.7 1.3 580
## 395 48 Female 1.0 0.3 310
## 396 45 Male 0.8 0.2 140
## 397 74 Male 1.0 0.3 175
## 398 78 Male 1.0 0.3 152
## 399 38 Male 0.8 0.2 208
## 400 27 Male 1.0 0.2 205
## 401 66 Female 0.7 0.2 162
## 402 50 Male 7.3 3.7 92
## 403 42 Female 0.5 0.1 162
## 404 65 Male 0.7 0.2 199
## 405 22 Male 0.8 0.2 198
## 406 31 Female 0.8 0.2 215
## 407 45 Male 0.7 0.2 180
## 408 12 Male 1.0 0.2 719
## 409 48 Male 2.4 1.1 554
## 410 48 Male 5.0 2.6 555
## 411 18 Male 1.4 0.6 215
## 412 23 Female 2.3 0.8 509
## 413 65 Male 4.9 2.7 190
## 414 48 Male 0.7 0.2 208
## 415 65 Male 1.4 0.6 260
## 416 70 Male 1.3 0.3 690
## 417 70 Male 0.6 0.1 862
## 418 11 Male 0.7 0.1 592
## 419 50 Male 4.2 2.3 450
## 420 55 Female 8.2 3.9 1350
## 421 55 Female 10.9 5.1 1350
## 422 26 Male 1.0 0.3 163
## 423 41 Male 1.2 0.5 246
## 424 53 Male 1.6 0.9 178
## 425 32 Female 0.7 0.1 240
## 426 58 Male 0.4 0.1 100
## 427 45 Male 1.3 0.6 166
## 428 65 Male 0.9 0.2 170
## 429 52 Female 0.6 0.1 194
## 430 73 Male 1.9 0.7 1750
## 431 53 Female 0.7 0.1 182
## 432 47 Female 0.8 0.2 236
## 433 29 Male 0.7 0.2 165
## 434 41 Female 0.9 0.2 201
## 435 30 Female 0.7 0.2 194
## 436 17 Female 0.5 0.1 206
## 437 23 Male 1.0 0.3 212
## 438 35 Male 1.6 0.7 157
## 439 65 Male 0.8 0.2 162
## 440 42 Female 0.8 0.2 168
## 441 49 Female 0.8 0.2 198
## 442 42 Female 2.3 1.1 292
## 443 42 Female 7.4 3.6 298
## 444 42 Female 0.7 0.2 152
## 445 61 Male 0.8 0.2 163
## 446 17 Male 0.9 0.2 279
## 447 54 Male 0.8 0.2 181
## 448 45 Female 23.3 12.8 1550
## 449 48 Female 0.8 0.2 142
## 450 48 Female 0.9 0.2 173
## 451 65 Male 7.9 4.3 282
## 452 35 Male 0.8 0.2 279
## 453 58 Male 0.9 0.2 1100
## 454 46 Male 0.7 0.2 224
## 455 28 Male 0.6 0.2 159
## 456 21 Female 0.6 0.1 186
## 457 32 Male 0.7 0.2 189
## 458 61 Male 0.8 0.2 192
## 459 26 Male 6.8 3.2 140
## 460 65 Male 1.1 0.5 686
## 461 22 Female 2.2 1.0 215
## 462 28 Female 0.8 0.2 309
## 463 38 Male 0.7 0.2 110
## 464 25 Male 0.8 0.1 130
## 465 45 Female 0.7 0.2 164
## 466 45 Female 0.6 0.1 270
## 467 28 Female 0.6 0.1 137
## 468 28 Female 1.0 0.3 90
## 469 66 Male 1.0 0.3 190
## 470 66 Male 0.8 0.2 165
## 471 66 Male 1.1 0.5 167
## 472 49 Female 0.6 0.1 185
## 473 42 Male 0.7 0.2 197
## 474 42 Male 1.0 0.3 154
## 475 35 Male 2.0 1.1 226
## 476 38 Male 2.2 1.0 310
## 477 38 Male 0.9 0.3 310
## 478 55 Male 0.6 0.2 220
## 479 33 Male 7.1 3.7 196
## 480 33 Male 3.4 1.6 186
## 481 7 Male 0.5 0.1 352
## 482 45 Male 2.3 1.3 282
## 483 45 Male 1.1 0.4 92
## 484 30 Male 0.8 0.2 182
## 485 62 Male 5.0 2.1 103
## 486 22 Female 6.7 3.2 850
## 487 42 Female 0.8 0.2 195
## 488 32 Male 0.7 0.2 276
## 489 60 Male 0.7 0.2 171
## 490 65 Male 0.8 0.1 146
## 491 53 Female 0.8 0.2 193
## 492 27 Male 1.0 0.3 180
## 493 35 Female 1.0 0.3 805
## 494 65 Male 0.7 0.2 265
## 495 25 Male 0.7 0.2 185
## 496 32 Male 0.7 0.2 165
## 497 24 Male 1.0 0.2 189
## 498 67 Male 2.2 1.1 198
## 499 68 Male 1.8 0.5 151
## 500 55 Male 3.6 1.6 349
## 501 70 Male 2.7 1.2 365
## 502 36 Male 2.8 1.5 305
## 503 42 Male 0.8 0.2 127
## 504 53 Male 19.8 10.4 238
## 505 32 Male 30.5 17.1 218
## 506 32 Male 32.6 14.1 219
## 507 56 Male 17.7 8.8 239
## 508 50 Male 0.9 0.3 194
## 509 46 Male 18.4 8.5 450
## 510 46 Male 20.0 10.0 254
## 511 37 Female 0.8 0.2 205
## 512 45 Male 2.2 1.6 320
## 513 56 Male 1.0 0.3 195
## 514 69 Male 0.9 0.2 215
## 515 49 Male 1.0 0.3 230
## 516 49 Male 3.9 2.1 189
## 517 60 Male 0.9 0.3 168
## 518 28 Male 0.9 0.2 215
## 519 45 Male 2.9 1.4 210
## 520 35 Male 26.3 12.1 108
## 521 62 Male 1.8 0.9 224
## 522 55 Male 4.4 2.9 230
## 523 46 Female 0.8 0.2 185
## 524 50 Male 0.6 0.2 137
## 525 29 Male 0.8 0.2 156
## 526 53 Female 0.9 0.2 210
## 527 46 Male 9.4 5.2 268
## 528 40 Male 3.5 1.6 298
## 529 45 Male 1.7 0.8 315
## 530 55 Male 3.3 1.5 214
## 531 22 Female 1.1 0.3 138
## 532 40 Male 30.8 18.3 285
## 533 62 Male 0.7 0.2 162
## 534 46 Female 1.4 0.4 298
## 535 39 Male 1.6 0.8 230
## 536 60 Male 19.6 9.5 466
## 537 46 Male 15.8 7.2 227
## 538 10 Female 0.8 0.1 395
## 539 52 Male 1.8 0.8 97
## 540 65 Female 0.7 0.2 406
## 541 42 Male 0.8 0.2 114
## 542 42 Male 0.8 0.2 198
## 543 62 Male 0.7 0.2 173
## 544 40 Male 1.2 0.6 204
## 545 54 Female 5.5 3.2 350
## 546 45 Female 0.7 0.2 153
## 547 45 Male 20.2 11.7 188
## 548 50 Female 27.7 10.8 380
## 549 42 Male 11.1 6.1 214
## 550 40 Female 2.1 1.0 768
## 551 46 Male 3.3 1.5 172
## 552 29 Male 1.2 0.4 160
## 553 45 Male 0.6 0.1 196
## 554 46 Male 10.2 4.2 232
## 555 73 Male 1.8 0.9 220
## 556 55 Male 0.8 0.2 290
## 557 51 Male 0.7 0.1 180
## 558 51 Male 2.9 1.2 189
## 559 51 Male 4.0 2.5 275
## 560 26 Male 42.8 19.7 390
## 561 66 Male 15.2 7.7 356
## 562 66 Male 16.6 7.6 315
## 563 66 Male 17.3 8.5 388
## 564 64 Male 1.4 0.5 298
## 565 38 Female 0.6 0.1 165
## 566 43 Male 22.5 11.8 143
## 567 50 Female 1.0 0.3 191
## 568 52 Male 2.7 1.4 251
## 569 20 Female 16.7 8.4 200
## 570 16 Male 7.7 4.1 268
## 571 16 Male 2.6 1.2 236
## 572 90 Male 1.1 0.3 215
## 573 32 Male 15.6 9.5 134
## 574 32 Male 3.7 1.6 612
## 575 32 Male 12.1 6.0 515
## 576 32 Male 25.0 13.7 560
## 577 32 Male 15.0 8.2 289
## 578 32 Male 12.7 8.4 190
## 579 60 Male 0.5 0.1 500
## 580 40 Male 0.6 0.1 98
## 581 52 Male 0.8 0.2 245
## 582 31 Male 1.3 0.5 184
## 583 38 Male 1.0 0.3 216
## AlamineAminotransfera ApsartateAminotransferase TotalProtiens Albumin
## 1 16 18 6.8 3.3
## 2 64 100 7.5 3.2
## 3 60 68 7.0 3.3
## 4 14 20 6.8 3.4
## 5 27 59 7.3 2.4
## 6 19 14 7.6 4.4
## 7 16 12 7.0 3.5
## 8 14 11 6.7 3.6
## 9 22 19 7.4 4.1
## 10 53 58 6.8 3.4
## 11 51 59 5.9 2.7
## 12 31 56 7.4 3.0
## 13 61 58 7.0 3.4
## 14 22 30 8.1 4.1
## 15 53 41 5.8 2.7
## 16 91 53 5.5 2.3
## 17 168 441 7.6 4.4
## 18 15 23 7.3 3.5
## 19 232 245 6.8 3.1
## 20 232 245 6.8 3.1
## 21 17 28 7.3 2.6
## 22 22 34 7.0 2.4
## 23 116 66 6.4 3.1
## 24 16 55 4.3 1.6
## 25 52 45 6.0 3.9
## 26 875 731 5.0 2.7
## 27 875 731 5.0 2.7
## 28 1680 850 7.2 4.0
## 29 20 30 3.9 1.9
## 30 13 21 6.0 3.2
## 31 45 111 5.2 1.5
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## 139 72 140 5.5 2.5
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## 213 82 127 5.5 3.1
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## 221 27 46 5.0 2.5
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## 223 40 28 8.0 4.0
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## 232 85 231 8.5 4.3
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## 242 24 46 6.5 3.1
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## 244 25 23 8.5 5.5
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## 272 152 231 6.5 3.2
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## 274 15 18 9.6 4.7
## 275 25 22 8.0 4.0
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## 302 21 30 6.7 3.2
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## 309 22 18 8.2 4.1
## 310 178 250 8.0 4.2
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## 312 43 47 7.2 3.5
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## 315 24 65 7.8 4.3
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## 333 80 113 6.2 2.9
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## 335 17 24 7.4 4.0
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## 337 198 143 6.2 3.2
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## 341 44 33 6.2 2.9
## 342 70 138 6.6 3.1
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## 382 28 43 8.1 3.3
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## 405 20 26 6.8 3.9
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## 410 284 190 6.5 3.3
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## 415 28 24 5.2 2.2
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## 441 23 20 7.0 4.3
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## 443 52 102 4.6 1.9
## 444 35 81 6.2 3.2
## 445 18 19 6.3 2.8
## 446 40 46 7.3 4.0
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## 464 23 42 8.0 4.0
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## 469 30 54 5.3 2.1
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## 471 13 56 7.1 4.1
## 472 17 26 6.6 2.9
## 473 64 33 5.8 2.4
## 474 38 21 6.8 3.9
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## 476 119 42 7.9 4.1
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## 478 24 32 5.1 2.4
## 479 622 497 6.9 3.6
## 480 779 844 7.3 3.2
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## 485 18 40 5.0 2.1
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## 504 39 221 8.1 2.5
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## 506 95 235 5.8 3.1
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## 508 190 73 7.5 3.9
## 509 119 230 7.5 3.3
## 510 140 540 5.4 3.0
## 511 31 36 9.2 4.6
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## 513 22 28 5.8 2.6
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## 516 65 181 6.9 3.0
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## 518 50 28 8.0 4.0
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## 520 168 630 9.2 2.0
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## 522 14 25 7.1 2.1
## 523 24 15 7.9 3.7
## 524 15 16 4.8 2.6
## 525 12 15 6.8 3.7
## 526 35 32 8.0 3.9
## 527 21 63 6.4 2.8
## 528 68 200 7.1 3.4
## 529 12 38 6.3 2.1
## 530 54 152 5.1 1.8
## 531 14 21 7.0 3.8
## 532 110 186 7.9 2.7
## 533 12 17 8.2 3.2
## 534 509 623 3.6 1.0
## 535 88 74 8.0 4.0
## 536 46 52 6.1 2.0
## 537 67 220 6.9 2.6
## 538 25 75 7.6 3.6
## 539 85 78 6.4 2.7
## 540 24 45 7.2 3.5
## 541 21 23 7.0 3.0
## 542 29 19 6.6 3.0
## 543 46 47 7.3 4.1
## 544 23 27 7.6 4.0
## 545 67 42 7.0 3.2
## 546 41 42 4.5 2.2
## 547 47 32 5.4 2.3
## 548 39 348 7.1 2.3
## 549 60 186 6.9 2.8
## 550 74 141 7.8 4.9
## 551 25 41 5.6 2.4
## 552 20 22 6.2 3.0
## 553 29 30 5.8 2.9
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## 557 25 27 6.1 3.1
## 558 80 125 6.2 3.1
## 559 382 330 7.5 4.0
## 560 75 138 7.5 2.6
## 561 321 562 6.5 2.2
## 562 233 384 6.9 2.0
## 563 173 367 7.8 2.6
## 564 31 83 7.2 2.6
## 565 22 34 5.9 2.9
## 566 22 143 6.6 2.1
## 567 22 31 7.8 4.0
## 568 20 40 6.0 1.7
## 569 91 101 6.9 3.5
## 570 213 168 7.1 4.0
## 571 131 90 5.4 2.6
## 572 46 134 6.9 3.0
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## 576 41 88 7.9 2.5
## 577 58 80 5.3 2.2
## 578 28 47 5.4 2.6
## 579 20 34 5.9 1.6
## 580 35 31 6.0 3.2
## 581 48 49 6.4 3.2
## 582 29 32 6.8 3.4
## 583 21 24 7.3 4.4
## AlbuminandGlobulinRatio LiverPatient
## 1 0.90 1
## 2 0.74 1
## 3 0.89 1
## 4 1.00 1
## 5 0.40 1
## 6 1.30 1
## 7 1.00 1
## 8 1.10 1
## 9 1.20 2
## 10 1.00 1
## 11 0.80 1
## 12 0.60 1
## 13 0.90 2
## 14 1.00 1
## 15 0.87 1
## 16 0.70 2
## 17 1.30 1
## 18 0.92 2
## 19 0.80 1
## 20 0.80 1
## 21 0.55 1
## 22 0.50 1
## 23 0.90 1
## 24 0.60 1
## 25 1.85 2
## 26 1.10 1
## 27 1.10 1
## 28 1.20 1
## 29 0.95 2
## 30 1.10 2
## 31 0.40 1
## 32 1.40 1
## 33 1.20 2
## 34 0.80 2
## 35 0.80 2
## 36 0.90 1
## 37 1.18 2
## 38 0.80 1
## 39 0.61 1
## 40 1.10 1
## 41 1.00 1
## 42 1.10 2
## 43 0.60 2
## 44 1.00 1
## 45 1.00 2
## 46 0.90 2
## 47 1.34 1
## 48 1.30 1
## 49 1.00 1
## 50 0.40 1
## 51 0.70 1
## 52 1.00 1
## 53 1.30 1
## 54 1.20 1
## 55 0.50 1
## 56 0.50 1
## 57 1.00 2
## 58 0.90 2
## 59 0.60 1
## 60 1.39 2
## 61 1.00 1
## 62 1.00 1
## 63 1.00 1
## 64 0.70 1
## 65 0.60 1
## 66 0.80 1
## 67 1.60 1
## 68 1.40 1
## 69 1.40 1
## 70 0.90 2
## 71 1.20 1
## 72 0.60 1
## 73 0.80 1
## 74 1.20 1
## 75 1.00 1
## 76 0.90 2
## 77 0.70 1
## 78 0.80 1
## 79 1.58 2
## 80 0.50 1
## 81 0.60 1
## 82 1.30 1
## 83 1.00 2
## 84 1.25 1
## 85 1.34 1
## 86 1.40 1
## 87 1.00 2
## 88 1.00 1
## 89 0.70 1
## 90 0.80 1
## 91 0.78 1
## 92 0.70 1
## 93 0.60 1
## 94 0.70 1
## 95 0.70 1
## 96 1.00 1
## 97 0.80 1
## 98 0.70 1
## 99 1.10 1
## 100 1.10 1
## 101 0.76 2
## 102 0.70 2
## 103 1.55 1
## 104 1.20 2
## 105 0.80 1
## 106 1.00 2
## 107 1.00 2
## 108 0.50 2
## 109 0.50 2
## 110 0.90 2
## 111 0.71 2
## 112 0.62 1
## 113 1.10 2
## 114 0.60 1
## 115 0.67 2
## 116 0.60 2
## 117 0.60 1
## 118 1.30 1
## 119 1.00 1
## 120 0.90 1
## 121 0.90 1
## 122 0.70 1
## 123 1.40 2
## 124 0.90 1
## 125 1.40 2
## 126 0.70 2
## 127 0.80 1
## 128 0.90 1
## 129 0.95 1
## 130 0.80 1
## 131 0.80 1
## 132 0.75 2
## 133 1.16 2
## 134 0.80 1
## 135 1.10 1
## 136 0.50 1
## 137 0.60 1
## 138 0.80 1
## 139 0.80 1
## 140 1.50 1
## 141 1.10 2
## 142 0.40 1
## 143 0.90 1
## 144 0.90 1
## 145 1.00 1
## 146 1.10 2
## 147 0.40 1
## 148 1.10 1
## 149 1.66 1
## 150 1.20 1
## 151 1.20 2
## 152 0.96 1
## 153 0.90 1
## 154 1.20 2
## 155 0.90 1
## 156 0.70 1
## 157 1.00 1
## 158 0.50 1
## 159 0.50 1
## 160 1.10 1
## 161 0.60 1
## 162 0.80 1
## 163 0.50 1
## 164 1.38 1
## 165 1.38 1
## 166 0.52 1
## 167 0.60 1
## 168 0.47 1
## 169 0.90 1
## 170 0.80 1
## 171 0.93 1
## 172 1.00 1
## 173 0.47 1
## 174 1.60 1
## 175 1.60 1
## 176 1.20 1
## 177 0.80 1
## 178 0.70 1
## 179 0.50 1
## 180 0.48 1
## 181 0.50 1
## 182 1.00 1
## 183 0.58 2
## 184 0.80 1
## 185 1.40 1
## 186 1.00 2
## 187 0.69 1
## 188 0.60 1
## 189 0.60 1
## 190 0.90 2
## 191 1.27 1
## 192 1.12 1
## 193 1.10 1
## 194 1.06 2
## 195 1.10 2
## 196 0.50 1
## 197 0.53 1
## 198 0.75 1
## 199 0.90 2
## 200 0.40 1
## 201 0.90 1
## 202 0.90 1
## 203 1.03 2
## 204 1.20 2
## 205 1.00 2
## 206 1.10 1
## 207 0.70 1
## 208 1.30 1
## 209 0.68 1
## 210 NA 1
## 211 1.40 1
## 212 0.80 1
## 213 1.20 2
## 214 0.80 1
## 215 1.00 1
## 216 1.90 2
## 217 1.00 2
## 218 1.50 1
## 219 0.70 2
## 220 1.10 1
## 221 1.00 1
## 222 1.40 1
## 223 1.00 1
## 224 1.70 2
## 225 1.10 1
## 226 1.00 1
## 227 1.00 1
## 228 1.40 1
## 229 0.90 2
## 230 1.00 1
## 231 1.30 2
## 232 1.00 1
## 233 0.60 1
## 234 0.90 1
## 235 0.80 1
## 236 0.80 1
## 237 0.90 2
## 238 1.00 1
## 239 1.80 2
## 240 0.80 1
## 241 1.00 2
## 242 NA 1
## 243 1.00 1
## 244 1.80 1
## 245 0.60 1
## 246 0.80 1
## 247 0.70 1
## 248 0.80 1
## 249 1.00 1
## 250 0.70 1
## 251 0.80 1
## 252 0.80 1
## 253 1.20 2
## 254 NA 2
## 255 1.40 2
## 256 1.00 1
## 257 1.20 1
## 258 1.10 1
## 259 1.40 2
## 260 0.60 1
## 261 0.50 1
## 262 1.00 1
## 263 1.30 1
## 264 0.70 1
## 265 1.38 2
## 266 1.00 2
## 267 1.50 1
## 268 1.80 1
## 269 0.50 1
## 270 1.30 1
## 271 1.00 1
## 272 0.90 2
## 273 1.00 1
## 274 1.20 1
## 275 1.00 1
## 276 1.00 2
## 277 1.20 2
## 278 0.80 1
## 279 1.00 1
## 280 1.00 1
## 281 1.00 1
## 282 1.30 2
## 283 0.90 1
## 284 0.50 2
## 285 1.00 1
## 286 0.90 1
## 287 1.20 1
## 288 1.00 2
## 289 0.90 1
## 290 1.30 1
## 291 1.00 1
## 292 1.10 2
## 293 0.90 1
## 294 1.20 2
## 295 1.10 1
## 296 0.90 1
## 297 1.00 1
## 298 1.70 2
## 299 0.90 1
## 300 1.20 2
## 301 1.10 2
## 302 0.80 1
## 303 1.00 1
## 304 1.00 2
## 305 1.40 1
## 306 0.90 2
## 307 0.40 1
## 308 1.00 1
## 309 1.00 1
## 310 1.10 1
## 311 1.30 1
## 312 0.90 1
## 313 NA 2
## 314 1.30 2
## 315 1.20 1
## 316 1.20 2
## 317 1.00 2
## 318 1.00 1
## 319 1.30 1
## 320 1.00 2
## 321 1.40 1
## 322 1.10 1
## 323 1.20 1
## 324 1.10 2
## 325 1.00 1
## 326 0.80 2
## 327 1.10 2
## 328 1.00 2
## 329 0.90 2
## 330 1.20 2
## 331 0.80 1
## 332 0.60 1
## 333 0.80 1
## 334 1.10 2
## 335 1.10 1
## 336 1.20 1
## 337 1.00 1
## 338 0.80 1
## 339 0.90 1
## 340 0.70 1
## 341 0.80 1
## 342 0.80 1
## 343 1.30 2
## 344 0.50 1
## 345 1.50 2
## 346 0.50 1
## 347 0.90 1
## 348 1.00 1
## 349 0.80 1
## 350 1.20 2
## 351 1.00 1
## 352 0.60 1
## 353 1.10 1
## 354 1.10 1
## 355 1.00 1
## 356 1.30 2
## 357 0.80 1
## 358 0.70 1
## 359 0.80 1
## 360 1.40 2
## 361 1.00 1
## 362 0.70 2
## 363 0.80 2
## 364 1.00 2
## 365 1.00 2
## 366 0.80 2
## 367 1.30 2
## 368 0.90 2
## 369 1.10 1
## 370 1.30 1
## 371 0.80 1
## 372 1.50 2
## 373 1.70 1
## 374 1.30 2
## 375 1.30 2
## 376 1.00 1
## 377 1.10 1
## 378 0.90 2
## 379 0.80 1
## 380 1.00 2
## 381 0.90 1
## 382 0.60 1
## 383 0.90 1
## 384 1.00 2
## 385 0.70 1
## 386 1.30 1
## 387 1.20 2
## 388 1.10 1
## 389 1.10 1
## 390 1.00 1
## 391 1.00 1
## 392 1.10 1
## 393 0.90 1
## 394 1.00 1
## 395 0.70 1
## 396 1.00 2
## 397 1.10 1
## 398 0.90 1
## 399 1.00 1
## 400 1.00 1
## 401 1.00 2
## 402 0.30 1
## 403 0.90 1
## 404 1.30 2
## 405 1.30 1
## 406 1.10 1
## 407 1.20 2
## 408 1.00 1
## 409 0.90 1
## 410 1.00 1
## 411 0.60 1
## 412 0.70 2
## 413 0.70 1
## 414 0.80 2
## 415 0.70 2
## 416 0.70 1
## 417 0.75 1
## 418 1.40 2
## 419 0.70 1
## 420 0.70 1
## 421 0.50 1
## 422 1.00 2
## 423 0.97 1
## 424 1.50 2
## 425 0.70 1
## 426 1.40 1
## 427 0.80 2
## 428 0.75 1
## 429 0.90 2
## 430 0.50 1
## 431 0.60 1
## 432 0.76 2
## 433 1.58 1
## 434 1.00 2
## 435 0.92 2
## 436 1.70 2
## 437 1.00 1
## 438 0.90 1
## 439 0.50 1
## 440 1.00 1
## 441 1.50 1
## 442 0.70 1
## 443 0.70 1
## 444 1.06 1
## 445 0.80 2
## 446 1.20 2
## 447 0.96 1
## 448 0.80 1
## 449 0.70 1
## 450 1.00 1
## 451 1.00 1
## 452 0.80 1
## 453 0.90 1
## 454 0.70 1
## 455 1.00 2
## 456 1.00 1
## 457 0.70 2
## 458 0.90 2
## 459 0.30 1
## 460 0.35 1
## 461 0.80 1
## 462 1.51 1
## 463 0.64 1
## 464 1.00 1
## 465 0.45 2
## 466 0.50 2
## 467 0.60 2
## 468 0.80 2
## 469 0.60 1
## 470 0.80 1
## 471 1.36 1
## 472 0.70 2
## 473 0.70 2
## 474 1.30 2
## 475 0.80 2
## 476 1.00 2
## 477 1.00 1
## 478 0.88 1
## 479 1.09 1
## 480 0.70 1
## 481 1.10 2
## 482 1.20 1
## 483 1.11 1
## 484 1.20 2
## 485 1.72 1
## 486 0.80 1
## 487 0.80 1
## 488 0.93 1
## 489 1.00 2
## 490 1.18 2
## 491 1.16 1
## 492 1.85 2
## 493 0.70 1
## 494 0.52 2
## 495 1.50 1
## 496 0.96 2
## 497 1.50 1
## 498 0.70 1
## 499 1.60 1
## 500 0.60 1
## 501 0.60 1
## 502 0.70 1
## 503 1.20 1
## 504 0.40 1
## 505 0.90 1
## 506 1.10 1
## 507 0.70 1
## 508 1.00 1
## 509 0.70 1
## 510 1.20 1
## 511 1.00 2
## 512 1.00 1
## 513 0.80 2
## 514 0.70 1
## 515 1.00 1
## 516 0.70 1
## 517 0.80 1
## 518 1.00 1
## 519 1.00 1
## 520 0.30 1
## 521 0.80 1
## 522 0.40 1
## 523 0.80 1
## 524 1.10 1
## 525 1.10 2
## 526 0.90 2
## 527 0.80 1
## 528 0.90 1
## 529 0.50 1
## 530 0.50 1
## 531 1.10 2
## 532 0.50 1
## 533 0.60 2
## 534 0.30 1
## 535 1.00 2
## 536 0.40 1
## 537 0.60 1
## 538 0.90 1
## 539 0.70 1
## 540 0.90 2
## 541 0.70 2
## 542 0.80 2
## 543 1.20 2
## 544 1.10 1
## 545 0.80 1
## 546 0.90 2
## 547 0.70 1
## 548 0.40 1
## 549 2.80 1
## 550 1.60 1
## 551 0.70 1
## 552 0.90 2
## 553 1.00 1
## 554 0.60 1
## 555 0.80 1
## 556 0.70 1
## 557 1.00 1
## 558 1.00 1
## 559 1.10 1
## 560 0.50 1
## 561 0.40 1
## 562 0.40 1
## 563 0.50 1
## 564 0.50 1
## 565 0.90 2
## 566 0.46 1
## 567 1.00 2
## 568 0.39 1
## 569 1.02 1
## 570 1.20 1
## 571 0.90 1
## 572 0.70 1
## 573 2.50 1
## 574 0.40 1
## 575 0.50 1
## 576 2.50 1
## 577 0.70 1
## 578 0.90 1
## 579 0.37 2
## 580 1.10 1
## 581 1.00 1
## 582 1.00 1
## 583 1.50 2Pada bagian (sep = ",") digunakan untuk memisah
nilai/karakter antar kolom agar rapi, sedangkan fungsi
data2 digunakan untuk memanggil/menampilkan seluruh data
yang diimport.
2. Eksplorasi Struktur Awal
2.1 Melihat data awal
head(data2)## Age Gender TotalBilirubin DirectBilirubin AlkalinePhosphotase
## 1 65 Female 0.7 0.1 187
## 2 62 Male 10.9 5.5 699
## 3 62 Male 7.3 4.1 490
## 4 58 Male 1.0 0.4 182
## 5 72 Male 3.9 2.0 195
## 6 46 Male 1.8 0.7 208
## AlamineAminotransfera ApsartateAminotransferase TotalProtiens Albumin
## 1 16 18 6.8 3.3
## 2 64 100 7.5 3.2
## 3 60 68 7.0 3.3
## 4 14 20 6.8 3.4
## 5 27 59 7.3 2.4
## 6 19 14 7.6 4.4
## AlbuminandGlobulinRatio LiverPatient
## 1 0.90 1
## 2 0.74 1
## 3 0.89 1
## 4 1.00 1
## 5 0.40 1
## 6 1.30 1fungsi head(data2) saja digunakan untuk menampilkan 6
baris pertama.
head(data2,3)## Age Gender TotalBilirubin DirectBilirubin AlkalinePhosphotase
## 1 65 Female 0.7 0.1 187
## 2 62 Male 10.9 5.5 699
## 3 62 Male 7.3 4.1 490
## AlamineAminotransfera ApsartateAminotransferase TotalProtiens Albumin
## 1 16 18 6.8 3.3
## 2 64 100 7.5 3.2
## 3 60 68 7.0 3.3
## AlbuminandGlobulinRatio LiverPatient
## 1 0.90 1
## 2 0.74 1
## 3 0.89 1untuk fungsi head(data2,3) digunakan untuk menampilkan 3
baris pertama. Tergantung kita inginmenampilkan baris ke-berapa.
tail(data2,10) #menampilkan 10 baris terakhir## Age Gender TotalBilirubin DirectBilirubin AlkalinePhosphotase
## 574 32 Male 3.7 1.6 612
## 575 32 Male 12.1 6.0 515
## 576 32 Male 25.0 13.7 560
## 577 32 Male 15.0 8.2 289
## 578 32 Male 12.7 8.4 190
## 579 60 Male 0.5 0.1 500
## 580 40 Male 0.6 0.1 98
## 581 52 Male 0.8 0.2 245
## 582 31 Male 1.3 0.5 184
## 583 38 Male 1.0 0.3 216
## AlamineAminotransfera ApsartateAminotransferase TotalProtiens Albumin
## 574 50 88 6.2 1.9
## 575 48 92 6.6 2.4
## 576 41 88 7.9 2.5
## 577 58 80 5.3 2.2
## 578 28 47 5.4 2.6
## 579 20 34 5.9 1.6
## 580 35 31 6.0 3.2
## 581 48 49 6.4 3.2
## 582 29 32 6.8 3.4
## 583 21 24 7.3 4.4
## AlbuminandGlobulinRatio LiverPatient
## 574 0.40 1
## 575 0.50 1
## 576 2.50 1
## 577 0.70 1
## 578 0.90 1
## 579 0.37 2
## 580 1.10 1
## 581 1.00 1
## 582 1.00 1
## 583 1.50 2fungsi tail() digunakan untuk menampilkan berapa baris
terakhir yang ingin kita tampilkan.
2.2 Struktur dan tipe data
str(data2)## 'data.frame': 583 obs. of 11 variables:
## $ Age : int 65 62 62 58 72 46 26 29 17 55 ...
## $ Gender : chr "Female" "Male" "Male" "Male" ...
## $ TotalBilirubin : num 0.7 10.9 7.3 1 3.9 1.8 0.9 0.9 0.9 0.7 ...
## $ DirectBilirubin : num 0.1 5.5 4.1 0.4 2 0.7 0.2 0.3 0.3 0.2 ...
## $ AlkalinePhosphotase : int 187 699 490 182 195 208 154 202 202 290 ...
## $ AlamineAminotransfera : int 16 64 60 14 27 19 16 14 22 53 ...
## $ ApsartateAminotransferase: int 18 100 68 20 59 14 12 11 19 58 ...
## $ TotalProtiens : num 6.8 7.5 7 6.8 7.3 7.6 7 6.7 7.4 6.8 ...
## $ Albumin : num 3.3 3.2 3.3 3.4 2.4 4.4 3.5 3.6 4.1 3.4 ...
## $ AlbuminandGlobulinRatio : num 0.9 0.74 0.89 1 0.4 1.3 1 1.1 1.2 1 ...
## $ LiverPatient : int 1 1 1 1 1 1 1 1 2 1 ...fungsi str() digunakan untuk menampilkan struktur data
secara ringkas.
typeof(data2$TotalBilirubin)## [1] "double"fungsi typeof() digunakan untuk melihat tipe data kolom
tertentu yang ingin kita ketahui. Contohnya seperti pada kolom data
TotalBilirubin tipe datanya adalah double.
class(data2)## [1] "data.frame"fungsi class() digunakan untuk melihat tipe keseluruhan
data kita di R. Pada data yang saya gunakan tipe datanya adalah
data.frame.
2.3 Ukuran dan Nama
nrow(data2)## [1] 583fungsi nrow() digunakan untuk melihat jumlah baris pada
data.
ncol(data2)## [1] 11fungsi ncol() digunakan untuk melihat jumlah kolom pada
data.
dim (data2)## [1] 583 11fungsi dim() digunakan untuk menampilkna jumlah baris
dan kolom sekaligus.
colnames(data2)## [1] "Age" "Gender"
## [3] "TotalBilirubin" "DirectBilirubin"
## [5] "AlkalinePhosphotase" "AlamineAminotransfera"
## [7] "ApsartateAminotransferase" "TotalProtiens"
## [9] "Albumin" "AlbuminandGlobulinRatio"
## [11] "LiverPatient"fungsi colnames() digunakan untuk melihat nama-nama
kolom yang ada pada data.
rownames(data2)## [1] "1" "2" "3" "4" "5" "6" "7" "8" "9" "10" "11" "12"
## [13] "13" "14" "15" "16" "17" "18" "19" "20" "21" "22" "23" "24"
## [25] "25" "26" "27" "28" "29" "30" "31" "32" "33" "34" "35" "36"
## [37] "37" "38" "39" "40" "41" "42" "43" "44" "45" "46" "47" "48"
## [49] "49" "50" "51" "52" "53" "54" "55" "56" "57" "58" "59" "60"
## [61] "61" "62" "63" "64" "65" "66" "67" "68" "69" "70" "71" "72"
## [73] "73" "74" "75" "76" "77" "78" "79" "80" "81" "82" "83" "84"
## [85] "85" "86" "87" "88" "89" "90" "91" "92" "93" "94" "95" "96"
## [97] "97" "98" "99" "100" "101" "102" "103" "104" "105" "106" "107" "108"
## [109] "109" "110" "111" "112" "113" "114" "115" "116" "117" "118" "119" "120"
## [121] "121" "122" "123" "124" "125" "126" "127" "128" "129" "130" "131" "132"
## [133] "133" "134" "135" "136" "137" "138" "139" "140" "141" "142" "143" "144"
## [145] "145" "146" "147" "148" "149" "150" "151" "152" "153" "154" "155" "156"
## [157] "157" "158" "159" "160" "161" "162" "163" "164" "165" "166" "167" "168"
## [169] "169" "170" "171" "172" "173" "174" "175" "176" "177" "178" "179" "180"
## [181] "181" "182" "183" "184" "185" "186" "187" "188" "189" "190" "191" "192"
## [193] "193" "194" "195" "196" "197" "198" "199" "200" "201" "202" "203" "204"
## [205] "205" "206" "207" "208" "209" "210" "211" "212" "213" "214" "215" "216"
## [217] "217" "218" "219" "220" "221" "222" "223" "224" "225" "226" "227" "228"
## [229] "229" "230" "231" "232" "233" "234" "235" "236" "237" "238" "239" "240"
## [241] "241" "242" "243" "244" "245" "246" "247" "248" "249" "250" "251" "252"
## [253] "253" "254" "255" "256" "257" "258" "259" "260" "261" "262" "263" "264"
## [265] "265" "266" "267" "268" "269" "270" "271" "272" "273" "274" "275" "276"
## [277] "277" "278" "279" "280" "281" "282" "283" "284" "285" "286" "287" "288"
## [289] "289" "290" "291" "292" "293" "294" "295" "296" "297" "298" "299" "300"
## [301] "301" "302" "303" "304" "305" "306" "307" "308" "309" "310" "311" "312"
## [313] "313" "314" "315" "316" "317" "318" "319" "320" "321" "322" "323" "324"
## [325] "325" "326" "327" "328" "329" "330" "331" "332" "333" "334" "335" "336"
## [337] "337" "338" "339" "340" "341" "342" "343" "344" "345" "346" "347" "348"
## [349] "349" "350" "351" "352" "353" "354" "355" "356" "357" "358" "359" "360"
## [361] "361" "362" "363" "364" "365" "366" "367" "368" "369" "370" "371" "372"
## [373] "373" "374" "375" "376" "377" "378" "379" "380" "381" "382" "383" "384"
## [385] "385" "386" "387" "388" "389" "390" "391" "392" "393" "394" "395" "396"
## [397] "397" "398" "399" "400" "401" "402" "403" "404" "405" "406" "407" "408"
## [409] "409" "410" "411" "412" "413" "414" "415" "416" "417" "418" "419" "420"
## [421] "421" "422" "423" "424" "425" "426" "427" "428" "429" "430" "431" "432"
## [433] "433" "434" "435" "436" "437" "438" "439" "440" "441" "442" "443" "444"
## [445] "445" "446" "447" "448" "449" "450" "451" "452" "453" "454" "455" "456"
## [457] "457" "458" "459" "460" "461" "462" "463" "464" "465" "466" "467" "468"
## [469] "469" "470" "471" "472" "473" "474" "475" "476" "477" "478" "479" "480"
## [481] "481" "482" "483" "484" "485" "486" "487" "488" "489" "490" "491" "492"
## [493] "493" "494" "495" "496" "497" "498" "499" "500" "501" "502" "503" "504"
## [505] "505" "506" "507" "508" "509" "510" "511" "512" "513" "514" "515" "516"
## [517] "517" "518" "519" "520" "521" "522" "523" "524" "525" "526" "527" "528"
## [529] "529" "530" "531" "532" "533" "534" "535" "536" "537" "538" "539" "540"
## [541] "541" "542" "543" "544" "545" "546" "547" "548" "549" "550" "551" "552"
## [553] "553" "554" "555" "556" "557" "558" "559" "560" "561" "562" "563" "564"
## [565] "565" "566" "567" "568" "569" "570" "571" "572" "573" "574" "575" "576"
## [577] "577" "578" "579" "580" "581" "582" "583"fungsi rownames() digunakan untuk melihat nama-nama
baris yang ada pada data.
2.4 Indexing dan subsetting
data2[1,] # menampilkan baris pertama## Age Gender TotalBilirubin DirectBilirubin AlkalinePhosphotase
## 1 65 Female 0.7 0.1 187
## AlamineAminotransfera ApsartateAminotransferase TotalProtiens Albumin
## 1 16 18 6.8 3.3
## AlbuminandGlobulinRatio LiverPatient
## 1 0.9 1data2[,5] # menampilkan kolom ke-lima## [1] 187 699 490 182 195 208 154 202 202 290 210 260 310 214 145
## [16] 183 342 165 293 293 610 482 542 231 194 289 289 240 128 188
## [31] 190 156 187 410 410 482 145 374 263 275 168 160 630 415 208
## [46] 275 150 230 176 206 170 161 253 198 272 272 198 175 367 145
## [61] 158 158 158 208 259 470 195 215 239 215 186 188 205 171 145
## [76] 162 518 1620 146 670 915 75 148 258 237 269 320 298 538 238
## [91] 214 308 298 204 168 282 298 215 265 312 161 243 224 225 170
## [106] 145 145 158 158 486 188 257 179 272 661 1580 1630 194 280 298
## [121] 300 290 188 178 177 201 802 248 1896 263 512 237 199 238 178
## [136] 1110 310 282 282 380 186 159 332 332 189 201 168 392 202 286
## [151] 180 218 182 178 290 298 462 196 196 282 750 1050 599 180 180
## [166] 282 332 292 962 950 200 298 750 175 175 198 482 1020 562 386
## [181] 250 218 170 171 201 298 750 191 614 218 314 257 272 206 209
## [196] 1124 664 142 169 1420 218 218 145 142 135 163 285 350 220 189
## [211] 190 219 160 401 180 100 116 159 289 125 147 192 265 175 400
## [226] 120 173 186 202 290 196 282 157 2110 285 360 300 158 190 196
## [241] 165 230 205 316 218 290 272 190 202 498 480 680 258 180 152
## [256] 859 901 335 182 285 245 505 228 185 247 348 195 140 358 110
## [271] 235 460 380 262 196 180 190 190 209 144 123 192 188 316 300
## [286] 575 192 155 239 315 250 174 245 191 340 202 234 159 190 195
## [301] 180 280 430 206 155 195 588 174 165 527 175 574 106 158 195
## [316] 179 182 198 216 310 63 198 205 302 171 158 358 174 192 211
## [331] 157 210 258 152 350 182 458 375 405 215 206 650 198 198 195
## [346] 230 115 216 358 158 145 195 144 621 150 178 256 205 176 146
## [361] 218 182 215 165 183 176 418 271 182 130 558 135 326 140 145
## [376] 206 168 202 192 185 331 188 172 159 490 152 105 160 160 102
## [391] 148 162 149 580 310 140 175 152 208 205 162 92 162 199 198
## [406] 215 180 719 554 555 215 509 190 208 260 690 862 592 450 1350
## [421] 1350 163 246 178 240 100 166 170 194 1750 182 236 165 201 194
## [436] 206 212 157 162 168 198 292 298 152 163 279 181 1550 142 173
## [451] 282 279 1100 224 159 186 189 192 140 686 215 309 110 130 164
## [466] 270 137 90 190 165 167 185 197 154 226 310 310 220 196 186
## [481] 352 282 92 182 103 850 195 276 171 146 193 180 805 265 185
## [496] 165 189 198 151 349 365 305 127 238 218 219 239 194 450 254
## [511] 205 320 195 215 230 189 168 215 210 108 224 230 185 137 156
## [526] 210 268 298 315 214 138 285 162 298 230 466 227 395 97 406
## [541] 114 198 173 204 350 153 188 380 214 768 172 160 196 232 220
## [556] 290 180 189 275 390 356 315 388 298 165 143 191 251 200 268
## [571] 236 215 134 612 515 560 289 190 500 98 245 184 216data2[1:3, 2:4] # menampilkan baris pertama sampai ketiga, dan kolom kedua sampai keempat## Gender TotalBilirubin DirectBilirubin
## 1 Female 0.7 0.1
## 2 Male 10.9 5.5
## 3 Male 7.3 4.1fungsi [baris,kolom] digunakan untuk memilih data baris
ke-berapa atau kolom ke-berapa yang ingin ditampilkan, atau bahkan hanya
menampilkan baris atau kolom saja.
data2$Gender #menampilkan kolom "gender" saja## [1] "Female" "Male" "Male" "Male" "Male" "Male" "Female" "Female"
## [9] "Male" "Male" "Male" "Male" "Male" "Female" "Male" "Male"
## [17] "Male" "Male" "Female" "Female" "Male" "Male" "Male" "Male"
## [25] "Male" "Male" "Male" "Male" "Male" "Female" "Male" "Male"
## [33] "Male" "Female" "Female" "Male" "Female" "Female" "Male" "Male"
## [41] "Male" "Male" "Male" "Male" "Female" "Male" "Male" "Male"
## [49] "Female" "Male" "Female" "Female" "Male" "Male" "Male" "Male"
## [57] "Male" "Female" "Male" "Male" "Female" "Male" "Male" "Male"
## [65] "Male" "Male" "Male" "Male" "Male" "Male" "Female" "Female"
## [73] "Female" "Male" "Male" "Female" "Male" "Female" "Male" "Female"
## [81] "Female" "Male" "Male" "Male" "Male" "Male" "Male" "Male"
## [89] "Male" "Male" "Male" "Male" "Male" "Male" "Male" "Male"
## [97] "Male" "Male" "Male" "Male" "Male" "Male" "Male" "Female"
## [105] "Male" "Male" "Male" "Male" "Male" "Male" "Female" "Male"
## [113] "Male" "Male" "Male" "Male" "Male" "Male" "Male" "Male"
## [121] "Male" "Male" "Male" "Female" "Male" "Male" "Male" "Male"
## [129] "Female" "Male" "Male" "Female" "Female" "Male" "Male" "Male"
## [137] "Female" "Male" "Male" "Male" "Male" "Female" "Male" "Male"
## [145] "Female" "Male" "Female" "Male" "Male" "Male" "Male" "Male"
## [153] "Female" "Male" "Male" "Male" "Male" "Male" "Male" "Male"
## [161] "Male" "Male" "Male" "Male" "Male" "Male" "Male" "Female"
## [169] "Male" "Male" "Male" "Female" "Male" "Male" "Male" "Male"
## [177] "Male" "Male" "Male" "Male" "Male" "Male" "Male" "Male"
## [185] "Male" "Male" "Male" "Male" "Male" "Female" "Male" "Male"
## [193] "Male" "Male" "Male" "Male" "Male" "Female" "Male" "Female"
## [201] "Male" "Male" "Female" "Male" "Male" "Male" "Male" "Male"
## [209] "Female" "Female" "Male" "Male" "Male" "Female" "Male" "Male"
## [217] "Male" "Male" "Male" "Male" "Male" "Male" "Male" "Male"
## [225] "Male" "Male" "Male" "Female" "Female" "Male" "Male" "Male"
## [233] "Male" "Male" "Female" "Male" "Male" "Female" "Female" "Male"
## [241] "Male" "Male" "Female" "Female" "Male" "Male" "Male" "Male"
## [249] "Male" "Male" "Male" "Male" "Male" "Female" "Female" "Male"
## [257] "Male" "Male" "Male" "Male" "Male" "Male" "Male" "Female"
## [265] "Male" "Male" "Male" "Female" "Male" "Male" "Male" "Male"
## [273] "Male" "Male" "Male" "Male" "Male" "Male" "Male" "Female"
## [281] "Male" "Male" "Male" "Male" "Male" "Male" "Female" "Male"
## [289] "Female" "Male" "Female" "Male" "Male" "Male" "Male" "Male"
## [297] "Female" "Female" "Female" "Female" "Male" "Female" "Female" "Male"
## [305] "Female" "Female" "Male" "Male" "Female" "Female" "Male" "Female"
## [313] "Male" "Female" "Male" "Male" "Male" "Male" "Male" "Male"
## [321] "Female" "Female" "Male" "Male" "Male" "Male" "Female" "Male"
## [329] "Male" "Male" "Male" "Male" "Male" "Female" "Female" "Female"
## [337] "Male" "Male" "Male" "Male" "Male" "Female" "Male" "Male"
## [345] "Male" "Male" "Male" "Male" "Male" "Female" "Male" "Male"
## [353] "Female" "Female" "Female" "Male" "Male" "Male" "Male" "Female"
## [361] "Male" "Female" "Male" "Female" "Male" "Male" "Male" "Male"
## [369] "Female" "Female" "Female" "Male" "Male" "Female" "Female" "Male"
## [377] "Male" "Female" "Female" "Male" "Male" "Male" "Female" "Female"
## [385] "Male" "Male" "Male" "Male" "Male" "Male" "Male" "Male"
## [393] "Male" "Male" "Female" "Male" "Male" "Male" "Male" "Male"
## [401] "Female" "Male" "Female" "Male" "Male" "Female" "Male" "Male"
## [409] "Male" "Male" "Male" "Female" "Male" "Male" "Male" "Male"
## [417] "Male" "Male" "Male" "Female" "Female" "Male" "Male" "Male"
## [425] "Female" "Male" "Male" "Male" "Female" "Male" "Female" "Female"
## [433] "Male" "Female" "Female" "Female" "Male" "Male" "Male" "Female"
## [441] "Female" "Female" "Female" "Female" "Male" "Male" "Male" "Female"
## [449] "Female" "Female" "Male" "Male" "Male" "Male" "Male" "Female"
## [457] "Male" "Male" "Male" "Male" "Female" "Female" "Male" "Male"
## [465] "Female" "Female" "Female" "Female" "Male" "Male" "Male" "Female"
## [473] "Male" "Male" "Male" "Male" "Male" "Male" "Male" "Male"
## [481] "Male" "Male" "Male" "Male" "Male" "Female" "Female" "Male"
## [489] "Male" "Male" "Female" "Male" "Female" "Male" "Male" "Male"
## [497] "Male" "Male" "Male" "Male" "Male" "Male" "Male" "Male"
## [505] "Male" "Male" "Male" "Male" "Male" "Male" "Female" "Male"
## [513] "Male" "Male" "Male" "Male" "Male" "Male" "Male" "Male"
## [521] "Male" "Male" "Female" "Male" "Male" "Female" "Male" "Male"
## [529] "Male" "Male" "Female" "Male" "Male" "Female" "Male" "Male"
## [537] "Male" "Female" "Male" "Female" "Male" "Male" "Male" "Male"
## [545] "Female" "Female" "Male" "Female" "Male" "Female" "Male" "Male"
## [553] "Male" "Male" "Male" "Male" "Male" "Male" "Male" "Male"
## [561] "Male" "Male" "Male" "Male" "Female" "Male" "Female" "Male"
## [569] "Female" "Male" "Male" "Male" "Male" "Male" "Male" "Male"
## [577] "Male" "Male" "Male" "Male" "Male" "Male" "Male"data2[["Age"]] #menampilkan kolom "Age" saja## [1] 65 62 62 58 72 46 26 29 17 55 57 72 64 74 61 25 38 33 40 40 51 51 62 40 63
## [26] 34 34 34 20 84 57 52 57 38 38 30 17 46 48 47 45 62 42 50 85 35 21 40 32 55
## [51] 45 34 38 38 42 42 33 48 51 64 31 58 58 57 57 57 54 37 66 60 19 75 75 52 68
## [76] 29 31 68 70 58 58 29 49 33 32 14 13 58 18 60 60 60 60 60 60 75 39 39 18 18
## [101] 27 27 17 55 63 36 36 36 36 36 24 48 27 74 50 50 48 32 32 32 32 32 58 64 28
## [126] 60 48 64 58 45 45 70 18 53 18 66 46 18 18 15 60 66 30 30 45 65 66 65 50 60
## [151] 56 50 46 52 34 34 32 72 72 50 60 60 60 39 39 48 55 47 60 60 72 44 55 31 31
## [176] 31 55 75 75 75 75 75 65 40 64 38 60 60 60 48 60 60 60 49 49 60 60 26 41 7
## [201] 49 49 38 21 21 45 40 40 70 45 28 42 22 8 38 66 55 49 6 37 37 47 47 50 70
## [226] 26 26 68 65 46 61 61 50 33 40 60 22 35 35 40 48 51 29 28 54 54 55 55 40 33
## [251] 33 33 65 35 38 38 50 44 36 42 42 33 18 38 38 4 62 43 40 26 37 4 21 30 33
## [276] 26 35 60 45 48 58 50 50 18 18 13 34 43 50 57 45 60 45 23 22 22 74 25 31 24
## [301] 58 51 50 50 55 54 48 30 45 48 51 54 27 30 26 22 44 35 38 14 30 30 36 12 60
## [326] 42 36 24 43 21 26 26 26 36 13 13 75 75 75 75 75 36 35 70 37 60 46 38 70 49
## [351] 37 37 26 48 48 19 33 33 37 69 24 65 55 42 21 40 16 60 42 58 54 33 48 25 56
## [376] 47 33 20 50 72 50 39 58 60 34 50 38 51 46 72 72 75 41 41 48 45 74 78 38 27
## [401] 66 50 42 65 22 31 45 12 48 48 18 23 65 48 65 70 70 11 50 55 55 26 41 53 32
## [426] 58 45 65 52 73 53 47 29 41 30 17 23 35 65 42 49 42 42 42 61 17 54 45 48 48
## [451] 65 35 58 46 28 21 32 61 26 65 22 28 38 25 45 45 28 28 66 66 66 49 42 42 35
## [476] 38 38 55 33 33 7 45 45 30 62 22 42 32 60 65 53 27 35 65 25 32 24 67 68 55
## [501] 70 36 42 53 32 32 56 50 46 46 37 45 56 69 49 49 60 28 45 35 62 55 46 50 29
## [526] 53 46 40 45 55 22 40 62 46 39 60 46 10 52 65 42 42 62 40 54 45 45 50 42 40
## [551] 46 29 45 46 73 55 51 51 51 26 66 66 66 64 38 43 50 52 20 16 16 90 32 32 32
## [576] 32 32 32 60 40 52 31 38fungsi $ ataupun [[]]digunakan untuk
menampilkan/memanggil kolom tertentu yang ingin kita tampilkan.
3. Pemeriksaan Data
3.1 Missing value
is.na(data2) ## Age Gender TotalBilirubin DirectBilirubin AlkalinePhosphotase
## [1,] FALSE FALSE FALSE FALSE FALSE
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## AlamineAminotransfera ApsartateAminotransferase TotalProtiens Albumin
## [1,] FALSE FALSE FALSE FALSE
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## [564,] FALSE FALSE FALSE FALSE
## [565,] FALSE FALSE FALSE FALSE
## [566,] FALSE FALSE FALSE FALSE
## [567,] FALSE FALSE FALSE FALSE
## [568,] FALSE FALSE FALSE FALSE
## [569,] FALSE FALSE FALSE FALSE
## [570,] FALSE FALSE FALSE FALSE
## [571,] FALSE FALSE FALSE FALSE
## [572,] FALSE FALSE FALSE FALSE
## [573,] FALSE FALSE FALSE FALSE
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## [575,] FALSE FALSE FALSE FALSE
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## [578,] FALSE FALSE FALSE FALSE
## [579,] FALSE FALSE FALSE FALSE
## [580,] FALSE FALSE FALSE FALSE
## [581,] FALSE FALSE FALSE FALSE
## [582,] FALSE FALSE FALSE FALSE
## [583,] FALSE FALSE FALSE FALSE
## AlbuminandGlobulinRatio LiverPatient
## [1,] FALSE FALSE
## [2,] FALSE FALSE
## [3,] FALSE FALSE
## [4,] FALSE FALSE
## [5,] FALSE FALSE
## [6,] FALSE FALSE
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## [571,] FALSE FALSE
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## [579,] FALSE FALSE
## [580,] FALSE FALSE
## [581,] FALSE FALSE
## [582,] FALSE FALSE
## [583,] FALSE FALSEfungsi is.na() digunakan untuk menampilkan apakah ada NA
atau data yang hilang/kosong.
sum(is.na(data2)) ## [1] 4fungsi sum(is.na()) digunakan untuk menampilkan ada
berapa data NA atau data hilang.
colSums(is.na(data2)) ## Age Gender TotalBilirubin
## 0 0 0
## DirectBilirubin AlkalinePhosphotase AlamineAminotransfera
## 0 0 0
## ApsartateAminotransferase TotalProtiens Albumin
## 0 0 0
## AlbuminandGlobulinRatio LiverPatient
## 4 0fungsi colSums(is.na()) digunakan untuk menampilkan data
NA atau data yang hilang tersebut.
which(is.na(data2$AlbuminandGlobulinRatio))## [1] 210 242 254 313fungsi which(is.na($)) digunakan untuk menemukan indeks
baris dengan NA atau data hilang dikolom tertentu.
Wow <-na.omit(data2)
Wow## Age Gender TotalBilirubin DirectBilirubin AlkalinePhosphotase
## 1 65 Female 0.7 0.1 187
## 2 62 Male 10.9 5.5 699
## 3 62 Male 7.3 4.1 490
## 4 58 Male 1.0 0.4 182
## 5 72 Male 3.9 2.0 195
## 6 46 Male 1.8 0.7 208
## 7 26 Female 0.9 0.2 154
## 8 29 Female 0.9 0.3 202
## 9 17 Male 0.9 0.3 202
## 10 55 Male 0.7 0.2 290
## 11 57 Male 0.6 0.1 210
## 12 72 Male 2.7 1.3 260
## 13 64 Male 0.9 0.3 310
## 14 74 Female 1.1 0.4 214
## 15 61 Male 0.7 0.2 145
## 16 25 Male 0.6 0.1 183
## 17 38 Male 1.8 0.8 342
## 18 33 Male 1.6 0.5 165
## 19 40 Female 0.9 0.3 293
## 20 40 Female 0.9 0.3 293
## 21 51 Male 2.2 1.0 610
## 22 51 Male 2.9 1.3 482
## 23 62 Male 6.8 3.0 542
## 24 40 Male 1.9 1.0 231
## 25 63 Male 0.9 0.2 194
## 26 34 Male 4.1 2.0 289
## 27 34 Male 4.1 2.0 289
## 28 34 Male 6.2 3.0 240
## 29 20 Male 1.1 0.5 128
## 30 84 Female 0.7 0.2 188
## 31 57 Male 4.0 1.9 190
## 32 52 Male 0.9 0.2 156
## 33 57 Male 1.0 0.3 187
## 34 38 Female 2.6 1.2 410
## 35 38 Female 2.6 1.2 410
## 36 30 Male 1.3 0.4 482
## 37 17 Female 0.7 0.2 145
## 38 46 Female 14.2 7.8 374
## 39 48 Male 1.4 0.6 263
## 40 47 Male 2.7 1.3 275
## 41 45 Male 2.4 1.1 168
## 42 62 Male 0.6 0.1 160
## 43 42 Male 6.8 3.2 630
## 44 50 Male 2.6 1.2 415
## 45 85 Female 1.0 0.3 208
## 46 35 Male 1.8 0.6 275
## 47 21 Male 3.9 1.8 150
## 48 40 Male 1.1 0.3 230
## 49 32 Female 0.6 0.1 176
## 50 55 Male 18.4 8.8 206
## 51 45 Female 0.7 0.2 170
## 52 34 Female 0.6 0.1 161
## 53 38 Male 3.1 1.6 253
## 54 38 Male 1.1 0.3 198
## 55 42 Male 8.9 4.5 272
## 56 42 Male 8.9 4.5 272
## 57 33 Male 0.8 0.2 198
## 58 48 Female 0.9 0.2 175
## 59 51 Male 0.8 0.2 367
## 60 64 Male 1.1 0.5 145
## 61 31 Female 0.8 0.2 158
## 62 58 Male 1.0 0.5 158
## 63 58 Male 1.0 0.5 158
## 64 57 Male 0.7 0.2 208
## 65 57 Male 1.3 0.4 259
## 66 57 Male 1.4 0.7 470
## 67 54 Male 2.2 1.2 195
## 68 37 Male 1.8 0.8 215
## 69 66 Male 0.7 0.2 239
## 70 60 Male 0.8 0.2 215
## 71 19 Female 0.7 0.2 186
## 72 75 Female 0.8 0.2 188
## 73 75 Female 0.8 0.2 205
## 74 52 Male 0.6 0.1 171
## 75 68 Male 0.7 0.1 145
## 76 29 Female 0.7 0.1 162
## 77 31 Male 0.9 0.2 518
## 78 68 Female 0.6 0.1 1620
## 79 70 Male 1.4 0.6 146
## 80 58 Female 2.8 1.3 670
## 81 58 Female 2.4 1.1 915
## 82 29 Male 1.0 0.3 75
## 83 49 Male 0.7 0.1 148
## 84 33 Male 2.0 1.0 258
## 85 32 Male 0.6 0.1 237
## 86 14 Male 1.4 0.5 269
## 87 13 Male 0.6 0.1 320
## 88 58 Male 0.8 0.2 298
## 89 18 Male 0.6 0.2 538
## 90 60 Male 4.0 1.9 238
## 91 60 Male 5.7 2.8 214
## 92 60 Male 6.8 3.2 308
## 93 60 Male 8.6 4.0 298
## 94 60 Male 5.8 2.7 204
## 95 60 Male 5.2 2.4 168
## 96 75 Male 0.9 0.2 282
## 97 39 Male 3.8 1.5 298
## 98 39 Male 6.6 3.0 215
## 99 18 Male 0.6 0.1 265
## 100 18 Male 0.7 0.1 312
## 101 27 Male 0.6 0.2 161
## 102 27 Male 0.7 0.2 243
## 103 17 Male 0.9 0.2 224
## 104 55 Female 0.8 0.2 225
## 105 63 Male 0.5 0.1 170
## 106 36 Male 5.3 2.3 145
## 107 36 Male 5.3 2.3 145
## 108 36 Male 0.8 0.2 158
## 109 36 Male 0.8 0.2 158
## 110 36 Male 0.9 0.1 486
## 111 24 Female 0.7 0.2 188
## 112 48 Male 3.2 1.6 257
## 113 27 Male 1.2 0.4 179
## 114 74 Male 0.6 0.1 272
## 115 50 Male 5.8 3.0 661
## 116 50 Male 7.3 3.6 1580
## 117 48 Male 0.7 0.1 1630
## 118 32 Male 12.7 6.2 194
## 119 32 Male 15.9 7.0 280
## 120 32 Male 18.0 8.2 298
## 121 32 Male 23.0 11.3 300
## 122 32 Male 22.7 10.2 290
## 123 58 Male 1.7 0.8 188
## 124 64 Female 0.8 0.2 178
## 125 28 Male 0.6 0.1 177
## 126 60 Male 1.8 0.5 201
## 127 48 Male 5.8 2.5 802
## 128 64 Male 3.0 1.4 248
## 129 58 Female 1.7 0.8 1896
## 130 45 Male 2.8 1.7 263
## 131 45 Male 3.2 1.4 512
## 132 70 Female 0.7 0.2 237
## 133 18 Female 0.8 0.2 199
## 134 53 Male 0.9 0.4 238
## 135 18 Male 1.8 0.7 178
## 136 66 Male 11.3 5.6 1110
## 137 46 Female 4.7 2.2 310
## 138 18 Male 0.8 0.2 282
## 139 18 Male 0.8 0.2 282
## 140 15 Male 0.8 0.2 380
## 141 60 Male 0.6 0.1 186
## 142 66 Female 4.2 2.1 159
## 143 30 Male 1.6 0.4 332
## 144 30 Male 1.6 0.4 332
## 145 45 Female 3.5 1.5 189
## 146 65 Male 0.8 0.2 201
## 147 66 Female 2.9 1.3 168
## 148 65 Male 0.7 0.1 392
## 149 50 Male 0.9 0.2 202
## 150 60 Male 0.8 0.2 286
## 151 56 Male 1.1 0.5 180
## 152 50 Male 1.6 0.8 218
## 153 46 Female 0.8 0.2 182
## 154 52 Male 0.6 0.1 178
## 155 34 Male 5.9 2.5 290
## 156 34 Male 8.7 4.0 298
## 157 32 Male 0.9 0.3 462
## 158 72 Male 0.7 0.1 196
## 159 72 Male 0.7 0.1 196
## 160 50 Male 1.2 0.4 282
## 161 60 Male 11.0 4.9 750
## 162 60 Male 11.5 5.0 1050
## 163 60 Male 5.8 2.7 599
## 164 39 Male 1.9 0.9 180
## 165 39 Male 1.9 0.9 180
## 166 48 Male 4.5 2.3 282
## 167 55 Male 75.0 3.6 332
## 168 47 Female 3.0 1.5 292
## 169 60 Male 22.8 12.6 962
## 170 60 Male 8.9 4.0 950
## 171 72 Male 1.7 0.8 200
## 172 44 Female 1.9 0.6 298
## 173 55 Male 14.1 7.6 750
## 174 31 Male 0.6 0.1 175
## 175 31 Male 0.6 0.1 175
## 176 31 Male 0.8 0.2 198
## 177 55 Male 0.8 0.2 482
## 178 75 Male 14.8 9.0 1020
## 179 75 Male 10.6 5.0 562
## 180 75 Male 8.0 4.6 386
## 181 75 Male 2.8 1.3 250
## 182 75 Male 2.9 1.3 218
## 183 65 Male 1.9 0.8 170
## 184 40 Male 0.6 0.1 171
## 185 64 Male 1.1 0.4 201
## 186 38 Male 1.5 0.4 298
## 187 60 Male 3.2 1.8 750
## 188 60 Male 2.1 1.0 191
## 189 60 Male 1.9 0.8 614
## 190 48 Female 0.8 0.2 218
## 191 60 Male 6.3 3.2 314
## 192 60 Male 5.8 3.0 257
## 193 60 Male 2.3 0.6 272
## 194 49 Male 1.3 0.4 206
## 195 49 Male 2.0 0.6 209
## 196 60 Male 2.4 1.0 1124
## 197 60 Male 2.0 1.1 664
## 198 26 Female 0.6 0.2 142
## 199 41 Male 0.9 0.2 169
## 200 7 Female 27.2 11.8 1420
## 201 49 Male 0.6 0.1 218
## 202 49 Male 0.6 0.1 218
## 203 38 Female 0.8 0.2 145
## 204 21 Male 1.0 0.3 142
## 205 21 Male 0.7 0.2 135
## 206 45 Male 2.5 1.2 163
## 207 40 Male 3.6 1.8 285
## 208 40 Male 3.9 1.7 350
## 209 70 Female 0.9 0.3 220
## 211 28 Male 0.8 0.3 190
## 212 42 Male 2.7 1.3 219
## 213 22 Male 2.7 1.0 160
## 214 8 Female 0.9 0.2 401
## 215 38 Male 1.7 1.0 180
## 216 66 Male 0.6 0.2 100
## 217 55 Male 0.9 0.2 116
## 218 49 Male 1.1 0.5 159
## 219 6 Male 0.6 0.1 289
## 220 37 Male 0.8 0.2 125
## 221 37 Male 0.8 0.2 147
## 222 47 Male 0.9 0.2 192
## 223 47 Male 0.9 0.2 265
## 224 50 Male 1.1 0.3 175
## 225 70 Male 1.7 0.5 400
## 226 26 Male 0.6 0.2 120
## 227 26 Male 1.3 0.4 173
## 228 68 Female 0.7 0.2 186
## 229 65 Female 1.0 0.3 202
## 230 46 Male 0.6 0.2 290
## 231 61 Male 1.5 0.6 196
## 232 61 Male 0.8 0.1 282
## 233 50 Male 2.7 1.6 157
## 234 33 Male 2.0 1.4 2110
## 235 40 Female 0.9 0.2 285
## 236 60 Male 1.5 0.6 360
## 237 22 Male 0.8 0.2 300
## 238 35 Female 0.9 0.3 158
## 239 35 Female 0.9 0.2 190
## 240 40 Male 0.9 0.3 196
## 241 48 Male 0.7 0.2 165
## 243 29 Female 0.8 0.2 205
## 244 28 Female 0.9 0.2 316
## 245 54 Male 0.8 0.2 218
## 246 54 Male 0.9 0.2 290
## 247 55 Male 1.8 9.0 272
## 248 55 Male 0.9 0.2 190
## 249 40 Male 0.7 0.1 202
## 250 33 Male 1.2 0.3 498
## 251 33 Male 2.1 1.3 480
## 252 33 Male 0.9 0.8 680
## 253 65 Male 1.1 0.3 258
## 255 38 Female 0.7 0.1 152
## 256 38 Male 1.7 0.7 859
## 257 50 Male 0.9 0.3 901
## 258 44 Male 0.8 0.2 335
## 259 36 Male 0.8 0.2 182
## 260 42 Male 30.5 14.2 285
## 261 42 Male 16.4 8.9 245
## 262 33 Male 1.5 7.0 505
## 263 18 Male 0.8 0.2 228
## 264 38 Female 0.8 0.2 185
## 265 38 Male 0.8 0.2 247
## 266 4 Male 0.9 0.2 348
## 267 62 Male 1.2 0.4 195
## 268 43 Female 0.9 0.3 140
## 269 40 Male 14.5 6.4 358
## 270 26 Male 0.6 0.1 110
## 271 37 Male 0.7 0.2 235
## 272 4 Male 0.8 0.2 460
## 273 21 Male 18.5 9.5 380
## 274 30 Male 0.7 0.2 262
## 275 33 Male 1.8 0.8 196
## 276 26 Male 1.9 0.8 180
## 277 35 Male 0.9 0.2 190
## 278 60 Male 2.0 0.8 190
## 279 45 Male 2.2 0.8 209
## 280 48 Female 1.0 1.4 144
## 281 58 Male 0.8 0.2 123
## 282 50 Male 0.7 0.2 192
## 283 50 Male 0.7 0.2 188
## 284 18 Male 1.3 0.7 316
## 285 18 Male 0.9 0.3 300
## 286 13 Male 1.5 0.5 575
## 287 34 Female 0.8 0.2 192
## 288 43 Male 1.3 0.6 155
## 289 50 Female 1.0 0.5 239
## 290 57 Male 4.5 2.3 315
## 291 45 Female 1.0 0.3 250
## 292 60 Male 0.7 0.2 174
## 293 45 Male 0.6 0.2 245
## 294 23 Male 1.1 0.5 191
## 295 22 Male 2.4 1.0 340
## 296 22 Male 0.6 0.2 202
## 297 74 Female 0.9 0.3 234
## 298 25 Female 0.9 0.3 159
## 299 31 Female 1.1 0.3 190
## 300 24 Female 0.9 0.2 195
## 301 58 Male 0.8 0.2 180
## 302 51 Female 0.9 0.2 280
## 303 50 Female 1.7 0.6 430
## 304 50 Male 0.7 0.2 206
## 305 55 Female 0.8 0.2 155
## 306 54 Female 1.4 0.7 195
## 307 48 Male 1.6 1.0 588
## 308 30 Male 0.8 0.2 174
## 309 45 Female 0.8 0.2 165
## 310 48 Female 1.1 0.7 527
## 311 51 Male 0.8 0.2 175
## 312 54 Female 23.2 12.6 574
## 314 30 Female 0.8 0.2 158
## 315 26 Male 2.0 0.9 195
## 316 22 Male 0.9 0.3 179
## 317 44 Male 0.9 0.2 182
## 318 35 Male 0.7 0.2 198
## 319 38 Male 3.7 2.2 216
## 320 14 Male 0.9 0.3 310
## 321 30 Female 0.7 0.2 63
## 322 30 Female 0.8 0.2 198
## 323 36 Male 1.7 0.5 205
## 324 12 Male 0.8 0.2 302
## 325 60 Male 2.6 1.2 171
## 326 42 Male 0.8 0.2 158
## 327 36 Female 1.2 0.4 358
## 328 24 Male 3.3 1.6 174
## 329 43 Male 0.8 0.2 192
## 330 21 Male 0.7 0.2 211
## 331 26 Male 2.0 0.9 157
## 332 26 Male 1.7 0.6 210
## 333 26 Male 7.1 3.3 258
## 334 36 Female 0.7 0.2 152
## 335 13 Female 0.7 0.2 350
## 336 13 Female 0.7 0.1 182
## 337 75 Male 6.7 3.6 458
## 338 75 Male 2.5 1.2 375
## 339 75 Male 1.8 0.8 405
## 340 75 Male 1.4 0.4 215
## 341 75 Male 0.9 0.2 206
## 342 36 Female 0.8 0.2 650
## 343 35 Male 0.8 0.2 198
## 344 70 Male 3.1 1.6 198
## 345 37 Male 0.8 0.2 195
## 346 60 Male 2.9 1.3 230
## 347 46 Male 0.6 0.2 115
## 348 38 Male 0.7 0.2 216
## 349 70 Male 1.3 0.4 358
## 350 49 Female 0.8 0.2 158
## 351 37 Male 1.8 0.8 145
## 352 37 Male 1.3 0.4 195
## 353 26 Female 0.7 0.2 144
## 354 48 Female 1.4 0.8 621
## 355 48 Female 0.8 0.2 150
## 356 19 Male 1.4 0.8 178
## 357 33 Male 0.7 0.2 256
## 358 33 Male 2.1 0.7 205
## 359 37 Male 0.7 0.2 176
## 360 69 Female 0.8 0.2 146
## 361 24 Male 0.7 0.2 218
## 362 65 Female 0.7 0.2 182
## 363 55 Male 1.1 0.3 215
## 364 42 Female 0.9 0.2 165
## 365 21 Male 0.8 0.2 183
## 366 40 Male 0.7 0.2 176
## 367 16 Male 0.7 0.2 418
## 368 60 Male 2.2 1.0 271
## 369 42 Female 0.8 0.2 182
## 370 58 Female 0.8 0.2 130
## 371 54 Female 22.6 11.4 558
## 372 33 Male 0.8 0.2 135
## 373 48 Male 0.7 0.2 326
## 374 25 Female 0.7 0.1 140
## 375 56 Female 0.7 0.1 145
## 376 47 Male 3.5 1.6 206
## 377 33 Male 0.7 0.1 168
## 378 20 Female 0.6 0.2 202
## 379 50 Female 0.7 0.1 192
## 380 72 Male 0.7 0.2 185
## 381 50 Male 1.7 0.8 331
## 382 39 Male 0.6 0.2 188
## 383 58 Female 0.7 0.1 172
## 384 60 Female 1.4 0.7 159
## 385 34 Male 3.7 2.1 490
## 386 50 Male 0.8 0.2 152
## 387 38 Male 2.7 1.4 105
## 388 51 Male 0.8 0.2 160
## 389 46 Male 0.8 0.2 160
## 390 72 Male 0.6 0.1 102
## 391 72 Male 0.8 0.2 148
## 392 75 Male 0.9 0.2 162
## 393 41 Male 7.5 4.3 149
## 394 41 Male 2.7 1.3 580
## 395 48 Female 1.0 0.3 310
## 396 45 Male 0.8 0.2 140
## 397 74 Male 1.0 0.3 175
## 398 78 Male 1.0 0.3 152
## 399 38 Male 0.8 0.2 208
## 400 27 Male 1.0 0.2 205
## 401 66 Female 0.7 0.2 162
## 402 50 Male 7.3 3.7 92
## 403 42 Female 0.5 0.1 162
## 404 65 Male 0.7 0.2 199
## 405 22 Male 0.8 0.2 198
## 406 31 Female 0.8 0.2 215
## 407 45 Male 0.7 0.2 180
## 408 12 Male 1.0 0.2 719
## 409 48 Male 2.4 1.1 554
## 410 48 Male 5.0 2.6 555
## 411 18 Male 1.4 0.6 215
## 412 23 Female 2.3 0.8 509
## 413 65 Male 4.9 2.7 190
## 414 48 Male 0.7 0.2 208
## 415 65 Male 1.4 0.6 260
## 416 70 Male 1.3 0.3 690
## 417 70 Male 0.6 0.1 862
## 418 11 Male 0.7 0.1 592
## 419 50 Male 4.2 2.3 450
## 420 55 Female 8.2 3.9 1350
## 421 55 Female 10.9 5.1 1350
## 422 26 Male 1.0 0.3 163
## 423 41 Male 1.2 0.5 246
## 424 53 Male 1.6 0.9 178
## 425 32 Female 0.7 0.1 240
## 426 58 Male 0.4 0.1 100
## 427 45 Male 1.3 0.6 166
## 428 65 Male 0.9 0.2 170
## 429 52 Female 0.6 0.1 194
## 430 73 Male 1.9 0.7 1750
## 431 53 Female 0.7 0.1 182
## 432 47 Female 0.8 0.2 236
## 433 29 Male 0.7 0.2 165
## 434 41 Female 0.9 0.2 201
## 435 30 Female 0.7 0.2 194
## 436 17 Female 0.5 0.1 206
## 437 23 Male 1.0 0.3 212
## 438 35 Male 1.6 0.7 157
## 439 65 Male 0.8 0.2 162
## 440 42 Female 0.8 0.2 168
## 441 49 Female 0.8 0.2 198
## 442 42 Female 2.3 1.1 292
## 443 42 Female 7.4 3.6 298
## 444 42 Female 0.7 0.2 152
## 445 61 Male 0.8 0.2 163
## 446 17 Male 0.9 0.2 279
## 447 54 Male 0.8 0.2 181
## 448 45 Female 23.3 12.8 1550
## 449 48 Female 0.8 0.2 142
## 450 48 Female 0.9 0.2 173
## 451 65 Male 7.9 4.3 282
## 452 35 Male 0.8 0.2 279
## 453 58 Male 0.9 0.2 1100
## 454 46 Male 0.7 0.2 224
## 455 28 Male 0.6 0.2 159
## 456 21 Female 0.6 0.1 186
## 457 32 Male 0.7 0.2 189
## 458 61 Male 0.8 0.2 192
## 459 26 Male 6.8 3.2 140
## 460 65 Male 1.1 0.5 686
## 461 22 Female 2.2 1.0 215
## 462 28 Female 0.8 0.2 309
## 463 38 Male 0.7 0.2 110
## 464 25 Male 0.8 0.1 130
## 465 45 Female 0.7 0.2 164
## 466 45 Female 0.6 0.1 270
## 467 28 Female 0.6 0.1 137
## 468 28 Female 1.0 0.3 90
## 469 66 Male 1.0 0.3 190
## 470 66 Male 0.8 0.2 165
## 471 66 Male 1.1 0.5 167
## 472 49 Female 0.6 0.1 185
## 473 42 Male 0.7 0.2 197
## 474 42 Male 1.0 0.3 154
## 475 35 Male 2.0 1.1 226
## 476 38 Male 2.2 1.0 310
## 477 38 Male 0.9 0.3 310
## 478 55 Male 0.6 0.2 220
## 479 33 Male 7.1 3.7 196
## 480 33 Male 3.4 1.6 186
## 481 7 Male 0.5 0.1 352
## 482 45 Male 2.3 1.3 282
## 483 45 Male 1.1 0.4 92
## 484 30 Male 0.8 0.2 182
## 485 62 Male 5.0 2.1 103
## 486 22 Female 6.7 3.2 850
## 487 42 Female 0.8 0.2 195
## 488 32 Male 0.7 0.2 276
## 489 60 Male 0.7 0.2 171
## 490 65 Male 0.8 0.1 146
## 491 53 Female 0.8 0.2 193
## 492 27 Male 1.0 0.3 180
## 493 35 Female 1.0 0.3 805
## 494 65 Male 0.7 0.2 265
## 495 25 Male 0.7 0.2 185
## 496 32 Male 0.7 0.2 165
## 497 24 Male 1.0 0.2 189
## 498 67 Male 2.2 1.1 198
## 499 68 Male 1.8 0.5 151
## 500 55 Male 3.6 1.6 349
## 501 70 Male 2.7 1.2 365
## 502 36 Male 2.8 1.5 305
## 503 42 Male 0.8 0.2 127
## 504 53 Male 19.8 10.4 238
## 505 32 Male 30.5 17.1 218
## 506 32 Male 32.6 14.1 219
## 507 56 Male 17.7 8.8 239
## 508 50 Male 0.9 0.3 194
## 509 46 Male 18.4 8.5 450
## 510 46 Male 20.0 10.0 254
## 511 37 Female 0.8 0.2 205
## 512 45 Male 2.2 1.6 320
## 513 56 Male 1.0 0.3 195
## 514 69 Male 0.9 0.2 215
## 515 49 Male 1.0 0.3 230
## 516 49 Male 3.9 2.1 189
## 517 60 Male 0.9 0.3 168
## 518 28 Male 0.9 0.2 215
## 519 45 Male 2.9 1.4 210
## 520 35 Male 26.3 12.1 108
## 521 62 Male 1.8 0.9 224
## 522 55 Male 4.4 2.9 230
## 523 46 Female 0.8 0.2 185
## 524 50 Male 0.6 0.2 137
## 525 29 Male 0.8 0.2 156
## 526 53 Female 0.9 0.2 210
## 527 46 Male 9.4 5.2 268
## 528 40 Male 3.5 1.6 298
## 529 45 Male 1.7 0.8 315
## 530 55 Male 3.3 1.5 214
## 531 22 Female 1.1 0.3 138
## 532 40 Male 30.8 18.3 285
## 533 62 Male 0.7 0.2 162
## 534 46 Female 1.4 0.4 298
## 535 39 Male 1.6 0.8 230
## 536 60 Male 19.6 9.5 466
## 537 46 Male 15.8 7.2 227
## 538 10 Female 0.8 0.1 395
## 539 52 Male 1.8 0.8 97
## 540 65 Female 0.7 0.2 406
## 541 42 Male 0.8 0.2 114
## 542 42 Male 0.8 0.2 198
## 543 62 Male 0.7 0.2 173
## 544 40 Male 1.2 0.6 204
## 545 54 Female 5.5 3.2 350
## 546 45 Female 0.7 0.2 153
## 547 45 Male 20.2 11.7 188
## 548 50 Female 27.7 10.8 380
## 549 42 Male 11.1 6.1 214
## 550 40 Female 2.1 1.0 768
## 551 46 Male 3.3 1.5 172
## 552 29 Male 1.2 0.4 160
## 553 45 Male 0.6 0.1 196
## 554 46 Male 10.2 4.2 232
## 555 73 Male 1.8 0.9 220
## 556 55 Male 0.8 0.2 290
## 557 51 Male 0.7 0.1 180
## 558 51 Male 2.9 1.2 189
## 559 51 Male 4.0 2.5 275
## 560 26 Male 42.8 19.7 390
## 561 66 Male 15.2 7.7 356
## 562 66 Male 16.6 7.6 315
## 563 66 Male 17.3 8.5 388
## 564 64 Male 1.4 0.5 298
## 565 38 Female 0.6 0.1 165
## 566 43 Male 22.5 11.8 143
## 567 50 Female 1.0 0.3 191
## 568 52 Male 2.7 1.4 251
## 569 20 Female 16.7 8.4 200
## 570 16 Male 7.7 4.1 268
## 571 16 Male 2.6 1.2 236
## 572 90 Male 1.1 0.3 215
## 573 32 Male 15.6 9.5 134
## 574 32 Male 3.7 1.6 612
## 575 32 Male 12.1 6.0 515
## 576 32 Male 25.0 13.7 560
## 577 32 Male 15.0 8.2 289
## 578 32 Male 12.7 8.4 190
## 579 60 Male 0.5 0.1 500
## 580 40 Male 0.6 0.1 98
## 581 52 Male 0.8 0.2 245
## 582 31 Male 1.3 0.5 184
## 583 38 Male 1.0 0.3 216
## AlamineAminotransfera ApsartateAminotransferase TotalProtiens Albumin
## 1 16 18 6.8 3.3
## 2 64 100 7.5 3.2
## 3 60 68 7.0 3.3
## 4 14 20 6.8 3.4
## 5 27 59 7.3 2.4
## 6 19 14 7.6 4.4
## 7 16 12 7.0 3.5
## 8 14 11 6.7 3.6
## 9 22 19 7.4 4.1
## 10 53 58 6.8 3.4
## 11 51 59 5.9 2.7
## 12 31 56 7.4 3.0
## 13 61 58 7.0 3.4
## 14 22 30 8.1 4.1
## 15 53 41 5.8 2.7
## 16 91 53 5.5 2.3
## 17 168 441 7.6 4.4
## 18 15 23 7.3 3.5
## 19 232 245 6.8 3.1
## 20 232 245 6.8 3.1
## 21 17 28 7.3 2.6
## 22 22 34 7.0 2.4
## 23 116 66 6.4 3.1
## 24 16 55 4.3 1.6
## 25 52 45 6.0 3.9
## 26 875 731 5.0 2.7
## 27 875 731 5.0 2.7
## 28 1680 850 7.2 4.0
## 29 20 30 3.9 1.9
## 30 13 21 6.0 3.2
## 31 45 111 5.2 1.5
## 32 35 44 4.9 2.9
## 33 19 23 5.2 2.9
## 34 59 57 5.6 3.0
## 35 59 57 5.6 3.0
## 36 102 80 6.9 3.3
## 37 18 36 7.2 3.9
## 38 38 77 4.3 2.0
## 39 38 66 5.8 2.2
## 40 123 73 6.2 3.3
## 41 33 50 5.1 2.6
## 42 42 110 4.9 2.6
## 43 25 47 6.1 2.3
## 44 407 576 6.4 3.2
## 45 17 15 7.0 3.6
## 46 48 178 6.5 3.2
## 47 36 27 6.8 3.9
## 48 1630 960 4.9 2.8
## 49 39 28 6.0 3.0
## 50 64 178 6.2 1.8
## 51 21 14 5.7 2.5
## 52 15 19 6.6 3.4
## 53 80 406 6.8 3.9
## 54 86 150 6.3 3.5
## 55 31 61 5.8 2.0
## 56 31 61 5.8 2.0
## 57 26 23 8.0 4.0
## 58 24 54 5.5 2.7
## 59 42 18 5.2 2.0
## 60 20 24 5.5 3.2
## 61 21 16 6.0 3.0
## 62 37 43 7.2 3.6
## 63 37 43 7.2 3.6
## 64 35 97 5.1 2.1
## 65 40 86 6.5 2.5
## 66 62 88 5.6 2.5
## 67 55 95 6.0 3.7
## 68 53 58 6.4 3.8
## 69 27 26 6.3 3.7
## 70 24 17 6.3 3.0
## 71 166 397 5.5 3.0
## 72 20 29 4.4 1.8
## 73 27 24 4.4 2.0
## 74 22 16 6.6 3.6
## 75 20 22 5.8 2.9
## 76 52 41 5.2 2.5
## 77 189 17 5.3 2.3
## 78 95 127 4.6 2.1
## 79 12 24 6.2 3.8
## 80 48 79 4.7 1.6
## 81 60 142 4.7 1.8
## 82 25 26 5.1 2.9
## 83 14 12 5.4 2.8
## 84 194 152 5.4 3.0
## 85 45 31 7.5 4.3
## 86 58 45 6.7 3.9
## 87 28 56 7.2 3.6
## 88 33 59 6.2 3.1
## 89 33 34 7.5 3.2
## 90 119 350 7.1 3.3
## 91 412 850 7.3 3.2
## 92 404 794 6.8 3.0
## 93 412 850 7.4 3.0
## 94 220 400 7.0 3.0
## 95 126 202 6.8 2.9
## 96 25 23 4.4 2.2
## 97 102 630 7.1 3.3
## 98 190 950 4.0 1.7
## 99 97 161 5.9 3.1
## 100 308 405 6.9 3.7
## 101 27 28 3.7 1.6
## 102 21 23 5.3 2.3
## 103 36 45 6.9 4.2
## 104 14 23 6.1 3.3
## 105 21 28 5.5 2.5
## 106 32 92 5.1 2.6
## 107 32 92 5.1 2.6
## 108 29 39 6.0 2.2
## 109 29 39 6.0 2.2
## 110 25 34 5.9 2.8
## 111 11 10 5.5 2.3
## 112 33 116 5.7 2.2
## 113 63 39 6.1 3.3
## 114 24 98 5.0 2.0
## 115 181 285 5.7 2.3
## 116 88 64 5.6 2.3
## 117 74 149 5.3 2.0
## 118 2000 2946 5.7 3.3
## 119 1350 1600 5.6 2.8
## 120 1250 1050 5.4 2.6
## 121 482 275 7.1 3.5
## 122 322 113 6.6 2.8
## 123 60 84 5.9 3.5
## 124 17 18 6.3 3.1
## 125 36 29 6.9 4.1
## 126 45 25 3.9 1.7
## 127 133 88 6.0 2.8
## 128 46 40 6.5 3.2
## 129 61 83 8.0 3.9
## 130 57 65 5.1 2.3
## 131 50 58 6.0 2.7
## 132 18 28 5.8 2.5
## 133 34 31 6.5 3.5
## 134 17 14 6.6 2.9
## 135 35 36 6.8 3.6
## 136 1250 4929 7.0 2.4
## 137 62 90 6.4 2.5
## 138 72 140 5.5 2.5
## 139 72 140 5.5 2.5
## 140 25 66 6.1 3.7
## 141 20 21 6.2 3.3
## 142 15 30 7.1 2.2
## 143 84 139 5.6 2.7
## 144 84 139 5.6 2.7
## 145 63 87 5.6 2.9
## 146 18 22 5.4 2.9
## 147 21 38 5.5 1.8
## 148 20 30 5.3 2.8
## 149 20 26 7.2 4.5
## 150 21 27 7.1 4.0
## 151 30 42 6.9 3.8
## 152 18 20 5.9 2.9
## 153 20 40 6.0 2.9
## 154 26 27 6.5 3.6
## 155 45 233 5.6 2.7
## 156 58 138 5.8 2.4
## 157 70 82 6.2 3.1
## 158 20 35 5.8 2.0
## 159 20 35 5.8 2.0
## 160 36 32 7.2 3.9
## 161 140 350 5.5 2.1
## 162 99 187 6.2 2.8
## 163 43 66 5.4 1.8
## 164 42 62 7.4 4.3
## 165 42 62 7.4 4.3
## 166 13 74 7.0 2.4
## 167 40 66 6.2 2.5
## 168 64 67 5.6 1.8
## 169 53 41 6.9 3.3
## 170 33 32 6.8 3.1
## 171 28 37 6.2 3.0
## 172 378 602 6.6 3.3
## 173 35 63 5.0 1.6
## 174 48 34 6.0 3.7
## 175 48 34 6.0 3.7
## 176 43 31 7.3 4.0
## 177 112 99 5.7 2.6
## 178 71 42 5.3 2.2
## 179 37 29 5.1 1.8
## 180 30 25 5.5 1.8
## 181 23 29 2.7 0.9
## 182 33 37 3.0 1.5
## 183 36 43 3.8 1.4
## 184 20 17 5.4 2.5
## 185 18 19 6.9 4.1
## 186 60 103 6.0 3.0
## 187 79 145 7.8 3.2
## 188 114 247 4.0 1.6
## 189 42 38 4.5 1.8
## 190 32 28 5.2 2.5
## 191 118 114 6.6 3.7
## 192 107 104 6.6 3.5
## 193 79 51 6.6 3.5
## 194 30 25 6.0 3.1
## 195 48 32 5.7 3.0
## 196 30 54 5.2 1.9
## 197 52 104 6.0 2.1
## 198 12 32 5.7 2.4
## 199 22 18 6.1 3.0
## 200 790 1050 6.1 2.0
## 201 50 53 5.0 2.4
## 202 50 53 5.0 2.4
## 203 19 23 6.1 3.1
## 204 27 21 6.4 3.5
## 205 27 26 6.4 3.3
## 206 28 22 7.6 4.0
## 207 50 60 7.0 2.9
## 208 950 1500 6.7 3.8
## 209 53 95 6.1 2.8
## 211 20 14 4.1 2.4
## 212 60 180 7.0 3.2
## 213 82 127 5.5 3.1
## 214 25 58 7.5 3.4
## 215 18 34 7.2 3.6
## 216 17 148 5.0 3.3
## 217 36 16 6.2 3.2
## 218 30 31 7.0 4.3
## 219 38 30 4.8 2.0
## 220 41 39 6.4 3.4
## 221 27 46 5.0 2.5
## 222 38 24 7.3 4.3
## 223 40 28 8.0 4.0
## 224 20 19 7.1 4.5
## 225 56 44 5.7 3.1
## 226 45 51 7.9 4.0
## 227 38 62 8.0 4.0
## 228 18 15 6.4 3.8
## 229 26 13 5.3 2.6
## 230 26 21 6.0 3.0
## 231 61 85 6.7 3.8
## 232 85 231 8.5 4.3
## 233 149 156 7.9 3.1
## 234 48 89 6.2 3.0
## 235 32 27 7.7 3.5
## 236 230 298 4.5 2.0
## 237 57 40 7.9 3.8
## 238 20 16 8.0 4.0
## 239 40 35 7.3 4.7
## 240 69 48 6.8 3.1
## 241 32 30 8.0 4.0
## 243 30 23 8.2 4.1
## 244 25 23 8.5 5.5
## 245 20 19 6.3 2.5
## 246 15 18 6.1 2.8
## 247 22 79 6.1 2.7
## 248 25 28 5.9 2.7
## 249 37 29 5.0 2.6
## 250 28 25 7.0 3.0
## 251 38 22 6.5 3.0
## 252 37 40 5.9 2.6
## 253 48 40 7.0 3.9
## 255 90 21 7.1 4.2
## 256 89 48 6.0 3.0
## 257 23 17 6.2 3.5
## 258 148 86 5.6 3.0
## 259 31 34 6.4 3.8
## 260 65 130 5.2 2.1
## 261 56 87 5.4 2.0
## 262 205 140 7.5 3.9
## 263 55 54 6.9 4.0
## 264 25 21 7.0 3.0
## 265 55 92 7.4 4.3
## 266 30 34 8.0 4.0
## 267 38 54 6.3 3.8
## 268 12 29 7.4 3.5
## 269 50 75 5.7 2.1
## 270 15 20 2.8 1.6
## 271 96 54 9.5 4.9
## 272 152 231 6.5 3.2
## 273 390 500 8.2 4.1
## 274 15 18 9.6 4.7
## 275 25 22 8.0 4.0
## 276 22 19 8.2 4.1
## 277 25 20 6.4 3.6
## 278 45 40 6.0 2.8
## 279 25 20 8.0 4.0
## 280 18 14 8.3 4.2
## 281 56 48 6.0 3.0
## 282 18 15 7.4 4.2
## 283 12 14 7.0 3.4
## 284 10 21 6.0 2.1
## 285 30 48 8.0 4.0
## 286 29 24 7.9 3.9
## 287 15 12 8.6 4.7
## 288 15 20 8.0 4.0
## 289 16 39 7.5 3.7
## 290 120 105 7.0 4.0
## 291 48 44 8.6 4.3
## 292 32 14 7.8 4.2
## 293 22 24 7.1 3.4
## 294 37 41 7.7 4.3
## 295 25 21 8.3 4.5
## 296 78 41 8.0 3.9
## 297 16 19 7.9 4.0
## 298 24 25 6.9 4.4
## 299 26 15 7.9 3.8
## 300 40 35 7.4 4.1
## 301 32 25 8.2 4.4
## 302 21 30 6.7 3.2
## 303 28 32 6.8 3.5
## 304 18 17 8.4 4.2
## 305 21 17 6.9 3.8
## 306 36 16 7.9 3.7
## 307 74 113 7.3 2.4
## 308 21 47 4.6 2.3
## 309 22 18 8.2 4.1
## 310 178 250 8.0 4.2
## 311 48 22 8.1 4.6
## 312 43 47 7.2 3.5
## 314 25 22 7.9 4.5
## 315 24 65 7.8 4.3
## 316 18 21 6.7 3.7
## 317 29 82 7.1 3.7
## 318 42 30 6.8 3.4
## 319 179 232 7.8 4.5
## 320 21 16 8.1 4.2
## 321 31 27 5.8 3.4
## 322 30 58 5.2 2.8
## 323 36 34 7.1 3.9
## 324 47 67 6.7 3.5
## 325 42 37 5.4 2.7
## 326 27 23 6.7 3.1
## 327 160 90 8.3 4.4
## 328 11 33 7.6 3.9
## 329 29 20 6.0 2.9
## 330 14 23 7.3 4.1
## 331 54 68 6.1 2.7
## 332 62 56 5.4 2.2
## 333 80 113 6.2 2.9
## 334 21 25 5.9 3.1
## 335 17 24 7.4 4.0
## 336 24 19 8.9 4.9
## 337 198 143 6.2 3.2
## 338 85 68 6.4 2.9
## 339 79 50 6.1 2.9
## 340 50 30 5.9 2.6
## 341 44 33 6.2 2.9
## 342 70 138 6.6 3.1
## 343 36 32 7.0 4.0
## 344 40 28 5.6 2.0
## 345 60 40 8.2 5.0
## 346 32 44 5.6 2.0
## 347 14 11 6.9 3.4
## 348 349 105 7.0 3.5
## 349 19 14 6.1 2.8
## 350 19 15 6.6 3.6
## 351 62 58 5.7 2.9
## 352 41 38 5.3 2.1
## 353 36 33 8.2 4.3
## 354 110 176 7.2 3.9
## 355 25 23 7.5 3.9
## 356 13 26 8.0 4.6
## 357 21 30 8.5 3.9
## 358 50 38 6.8 3.0
## 359 28 34 5.6 2.6
## 360 42 70 8.4 4.9
## 361 47 26 6.6 3.3
## 362 23 28 6.8 2.9
## 363 21 15 6.2 2.9
## 364 26 29 8.5 4.4
## 365 33 57 6.8 3.5
## 366 28 43 5.3 2.4
## 367 28 35 7.2 4.1
## 368 45 52 6.1 2.9
## 369 22 20 7.2 3.9
## 370 24 25 7.0 4.0
## 371 30 37 7.8 3.4
## 372 30 29 7.2 4.4
## 373 29 17 8.7 5.5
## 374 32 25 7.6 4.3
## 375 26 23 7.0 4.0
## 376 32 31 6.8 3.4
## 377 35 33 7.0 3.7
## 378 12 13 6.1 3.0
## 379 20 41 7.3 3.3
## 380 16 22 7.3 3.7
## 381 36 53 7.3 3.4
## 382 28 43 8.1 3.3
## 383 27 22 6.7 3.2
## 384 10 12 4.9 2.5
## 385 115 91 6.5 2.8
## 386 29 30 7.4 4.1
## 387 25 21 7.5 4.2
## 388 34 20 6.9 3.7
## 389 31 40 7.3 3.8
## 390 31 35 6.3 3.2
## 391 23 35 6.0 3.0
## 392 25 20 6.9 3.7
## 393 94 92 6.3 3.1
## 394 142 68 8.0 4.0
## 395 37 56 5.9 2.5
## 396 24 20 6.3 3.2
## 397 30 32 6.4 3.4
## 398 28 70 6.3 3.1
## 399 25 50 7.1 3.7
## 400 137 145 6.0 3.0
## 401 24 20 6.4 3.2
## 402 44 236 6.8 1.6
## 403 155 108 8.1 4.0
## 404 19 22 6.3 3.6
## 405 20 26 6.8 3.9
## 406 15 21 7.6 4.0
## 407 18 58 6.7 3.7
## 408 157 108 7.2 3.7
## 409 141 73 7.5 3.6
## 410 284 190 6.5 3.3
## 411 440 850 5.0 1.9
## 412 28 44 6.9 2.9
## 413 33 71 7.1 2.9
## 414 15 30 4.6 2.1
## 415 28 24 5.2 2.2
## 416 93 40 3.6 2.7
## 417 76 180 6.3 2.7
## 418 26 29 7.1 4.2
## 419 69 50 7.0 3.0
## 420 52 65 6.7 2.9
## 421 48 57 6.4 2.3
## 422 48 71 7.1 3.7
## 423 34 42 6.9 3.4
## 424 44 59 6.5 3.9
## 425 12 15 7.0 3.0
## 426 59 126 4.3 2.5
## 427 49 42 5.6 2.5
## 428 33 66 7.0 3.0
## 429 10 12 6.9 3.3
## 430 102 141 5.5 2.0
## 431 20 33 4.8 1.9
## 432 10 13 6.7 2.9
## 433 55 87 7.5 4.6
## 434 31 24 7.6 3.8
## 435 32 36 7.5 3.6
## 436 28 21 7.1 4.5
## 437 41 80 6.2 3.1
## 438 15 44 5.2 2.5
## 439 30 90 3.8 1.4
## 440 25 18 6.2 3.1
## 441 23 20 7.0 4.3
## 442 29 39 4.1 1.8
## 443 52 102 4.6 1.9
## 444 35 81 6.2 3.2
## 445 18 19 6.3 2.8
## 446 40 46 7.3 4.0
## 447 35 20 5.5 2.7
## 448 425 511 7.7 3.5
## 449 26 25 6.0 2.6
## 450 26 27 6.2 3.1
## 451 50 72 6.0 3.0
## 452 20 25 7.2 3.2
## 453 25 36 7.1 3.5
## 454 40 23 7.1 3.0
## 455 15 16 7.0 3.5
## 456 25 22 6.8 3.4
## 457 22 43 7.4 3.1
## 458 28 35 6.9 3.4
## 459 37 19 3.6 0.9
## 460 16 46 5.7 1.5
## 461 159 51 5.5 2.5
## 462 55 23 6.8 4.1
## 463 22 18 6.4 2.5
## 464 23 42 8.0 4.0
## 465 21 53 4.5 1.4
## 466 23 42 5.1 2.0
## 467 22 16 4.9 1.9
## 468 18 108 6.8 3.1
## 469 30 54 5.3 2.1
## 470 22 32 4.4 2.0
## 471 13 56 7.1 4.1
## 472 17 26 6.6 2.9
## 473 64 33 5.8 2.4
## 474 38 21 6.8 3.9
## 475 33 135 6.0 2.7
## 476 119 42 7.9 4.1
## 477 15 25 5.5 2.7
## 478 24 32 5.1 2.4
## 479 622 497 6.9 3.6
## 480 779 844 7.3 3.2
## 481 28 51 7.9 4.2
## 482 132 368 7.3 4.0
## 483 91 188 7.2 3.8
## 484 46 57 7.8 4.3
## 485 18 40 5.0 2.1
## 486 154 248 6.2 2.8
## 487 18 15 6.7 3.0
## 488 102 190 6.0 2.9
## 489 31 26 7.0 3.5
## 490 17 29 5.9 3.2
## 491 96 57 6.7 3.6
## 492 56 111 6.8 3.9
## 493 133 103 7.9 3.3
## 494 30 28 5.2 1.8
## 495 196 401 6.5 3.9
## 496 31 29 6.1 3.0
## 497 52 31 8.0 4.8
## 498 42 39 7.2 3.0
## 499 18 22 6.5 4.0
## 500 40 70 7.2 2.9
## 501 62 55 6.0 2.4
## 502 28 76 5.9 2.5
## 503 29 30 4.9 2.7
## 504 39 221 8.1 2.5
## 505 39 79 5.5 2.7
## 506 95 235 5.8 3.1
## 507 43 185 5.6 2.4
## 508 190 73 7.5 3.9
## 509 119 230 7.5 3.3
## 510 140 540 5.4 3.0
## 511 31 36 9.2 4.6
## 512 37 48 6.8 3.4
## 513 22 28 5.8 2.6
## 514 32 24 6.9 3.0
## 515 48 58 8.4 4.2
## 516 65 181 6.9 3.0
## 517 16 24 6.7 3.0
## 518 50 28 8.0 4.0
## 519 74 68 7.2 3.6
## 520 168 630 9.2 2.0
## 521 69 155 8.6 4.0
## 522 14 25 7.1 2.1
## 523 24 15 7.9 3.7
## 524 15 16 4.8 2.6
## 525 12 15 6.8 3.7
## 526 35 32 8.0 3.9
## 527 21 63 6.4 2.8
## 528 68 200 7.1 3.4
## 529 12 38 6.3 2.1
## 530 54 152 5.1 1.8
## 531 14 21 7.0 3.8
## 532 110 186 7.9 2.7
## 533 12 17 8.2 3.2
## 534 509 623 3.6 1.0
## 535 88 74 8.0 4.0
## 536 46 52 6.1 2.0
## 537 67 220 6.9 2.6
## 538 25 75 7.6 3.6
## 539 85 78 6.4 2.7
## 540 24 45 7.2 3.5
## 541 21 23 7.0 3.0
## 542 29 19 6.6 3.0
## 543 46 47 7.3 4.1
## 544 23 27 7.6 4.0
## 545 67 42 7.0 3.2
## 546 41 42 4.5 2.2
## 547 47 32 5.4 2.3
## 548 39 348 7.1 2.3
## 549 60 186 6.9 2.8
## 550 74 141 7.8 4.9
## 551 25 41 5.6 2.4
## 552 20 22 6.2 3.0
## 553 29 30 5.8 2.9
## 554 58 140 7.0 2.7
## 555 20 43 6.5 3.0
## 556 139 87 7.0 3.0
## 557 25 27 6.1 3.1
## 558 80 125 6.2 3.1
## 559 382 330 7.5 4.0
## 560 75 138 7.5 2.6
## 561 321 562 6.5 2.2
## 562 233 384 6.9 2.0
## 563 173 367 7.8 2.6
## 564 31 83 7.2 2.6
## 565 22 34 5.9 2.9
## 566 22 143 6.6 2.1
## 567 22 31 7.8 4.0
## 568 20 40 6.0 1.7
## 569 91 101 6.9 3.5
## 570 213 168 7.1 4.0
## 571 131 90 5.4 2.6
## 572 46 134 6.9 3.0
## 573 54 125 5.6 4.0
## 574 50 88 6.2 1.9
## 575 48 92 6.6 2.4
## 576 41 88 7.9 2.5
## 577 58 80 5.3 2.2
## 578 28 47 5.4 2.6
## 579 20 34 5.9 1.6
## 580 35 31 6.0 3.2
## 581 48 49 6.4 3.2
## 582 29 32 6.8 3.4
## 583 21 24 7.3 4.4
## AlbuminandGlobulinRatio LiverPatient
## 1 0.90 1
## 2 0.74 1
## 3 0.89 1
## 4 1.00 1
## 5 0.40 1
## 6 1.30 1
## 7 1.00 1
## 8 1.10 1
## 9 1.20 2
## 10 1.00 1
## 11 0.80 1
## 12 0.60 1
## 13 0.90 2
## 14 1.00 1
## 15 0.87 1
## 16 0.70 2
## 17 1.30 1
## 18 0.92 2
## 19 0.80 1
## 20 0.80 1
## 21 0.55 1
## 22 0.50 1
## 23 0.90 1
## 24 0.60 1
## 25 1.85 2
## 26 1.10 1
## 27 1.10 1
## 28 1.20 1
## 29 0.95 2
## 30 1.10 2
## 31 0.40 1
## 32 1.40 1
## 33 1.20 2
## 34 0.80 2
## 35 0.80 2
## 36 0.90 1
## 37 1.18 2
## 38 0.80 1
## 39 0.61 1
## 40 1.10 1
## 41 1.00 1
## 42 1.10 2
## 43 0.60 2
## 44 1.00 1
## 45 1.00 2
## 46 0.90 2
## 47 1.34 1
## 48 1.30 1
## 49 1.00 1
## 50 0.40 1
## 51 0.70 1
## 52 1.00 1
## 53 1.30 1
## 54 1.20 1
## 55 0.50 1
## 56 0.50 1
## 57 1.00 2
## 58 0.90 2
## 59 0.60 1
## 60 1.39 2
## 61 1.00 1
## 62 1.00 1
## 63 1.00 1
## 64 0.70 1
## 65 0.60 1
## 66 0.80 1
## 67 1.60 1
## 68 1.40 1
## 69 1.40 1
## 70 0.90 2
## 71 1.20 1
## 72 0.60 1
## 73 0.80 1
## 74 1.20 1
## 75 1.00 1
## 76 0.90 2
## 77 0.70 1
## 78 0.80 1
## 79 1.58 2
## 80 0.50 1
## 81 0.60 1
## 82 1.30 1
## 83 1.00 2
## 84 1.25 1
## 85 1.34 1
## 86 1.40 1
## 87 1.00 2
## 88 1.00 1
## 89 0.70 1
## 90 0.80 1
## 91 0.78 1
## 92 0.70 1
## 93 0.60 1
## 94 0.70 1
## 95 0.70 1
## 96 1.00 1
## 97 0.80 1
## 98 0.70 1
## 99 1.10 1
## 100 1.10 1
## 101 0.76 2
## 102 0.70 2
## 103 1.55 1
## 104 1.20 2
## 105 0.80 1
## 106 1.00 2
## 107 1.00 2
## 108 0.50 2
## 109 0.50 2
## 110 0.90 2
## 111 0.71 2
## 112 0.62 1
## 113 1.10 2
## 114 0.60 1
## 115 0.67 2
## 116 0.60 2
## 117 0.60 1
## 118 1.30 1
## 119 1.00 1
## 120 0.90 1
## 121 0.90 1
## 122 0.70 1
## 123 1.40 2
## 124 0.90 1
## 125 1.40 2
## 126 0.70 2
## 127 0.80 1
## 128 0.90 1
## 129 0.95 1
## 130 0.80 1
## 131 0.80 1
## 132 0.75 2
## 133 1.16 2
## 134 0.80 1
## 135 1.10 1
## 136 0.50 1
## 137 0.60 1
## 138 0.80 1
## 139 0.80 1
## 140 1.50 1
## 141 1.10 2
## 142 0.40 1
## 143 0.90 1
## 144 0.90 1
## 145 1.00 1
## 146 1.10 2
## 147 0.40 1
## 148 1.10 1
## 149 1.66 1
## 150 1.20 1
## 151 1.20 2
## 152 0.96 1
## 153 0.90 1
## 154 1.20 2
## 155 0.90 1
## 156 0.70 1
## 157 1.00 1
## 158 0.50 1
## 159 0.50 1
## 160 1.10 1
## 161 0.60 1
## 162 0.80 1
## 163 0.50 1
## 164 1.38 1
## 165 1.38 1
## 166 0.52 1
## 167 0.60 1
## 168 0.47 1
## 169 0.90 1
## 170 0.80 1
## 171 0.93 1
## 172 1.00 1
## 173 0.47 1
## 174 1.60 1
## 175 1.60 1
## 176 1.20 1
## 177 0.80 1
## 178 0.70 1
## 179 0.50 1
## 180 0.48 1
## 181 0.50 1
## 182 1.00 1
## 183 0.58 2
## 184 0.80 1
## 185 1.40 1
## 186 1.00 2
## 187 0.69 1
## 188 0.60 1
## 189 0.60 1
## 190 0.90 2
## 191 1.27 1
## 192 1.12 1
## 193 1.10 1
## 194 1.06 2
## 195 1.10 2
## 196 0.50 1
## 197 0.53 1
## 198 0.75 1
## 199 0.90 2
## 200 0.40 1
## 201 0.90 1
## 202 0.90 1
## 203 1.03 2
## 204 1.20 2
## 205 1.00 2
## 206 1.10 1
## 207 0.70 1
## 208 1.30 1
## 209 0.68 1
## 211 1.40 1
## 212 0.80 1
## 213 1.20 2
## 214 0.80 1
## 215 1.00 1
## 216 1.90 2
## 217 1.00 2
## 218 1.50 1
## 219 0.70 2
## 220 1.10 1
## 221 1.00 1
## 222 1.40 1
## 223 1.00 1
## 224 1.70 2
## 225 1.10 1
## 226 1.00 1
## 227 1.00 1
## 228 1.40 1
## 229 0.90 2
## 230 1.00 1
## 231 1.30 2
## 232 1.00 1
## 233 0.60 1
## 234 0.90 1
## 235 0.80 1
## 236 0.80 1
## 237 0.90 2
## 238 1.00 1
## 239 1.80 2
## 240 0.80 1
## 241 1.00 2
## 243 1.00 1
## 244 1.80 1
## 245 0.60 1
## 246 0.80 1
## 247 0.70 1
## 248 0.80 1
## 249 1.00 1
## 250 0.70 1
## 251 0.80 1
## 252 0.80 1
## 253 1.20 2
## 255 1.40 2
## 256 1.00 1
## 257 1.20 1
## 258 1.10 1
## 259 1.40 2
## 260 0.60 1
## 261 0.50 1
## 262 1.00 1
## 263 1.30 1
## 264 0.70 1
## 265 1.38 2
## 266 1.00 2
## 267 1.50 1
## 268 1.80 1
## 269 0.50 1
## 270 1.30 1
## 271 1.00 1
## 272 0.90 2
## 273 1.00 1
## 274 1.20 1
## 275 1.00 1
## 276 1.00 2
## 277 1.20 2
## 278 0.80 1
## 279 1.00 1
## 280 1.00 1
## 281 1.00 1
## 282 1.30 2
## 283 0.90 1
## 284 0.50 2
## 285 1.00 1
## 286 0.90 1
## 287 1.20 1
## 288 1.00 2
## 289 0.90 1
## 290 1.30 1
## 291 1.00 1
## 292 1.10 2
## 293 0.90 1
## 294 1.20 2
## 295 1.10 1
## 296 0.90 1
## 297 1.00 1
## 298 1.70 2
## 299 0.90 1
## 300 1.20 2
## 301 1.10 2
## 302 0.80 1
## 303 1.00 1
## 304 1.00 2
## 305 1.40 1
## 306 0.90 2
## 307 0.40 1
## 308 1.00 1
## 309 1.00 1
## 310 1.10 1
## 311 1.30 1
## 312 0.90 1
## 314 1.30 2
## 315 1.20 1
## 316 1.20 2
## 317 1.00 2
## 318 1.00 1
## 319 1.30 1
## 320 1.00 2
## 321 1.40 1
## 322 1.10 1
## 323 1.20 1
## 324 1.10 2
## 325 1.00 1
## 326 0.80 2
## 327 1.10 2
## 328 1.00 2
## 329 0.90 2
## 330 1.20 2
## 331 0.80 1
## 332 0.60 1
## 333 0.80 1
## 334 1.10 2
## 335 1.10 1
## 336 1.20 1
## 337 1.00 1
## 338 0.80 1
## 339 0.90 1
## 340 0.70 1
## 341 0.80 1
## 342 0.80 1
## 343 1.30 2
## 344 0.50 1
## 345 1.50 2
## 346 0.50 1
## 347 0.90 1
## 348 1.00 1
## 349 0.80 1
## 350 1.20 2
## 351 1.00 1
## 352 0.60 1
## 353 1.10 1
## 354 1.10 1
## 355 1.00 1
## 356 1.30 2
## 357 0.80 1
## 358 0.70 1
## 359 0.80 1
## 360 1.40 2
## 361 1.00 1
## 362 0.70 2
## 363 0.80 2
## 364 1.00 2
## 365 1.00 2
## 366 0.80 2
## 367 1.30 2
## 368 0.90 2
## 369 1.10 1
## 370 1.30 1
## 371 0.80 1
## 372 1.50 2
## 373 1.70 1
## 374 1.30 2
## 375 1.30 2
## 376 1.00 1
## 377 1.10 1
## 378 0.90 2
## 379 0.80 1
## 380 1.00 2
## 381 0.90 1
## 382 0.60 1
## 383 0.90 1
## 384 1.00 2
## 385 0.70 1
## 386 1.30 1
## 387 1.20 2
## 388 1.10 1
## 389 1.10 1
## 390 1.00 1
## 391 1.00 1
## 392 1.10 1
## 393 0.90 1
## 394 1.00 1
## 395 0.70 1
## 396 1.00 2
## 397 1.10 1
## 398 0.90 1
## 399 1.00 1
## 400 1.00 1
## 401 1.00 2
## 402 0.30 1
## 403 0.90 1
## 404 1.30 2
## 405 1.30 1
## 406 1.10 1
## 407 1.20 2
## 408 1.00 1
## 409 0.90 1
## 410 1.00 1
## 411 0.60 1
## 412 0.70 2
## 413 0.70 1
## 414 0.80 2
## 415 0.70 2
## 416 0.70 1
## 417 0.75 1
## 418 1.40 2
## 419 0.70 1
## 420 0.70 1
## 421 0.50 1
## 422 1.00 2
## 423 0.97 1
## 424 1.50 2
## 425 0.70 1
## 426 1.40 1
## 427 0.80 2
## 428 0.75 1
## 429 0.90 2
## 430 0.50 1
## 431 0.60 1
## 432 0.76 2
## 433 1.58 1
## 434 1.00 2
## 435 0.92 2
## 436 1.70 2
## 437 1.00 1
## 438 0.90 1
## 439 0.50 1
## 440 1.00 1
## 441 1.50 1
## 442 0.70 1
## 443 0.70 1
## 444 1.06 1
## 445 0.80 2
## 446 1.20 2
## 447 0.96 1
## 448 0.80 1
## 449 0.70 1
## 450 1.00 1
## 451 1.00 1
## 452 0.80 1
## 453 0.90 1
## 454 0.70 1
## 455 1.00 2
## 456 1.00 1
## 457 0.70 2
## 458 0.90 2
## 459 0.30 1
## 460 0.35 1
## 461 0.80 1
## 462 1.51 1
## 463 0.64 1
## 464 1.00 1
## 465 0.45 2
## 466 0.50 2
## 467 0.60 2
## 468 0.80 2
## 469 0.60 1
## 470 0.80 1
## 471 1.36 1
## 472 0.70 2
## 473 0.70 2
## 474 1.30 2
## 475 0.80 2
## 476 1.00 2
## 477 1.00 1
## 478 0.88 1
## 479 1.09 1
## 480 0.70 1
## 481 1.10 2
## 482 1.20 1
## 483 1.11 1
## 484 1.20 2
## 485 1.72 1
## 486 0.80 1
## 487 0.80 1
## 488 0.93 1
## 489 1.00 2
## 490 1.18 2
## 491 1.16 1
## 492 1.85 2
## 493 0.70 1
## 494 0.52 2
## 495 1.50 1
## 496 0.96 2
## 497 1.50 1
## 498 0.70 1
## 499 1.60 1
## 500 0.60 1
## 501 0.60 1
## 502 0.70 1
## 503 1.20 1
## 504 0.40 1
## 505 0.90 1
## 506 1.10 1
## 507 0.70 1
## 508 1.00 1
## 509 0.70 1
## 510 1.20 1
## 511 1.00 2
## 512 1.00 1
## 513 0.80 2
## 514 0.70 1
## 515 1.00 1
## 516 0.70 1
## 517 0.80 1
## 518 1.00 1
## 519 1.00 1
## 520 0.30 1
## 521 0.80 1
## 522 0.40 1
## 523 0.80 1
## 524 1.10 1
## 525 1.10 2
## 526 0.90 2
## 527 0.80 1
## 528 0.90 1
## 529 0.50 1
## 530 0.50 1
## 531 1.10 2
## 532 0.50 1
## 533 0.60 2
## 534 0.30 1
## 535 1.00 2
## 536 0.40 1
## 537 0.60 1
## 538 0.90 1
## 539 0.70 1
## 540 0.90 2
## 541 0.70 2
## 542 0.80 2
## 543 1.20 2
## 544 1.10 1
## 545 0.80 1
## 546 0.90 2
## 547 0.70 1
## 548 0.40 1
## 549 2.80 1
## 550 1.60 1
## 551 0.70 1
## 552 0.90 2
## 553 1.00 1
## 554 0.60 1
## 555 0.80 1
## 556 0.70 1
## 557 1.00 1
## 558 1.00 1
## 559 1.10 1
## 560 0.50 1
## 561 0.40 1
## 562 0.40 1
## 563 0.50 1
## 564 0.50 1
## 565 0.90 2
## 566 0.46 1
## 567 1.00 2
## 568 0.39 1
## 569 1.02 1
## 570 1.20 1
## 571 0.90 1
## 572 0.70 1
## 573 2.50 1
## 574 0.40 1
## 575 0.50 1
## 576 2.50 1
## 577 0.70 1
## 578 0.90 1
## 579 0.37 2
## 580 1.10 1
## 581 1.00 1
## 582 1.00 1
## 583 1.50 2Wow[!is.na(Wow)] #digunakan untuk mengecek apakah benar sudah tidak ada NA## [1] "65" "62" "62" "58" "72" "46" "26" "29"
## [9] "17" "55" "57" "72" "64" "74" "61" "25"
## [17] "38" "33" "40" "40" "51" "51" "62" "40"
## [25] "63" "34" "34" "34" "20" "84" "57" "52"
## [33] "57" "38" "38" "30" "17" "46" "48" "47"
## [41] "45" "62" "42" "50" "85" "35" "21" "40"
## [49] "32" "55" "45" "34" "38" "38" "42" "42"
## [57] "33" "48" "51" "64" "31" "58" "58" "57"
## [65] "57" "57" "54" "37" "66" "60" "19" "75"
## [73] "75" "52" "68" "29" "31" "68" "70" "58"
## [81] "58" "29" "49" "33" "32" "14" "13" "58"
## [89] "18" "60" "60" "60" "60" "60" "60" "75"
## [97] "39" "39" "18" "18" "27" "27" "17" "55"
## [105] "63" "36" "36" "36" "36" "36" "24" "48"
## [113] "27" "74" "50" "50" "48" "32" "32" "32"
## [121] "32" "32" "58" "64" "28" "60" "48" "64"
## [129] "58" "45" "45" "70" "18" "53" "18" "66"
## [137] "46" "18" "18" "15" "60" "66" "30" "30"
## [145] "45" "65" "66" "65" "50" "60" "56" "50"
## [153] "46" "52" "34" "34" "32" "72" "72" "50"
## [161] "60" "60" "60" "39" "39" "48" "55" "47"
## [169] "60" "60" "72" "44" "55" "31" "31" "31"
## [177] "55" "75" "75" "75" "75" "75" "65" "40"
## [185] "64" "38" "60" "60" "60" "48" "60" "60"
## [193] "60" "49" "49" "60" "60" "26" "41" " 7"
## [201] "49" "49" "38" "21" "21" "45" "40" "40"
## [209] "70" "28" "42" "22" " 8" "38" "66" "55"
## [217] "49" " 6" "37" "37" "47" "47" "50" "70"
## [225] "26" "26" "68" "65" "46" "61" "61" "50"
## [233] "33" "40" "60" "22" "35" "35" "40" "48"
## [241] "29" "28" "54" "54" "55" "55" "40" "33"
## [249] "33" "33" "65" "38" "38" "50" "44" "36"
## [257] "42" "42" "33" "18" "38" "38" " 4" "62"
## [265] "43" "40" "26" "37" " 4" "21" "30" "33"
## [273] "26" "35" "60" "45" "48" "58" "50" "50"
## [281] "18" "18" "13" "34" "43" "50" "57" "45"
## [289] "60" "45" "23" "22" "22" "74" "25" "31"
## [297] "24" "58" "51" "50" "50" "55" "54" "48"
## [305] "30" "45" "48" "51" "54" "30" "26" "22"
## [313] "44" "35" "38" "14" "30" "30" "36" "12"
## [321] "60" "42" "36" "24" "43" "21" "26" "26"
## [329] "26" "36" "13" "13" "75" "75" "75" "75"
## [337] "75" "36" "35" "70" "37" "60" "46" "38"
## [345] "70" "49" "37" "37" "26" "48" "48" "19"
## [353] "33" "33" "37" "69" "24" "65" "55" "42"
## [361] "21" "40" "16" "60" "42" "58" "54" "33"
## [369] "48" "25" "56" "47" "33" "20" "50" "72"
## [377] "50" "39" "58" "60" "34" "50" "38" "51"
## [385] "46" "72" "72" "75" "41" "41" "48" "45"
## [393] "74" "78" "38" "27" "66" "50" "42" "65"
## [401] "22" "31" "45" "12" "48" "48" "18" "23"
## [409] "65" "48" "65" "70" "70" "11" "50" "55"
## [417] "55" "26" "41" "53" "32" "58" "45" "65"
## [425] "52" "73" "53" "47" "29" "41" "30" "17"
## [433] "23" "35" "65" "42" "49" "42" "42" "42"
## [441] "61" "17" "54" "45" "48" "48" "65" "35"
## [449] "58" "46" "28" "21" "32" "61" "26" "65"
## [457] "22" "28" "38" "25" "45" "45" "28" "28"
## [465] "66" "66" "66" "49" "42" "42" "35" "38"
## [473] "38" "55" "33" "33" " 7" "45" "45" "30"
## [481] "62" "22" "42" "32" "60" "65" "53" "27"
## [489] "35" "65" "25" "32" "24" "67" "68" "55"
## [497] "70" "36" "42" "53" "32" "32" "56" "50"
## [505] "46" "46" "37" "45" "56" "69" "49" "49"
## [513] "60" "28" "45" "35" "62" "55" "46" "50"
## [521] "29" "53" "46" "40" "45" "55" "22" "40"
## [529] "62" "46" "39" "60" "46" "10" "52" "65"
## [537] "42" "42" "62" "40" "54" "45" "45" "50"
## [545] "42" "40" "46" "29" "45" "46" "73" "55"
## [553] "51" "51" "51" "26" "66" "66" "66" "64"
## [561] "38" "43" "50" "52" "20" "16" "16" "90"
## [569] "32" "32" "32" "32" "32" "32" "60" "40"
## [577] "52" "31" "38" "Female" "Male" "Male" "Male" "Male"
## [585] "Male" "Female" "Female" "Male" "Male" "Male" "Male" "Male"
## [593] "Female" "Male" "Male" "Male" "Male" "Female" "Female" "Male"
## [601] "Male" "Male" "Male" "Male" "Male" "Male" "Male" "Male"
## [609] "Female" "Male" "Male" "Male" "Female" "Female" "Male" "Female"
## [617] "Female" "Male" "Male" "Male" "Male" "Male" "Male" "Female"
## [625] "Male" "Male" "Male" "Female" "Male" "Female" "Female" "Male"
## [633] "Male" "Male" "Male" "Male" "Female" "Male" "Male" "Female"
## [641] "Male" "Male" "Male" "Male" "Male" "Male" "Male" "Male"
## [649] "Male" "Female" "Female" "Female" "Male" "Male" "Female" "Male"
## [657] "Female" "Male" "Female" "Female" "Male" "Male" "Male" "Male"
## [665] "Male" "Male" "Male" "Male" "Male" "Male" "Male" "Male"
## [673] "Male" "Male" "Male" "Male" "Male" "Male" "Male" "Male"
## [681] "Male" "Male" "Female" "Male" "Male" "Male" "Male" "Male"
## [689] "Male" "Female" "Male" "Male" "Male" "Male" "Male" "Male"
## [697] "Male" "Male" "Male" "Male" "Male" "Male" "Female" "Male"
## [705] "Male" "Male" "Male" "Female" "Male" "Male" "Female" "Female"
## [713] "Male" "Male" "Male" "Female" "Male" "Male" "Male" "Male"
## [721] "Female" "Male" "Male" "Female" "Male" "Female" "Male" "Male"
## [729] "Male" "Male" "Male" "Female" "Male" "Male" "Male" "Male"
## [737] "Male" "Male" "Male" "Male" "Male" "Male" "Male" "Male"
## [745] "Male" "Male" "Female" "Male" "Male" "Male" "Female" "Male"
## [753] "Male" "Male" "Male" "Male" "Male" "Male" "Male" "Male"
## [761] "Male" "Male" "Male" "Male" "Male" "Male" "Male" "Male"
## [769] "Female" "Male" "Male" "Male" "Male" "Male" "Male" "Male"
## [777] "Female" "Male" "Female" "Male" "Male" "Female" "Male" "Male"
## [785] "Male" "Male" "Male" "Female" "Male" "Male" "Male" "Female"
## [793] "Male" "Male" "Male" "Male" "Male" "Male" "Male" "Male"
## [801] "Male" "Male" "Male" "Male" "Male" "Female" "Female" "Male"
## [809] "Male" "Male" "Male" "Male" "Female" "Male" "Male" "Female"
## [817] "Female" "Male" "Male" "Female" "Female" "Male" "Male" "Male"
## [825] "Male" "Male" "Male" "Male" "Male" "Male" "Female" "Male"
## [833] "Male" "Male" "Male" "Male" "Male" "Male" "Male" "Female"
## [841] "Male" "Male" "Male" "Female" "Male" "Male" "Male" "Male"
## [849] "Male" "Male" "Male" "Male" "Male" "Male" "Male" "Female"
## [857] "Male" "Male" "Male" "Male" "Male" "Male" "Female" "Male"
## [865] "Female" "Male" "Female" "Male" "Male" "Male" "Male" "Male"
## [873] "Female" "Female" "Female" "Female" "Male" "Female" "Female" "Male"
## [881] "Female" "Female" "Male" "Male" "Female" "Female" "Male" "Female"
## [889] "Female" "Male" "Male" "Male" "Male" "Male" "Male" "Female"
## [897] "Female" "Male" "Male" "Male" "Male" "Female" "Male" "Male"
## [905] "Male" "Male" "Male" "Male" "Female" "Female" "Female" "Male"
## [913] "Male" "Male" "Male" "Male" "Female" "Male" "Male" "Male"
## [921] "Male" "Male" "Male" "Male" "Female" "Male" "Male" "Female"
## [929] "Female" "Female" "Male" "Male" "Male" "Male" "Female" "Male"
## [937] "Female" "Male" "Female" "Male" "Male" "Male" "Male" "Female"
## [945] "Female" "Female" "Male" "Male" "Female" "Female" "Male" "Male"
## [953] "Female" "Female" "Male" "Male" "Male" "Female" "Female" "Male"
## [961] "Male" "Male" "Male" "Male" "Male" "Male" "Male" "Male"
## [969] "Male" "Female" "Male" "Male" "Male" "Male" "Male" "Female"
## [977] "Male" "Female" "Male" "Male" "Female" "Male" "Male" "Male"
## [985] "Male" "Male" "Female" "Male" "Male" "Male" "Male" "Male"
## [993] "Male" "Male" "Female" "Female" "Male" "Male" "Male" "Female"
## [1001] "Male" "Male" "Male" "Female" "Male" "Female" "Female" "Male"
## [1009] "Female" "Female" "Female" "Male" "Male" "Male" "Female" "Female"
## [1017] "Female" "Female" "Female" "Male" "Male" "Male" "Female" "Female"
## [1025] "Female" "Male" "Male" "Male" "Male" "Male" "Female" "Male"
## [1033] "Male" "Male" "Male" "Female" "Female" "Male" "Male" "Female"
## [1041] "Female" "Female" "Female" "Male" "Male" "Male" "Female" "Male"
## [1049] "Male" "Male" "Male" "Male" "Male" "Male" "Male" "Male"
## [1057] "Male" "Male" "Male" "Male" "Female" "Female" "Male" "Male"
## [1065] "Male" "Female" "Male" "Female" "Male" "Male" "Male" "Male"
## [1073] "Male" "Male" "Male" "Male" "Male" "Male" "Male" "Male"
## [1081] "Male" "Male" "Male" "Male" "Male" "Female" "Male" "Male"
## [1089] "Male" "Male" "Male" "Male" "Male" "Male" "Male" "Male"
## [1097] "Male" "Female" "Male" "Male" "Female" "Male" "Male" "Male"
## [1105] "Male" "Female" "Male" "Male" "Female" "Male" "Male" "Male"
## [1113] "Female" "Male" "Female" "Male" "Male" "Male" "Male" "Female"
## [1121] "Female" "Male" "Female" "Male" "Female" "Male" "Male" "Male"
## [1129] "Male" "Male" "Male" "Male" "Male" "Male" "Male" "Male"
## [1137] "Male" "Male" "Male" "Female" "Male" "Female" "Male" "Female"
## [1145] "Male" "Male" "Male" "Male" "Male" "Male" "Male" "Male"
## [1153] "Male" "Male" "Male" "Male" "Male" "Male" " 0.7" "10.9"
## [1161] " 7.3" " 1.0" " 3.9" " 1.8" " 0.9" " 0.9" " 0.9" " 0.7"
## [1169] " 0.6" " 2.7" " 0.9" " 1.1" " 0.7" " 0.6" " 1.8" " 1.6"
## [1177] " 0.9" " 0.9" " 2.2" " 2.9" " 6.8" " 1.9" " 0.9" " 4.1"
## [1185] " 4.1" " 6.2" " 1.1" " 0.7" " 4.0" " 0.9" " 1.0" " 2.6"
## [1193] " 2.6" " 1.3" " 0.7" "14.2" " 1.4" " 2.7" " 2.4" " 0.6"
## [1201] " 6.8" " 2.6" " 1.0" " 1.8" " 3.9" " 1.1" " 0.6" "18.4"
## [1209] " 0.7" " 0.6" " 3.1" " 1.1" " 8.9" " 8.9" " 0.8" " 0.9"
## [1217] " 0.8" " 1.1" " 0.8" " 1.0" " 1.0" " 0.7" " 1.3" " 1.4"
## [1225] " 2.2" " 1.8" " 0.7" " 0.8" " 0.7" " 0.8" " 0.8" " 0.6"
## [1233] " 0.7" " 0.7" " 0.9" " 0.6" " 1.4" " 2.8" " 2.4" " 1.0"
## [1241] " 0.7" " 2.0" " 0.6" " 1.4" " 0.6" " 0.8" " 0.6" " 4.0"
## [1249] " 5.7" " 6.8" " 8.6" " 5.8" " 5.2" " 0.9" " 3.8" " 6.6"
## [1257] " 0.6" " 0.7" " 0.6" " 0.7" " 0.9" " 0.8" " 0.5" " 5.3"
## [1265] " 5.3" " 0.8" " 0.8" " 0.9" " 0.7" " 3.2" " 1.2" " 0.6"
## [1273] " 5.8" " 7.3" " 0.7" "12.7" "15.9" "18.0" "23.0" "22.7"
## [1281] " 1.7" " 0.8" " 0.6" " 1.8" " 5.8" " 3.0" " 1.7" " 2.8"
## [1289] " 3.2" " 0.7" " 0.8" " 0.9" " 1.8" "11.3" " 4.7" " 0.8"
## [1297] " 0.8" " 0.8" " 0.6" " 4.2" " 1.6" " 1.6" " 3.5" " 0.8"
## [1305] " 2.9" " 0.7" " 0.9" " 0.8" " 1.1" " 1.6" " 0.8" " 0.6"
## [1313] " 5.9" " 8.7" " 0.9" " 0.7" " 0.7" " 1.2" "11.0" "11.5"
## [1321] " 5.8" " 1.9" " 1.9" " 4.5" "75.0" " 3.0" "22.8" " 8.9"
## [1329] " 1.7" " 1.9" "14.1" " 0.6" " 0.6" " 0.8" " 0.8" "14.8"
## [1337] "10.6" " 8.0" " 2.8" " 2.9" " 1.9" " 0.6" " 1.1" " 1.5"
## [1345] " 3.2" " 2.1" " 1.9" " 0.8" " 6.3" " 5.8" " 2.3" " 1.3"
## [1353] " 2.0" " 2.4" " 2.0" " 0.6" " 0.9" "27.2" " 0.6" " 0.6"
## [1361] " 0.8" " 1.0" " 0.7" " 2.5" " 3.6" " 3.9" " 0.9" " 0.8"
## [1369] " 2.7" " 2.7" " 0.9" " 1.7" " 0.6" " 0.9" " 1.1" " 0.6"
## [1377] " 0.8" " 0.8" " 0.9" " 0.9" " 1.1" " 1.7" " 0.6" " 1.3"
## [1385] " 0.7" " 1.0" " 0.6" " 1.5" " 0.8" " 2.7" " 2.0" " 0.9"
## [1393] " 1.5" " 0.8" " 0.9" " 0.9" " 0.9" " 0.7" " 0.8" " 0.9"
## [1401] " 0.8" " 0.9" " 1.8" " 0.9" " 0.7" " 1.2" " 2.1" " 0.9"
## [1409] " 1.1" " 0.7" " 1.7" " 0.9" " 0.8" " 0.8" "30.5" "16.4"
## [1417] " 1.5" " 0.8" " 0.8" " 0.8" " 0.9" " 1.2" " 0.9" "14.5"
## [1425] " 0.6" " 0.7" " 0.8" "18.5" " 0.7" " 1.8" " 1.9" " 0.9"
## [1433] " 2.0" " 2.2" " 1.0" " 0.8" " 0.7" " 0.7" " 1.3" " 0.9"
## [1441] " 1.5" " 0.8" " 1.3" " 1.0" " 4.5" " 1.0" " 0.7" " 0.6"
## [1449] " 1.1" " 2.4" " 0.6" " 0.9" " 0.9" " 1.1" " 0.9" " 0.8"
## [1457] " 0.9" " 1.7" " 0.7" " 0.8" " 1.4" " 1.6" " 0.8" " 0.8"
## [1465] " 1.1" " 0.8" "23.2" " 0.8" " 2.0" " 0.9" " 0.9" " 0.7"
## [1473] " 3.7" " 0.9" " 0.7" " 0.8" " 1.7" " 0.8" " 2.6" " 0.8"
## [1481] " 1.2" " 3.3" " 0.8" " 0.7" " 2.0" " 1.7" " 7.1" " 0.7"
## [1489] " 0.7" " 0.7" " 6.7" " 2.5" " 1.8" " 1.4" " 0.9" " 0.8"
## [1497] " 0.8" " 3.1" " 0.8" " 2.9" " 0.6" " 0.7" " 1.3" " 0.8"
## [1505] " 1.8" " 1.3" " 0.7" " 1.4" " 0.8" " 1.4" " 0.7" " 2.1"
## [1513] " 0.7" " 0.8" " 0.7" " 0.7" " 1.1" " 0.9" " 0.8" " 0.7"
## [1521] " 0.7" " 2.2" " 0.8" " 0.8" "22.6" " 0.8" " 0.7" " 0.7"
## [1529] " 0.7" " 3.5" " 0.7" " 0.6" " 0.7" " 0.7" " 1.7" " 0.6"
## [1537] " 0.7" " 1.4" " 3.7" " 0.8" " 2.7" " 0.8" " 0.8" " 0.6"
## [1545] " 0.8" " 0.9" " 7.5" " 2.7" " 1.0" " 0.8" " 1.0" " 1.0"
## [1553] " 0.8" " 1.0" " 0.7" " 7.3" " 0.5" " 0.7" " 0.8" " 0.8"
## [1561] " 0.7" " 1.0" " 2.4" " 5.0" " 1.4" " 2.3" " 4.9" " 0.7"
## [1569] " 1.4" " 1.3" " 0.6" " 0.7" " 4.2" " 8.2" "10.9" " 1.0"
## [1577] " 1.2" " 1.6" " 0.7" " 0.4" " 1.3" " 0.9" " 0.6" " 1.9"
## [1585] " 0.7" " 0.8" " 0.7" " 0.9" " 0.7" " 0.5" " 1.0" " 1.6"
## [1593] " 0.8" " 0.8" " 0.8" " 2.3" " 7.4" " 0.7" " 0.8" " 0.9"
## [1601] " 0.8" "23.3" " 0.8" " 0.9" " 7.9" " 0.8" " 0.9" " 0.7"
## [1609] " 0.6" " 0.6" " 0.7" " 0.8" " 6.8" " 1.1" " 2.2" " 0.8"
## [1617] " 0.7" " 0.8" " 0.7" " 0.6" " 0.6" " 1.0" " 1.0" " 0.8"
## [1625] " 1.1" " 0.6" " 0.7" " 1.0" " 2.0" " 2.2" " 0.9" " 0.6"
## [1633] " 7.1" " 3.4" " 0.5" " 2.3" " 1.1" " 0.8" " 5.0" " 6.7"
## [1641] " 0.8" " 0.7" " 0.7" " 0.8" " 0.8" " 1.0" " 1.0" " 0.7"
## [1649] " 0.7" " 0.7" " 1.0" " 2.2" " 1.8" " 3.6" " 2.7" " 2.8"
## [1657] " 0.8" "19.8" "30.5" "32.6" "17.7" " 0.9" "18.4" "20.0"
## [1665] " 0.8" " 2.2" " 1.0" " 0.9" " 1.0" " 3.9" " 0.9" " 0.9"
## [1673] " 2.9" "26.3" " 1.8" " 4.4" " 0.8" " 0.6" " 0.8" " 0.9"
## [1681] " 9.4" " 3.5" " 1.7" " 3.3" " 1.1" "30.8" " 0.7" " 1.4"
## [1689] " 1.6" "19.6" "15.8" " 0.8" " 1.8" " 0.7" " 0.8" " 0.8"
## [1697] " 0.7" " 1.2" " 5.5" " 0.7" "20.2" "27.7" "11.1" " 2.1"
## [1705] " 3.3" " 1.2" " 0.6" "10.2" " 1.8" " 0.8" " 0.7" " 2.9"
## [1713] " 4.0" "42.8" "15.2" "16.6" "17.3" " 1.4" " 0.6" "22.5"
## [1721] " 1.0" " 2.7" "16.7" " 7.7" " 2.6" " 1.1" "15.6" " 3.7"
## [1729] "12.1" "25.0" "15.0" "12.7" " 0.5" " 0.6" " 0.8" " 1.3"
## [1737] " 1.0" " 0.1" " 5.5" " 4.1" " 0.4" " 2.0" " 0.7" " 0.2"
## [1745] " 0.3" " 0.3" " 0.2" " 0.1" " 1.3" " 0.3" " 0.4" " 0.2"
## [1753] " 0.1" " 0.8" " 0.5" " 0.3" " 0.3" " 1.0" " 1.3" " 3.0"
## [1761] " 1.0" " 0.2" " 2.0" " 2.0" " 3.0" " 0.5" " 0.2" " 1.9"
## [1769] " 0.2" " 0.3" " 1.2" " 1.2" " 0.4" " 0.2" " 7.8" " 0.6"
## [1777] " 1.3" " 1.1" " 0.1" " 3.2" " 1.2" " 0.3" " 0.6" " 1.8"
## [1785] " 0.3" " 0.1" " 8.8" " 0.2" " 0.1" " 1.6" " 0.3" " 4.5"
## [1793] " 4.5" " 0.2" " 0.2" " 0.2" " 0.5" " 0.2" " 0.5" " 0.5"
## [1801] " 0.2" " 0.4" " 0.7" " 1.2" " 0.8" " 0.2" " 0.2" " 0.2"
## [1809] " 0.2" " 0.2" " 0.1" " 0.1" " 0.1" " 0.2" " 0.1" " 0.6"
## [1817] " 1.3" " 1.1" " 0.3" " 0.1" " 1.0" " 0.1" " 0.5" " 0.1"
## [1825] " 0.2" " 0.2" " 1.9" " 2.8" " 3.2" " 4.0" " 2.7" " 2.4"
## [1833] " 0.2" " 1.5" " 3.0" " 0.1" " 0.1" " 0.2" " 0.2" " 0.2"
## [1841] " 0.2" " 0.1" " 2.3" " 2.3" " 0.2" " 0.2" " 0.1" " 0.2"
## [1849] " 1.6" " 0.4" " 0.1" " 3.0" " 3.6" " 0.1" " 6.2" " 7.0"
## [1857] " 8.2" "11.3" "10.2" " 0.8" " 0.2" " 0.1" " 0.5" " 2.5"
## [1865] " 1.4" " 0.8" " 1.7" " 1.4" " 0.2" " 0.2" " 0.4" " 0.7"
## [1873] " 5.6" " 2.2" " 0.2" " 0.2" " 0.2" " 0.1" " 2.1" " 0.4"
## [1881] " 0.4" " 1.5" " 0.2" " 1.3" " 0.1" " 0.2" " 0.2" " 0.5"
## [1889] " 0.8" " 0.2" " 0.1" " 2.5" " 4.0" " 0.3" " 0.1" " 0.1"
## [1897] " 0.4" " 4.9" " 5.0" " 2.7" " 0.9" " 0.9" " 2.3" " 3.6"
## [1905] " 1.5" "12.6" " 4.0" " 0.8" " 0.6" " 7.6" " 0.1" " 0.1"
## [1913] " 0.2" " 0.2" " 9.0" " 5.0" " 4.6" " 1.3" " 1.3" " 0.8"
## [1921] " 0.1" " 0.4" " 0.4" " 1.8" " 1.0" " 0.8" " 0.2" " 3.2"
## [1929] " 3.0" " 0.6" " 0.4" " 0.6" " 1.0" " 1.1" " 0.2" " 0.2"
## [1937] "11.8" " 0.1" " 0.1" " 0.2" " 0.3" " 0.2" " 1.2" " 1.8"
## [1945] " 1.7" " 0.3" " 0.3" " 1.3" " 1.0" " 0.2" " 1.0" " 0.2"
## [1953] " 0.2" " 0.5" " 0.1" " 0.2" " 0.2" " 0.2" " 0.2" " 0.3"
## [1961] " 0.5" " 0.2" " 0.4" " 0.2" " 0.3" " 0.2" " 0.6" " 0.1"
## [1969] " 1.6" " 1.4" " 0.2" " 0.6" " 0.2" " 0.3" " 0.2" " 0.3"
## [1977] " 0.2" " 0.2" " 0.2" " 0.2" " 0.2" " 9.0" " 0.2" " 0.1"
## [1985] " 0.3" " 1.3" " 0.8" " 0.3" " 0.1" " 0.7" " 0.3" " 0.2"
## [1993] " 0.2" "14.2" " 8.9" " 7.0" " 0.2" " 0.2" " 0.2" " 0.2"
## [2001] " 0.4" " 0.3" " 6.4" " 0.1" " 0.2" " 0.2" " 9.5" " 0.2"
## [2009] " 0.8" " 0.8" " 0.2" " 0.8" " 0.8" " 1.4" " 0.2" " 0.2"
## [2017] " 0.2" " 0.7" " 0.3" " 0.5" " 0.2" " 0.6" " 0.5" " 2.3"
## [2025] " 0.3" " 0.2" " 0.2" " 0.5" " 1.0" " 0.2" " 0.3" " 0.3"
## [2033] " 0.3" " 0.2" " 0.2" " 0.2" " 0.6" " 0.2" " 0.2" " 0.7"
## [2041] " 1.0" " 0.2" " 0.2" " 0.7" " 0.2" "12.6" " 0.2" " 0.9"
## [2049] " 0.3" " 0.2" " 0.2" " 2.2" " 0.3" " 0.2" " 0.2" " 0.5"
## [2057] " 0.2" " 1.2" " 0.2" " 0.4" " 1.6" " 0.2" " 0.2" " 0.9"
## [2065] " 0.6" " 3.3" " 0.2" " 0.2" " 0.1" " 3.6" " 1.2" " 0.8"
## [2073] " 0.4" " 0.2" " 0.2" " 0.2" " 1.6" " 0.2" " 1.3" " 0.2"
## [2081] " 0.2" " 0.4" " 0.2" " 0.8" " 0.4" " 0.2" " 0.8" " 0.2"
## [2089] " 0.8" " 0.2" " 0.7" " 0.2" " 0.2" " 0.2" " 0.2" " 0.3"
## [2097] " 0.2" " 0.2" " 0.2" " 0.2" " 1.0" " 0.2" " 0.2" "11.4"
## [2105] " 0.2" " 0.2" " 0.1" " 0.1" " 1.6" " 0.1" " 0.2" " 0.1"
## [2113] " 0.2" " 0.8" " 0.2" " 0.1" " 0.7" " 2.1" " 0.2" " 1.4"
## [2121] " 0.2" " 0.2" " 0.1" " 0.2" " 0.2" " 4.3" " 1.3" " 0.3"
## [2129] " 0.2" " 0.3" " 0.3" " 0.2" " 0.2" " 0.2" " 3.7" " 0.1"
## [2137] " 0.2" " 0.2" " 0.2" " 0.2" " 0.2" " 1.1" " 2.6" " 0.6"
## [2145] " 0.8" " 2.7" " 0.2" " 0.6" " 0.3" " 0.1" " 0.1" " 2.3"
## [2153] " 3.9" " 5.1" " 0.3" " 0.5" " 0.9" " 0.1" " 0.1" " 0.6"
## [2161] " 0.2" " 0.1" " 0.7" " 0.1" " 0.2" " 0.2" " 0.2" " 0.2"
## [2169] " 0.1" " 0.3" " 0.7" " 0.2" " 0.2" " 0.2" " 1.1" " 3.6"
## [2177] " 0.2" " 0.2" " 0.2" " 0.2" "12.8" " 0.2" " 0.2" " 4.3"
## [2185] " 0.2" " 0.2" " 0.2" " 0.2" " 0.1" " 0.2" " 0.2" " 3.2"
## [2193] " 0.5" " 1.0" " 0.2" " 0.2" " 0.1" " 0.2" " 0.1" " 0.1"
## [2201] " 0.3" " 0.3" " 0.2" " 0.5" " 0.1" " 0.2" " 0.3" " 1.1"
## [2209] " 1.0" " 0.3" " 0.2" " 3.7" " 1.6" " 0.1" " 1.3" " 0.4"
## [2217] " 0.2" " 2.1" " 3.2" " 0.2" " 0.2" " 0.2" " 0.1" " 0.2"
## [2225] " 0.3" " 0.3" " 0.2" " 0.2" " 0.2" " 0.2" " 1.1" " 0.5"
## [2233] " 1.6" " 1.2" " 1.5" " 0.2" "10.4" "17.1" "14.1" " 8.8"
## [2241] " 0.3" " 8.5" "10.0" " 0.2" " 1.6" " 0.3" " 0.2" " 0.3"
## [2249] " 2.1" " 0.3" " 0.2" " 1.4" "12.1" " 0.9" " 2.9" " 0.2"
## [2257] " 0.2" " 0.2" " 0.2" " 5.2" " 1.6" " 0.8" " 1.5" " 0.3"
## [2265] "18.3" " 0.2" " 0.4" " 0.8" " 9.5" " 7.2" " 0.1" " 0.8"
## [2273] " 0.2" " 0.2" " 0.2" " 0.2" " 0.6" " 3.2" " 0.2" "11.7"
## [2281] "10.8" " 6.1" " 1.0" " 1.5" " 0.4" " 0.1" " 4.2" " 0.9"
## [2289] " 0.2" " 0.1" " 1.2" " 2.5" "19.7" " 7.7" " 7.6" " 8.5"
## [2297] " 0.5" " 0.1" "11.8" " 0.3" " 1.4" " 8.4" " 4.1" " 1.2"
## [2305] " 0.3" " 9.5" " 1.6" " 6.0" "13.7" " 8.2" " 8.4" " 0.1"
## [2313] " 0.1" " 0.2" " 0.5" " 0.3" " 187" " 699" " 490" " 182"
## [2321] " 195" " 208" " 154" " 202" " 202" " 290" " 210" " 260"
## [2329] " 310" " 214" " 145" " 183" " 342" " 165" " 293" " 293"
## [2337] " 610" " 482" " 542" " 231" " 194" " 289" " 289" " 240"
## [2345] " 128" " 188" " 190" " 156" " 187" " 410" " 410" " 482"
## [2353] " 145" " 374" " 263" " 275" " 168" " 160" " 630" " 415"
## [2361] " 208" " 275" " 150" " 230" " 176" " 206" " 170" " 161"
## [2369] " 253" " 198" " 272" " 272" " 198" " 175" " 367" " 145"
## [2377] " 158" " 158" " 158" " 208" " 259" " 470" " 195" " 215"
## [2385] " 239" " 215" " 186" " 188" " 205" " 171" " 145" " 162"
## [2393] " 518" "1620" " 146" " 670" " 915" " 75" " 148" " 258"
## [2401] " 237" " 269" " 320" " 298" " 538" " 238" " 214" " 308"
## [2409] " 298" " 204" " 168" " 282" " 298" " 215" " 265" " 312"
## [2417] " 161" " 243" " 224" " 225" " 170" " 145" " 145" " 158"
## [2425] " 158" " 486" " 188" " 257" " 179" " 272" " 661" "1580"
## [2433] "1630" " 194" " 280" " 298" " 300" " 290" " 188" " 178"
## [2441] " 177" " 201" " 802" " 248" "1896" " 263" " 512" " 237"
## [2449] " 199" " 238" " 178" "1110" " 310" " 282" " 282" " 380"
## [2457] " 186" " 159" " 332" " 332" " 189" " 201" " 168" " 392"
## [2465] " 202" " 286" " 180" " 218" " 182" " 178" " 290" " 298"
## [2473] " 462" " 196" " 196" " 282" " 750" "1050" " 599" " 180"
## [2481] " 180" " 282" " 332" " 292" " 962" " 950" " 200" " 298"
## [2489] " 750" " 175" " 175" " 198" " 482" "1020" " 562" " 386"
## [2497] " 250" " 218" " 170" " 171" " 201" " 298" " 750" " 191"
## [2505] " 614" " 218" " 314" " 257" " 272" " 206" " 209" "1124"
## [2513] " 664" " 142" " 169" "1420" " 218" " 218" " 145" " 142"
## [2521] " 135" " 163" " 285" " 350" " 220" " 190" " 219" " 160"
## [2529] " 401" " 180" " 100" " 116" " 159" " 289" " 125" " 147"
## [2537] " 192" " 265" " 175" " 400" " 120" " 173" " 186" " 202"
## [2545] " 290" " 196" " 282" " 157" "2110" " 285" " 360" " 300"
## [2553] " 158" " 190" " 196" " 165" " 205" " 316" " 218" " 290"
## [2561] " 272" " 190" " 202" " 498" " 480" " 680" " 258" " 152"
## [2569] " 859" " 901" " 335" " 182" " 285" " 245" " 505" " 228"
## [2577] " 185" " 247" " 348" " 195" " 140" " 358" " 110" " 235"
## [2585] " 460" " 380" " 262" " 196" " 180" " 190" " 190" " 209"
## [2593] " 144" " 123" " 192" " 188" " 316" " 300" " 575" " 192"
## [2601] " 155" " 239" " 315" " 250" " 174" " 245" " 191" " 340"
## [2609] " 202" " 234" " 159" " 190" " 195" " 180" " 280" " 430"
## [2617] " 206" " 155" " 195" " 588" " 174" " 165" " 527" " 175"
## [2625] " 574" " 158" " 195" " 179" " 182" " 198" " 216" " 310"
## [2633] " 63" " 198" " 205" " 302" " 171" " 158" " 358" " 174"
## [2641] " 192" " 211" " 157" " 210" " 258" " 152" " 350" " 182"
## [2649] " 458" " 375" " 405" " 215" " 206" " 650" " 198" " 198"
## [2657] " 195" " 230" " 115" " 216" " 358" " 158" " 145" " 195"
## [2665] " 144" " 621" " 150" " 178" " 256" " 205" " 176" " 146"
## [2673] " 218" " 182" " 215" " 165" " 183" " 176" " 418" " 271"
## [2681] " 182" " 130" " 558" " 135" " 326" " 140" " 145" " 206"
## [2689] " 168" " 202" " 192" " 185" " 331" " 188" " 172" " 159"
## [2697] " 490" " 152" " 105" " 160" " 160" " 102" " 148" " 162"
## [2705] " 149" " 580" " 310" " 140" " 175" " 152" " 208" " 205"
## [2713] " 162" " 92" " 162" " 199" " 198" " 215" " 180" " 719"
## [2721] " 554" " 555" " 215" " 509" " 190" " 208" " 260" " 690"
## [2729] " 862" " 592" " 450" "1350" "1350" " 163" " 246" " 178"
## [2737] " 240" " 100" " 166" " 170" " 194" "1750" " 182" " 236"
## [2745] " 165" " 201" " 194" " 206" " 212" " 157" " 162" " 168"
## [2753] " 198" " 292" " 298" " 152" " 163" " 279" " 181" "1550"
## [2761] " 142" " 173" " 282" " 279" "1100" " 224" " 159" " 186"
## [2769] " 189" " 192" " 140" " 686" " 215" " 309" " 110" " 130"
## [2777] " 164" " 270" " 137" " 90" " 190" " 165" " 167" " 185"
## [2785] " 197" " 154" " 226" " 310" " 310" " 220" " 196" " 186"
## [2793] " 352" " 282" " 92" " 182" " 103" " 850" " 195" " 276"
## [2801] " 171" " 146" " 193" " 180" " 805" " 265" " 185" " 165"
## [2809] " 189" " 198" " 151" " 349" " 365" " 305" " 127" " 238"
## [2817] " 218" " 219" " 239" " 194" " 450" " 254" " 205" " 320"
## [2825] " 195" " 215" " 230" " 189" " 168" " 215" " 210" " 108"
## [2833] " 224" " 230" " 185" " 137" " 156" " 210" " 268" " 298"
## [2841] " 315" " 214" " 138" " 285" " 162" " 298" " 230" " 466"
## [2849] " 227" " 395" " 97" " 406" " 114" " 198" " 173" " 204"
## [2857] " 350" " 153" " 188" " 380" " 214" " 768" " 172" " 160"
## [2865] " 196" " 232" " 220" " 290" " 180" " 189" " 275" " 390"
## [2873] " 356" " 315" " 388" " 298" " 165" " 143" " 191" " 251"
## [2881] " 200" " 268" " 236" " 215" " 134" " 612" " 515" " 560"
## [2889] " 289" " 190" " 500" " 98" " 245" " 184" " 216" " 16"
## [2897] " 64" " 60" " 14" " 27" " 19" " 16" " 14" " 22"
## [2905] " 53" " 51" " 31" " 61" " 22" " 53" " 91" " 168"
## [2913] " 15" " 232" " 232" " 17" " 22" " 116" " 16" " 52"
## [2921] " 875" " 875" "1680" " 20" " 13" " 45" " 35" " 19"
## [2929] " 59" " 59" " 102" " 18" " 38" " 38" " 123" " 33"
## [2937] " 42" " 25" " 407" " 17" " 48" " 36" "1630" " 39"
## [2945] " 64" " 21" " 15" " 80" " 86" " 31" " 31" " 26"
## [2953] " 24" " 42" " 20" " 21" " 37" " 37" " 35" " 40"
## [2961] " 62" " 55" " 53" " 27" " 24" " 166" " 20" " 27"
## [2969] " 22" " 20" " 52" " 189" " 95" " 12" " 48" " 60"
## [2977] " 25" " 14" " 194" " 45" " 58" " 28" " 33" " 33"
## [2985] " 119" " 412" " 404" " 412" " 220" " 126" " 25" " 102"
## [2993] " 190" " 97" " 308" " 27" " 21" " 36" " 14" " 21"
## [3001] " 32" " 32" " 29" " 29" " 25" " 11" " 33" " 63"
## [3009] " 24" " 181" " 88" " 74" "2000" "1350" "1250" " 482"
## [3017] " 322" " 60" " 17" " 36" " 45" " 133" " 46" " 61"
## [3025] " 57" " 50" " 18" " 34" " 17" " 35" "1250" " 62"
## [3033] " 72" " 72" " 25" " 20" " 15" " 84" " 84" " 63"
## [3041] " 18" " 21" " 20" " 20" " 21" " 30" " 18" " 20"
## [3049] " 26" " 45" " 58" " 70" " 20" " 20" " 36" " 140"
## [3057] " 99" " 43" " 42" " 42" " 13" " 40" " 64" " 53"
## [3065] " 33" " 28" " 378" " 35" " 48" " 48" " 43" " 112"
## [3073] " 71" " 37" " 30" " 23" " 33" " 36" " 20" " 18"
## [3081] " 60" " 79" " 114" " 42" " 32" " 118" " 107" " 79"
## [3089] " 30" " 48" " 30" " 52" " 12" " 22" " 790" " 50"
## [3097] " 50" " 19" " 27" " 27" " 28" " 50" " 950" " 53"
## [3105] " 20" " 60" " 82" " 25" " 18" " 17" " 36" " 30"
## [3113] " 38" " 41" " 27" " 38" " 40" " 20" " 56" " 45"
## [3121] " 38" " 18" " 26" " 26" " 61" " 85" " 149" " 48"
## [3129] " 32" " 230" " 57" " 20" " 40" " 69" " 32" " 30"
## [3137] " 25" " 20" " 15" " 22" " 25" " 37" " 28" " 38"
## [3145] " 37" " 48" " 90" " 89" " 23" " 148" " 31" " 65"
## [3153] " 56" " 205" " 55" " 25" " 55" " 30" " 38" " 12"
## [3161] " 50" " 15" " 96" " 152" " 390" " 15" " 25" " 22"
## [3169] " 25" " 45" " 25" " 18" " 56" " 18" " 12" " 10"
## [3177] " 30" " 29" " 15" " 15" " 16" " 120" " 48" " 32"
## [3185] " 22" " 37" " 25" " 78" " 16" " 24" " 26" " 40"
## [3193] " 32" " 21" " 28" " 18" " 21" " 36" " 74" " 21"
## [3201] " 22" " 178" " 48" " 43" " 25" " 24" " 18" " 29"
## [3209] " 42" " 179" " 21" " 31" " 30" " 36" " 47" " 42"
## [3217] " 27" " 160" " 11" " 29" " 14" " 54" " 62" " 80"
## [3225] " 21" " 17" " 24" " 198" " 85" " 79" " 50" " 44"
## [3233] " 70" " 36" " 40" " 60" " 32" " 14" " 349" " 19"
## [3241] " 19" " 62" " 41" " 36" " 110" " 25" " 13" " 21"
## [3249] " 50" " 28" " 42" " 47" " 23" " 21" " 26" " 33"
## [3257] " 28" " 28" " 45" " 22" " 24" " 30" " 30" " 29"
## [3265] " 32" " 26" " 32" " 35" " 12" " 20" " 16" " 36"
## [3273] " 28" " 27" " 10" " 115" " 29" " 25" " 34" " 31"
## [3281] " 31" " 23" " 25" " 94" " 142" " 37" " 24" " 30"
## [3289] " 28" " 25" " 137" " 24" " 44" " 155" " 19" " 20"
## [3297] " 15" " 18" " 157" " 141" " 284" " 440" " 28" " 33"
## [3305] " 15" " 28" " 93" " 76" " 26" " 69" " 52" " 48"
## [3313] " 48" " 34" " 44" " 12" " 59" " 49" " 33" " 10"
## [3321] " 102" " 20" " 10" " 55" " 31" " 32" " 28" " 41"
## [3329] " 15" " 30" " 25" " 23" " 29" " 52" " 35" " 18"
## [3337] " 40" " 35" " 425" " 26" " 26" " 50" " 20" " 25"
## [3345] " 40" " 15" " 25" " 22" " 28" " 37" " 16" " 159"
## [3353] " 55" " 22" " 23" " 21" " 23" " 22" " 18" " 30"
## [3361] " 22" " 13" " 17" " 64" " 38" " 33" " 119" " 15"
## [3369] " 24" " 622" " 779" " 28" " 132" " 91" " 46" " 18"
## [3377] " 154" " 18" " 102" " 31" " 17" " 96" " 56" " 133"
## [3385] " 30" " 196" " 31" " 52" " 42" " 18" " 40" " 62"
## [3393] " 28" " 29" " 39" " 39" " 95" " 43" " 190" " 119"
## [3401] " 140" " 31" " 37" " 22" " 32" " 48" " 65" " 16"
## [3409] " 50" " 74" " 168" " 69" " 14" " 24" " 15" " 12"
## [3417] " 35" " 21" " 68" " 12" " 54" " 14" " 110" " 12"
## [3425] " 509" " 88" " 46" " 67" " 25" " 85" " 24" " 21"
## [3433] " 29" " 46" " 23" " 67" " 41" " 47" " 39" " 60"
## [3441] " 74" " 25" " 20" " 29" " 58" " 20" " 139" " 25"
## [3449] " 80" " 382" " 75" " 321" " 233" " 173" " 31" " 22"
## [3457] " 22" " 22" " 20" " 91" " 213" " 131" " 46" " 54"
## [3465] " 50" " 48" " 41" " 58" " 28" " 20" " 35" " 48"
## [3473] " 29" " 21" " 18" " 100" " 68" " 20" " 59" " 14"
## [3481] " 12" " 11" " 19" " 58" " 59" " 56" " 58" " 30"
## [3489] " 41" " 53" " 441" " 23" " 245" " 245" " 28" " 34"
## [3497] " 66" " 55" " 45" " 731" " 731" " 850" " 30" " 21"
## [3505] " 111" " 44" " 23" " 57" " 57" " 80" " 36" " 77"
## [3513] " 66" " 73" " 50" " 110" " 47" " 576" " 15" " 178"
## [3521] " 27" " 960" " 28" " 178" " 14" " 19" " 406" " 150"
## [3529] " 61" " 61" " 23" " 54" " 18" " 24" " 16" " 43"
## [3537] " 43" " 97" " 86" " 88" " 95" " 58" " 26" " 17"
## [3545] " 397" " 29" " 24" " 16" " 22" " 41" " 17" " 127"
## [3553] " 24" " 79" " 142" " 26" " 12" " 152" " 31" " 45"
## [3561] " 56" " 59" " 34" " 350" " 850" " 794" " 850" " 400"
## [3569] " 202" " 23" " 630" " 950" " 161" " 405" " 28" " 23"
## [3577] " 45" " 23" " 28" " 92" " 92" " 39" " 39" " 34"
## [3585] " 10" " 116" " 39" " 98" " 285" " 64" " 149" "2946"
## [3593] "1600" "1050" " 275" " 113" " 84" " 18" " 29" " 25"
## [3601] " 88" " 40" " 83" " 65" " 58" " 28" " 31" " 14"
## [3609] " 36" "4929" " 90" " 140" " 140" " 66" " 21" " 30"
## [3617] " 139" " 139" " 87" " 22" " 38" " 30" " 26" " 27"
## [3625] " 42" " 20" " 40" " 27" " 233" " 138" " 82" " 35"
## [3633] " 35" " 32" " 350" " 187" " 66" " 62" " 62" " 74"
## [3641] " 66" " 67" " 41" " 32" " 37" " 602" " 63" " 34"
## [3649] " 34" " 31" " 99" " 42" " 29" " 25" " 29" " 37"
## [3657] " 43" " 17" " 19" " 103" " 145" " 247" " 38" " 28"
## [3665] " 114" " 104" " 51" " 25" " 32" " 54" " 104" " 32"
## [3673] " 18" "1050" " 53" " 53" " 23" " 21" " 26" " 22"
## [3681] " 60" "1500" " 95" " 14" " 180" " 127" " 58" " 34"
## [3689] " 148" " 16" " 31" " 30" " 39" " 46" " 24" " 28"
## [3697] " 19" " 44" " 51" " 62" " 15" " 13" " 21" " 85"
## [3705] " 231" " 156" " 89" " 27" " 298" " 40" " 16" " 35"
## [3713] " 48" " 30" " 23" " 23" " 19" " 18" " 79" " 28"
## [3721] " 29" " 25" " 22" " 40" " 40" " 21" " 48" " 17"
## [3729] " 86" " 34" " 130" " 87" " 140" " 54" " 21" " 92"
## [3737] " 34" " 54" " 29" " 75" " 20" " 54" " 231" " 500"
## [3745] " 18" " 22" " 19" " 20" " 40" " 20" " 14" " 48"
## [3753] " 15" " 14" " 21" " 48" " 24" " 12" " 20" " 39"
## [3761] " 105" " 44" " 14" " 24" " 41" " 21" " 41" " 19"
## [3769] " 25" " 15" " 35" " 25" " 30" " 32" " 17" " 17"
## [3777] " 16" " 113" " 47" " 18" " 250" " 22" " 47" " 22"
## [3785] " 65" " 21" " 82" " 30" " 232" " 16" " 27" " 58"
## [3793] " 34" " 67" " 37" " 23" " 90" " 33" " 20" " 23"
## [3801] " 68" " 56" " 113" " 25" " 24" " 19" " 143" " 68"
## [3809] " 50" " 30" " 33" " 138" " 32" " 28" " 40" " 44"
## [3817] " 11" " 105" " 14" " 15" " 58" " 38" " 33" " 176"
## [3825] " 23" " 26" " 30" " 38" " 34" " 70" " 26" " 28"
## [3833] " 15" " 29" " 57" " 43" " 35" " 52" " 20" " 25"
## [3841] " 37" " 29" " 17" " 25" " 23" " 31" " 33" " 13"
## [3849] " 41" " 22" " 53" " 43" " 22" " 12" " 91" " 30"
## [3857] " 21" " 20" " 40" " 35" " 35" " 20" " 92" " 68"
## [3865] " 56" " 20" " 32" " 70" " 50" " 145" " 20" " 236"
## [3873] " 108" " 22" " 26" " 21" " 58" " 108" " 73" " 190"
## [3881] " 850" " 44" " 71" " 30" " 24" " 40" " 180" " 29"
## [3889] " 50" " 65" " 57" " 71" " 42" " 59" " 15" " 126"
## [3897] " 42" " 66" " 12" " 141" " 33" " 13" " 87" " 24"
## [3905] " 36" " 21" " 80" " 44" " 90" " 18" " 20" " 39"
## [3913] " 102" " 81" " 19" " 46" " 20" " 511" " 25" " 27"
## [3921] " 72" " 25" " 36" " 23" " 16" " 22" " 43" " 35"
## [3929] " 19" " 46" " 51" " 23" " 18" " 42" " 53" " 42"
## [3937] " 16" " 108" " 54" " 32" " 56" " 26" " 33" " 21"
## [3945] " 135" " 42" " 25" " 32" " 497" " 844" " 51" " 368"
## [3953] " 188" " 57" " 40" " 248" " 15" " 190" " 26" " 29"
## [3961] " 57" " 111" " 103" " 28" " 401" " 29" " 31" " 39"
## [3969] " 22" " 70" " 55" " 76" " 30" " 221" " 79" " 235"
## [3977] " 185" " 73" " 230" " 540" " 36" " 48" " 28" " 24"
## [3985] " 58" " 181" " 24" " 28" " 68" " 630" " 155" " 25"
## [3993] " 15" " 16" " 15" " 32" " 63" " 200" " 38" " 152"
## [4001] " 21" " 186" " 17" " 623" " 74" " 52" " 220" " 75"
## [4009] " 78" " 45" " 23" " 19" " 47" " 27" " 42" " 42"
## [4017] " 32" " 348" " 186" " 141" " 41" " 22" " 30" " 140"
## [4025] " 43" " 87" " 27" " 125" " 330" " 138" " 562" " 384"
## [4033] " 367" " 83" " 34" " 143" " 31" " 40" " 101" " 168"
## [4041] " 90" " 134" " 125" " 88" " 92" " 88" " 80" " 47"
## [4049] " 34" " 31" " 49" " 32" " 24" "6.8" "7.5" "7.0"
## [4057] "6.8" "7.3" "7.6" "7.0" "6.7" "7.4" "6.8" "5.9"
## [4065] "7.4" "7.0" "8.1" "5.8" "5.5" "7.6" "7.3" "6.8"
## [4073] "6.8" "7.3" "7.0" "6.4" "4.3" "6.0" "5.0" "5.0"
## [4081] "7.2" "3.9" "6.0" "5.2" "4.9" "5.2" "5.6" "5.6"
## [4089] "6.9" "7.2" "4.3" "5.8" "6.2" "5.1" "4.9" "6.1"
## [4097] "6.4" "7.0" "6.5" "6.8" "4.9" "6.0" "6.2" "5.7"
## [4105] "6.6" "6.8" "6.3" "5.8" "5.8" "8.0" "5.5" "5.2"
## [4113] "5.5" "6.0" "7.2" "7.2" "5.1" "6.5" "5.6" "6.0"
## [4121] "6.4" "6.3" "6.3" "5.5" "4.4" "4.4" "6.6" "5.8"
## [4129] "5.2" "5.3" "4.6" "6.2" "4.7" "4.7" "5.1" "5.4"
## [4137] "5.4" "7.5" "6.7" "7.2" "6.2" "7.5" "7.1" "7.3"
## [4145] "6.8" "7.4" "7.0" "6.8" "4.4" "7.1" "4.0" "5.9"
## [4153] "6.9" "3.7" "5.3" "6.9" "6.1" "5.5" "5.1" "5.1"
## [4161] "6.0" "6.0" "5.9" "5.5" "5.7" "6.1" "5.0" "5.7"
## [4169] "5.6" "5.3" "5.7" "5.6" "5.4" "7.1" "6.6" "5.9"
## [4177] "6.3" "6.9" "3.9" "6.0" "6.5" "8.0" "5.1" "6.0"
## [4185] "5.8" "6.5" "6.6" "6.8" "7.0" "6.4" "5.5" "5.5"
## [4193] "6.1" "6.2" "7.1" "5.6" "5.6" "5.6" "5.4" "5.5"
## [4201] "5.3" "7.2" "7.1" "6.9" "5.9" "6.0" "6.5" "5.6"
## [4209] "5.8" "6.2" "5.8" "5.8" "7.2" "5.5" "6.2" "5.4"
## [4217] "7.4" "7.4" "7.0" "6.2" "5.6" "6.9" "6.8" "6.2"
## [4225] "6.6" "5.0" "6.0" "6.0" "7.3" "5.7" "5.3" "5.1"
## [4233] "5.5" "2.7" "3.0" "3.8" "5.4" "6.9" "6.0" "7.8"
## [4241] "4.0" "4.5" "5.2" "6.6" "6.6" "6.6" "6.0" "5.7"
## [4249] "5.2" "6.0" "5.7" "6.1" "6.1" "5.0" "5.0" "6.1"
## [4257] "6.4" "6.4" "7.6" "7.0" "6.7" "6.1" "4.1" "7.0"
## [4265] "5.5" "7.5" "7.2" "5.0" "6.2" "7.0" "4.8" "6.4"
## [4273] "5.0" "7.3" "8.0" "7.1" "5.7" "7.9" "8.0" "6.4"
## [4281] "5.3" "6.0" "6.7" "8.5" "7.9" "6.2" "7.7" "4.5"
## [4289] "7.9" "8.0" "7.3" "6.8" "8.0" "8.2" "8.5" "6.3"
## [4297] "6.1" "6.1" "5.9" "5.0" "7.0" "6.5" "5.9" "7.0"
## [4305] "7.1" "6.0" "6.2" "5.6" "6.4" "5.2" "5.4" "7.5"
## [4313] "6.9" "7.0" "7.4" "8.0" "6.3" "7.4" "5.7" "2.8"
## [4321] "9.5" "6.5" "8.2" "9.6" "8.0" "8.2" "6.4" "6.0"
## [4329] "8.0" "8.3" "6.0" "7.4" "7.0" "6.0" "8.0" "7.9"
## [4337] "8.6" "8.0" "7.5" "7.0" "8.6" "7.8" "7.1" "7.7"
## [4345] "8.3" "8.0" "7.9" "6.9" "7.9" "7.4" "8.2" "6.7"
## [4353] "6.8" "8.4" "6.9" "7.9" "7.3" "4.6" "8.2" "8.0"
## [4361] "8.1" "7.2" "7.9" "7.8" "6.7" "7.1" "6.8" "7.8"
## [4369] "8.1" "5.8" "5.2" "7.1" "6.7" "5.4" "6.7" "8.3"
## [4377] "7.6" "6.0" "7.3" "6.1" "5.4" "6.2" "5.9" "7.4"
## [4385] "8.9" "6.2" "6.4" "6.1" "5.9" "6.2" "6.6" "7.0"
## [4393] "5.6" "8.2" "5.6" "6.9" "7.0" "6.1" "6.6" "5.7"
## [4401] "5.3" "8.2" "7.2" "7.5" "8.0" "8.5" "6.8" "5.6"
## [4409] "8.4" "6.6" "6.8" "6.2" "8.5" "6.8" "5.3" "7.2"
## [4417] "6.1" "7.2" "7.0" "7.8" "7.2" "8.7" "7.6" "7.0"
## [4425] "6.8" "7.0" "6.1" "7.3" "7.3" "7.3" "8.1" "6.7"
## [4433] "4.9" "6.5" "7.4" "7.5" "6.9" "7.3" "6.3" "6.0"
## [4441] "6.9" "6.3" "8.0" "5.9" "6.3" "6.4" "6.3" "7.1"
## [4449] "6.0" "6.4" "6.8" "8.1" "6.3" "6.8" "7.6" "6.7"
## [4457] "7.2" "7.5" "6.5" "5.0" "6.9" "7.1" "4.6" "5.2"
## [4465] "3.6" "6.3" "7.1" "7.0" "6.7" "6.4" "7.1" "6.9"
## [4473] "6.5" "7.0" "4.3" "5.6" "7.0" "6.9" "5.5" "4.8"
## [4481] "6.7" "7.5" "7.6" "7.5" "7.1" "6.2" "5.2" "3.8"
## [4489] "6.2" "7.0" "4.1" "4.6" "6.2" "6.3" "7.3" "5.5"
## [4497] "7.7" "6.0" "6.2" "6.0" "7.2" "7.1" "7.1" "7.0"
## [4505] "6.8" "7.4" "6.9" "3.6" "5.7" "5.5" "6.8" "6.4"
## [4513] "8.0" "4.5" "5.1" "4.9" "6.8" "5.3" "4.4" "7.1"
## [4521] "6.6" "5.8" "6.8" "6.0" "7.9" "5.5" "5.1" "6.9"
## [4529] "7.3" "7.9" "7.3" "7.2" "7.8" "5.0" "6.2" "6.7"
## [4537] "6.0" "7.0" "5.9" "6.7" "6.8" "7.9" "5.2" "6.5"
## [4545] "6.1" "8.0" "7.2" "6.5" "7.2" "6.0" "5.9" "4.9"
## [4553] "8.1" "5.5" "5.8" "5.6" "7.5" "7.5" "5.4" "9.2"
## [4561] "6.8" "5.8" "6.9" "8.4" "6.9" "6.7" "8.0" "7.2"
## [4569] "9.2" "8.6" "7.1" "7.9" "4.8" "6.8" "8.0" "6.4"
## [4577] "7.1" "6.3" "5.1" "7.0" "7.9" "8.2" "3.6" "8.0"
## [4585] "6.1" "6.9" "7.6" "6.4" "7.2" "7.0" "6.6" "7.3"
## [4593] "7.6" "7.0" "4.5" "5.4" "7.1" "6.9" "7.8" "5.6"
## [4601] "6.2" "5.8" "7.0" "6.5" "7.0" "6.1" "6.2" "7.5"
## [4609] "7.5" "6.5" "6.9" "7.8" "7.2" "5.9" "6.6" "7.8"
## [4617] "6.0" "6.9" "7.1" "5.4" "6.9" "5.6" "6.2" "6.6"
## [4625] "7.9" "5.3" "5.4" "5.9" "6.0" "6.4" "6.8" "7.3"
## [4633] "3.3" "3.2" "3.3" "3.4" "2.4" "4.4" "3.5" "3.6"
## [4641] "4.1" "3.4" "2.7" "3.0" "3.4" "4.1" "2.7" "2.3"
## [4649] "4.4" "3.5" "3.1" "3.1" "2.6" "2.4" "3.1" "1.6"
## [4657] "3.9" "2.7" "2.7" "4.0" "1.9" "3.2" "1.5" "2.9"
## [4665] "2.9" "3.0" "3.0" "3.3" "3.9" "2.0" "2.2" "3.3"
## [4673] "2.6" "2.6" "2.3" "3.2" "3.6" "3.2" "3.9" "2.8"
## [4681] "3.0" "1.8" "2.5" "3.4" "3.9" "3.5" "2.0" "2.0"
## [4689] "4.0" "2.7" "2.0" "3.2" "3.0" "3.6" "3.6" "2.1"
## [4697] "2.5" "2.5" "3.7" "3.8" "3.7" "3.0" "3.0" "1.8"
## [4705] "2.0" "3.6" "2.9" "2.5" "2.3" "2.1" "3.8" "1.6"
## [4713] "1.8" "2.9" "2.8" "3.0" "4.3" "3.9" "3.6" "3.1"
## [4721] "3.2" "3.3" "3.2" "3.0" "3.0" "3.0" "2.9" "2.2"
## [4729] "3.3" "1.7" "3.1" "3.7" "1.6" "2.3" "4.2" "3.3"
## [4737] "2.5" "2.6" "2.6" "2.2" "2.2" "2.8" "2.3" "2.2"
## [4745] "3.3" "2.0" "2.3" "2.3" "2.0" "3.3" "2.8" "2.6"
## [4753] "3.5" "2.8" "3.5" "3.1" "4.1" "1.7" "2.8" "3.2"
## [4761] "3.9" "2.3" "2.7" "2.5" "3.5" "2.9" "3.6" "2.4"
## [4769] "2.5" "2.5" "2.5" "3.7" "3.3" "2.2" "2.7" "2.7"
## [4777] "2.9" "2.9" "1.8" "2.8" "4.5" "4.0" "3.8" "2.9"
## [4785] "2.9" "3.6" "2.7" "2.4" "3.1" "2.0" "2.0" "3.9"
## [4793] "2.1" "2.8" "1.8" "4.3" "4.3" "2.4" "2.5" "1.8"
## [4801] "3.3" "3.1" "3.0" "3.3" "1.6" "3.7" "3.7" "4.0"
## [4809] "2.6" "2.2" "1.8" "1.8" "0.9" "1.5" "1.4" "2.5"
## [4817] "4.1" "3.0" "3.2" "1.6" "1.8" "2.5" "3.7" "3.5"
## [4825] "3.5" "3.1" "3.0" "1.9" "2.1" "2.4" "3.0" "2.0"
## [4833] "2.4" "2.4" "3.1" "3.5" "3.3" "4.0" "2.9" "3.8"
## [4841] "2.8" "2.4" "3.2" "3.1" "3.4" "3.6" "3.3" "3.2"
## [4849] "4.3" "2.0" "3.4" "2.5" "4.3" "4.0" "4.5" "3.1"
## [4857] "4.0" "4.0" "3.8" "2.6" "3.0" "3.8" "4.3" "3.1"
## [4865] "3.0" "3.5" "2.0" "3.8" "4.0" "4.7" "3.1" "4.0"
## [4873] "4.1" "5.5" "2.5" "2.8" "2.7" "2.7" "2.6" "3.0"
## [4881] "3.0" "2.6" "3.9" "4.2" "3.0" "3.5" "3.0" "3.8"
## [4889] "2.1" "2.0" "3.9" "4.0" "3.0" "4.3" "4.0" "3.8"
## [4897] "3.5" "2.1" "1.6" "4.9" "3.2" "4.1" "4.7" "4.0"
## [4905] "4.1" "3.6" "2.8" "4.0" "4.2" "3.0" "4.2" "3.4"
## [4913] "2.1" "4.0" "3.9" "4.7" "4.0" "3.7" "4.0" "4.3"
## [4921] "4.2" "3.4" "4.3" "4.5" "3.9" "4.0" "4.4" "3.8"
## [4929] "4.1" "4.4" "3.2" "3.5" "4.2" "3.8" "3.7" "2.4"
## [4937] "2.3" "4.1" "4.2" "4.6" "3.5" "4.5" "4.3" "3.7"
## [4945] "3.7" "3.4" "4.5" "4.2" "3.4" "2.8" "3.9" "3.5"
## [4953] "2.7" "3.1" "4.4" "3.9" "2.9" "4.1" "2.7" "2.2"
## [4961] "2.9" "3.1" "4.0" "4.9" "3.2" "2.9" "2.9" "2.6"
## [4969] "2.9" "3.1" "4.0" "2.0" "5.0" "2.0" "3.4" "3.5"
## [4977] "2.8" "3.6" "2.9" "2.1" "4.3" "3.9" "3.9" "4.6"
## [4985] "3.9" "3.0" "2.6" "4.9" "3.3" "2.9" "2.9" "4.4"
## [4993] "3.5" "2.4" "4.1" "2.9" "3.9" "4.0" "3.4" "4.4"
## [5001] "5.5" "4.3" "4.0" "3.4" "3.7" "3.0" "3.3" "3.7"
## [5009] "3.4" "3.3" "3.2" "2.5" "2.8" "4.1" "4.2" "3.7"
## [5017] "3.8" "3.2" "3.0" "3.7" "3.1" "4.0" "2.5" "3.2"
## [5025] "3.4" "3.1" "3.7" "3.0" "3.2" "1.6" "4.0" "3.6"
## [5033] "3.9" "4.0" "3.7" "3.7" "3.6" "3.3" "1.9" "2.9"
## [5041] "2.9" "2.1" "2.2" "2.7" "2.7" "4.2" "3.0" "2.9"
## [5049] "2.3" "3.7" "3.4" "3.9" "3.0" "2.5" "2.5" "3.0"
## [5057] "3.3" "2.0" "1.9" "2.9" "4.6" "3.8" "3.6" "4.5"
## [5065] "3.1" "2.5" "1.4" "3.1" "4.3" "1.8" "1.9" "3.2"
## [5073] "2.8" "4.0" "2.7" "3.5" "2.6" "3.1" "3.0" "3.2"
## [5081] "3.5" "3.0" "3.5" "3.4" "3.1" "3.4" "0.9" "1.5"
## [5089] "2.5" "4.1" "2.5" "4.0" "1.4" "2.0" "1.9" "3.1"
## [5097] "2.1" "2.0" "4.1" "2.9" "2.4" "3.9" "2.7" "4.1"
## [5105] "2.7" "2.4" "3.6" "3.2" "4.2" "4.0" "3.8" "4.3"
## [5113] "2.1" "2.8" "3.0" "2.9" "3.5" "3.2" "3.6" "3.9"
## [5121] "3.3" "1.8" "3.9" "3.0" "4.8" "3.0" "4.0" "2.9"
## [5129] "2.4" "2.5" "2.7" "2.5" "2.7" "3.1" "2.4" "3.9"
## [5137] "3.3" "3.0" "4.6" "3.4" "2.6" "3.0" "4.2" "3.0"
## [5145] "3.0" "4.0" "3.6" "2.0" "4.0" "2.1" "3.7" "2.6"
## [5153] "3.7" "3.9" "2.8" "3.4" "2.1" "1.8" "3.8" "2.7"
## [5161] "3.2" "1.0" "4.0" "2.0" "2.6" "3.6" "2.7" "3.5"
## [5169] "3.0" "3.0" "4.1" "4.0" "3.2" "2.2" "2.3" "2.3"
## [5177] "2.8" "4.9" "2.4" "3.0" "2.9" "2.7" "3.0" "3.0"
## [5185] "3.1" "3.1" "4.0" "2.6" "2.2" "2.0" "2.6" "2.6"
## [5193] "2.9" "2.1" "4.0" "1.7" "3.5" "4.0" "2.6" "3.0"
## [5201] "4.0" "1.9" "2.4" "2.5" "2.2" "2.6" "1.6" "3.2"
## [5209] "3.2" "3.4" "4.4" "0.90" "0.74" "0.89" "1.00" "0.40"
## [5217] "1.30" "1.00" "1.10" "1.20" "1.00" "0.80" "0.60" "0.90"
## [5225] "1.00" "0.87" "0.70" "1.30" "0.92" "0.80" "0.80" "0.55"
## [5233] "0.50" "0.90" "0.60" "1.85" "1.10" "1.10" "1.20" "0.95"
## [5241] "1.10" "0.40" "1.40" "1.20" "0.80" "0.80" "0.90" "1.18"
## [5249] "0.80" "0.61" "1.10" "1.00" "1.10" "0.60" "1.00" "1.00"
## [5257] "0.90" "1.34" "1.30" "1.00" "0.40" "0.70" "1.00" "1.30"
## [5265] "1.20" "0.50" "0.50" "1.00" "0.90" "0.60" "1.39" "1.00"
## [5273] "1.00" "1.00" "0.70" "0.60" "0.80" "1.60" "1.40" "1.40"
## [5281] "0.90" "1.20" "0.60" "0.80" "1.20" "1.00" "0.90" "0.70"
## [5289] "0.80" "1.58" "0.50" "0.60" "1.30" "1.00" "1.25" "1.34"
## [5297] "1.40" "1.00" "1.00" "0.70" "0.80" "0.78" "0.70" "0.60"
## [5305] "0.70" "0.70" "1.00" "0.80" "0.70" "1.10" "1.10" "0.76"
## [5313] "0.70" "1.55" "1.20" "0.80" "1.00" "1.00" "0.50" "0.50"
## [5321] "0.90" "0.71" "0.62" "1.10" "0.60" "0.67" "0.60" "0.60"
## [5329] "1.30" "1.00" "0.90" "0.90" "0.70" "1.40" "0.90" "1.40"
## [5337] "0.70" "0.80" "0.90" "0.95" "0.80" "0.80" "0.75" "1.16"
## [5345] "0.80" "1.10" "0.50" "0.60" "0.80" "0.80" "1.50" "1.10"
## [5353] "0.40" "0.90" "0.90" "1.00" "1.10" "0.40" "1.10" "1.66"
## [5361] "1.20" "1.20" "0.96" "0.90" "1.20" "0.90" "0.70" "1.00"
## [5369] "0.50" "0.50" "1.10" "0.60" "0.80" "0.50" "1.38" "1.38"
## [5377] "0.52" "0.60" "0.47" "0.90" "0.80" "0.93" "1.00" "0.47"
## [5385] "1.60" "1.60" "1.20" "0.80" "0.70" "0.50" "0.48" "0.50"
## [5393] "1.00" "0.58" "0.80" "1.40" "1.00" "0.69" "0.60" "0.60"
## [5401] "0.90" "1.27" "1.12" "1.10" "1.06" "1.10" "0.50" "0.53"
## [5409] "0.75" "0.90" "0.40" "0.90" "0.90" "1.03" "1.20" "1.00"
## [5417] "1.10" "0.70" "1.30" "0.68" "1.40" "0.80" "1.20" "0.80"
## [5425] "1.00" "1.90" "1.00" "1.50" "0.70" "1.10" "1.00" "1.40"
## [5433] "1.00" "1.70" "1.10" "1.00" "1.00" "1.40" "0.90" "1.00"
## [5441] "1.30" "1.00" "0.60" "0.90" "0.80" "0.80" "0.90" "1.00"
## [5449] "1.80" "0.80" "1.00" "1.00" "1.80" "0.60" "0.80" "0.70"
## [5457] "0.80" "1.00" "0.70" "0.80" "0.80" "1.20" "1.40" "1.00"
## [5465] "1.20" "1.10" "1.40" "0.60" "0.50" "1.00" "1.30" "0.70"
## [5473] "1.38" "1.00" "1.50" "1.80" "0.50" "1.30" "1.00" "0.90"
## [5481] "1.00" "1.20" "1.00" "1.00" "1.20" "0.80" "1.00" "1.00"
## [5489] "1.00" "1.30" "0.90" "0.50" "1.00" "0.90" "1.20" "1.00"
## [5497] "0.90" "1.30" "1.00" "1.10" "0.90" "1.20" "1.10" "0.90"
## [5505] "1.00" "1.70" "0.90" "1.20" "1.10" "0.80" "1.00" "1.00"
## [5513] "1.40" "0.90" "0.40" "1.00" "1.00" "1.10" "1.30" "0.90"
## [5521] "1.30" "1.20" "1.20" "1.00" "1.00" "1.30" "1.00" "1.40"
## [5529] "1.10" "1.20" "1.10" "1.00" "0.80" "1.10" "1.00" "0.90"
## [5537] "1.20" "0.80" "0.60" "0.80" "1.10" "1.10" "1.20" "1.00"
## [5545] "0.80" "0.90" "0.70" "0.80" "0.80" "1.30" "0.50" "1.50"
## [5553] "0.50" "0.90" "1.00" "0.80" "1.20" "1.00" "0.60" "1.10"
## [5561] "1.10" "1.00" "1.30" "0.80" "0.70" "0.80" "1.40" "1.00"
## [5569] "0.70" "0.80" "1.00" "1.00" "0.80" "1.30" "0.90" "1.10"
## [5577] "1.30" "0.80" "1.50" "1.70" "1.30" "1.30" "1.00" "1.10"
## [5585] "0.90" "0.80" "1.00" "0.90" "0.60" "0.90" "1.00" "0.70"
## [5593] "1.30" "1.20" "1.10" "1.10" "1.00" "1.00" "1.10" "0.90"
## [5601] "1.00" "0.70" "1.00" "1.10" "0.90" "1.00" "1.00" "1.00"
## [5609] "0.30" "0.90" "1.30" "1.30" "1.10" "1.20" "1.00" "0.90"
## [5617] "1.00" "0.60" "0.70" "0.70" "0.80" "0.70" "0.70" "0.75"
## [5625] "1.40" "0.70" "0.70" "0.50" "1.00" "0.97" "1.50" "0.70"
## [5633] "1.40" "0.80" "0.75" "0.90" "0.50" "0.60" "0.76" "1.58"
## [5641] "1.00" "0.92" "1.70" "1.00" "0.90" "0.50" "1.00" "1.50"
## [5649] "0.70" "0.70" "1.06" "0.80" "1.20" "0.96" "0.80" "0.70"
## [5657] "1.00" "1.00" "0.80" "0.90" "0.70" "1.00" "1.00" "0.70"
## [5665] "0.90" "0.30" "0.35" "0.80" "1.51" "0.64" "1.00" "0.45"
## [5673] "0.50" "0.60" "0.80" "0.60" "0.80" "1.36" "0.70" "0.70"
## [5681] "1.30" "0.80" "1.00" "1.00" "0.88" "1.09" "0.70" "1.10"
## [5689] "1.20" "1.11" "1.20" "1.72" "0.80" "0.80" "0.93" "1.00"
## [5697] "1.18" "1.16" "1.85" "0.70" "0.52" "1.50" "0.96" "1.50"
## [5705] "0.70" "1.60" "0.60" "0.60" "0.70" "1.20" "0.40" "0.90"
## [5713] "1.10" "0.70" "1.00" "0.70" "1.20" "1.00" "1.00" "0.80"
## [5721] "0.70" "1.00" "0.70" "0.80" "1.00" "1.00" "0.30" "0.80"
## [5729] "0.40" "0.80" "1.10" "1.10" "0.90" "0.80" "0.90" "0.50"
## [5737] "0.50" "1.10" "0.50" "0.60" "0.30" "1.00" "0.40" "0.60"
## [5745] "0.90" "0.70" "0.90" "0.70" "0.80" "1.20" "1.10" "0.80"
## [5753] "0.90" "0.70" "0.40" "2.80" "1.60" "0.70" "0.90" "1.00"
## [5761] "0.60" "0.80" "0.70" "1.00" "1.00" "1.10" "0.50" "0.40"
## [5769] "0.40" "0.50" "0.50" "0.90" "0.46" "1.00" "0.39" "1.02"
## [5777] "1.20" "0.90" "0.70" "2.50" "0.40" "0.50" "2.50" "0.70"
## [5785] "0.90" "0.37" "1.10" "1.00" "1.00" "1.50" "1" "1"
## [5793] "1" "1" "1" "1" "1" "1" "2" "1"
## [5801] "1" "1" "2" "1" "1" "2" "1" "2"
## [5809] "1" "1" "1" "1" "1" "1" "2" "1"
## [5817] "1" "1" "2" "2" "1" "1" "2" "2"
## [5825] "2" "1" "2" "1" "1" "1" "1" "2"
## [5833] "2" "1" "2" "2" "1" "1" "1" "1"
## [5841] "1" "1" "1" "1" "1" "1" "2" "2"
## [5849] "1" "2" "1" "1" "1" "1" "1" "1"
## [5857] "1" "1" "1" "2" "1" "1" "1" "1"
## [5865] "1" "2" "1" "1" "2" "1" "1" "1"
## [5873] "2" "1" "1" "1" "2" "1" "1" "1"
## [5881] "1" "1" "1" "1" "1" "1" "1" "1"
## [5889] "1" "1" "2" "2" "1" "2" "1" "2"
## [5897] "2" "2" "2" "2" "2" "1" "2" "1"
## [5905] "2" "2" "1" "1" "1" "1" "1" "1"
## [5913] "2" "1" "2" "2" "1" "1" "1" "1"
## [5921] "1" "2" "2" "1" "1" "1" "1" "1"
## [5929] "1" "1" "2" "1" "1" "1" "1" "2"
## [5937] "1" "1" "1" "1" "2" "1" "1" "2"
## [5945] "1" "1" "1" "1" "1" "1" "1" "1"
## [5953] "1" "1" "1" "1" "1" "1" "1" "1"
## [5961] "1" "1" "1" "1" "1" "1" "1" "1"
## [5969] "1" "1" "1" "1" "2" "1" "1" "2"
## [5977] "1" "1" "1" "2" "1" "1" "1" "2"
## [5985] "2" "1" "1" "1" "2" "1" "1" "1"
## [5993] "2" "2" "2" "1" "1" "1" "1" "1"
## [6001] "1" "2" "1" "1" "2" "2" "1" "2"
## [6009] "1" "1" "1" "1" "2" "1" "1" "1"
## [6017] "1" "2" "1" "2" "1" "1" "1" "1"
## [6025] "1" "2" "1" "2" "1" "2" "1" "1"
## [6033] "1" "1" "1" "1" "1" "1" "1" "1"
## [6041] "2" "2" "1" "1" "1" "2" "1" "1"
## [6049] "1" "1" "1" "2" "2" "1" "1" "1"
## [6057] "1" "1" "2" "1" "1" "1" "2" "2"
## [6065] "1" "1" "1" "1" "2" "1" "2" "1"
## [6073] "1" "1" "2" "1" "1" "1" "2" "1"
## [6081] "2" "1" "1" "1" "2" "1" "2" "2"
## [6089] "1" "1" "2" "1" "2" "1" "1" "1"
## [6097] "1" "1" "1" "2" "1" "2" "2" "1"
## [6105] "1" "2" "1" "1" "1" "2" "1" "2"
## [6113] "2" "2" "2" "2" "1" "1" "1" "2"
## [6121] "1" "1" "1" "1" "1" "1" "1" "1"
## [6129] "2" "1" "2" "1" "1" "1" "1" "2"
## [6137] "1" "1" "1" "1" "1" "2" "1" "1"
## [6145] "1" "2" "1" "2" "2" "2" "2" "2"
## [6153] "2" "2" "1" "1" "1" "2" "1" "2"
## [6161] "2" "1" "1" "2" "1" "2" "1" "1"
## [6169] "1" "2" "1" "1" "2" "1" "1" "1"
## [6177] "1" "1" "1" "1" "1" "2" "1" "1"
## [6185] "1" "1" "2" "1" "1" "2" "1" "1"
## [6193] "2" "1" "1" "1" "1" "2" "1" "2"
## [6201] "2" "1" "1" "2" "1" "1" "1" "2"
## [6209] "1" "2" "1" "1" "2" "1" "2" "1"
## [6217] "1" "2" "1" "2" "2" "2" "1" "1"
## [6225] "1" "1" "1" "1" "1" "1" "2" "2"
## [6233] "1" "1" "1" "1" "1" "1" "1" "1"
## [6241] "2" "1" "2" "2" "1" "1" "1" "1"
## [6249] "1" "1" "2" "2" "2" "2" "1" "1"
## [6257] "1" "2" "2" "2" "2" "2" "1" "1"
## [6265] "1" "1" "2" "1" "1" "2" "1" "1"
## [6273] "1" "1" "2" "2" "1" "2" "1" "2"
## [6281] "1" "2" "1" "1" "1" "1" "1" "1"
## [6289] "1" "1" "1" "1" "1" "1" "1" "1"
## [6297] "2" "1" "2" "1" "1" "1" "1" "1"
## [6305] "1" "1" "1" "1" "1" "1" "2" "2"
## [6313] "1" "1" "1" "1" "2" "1" "2" "1"
## [6321] "2" "1" "1" "1" "1" "2" "2" "2"
## [6329] "2" "1" "1" "2" "1" "1" "1" "1"
## [6337] "1" "2" "1" "1" "1" "1" "1" "1"
## [6345] "1" "1" "1" "1" "1" "1" "2" "1"
## [6353] "2" "1" "1" "1" "1" "1" "1" "1"
## [6361] "1" "1" "1" "1" "2" "1" "1" "1"
## [6369] "2"sum(is.na(Wow)) #digunakan untuk menampilkan apakah jumlah NA sudah 0## [1] 0fungsi <-na.omit() digunakan untuk menghapus baris
yang ada NA-nya atau baris yang ada data hilang. Data yang sudah tidak
mengandung NA-nya disimpan pada data nama baru, disini diberi nama
Wow.
3.2 Statistik Deskriptif Awal
summary(data2)## Age Gender TotalBilirubin DirectBilirubin
## Min. : 4.00 Length:583 Min. : 0.400 Min. : 0.100
## 1st Qu.:33.00 Class :character 1st Qu.: 0.800 1st Qu.: 0.200
## Median :45.00 Mode :character Median : 1.000 Median : 0.300
## Mean :44.75 Mean : 3.299 Mean : 1.486
## 3rd Qu.:58.00 3rd Qu.: 2.600 3rd Qu.: 1.300
## Max. :90.00 Max. :75.000 Max. :19.700
##
## AlkalinePhosphotase AlamineAminotransfera ApsartateAminotransferase
## Min. : 63.0 Min. : 10.00 Min. : 10.0
## 1st Qu.: 175.5 1st Qu.: 23.00 1st Qu.: 25.0
## Median : 208.0 Median : 35.00 Median : 42.0
## Mean : 290.6 Mean : 80.71 Mean : 109.9
## 3rd Qu.: 298.0 3rd Qu.: 60.50 3rd Qu.: 87.0
## Max. :2110.0 Max. :2000.00 Max. :4929.0
##
## TotalProtiens Albumin AlbuminandGlobulinRatio LiverPatient
## Min. :2.700 Min. :0.900 Min. :0.3000 Min. :1.000
## 1st Qu.:5.800 1st Qu.:2.600 1st Qu.:0.7000 1st Qu.:1.000
## Median :6.600 Median :3.100 Median :0.9300 Median :1.000
## Mean :6.483 Mean :3.142 Mean :0.9471 Mean :1.286
## 3rd Qu.:7.200 3rd Qu.:3.800 3rd Qu.:1.1000 3rd Qu.:2.000
## Max. :9.600 Max. :5.500 Max. :2.8000 Max. :2.000
## NA's :4fungsi summary()digunakan untuk melihat ringkasan
statistik atau deskripsi singkat dari setiap kolom yang ada pada data,
seperti nilai minimum, maksimum, quartil, mean, dan median.
summary(data2$TotalBilirubin)## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 0.400 0.800 1.000 3.299 2.600 75.000fungsi summary( $ ) digunakan untuk menampilkan
ringkasan statistik pada kolom tertentu. Pada contoh itu menampilkan
ringkasan statistik untuk kolom TotalBilirubin.
mean(data2$Age, na.rm = TRUE)## [1] 44.74614fungsi mean( $ ,na.rm = TRUE)digunakan untuk menghitung
nilai rata-rata dari kolom tertentu dengan mengabaikan jiak ada nilai
yang hilang pada kolom tersebut.
min(data2$Age, na.rm = TRUE)## [1] 4fungsi min( $ ,na.rm = TRUE)digunakan untuk menghitung
nilai terkecil dari kolom tertentu dengan mengabaikan jiak ada nilai
yang hilang pada kolom tersebut.
3.3 Outlier Sederhana (Boxplot)
boxplot(data2$Age, main = "Boxplot Usia", col="red")
Fungsi tersebut digunakan untuk menampilkan distribusi data dari kolom
Age dan mendeteksi outlier secara visual dalam bentuk boxplot, dengan
judul “Boxplot Usia” dan diagram berwarna merah.
boxplot(data2$Albumin ~ data2$TotalProtiens, col= "green")
Fungsi (~) digunakan untuk membandingkan distribusi nilai
Albumin pada setiap kelompok nilai Total Proteins dan melihat
kemungkinan adanya outlier, dengan diagram berwarna hijau.