Affairs <- read.csv("Affairs.csv")
print(Affairs)## X affairs gender age yearsmarried children religiousness education
## 1 4 0 male 37.0 10.000 no 3 18
## 2 5 0 female 27.0 4.000 no 4 14
## 3 11 0 female 32.0 15.000 yes 1 12
## 4 16 0 male 57.0 15.000 yes 5 18
## 5 23 0 male 22.0 0.750 no 2 17
## 6 29 0 female 32.0 1.500 no 2 17
## 7 44 0 female 22.0 0.750 no 2 12
## 8 45 0 male 57.0 15.000 yes 2 14
## 9 47 0 female 32.0 15.000 yes 4 16
## 10 49 0 male 22.0 1.500 no 4 14
## 11 50 0 male 37.0 15.000 yes 2 20
## 12 55 0 male 27.0 4.000 yes 4 18
## 13 64 0 male 47.0 15.000 yes 5 17
## 14 80 0 female 22.0 1.500 no 2 17
## 15 86 0 female 27.0 4.000 no 4 14
## 16 93 0 female 37.0 15.000 yes 1 17
## 17 108 0 female 37.0 15.000 yes 2 18
## 18 114 0 female 22.0 0.750 no 3 16
## 19 115 0 female 22.0 1.500 no 2 16
## 20 116 0 female 27.0 10.000 yes 2 14
## 21 123 0 female 22.0 1.500 no 2 16
## 22 127 0 female 22.0 1.500 no 2 16
## 23 129 0 female 27.0 10.000 yes 4 16
## 24 134 0 female 32.0 10.000 yes 3 14
## 25 137 0 male 37.0 4.000 yes 2 20
## 26 139 0 female 22.0 1.500 no 2 18
## 27 147 0 female 27.0 7.000 no 4 16
## 28 151 0 male 42.0 15.000 yes 5 20
## 29 153 0 male 27.0 4.000 yes 3 16
## 30 155 0 female 27.0 4.000 yes 3 17
## 31 162 0 male 42.0 15.000 yes 4 20
## 32 163 0 female 22.0 1.500 no 3 16
## 33 165 0 male 27.0 0.417 no 4 17
## 34 168 0 female 42.0 15.000 yes 5 14
## 35 170 0 male 32.0 4.000 yes 1 18
## 36 172 0 female 22.0 1.500 no 4 16
## 37 184 0 female 42.0 15.000 yes 3 12
## 38 187 0 female 22.0 4.000 no 4 17
## 39 192 0 male 22.0 1.500 yes 1 14
## 40 194 0 female 22.0 0.750 no 3 16
## 41 210 0 male 32.0 10.000 yes 5 20
## 42 217 0 male 52.0 15.000 yes 5 18
## 43 220 0 female 22.0 0.417 no 5 14
## 44 224 0 female 27.0 4.000 yes 2 18
## 45 227 0 female 32.0 7.000 yes 5 17
## 46 228 0 male 22.0 4.000 no 3 16
## 47 239 0 female 27.0 7.000 yes 4 18
## 48 241 0 female 42.0 15.000 yes 2 18
## 49 245 0 male 27.0 1.500 yes 4 16
## 50 249 0 male 42.0 15.000 yes 2 20
## 51 262 0 female 22.0 0.750 no 5 14
## 52 265 0 male 32.0 7.000 yes 2 20
## 53 267 0 male 27.0 4.000 yes 5 20
## 54 269 0 male 27.0 10.000 yes 4 20
## 55 271 0 male 22.0 4.000 no 1 18
## 56 277 0 female 37.0 15.000 yes 4 14
## 57 290 0 male 22.0 1.500 yes 5 16
## 58 292 0 female 37.0 15.000 yes 4 17
## 59 293 0 female 27.0 0.750 no 4 17
## 60 295 0 male 32.0 10.000 yes 4 20
## 61 299 0 female 47.0 15.000 yes 5 14
## 62 320 0 male 37.0 10.000 yes 3 20
## 63 321 0 female 22.0 0.750 no 2 16
## 64 324 0 male 27.0 4.000 no 2 18
## 65 334 0 male 32.0 7.000 no 4 20
## 66 351 0 male 42.0 15.000 yes 2 17
## 67 355 0 male 37.0 10.000 yes 4 20
## 68 361 0 female 47.0 15.000 yes 3 17
## 69 362 0 female 22.0 1.500 no 5 16
## 70 366 0 female 27.0 1.500 no 2 16
## 71 370 0 female 27.0 4.000 no 3 17
## 72 374 0 female 32.0 10.000 yes 5 14
## 73 378 0 female 22.0 0.125 no 2 12
## 74 381 0 male 47.0 15.000 yes 4 14
## 75 382 0 male 32.0 15.000 yes 1 14
## 76 383 0 male 27.0 7.000 yes 4 16
## 77 384 0 female 22.0 1.500 yes 3 16
## 78 400 0 male 27.0 4.000 yes 3 17
## 79 403 0 female 22.0 1.500 no 3 16
## 80 409 0 male 57.0 15.000 yes 2 14
## 81 412 0 male 17.5 1.500 yes 3 18
## 82 413 0 male 57.0 15.000 yes 4 20
## 83 416 0 female 22.0 0.750 no 2 16
## 84 418 0 male 42.0 4.000 no 4 17
## 85 422 0 female 22.0 1.500 yes 4 12
## 86 435 0 female 22.0 0.417 no 1 17
## 87 439 0 female 32.0 15.000 yes 4 17
## 88 445 0 female 27.0 1.500 no 3 18
## 89 447 0 female 22.0 1.500 yes 3 14
## 90 448 0 female 37.0 15.000 yes 3 14
## 91 449 0 female 32.0 15.000 yes 4 14
## 92 478 0 male 37.0 10.000 yes 2 14
## 93 482 0 male 37.0 10.000 yes 4 16
## 94 486 0 male 57.0 15.000 yes 5 20
## 95 489 0 male 27.0 0.417 no 1 16
## 96 490 0 female 42.0 15.000 yes 5 14
## 97 491 0 male 57.0 15.000 yes 3 16
## 98 492 0 male 37.0 10.000 yes 1 16
## 99 503 0 male 37.0 15.000 yes 3 17
## 100 508 0 male 37.0 15.000 yes 4 20
## 101 509 0 female 27.0 10.000 yes 5 14
## 102 512 0 male 37.0 10.000 yes 2 18
## 103 515 0 female 22.0 0.125 no 4 12
## 104 517 0 male 57.0 15.000 yes 5 20
## 105 532 0 female 37.0 15.000 yes 4 18
## 106 533 0 male 22.0 4.000 yes 4 14
## 107 535 0 male 27.0 7.000 yes 4 18
## 108 537 0 male 57.0 15.000 yes 4 20
## 109 538 0 male 32.0 15.000 yes 3 14
## 110 543 0 female 22.0 1.500 no 2 14
## 111 547 0 female 32.0 7.000 yes 4 17
## 112 550 0 female 37.0 15.000 yes 4 17
## 113 558 0 female 32.0 1.500 no 5 18
## 114 571 0 male 42.0 10.000 yes 5 20
## 115 578 0 female 27.0 7.000 no 3 16
## 116 583 0 male 37.0 15.000 no 4 20
## 117 586 0 male 37.0 15.000 yes 4 14
## 118 594 0 male 32.0 10.000 no 5 18
## 119 597 0 female 22.0 0.750 no 4 16
## 120 602 0 female 27.0 7.000 yes 4 12
## 121 603 0 female 27.0 7.000 yes 2 16
## 122 604 0 female 42.0 15.000 yes 5 18
## 123 612 0 male 42.0 15.000 yes 4 17
## 124 613 0 female 27.0 7.000 yes 2 16
## 125 621 0 female 22.0 1.500 no 3 16
## 126 627 0 male 37.0 15.000 yes 5 20
## 127 630 0 female 22.0 0.125 no 2 14
## 128 631 0 male 27.0 1.500 no 4 16
## 129 632 0 male 32.0 1.500 no 2 18
## 130 639 0 male 27.0 1.500 no 2 17
## 131 645 0 female 27.0 10.000 yes 4 16
## 132 647 0 male 42.0 15.000 yes 4 18
## 133 648 0 female 27.0 1.500 no 2 16
## 134 651 0 male 27.0 4.000 no 2 18
## 135 655 0 female 32.0 10.000 yes 3 14
## 136 667 0 female 32.0 15.000 yes 3 18
## 137 670 0 female 22.0 0.750 no 2 18
## 138 671 0 female 37.0 15.000 yes 2 16
## 139 673 0 male 27.0 4.000 yes 4 20
## 140 701 0 male 27.0 4.000 no 1 20
## 141 705 0 female 27.0 10.000 yes 2 12
## 142 706 0 female 32.0 15.000 yes 5 18
## 143 709 0 male 27.0 7.000 yes 5 12
## 144 717 0 male 52.0 15.000 yes 2 18
## 145 719 0 male 27.0 4.000 no 3 20
## 146 723 0 male 37.0 4.000 yes 1 18
## 147 724 0 male 27.0 4.000 yes 4 14
## 148 726 0 female 52.0 15.000 yes 5 12
## 149 734 0 female 57.0 15.000 yes 4 16
## 150 735 0 male 27.0 7.000 yes 1 16
## 151 736 0 male 37.0 7.000 yes 4 20
## 152 737 0 male 22.0 0.750 no 2 14
## 153 739 0 male 32.0 4.000 yes 2 18
## 154 743 0 male 37.0 15.000 yes 4 20
## 155 745 0 male 22.0 0.750 yes 2 14
## 156 747 0 male 42.0 15.000 yes 4 20
## 157 751 0 female 52.0 15.000 yes 5 17
## 158 752 0 female 37.0 15.000 yes 4 14
## 159 754 0 male 27.0 7.000 yes 4 14
## 160 760 0 male 32.0 4.000 yes 2 16
## 161 763 0 female 27.0 4.000 yes 2 18
## 162 774 0 female 27.0 4.000 yes 2 18
## 163 776 0 male 37.0 15.000 yes 5 18
## 164 779 0 female 47.0 15.000 yes 5 12
## 165 784 0 female 32.0 10.000 yes 3 17
## 166 788 0 female 27.0 1.500 yes 4 17
## 167 794 0 female 57.0 15.000 yes 2 18
## 168 795 0 female 22.0 1.500 no 4 14
## 169 798 0 male 42.0 15.000 yes 3 14
## 170 800 0 male 57.0 15.000 yes 4 9
## 171 803 0 male 57.0 15.000 yes 4 20
## 172 807 0 female 22.0 0.125 no 4 14
## 173 812 0 female 32.0 10.000 yes 4 14
## 174 820 0 female 42.0 15.000 yes 3 18
## 175 823 0 female 27.0 1.500 no 2 18
## 176 830 0 male 32.0 0.125 yes 2 18
## 177 843 0 female 27.0 4.000 no 3 16
## 178 848 0 female 27.0 10.000 yes 2 16
## 179 851 0 female 32.0 7.000 yes 4 16
## 180 854 0 female 37.0 15.000 yes 4 14
## 181 856 0 female 42.0 15.000 yes 5 17
## 182 857 0 male 32.0 1.500 yes 4 14
## 183 859 0 female 32.0 4.000 yes 3 17
## 184 863 0 female 37.0 7.000 no 4 18
## 185 865 0 female 22.0 0.417 yes 3 14
## 186 867 0 female 27.0 7.000 yes 4 14
## 187 870 0 male 27.0 0.750 no 3 16
## 188 873 0 male 27.0 4.000 yes 2 20
## 189 875 0 male 32.0 10.000 yes 4 16
## 190 876 0 male 32.0 15.000 yes 1 14
## 191 877 0 male 22.0 0.750 no 3 17
## 192 880 0 female 27.0 7.000 yes 4 17
## 193 903 0 male 27.0 0.417 yes 4 20
## 194 904 0 male 37.0 15.000 yes 4 20
## 195 905 0 female 37.0 15.000 yes 2 14
## 196 908 0 male 22.0 4.000 yes 1 18
## 197 909 0 male 37.0 15.000 yes 4 17
## 198 910 0 female 22.0 1.500 no 2 14
## 199 912 0 male 52.0 15.000 yes 4 14
## 200 914 0 female 22.0 1.500 no 4 17
## 201 915 0 male 32.0 4.000 yes 5 14
## 202 916 0 male 32.0 4.000 yes 2 14
## 203 920 0 female 22.0 1.500 no 3 16
## 204 921 0 male 27.0 0.750 no 2 18
## 205 925 0 female 22.0 7.000 yes 2 14
## 206 926 0 female 27.0 0.750 no 2 17
## 207 929 0 female 37.0 15.000 yes 4 12
## 208 931 0 female 22.0 1.500 no 1 14
## 209 945 0 female 37.0 10.000 no 2 12
## 210 947 0 female 37.0 15.000 yes 4 18
## 211 949 0 female 42.0 15.000 yes 3 12
## 212 950 0 male 22.0 4.000 no 2 18
## 213 961 0 male 52.0 7.000 yes 2 20
## 214 965 0 male 27.0 0.750 no 2 17
## 215 966 0 female 27.0 4.000 no 2 17
## 216 967 0 male 42.0 1.500 no 5 20
## 217 987 0 male 22.0 1.500 no 4 17
## 218 990 0 male 22.0 4.000 no 4 17
## 219 992 0 female 22.0 4.000 yes 1 14
## 220 995 0 male 37.0 15.000 yes 5 20
## 221 1009 0 female 37.0 10.000 yes 3 16
## 222 1021 0 male 42.0 15.000 yes 4 17
## 223 1026 0 female 47.0 15.000 yes 4 17
## 224 1027 0 male 22.0 1.500 no 4 16
## 225 1030 0 female 32.0 10.000 yes 3 12
## 226 1031 0 female 22.0 7.000 yes 1 14
## 227 1034 0 female 32.0 10.000 yes 4 17
## 228 1037 0 male 27.0 1.500 yes 2 16
## 229 1038 0 male 37.0 15.000 yes 4 14
## 230 1039 0 male 42.0 4.000 yes 3 14
## 231 1045 0 female 37.0 15.000 yes 5 14
## 232 1046 0 female 32.0 7.000 yes 4 17
## 233 1054 0 female 42.0 15.000 yes 4 18
## 234 1059 0 male 27.0 4.000 no 4 18
## 235 1063 0 male 22.0 0.750 no 4 18
## 236 1068 0 male 27.0 4.000 yes 4 14
## 237 1070 0 female 22.0 0.750 no 5 18
## 238 1072 0 female 52.0 15.000 yes 5 9
## 239 1073 0 male 32.0 10.000 yes 3 14
## 240 1077 0 female 37.0 15.000 yes 4 16
## 241 1081 0 male 32.0 7.000 yes 2 20
## 242 1083 0 female 42.0 15.000 yes 3 18
## 243 1084 0 male 32.0 15.000 yes 1 16
## 244 1086 0 male 27.0 4.000 yes 3 18
## 245 1087 0 female 32.0 15.000 yes 4 12
## 246 1089 0 male 22.0 0.750 yes 3 14
## 247 1096 0 female 22.0 1.500 no 3 16
## 248 1102 0 female 42.0 15.000 yes 4 14
## 249 1103 0 female 52.0 15.000 yes 3 16
## 250 1107 0 male 37.0 15.000 yes 5 20
## 251 1109 0 female 47.0 15.000 yes 4 12
## 252 1115 0 male 57.0 15.000 yes 2 20
## 253 1119 0 male 32.0 7.000 yes 4 17
## 254 1124 0 female 27.0 7.000 yes 4 17
## 255 1126 0 male 22.0 1.500 no 1 18
## 256 1128 0 female 22.0 4.000 yes 3 9
## 257 1129 0 female 22.0 1.500 no 2 14
## 258 1130 0 male 42.0 15.000 yes 2 20
## 259 1133 0 male 57.0 15.000 yes 4 9
## 260 1140 0 female 27.0 7.000 yes 2 18
## 261 1143 0 female 22.0 4.000 yes 3 14
## 262 1146 0 male 37.0 15.000 yes 4 14
## 263 1153 0 male 32.0 7.000 yes 1 18
## 264 1156 0 female 22.0 1.500 no 2 14
## 265 1157 0 female 22.0 1.500 yes 3 12
## 266 1158 0 male 52.0 15.000 yes 2 14
## 267 1160 0 female 37.0 15.000 yes 2 14
## 268 1161 0 female 32.0 10.000 yes 2 14
## 269 1166 0 male 42.0 15.000 yes 4 20
## 270 1177 0 female 27.0 4.000 yes 3 18
## 271 1178 0 male 37.0 15.000 yes 4 20
## 272 1180 0 male 27.0 1.500 no 3 18
## 273 1187 0 female 22.0 0.125 no 2 16
## 274 1191 0 male 32.0 10.000 yes 2 20
## 275 1195 0 female 27.0 4.000 no 4 18
## 276 1207 0 female 27.0 7.000 yes 2 12
## 277 1208 0 male 32.0 4.000 yes 5 18
## 278 1209 0 female 37.0 15.000 yes 2 17
## 279 1211 0 male 47.0 15.000 no 4 20
## 280 1215 0 male 27.0 1.500 no 1 18
## 281 1221 0 male 37.0 15.000 yes 4 20
## 282 1226 0 female 32.0 15.000 yes 4 18
## 283 1229 0 female 32.0 7.000 yes 4 17
## 284 1231 0 female 42.0 15.000 yes 3 14
## 285 1234 0 female 27.0 7.000 yes 3 16
## 286 1235 0 male 27.0 1.500 no 3 16
## 287 1242 0 male 22.0 1.500 no 3 16
## 288 1245 0 male 27.0 4.000 yes 3 16
## 289 1260 0 female 27.0 7.000 yes 3 12
## 290 1266 0 female 37.0 15.000 yes 2 18
## 291 1271 0 female 37.0 7.000 yes 3 14
## 292 1273 0 male 22.0 1.500 no 2 16
## 293 1276 0 male 37.0 15.000 yes 5 20
## 294 1280 0 female 22.0 1.500 no 4 16
## 295 1282 0 female 32.0 10.000 yes 4 16
## 296 1285 0 male 27.0 4.000 no 2 17
## 297 1295 0 female 22.0 0.417 no 4 14
## 298 1298 0 female 27.0 4.000 no 2 18
## 299 1299 0 male 37.0 15.000 yes 4 18
## 300 1304 0 male 37.0 10.000 yes 5 20
## 301 1305 0 female 27.0 7.000 yes 2 14
## 302 1311 0 male 32.0 4.000 yes 2 16
## 303 1314 0 male 32.0 4.000 yes 2 16
## 304 1319 0 male 22.0 1.500 no 3 18
## 305 1322 0 female 22.0 4.000 yes 4 14
## 306 1324 0 female 17.5 0.750 no 2 18
## 307 1327 0 male 32.0 10.000 yes 4 20
## 308 1328 0 female 32.0 0.750 no 5 14
## 309 1330 0 male 37.0 15.000 yes 4 17
## 310 1332 0 male 32.0 4.000 no 3 14
## 311 1333 0 female 27.0 1.500 no 2 17
## 312 1336 0 female 22.0 7.000 yes 4 14
## 313 1341 0 male 47.0 15.000 yes 5 14
## 314 1344 0 male 27.0 4.000 yes 1 16
## 315 1352 0 female 37.0 15.000 yes 5 14
## 316 1358 0 male 42.0 4.000 yes 4 18
## 317 1359 0 female 32.0 4.000 yes 2 14
## 318 1361 0 male 52.0 15.000 yes 2 14
## 319 1364 0 female 22.0 1.500 no 2 16
## 320 1368 0 male 52.0 15.000 yes 4 12
## 321 1384 0 female 22.0 0.417 no 3 17
## 322 1390 0 female 22.0 1.500 no 2 16
## 323 1393 0 male 27.0 4.000 yes 4 20
## 324 1394 0 female 32.0 15.000 yes 4 14
## 325 1402 0 female 27.0 1.500 no 2 16
## 326 1407 0 male 32.0 4.000 no 1 20
## 327 1408 0 male 37.0 15.000 yes 3 20
## 328 1412 0 female 32.0 10.000 no 2 16
## 329 1413 0 female 32.0 10.000 yes 5 14
## 330 1416 0 male 37.0 1.500 yes 4 18
## 331 1417 0 male 32.0 1.500 no 2 18
## 332 1418 0 female 32.0 10.000 yes 4 14
## 333 1419 0 female 47.0 15.000 yes 4 18
## 334 1420 0 female 27.0 10.000 yes 5 12
## 335 1423 0 male 27.0 4.000 yes 3 16
## 336 1424 0 female 37.0 15.000 yes 4 12
## 337 1432 0 female 27.0 0.750 no 4 16
## 338 1433 0 female 37.0 15.000 yes 4 16
## 339 1437 0 female 32.0 15.000 yes 3 16
## 340 1438 0 female 27.0 10.000 yes 2 16
## 341 1439 0 male 27.0 7.000 no 2 20
## 342 1446 0 female 37.0 15.000 yes 2 14
## 343 1450 0 male 27.0 1.500 yes 2 17
## 344 1451 0 female 22.0 0.750 yes 2 14
## 345 1452 0 male 22.0 4.000 yes 4 14
## 346 1453 0 male 42.0 0.125 no 4 17
## 347 1456 0 male 27.0 1.500 yes 4 18
## 348 1464 0 male 27.0 7.000 yes 3 16
## 349 1469 0 female 52.0 15.000 yes 4 14
## 350 1473 0 male 27.0 1.500 no 5 20
## 351 1481 0 female 27.0 1.500 no 2 16
## 352 1482 0 female 27.0 1.500 no 3 17
## 353 1496 0 male 22.0 0.125 no 5 16
## 354 1497 0 female 27.0 4.000 yes 4 16
## 355 1504 0 female 27.0 4.000 yes 4 12
## 356 1513 0 female 47.0 15.000 yes 2 14
## 357 1515 0 female 32.0 15.000 yes 3 14
## 358 1534 0 male 42.0 7.000 yes 2 16
## 359 1535 0 male 22.0 0.750 no 4 16
## 360 1536 0 male 27.0 0.125 no 3 20
## 361 1540 0 male 32.0 10.000 yes 3 20
## 362 1551 0 female 22.0 0.417 no 5 14
## 363 1555 0 female 47.0 15.000 yes 5 14
## 364 1557 0 female 32.0 10.000 yes 3 14
## 365 1566 0 male 57.0 15.000 yes 4 17
## 366 1567 0 male 27.0 4.000 yes 3 20
## 367 1576 0 female 32.0 7.000 yes 4 17
## 368 1584 0 female 37.0 10.000 yes 4 16
## 369 1585 0 female 32.0 10.000 yes 1 18
## 370 1590 0 female 22.0 4.000 no 3 14
## 371 1594 0 female 27.0 7.000 yes 4 14
## 372 1595 0 male 57.0 15.000 yes 5 18
## 373 1603 0 male 32.0 7.000 yes 2 18
## 374 1608 0 female 27.0 1.500 no 4 17
## 375 1609 0 male 22.0 1.500 no 4 14
## 376 1615 0 female 22.0 1.500 yes 4 14
## 377 1616 0 female 32.0 7.000 yes 3 16
## 378 1617 0 female 47.0 15.000 yes 3 16
## 379 1620 0 female 22.0 0.750 no 3 16
## 380 1621 0 female 22.0 1.500 yes 2 14
## 381 1637 0 female 27.0 4.000 yes 1 16
## 382 1638 0 male 52.0 15.000 yes 4 16
## 383 1650 0 male 32.0 10.000 yes 4 20
## 384 1654 0 male 47.0 15.000 yes 4 16
## 385 1665 0 female 27.0 7.000 yes 2 14
## 386 1670 0 female 22.0 1.500 no 4 14
## 387 1671 0 female 32.0 10.000 yes 2 16
## 388 1675 0 female 22.0 0.750 no 2 16
## 389 1688 0 female 22.0 1.500 no 2 16
## 390 1691 0 female 42.0 15.000 yes 3 18
## 391 1695 0 female 27.0 7.000 yes 5 14
## 392 1698 0 male 42.0 15.000 yes 4 16
## 393 1704 0 female 57.0 15.000 yes 3 18
## 394 1705 0 male 42.0 15.000 yes 3 18
## 395 1711 0 female 32.0 7.000 yes 2 14
## 396 1719 0 male 22.0 4.000 no 5 12
## 397 1723 0 female 22.0 1.500 no 1 16
## 398 1726 0 female 22.0 0.750 no 1 14
## 399 1749 0 female 32.0 15.000 yes 4 12
## 400 1752 0 male 22.0 1.500 no 2 18
## 401 1754 0 male 27.0 4.000 yes 5 17
## 402 1758 0 female 27.0 4.000 yes 4 12
## 403 1761 0 male 42.0 15.000 yes 5 18
## 404 1773 0 male 32.0 1.500 no 2 20
## 405 1775 0 male 57.0 15.000 no 4 9
## 406 1786 0 male 37.0 7.000 no 4 18
## 407 1793 0 male 52.0 15.000 yes 2 17
## 408 1799 0 male 47.0 15.000 yes 4 17
## 409 1803 0 female 27.0 7.000 no 2 17
## 410 1806 0 female 27.0 7.000 yes 4 14
## 411 1807 0 female 22.0 4.000 no 2 14
## 412 1808 0 male 37.0 7.000 yes 2 20
## 413 1814 0 male 27.0 7.000 no 4 12
## 414 1815 0 male 42.0 10.000 yes 4 18
## 415 1818 0 female 22.0 1.500 no 3 14
## 416 1827 0 female 22.0 4.000 yes 2 14
## 417 1834 0 female 57.0 15.000 no 4 20
## 418 1835 0 male 37.0 15.000 yes 4 14
## 419 1843 0 female 27.0 7.000 yes 3 18
## 420 1846 0 female 17.5 10.000 no 4 14
## 421 1850 0 male 22.0 4.000 yes 4 16
## 422 1851 0 female 27.0 4.000 yes 2 16
## 423 1854 0 female 37.0 15.000 yes 2 14
## 424 1859 0 female 22.0 1.500 no 5 14
## 425 1861 0 male 27.0 7.000 yes 2 20
## 426 1866 0 male 27.0 4.000 yes 4 14
## 427 1873 0 male 22.0 0.125 no 1 16
## 428 1875 0 female 27.0 7.000 yes 4 14
## 429 1885 0 female 32.0 15.000 yes 5 16
## 430 1892 0 male 32.0 10.000 yes 4 18
## 431 1895 0 female 32.0 15.000 yes 2 14
## 432 1896 0 female 22.0 1.500 no 3 17
## 433 1897 0 male 27.0 4.000 yes 4 17
## 434 1899 0 female 52.0 15.000 yes 5 14
## 435 1904 0 female 27.0 7.000 yes 2 12
## 436 1905 0 female 27.0 7.000 yes 3 12
## 437 1908 0 female 42.0 15.000 yes 2 14
## 438 1916 0 female 42.0 15.000 yes 4 14
## 439 1918 0 male 27.0 7.000 yes 4 14
## 440 1920 0 male 27.0 7.000 yes 2 20
## 441 1930 0 female 42.0 15.000 yes 3 12
## 442 1940 0 male 27.0 4.000 yes 3 16
## 443 1947 0 female 27.0 7.000 yes 3 14
## 444 1949 0 female 22.0 1.500 no 2 14
## 445 1951 0 female 27.0 4.000 yes 4 14
## 446 1952 0 female 22.0 4.000 no 4 14
## 447 1960 0 female 22.0 1.500 no 2 16
## 448 9001 0 male 47.0 15.000 no 4 14
## 449 9012 0 male 37.0 10.000 yes 2 18
## 450 9023 0 male 37.0 15.000 yes 3 17
## 451 9029 0 female 27.0 4.000 yes 2 16
## 452 6 3 male 27.0 1.500 no 3 18
## 453 12 3 female 27.0 4.000 yes 3 17
## 454 43 7 male 37.0 15.000 yes 5 18
## 455 53 12 female 32.0 10.000 yes 3 17
## 456 67 1 male 22.0 0.125 no 4 16
## 457 79 1 female 22.0 1.500 yes 2 14
## 458 122 12 male 37.0 15.000 yes 4 14
## 459 126 7 female 22.0 1.500 no 2 14
## 460 133 2 male 37.0 15.000 yes 2 18
## 461 138 3 female 32.0 15.000 yes 4 12
## 462 154 1 female 37.0 15.000 yes 4 14
## 463 159 7 female 42.0 15.000 yes 3 17
## 464 174 12 female 42.0 15.000 yes 5 9
## 465 176 12 male 37.0 10.000 yes 2 20
## 466 181 12 female 32.0 15.000 yes 3 14
## 467 182 3 male 27.0 4.000 no 1 18
## 468 186 7 male 37.0 10.000 yes 2 18
## 469 189 7 female 27.0 4.000 no 3 17
## 470 204 1 male 42.0 15.000 yes 4 16
## 471 215 1 female 47.0 15.000 yes 5 14
## 472 232 7 female 27.0 4.000 yes 3 18
## 473 233 1 female 27.0 7.000 yes 5 14
## 474 252 12 male 27.0 1.500 yes 3 17
## 475 253 12 female 27.0 7.000 yes 4 14
## 476 274 3 female 42.0 15.000 yes 4 16
## 477 275 7 female 27.0 10.000 yes 4 12
## 478 287 1 male 27.0 1.500 no 2 18
## 479 288 1 male 32.0 4.000 no 4 20
## 480 325 1 female 27.0 7.000 yes 3 14
## 481 328 3 female 32.0 10.000 yes 4 14
## 482 344 3 male 27.0 4.000 yes 2 18
## 483 353 1 female 17.5 0.750 no 5 14
## 484 354 1 female 32.0 10.000 yes 4 18
## 485 367 7 female 32.0 7.000 yes 2 17
## 486 369 7 male 37.0 15.000 yes 2 20
## 487 390 7 female 37.0 10.000 no 1 20
## 488 392 12 female 32.0 10.000 yes 2 16
## 489 423 7 male 52.0 15.000 yes 2 20
## 490 432 7 female 42.0 15.000 yes 1 12
## 491 436 1 male 52.0 15.000 yes 2 20
## 492 483 2 male 37.0 15.000 yes 3 18
## 493 513 12 female 22.0 4.000 no 3 12
## 494 516 12 male 27.0 7.000 yes 1 18
## 495 518 1 male 27.0 4.000 yes 3 18
## 496 520 12 male 47.0 15.000 yes 4 17
## 497 526 12 female 42.0 15.000 yes 4 12
## 498 528 7 male 27.0 4.000 no 3 14
## 499 553 7 female 32.0 7.000 yes 4 18
## 500 576 1 male 32.0 0.417 yes 3 12
## 501 611 3 male 47.0 15.000 yes 5 16
## 502 625 12 male 37.0 15.000 yes 2 20
## 503 635 7 male 22.0 4.000 yes 2 17
## 504 646 1 male 27.0 4.000 no 2 14
## 505 657 7 female 52.0 15.000 yes 5 16
## 506 659 1 male 27.0 4.000 no 3 14
## 507 666 1 female 27.0 10.000 yes 4 16
## 508 679 1 male 32.0 7.000 yes 3 14
## 509 729 7 male 32.0 7.000 yes 2 18
## 510 755 3 male 22.0 1.500 no 1 14
## 511 758 7 male 22.0 4.000 yes 3 18
## 512 770 7 male 42.0 15.000 yes 4 20
## 513 786 2 female 57.0 15.000 yes 1 18
## 514 797 7 female 32.0 4.000 yes 3 18
## 515 811 1 male 27.0 4.000 yes 1 16
## 516 834 7 male 32.0 7.000 yes 4 16
## 517 858 2 male 57.0 15.000 yes 1 17
## 518 885 7 female 42.0 15.000 yes 4 14
## 519 893 7 male 37.0 10.000 yes 1 18
## 520 927 3 male 42.0 15.000 yes 3 17
## 521 928 1 female 52.0 15.000 yes 3 14
## 522 933 2 female 27.0 7.000 yes 3 17
## 523 951 12 male 32.0 7.000 yes 2 12
## 524 968 1 male 22.0 4.000 no 4 14
## 525 972 3 male 27.0 7.000 yes 3 18
## 526 975 12 female 37.0 15.000 yes 1 18
## 527 977 7 female 32.0 15.000 yes 3 17
## 528 981 7 female 27.0 7.000 no 2 17
## 529 986 1 female 32.0 7.000 yes 3 17
## 530 1002 1 male 32.0 1.500 yes 2 14
## 531 1007 12 female 42.0 15.000 yes 4 14
## 532 1011 7 male 32.0 10.000 yes 3 14
## 533 1035 7 male 37.0 4.000 yes 1 20
## 534 1050 1 female 27.0 4.000 yes 2 16
## 535 1056 12 female 42.0 15.000 yes 3 14
## 536 1057 1 male 27.0 10.000 yes 5 20
## 537 1075 12 male 37.0 10.000 yes 2 20
## 538 1080 12 female 27.0 7.000 yes 1 14
## 539 1125 3 female 27.0 7.000 yes 4 12
## 540 1131 3 male 32.0 10.000 yes 2 14
## 541 1138 12 female 17.5 0.750 yes 2 12
## 542 1150 12 female 32.0 15.000 yes 3 18
## 543 1163 2 female 22.0 7.000 no 4 14
## 544 1169 1 male 32.0 7.000 yes 4 20
## 545 1198 7 male 27.0 4.000 yes 2 18
## 546 1204 1 female 22.0 1.500 yes 5 14
## 547 1218 12 female 32.0 15.000 no 3 17
## 548 1230 12 female 42.0 15.000 yes 2 12
## 549 1236 7 male 42.0 15.000 yes 3 20
## 550 1247 12 male 32.0 10.000 no 2 18
## 551 1259 12 female 32.0 15.000 yes 3 9
## 552 1294 7 male 57.0 15.000 yes 5 20
## 553 1353 12 male 47.0 15.000 yes 4 20
## 554 1370 2 female 42.0 15.000 yes 2 17
## 555 1427 12 male 37.0 15.000 yes 3 17
## 556 1445 12 male 37.0 15.000 yes 5 17
## 557 1460 7 male 27.0 10.000 yes 2 20
## 558 1480 2 male 37.0 15.000 yes 2 16
## 559 1505 12 female 32.0 15.000 yes 1 14
## 560 1543 7 male 32.0 10.000 yes 3 17
## 561 1548 2 male 37.0 15.000 yes 4 18
## 562 1550 7 female 27.0 1.500 no 2 17
## 563 1561 3 female 47.0 15.000 yes 2 17
## 564 1564 12 male 37.0 15.000 yes 2 17
## 565 1573 12 female 27.0 4.000 no 2 14
## 566 1575 2 female 27.0 10.000 yes 4 14
## 567 1599 1 female 22.0 4.000 yes 3 16
## 568 1622 12 male 52.0 7.000 no 4 16
## 569 1629 2 female 27.0 4.000 yes 1 16
## 570 1664 7 female 37.0 15.000 yes 2 17
## 571 1669 2 female 27.0 4.000 no 1 17
## 572 1674 12 female 17.5 0.750 yes 2 12
## 573 1682 7 female 32.0 15.000 yes 5 18
## 574 1685 7 female 22.0 4.000 no 1 16
## 575 1697 2 male 32.0 4.000 yes 4 18
## 576 1716 1 female 22.0 1.500 yes 3 18
## 577 1730 3 female 42.0 15.000 yes 2 17
## 578 1731 1 male 32.0 7.000 yes 4 16
## 579 1732 12 male 37.0 15.000 no 3 14
## 580 1743 1 male 42.0 15.000 yes 3 16
## 581 1751 1 male 27.0 4.000 yes 1 18
## 582 1757 2 male 37.0 15.000 yes 4 20
## 583 1763 7 male 37.0 15.000 yes 3 20
## 584 1766 3 male 22.0 1.500 no 2 12
## 585 1772 3 male 32.0 4.000 yes 3 20
## 586 1776 2 male 32.0 15.000 yes 5 20
## 587 1782 12 female 52.0 15.000 yes 1 18
## 588 1784 12 male 47.0 15.000 no 1 18
## 589 1791 3 female 32.0 15.000 yes 4 16
## 590 1831 7 female 32.0 15.000 yes 3 14
## 591 1840 7 female 27.0 7.000 yes 4 16
## 592 1844 12 male 42.0 15.000 yes 3 18
## 593 1856 7 female 42.0 15.000 yes 2 14
## 594 1876 12 male 27.0 7.000 yes 2 17
## 595 1929 3 male 32.0 10.000 yes 4 14
## 596 1935 7 male 47.0 15.000 yes 3 16
## 597 1938 1 male 22.0 1.500 yes 1 12
## 598 1941 7 female 32.0 10.000 yes 2 18
## 599 1954 2 male 32.0 10.000 yes 2 17
## 600 1959 2 male 22.0 7.000 yes 3 18
## 601 9010 1 female 32.0 15.000 yes 3 14
## occupation rating
## 1 7 4
## 2 6 4
## 3 1 4
## 4 6 5
## 5 6 3
## 6 5 5
## 7 1 3
## 8 4 4
## 9 1 2
## 10 4 5
## 11 7 2
## 12 6 4
## 13 6 4
## 14 5 4
## 15 5 4
## 16 5 5
## 17 4 3
## 18 5 4
## 19 5 5
## 20 1 5
## 21 5 5
## 22 5 5
## 23 5 4
## 24 1 5
## 25 6 4
## 26 5 5
## 27 1 5
## 28 6 4
## 29 5 5
## 30 5 4
## 31 6 3
## 32 5 5
## 33 6 4
## 34 5 4
## 35 6 4
## 36 5 3
## 37 1 4
## 38 5 5
## 39 3 5
## 40 1 5
## 41 6 5
## 42 6 3
## 43 1 4
## 44 6 1
## 45 5 3
## 46 5 5
## 47 6 5
## 48 5 4
## 49 3 5
## 50 6 4
## 51 3 5
## 52 6 4
## 53 6 5
## 54 6 4
## 55 5 5
## 56 3 1
## 57 4 4
## 58 1 5
## 59 5 4
## 60 6 4
## 61 7 2
## 62 6 4
## 63 5 5
## 64 4 5
## 65 6 4
## 66 3 5
## 67 6 4
## 68 6 5
## 69 5 5
## 70 6 4
## 71 5 5
## 72 4 5
## 73 5 5
## 74 4 3
## 75 5 5
## 76 5 5
## 77 5 5
## 78 6 5
## 79 5 5
## 80 7 2
## 81 6 5
## 82 6 5
## 83 3 4
## 84 3 3
## 85 1 5
## 86 6 4
## 87 5 5
## 88 5 2
## 89 1 5
## 90 1 4
## 91 3 4
## 92 5 3
## 93 5 4
## 94 5 3
## 95 3 4
## 96 1 5
## 97 6 1
## 98 6 4
## 99 5 5
## 100 6 5
## 101 1 5
## 102 6 4
## 103 4 5
## 104 6 5
## 105 6 4
## 106 6 4
## 107 5 4
## 108 5 4
## 109 6 3
## 110 5 4
## 111 1 5
## 112 6 5
## 113 5 5
## 114 7 4
## 115 5 4
## 116 6 5
## 117 3 2
## 118 6 4
## 119 1 5
## 120 2 4
## 121 2 5
## 122 5 4
## 123 5 3
## 124 1 2
## 125 5 5
## 126 6 5
## 127 4 5
## 128 5 5
## 129 6 5
## 130 6 5
## 131 1 3
## 132 6 5
## 133 6 5
## 134 6 3
## 135 5 3
## 136 5 4
## 137 6 5
## 138 1 4
## 139 5 5
## 140 5 4
## 141 1 4
## 142 6 4
## 143 5 3
## 144 5 4
## 145 6 3
## 146 5 4
## 147 5 4
## 148 1 3
## 149 6 4
## 150 5 4
## 151 6 3
## 152 4 3
## 153 5 3
## 154 6 3
## 155 4 3
## 156 6 3
## 157 1 1
## 158 1 2
## 159 5 3
## 160 5 5
## 161 6 5
## 162 5 5
## 163 6 5
## 164 5 4
## 165 1 4
## 166 1 2
## 167 5 2
## 168 5 4
## 169 3 4
## 170 2 2
## 171 6 5
## 172 4 5
## 173 1 5
## 174 5 4
## 175 6 5
## 176 5 2
## 177 5 4
## 178 1 4
## 179 1 3
## 180 5 4
## 181 6 2
## 182 6 5
## 183 5 3
## 184 5 5
## 185 3 5
## 186 1 5
## 187 5 5
## 188 5 5
## 189 4 5
## 190 5 5
## 191 4 5
## 192 1 4
## 193 5 4
## 194 5 4
## 195 1 3
## 196 5 4
## 197 5 3
## 198 4 5
## 199 6 2
## 200 5 5
## 201 3 5
## 202 3 5
## 203 6 5
## 204 3 3
## 205 5 2
## 206 5 3
## 207 1 2
## 208 1 5
## 209 4 4
## 210 5 3
## 211 3 3
## 212 5 5
## 213 6 2
## 214 5 5
## 215 4 5
## 216 6 5
## 217 6 5
## 218 5 3
## 219 5 4
## 220 4 5
## 221 6 3
## 222 6 5
## 223 5 5
## 224 5 4
## 225 1 4
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## 227 5 4
## 228 2 4
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## 230 4 5
## 231 5 4
## 232 5 5
## 233 6 5
## 234 6 4
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## 236 5 3
## 237 1 5
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## 239 5 5
## 240 4 4
## 241 5 4
## 242 1 4
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## 244 5 5
## 245 3 4
## 246 2 4
## 247 5 3
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## 249 5 4
## 250 6 4
## 251 2 3
## 252 6 4
## 253 5 5
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## 273 6 3
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## 279 6 4
## 280 5 5
## 281 6 4
## 282 1 4
## 283 5 4
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## 289 1 2
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## 291 4 4
## 292 5 5
## 293 5 4
## 294 5 3
## 295 1 5
## 296 5 3
## 297 5 5
## 298 5 5
## 299 5 3
## 300 7 4
## 301 4 2
## 302 5 5
## 303 6 4
## 304 4 5
## 305 3 4
## 306 5 4
## 307 4 5
## 308 3 3
## 309 5 3
## 310 4 5
## 311 3 2
## 312 1 5
## 313 6 5
## 314 4 4
## 315 1 3
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## 317 1 5
## 318 7 4
## 319 1 4
## 320 2 4
## 321 1 5
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## 327 6 4
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## 330 5 3
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## 336 4 2
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## 342 1 3
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## 348 6 3
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## 367 1 5
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## 381 5 5
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## 384 6 4
## 385 1 2
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## 395 1 2
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## 411 3 3
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## 415 1 5
## 416 1 3
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## 418 4 3
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## 420 4 5
## 421 5 5
## 422 1 4
## 423 5 1
## 424 1 4
## 425 5 4
## 426 5 5
## 427 3 5
## 428 1 4
## 429 5 3
## 430 5 4
## 431 3 4
## 432 5 5
## 433 4 4
## 434 1 5
## 435 1 2
## 436 1 4
## 437 1 4
## 438 5 4
## 439 3 3
## 440 6 2
## 441 3 3
## 442 3 5
## 443 1 4
## 444 4 5
## 445 1 4
## 446 5 5
## 447 4 5
## 448 5 4
## 449 6 2
## 450 5 4
## 451 1 4
## 452 4 4
## 453 1 5
## 454 6 2
## 455 5 2
## 456 5 5
## 457 1 5
## 458 5 2
## 459 3 4
## 460 6 4
## 461 3 2
## 462 4 2
## 463 1 4
## 464 4 1
## 465 6 2
## 466 1 2
## 467 6 5
## 468 7 3
## 469 5 5
## 470 5 5
## 471 4 5
## 472 5 4
## 473 1 4
## 474 5 4
## 475 6 2
## 476 5 4
## 477 7 3
## 478 5 2
## 479 6 4
## 480 1 3
## 481 1 4
## 482 7 2
## 483 4 5
## 484 1 5
## 485 6 4
## 486 6 4
## 487 5 3
## 488 5 5
## 489 6 4
## 490 1 3
## 491 6 3
## 492 6 5
## 493 3 4
## 494 6 2
## 495 5 5
## 496 6 5
## 497 1 1
## 498 3 4
## 499 4 5
## 500 3 4
## 501 5 4
## 502 5 4
## 503 6 4
## 504 4 5
## 505 1 3
## 506 3 3
## 507 1 4
## 508 7 4
## 509 4 1
## 510 3 2
## 511 6 4
## 512 6 4
## 513 5 4
## 514 5 2
## 515 4 4
## 516 1 4
## 517 4 4
## 518 5 2
## 519 5 3
## 520 6 1
## 521 4 4
## 522 5 3
## 523 4 2
## 524 2 5
## 525 6 4
## 526 5 5
## 527 1 3
## 528 5 5
## 529 5 3
## 530 2 4
## 531 1 2
## 532 5 4
## 533 6 3
## 534 5 3
## 535 4 3
## 536 6 5
## 537 6 2
## 538 3 3
## 539 1 2
## 540 4 4
## 541 1 3
## 542 5 4
## 543 4 3
## 544 6 5
## 545 6 2
## 546 5 3
## 547 5 1
## 548 1 2
## 549 5 4
## 550 4 2
## 551 1 1
## 552 4 5
## 553 6 4
## 554 6 3
## 555 6 3
## 556 5 2
## 557 6 4
## 558 5 4
## 559 5 2
## 560 6 3
## 561 5 1
## 562 5 5
## 563 5 2
## 564 5 4
## 565 5 5
## 566 1 5
## 567 1 3
## 568 5 5
## 569 3 5
## 570 6 4
## 571 3 1
## 572 3 5
## 573 5 4
## 574 3 5
## 575 6 4
## 576 5 2
## 577 5 4
## 578 4 4
## 579 6 2
## 580 6 3
## 581 5 4
## 582 7 3
## 583 6 4
## 584 3 3
## 585 6 2
## 586 6 5
## 587 5 5
## 588 6 5
## 589 4 4
## 590 3 2
## 591 1 2
## 592 6 2
## 593 3 2
## 594 5 4
## 595 4 3
## 596 4 2
## 597 2 5
## 598 5 4
## 599 6 5
## 600 6 2
## 601 1 5#1 Overview of the data Affairs
summary(Affairs)## X affairs gender age
## Min. : 4 Min. : 0.000 Length:601 Min. :17.50
## 1st Qu.: 528 1st Qu.: 0.000 Class :character 1st Qu.:27.00
## Median :1009 Median : 0.000 Mode :character Median :32.00
## Mean :1060 Mean : 1.456 Mean :32.49
## 3rd Qu.:1453 3rd Qu.: 0.000 3rd Qu.:37.00
## Max. :9029 Max. :12.000 Max. :57.00
## yearsmarried children religiousness education
## Min. : 0.125 Length:601 Min. :1.000 Min. : 9.00
## 1st Qu.: 4.000 Class :character 1st Qu.:2.000 1st Qu.:14.00
## Median : 7.000 Mode :character Median :3.000 Median :16.00
## Mean : 8.178 Mean :3.116 Mean :16.17
## 3rd Qu.:15.000 3rd Qu.:4.000 3rd Qu.:18.00
## Max. :15.000 Max. :5.000 Max. :20.00
## occupation rating
## Min. :1.000 Min. :1.000
## 1st Qu.:3.000 1st Qu.:3.000
## Median :5.000 Median :4.000
## Mean :4.195 Mean :3.932
## 3rd Qu.:6.000 3rd Qu.:5.000
## Max. :7.000 Max. :5.000#1 the mean and the median of the data
summary(Affairs$age)## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 17.50 27.00 32.00 32.49 37.00 57.00mean(Affairs$age)## [1] 32.48752median(Affairs$age)## [1] 32mean(Affairs$yearsmarried)## [1] 8.177696median(Affairs$yearsmarried)## [1] 7mean(Affairs$religiousness)## [1] 3.116473median(Affairs$religiousness)## [1] 3#2 create a new data frame and rename it
df_exp1 <- Affairs[c(10:21),c(2:4,6:7)]#3 new column names
colnames(df_exp1)[colnames(df_exp1) == "c1"] ="gen"
colnames(df_exp1)[colnames(df_exp1) == "affairs"] ="aff"
colnames(df_exp1)[colnames(df_exp1) == "age"] ="yearsold"
colnames(df_exp1)[colnames(df_exp1) == "children"] ="kids"
colnames(df_exp1)[colnames(df_exp1) == "religiousness"] ="religion"#4 summary of new data frame, mean and median of two attribute.
summary(df_exp1)## aff gender yearsold kids
## Min. :0 Length:12 Min. :22.00 Length:12
## 1st Qu.:0 Class :character 1st Qu.:22.00 Class :character
## Median :0 Mode :character Median :27.00 Mode :character
## Mean :0 Mean :29.08
## 3rd Qu.:0 3rd Qu.:37.00
## Max. :0 Max. :47.00
## religion
## Min. :1.00
## 1st Qu.:2.00
## Median :2.00
## Mean :2.75
## 3rd Qu.:4.00
## Max. :5.00summary(df_exp1$yearsold)## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 22.00 22.00 27.00 29.08 37.00 47.00mean(df_exp1$yearsold)## [1] 29.08333median(df_exp1$yearsold)## [1] 27mean(df_exp1$religion)## [1] 2.75median(df_exp1$religion)## [1] 2#4 COMPARING THE MEAN AND THE MEDIAN: the mean of the age in the original data is 32.48752 while in the dataframe is 29.08333, so it greater in the Affairs data than the df_exp1. it was the oppsoite for the religiousness (bigger for df_exp1 than Affairs). For the median, both in religion and age is bigger in Affairs dataset.
#5 For at least 3 values in a column please rename so that every value in that column is renamed.
replace(df_exp1$aff,df_exp1$aff<=0,"Zero")## [1] "Zero" "Zero" "Zero" "Zero" "Zero" "Zero" "Zero" "Zero" "Zero" "Zero"
## [11] "Zero" "Zero"replace(df_exp1$gender,df_exp1$gender<="male","M")## [1] "M" "M" "M" "M" "M" "M" "M" "M" "M" "M" "M" "M"replace(df_exp1$yearsold,df_exp1$yearsold>=30,"over30YO")## [1] "22" "over30YO" "27" "over30YO" "22" "27"
## [7] "over30YO" "over30YO" "22" "22" "27" "22"#6 Display enough rows to see examples of all of steps 1-5 above.
head(df_exp1,n=15)## aff gender yearsold kids religion
## 10 0 male 22 no 4
## 11 0 male 37 yes 2
## 12 0 male 27 yes 4
## 13 0 male 47 yes 5
## 14 0 female 22 no 2
## 15 0 female 27 no 4
## 16 0 female 37 yes 1
## 17 0 female 37 yes 2
## 18 0 female 22 no 3
## 19 0 female 22 no 2
## 20 0 female 27 yes 2
## 21 0 female 22 no 2library(readr)
library(curl)## Using libcurl 7.79.1 with LibreSSL/3.3.6##
## Attaching package: 'curl'## The following object is masked from 'package:readr':
##
## parse_dateurl_Affairs<-"https://raw.githubusercontent.com/SalouaDaouki/R--Week-2-Assignment-/main/Data/Affairs.csv"
Affairsdata<-read.csv(url_Affairs)
head(Affairsdata)## X affairs gender age yearsmarried children religiousness education
## 1 4 0 male 37 10.00 no 3 18
## 2 5 0 female 27 4.00 no 4 14
## 3 11 0 female 32 15.00 yes 1 12
## 4 16 0 male 57 15.00 yes 5 18
## 5 23 0 male 22 0.75 no 2 17
## 6 29 0 female 32 1.50 no 2 17
## occupation rating
## 1 7 4
## 2 6 4
## 3 1 4
## 4 6 5
## 5 6 3
## 6 5 5