Titanic Data Analysis
Table containing the average age of male and female survivors
age.survived <- titanic[which(titanic$Survived=="1"), names(titanic) %in% c("Age", "Survived", "Sex")]
age.survived
## Survived Sex Age
## 2 1 female 38.0
## 3 1 female 26.0
## 4 1 female 35.0
## 9 1 female 27.0
## 10 1 female 14.0
## 11 1 female 4.0
## 12 1 female 58.0
## 16 1 female 55.0
## 18 1 male 29.7
## 20 1 female 29.7
## 22 1 male 34.0
## 23 1 female 15.0
## 24 1 male 28.0
## 26 1 female 38.0
## 29 1 female 29.7
## 32 1 female 29.7
## 33 1 female 29.7
## 37 1 male 29.7
## 40 1 female 14.0
## 44 1 female 3.0
## 45 1 female 19.0
## 48 1 female 29.7
## 53 1 female 49.0
## 54 1 female 29.0
## 56 1 male 29.7
## 57 1 female 21.0
## 59 1 female 5.0
## 65 1 male 29.7
## 66 1 female 29.0
## 68 1 female 17.0
## 74 1 male 32.0
## 78 1 male 0.8
## 79 1 female 30.0
## 81 1 male 29.0
## 82 1 female 29.7
## 84 1 female 17.0
## 85 1 female 33.0
## 88 1 female 23.0
## 97 1 male 23.0
## 98 1 female 34.0
## 106 1 female 21.0
## 107 1 male 29.7
## 109 1 female 29.7
## 123 1 female 32.5
## 125 1 male 12.0
## 127 1 male 24.0
## 128 1 female 29.7
## 133 1 female 29.0
## 136 1 female 19.0
## 141 1 female 22.0
## 142 1 female 24.0
## 146 1 male 27.0
## 151 1 female 22.0
## 156 1 female 16.0
## 161 1 female 40.0
## 165 1 male 9.0
## 166 1 female 29.7
## 172 1 female 1.0
## 183 1 male 1.0
## 184 1 female 4.0
## 186 1 female 29.7
## 187 1 male 45.0
## 190 1 female 32.0
## 192 1 female 19.0
## 193 1 male 3.0
## 194 1 female 44.0
## 195 1 female 58.0
## 198 1 female 29.7
## 204 1 male 18.0
## 207 1 male 26.0
## 208 1 female 16.0
## 209 1 male 40.0
## 211 1 female 35.0
## 215 1 female 31.0
## 216 1 female 27.0
## 218 1 female 32.0
## 220 1 male 16.0
## 224 1 male 38.0
## 226 1 male 19.0
## 230 1 female 35.0
## 233 1 female 5.0
## 237 1 female 8.0
## 241 1 female 29.7
## 247 1 female 24.0
## 248 1 male 37.0
## 255 1 female 29.0
## 256 1 female 29.7
## 257 1 female 30.0
## 258 1 female 35.0
## 259 1 female 50.0
## 261 1 male 3.0
## 267 1 male 25.0
## 268 1 female 58.0
## 269 1 female 35.0
## 271 1 male 25.0
## 272 1 female 41.0
## 274 1 female 29.7
## 275 1 female 63.0
## 279 1 female 35.0
## 283 1 male 19.0
## 286 1 male 30.0
## 288 1 male 42.0
## 289 1 female 22.0
## 290 1 female 26.0
## 291 1 female 19.0
## 298 1 male 29.7
## 299 1 female 50.0
## 300 1 female 29.7
## 301 1 male 29.7
## 303 1 female 29.7
## 305 1 male 0.9
## 306 1 female 29.7
## 307 1 female 17.0
## 309 1 female 30.0
## 310 1 female 24.0
## 311 1 female 18.0
## 315 1 female 26.0
## 316 1 female 24.0
## 318 1 female 31.0
## 319 1 female 40.0
## 322 1 female 30.0
## 323 1 female 22.0
## 325 1 female 36.0
## 327 1 female 36.0
## 328 1 female 31.0
## 329 1 female 16.0
## 330 1 female 29.7
## 334 1 female 29.7
## 337 1 female 41.0
## 338 1 male 45.0
## 340 1 male 2.0
## 341 1 female 24.0
## 345 1 female 24.0
## 346 1 female 40.0
## 347 1 female 29.7
## 348 1 male 3.0
## 356 1 female 22.0
## 358 1 female 29.7
## 359 1 female 29.7
## 366 1 female 60.0
## 367 1 female 29.7
## 368 1 female 29.7
## 369 1 female 24.0
## 370 1 male 25.0
## 375 1 female 29.7
## 376 1 female 22.0
## 380 1 female 42.0
## 381 1 female 1.0
## 383 1 female 35.0
## 387 1 female 36.0
## 389 1 female 17.0
## 390 1 male 36.0
## 391 1 male 21.0
## 393 1 female 23.0
## 394 1 female 24.0
## 399 1 female 28.0
## 400 1 male 39.0
## 407 1 male 3.0
## 412 1 female 33.0
## 414 1 male 44.0
## 416 1 female 34.0
## 417 1 female 18.0
## 426 1 female 28.0
## 427 1 female 19.0
## 429 1 male 32.0
## 430 1 male 28.0
## 431 1 female 29.7
## 432 1 female 42.0
## 435 1 female 14.0
## 437 1 female 24.0
## 440 1 female 45.0
## 443 1 female 28.0
## 444 1 male 29.7
## 445 1 male 4.0
## 446 1 female 13.0
## 447 1 male 34.0
## 448 1 female 5.0
## 449 1 male 52.0
## 453 1 male 49.0
## 455 1 male 29.0
## 457 1 female 29.7
## 458 1 female 50.0
## 460 1 male 48.0
## 469 1 female 0.8
## 472 1 female 33.0
## 473 1 female 23.0
## 479 1 female 2.0
## 483 1 female 63.0
## 484 1 male 25.0
## 486 1 female 35.0
## 489 1 male 9.0
## 496 1 female 54.0
## 504 1 female 16.0
## 506 1 female 33.0
## 507 1 male 29.7
## 509 1 male 26.0
## 510 1 male 29.0
## 512 1 male 36.0
## 513 1 female 54.0
## 516 1 female 34.0
## 518 1 female 36.0
## 520 1 female 30.0
## 523 1 female 44.0
## 526 1 female 50.0
## 530 1 female 2.0
## 533 1 female 29.7
## 535 1 female 7.0
## 537 1 female 30.0
## 539 1 female 22.0
## 540 1 female 36.0
## 543 1 male 32.0
## 546 1 female 19.0
## 547 1 male 29.7
## 549 1 male 8.0
## 550 1 male 17.0
## 553 1 male 22.0
## 554 1 female 22.0
## 556 1 female 48.0
## 558 1 female 39.0
## 559 1 female 36.0
## 569 1 male 32.0
## 570 1 male 62.0
## 571 1 female 53.0
## 572 1 male 36.0
## 573 1 female 29.7
## 576 1 female 34.0
## 577 1 female 39.0
## 579 1 male 32.0
## 580 1 female 25.0
## 581 1 female 39.0
## 585 1 female 18.0
## 587 1 male 60.0
## 591 1 female 52.0
## 596 1 female 29.7
## 599 1 male 49.0
## 600 1 female 24.0
## 604 1 male 35.0
## 607 1 male 27.0
## 608 1 female 22.0
## 609 1 female 40.0
## 612 1 female 29.7
## 615 1 female 24.0
## 618 1 female 4.0
## 621 1 male 42.0
## 622 1 male 20.0
## 627 1 female 21.0
## 630 1 male 80.0
## 632 1 male 32.0
## 635 1 female 28.0
## 641 1 female 24.0
## 643 1 male 29.7
## 644 1 female 0.8
## 645 1 male 48.0
## 647 1 male 56.0
## 649 1 female 23.0
## 651 1 female 18.0
## 653 1 female 29.7
## 660 1 male 50.0
## 664 1 male 20.0
## 669 1 female 29.7
## 670 1 female 40.0
## 673 1 male 31.0
## 677 1 female 18.0
## 679 1 male 36.0
## 681 1 male 27.0
## 689 1 female 15.0
## 690 1 male 31.0
## 691 1 female 4.0
## 692 1 male 29.7
## 697 1 female 29.7
## 700 1 female 18.0
## 701 1 male 35.0
## 706 1 female 45.0
## 707 1 male 42.0
## 708 1 female 22.0
## 709 1 male 29.7
## 710 1 female 24.0
## 712 1 male 48.0
## 716 1 female 38.0
## 717 1 female 27.0
## 720 1 female 6.0
## 724 1 male 27.0
## 726 1 female 30.0
## 727 1 female 29.7
## 730 1 female 29.0
## 737 1 male 35.0
## 740 1 male 29.7
## 742 1 female 21.0
## 744 1 male 31.0
## 747 1 female 30.0
## 750 1 female 4.0
## 751 1 male 6.0
## 754 1 female 48.0
## 755 1 male 0.7
## 759 1 female 33.0
## 762 1 male 20.0
## 763 1 female 36.0
## 765 1 female 51.0
## 774 1 female 54.0
## 777 1 female 5.0
## 779 1 female 43.0
## 780 1 female 13.0
## 781 1 female 17.0
## 786 1 female 18.0
## 788 1 male 1.0
## 796 1 female 49.0
## 797 1 female 31.0
## 801 1 female 31.0
## 802 1 male 11.0
## 803 1 male 0.4
## 804 1 male 27.0
## 809 1 female 33.0
## 820 1 female 52.0
## 821 1 male 27.0
## 823 1 female 27.0
## 827 1 male 1.0
## 828 1 male 29.7
## 829 1 female 15.0
## 830 1 male 0.8
## 834 1 female 39.0
## 837 1 male 32.0
## 838 1 male 29.7
## 841 1 female 30.0
## 848 1 female 29.7
## 852 1 female 16.0
## 854 1 female 18.0
## 855 1 female 45.0
## 856 1 male 51.0
## 857 1 female 24.0
## 861 1 female 48.0
## 864 1 female 42.0
## 865 1 female 27.0
## 868 1 male 4.0
## 870 1 female 47.0
## 873 1 female 28.0
## 874 1 female 15.0
## 878 1 female 56.0
## 879 1 female 25.0
## 886 1 female 19.0
## 888 1 male 26.0
mytable <- aggregate(Age ~ Sex, data=age.survived, mean)
mytable
## Sex Age
## 1 female 28.79784
## 2 male 27.63119
Avg. age of female survivors is 28.79 and males survivors is 27.63
t.test to check whether “The Titanic survivors were younger than the passengers who died.”
aggregate(titanic$Age, list(Survived=titanic$Survived), mean)
## Survived x
## 1 0 30.41530
## 2 1 28.42382
t.test(titanic$Age, titanic$Survived, paired=TRUE)
##
## Paired t-test
##
## data: titanic$Age and titanic$Survived
## t = 67.065, df = 888, p-value < 2.2e-16
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
## 28.41458 30.12782
## sample estimates:
## mean of the differences
## 29.2712
The p-value is 2.2e-16 which is less than 0.05. Therefore, the hypothesis that the average age of people who survived and and who did not survive is insignificant, can be rejected. And from the aggregate command it is clear that the avg age of survibors is lower than the avg age of people wo did not survive.