Load Libraries
library(tidyverse) # for the map() command
## ── Attaching packages ─────────────────────────────────────── tidyverse 1.3.2 ──
## ✔ ggplot2 3.3.6 ✔ purrr 0.3.4
## ✔ tibble 3.1.8 ✔ dplyr 1.0.9
## ✔ tidyr 1.2.0 ✔ stringr 1.4.1
## ✔ readr 2.1.2 ✔ forcats 0.5.2
## ── Conflicts ────────────────────────────────────────── tidyverse_conflicts() ──
## ✖ dplyr::filter() masks stats::filter()
## ✖ dplyr::lag() masks stats::lag()
Import Data
# use the read.csv() command to import your downloaded CSV file
df <- read.csv(file="data/cleangss.csv", header=T)
Viewing Data
# use the names() command to view the list of columns or variables in your dataframe
names(df)
## [1] "id" "wrkwayup" "rank" "racdif1" "racdif2"
## [6] "racdif3" "racdif4" "rushed" "helpblk" "wlthwhts"
## [11] "wlthblks" "workblks" "workwhts" "intlwhts" "intlblks"
## [16] "liveblks" "livewhts" "marblk" "marwht" "racwork"
## [21] "discaff" "fejobaff" "discaffm" "discaffw" "fehire"
## [26] "evbrkdwn" "goodlife" "taxrich" "taxmid" "taxpoor"
## [31] "meovrwrk" "coninc" "racecen1" "racecen2" "race"
## [36] "uscitzn" "lotr1" "lotr2" "lotr3" "lotr4"
## [41] "lotr5" "lotr6" "hope1" "hope2" "hope3"
## [46] "hope4" "hope5" "hope6" "goodlife_tri"
# use the head() command to view the first few lines of your dataframe
head(df)
## id wrkwayup rank racdif1 racdif2 racdif3 racdif4 rushed helpblk wlthwhts
## 1 1 2 10 1 1 1 1 NA NA 4
## 2 2 4 6 1 1 NA 1 NA 3 6
## 3 3 NA 7 NA NA NA NA 0 1 NA
## 4 4 5 8 1 1 1 1 NA NA 4
## 5 5 NA 8 NA NA NA NA 1 3 NA
## 6 6 3 6 1 1 1 1 NA 3 3
## wlthblks workblks workwhts intlwhts intlblks liveblks livewhts marblk marwht
## 1 3 4 4 4 4 3 3 3 3
## 2 4 4 4 3 4 3 3 5 5
## 3 NA NA NA NA NA NA NA NA NA
## 4 4 2 2 4 4 5 5 3 3
## 5 NA NA NA NA NA NA NA NA NA
## 6 2 4 4 5 5 3 3 3 3
## racwork discaff fejobaff discaffm discaffw fehire evbrkdwn goodlife taxrich
## 1 1 0 2 1 NA NA NA NA 1
## 2 NA NA 1 1 NA NA 0 1 1
## 3 NA 1 NA NA NA NA 0 3 NA
## 4 1 1 0 1 NA NA NA NA 1
## 5 1 1 NA NA NA NA NA 2 NA
## 6 NA NA 1 2 NA NA 0 3 1
## taxmid taxpoor meovrwrk coninc racecen1 racecen2 race uscitzn lotr1
## 1 2 3 NA 2.88748724 white <NA> white NA <NA>
## 2 3 3 3 -0.08526274 white <NA> white NA Neutral
## 3 NA NA NA 0.39370676 white <NA> white NA Neutral
## 4 3 1 1 2.88748724 white <NA> white NA <NA>
## 5 NA NA NA 2.88748724 white <NA> white NA Agree
## 6 3 3 NA 0.13245067 white <NA> white 1 Agree
## lotr2 lotr3 lotr4 lotr5 lotr6 hope1 hope2
## 1 <NA> <NA> <NA> <NA> <NA> <NA> <NA>
## 2 Disagree Agree Disagree Disagree Agree Mostly true Somewhat false
## 3 Neutral Neutral Neutral Neutral Neutral Mostly true <NA>
## 4 <NA> <NA> <NA> <NA> <NA> <NA> <NA>
## 5 Disagree Agree Disagree Disagree Agree Mostly true Slightly false
## 6 Disagree Agree Disagree Disagree Agree Mostly true Mostly true
## hope3 hope4 hope5 hope6 goodlife_tri
## 1 <NA> <NA> <NA> <NA> NA
## 2 Somewhat true Slightly true Somewhat false Definitely true 1
## 3 <NA> Mostly true <NA> <NA> 2
## 4 <NA> <NA> <NA> <NA> NA
## 5 Slightly true Somewhat true Somewhat true Mostly true 1
## 6 Mostly true Mostly true Mostly true Mostly true 2
# use the str() command to see what kinds of variables are in your dataframe (string)
str(df)
## 'data.frame': 2867 obs. of 49 variables:
## $ id : int 1 2 3 4 5 6 7 8 9 10 ...
## $ wrkwayup : int 2 4 NA 5 NA 3 4 NA NA NA ...
## $ rank : int 10 6 7 8 8 6 6 7 6 7 ...
## $ racdif1 : int 1 1 NA 1 NA 1 1 NA NA NA ...
## $ racdif2 : int 1 1 NA 1 NA 1 1 NA 1 NA ...
## $ racdif3 : int 1 NA NA 1 NA 1 1 NA NA NA ...
## $ racdif4 : int 1 1 NA 1 NA 1 1 NA NA NA ...
## $ rushed : int NA NA 0 NA 1 NA NA 0 NA 0 ...
## $ helpblk : int NA 3 1 NA 3 3 NA 3 NA 1 ...
## $ wlthwhts : int 4 6 NA 4 NA 3 4 NA 3 NA ...
## $ wlthblks : int 3 4 NA 4 NA 2 3 NA 2 NA ...
## $ workblks : int 4 4 NA 2 NA 4 3 NA 2 NA ...
## $ workwhts : int 4 4 NA 2 NA 4 4 NA 4 NA ...
## $ intlwhts : int 4 3 NA 4 NA 5 4 NA 5 NA ...
## $ intlblks : int 4 4 NA 4 NA 5 4 NA 4 NA ...
## $ liveblks : int 3 3 NA 5 NA 3 4 NA 2 NA ...
## $ livewhts : int 3 3 NA 5 NA 3 4 NA 3 NA ...
## $ marblk : int 3 5 NA 3 NA 3 3 NA 4 NA ...
## $ marwht : int 3 5 NA 3 NA 3 3 NA 4 NA ...
## $ racwork : int 1 NA NA 1 1 NA 1 1 1 NA ...
## $ discaff : int 0 NA 1 1 1 NA 1 1 2 2 ...
## $ fejobaff : int 2 1 NA 0 NA 1 0 NA 0 NA ...
## $ discaffm : int 1 1 NA 1 NA 2 2 NA 2 NA ...
## $ discaffw : int NA NA NA NA NA NA NA NA NA NA ...
## $ fehire : int NA NA NA NA NA NA NA NA NA NA ...
## $ evbrkdwn : int NA 0 0 NA NA 0 NA 1 NA NA ...
## $ goodlife : int NA 1 3 NA 2 3 NA 3 NA NA ...
## $ taxrich : int 1 1 NA 1 NA 1 2 NA NA NA ...
## $ taxmid : int 2 3 NA 3 NA 3 2 NA NA NA ...
## $ taxpoor : int 3 3 NA 1 NA 3 2 NA NA NA ...
## $ meovrwrk : int NA 3 NA 1 NA NA NA NA 1 NA ...
## $ coninc : num 2.8875 -0.0853 0.3937 2.8875 2.8875 ...
## $ racecen1 : chr "white" "white" "white" "white" ...
## $ racecen2 : chr NA NA NA NA ...
## $ race : chr "white" "white" "white" "white" ...
## $ uscitzn : int NA NA NA NA NA 1 0 0 0 NA ...
## $ lotr1 : chr NA "Neutral" "Neutral" NA ...
## $ lotr2 : chr NA "Disagree" "Neutral" NA ...
## $ lotr3 : chr NA "Agree" "Neutral" NA ...
## $ lotr4 : chr NA "Disagree" "Neutral" NA ...
## $ lotr5 : chr NA "Disagree" "Neutral" NA ...
## $ lotr6 : chr NA "Agree" "Neutral" NA ...
## $ hope1 : chr NA "Mostly true" "Mostly true" NA ...
## $ hope2 : chr NA "Somewhat false" NA NA ...
## $ hope3 : chr NA "Somewhat true" NA NA ...
## $ hope4 : chr NA "Slightly true" "Mostly true" NA ...
## $ hope5 : chr NA "Somewhat false" NA NA ...
## $ hope6 : chr NA "Definitely true" NA NA ...
## $ goodlife_tri: int NA 1 2 NA 1 2 NA 2 NA NA ...
Subsetting Data
# use the subset() command to select which columns to keep in your dataframe
d <- subset(df, select=c(id, hope1, hope2,
coninc))
# this is the code you'll need for the lab. for the homework assignment, you'll need to customize the code so that the variables are the ones you've chosen.
d <- subset(df, select=c(id,
wlthwhts,
wlthblks,
workwhts,
workblks,
intlwhts,
intlblks,
coninc,
lotr1, lotr2, lotr3, lotr4, lotr5, lotr6,
hope1, hope2, hope3, hope4, hope5, hope6,
race,
goodlife_tri))
Basic Data
Checking
Checking Values
# use the map() command to view tables for all of your columns/variables at once
d %>%
map(table, useNA = "always")
## $id
##
## 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16
## 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32
## 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48
## 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64
## 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80
## 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96
## 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112
## 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128
## 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144
## 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160
## 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176
## 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192
## 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208
## 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224
## 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240
## 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## 241 242 243 244 245 246 247 248 249 250 251 252 253 254 255 256
## 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## 257 258 259 260 261 262 263 264 265 266 267 268 269 270 271 272
## 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## 273 274 275 276 277 278 279 280 281 282 283 284 285 286 287 288
## 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## 289 290 291 292 293 294 295 296 297 298 299 300 301 302 303 304
## 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## 305 306 307 308 309 310 311 312 313 314 315 316 317 318 319 320
## 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## 321 322 323 324 325 326 327 328 329 330 331 332 333 334 335 336
## 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## 337 338 339 340 341 342 343 344 345 346 347 348 349 350 351 352
## 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## 353 354 355 356 357 358 359 360 361 362 363 364 365 366 367 368
## 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## 369 370 371 372 373 374 375 376 377 378 379 380 381 382 383 384
## 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## 385 386 387 388 389 390 391 392 393 394 395 396 397 398 399 400
## 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## 401 402 403 404 405 406 407 408 409 410 411 412 413 414 415 416
## 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## 417 418 419 420 421 422 423 424 425 426 427 428 429 430 431 432
## 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## 433 434 435 436 437 438 439 440 441 442 443 444 445 446 447 448
## 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## 449 450 451 452 453 454 455 456 457 458 459 460 461 462 463 464
## 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## 465 466 467 468 469 470 471 472 473 474 475 476 477 478 479 480
## 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## 481 482 483 484 485 486 487 488 489 490 491 492 493 494 495 496
## 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## 497 498 499 500 501 502 503 504 505 506 507 508 509 510 511 512
## 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## 513 514 515 516 517 518 519 520 521 522 523 524 525 526 527 528
## 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## 529 530 531 532 533 534 535 536 537 538 539 540 541 542 543 544
## 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## 545 546 547 548 549 550 551 552 553 554 555 556 557 558 559 560
## 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## 561 562 563 564 565 566 567 568 569 570 571 572 573 574 575 576
## 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## 577 578 579 580 581 582 583 584 585 586 587 588 589 590 591 592
## 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## 593 594 595 596 597 598 599 600 601 602 603 604 605 606 607 608
## 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## 609 610 611 612 613 614 615 616 617 618 619 620 621 622 623 624
## 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## 625 626 627 628 629 630 631 632 633 634 635 636 637 638 639 640
## 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## 641 642 643 644 645 646 647 648 649 650 651 652 653 654 655 656
## 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## 657 658 659 660 661 662 663 664 665 666 667 668 669 670 671 672
## 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## 673 674 675 676 677 678 679 680 681 682 683 684 685 686 687 688
## 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## 689 690 691 692 693 694 695 696 697 698 699 700 701 702 703 704
## 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## 705 706 707 708 709 710 711 712 713 714 715 716 717 718 719 720
## 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## 721 722 723 724 725 726 727 728 729 730 731 732 733 734 735 736
## 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## 737 738 739 740 741 742 743 744 745 746 747 748 749 750 751 752
## 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## 753 754 755 756 757 758 759 760 761 762 763 764 765 766 767 768
## 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## 769 770 771 772 773 774 775 776 777 778 779 780 781 782 783 784
## 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## 785 786 787 788 789 790 791 792 793 794 795 796 797 798 799 800
## 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## 801 802 803 804 805 806 807 808 809 810 811 812 813 814 815 816
## 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## 817 818 819 820 821 822 823 824 825 826 827 828 829 830 831 832
## 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## 833 834 835 836 837 838 839 840 841 842 843 844 845 846 847 848
## 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## 849 850 851 852 853 854 855 856 857 858 859 860 861 862 863 864
## 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## 865 866 867 868 869 870 871 872 873 874 875 876 877 878 879 880
## 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## 881 882 883 884 885 886 887 888 889 890 891 892 893 894 895 896
## 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## 897 898 899 900 901 902 903 904 905 906 907 908 909 910 911 912
## 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## 913 914 915 916 917 918 919 920 921 922 923 924 925 926 927 928
## 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## 929 930 931 932 933 934 935 936 937 938 939 940 941 942 943 944
## 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## 945 946 947 948 949 950 951 952 953 954 955 956 957 958 959 960
## 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## 961 962 963 964 965 966 967 968 969 970 971 972 973 974 975 976
## 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## 977 978 979 980 981 982 983 984 985 986 987 988 989 990 991 992
## 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## 993 994 995 996 997 998 999 1000 1001 1002 1003 1004 1005 1006 1007 1008
## 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## 1009 1010 1011 1012 1013 1014 1015 1016 1017 1018 1019 1020 1021 1022 1023 1024
## 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## 1025 1026 1027 1028 1029 1030 1031 1032 1033 1034 1035 1036 1037 1038 1039 1040
## 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## 1041 1042 1043 1044 1045 1046 1047 1048 1049 1050 1051 1052 1053 1054 1055 1056
## 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## 1057 1058 1059 1060 1061 1062 1063 1064 1065 1066 1067 1068 1069 1070 1071 1072
## 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## 1073 1074 1075 1076 1077 1078 1079 1080 1081 1082 1083 1084 1085 1086 1087 1088
## 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## 1089 1090 1091 1092 1093 1094 1095 1096 1097 1098 1099 1100 1101 1102 1103 1104
## 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## 1105 1106 1107 1108 1109 1110 1111 1112 1113 1114 1115 1116 1117 1118 1119 1120
## 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## 1121 1122 1123 1124 1125 1126 1127 1128 1129 1130 1131 1132 1133 1134 1135 1136
## 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## 1137 1138 1139 1140 1141 1142 1143 1144 1145 1146 1147 1148 1149 1150 1151 1152
## 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## 1153 1154 1155 1156 1157 1158 1159 1160 1161 1162 1163 1164 1165 1166 1167 1168
## 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## 1169 1170 1171 1172 1173 1174 1175 1176 1177 1178 1179 1180 1181 1182 1183 1184
## 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## 1185 1186 1187 1188 1189 1190 1191 1192 1193 1194 1195 1196 1197 1198 1199 1200
## 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## 1201 1202 1203 1204 1205 1206 1207 1208 1209 1210 1211 1212 1213 1214 1215 1216
## 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## 1217 1218 1219 1220 1221 1222 1223 1224 1225 1226 1227 1228 1229 1230 1231 1232
## 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## 1233 1234 1235 1236 1237 1238 1239 1240 1241 1242 1243 1244 1245 1246 1247 1248
## 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## 1249 1250 1251 1252 1253 1254 1255 1256 1257 1258 1259 1260 1261 1262 1263 1264
## 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## 1265 1266 1267 1268 1269 1270 1271 1272 1273 1274 1275 1276 1277 1278 1279 1280
## 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## 1281 1282 1283 1284 1285 1286 1287 1288 1289 1290 1291 1292 1293 1294 1295 1296
## 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## 1297 1298 1299 1300 1301 1302 1303 1304 1305 1306 1307 1308 1309 1310 1311 1312
## 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## 1313 1314 1315 1316 1317 1318 1319 1320 1321 1322 1323 1324 1325 1326 1327 1328
## 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## 1329 1330 1331 1332 1333 1334 1335 1336 1337 1338 1339 1340 1341 1342 1343 1344
## 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## 1345 1346 1347 1348 1349 1350 1351 1352 1353 1354 1355 1356 1357 1358 1359 1360
## 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## 1361 1362 1363 1364 1365 1366 1367 1368 1369 1370 1371 1372 1373 1374 1375 1376
## 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## 1377 1378 1379 1380 1381 1382 1383 1384 1385 1386 1387 1388 1389 1390 1391 1392
## 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## 1393 1394 1395 1396 1397 1398 1399 1400 1401 1402 1403 1404 1405 1406 1407 1408
## 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## 1409 1410 1411 1412 1413 1414 1415 1416 1417 1418 1419 1420 1421 1422 1423 1424
## 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## 1425 1426 1427 1428 1429 1430 1431 1432 1433 1434 1435 1436 1437 1438 1439 1440
## 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## 1441 1442 1443 1444 1445 1446 1447 1448 1449 1450 1451 1452 1453 1454 1455 1456
## 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## 1457 1458 1459 1460 1461 1462 1463 1464 1465 1466 1467 1468 1469 1470 1471 1472
## 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## 1473 1474 1475 1476 1477 1478 1479 1480 1481 1482 1483 1484 1485 1486 1487 1488
## 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## 1489 1490 1491 1492 1493 1494 1495 1496 1497 1498 1499 1500 1501 1502 1503 1504
## 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## 1505 1506 1507 1508 1509 1510 1511 1512 1513 1514 1515 1516 1517 1518 1519 1520
## 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## 1521 1522 1523 1524 1525 1526 1527 1528 1529 1530 1531 1532 1533 1534 1535 1536
## 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
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## 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
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## 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
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## 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## 1585 1586 1587 1588 1589 1590 1591 1592 1593 1594 1595 1596 1597 1598 1599 1600
## 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## 1601 1602 1603 1604 1605 1606 1607 1608 1609 1610 1611 1612 1613 1614 1615 1616
## 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
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## 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
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## 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## 1649 1650 1651 1652 1653 1654 1655 1656 1657 1658 1659 1660 1661 1662 1663 1664
## 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## 1665 1666 1667 1668 1669 1670 1671 1672 1673 1674 1675 1676 1677 1678 1679 1680
## 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## 1681 1682 1683 1684 1685 1686 1687 1688 1689 1690 1691 1692 1693 1694 1695 1696
## 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## 1697 1698 1699 1700 1701 1702 1703 1704 1705 1706 1707 1708 1709 1710 1711 1712
## 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## 1713 1714 1715 1716 1717 1718 1719 1720 1721 1722 1723 1724 1725 1726 1727 1728
## 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## 1729 1730 1731 1732 1733 1734 1735 1736 1737 1738 1739 1740 1741 1742 1743 1744
## 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## 1745 1746 1747 1748 1749 1750 1751 1752 1753 1754 1755 1756 1757 1758 1759 1760
## 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## 1761 1762 1763 1764 1765 1766 1767 1768 1769 1770 1771 1772 1773 1774 1775 1776
## 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## 1777 1778 1779 1780 1781 1782 1783 1784 1785 1786 1787 1788 1789 1790 1791 1792
## 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## 1793 1794 1795 1796 1797 1798 1799 1800 1801 1802 1803 1804 1805 1806 1807 1808
## 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## 1809 1810 1811 1812 1813 1814 1815 1816 1817 1818 1819 1820 1821 1822 1823 1824
## 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## 1825 1826 1827 1828 1829 1830 1831 1832 1833 1834 1835 1836 1837 1838 1839 1840
## 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## 1841 1842 1843 1844 1845 1846 1847 1848 1849 1850 1851 1852 1853 1854 1855 1856
## 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## 1857 1858 1859 1860 1861 1862 1863 1864 1865 1866 1867 1868 1869 1870 1871 1872
## 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## 1873 1874 1875 1876 1877 1878 1879 1880 1881 1882 1883 1884 1885 1886 1887 1888
## 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## 1889 1890 1891 1892 1893 1894 1895 1896 1897 1898 1899 1900 1901 1902 1903 1904
## 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## 1905 1906 1907 1908 1909 1910 1911 1912 1913 1914 1915 1916 1917 1918 1919 1920
## 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## 1921 1922 1923 1924 1925 1926 1927 1928 1929 1930 1931 1932 1933 1934 1935 1936
## 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## 1937 1938 1939 1940 1941 1942 1943 1944 1945 1946 1947 1948 1949 1950 1951 1952
## 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## 1953 1954 1955 1956 1957 1958 1959 1960 1961 1962 1963 1964 1965 1966 1967 1968
## 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## 1969 1970 1971 1972 1973 1974 1975 1976 1977 1978 1979 1980 1981 1982 1983 1984
## 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## 1985 1986 1987 1988 1989 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000
## 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016
## 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## 2017 2018 2019 2020 2021 2022 2023 2024 2025 2026 2027 2028 2029 2030 2031 2032
## 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## 2033 2034 2035 2036 2037 2038 2039 2040 2041 2042 2043 2044 2045 2046 2047 2048
## 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## 2049 2050 2051 2052 2053 2054 2055 2056 2057 2058 2059 2060 2061 2062 2063 2064
## 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## 2065 2066 2067 2068 2069 2070 2071 2072 2073 2074 2075 2076 2077 2078 2079 2080
## 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## 2081 2082 2083 2084 2085 2086 2087 2088 2089 2090 2091 2092 2093 2094 2095 2096
## 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## 2097 2098 2099 2100 2101 2102 2103 2104 2105 2106 2107 2108 2109 2110 2111 2112
## 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## 2113 2114 2115 2116 2117 2118 2119 2120 2121 2122 2123 2124 2125 2126 2127 2128
## 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## 2129 2130 2131 2132 2133 2134 2135 2136 2137 2138 2139 2140 2141 2142 2143 2144
## 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## 2145 2146 2147 2148 2149 2150 2151 2152 2153 2154 2155 2156 2157 2158 2159 2160
## 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## 2161 2162 2163 2164 2165 2166 2167 2168 2169 2170 2171 2172 2173 2174 2175 2176
## 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## 2177 2178 2179 2180 2181 2182 2183 2184 2185 2186 2187 2188 2189 2190 2191 2192
## 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## 2193 2194 2195 2196 2197 2198 2199 2200 2201 2202 2203 2204 2205 2206 2207 2208
## 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## 2209 2210 2211 2212 2213 2214 2215 2216 2217 2218 2219 2220 2221 2222 2223 2224
## 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## 2225 2226 2227 2228 2229 2230 2231 2232 2233 2234 2235 2236 2237 2238 2239 2240
## 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## 2241 2242 2243 2244 2245 2246 2247 2248 2249 2250 2251 2252 2253 2254 2255 2256
## 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## 2257 2258 2259 2260 2261 2262 2263 2264 2265 2266 2267 2268 2269 2270 2271 2272
## 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## 2273 2274 2275 2276 2277 2278 2279 2280 2281 2282 2283 2284 2285 2286 2287 2288
## 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## 2289 2290 2291 2292 2293 2294 2295 2296 2297 2298 2299 2300 2301 2302 2303 2304
## 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## 2305 2306 2307 2308 2309 2310 2311 2312 2313 2314 2315 2316 2317 2318 2319 2320
## 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## 2321 2322 2323 2324 2325 2326 2327 2328 2329 2330 2331 2332 2333 2334 2335 2336
## 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## 2337 2338 2339 2340 2341 2342 2343 2344 2345 2346 2347 2348 2349 2350 2351 2352
## 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## 2353 2354 2355 2356 2357 2358 2359 2360 2361 2362 2363 2364 2365 2366 2367 2368
## 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## 2369 2370 2371 2372 2373 2374 2375 2376 2377 2378 2379 2380 2381 2382 2383 2384
## 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## 2385 2386 2387 2388 2389 2390 2391 2392 2393 2394 2395 2396 2397 2398 2399 2400
## 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## 2401 2402 2403 2404 2405 2406 2407 2408 2409 2410 2411 2412 2413 2414 2415 2416
## 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## 2417 2418 2419 2420 2421 2422 2423 2424 2425 2426 2427 2428 2429 2430 2431 2432
## 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## 2433 2434 2435 2436 2437 2438 2439 2440 2441 2442 2443 2444 2445 2446 2447 2448
## 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## 2449 2450 2451 2452 2453 2454 2455 2456 2457 2458 2459 2460 2461 2462 2463 2464
## 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## 2465 2466 2467 2468 2469 2470 2471 2472 2473 2474 2475 2476 2477 2478 2479 2480
## 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## 2481 2482 2483 2484 2485 2486 2487 2488 2489 2490 2491 2492 2493 2494 2495 2496
## 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## 2497 2498 2499 2500 2501 2502 2503 2504 2505 2506 2507 2508 2509 2510 2511 2512
## 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## 2513 2514 2515 2516 2517 2518 2519 2520 2521 2522 2523 2524 2525 2526 2527 2528
## 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## 2529 2530 2531 2532 2533 2534 2535 2536 2537 2538 2539 2540 2541 2542 2543 2544
## 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## 2545 2546 2547 2548 2549 2550 2551 2552 2553 2554 2555 2556 2557 2558 2559 2560
## 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## 2561 2562 2563 2564 2565 2566 2567 2568 2569 2570 2571 2572 2573 2574 2575 2576
## 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## 2577 2578 2579 2580 2581 2582 2583 2584 2585 2586 2587 2588 2589 2590 2591 2592
## 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## 2593 2594 2595 2596 2597 2598 2599 2600 2601 2602 2603 2604 2605 2606 2607 2608
## 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## 2609 2610 2611 2612 2613 2614 2615 2616 2617 2618 2619 2620 2621 2622 2623 2624
## 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## 2625 2626 2627 2628 2629 2630 2631 2632 2633 2634 2635 2636 2637 2638 2639 2640
## 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## 2641 2642 2643 2644 2645 2646 2647 2648 2649 2650 2651 2652 2653 2654 2655 2656
## 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## 2657 2658 2659 2660 2661 2662 2663 2664 2665 2666 2667 2668 2669 2670 2671 2672
## 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## 2673 2674 2675 2676 2677 2678 2679 2680 2681 2682 2683 2684 2685 2686 2687 2688
## 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## 2689 2690 2691 2692 2693 2694 2695 2696 2697 2698 2699 2700 2701 2702 2703 2704
## 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## 2705 2706 2707 2708 2709 2710 2711 2712 2713 2714 2715 2716 2717 2718 2719 2720
## 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## 2721 2722 2723 2724 2725 2726 2727 2728 2729 2730 2731 2732 2733 2734 2735 2736
## 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## 2737 2738 2739 2740 2741 2742 2743 2744 2745 2746 2747 2748 2749 2750 2751 2752
## 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## 2753 2754 2755 2756 2757 2758 2759 2760 2761 2762 2763 2764 2765 2766 2767 2768
## 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## 2769 2770 2771 2772 2773 2774 2775 2776 2777 2778 2779 2780 2781 2782 2783 2784
## 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## 2785 2786 2787 2788 2789 2790 2791 2792 2793 2794 2795 2796 2797 2798 2799 2800
## 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## 2801 2802 2803 2804 2805 2806 2807 2808 2809 2810 2811 2812 2813 2814 2815 2816
## 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## 2817 2818 2819 2820 2821 2822 2823 2824 2825 2826 2827 2828 2829 2830 2831 2832
## 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## 2833 2834 2835 2836 2837 2838 2839 2840 2841 2842 2843 2844 2845 2846 2847 2848
## 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## 2849 2850 2851 2852 2853 2854 2855 2856 2857 2858 2859 2860 2861 2862 2863 2864
## 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## 2865 2866 2867 <NA>
## 1 1 1 0
##
## $wlthwhts
##
## 1 2 3 4 5 6 7 <NA>
## 7 22 110 985 458 186 95 1004
##
## $wlthblks
##
## 1 2 3 4 5 6 7 <NA>
## 61 332 712 627 95 20 9 1011
##
## $workwhts
##
## 1 2 3 4 5 6 7 <NA>
## 21 40 165 943 402 185 102 1009
##
## $workblks
##
## 1 2 3 4 5 6 7 <NA>
## 55 118 332 939 243 100 66 1014
##
## $intlwhts
##
## 1 2 3 4 5 6 7 <NA>
## 13 33 139 894 402 237 131 1018
##
## $intlblks
##
## 1 2 3 4 5 6 7 <NA>
## 22 39 176 1015 356 149 88 1022
##
## $coninc
##
## -1.045601 -1.04560078090143 -1.03449319872289 -1.034493
## 161 110 14 22
## -1.008368 -1.00836758966798 -0.982242 -0.982241980613072
## 27 8 14 10
## -0.964824907909798 -0.9648249 -0.947407835206525 -0.9474078
## 4 9 6 13
## -0.9299908 -0.929990762503251 -0.9125737 -0.912573689799978
## 9 6 16 7
## -0.8864481 -0.886448080745068 -0.8472597 -0.847259667162702
## 35 13 62 26
## -0.803717 -0.803716985404518 -0.760174303646334 -0.7601743
## 53 18 19 46
## -0.716631621888151 -0.7166316 -0.673088940129967 -0.6730889
## 17 48 18 77
## -0.6295463 -0.629546258371783 -0.564232235734507 -0.5642322
## 65 36 31 84
## -0.4771469 -0.477146872218139 -0.390061508701772 -0.3900615
## 89 42 34 92
## -0.2594335 -0.25943346342722 -0.08526274 -0.0852627363944849
## 154 64 141 66
## 0.132450672396434 0.1324507 0.393706762945537 0.3937068
## 87 171 74 140
## 0.6985055 0.698505535252824 1.04684698931829 1.046847
## 113 66 49 77
## 1.395188 1.39518844338377 1.74352989744924 1.74353
## 54 45 34 27
## 2.887487 2.88748723852696 <NA>
## 69 95 0
##
## $lotr1
##
## Agree Disagree Neutral Strongly agree
## 683 196 271 227
## Strongly disagree <NA>
## 70 1420
##
## $lotr2
##
## Agree Disagree Neutral Strongly agree
## 351 576 313 80
## Strongly disagree <NA>
## 128 1419
##
## $lotr3
##
## Agree Disagree Neutral Strongly agree
## 724 144 281 256
## Strongly disagree <NA>
## 43 1419
##
## $lotr4
##
## Agree Disagree Neutral Strongly agree
## 196 710 299 55
## Strongly disagree <NA>
## 189 1418
##
## $lotr5
##
## Agree Disagree Neutral Strongly agree
## 225 699 270 61
## Strongly disagree <NA>
## 190 1422
##
## $lotr6
##
## Agree Disagree Neutral Strongly agree
## 843 90 196 280
## Strongly disagree <NA>
## 39 1419
##
## $hope1
##
## Definitely false Definitely true Mostly false Mostly true
## 19 346 23 543
## Slightly false Slightly true Somewhat false Somewhat true
## 28 154 27 299
## <NA>
## 1428
##
## $hope2
##
## Definitely false Definitely true Mostly false Mostly true
## 53 274 53 403
## Slightly false Slightly true Somewhat false Somewhat true
## 67 229 68 291
## <NA>
## 1429
##
## $hope3
##
## Definitely false Definitely true Mostly false Mostly true
## 20 286 36 472
## Slightly false Slightly true Somewhat false Somewhat true
## 52 191 38 347
## <NA>
## 1425
##
## $hope4
##
## Definitely false Definitely true Mostly false Mostly true
## 51 225 48 426
## Slightly false Slightly true Somewhat false Somewhat true
## 83 215 57 337
## <NA>
## 1425
##
## $hope5
##
## Definitely false Definitely true Mostly false Mostly true
## 23 286 25 439
## Slightly false Slightly true Somewhat false Somewhat true
## 54 199 38 374
## <NA>
## 1429
##
## $hope6
##
## Definitely false Definitely true Mostly false Mostly true
## 57 153 51 404
## Slightly false Slightly true Somewhat false Somewhat true
## 104 251 62 357
## <NA>
## 1428
##
## $race
##
## aian asian black hispanic multi nhpi other white
## 45 63 417 101 234 19 11 1953
## <NA>
## 24
##
## $goodlife_tri
##
## 0 1 2 <NA>
## 102 724 832 1209
Recoding Variables
# use the table() command to view a table of a single variable at a time
table(d$lotr1, useNA = "always")
##
## Agree Disagree Neutral Strongly agree
## 683 196 271 227
## Strongly disagree <NA>
## 70 1420
# use the below code to recode a variable. in this example, the variable 'lotr1' is being transformed into lotr1_rc, and the option "Strongly disagree" is being recoded to "0", "Disagree" to 1, etc.
d$lotr1_rc[d$lotr1 == "Strongly disagree"] <- 0
d$lotr1_rc[d$lotr1 == "Disagree"] <- 1
d$lotr1_rc[d$lotr1 == "Neutral"] <- 2
d$lotr1_rc[d$lotr1 == "Agree"] <- 3
d$lotr1_rc[d$lotr1 == "Strongly agree"] <- 4
# afterwards, use the table() command to check your new variable
table(d$lotr1_rc, useNA = "always")
##
## 0 1 2 3 4 <NA>
## 70 196 271 683 227 1420
# use the table() command to view a table of a single variable at a time
table(d$lotr2, useNA = "always")
##
## Agree Disagree Neutral Strongly agree
## 351 576 313 80
## Strongly disagree <NA>
## 128 1419
# use the below code to recode a variable. in this example, the variable 'lotr1' is being transformed into lotr1_rc, and the option "Strongly disagree" is being recoded to "0", "Disagree" to 1, etc.
d$lotr2_rc[d$lotr2 == "Strongly disagree"] <- 0
d$lotr2_rc[d$lotr2 == "Disagree"] <- 1
d$lotr2_rc[d$lotr2 == "Neutral"] <- 2
d$lotr2_rc[d$lotr2 == "Agree"] <- 3
d$lotr2_rc[d$lotr2 == "Strongly agree"] <- 4
# afterwards, use the table() command to check your new variable
table(d$lotr2_rc, useNA = "always")
##
## 0 1 2 3 4 <NA>
## 128 576 313 351 80 1419
# use the table() command to view a table of a single variable at a time
table(d$lotr3, useNA = "always")
##
## Agree Disagree Neutral Strongly agree
## 724 144 281 256
## Strongly disagree <NA>
## 43 1419
# use the below code to recode a variable. in this example, the variable 'lotr3' is being transformed into lotr3_rc, and the option "Strongly disagree" is being recoded to "0", "Disagree" to 1, etc.
d$lotr3_rc[d$lotr3 == "Strongly disagree"] <- 0
d$lotr3_rc[d$lotr3 == "Disagree"] <- 1
d$lotr3_rc[d$lotr3 == "Neutral"] <- 2
d$lotr3_rc[d$lotr3 == "Agree"] <- 3
d$lotr3_rc[d$lotr3 == "Strongly agree"] <- 4
# afterwards, use the table() command to check your new variable
table(d$lotr3_rc, useNA = "always")
##
## 0 1 2 3 4 <NA>
## 43 144 281 724 256 1419
# use the table() command to view a table of a single variable at a time
table(d$lotr4, useNA = "always")
##
## Agree Disagree Neutral Strongly agree
## 196 710 299 55
## Strongly disagree <NA>
## 189 1418
# use the below code to recode a variable. in this example, the variable 'lotr4' is being transformed into lotr4_rc, and the option "Strongly disagree" is being recoded to "0", "Disagree" to 1, etc.
d$lotr4_rc[d$lotr4 == "Strongly disagree"] <- 0
d$lotr4_rc[d$lotr4 == "Disagree"] <- 1
d$lotr4_rc[d$lotr4 == "Neutral"] <- 2
d$lotr4_rc[d$lotr4 == "Agree"] <- 3
d$lotr4_rc[d$lotr4 == "Strongly agree"] <- 4
# afterwards, use the table() command to check your new variable
table(d$lotr4_rc, useNA = "always")
##
## 0 1 2 3 4 <NA>
## 189 710 299 196 55 1418
# use the table() command to view a table of a single variable at a time
table(d$lotr5, useNA = "always")
##
## Agree Disagree Neutral Strongly agree
## 225 699 270 61
## Strongly disagree <NA>
## 190 1422
# use the below code to recode a variable. in this example, the variable 'lotr5' is being transformed into lotr5_rc, and the option "Strongly disagree" is being recoded to "0", "Disagree" to 1, etc.
d$lotr5_rc[d$lotr5 == "Strongly disagree"] <- 0
d$lotr5_rc[d$lotr5 == "Disagree"] <- 1
d$lotr5_rc[d$lotr5 == "Neutral"] <- 2
d$lotr5_rc[d$lotr5 == "Agree"] <- 3
d$lotr5_rc[d$lotr5 == "Strongly agree"] <- 4
# afterwards, use the table() command to check your new variable
table(d$lotr5_rc, useNA = "always")
##
## 0 1 2 3 4 <NA>
## 190 699 270 225 61 1422
# use the table() command to view a table of a single variable at a time
table(d$lotr6, useNA = "always")
##
## Agree Disagree Neutral Strongly agree
## 843 90 196 280
## Strongly disagree <NA>
## 39 1419
# use the below code to recode a variable. in this example, the variable 'lotr6' is being transformed into lotr6_rc, and the option "Strongly disagree" is being recoded to "0", "Disagree" to 1, etc.
d$lotr6_rc[d$lotr6 == "Strongly disagree"] <- 0
d$lotr6_rc[d$lotr6 == "Disagree"] <- 1
d$lotr6_rc[d$lotr6 == "Neutral"] <- 2
d$lotr6_rc[d$lotr6 == "Agree"] <- 3
d$lotr6_rc[d$lotr6 == "Strongly agree"] <- 4
# afterwards, use the table() command to check your new variable
table(d$lotr6_rc, useNA = "always")
##
## 0 1 2 3 4 <NA>
## 39 90 196 843 280 1419
Factor Scores/Composite
Variables
# use the str() command to check that your recoded variable is numeric so you can use mathematical operators on it
str(d)
## 'data.frame': 2867 obs. of 28 variables:
## $ id : int 1 2 3 4 5 6 7 8 9 10 ...
## $ wlthwhts : int 4 6 NA 4 NA 3 4 NA 3 NA ...
## $ wlthblks : int 3 4 NA 4 NA 2 3 NA 2 NA ...
## $ workwhts : int 4 4 NA 2 NA 4 4 NA 4 NA ...
## $ workblks : int 4 4 NA 2 NA 4 3 NA 2 NA ...
## $ intlwhts : int 4 3 NA 4 NA 5 4 NA 5 NA ...
## $ intlblks : int 4 4 NA 4 NA 5 4 NA 4 NA ...
## $ coninc : num 2.8875 -0.0853 0.3937 2.8875 2.8875 ...
## $ lotr1 : chr NA "Neutral" "Neutral" NA ...
## $ lotr2 : chr NA "Disagree" "Neutral" NA ...
## $ lotr3 : chr NA "Agree" "Neutral" NA ...
## $ lotr4 : chr NA "Disagree" "Neutral" NA ...
## $ lotr5 : chr NA "Disagree" "Neutral" NA ...
## $ lotr6 : chr NA "Agree" "Neutral" NA ...
## $ hope1 : chr NA "Mostly true" "Mostly true" NA ...
## $ hope2 : chr NA "Somewhat false" NA NA ...
## $ hope3 : chr NA "Somewhat true" NA NA ...
## $ hope4 : chr NA "Slightly true" "Mostly true" NA ...
## $ hope5 : chr NA "Somewhat false" NA NA ...
## $ hope6 : chr NA "Definitely true" NA NA ...
## $ race : chr "white" "white" "white" "white" ...
## $ goodlife_tri: int NA 1 2 NA 1 2 NA 2 NA NA ...
## $ lotr1_rc : num NA 2 2 NA 3 3 NA 2 NA 3 ...
## $ lotr2_rc : num NA 1 2 NA 1 1 NA 2 NA 3 ...
## $ lotr3_rc : num NA 3 2 NA 3 3 NA 4 NA 1 ...
## $ lotr4_rc : num NA 1 2 NA 1 1 NA 4 NA 3 ...
## $ lotr5_rc : num NA 1 2 NA 1 1 NA 2 NA 1 ...
## $ lotr6_rc : num NA 3 2 NA 3 3 NA 4 NA 3 ...
# create your composite variable by adding the individual items and dividing by the total number of items
d$lotr <- (d$lotr1_rc + d$lotr2_rc + d$lotr3_rc + d$lotr4_rc + d$lotr5_rc + d$lotr6_rc)/6
Exporting Data
# use the subset() command to finalize your current dataframe
# in the version of the subset() command below, a dash is added to the 'c' argument so that instead of keeping the columns listed in the parentheses, R will drop them instead
d2 <- subset(d, select=-c(lotr1, lotr2, lotr3, lotr4, lotr5, lotr6,
lotr1_rc, lotr2_rc, lotr3_rc, lotr4_rc, lotr5_rc, lotr6_rc))
# use the write.csv() command to export your finalized dataframe
write.csv(d2, file="data/gss_final.csv", row.names = F)