#Solo fecha
as.Date("2024-07-24")## [1] "2024-07-24"#Fecha y hora
as.POSIXct("2024-07-24 18:43:00")## [1] "2024-07-24 18:43:00 -05"Sys.time()## [1] "2024-07-23 21:15:20 -05"Sys.Date()## [1] "2024-07-23"nombres <- c("Nazly","Astrid","Natalia","Juan","Nazly")
table(nombres)## nombres
## Astrid Juan Natalia Nazly
## 1 1 1 2codes <- c(Italy=380, Canada=124, Egypt=818)
codes## Italy Canada Egypt
## 380 124 818#Acceder a posiciones especificas
codes[2]## Canada
## 124codes[1:2]## Italy Canada
## 380 124codes[c(1,3)]## Italy Egypt
## 380 818codes["Canada"]## Canada
## 124class(nombres)## [1] "character"#Funciones para generar vectores
#secuencia del 1 al 10 contando de 2 en 2
seq(2,10,by=2)## [1] 2 4 6 8 10#Quiero que me muestre 5 valores del 0 al 100
seq(0,100,length.out=5)## [1] 0 25 50 75 100a<-c(1,"a",3.14)mat<-matrix(1:12,nrow = 3,ncol = 4, byrow=TRUE)
mat## [,1] [,2] [,3] [,4]
## [1,] 1 2 3 4
## [2,] 5 6 7 8
## [3,] 9 10 11 12rownames(mat)<-c("F1","F2","F3")
colnames(mat)<-c("C1","C2","C3","C4")
# Acceder a posiciones especificas [fila,columna]
mat[,3]## F1 F2 F3
## 3 7 11b<-list(numero=1,texto="hola",logico=TRUE, decimal=pi)
b## $numero
## [1] 1
##
## $texto
## [1] "hola"
##
## $logico
## [1] TRUE
##
## $decimal
## [1] 3.141593calificacionesDip<-list(name="Astrid",student_id=1234,grades=c(5,4.8),final="A")
calificacionesDip## $name
## [1] "Astrid"
##
## $student_id
## [1] 1234
##
## $grades
## [1] 5.0 4.8
##
## $final
## [1] "A"#Acceder a los elementos
calificacionesDip$student_id## [1] 1234calificacionesDip[["student_id"]]## [1] 1234calificacionesDip[[2]]## [1] 1234#Dataframe
df_mat<-as.data.frame(mat)
df_mat## C1 C2 C3 C4
## F1 1 2 3 4
## F2 5 6 7 8
## F3 9 10 11 12df<-data.frame(nombres=c("Carlos","Juan","Luis","Camilo"), edades=c(23,24,25,26))
df## nombres edades
## 1 Carlos 23
## 2 Juan 24
## 3 Luis 25
## 4 Camilo 26city<-c("Bogota","Cali","Medellin")
temp_c<-c(14,34,28)
df_city_temp <- data.frame(city,temp_c)
df_city_temp## city temp_c
## 1 Bogota 14
## 2 Cali 34
## 3 Medellin 28## Ejercicio agregar una columna al data frame con la temp en F usando una función
CaF<-function(x){
return (9*x/5+32)
}
CaF(14)## [1] 57.2temp_f<-CaF(temp_c)
df_city_temp$temp_f <- temp_f
# Sacar solo los grados celsius menores a 30
df_city_temp[temp_c<30,]## city temp_c temp_f
## 1 Bogota 14 57.2
## 3 Medellin 28 82.4data("BJsales")
BJsales## Time Series:
## Start = 1
## End = 150
## Frequency = 1
## [1] 200.1 199.5 199.4 198.9 199.0 200.2 198.6 200.0 200.3 201.2 201.6 201.5
## [13] 201.5 203.5 204.9 207.1 210.5 210.5 209.8 208.8 209.5 213.2 213.7 215.1
## [25] 218.7 219.8 220.5 223.8 222.8 223.8 221.7 222.3 220.8 219.4 220.1 220.6
## [37] 218.9 217.8 217.7 215.0 215.3 215.9 216.7 216.7 217.7 218.7 222.9 224.9
## [49] 222.2 220.7 220.0 218.7 217.0 215.9 215.8 214.1 212.3 213.9 214.6 213.6
## [61] 212.1 211.4 213.1 212.9 213.3 211.5 212.3 213.0 211.0 210.7 210.1 211.4
## [73] 210.0 209.7 208.8 208.8 208.8 210.6 211.9 212.8 212.5 214.8 215.3 217.5
## [85] 218.8 220.7 222.2 226.7 228.4 233.2 235.7 237.1 240.6 243.8 245.3 246.0
## [97] 246.3 247.7 247.6 247.8 249.4 249.0 249.9 250.5 251.5 249.0 247.6 248.8
## [109] 250.4 250.7 253.0 253.7 255.0 256.2 256.0 257.4 260.4 260.0 261.3 260.4
## [121] 261.6 260.8 259.8 259.0 258.9 257.4 257.7 257.9 257.4 257.3 257.6 258.9
## [133] 257.8 257.7 257.2 257.5 256.8 257.5 257.0 257.6 257.3 257.5 259.6 261.1
## [145] 262.9 263.3 262.8 261.8 262.2 262.7data("HairEyeColor")
HairEyeColor["Blond","Blue","Female"]## [1] 64# Fijar una semilla para reproducibilidad
set.seed(123)
# Crear un vector de 1000 números enteros
vector <- sample(1:1000, 1000, replace = TRUE)
# Reemplazar aleatoriamente algunos valores por NA
num_na <- 100
# Número de valores NA deseados
na_indices <- sample(1:1000, num_na)
vector[na_indices] <- NA
print(vector)## [1] 415 463 179 526 NA 938 818 118 NA 229 244 14 374 665
## [15] 602 603 768 NA 91 953 348 649 989 355 840 26 519 426
## [29] 649 766 211 932 590 593 555 871 373 844 143 NA 490 621
## [43] 775 NA 937 842 23 923 956 309 135 821 923 224 NA 217
## [57] 290 NA 581 72 588 575 141 722 865 859 NA 294 277 463
## [71] 41 431 90 NA 223 528 NA 606 774 747 456 598 854 NA
## [85] 159 752 209 374 818 34 516 13 69 895 755 409 308 278
## [99] 89 928 537 983 291 424 880 286 908 671 121 110 158 NA
## [113] 483 910 477 480 711 67 663 890 847 85 165 648 51 74
## [127] 178 362 236 610 330 726 127 972 212 686 785 958 814 310
## [141] 931 744 878 243 862 847 792 NA 983 619 NA 477 975 151
## [155] NA 614 767 160 391 155 426 5 326 784 280 800 789 NA
## [169] 843 932 238 764 339 985 39 822 986 137 455 738 560 589
## [183] 83 696 879 39 196 769 680 286 606 500 985 NA 344 310
## [197] 459 944 20 NA 195 861 164 52 876 534 177 554 NA 84
## [211] 523 633 951 392 302 597 877 NA 619 589 430 710 761 712
## [225] 428 672 250 804 429 398 528 983 381 545 40 936 NA 473
## [239] 200 978 125 265 775 903 186 573 NA 458 152 831 54 919
## [253] 538 235 289 185 765 NA 627 522 309 54 205 875 NA NA
## [267] 564 794 391 409 727 346 160 468 509 920 57 457 617 357
## [281] NA 270 878 646 347 129 NA 618 881 NA 337 797 26 539
## [295] 981 519 956 757 666 553 724 390 498 222 671 861 657 960
## [309] 421 57 660 163 985 238 673 578 516 330 225 389 NA 537
## [323] 648 55 217 597 557 658 NA 415 134 711 957 873 688 913
## [337] 757 941 NA 447 821 104 821 NA 711 468 NA NA 401 737
## [351] 258 177 386 141 NA 945 963 466 130 165 703 588 377 781
## [365] 170 445 710 874 234 NA 508 880 64 80 483 NA 987 475
## [379] 291 NA 343 323 NA 560 450 111 791 963 905 317 NA 222
## [393] 287 734 585 292 226 NA 890 684 297 860 605 637 811 39
## [407] NA 165 619 33 NA 396 866 277 209 76 94 803 30 217
## [421] 946 175 374 323 115 377 850 608 465 358 682 424 938 96
## [435] 538 397 404 742 148 989 980 862 937 392 935 714 593 447
## [449] 338 744 243 106 887 11 625 364 386 403 461 NA 31 926
## [463] 115 790 94 NA 16 709 NA NA NA 464 412 177 524 437
## [477] 924 NA NA 204 175 947 373 646 464 384 122 NA 403 315
## [491] 259 494 865 760 289 48 331 100 108 301 10 170 280 348
## [505] 999 402 209 468 827 649 309 395 108 8 626 261 541 306
## [519] 326 74 282 585 267 887 262 736 204 723 219 NA 352 667
## [533] NA 119 NA 856 924 579 622 936 NA 36 55 490 NA 891
## [547] 632 862 304 10 NA 422 612 NA 793 388 463 180 278 373
## [561] 241 24 679 559 NA 703 37 686 566 303 719 912 19 712
## [575] 671 NA 549 615 244 48 188 958 464 393 139 299 371 670
## [589] 189 970 NA 189 418 NA 382 38 84 319 686 846 838 402
## [603] 642 120 NA 331 533 441 199 499 599 NA 315 714 NA 81
## [617] 55 134 NA 756 6 128 879 668 800 49 739 476 239 340
## [631] 193 709 459 303 148 898 190 624 191 446 119 NA 522 627
## [645] 59 817 NA 61 422 108 NA 373 535 115 NA 600 644 NA
## [659] 413 NA 983 763 203 758 993 246 440 947 690 251 NA 643
## [673] 545 990 162 322 576 168 442 788 78 665 493 199 424 445
## [687] 995 95 918 464 NA 342 221 696 161 620 448 242 693 927
## [701] 814 968 536 828 926 407 229 NA NA 474 699 441 NA 23
## [715] 218 484 301 648 79 511 507 164 237 579 807 929 422 493
## [729] 730 796 986 NA 599 693 358 650 877 358 41 904 129 848
## [743] 886 450 232 NA 396 730 840 639 41 264 697 201 52 225
## [757] 67 680 770 577 457 903 973 541 20 NA 124 592 775 740
## [771] 45 332 281 91 653 980 138 606 127 425 780 8 839 271
## [785] 595 NA 747 167 499 255 599 634 931 902 71 772 970 81
## [799] 944 776 437 579 876 896 437 750 997 270 412 NA 137 673
## [813] 628 NA 64 531 229 NA 129 220 692 222 836 507 602 122
## [827] 331 901 502 NA NA 291 929 743 709 829 919 169 729 447
## [841] 561 341 69 320 504 76 2 886 786 772 106 111 855 374
## [855] 72 449 888 971 229 NA 719 335 953 56 618 271 NA 436
## [869] 876 957 601 292 387 263 68 NA 744 NA 357 792 742 836
## [883] 835 523 586 256 349 471 901 88 416 857 NA 586 463 755
## [897] 700 287 NA 685 827 280 512 NA 242 778 NA 328 172 298
## [911] 160 679 NA 678 529 468 384 929 741 970 365 970 898 591
## [925] 471 879 1000 227 NA 838 622 NA 943 243 265 535 793 911
## [939] NA 112 456 93 489 789 631 48 969 482 248 105 171 696
## [953] 459 516 839 312 562 892 139 758 481 843 828 250 742 597
## [967] 330 633 626 195 99 98 58 424 988 525 8 258 635 262
## [981] 599 NA 928 206 199 NA 840 870 459 234 529 839 55 892
## [995] 531 753 488 271 942 398mean(vector)## [1] NAind<-is.na(vector)
ind## [1] FALSE FALSE FALSE FALSE TRUE FALSE FALSE FALSE TRUE FALSE FALSE FALSE
## [13] FALSE FALSE FALSE FALSE FALSE TRUE FALSE FALSE FALSE FALSE FALSE FALSE
## [25] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [37] FALSE FALSE FALSE TRUE FALSE FALSE FALSE TRUE FALSE FALSE FALSE FALSE
## [49] FALSE FALSE FALSE FALSE FALSE FALSE TRUE FALSE FALSE TRUE FALSE FALSE
## [61] FALSE FALSE FALSE FALSE FALSE FALSE TRUE FALSE FALSE FALSE FALSE FALSE
## [73] FALSE TRUE FALSE FALSE TRUE FALSE FALSE FALSE FALSE FALSE FALSE TRUE
## [85] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [97] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [109] FALSE FALSE FALSE TRUE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [121] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [133] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [145] FALSE FALSE FALSE TRUE FALSE FALSE TRUE FALSE FALSE FALSE TRUE FALSE
## [157] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE TRUE
## [169] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [181] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [193] FALSE TRUE FALSE FALSE FALSE FALSE FALSE TRUE FALSE FALSE FALSE FALSE
## [205] FALSE FALSE FALSE FALSE TRUE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [217] FALSE TRUE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [229] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE TRUE FALSE FALSE FALSE
## [241] FALSE FALSE FALSE FALSE FALSE FALSE TRUE FALSE FALSE FALSE FALSE FALSE
## [253] FALSE FALSE FALSE FALSE FALSE TRUE FALSE FALSE FALSE FALSE FALSE FALSE
## [265] TRUE TRUE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [277] FALSE FALSE FALSE FALSE TRUE FALSE FALSE FALSE FALSE FALSE TRUE FALSE
## [289] FALSE TRUE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [301] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [313] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE TRUE FALSE FALSE FALSE
## [325] FALSE FALSE FALSE FALSE TRUE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [337] FALSE FALSE TRUE FALSE FALSE FALSE FALSE TRUE FALSE FALSE TRUE TRUE
## [349] FALSE FALSE FALSE FALSE FALSE FALSE TRUE FALSE FALSE FALSE FALSE FALSE
## [361] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE TRUE FALSE FALSE
## [373] FALSE FALSE FALSE TRUE FALSE FALSE FALSE TRUE FALSE FALSE TRUE FALSE
## [385] FALSE FALSE FALSE FALSE FALSE FALSE TRUE FALSE FALSE FALSE FALSE FALSE
## [397] FALSE TRUE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE TRUE FALSE
## [409] FALSE FALSE TRUE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [421] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [433] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [445] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [457] FALSE FALSE FALSE TRUE FALSE FALSE FALSE FALSE FALSE TRUE FALSE FALSE
## [469] TRUE TRUE TRUE FALSE FALSE FALSE FALSE FALSE FALSE TRUE TRUE FALSE
## [481] FALSE FALSE FALSE FALSE FALSE FALSE FALSE TRUE FALSE FALSE FALSE FALSE
## [493] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [505] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [517] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [529] FALSE TRUE FALSE FALSE TRUE FALSE TRUE FALSE FALSE FALSE FALSE FALSE
## [541] TRUE FALSE FALSE FALSE TRUE FALSE FALSE FALSE FALSE FALSE TRUE FALSE
## [553] FALSE TRUE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [565] TRUE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE TRUE
## [577] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [589] FALSE FALSE TRUE FALSE FALSE TRUE FALSE FALSE FALSE FALSE FALSE FALSE
## [601] FALSE FALSE FALSE FALSE TRUE FALSE FALSE FALSE FALSE FALSE FALSE TRUE
## [613] FALSE FALSE TRUE FALSE FALSE FALSE TRUE FALSE FALSE FALSE FALSE FALSE
## [625] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [637] FALSE FALSE FALSE FALSE FALSE TRUE FALSE FALSE FALSE FALSE TRUE FALSE
## [649] FALSE FALSE TRUE FALSE FALSE FALSE TRUE FALSE FALSE TRUE FALSE TRUE
## [661] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE TRUE FALSE
## [673] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [685] FALSE FALSE FALSE FALSE FALSE FALSE TRUE FALSE FALSE FALSE FALSE FALSE
## [697] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE TRUE
## [709] TRUE FALSE FALSE FALSE TRUE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [721] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE TRUE
## [733] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [745] FALSE TRUE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [757] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE TRUE FALSE FALSE
## [769] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [781] FALSE FALSE FALSE FALSE FALSE TRUE FALSE FALSE FALSE FALSE FALSE FALSE
## [793] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [805] FALSE FALSE FALSE FALSE FALSE TRUE FALSE FALSE FALSE TRUE FALSE FALSE
## [817] FALSE TRUE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [829] FALSE TRUE TRUE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [841] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [853] FALSE FALSE FALSE FALSE FALSE FALSE FALSE TRUE FALSE FALSE FALSE FALSE
## [865] FALSE FALSE TRUE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE TRUE
## [877] FALSE TRUE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [889] FALSE FALSE FALSE FALSE TRUE FALSE FALSE FALSE FALSE FALSE TRUE FALSE
## [901] FALSE FALSE FALSE TRUE FALSE FALSE TRUE FALSE FALSE FALSE FALSE FALSE
## [913] TRUE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [925] FALSE FALSE FALSE FALSE TRUE FALSE FALSE TRUE FALSE FALSE FALSE FALSE
## [937] FALSE FALSE TRUE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [949] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [961] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [973] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE TRUE FALSE FALSE
## [985] FALSE TRUE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [997] FALSE FALSE FALSE FALSEsum(ind)## [1] 100mean(vector[!ind])## [1] 498.2311s1<-summary(vector)
s2<-summary(vector[!ind])
ls()## [1] "a" "b" "BJsales"
## [4] "BJsales.lead" "CaF" "calificacionesDip"
## [7] "city" "codes" "df"
## [10] "df_city_temp" "df_mat" "HairEyeColor"
## [13] "ind" "mat" "na_indices"
## [16] "nombres" "num_na" "s1"
## [19] "s2" "temp_c" "temp_f"
## [22] "vector"d<-c(1,2,3,4,5)
logi<-c(TRUE,FALSE,TRUE,FALSE,TRUE)
d[!logi]## [1] 2 4print(s2)## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 2.0 243.8 483.5 498.2 747.8 1000.0#install.packages(c("dslabs","Rtools"))
library(dslabs)
data("murders")
levels(murders$region)## [1] "Northeast" "South" "North Central" "West"nlevels(murders$region)## [1] 4murders## state abb region population total
## 1 Alabama AL South 4779736 135
## 2 Alaska AK West 710231 19
## 3 Arizona AZ West 6392017 232
## 4 Arkansas AR South 2915918 93
## 5 California CA West 37253956 1257
## 6 Colorado CO West 5029196 65
## 7 Connecticut CT Northeast 3574097 97
## 8 Delaware DE South 897934 38
## 9 District of Columbia DC South 601723 99
## 10 Florida FL South 19687653 669
## 11 Georgia GA South 9920000 376
## 12 Hawaii HI West 1360301 7
## 13 Idaho ID West 1567582 12
## 14 Illinois IL North Central 12830632 364
## 15 Indiana IN North Central 6483802 142
## 16 Iowa IA North Central 3046355 21
## 17 Kansas KS North Central 2853118 63
## 18 Kentucky KY South 4339367 116
## 19 Louisiana LA South 4533372 351
## 20 Maine ME Northeast 1328361 11
## 21 Maryland MD South 5773552 293
## 22 Massachusetts MA Northeast 6547629 118
## 23 Michigan MI North Central 9883640 413
## 24 Minnesota MN North Central 5303925 53
## 25 Mississippi MS South 2967297 120
## 26 Missouri MO North Central 5988927 321
## 27 Montana MT West 989415 12
## 28 Nebraska NE North Central 1826341 32
## 29 Nevada NV West 2700551 84
## 30 New Hampshire NH Northeast 1316470 5
## 31 New Jersey NJ Northeast 8791894 246
## 32 New Mexico NM West 2059179 67
## 33 New York NY Northeast 19378102 517
## 34 North Carolina NC South 9535483 286
## 35 North Dakota ND North Central 672591 4
## 36 Ohio OH North Central 11536504 310
## 37 Oklahoma OK South 3751351 111
## 38 Oregon OR West 3831074 36
## 39 Pennsylvania PA Northeast 12702379 457
## 40 Rhode Island RI Northeast 1052567 16
## 41 South Carolina SC South 4625364 207
## 42 South Dakota SD North Central 814180 8
## 43 Tennessee TN South 6346105 219
## 44 Texas TX South 25145561 805
## 45 Utah UT West 2763885 22
## 46 Vermont VT Northeast 625741 2
## 47 Virginia VA South 8001024 250
## 48 Washington WA West 6724540 93
## 49 West Virginia WV South 1852994 27
## 50 Wisconsin WI North Central 5686986 97
## 51 Wyoming WY West 563626 5head(murders,3)## state abb region population total
## 1 Alabama AL South 4779736 135
## 2 Alaska AK West 710231 19
## 3 Arizona AZ West 6392017 232str(murders)## 'data.frame': 51 obs. of 5 variables:
## $ state : chr "Alabama" "Alaska" "Arizona" "Arkansas" ...
## $ abb : chr "AL" "AK" "AZ" "AR" ...
## $ region : Factor w/ 4 levels "Northeast","South",..: 2 4 4 2 4 4 1 2 2 2 ...
## $ population: num 4779736 710231 6392017 2915918 37253956 ...
## $ total : num 135 19 232 93 1257 ...class(murders)## [1] "data.frame"names(murders)## [1] "state" "abb" "region" "population" "total"# Valor máximo en total de asesinatos
sort(murders$total) #orden## [1] 2 4 5 5 7 8 11 12 12 16 19 21 22 27 32
## [16] 36 38 53 63 65 67 84 93 93 97 97 99 111 116 118
## [31] 120 135 142 207 219 232 246 250 286 293 310 321 351 364 376
## [46] 413 457 517 669 805 1257order(murders$total,decreasing = TRUE) #entrega posiciones de menor a mayor donde queda la variable según su orden## [1] 5 44 10 33 39 23 11 14 19 26 36 21 34 47 31 3 43 41 15 1 25 22 18 37 9
## [26] 7 50 4 48 29 32 6 17 24 8 38 28 49 45 16 2 40 13 27 20 42 12 30 51 35
## [51] 46murders$state[5]## [1] "California"attach(murders)
state[5]## [1] "California"max(total)## [1] 1257pos_max<-which.max(total)
pos_max## [1] 5min(total)## [1] 2pos_min<-which.min(total)
pos_min## [1] 46state[pos_max]## [1] "California"state[pos_min]## [1] "Vermont"