Vectores

nombre <- c("Juan", "Carlos", "Alberto")
table(nombre)

## nombre
## Alberto  Carlos    Juan 
##       1       1       1

names(nombre)

## NULL

#posiciones especificas
nombre[2]

## [1] "Carlos"

nombre[c(1,3)]

## [1] "Juan"    "Alberto"

nombre["Juan"]

## [1] NA

class(nombre)

## [1] "character"

#Funciones para generara vectores

seq(1:10)

##  [1]  1  2  3  4  5  6  7  8  9 10

help("seq")

## starting httpd help server ... done

seq(1,10,by=2)

## [1] 1 3 5 7 9

# el 1 representa desde donde empieza a contar la funcion si es uno se refiere a par y si es 2 impar
seq(0,100,length.out=5)

## [1]   0  25  50  75 100

a<- c(1,"a",3.14)
#los vectoressolo permiter contener varaibles del mismo tipo

#Matrices

mat<- matrix(1:9, nrow = 3)
mat

##      [,1] [,2] [,3]
## [1,]    1    4    7
## [2,]    2    5    8
## [3,]    3    6    9

mat<- matrix(1:9, nrow = 3, byrow = TRUE)
mat

##      [,1] [,2] [,3]
## [1,]    1    2    3
## [2,]    4    5    6
## [3,]    7    8    9

mat<- matrix(1:9, nrow = 3, ncol = 4, byrow = TRUE)

## Warning in matrix(1:9, nrow = 3, ncol = 4, byrow = TRUE): data length [9] is
## not a sub-multiple or multiple of the number of columns [4]

mat

##      [,1] [,2] [,3] [,4]
## [1,]    1    2    3    4
## [2,]    5    6    7    8
## [3,]    9    1    2    3

rownames(mat)<-c("F1","F2","F3")
colnames(mat)<-c("C1","C2","C3","C4")

#mat[x,y]
#mat[2,3]
#mat[3,]
#mat[,6]
mat[1:2,]

##    C1 C2 C3 C4
## F1  1  2  3  4
## F2  5  6  7  8

#Listas

Calificaciones<- list(name="Atrid", student_id="1234", grade=c(5,4.8), final="A")

#acceder a los elementos
Calificaciones$student_id

## [1] "1234"

Calificaciones[["student_id"]]

## [1] "1234"

Calificaciones[[2]]

## [1] "1234"

#Dataframe

#convertir matriz a data frame
df<-as.data.frame(mat)
df

##    C1 C2 C3 C4
## F1  1  2  3  4
## F2  5  6  7  8
## F3  9  1  2  3

#columnas es igual a variables y filas los registros de las mismas

df1<-data.frame(nombres=c("Juliana","Andres","Maria"),edades=c(23,35,34))
df1

##   nombres edades
## 1 Juliana     23
## 2  Andres     35
## 3   Maria     34

city<-c("Bogota","Cali","Medellin")
temp_c<-c(14,34,28)
DFT<-data.frame(city,temp_c)
DFT

##       city temp_c
## 1   Bogota     14
## 2     Cali     34
## 3 Medellin     28

#1temp_f<-(temp_c*9/5)+32
CaF<-function(x){
  return(9*x/5+32)
}
temp_f1<-CaF(temp_c)
DFT$temp_f1 <- temp_f1

DFT[temp_c<30,]

##       city temp_c temp_f1
## 1   Bogota     14    57.2
## 3 Medellin     28    82.4

#ff

# Fijar una semilla para reproducibilidad set.seed(123) 
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] 917 394  NA 623 467 835 974 867 726 694   1 639 366  NA 463 168 812 418
##   [19]  NA 874 565 867 962 105 129 433  90  NA 205  NA 934 388 672 476 782 868
##   [37] 364 556 289  24 380 411 791 141 823  90 985 975 879 228 279 647 829 109
##   [55] 227 818 634 297 747 743 316 616  NA 424  NA 766 723  NA 293 192 335 297
##   [73] 764 241  NA 872 968  NA 122 860 301 753  79 158 463 664 792 144 286 801
##   [91] 709 348 227  NA 382 316  79 682 704 399  44 999  NA 282 332  NA 323 550
##  [109] 377 527 776 424 145 562 614 235 957  26 122 606 331 138  40 332 994 319
##  [127] 650 505 883 453  NA 470  64 271 518 778 960 724 656 177 613 651  85 172
##  [145] 644 402 446 579 383 379  48 742 454 684 699 755 875  NA 436 545 317 169
##  [163] 277 500  NA 659 337 888 547   3 554 682 832  NA 558 781 194 530 859 636
##  [181] 792 496 387 994 568 386 846 498 672 554 926 676 145 920 184 887 226 986
##  [199] 212 965 255  49 324 996  93 387 814 688 129  82  NA 913 642 227 549 328
##  [217] 594 621  NA 678 357 867 468  70 642  NA 793 701  NA 643 294 665 333 499
##  [235] 328 927 272 620 860 870  NA 240  NA 874  15 242 270 705 516  NA 287  29
##  [253] 354 881 315 477 975 359 672  NA 847 301  NA 334 420 688 332 623 167 704
##  [271] 208 114 344 401 449 540 384  NA  NA 856 721 903 741 625 244 142  56 972
##  [289] 276 769 509 377 796  61 367 583 922 105 725 595 755 667 862 409 832 503
##  [307] 956 504 363  41 492 553 537 622 316 207  11  34 976 158 110  NA 470 803
##  [325] 335  43 824 309  19 869 474 625  89 506  44 437  NA 208 918 411 141 345
##  [343] 932  33 751 502 248 242 159  76 970 130 996 821 328 977 741 367 416 613
##  [361] 202 438 544 689 278  14  94 933 670 752 503 292 427 882  96  NA 257 146
##  [379]  NA 841 681 338 817  NA 255 644 460 119 203 286  NA 370 736 659 412  NA
##  [397] 292 851  NA  NA  NA 840 130 635 234 673 134 368 719  NA 514 155 338 128
##  [415] 890  82 990 423 825 187 394 268 399 176 689 741 756 734  NA 977 826 248
##  [433] 821  NA 880  NA  NA  NA 490 209  NA 985 549 344 852  75 988  NA 841 675
##  [451]  NA 545 607 718 437 212 924 436 480  NA 229 156  57  NA  NA 826 773 726
##  [469] 457 634 355 636   7 255 427 158 252 866 727 977  NA 854 115  87 625 163
##  [487]  NA 496 217 740  21 569 720 228 306 318  NA 804  48 282 116  NA 631 100
##  [505] 987 357 502 838 775  NA 362 670 607  27 517 833 657 204  17  NA 183  NA
##  [523] 448 843 112  NA 966 327  91 878 686  NA 546 229 732 177  NA 881  NA 535
##  [541] 333 846  57 666 589 769 829 522   5 353 710 794 795  69 724 384 420 521
##  [559] 624 711 868 767 609  60  46 965 932 252 553 964 986 882 382 506 724 564
##  [577] 353 175 840  NA 440  NA 593 232 861 156 775 285 121 679  54  NA 205 238
##  [595] 789 351 282 732 860 501 263 182 549 739 893  41  NA 795 638 514 465 112
##  [613]  97 615 751  NA 864 912 831 952 541 537 990 732 531 954 865 767 807 268
##  [631] 297 227 210 613 596 498 855 942 110 707 330 354 711 464 178 976  NA 252
##  [649]  NA 514  NA 174 886  NA 442 877 367 572  18 732  NA  65 823 297 840 401
##  [667] 426 163  94 140  NA 655 409 856  NA 448 584  NA 116 877 143 478 644 664
##  [685]  NA  76 757 203 996 265 254  51 414  28 501  10 710 629 490 871 595 255
##  [703] 780 903 260 296 148  92 551  NA  NA  64 158 714 151 130 753 545  76  61
##  [721] 945 592  34 466 553 660 729 639 200  NA 594 945 368 602 273 231 650 266
##  [739] 383  78 865 365 190 184  57 522 723 353 773 392 703 381 139 493 126 917
##  [757]  98  41 996 330 606 855 610 109 527 846  NA 723 529 338 712 777 661 307
##  [775]  57 283 424 587 476  15 220 261 723  NA 600  17 452 959 833 398 991 572
##  [793]  71 189 669 543 140 781 470  28 866 112  18 528 914 848  NA 285 640 379
##  [811] 826  NA 405 746 653 527 629 607  68 840 957 222 142 847 188 261 227 719
##  [829] 163 358  33 255 290 821 217 517 225 160 783 590  86 300 838 855  NA 944
##  [847] 480 158 674 463  NA 274 408 784 535 458 754 253 918 560 392 676 210 673
##  [865] 103 677 152 856 821  NA 636 477 231 734   4 406 918 973  NA 555 613 708
##  [883]  NA 563  86 517 747 101 749 703 304 100 165 225  NA 759 229 500 385 176
##  [901] 962 382  26 368  25 620 492   3  NA 649 828 220  NA 220  NA 134 276  52
##  [919] 754   9 344 451 865 429  46  88 106 240 803 152 833 976 860 791 946  NA
##  [937] 777  NA  NA 664 308  82 829 331 562 683 321 380 391 279 701 914 566 582
##  [955]  83 761 203 932 819 682 521 259 445 739 275 278  NA 221 933  79 209 727
##  [973]  NA 801 206  NA 827 558 524 256 493 386 438 700 179 101 525 178 976 488
##  [991] 213 625 343 419  NA  NA 844 164 558  NA

#is.na = sirve para darle un valor logico a una variable

Paquetes

library(dslabs)
data("murders")

nlevels(murders$region)

## [1] 4

murders

##                   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     5

head(murders,3)

##     state abb region population total
## 1 Alabama  AL  South    4779736   135
## 2  Alaska  AK   West     710231    19
## 3 Arizona  AZ   West    6392017   232

str(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"

sort(murders$total)

##  [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 1257

order(murders$total,decreasing = TRUE)

##  [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] 46

murders$state[5]

## [1] "California"

murders$state[46]

## [1] "Vermont"

attach(murders)

state[5]

## [1] "California"

max(total)

## [1] 1257

pos_max<-which.max(total)
pos_max

## [1] 5

min(total)

## [1] 2

pos_min<-which.min(total)
pos_min

## [1] 46

state[pos_max]

## [1] "California"

state[pos_min]

## [1] "Vermont"

ejercicio3

Juan Pablo Agudelo Muñoz

2024-07-23

Fechas

Vectores

Paquetes