nombre <- c(“juan”, “manuela”, “jose”)
table(nombre)
codes <- c(Italy=380, Canada=124, egipto=150) codes names(codes) table(codes)
#Acceder a posiciones especificas codes[2]
codes[1:3]
codes[c(1,3)]
class(codes)
seq(1:10) help(“seq”)
seq(0,100,by=5)
seq(0,100, length.out=5)
a<- 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")
mat## C1 C2 C3 C4
## F1 1 2 3 4
## F2 5 6 7 8
## F3 9 10 11 12mat[1,2]## [1] 2{
mat
}## C1 C2 C3 C4
## F1 1 2 3 4
## F2 5 6 7 8
## F3 9 10 11 12x <-list(numero=1, text="Hello", logico=TRUE)
print(x)## $numero
## [1] 1
##
## $text
## [1] "Hello"
##
## $logico
## [1] TRUEx## $numero
## [1] 1
##
## $text
## [1] "Hello"
##
## $logico
## [1] TRUEcalificacionesdip <-list(name="Juan", studentID=2180838,grades=c(5,4.8), final="A")
calificacionesdip## $name
## [1] "Juan"
##
## $studentID
## [1] 2180838
##
## $grades
## [1] 5.0 4.8
##
## $final
## [1] "A"calificacionesdip$studentID## [1] 2180838calificacionesdip[["studentID"]]## [1] 2180838calificacionesdip[[1]]## [1] "Juan"#Dataframe
df<- as.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(nombre=c("Juan","Maria","Manuela"), edades=c(23,25,26))
df## nombre edades
## 1 Juan 23
## 2 Maria 25
## 3 Manuela 26city<- c("cali","bogota","medellin")
temp<- c(14,35,25)
temf<-0
df_city_temp<-data.frame(city,temp)
df_city_temp## city temp
## 1 cali 14
## 2 bogota 35
## 3 medellin 25caf<- function(x){
return(9*x/5+32)
}
caf(temp)## [1] 57.2 95.0 77.0tempf<-caf(temp)
df_city_temp$temf<-temf
df_city_temp## city temp temf
## 1 cali 14 0
## 2 bogota 35 0
## 3 medellin 25 0df_city_temp$city## [1] "cali" "bogota" "medellin"df_city_temp[temp>60]## data frame with 0 columns and 3 rowsdata()
data("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] 64d <- c(1,2,3,4,5)
logi<- c(TRUE,FALSE,TRUE,FALSE,TRUE)
d[logi]## [1] 1 3 5install.packages(“dslabs”) library(dslabs) data(“murders”)
library(dslabs)
data("murders")
murders$region## [1] South West West South West
## [6] West Northeast South South South
## [11] South West West North Central North Central
## [16] North Central North Central South South Northeast
## [21] South Northeast North Central North Central South
## [26] North Central West North Central West Northeast
## [31] Northeast West Northeast South North Central
## [36] North Central South West Northeast Northeast
## [41] South North Central South South West
## [46] Northeast South West South North Central
## [51] West
## Levels: Northeast South North Central Westlevels(murders$region)## [1] "Northeast" "South" "North Central" "West"nlevels(murders$region)## [1] 4head(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 65murders## 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 5str(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$total)## [1] 2 4 5 5 7 8 11 12 12 16 19 21 22 27 32
## [16] 36 38 53 63 65 67 84 93 93 97 97 99 111 116 118
## [31] 120 135 142 207 219 232 246 250 286 293 310 321 351 364 376
## [46] 413 457 517 669 805 1257order(murders$total)## [1] 46 35 30 51 12 42 20 13 27 40 2 16 45 49 28 38 8 24 17 6 32 29 4 48 7
## [26] 50 9 37 18 22 25 1 15 41 43 3 31 47 34 21 36 26 19 14 11 23 39 33 10 44
## [51] 5murders## 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 5murders$state[5]## [1] "California"murders$state[46]## [1] "Vermont"attach(murders)
state[5]## [1] "California"max(total)## [1] 1257pos_max<-which.max(total)
pos_max## [1] 5pos_min<-which.min(total)
pos_min## [1] 46min(total)## [1] 2state[pos_max]## [1] "California"state[pos_min]## [1] "Vermont"data("na_example")
na_example## [1] 2 1 3 2 1 3 1 4 3 2 2 NA 2 2 1 4 NA 1 1 2 1 2 2 1
## [25] 2 5 NA 2 2 3 1 2 4 1 1 1 4 5 2 3 4 1 2 4 1 1 2 1
## [49] 5 NA NA NA 1 1 5 1 3 1 NA 4 4 7 3 2 NA NA 1 NA 4 1 2 2
## [73] 3 2 1 2 2 4 3 4 2 3 1 3 2 1 1 1 3 1 NA 3 1 2 2 1
## [97] 2 2 1 1 4 1 1 2 3 3 2 2 3 3 3 4 1 1 1 2 NA 4 3 4
## [121] 3 1 2 1 NA NA NA NA 1 5 1 2 1 3 5 3 2 2 NA NA NA NA 3 5
## [145] 3 1 1 4 2 4 3 3 NA 2 3 2 6 NA 1 1 2 2 1 3 1 1 5 NA
## [169] NA 2 4 NA 2 5 1 4 3 3 NA 4 3 1 4 1 1 3 1 1 NA NA 3 5
## [193] 2 2 2 3 1 2 2 3 2 1 NA 2 NA 1 NA NA 2 1 1 NA 3 NA 1 2
## [217] 2 1 3 2 2 1 1 2 3 1 1 1 4 3 4 2 2 1 4 1 NA 5 1 4
## [241] NA 3 NA NA 1 1 5 2 3 3 2 4 NA 3 2 5 NA 2 3 4 6 2 2 2
## [265] NA 2 NA 2 NA 3 3 2 2 4 3 1 4 2 NA 2 4 NA 6 2 3 1 NA 2
## [289] 2 NA 1 1 3 2 3 3 1 NA 1 4 2 1 1 3 2 1 2 3 1 NA 2 3
## [313] 3 2 1 2 3 5 5 1 2 3 3 1 NA NA 1 2 4 NA 2 1 1 1 3 2
## [337] 1 1 3 4 NA 1 2 1 1 3 3 NA 1 1 3 5 3 2 3 4 1 4 3 1
## [361] NA 2 1 2 2 1 2 2 6 1 2 4 5 NA 3 4 2 1 1 4 2 1 1 1
## [385] 1 2 1 4 4 1 3 NA 3 3 NA 2 NA 1 2 1 1 4 2 1 4 4 NA 1
## [409] 2 NA 3 2 2 2 1 4 3 6 1 2 3 1 3 2 2 2 1 1 3 2 1 1
## [433] 1 3 2 2 NA 4 4 4 1 1 NA 4 3 NA 1 3 1 3 2 4 2 2 2 3
## [457] 2 1 4 3 NA 1 4 3 1 3 2 NA 3 NA 1 3 1 4 1 1 1 2 4 3
## [481] 1 2 2 2 3 2 3 1 1 NA 3 2 1 1 2 NA 2 2 2 3 3 1 1 2
## [505] NA 1 2 1 1 3 3 1 3 1 1 1 1 1 2 5 1 1 2 2 1 1 NA 1
## [529] 4 1 2 4 1 3 2 NA 1 1 NA 2 1 1 4 2 3 3 1 5 3 1 1 2
## [553] NA 1 1 3 1 3 2 4 NA 2 3 2 1 2 1 1 1 2 2 3 1 5 2 NA
## [577] 2 NA 3 2 2 2 1 5 3 2 3 1 NA 3 1 2 2 2 1 2 2 4 NA 6
## [601] 1 2 NA 1 1 2 2 3 NA 3 2 3 3 4 2 NA 2 NA 4 NA 1 1 2 2
## [625] 3 1 1 1 3 NA 2 5 NA 7 1 NA 4 3 3 1 NA 1 1 1 1 3 2 4
## [649] 2 2 3 NA NA 1 4 3 2 2 2 3 2 4 2 2 4 NA NA NA 6 3 3 1
## [673] 4 4 2 1 NA 1 6 NA 3 3 2 1 1 6 NA 1 5 1 NA 2 6 2 NA 4
## [697] 1 3 1 2 NA 1 1 3 1 2 4 2 1 3 2 4 3 2 2 1 1 5 6 4
## [721] 2 2 2 2 4 NA 1 2 2 2 2 4 5 NA NA NA 4 3 3 3 2 4 2 4
## [745] NA NA NA NA 2 1 NA 2 4 3 2 NA 2 3 1 3 4 NA 1 2 1 2 NA 3
## [769] 1 2 1 2 1 2 1 2 2 2 2 1 1 3 3 1 3 4 3 NA NA 4 2 3
## [793] 2 1 3 2 4 2 2 3 1 2 4 3 3 4 NA 1 4 2 1 1 1 3 1 5
## [817] 2 2 4 2 NA 1 3 1 2 NA 1 2 1 2 1 NA 1 3 2 3 2 NA 2 1
## [841] 4 2 NA NA NA 2 4 2 NA NA 3 1 NA 5 5 2 2 2 NA 2 1 3 1 3
## [865] 2 4 2 4 NA 4 1 2 3 2 3 3 2 3 2 2 2 1 3 2 4 2 NA 3
## [889] 3 2 2 NA NA 3 2 1 2 4 1 1 1 1 4 3 2 NA 3 2 NA 1 NA 3
## [913] 2 1 1 1 2 NA 2 2 3 3 2 NA NA 4 5 2 2 2 1 2 3 1 3 3
## [937] 4 3 NA 1 1 1 NA 4 3 5 1 1 2 NA 2 2 2 2 5 2 2 3 1 2
## [961] 3 NA 1 2 NA NA 2 NA 3 1 1 2 5 3 5 1 1 4 NA 2 1 3 1 1
## [985] 2 4 3 3 3 NA 1 1 2 2 1 1 2 2 NA 2