MATRICES

Constr.

B = matrix(c(2, 4, 3, 1, 5, 7), 
           byrow = F,  
           nrow=3, 
           ncol=2)
##
B   
##      [,1] [,2]
## [1,]    2    1
## [2,]    4    5
## [3,]    3    7
### Transponiendo 
t(B)
##      [,1] [,2] [,3]
## [1,]    2    4    3
## [2,]    1    5    7
B
##      [,1] [,2]
## [1,]    2    1
## [2,]    4    5
## [3,]    3    7
### Combinando
#### Por columnas
C = matrix(c(7,4,2),
           nrow = 3,
           ncol = 1)
C
##      [,1]
## [1,]    7
## [2,]    4
## [3,]    2
cbind(B,C)
##      [,1] [,2] [,3]
## [1,]    2    1    7
## [2,]    4    5    4
## [3,]    3    7    2
#### POr filas
D <- matrix(c(6,2),
            nrow = 1,
            ncol = 2)
D
##      [,1] [,2]
## [1,]    6    2
B
##      [,1] [,2]
## [1,]    2    1
## [2,]    4    5
## [3,]    3    7
rbind(B,D)
##      [,1] [,2]
## [1,]    2    1
## [2,]    4    5
## [3,]    3    7
## [4,]    6    2
### Desarmando
B
##      [,1] [,2]
## [1,]    2    1
## [2,]    4    5
## [3,]    3    7
c(B)
## [1] 2 4 3 1 5 7
# LISTS
n <- c(2,3,5)
s <- c("a", "b", "c", "d", "e")
b <- c(TRUE, FALSE, TRUE, FALSE, FALSE)

x <- list(n, s, b, 5)
x
## [[1]]
## [1] 2 3 5
## 
## [[2]]
## [1] "a" "b" "c" "d" "e"
## 
## [[3]]
## [1]  TRUE FALSE  TRUE FALSE FALSE
## 
## [[4]]
## [1] 5
### Rodajas de la lista
x[c(2,4)]
## [[1]]
## [1] "a" "b" "c" "d" "e"
## 
## [[2]]
## [1] 5
x[1]
## [[1]]
## [1] 2 3 5
### Accesando directamente a un miembro de la lista
x
## [[1]]
## [1] 2 3 5
## 
## [[2]]
## [1] "a" "b" "c" "d" "e"
## 
## [[3]]
## [1]  TRUE FALSE  TRUE FALSE FALSE
## 
## [[4]]
## [1] 5
x[[1]]
## [1] 2 3 5
x[[1]] [1]
## [1] 2
x[[1]] [2]
## [1] 3
x[[1]] [2] *3
## [1] 9
#### modificando
x[[2]]
## [1] "a" "b" "c" "d" "e"
x[[2]] [2]
## [1] "b"
x[[2]] [2] = "z"
x[[2]]
## [1] "a" "z" "c" "d" "e"
s   # No se altera el objeto original
## [1] "a" "b" "c" "d" "e"

Listas con nombres

v <- list(rob=c(2, 3, 5), 
          juan=c("aa", "bb"))
v 
## $rob
## [1] 2 3 5
## 
## $juan
## [1] "aa" "bb"
v["rob"]
## $rob
## [1] 2 3 5
v[["rob"]]
## [1] 2 3 5
v$rob
## [1] 2 3 5

attachment

‘bob’ no existe como objeto. pero si se desea llegar directamente, se puede:

attach(v)
rob
## [1] 2 3 5
detach(v)

# Data Frame

n <- c(2,4,6)
s <- c('a','b','c')
b <- c(TRUE,FALSE,TRUE)

datos <- data.frame(n,s,b)
datos
##   n s     b
## 1 2 a  TRUE
## 2 4 b FALSE
## 3 6 c  TRUE
datos[2,2]
## [1] "b"
datos[2,3]
## [1] FALSE
datos[2,"b"]
## [1] FALSE
datos[2,"n"]
## [1] 4
nrow(datos)
## [1] 3
ncol(datos)
## [1] 3
## Built-in data frame
mtcars
##                      mpg cyl  disp  hp drat    wt  qsec vs am gear carb
## Mazda RX4           21.0   6 160.0 110 3.90 2.620 16.46  0  1    4    4
## Mazda RX4 Wag       21.0   6 160.0 110 3.90 2.875 17.02  0  1    4    4
## Datsun 710          22.8   4 108.0  93 3.85 2.320 18.61  1  1    4    1
## Hornet 4 Drive      21.4   6 258.0 110 3.08 3.215 19.44  1  0    3    1
## Hornet Sportabout   18.7   8 360.0 175 3.15 3.440 17.02  0  0    3    2
## Valiant             18.1   6 225.0 105 2.76 3.460 20.22  1  0    3    1
## Duster 360          14.3   8 360.0 245 3.21 3.570 15.84  0  0    3    4
## Merc 240D           24.4   4 146.7  62 3.69 3.190 20.00  1  0    4    2
## Merc 230            22.8   4 140.8  95 3.92 3.150 22.90  1  0    4    2
## Merc 280            19.2   6 167.6 123 3.92 3.440 18.30  1  0    4    4
## Merc 280C           17.8   6 167.6 123 3.92 3.440 18.90  1  0    4    4
## Merc 450SE          16.4   8 275.8 180 3.07 4.070 17.40  0  0    3    3
## Merc 450SL          17.3   8 275.8 180 3.07 3.730 17.60  0  0    3    3
## Merc 450SLC         15.2   8 275.8 180 3.07 3.780 18.00  0  0    3    3
## Cadillac Fleetwood  10.4   8 472.0 205 2.93 5.250 17.98  0  0    3    4
## Lincoln Continental 10.4   8 460.0 215 3.00 5.424 17.82  0  0    3    4
## Chrysler Imperial   14.7   8 440.0 230 3.23 5.345 17.42  0  0    3    4
## Fiat 128            32.4   4  78.7  66 4.08 2.200 19.47  1  1    4    1
## Honda Civic         30.4   4  75.7  52 4.93 1.615 18.52  1  1    4    2
## Toyota Corolla      33.9   4  71.1  65 4.22 1.835 19.90  1  1    4    1
## Toyota Corona       21.5   4 120.1  97 3.70 2.465 20.01  1  0    3    1
## Dodge Challenger    15.5   8 318.0 150 2.76 3.520 16.87  0  0    3    2
## AMC Javelin         15.2   8 304.0 150 3.15 3.435 17.30  0  0    3    2
## Camaro Z28          13.3   8 350.0 245 3.73 3.840 15.41  0  0    3    4
## Pontiac Firebird    19.2   8 400.0 175 3.08 3.845 17.05  0  0    3    2
## Fiat X1-9           27.3   4  79.0  66 4.08 1.935 18.90  1  1    4    1
## Porsche 914-2       26.0   4 120.3  91 4.43 2.140 16.70  0  1    5    2
## Lotus Europa        30.4   4  95.1 113 3.77 1.513 16.90  1  1    5    2
## Ford Pantera L      15.8   8 351.0 264 4.22 3.170 14.50  0  1    5    4
## Ferrari Dino        19.7   6 145.0 175 3.62 2.770 15.50  0  1    5    6
## Maserati Bora       15.0   8 301.0 335 3.54 3.570 14.60  0  1    5    8
## Volvo 142E          21.4   4 121.0 109 4.11 2.780 18.60  1  1    4    2
head(mtcars)
##                    mpg cyl disp  hp drat    wt  qsec vs am gear carb
## Mazda RX4         21.0   6  160 110 3.90 2.620 16.46  0  1    4    4
## Mazda RX4 Wag     21.0   6  160 110 3.90 2.875 17.02  0  1    4    4
## Datsun 710        22.8   4  108  93 3.85 2.320 18.61  1  1    4    1
## Hornet 4 Drive    21.4   6  258 110 3.08 3.215 19.44  1  0    3    1
## Hornet Sportabout 18.7   8  360 175 3.15 3.440 17.02  0  0    3    2
## Valiant           18.1   6  225 105 2.76 3.460 20.22  1  0    3    1
tail(mtcars)
##                 mpg cyl  disp  hp drat    wt qsec vs am gear carb
## Porsche 914-2  26.0   4 120.3  91 4.43 2.140 16.7  0  1    5    2
## Lotus Europa   30.4   4  95.1 113 3.77 1.513 16.9  1  1    5    2
## Ford Pantera L 15.8   8 351.0 264 4.22 3.170 14.5  0  1    5    4
## Ferrari Dino   19.7   6 145.0 175 3.62 2.770 15.5  0  1    5    6
## Maserati Bora  15.0   8 301.0 335 3.54 3.570 14.6  0  1    5    8
## Volvo 142E     21.4   4 121.0 109 4.11 2.780 18.6  1  1    4    2