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Complemento nociones básicas de R
Variables
R no requiere ningún tipo de comando para declarar variables. Sencillamente crea la variable
mediante asignación de su valor
x <- 3 x
[1] 3
Una vez declarada la variable podemos utilizar en cálculos
x ^ 3
[1] 27
x + 5
## [1] 8
Si deseamos cambiar el valor de la variable, solo debemos asignarle un nuevo valor
x <- 3 + 7
x
[1] 10
x ^ 4
## [1] 10000
Vectores
Para representar un nuevo vector de elemento debemos concatenar el vector de la siguiente manera
x <- c ( 1 , 4 , 9 , 2.25, 1 / 4 )
x
[1] 1.00 4.00 9.00 2.25 0.
length (x)
[1] 5
class (x)
[1] “numeric”
sqrt (x)
[1] 1.0 2.0 3.0 1.5 0.
Primeras funciones class (c)
[1] “function”
class (length)
[1] “function”
length
function (x) .Primitive(“length”)
Operaciones sencillas con vectores
x + 1
## [1] 2.00 5.00 10.00 3.25 1.
y <- 1 : 10
x + y
[1] 2.00 6.00 12.00 6.25 5.25 7.00 11.00 17.00 11.25 10.
x ***** y
[1] 1.00 8.00 27.00 9.00 1.25 6.00 28.00 72.00 20.25 2.
x ^ 2
## [1] 1.0000 16.0000 81.0000 5.0625 0.
## [47] 93 95 97 99
seq ( 1 , 100 , 10 )
## [1] 1 11 21 31 41 51 61 71 81 91
seq ( 1 , 100 , length= 10 )
## [1] 1 12 23 34 45 56 67 78 89 100
x <- seq ( 1 , 100 , length= 10 )
x
[1] 1 12 23 34 45 56 67 78 89 100
length (x)
[1] 10
y <- seq ( 2 , 100 , length= 50 )
y
[1] 2 4 6 8 10 12 14 16 18 20 22 24 26 28 30 32
34
[18] 36 38 40 42 44 46 48 50 52 54 56 58 60 62 64 66
68
[35] 70 72 74 76 78 80 82 84 86 88 90 92 94 96 98 100
length (y)
[1] 50
z <- c (x, y) z
[1] 1 12 23 34 45 56 67 78 89 100 2 4 6 8 10 12
14
[18] 16 18 20 22 24 26 28 30 32 34 36 38 40 42 44 46
48
[35] 50 52 54 56 58 60 62 64 66 68 70 72 74 76 78 80
82
[52] 84 86 88 90 92 94 96 98 100
z + c ( 1 , 2 )
## [1] 2 14 24 36 46 58 68 80 90 102 3 6 7 10 11 14
## [18] 18 19 22 23 26 27 30 31 34 35 38 39 42 43 46 47
## [35] 51 54 55 58 59 62 63 66 67 70 71 74 75 78 79 82
## [52] 86 87 90 91 94 95 98 99 102
z <- c (z, z, z) z
[1] 1 12 23 34 45 56 67 78 89 100 2 4 6 8 10 12
14
[18] 16 18 20 22 24 26 28 30 32 34 36 38 40 42 44 46
48
[35] 50 52 54 56 58 60 62 64 66 68 70 72 74 76 78 80
82
[52] 84 86 88 90 92 94 96 98 100 1 12 23 34 45 56 67
78
[69] 89 100 2 4 6 8 10 12 14 16 18 20 22 24 26 28
30
[86] 32 34 36 38 40 42 44 46 48 50 52 54 56 58 60 62
64
[103] 66 68 70 72 74 76 78 80 82 84 86 88 90 92 94 96
98
[120] 100 1 12 23 34 45 56 67 78 89 100 2 4 6 8 10
12
[137] 14 16 18 20 22 24 26 28 30 32 34 36 38 40 42 44
46
[154] 48 50 52 54 56 58 60 62 64 66 68 70 72 74 76 78
80
[171] 82 84 86 88 90 92 94 96 98 100
length (z)
[1] 180
Generar vectores con rep
Para ver la ayuda digite help (rep)
rep ( 1 : 10 , 4 )
## [1] 1 2 3 4 5 6 7 8 9 10 1 2 3 4 5 6 7 8 9 10 1 2
## [24] 4 5 6 7 8 9 10 1 2 3 4 5 6 7 8 9 10
## [1] 31 33 35 37 39 41 43 45 47 49 51 53 55 57 59 61 63 65 67 69 71 73
## [24] 77 79 81 83 85 87 89 91 93 95 97 99
x[x < 20 ]
## [1] 1 3 5 7 9 11 13 15 17 19
x[x == 9 ]
## [1] 9
x[x != 9 ]
## [1] 1 3 5 7 11 13 15 17 19 21 23 25 27 29 31 33 35 37 39 41 43 45
## [24] 49 51 53 55 57 59 61 63 65 67 69 71 73 75 77 79 81 83 85 87 89 91
## [47] 95 97 99
Indexado de vectores con %in%
y <- seq ( 101 , 200 , 2 )
y %in% c ( 101 , 127 , 141 )
## [1] TRUE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [12] FALSE FALSE TRUE FALSE FALSE FALSE FALSE FALSE FALSE TRUE FALSE
## [23] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [34] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [45] FALSE FALSE FALSE FALSE FALSE FALSE
y
## [1] 101 103 105 107 109 111 113 115 117 119 121 123 125 127 129 131
## [18] 135 137 139 141 143 145 147 149 151 153 155 157 159 161 163 165
## [35] 169 171 173 175 177 179 181 183 185 187 189 191 193 195 197 199
y[y %in% c ( 101 , 127 , 141 )]
## [1] 101 127 141
Indexado de vectores con condiciones múltiples z <- c (x, y) z
[1] 1 3 5 7 9 11 13 15 17 19 21 23 25 27 29 31
33
[18] 35 37 39 41 43 45 47 49 51 53 55 57 59 61 63 65
67
[35] 69 71 73 75 77 79 81 83 85 87 89 91 93 95 97 99
101
[52] 103 105 107 109 111 113 115 117 119 121 123 125 127 129 131 133
135
[69] 137 139 141 143 145 147 149 151 153 155 157 159 161 163 165 167
169
[86] 171 173 175 177 179 181 183 185 187 189 191 193 195 197 199
z > 150
## [1] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [12] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [23] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [34] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [45] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [56] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [67] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE TRUE TRUE
## [78] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
## [89] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
## [100] TRUE
z[z > 150 ]
## [1] 151 153 155 157 159 161 163 165 167 169 171 173 175 177 179 181
## [18] 185 187 189 191 193 195 197 199
z[z < 30 | z > 150 ]
## [1] 1 3 5 7 9 11 13 15 17 19 21 23 25 27 29 151
## [18] 155 157 159 161 163 165 167 169 171 173 175 177 179 181 183 185
## [35] 189 191 193 195 197 199
z[z >= 30 & z <= 150 ]
## [34] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
## [45] TRUE TRUE TRUE TRUE TRUE TRUE
sum (! cond)
[1] 30
length (x[cond])
[1] 20
length (x[! cond])
[1] 30
as.numeric (cond)
[1] 0 0 0 0 0 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 0 0 0 0 0 0 0 0
0 0
[36] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
Funciones predefinidas summary (x)
Min. 1st Qu. Median Mean 3rd Qu. Max.
1.0 25.5 50.0 50.0 74.5 99.
mean (x)
[1] 50
sd (x)
[1] 29.
median (x)
[1] 50
max (x)
[1] 99
min (x)
[1] 1
range (x)
[1] 1 99
quantile (x)
0% 25% 50% 75% 100%
1.0 25.5 50.0 74.5 99.
Matrices Construir una matriz
Para la ayuda de la función escriba help (matrix)
z <- 1 : 12
M <- matrix (z, nrow= 3 )
M
## [,1] [,2] [,3] [,4]
## [1,] 1 4 7 10
## [2,] 2 5 8 11
## [3,] 3 6 9 12
z
[1] 1 2 3 4 5 6 7 8 9 10 11 12
class (M)
[1] “matrix”
dim (M)
[1] 3 4
summary (M)
V1 V2 V3 V
Min. :1.0 Min. :4.0 Min. :7.0 Min. :10.
1st Qu.:1.5 1st Qu.:4.5 1st Qu.:7.5 1st Qu.:10.
Median :2.0 Median :5.0 Median :8.0 Median :11.
Mean :2.0 Mean :5.0 Mean :8.0 Mean :11.
cbind (M, M)
[,1] [,2] [,3] [,4] [,5] [,6] [,7] [,8] [,9] [,10] [,11] [,12] [,13]
x 1 2 3 4 5 6 7 8 9 10 1 2 3
y 1 2 3 4 5 6 7 8 9 10 1 2 3
z 1 2 3 4 5 6 7 8 9 10 1 2 3
[,14] [,15] [,16] [,17] [,18] [,19] [,20]
x 4 5 6 7 8 9 10
y 4 5 6 7 8 9 10
z 4 5 6 7 8 9 10
Transponer una matriz M
[,1] [,2] [,3] [,4] [,5] [,6] [,7] [,8] [,9] [,10]
x 1 2 3 4 5 6 7 8 9 10
y 1 2 3 4 5 6 7 8 9 10
z 1 2 3 4 5 6 7 8 9 10
t (M)
x y z
[1,] 1 1 1
[2,] 2 2 2
[3,] 3 3 3
[4,] 4 4 4
[5,] 5 5 5
[6,] 6 6 6
[7,] 7 7 7
[8,] 8 8 8
[9,] 9 9 9
[10,] 10 10 10
class (t)
[1] “function”
dim ( t (M))
[1] 10 3
Multiplicación entre matrices
Para multiplicar entre matrices utilice lo siguiente %*%:
M * M
## [,1] [,2] [,3] [,4] [,5] [,6] [,7] [,8] [,9] [,10]
x 1 4 9 16 25 36 49 64 81 100
y 1 4 9 16 25 36 49 64 81 100
z 1 4 9 16 25 36 49 64 81 100
M %%t* (M)
x y z
x 385 385 385
y 385 385 385
z 385 385 385
Operaciones con matrices: funciones predefinidas sum (M)
[1] 165
rowSums (M)
x y z
55 55 55
colSums (M)
[1] 3 6 9 12 15 18 21 24 27 30
rowMeans (M)
x y z
5.5 5.5 5.
colMeans (M)
[1] 1 2 3 4 5 6 7 8 9 10
M[, 1 ]
x y z
1 1 1
M[ 1 : 2 , ]
## [,1] [,2] [,3] [,4] [,5] [,6] [,7] [,8] [,9] [,10]
x 1 2 3 4 5 6 7 8 9 10
y 1 2 3 4 5 6 7 8 9 10
M[ 1 : 2 , 2 : 3 ]
## [,1] [,2]
x 2 3
y 2 3
M[ 1 , c ( 1 , 4 )]
## [1] 1 4
M[ - 1 ,]
## [,1] [,2] [,3] [,4] [,5] [,6] [,7] [,8] [,9] [,10]
y 1 2 3 4 5 6 7 8 9 10
z 1 2 3 4 5 6 7 8 9 10
M[ - c ( 1 , 2 ),]
## [1] 1 2 3 4 5 6 7 8 9 10
Valores Perdidos
Un valor perdido se denota como NA (‘Not Available’ / Missing Values)
class (NA)
[1] “logical”
x <- rnorm ( 100 )
idx <- sample ( length (x), 10 )
idx
x[idx]
x2 <- x NA en las funciones
Cuando en los objetos hay valores NA las funciones no trabajan de forma adecuada.
summary (x)
[1]
[1] 0.48140246 -1.25438824 0.43637005 0.10170100 -0.
[6] 0.51871286 -0.04006789 -0.34833641 -1.02026599 -0.
- x x2[idx] <- NA
[1] 2.460491400 -0.989672187 -0.733562158 0.446869438 -0.
[6] -2.089056325 NA 2.003798474 2.879552568 0.
[11] -0.146436980 0.293767454 2.376869437 -0.709277219 -0.
[16] -0.552640828 1.114521816 0.275690979 -0.476080386 -0.
[21] -0.055346313 -1.220338566 NA 0.203108405 -0.
[26] 0.414756468 -0.674746617 0.968628374 0.391101042 0.
[31] 0.148390378 -0.724031294 1.584410433 NA -1.
[36] 0.312395375 -0.550137672 0.078965341 0.226975622 -0.
[41] NA NA -0.468640864 0.132528834 0.
[46] -0.109778264 0.462969932 0.198948808 -0.914050237 -0.
[51] -0.925615008 0.445396509 -0.067783435 0.479883253 -0.
[56] NA -1.727086193 1.339306531 1.148439872 -0.
[61] -1.089754411 0.430632420 0.098041925 -0.607280524 1.
[66] NA NA 0.432061797 0.040583975 0.
[71] -1.087568794 0.236805285 -1.297783394 -0.127382414 0.
[76] -1.845351783 0.211207141 0.061888953 NA -1.
[81] 1.707006915 -0.610353010 0.544673138 1.267711209 -0.
[86] 2.123144597 0.835974335 -1.798179950 0.512522698 -0.
[91] -2.534751466 0.305806101 1.890898979 -0.123456187 -0.
[96] 1.424617821 -0.224555669 -0.971919271 NA -0.
Forma general
NombreDeFuncion <- function (arg 1 , arg 2 , ...) expresión
Ejemplo myFun <- function (x, y) x + y
myFun ( 3 , 4 )
## [1] 7
class (myFun)
[1] “function”
También se puede definir una función a partir de otras funciones
foo <- function (x, ...){ mx <- mean (x, ...) medx <- median (x, ...) sdx <- sd (x, ...) c (mx, medx, sdx) }
foo ( 1 : 10 ) # Función que calcula la media, mediana y la desviación estándar
## [1] 5.50000 5.50000 3.
Lo anterior también funciona con matrices
M <- matrix ( c ( 1 : 30 ),nrow = 3 ,byrow = TRUE)
M
## [,1] [,2] [,3] [,4] [,5] [,6] [,7] [,8] [,9] [,10]
## [1,] 1 2 3 4 5 6 7 8 9 10
## [2,] 11 12 13 14 15 16 17 18 19 20
## [3,] 21 22 23 24 25 26 27 28 29 30
apply (M, 1 , foo) # Aplicando la función foo en forma fila
## [,1] [,2] [,3]
## [1,] 5.50000 15.50000 25.
## [2,] 5.50000 15.50000 25.
## [3,] 3.02765 3.02765 3.
apply (M, 2 , foo) # Aplicando la función foo en forma columna
## [,1] [,2] [,3] [,4] [,5] [,6] [,7] [,8] [,9] [,10]
## [1,] 11 12 13 14 15 16 17 18 19 20
## [2,] 11 12 13 14 15 16 17 18 19 20
## [3,] 10 10 10 10 10 10 10 10 10 10
Funciones con valores predeterminados
function.suma<- function (A= 10 ,B= 5 ) A + B
function.suma ()
[1] 15
function.suma ( 10 , 3 )
## [1] 13
x
[1] 2.460491400 -0.989672187 -0.733562158 0.446869438 -0.
[6] -2.089056325 -1.254388240 2.003798474 2.879552568 0.
[11] -0.146436980 0.293767454 2.376869437 -0.709277219 -0.
[16] -0.552640828 1.114521816 0.275690979 -0.476080386 -0.
[21] -0.055346313 -1.220338566 0.481402461 0.203108405 -0.
[26] 0.414756468 -0.674746617 0.968628374 0.391101042 0.
[31] 0.148390378 -0.724031294 1.584410433 0.436370045 -1.
[36] 0.312395375 -0.550137672 0.078965341 0.226975622 -0.
[41] 0.101701005 -0.348336413 -0.468640864 0.132528834 0.
[46] -0.109778264 0.462969932 0.198948808 -0.914050237 -0.
[51] -0.925615008 0.445396509 -0.067783435 0.479883253 -0.
[56] 0.518712855 -1.727086193 1.339306531 1.148439872 -0.
[61] -1.089754411 0.430632420 0.098041925 -0.607280524 1.
[66] -0.767561732 -0.348508327 0.432061797 0.040583975 0.
[71] -1.087568794 0.236805285 -1.297783394 -0.127382414 0.
[76] -1.845351783 0.211207141 0.061888953 -0.040067888 -1.
