Introducción de datos
Vectores
v1 <-c(100, 500, 900, 225, 25)
v1
## [1] 100 500 900 225 25
v2 <-c("uno", "dos", "tres", "cuatro")
v2
## [1] "uno" "dos" "tres" "cuatro"
v1[1:3]
## [1] 100 500 900
v1*5
## [1] 500 2500 4500 1125 125
v1[-1]
## [1] 500 900 225 25
v1[c(1,3,5)]
## [1] 100 900 25
v1[-c(3)]
## [1] 100 500 225 25
data <-c(1, 2, 2, 2, 3, 3, 3, 3)
fdata <- factor(data)
fdata
## [1] 1 2 2 2 3 3 3 3
## Levels: 1 2 3
factor(1:3, labels = c("A", "B", "C"))
## [1] A B C
## Levels: A B C
factor(1:5, exclude = 4)
## [1] 1 2 3 <NA> 5
## Levels: 1 2 3 5
data1 <-c(10, 20, 20, 50, 10, 20, 10, 50, 20)
fdata1 <-factor(data1)
mdata1 <-factor(data1, levels<- c(10, 20, 50), ordered = TRUE)
mdata1
## [1] 10 20 20 50 10 20 10 50 20
## Levels: 10 < 20 < 50
Matrices
A <-matrix(c(3, 2, 1, -1, 4, 0, 2, 5, 4), nrow = 3, ncol = 3, byrow =T)
A
## [,1] [,2] [,3]
## [1,] 3 2 1
## [2,] -1 4 0
## [3,] 2 5 4
U <-matrix(1, nrow = 3, ncol = 4)
U
## [,1] [,2] [,3] [,4]
## [1,] 1 1 1 1
## [2,] 1 1 1 1
## [3,] 1 1 1 1
I5 <-diag(A)
I5
## [1] 3 4 4
D <-diag(A)
D
## [1] 3 4 4
solve(A)
## [,1] [,2] [,3]
## [1,] 0.37209302 -0.06976744 -0.09302326
## [2,] 0.09302326 0.23255814 -0.02325581
## [3,] -0.30232558 -0.25581395 0.32558140
t(A)
## [,1] [,2] [,3]
## [1,] 3 -1 2
## [2,] 2 4 5
## [3,] 1 0 4
M <-matrix(c(1, 2, 3, 4, 5, 6), nrow = 2, ncol = 3, byrow =T,
dimnames = list(c("Supermercado", "Tienda"),
c("Frutas", "Verduras", "Bebidas")))
M
## Frutas Verduras Bebidas
## Supermercado 1 2 3
## Tienda 4 5 6
M1 <-matrix(1:4, 2, 2)
M1
## [,1] [,2]
## [1,] 1 3
## [2,] 2 4
M2 <-matrix(5:8, 2, 2)
M2
## [,1] [,2]
## [1,] 5 7
## [2,] 6 8
M3 <-matrix(9:12, 2, 2)
M3
## [,1] [,2]
## [1,] 9 11
## [2,] 10 12
rbind(M1, M2, M3)
## [,1] [,2]
## [1,] 1 3
## [2,] 2 4
## [3,] 5 7
## [4,] 6 8
## [5,] 9 11
## [6,] 10 12
cbind(M1, M2, M3)
## [,1] [,2] [,3] [,4] [,5] [,6]
## [1,] 1 3 5 7 9 11
## [2,] 2 4 6 8 10 12
Arrays
array(1:5, c(2, 3))
## [,1] [,2] [,3]
## [1,] 1 3 5
## [2,] 2 4 1
array(1:5, c(2, 2))
## [,1] [,2]
## [1,] 1 3
## [2,] 2 4
array(1:5, c(2, 3, 3))
## , , 1
##
## [,1] [,2] [,3]
## [1,] 1 3 5
## [2,] 2 4 1
##
## , , 2
##
## [,1] [,2] [,3]
## [1,] 2 4 1
## [2,] 3 5 2
##
## , , 3
##
## [,1] [,2] [,3]
## [1,] 3 5 2
## [2,] 4 1 3
array(1:5, c(2, 3, 4))
## , , 1
##
## [,1] [,2] [,3]
## [1,] 1 3 5
## [2,] 2 4 1
##
## , , 2
##
## [,1] [,2] [,3]
## [1,] 2 4 1
## [2,] 3 5 2
##
## , , 3
##
## [,1] [,2] [,3]
## [1,] 3 5 2
## [2,] 4 1 3
##
## , , 4
##
## [,1] [,2] [,3]
## [1,] 4 1 3
## [2,] 5 2 4
array(1:5, c(2, 3, 5))
## , , 1
##
## [,1] [,2] [,3]
## [1,] 1 3 5
## [2,] 2 4 1
##
## , , 2
##
## [,1] [,2] [,3]
## [1,] 2 4 1
## [2,] 3 5 2
##
## , , 3
##
## [,1] [,2] [,3]
## [1,] 3 5 2
## [2,] 4 1 3
##
## , , 4
##
## [,1] [,2] [,3]
## [1,] 4 1 3
## [2,] 5 2 4
##
## , , 5
##
## [,1] [,2] [,3]
## [1,] 5 2 4
## [2,] 1 3 5
Listas
lista1 <-list(nombre = "José", "MarÃa", no.Hijos =c(10, 12, 34))
lista1
## $nombre
## [1] "José"
##
## [[2]]
## [1] "MarÃa"
##
## $no.Hijos
## [1] 10 12 34
DataFrames
IDpacientes<- c(1, 2, 3, 4)
Edad <- c(25, 27, 28, 27)
Diabetes <-c("Tipo1", "Tipo2", "Tipo3", "Tipo2")
Estado <- c("Malo", "Mejorado", "Excelente", "Malo")
datos1 <- data.frame(IDpacientes, Edad, Diabetes, Estado)
datos1
## IDpacientes Edad Diabetes Estado
## 1 1 25 Tipo1 Malo
## 2 2 27 Tipo2 Mejorado
## 3 3 28 Tipo3 Excelente
## 4 4 27 Tipo2 Malo
Alumno <- c("Pérez", "Gonzáles", "Rodriguez", "Alonso", "López",
"Tello", "Dominguez")
Altura <- c(1.81, 1.70, 1.75, 1.90, 1.85, 1.75, 1.81)
Peso <- c(85, 72, 80, 95, 85, 72, 84)
data3 <-data.frame(Alumno, Altura, Peso)
data3
## Alumno Altura Peso
## 1 Pérez 1.81 85
## 2 Gonzáles 1.70 72
## 3 Rodriguez 1.75 80
## 4 Alonso 1.90 95
## 5 López 1.85 85
## 6 Tello 1.75 72
## 7 Dominguez 1.81 84
mean(Altura)
## [1] 1.795714
mean(Peso)
## [1] 81.85714
library(vcd)
## Loading required package: grid
data("Arthritis")
str(Arthritis)
## 'data.frame': 84 obs. of 5 variables:
## $ ID : int 57 46 77 17 36 23 75 39 33 55 ...
## $ Treatment: Factor w/ 2 levels "Placebo","Treated": 2 2 2 2 2 2 2 2 2 2 ...
## $ Sex : Factor w/ 2 levels "Female","Male": 2 2 2 2 2 2 2 2 2 2 ...
## $ Age : int 27 29 30 32 46 58 59 59 63 63 ...
## $ Improved : Ord.factor w/ 3 levels "None"<"Some"<..: 2 1 1 3 3 3 1 3 1 1 ...
conteo <- table(Arthritis$Sex)
conteo
##
## Female Male
## 59 25
barplot(conteo, main = "Gráfico de Barras",
xlab = "Mejoramiento",
ylab = "Frecuencia",
col = "red")
library(ggplot2)
ggplot(Arthritis, aes(x=Sex, fill = factor(Sex)))+geom_bar()+
labs(fill = "Sex")
ggplot(Arthritis, aes(x=Improved, fill = factor(Improved)))+
geom_bar()+
ggtitle("Respuesta al Tratamiento")+
xlab("Tratamiento")+
ylab("Frecuecnia")+
labs(fill = "Improved")
ggplot(Arthritis, aes(x=Improved, fill = factor(Improved)))+
geom_bar()+
ggtitle("Respuesta al Tratamiento")+
xlab("Tratamiento")+
ylab("Frecuecnia")+
labs(fill = "Improved")+
coord_flip()
ggplot(Arthritis, aes(x=Treatment, fill = factor(Improved)))+
geom_bar()+
xlab("Tratamiento")+
ylab("Frecuencia")+
labs(fill = "Improved")
ggplot(Arthritis, aes(x=Treatment, fill = factor(Improved)))+
geom_bar()
ggplot(Arthritis, aes(x=Treatment, fill = factor(Improved)))+
geom_bar(position = "dodge")+
ggtitle("Gráfico de barras agrupadas")+
xlab("Tratamiento")+
ylab("Frecuencia")+
labs(fill = "Improved")
