### Asignación de Variables

x<-3
y<-2

Impresión de Resultados

x
## [1] 3
y
## [1] 2

Operaciones Aritméticas

suma <- x+y
suma
## [1] 5
resta <- x-y
resta
## [1] 1
multiplicacion <- x*y
multiplicacion
## [1] 6
division <- x/y
division
## [1] 1.5
division_entera <- x%/%y
division_entera
## [1] 1
potencia <- x^2
potencia
## [1] 9
potencia<- y^2
potencia
## [1] 4

Funciones Matemáticas

raiz_cuadrada <- sqrt(x)
raiz_cuadrada
## [1] 1.732051
raiz_cubica <- x^(1/3)
raiz_cubica
## [1] 1.44225
exponencial <- exp(1)
exponencial 
## [1] 2.718282
z <- -4
z 
## [1] -4
absoluto <- abs(z)
absoluto
## [1] 4
signo <- sign(z)
signo
## [1] -1
signo2 <- sign(x)
signo2
## [1] 1
redondeo_arriba <- ceiling(x/y)
redondeo_arriba
## [1] 2
redondeo_abajo <- floor(x/y)
redondeo_abajo
## [1] 1
truncar <- trunc(division)
truncar
## [1] 1

Constantes

pi
## [1] 3.141593
radio<-5
area_circulo <- pi*radio^2
area_circulo
## [1] 78.53982

Vectores

a <- c(1,2,3,4,5)
a
## [1] 1 2 3 4 5
?c

longitud <- length(a)
longitud
## [1] 5
promedio <- mean(a)
promedio
## [1] 3
resumen <- summary(a)
resumen
##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
##       1       2       3       3       4       5
orden_ascendente <- sort(a)
orden_ascendente
## [1] 1 2 3 4 5
orden_descendente <- sort(a, decreasing=TRUE)
orden_descendente
## [1] 5 4 3 2 1
b <- c(1:5)
b
## [1] 1 2 3 4 5
suma_vectores <- a+b
suma_vectores
## [1]  2  4  6  8 10
plot(a,b, type="b", main="Ventas Totales por Semana", xlab="Semana", ylab="Mxn")

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