x <- 3
y <- 2
x
## [1] 3
y
## [1] 2
suma <- x + y
suma
## [1] 5
resta <- x - y
resta
## [1] 1
multiplicacion <- x * y 
multiplicacion
## [1] 6
division <- x%/%y
division
## [1] 1
residuo <- x%%y
residuo
## [1] 1
potencia <- x^y
potencia
## [1] 9
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
absoluto <- abs(x)
absoluto
## [1] 3
signo <- sign(x)
signo
## [1] 1
redondeo_arriba <- ceiling(division)
redondeo_arriba
## [1] 1
redondeo_abajo <- floor(division)
redondeo_abajo
## [1] 1
truncar <- trunc(division)
truncar
## [1] 1
pi
## [1] 3.141593
radio <- 5
area_circulo <- pi*radio^2
area_circulo
## [1] 78.53982
a <- c(1,2,3,4,5)
a
## [1] 1 2 3 4 5
oden_descendente <- sort(a, decreasing = TRUE)
oden_descendente
## [1] 5 4 3 2 1
d <- c(1,2,3,4,5)
d
## [1] 1 2 3 4 5
suma_vectores<- a + d
suma_vectores
## [1]  2  4  6  8 10
plot(a,d, main = "Ventas por mes", xlab = "Mes", ylab = "Millones")

?plot
## Help on topic 'plot' was found in the following packages:
## 
##   Package               Library
##   graphics              /Library/Frameworks/R.framework/Versions/4.3-x86_64/Resources/library
##   base                  /Library/Frameworks/R.framework/Resources/library
## 
## 
## Using the first match ...
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