R functions work on bigger data structures

R functions work on bigger data

R functions work on bigger data structures like vectors and matrices by default

> x <- seq(0, 10, 0.1)

> y <- sin(x)

> plot(x, y)

> plot(x,y^2)

> plot(x,y^3)

> plot(X+y^2)

> plot(x, y, type = "l")

R functions work on bigger data structures like vectors

> y1 <- cos(x)

> lines(x, y1, col = "red")

> x1 <- 1:10

> text(x1, sin(x1), x1, col = "green")

> 

> plot(x, y, xlim = c(-10, 10), ylim = c(-2, 2),

+      xlab = "x-Values", ylab = "y-Values", main = "Example Graphics")

> par(mfrow = c(2, 2))

> x <- rnorm(100)

> plot(x)

> hist(x)

> 

> qqnorm(x)

> qqline(x, col="red")

> hist(x, probability = TRUE)

> 

> xx <- seq(-3, 3, 0.1)

> lines(xx, dnorm(xx, 0, 1),col = "red")

> 

> x <- rnorm(100)

> shapiro.test(x)

Shapiro-Wilk normality test

data:  x

W = 0.98229, p-value = 0.1998

> 

> par(mfrow=c(2,2))

> 

> y <- runif(100)

> plot(y)

> hist(y)

> 

> qqnorm(y)

> 

> qqline(y, col="red")

> mean(y)

[1] 0.5052021

> var(y)

[1] 0.06479489

> min(y)

[1] 0.007599009

> max(y)

[1] 0.9939666

> hist(y, probability=TRUE)

> 

> yy <- seq(min(y), max(y), length = 50)

> lines(yy, dnorm(yy, mean(y), sd(y)), col = "red")

> shapiro.test(y)

Shapiro-Wilk normality test

data:  y

W = 0.96773, p-value = 0.01486

> par(mfrow=c(1, 1))

> boxplot(x, y)

> x <- c(2, 4, 6, 5, 8)

> f <- as.factor(x)

> as.numeric(f)

[1] 1 2 4 3 5

> attributes(f)

$`levels`

[1] "2" "4" "5" "6" "8"

$class

[1] "factor"

> attr(f, "class")

[1] "factor"

> class(f)

[1] "factor"

> x <- c(1, 2, 5, 7, 3, 4, 5, 8)

> dat <- data.frame(f = c("a", "a", "a", "b", "b", "b"),

+                   x = c(1, 4, 3, 3, 5, 7)

+ )

> colMeans(dat[2:8])

Error in `[.data.frame`(dat, 2:8) : undefined columns selected

> system.time({

+     x <- rnorm(10000)

+     plot(x)

+ })

   user  system elapsed 

   0.06    0.27    0.35