plot(c(1,2,3), c(1,2,4))
plot(c(-3,3), c(-1,5), type = "b", xlab="x", ylab="y")
x <- c(1,2,3)
y <- c(1,3,8)
plot(x,y)
lmout <- lm(y ~ x)
abline(lmout)
lines(c(1.5,2.5),c(3,3))
data("mtcars")
d1 = density(mtcars$cyl,from=0,to=100)
d2 = density(mtcars$mpg,from=0,to=100)
plot(d1,main="",xlab="")
lines(d2, col = "purple")
plot(mtcars$cyl,mtcars$mpg, type = "n")
points(mtcars$cyl,mtcars$mpg,pch="+")
text(5,25,": )")
f = function(x) return(1-exp(-x))
curve(f,0,2)
polygon(c(1.2,1.8,1.8,1.2),c(0,0,f(1.3),f(1.3)),col="red")
library(lattice)
a <- 1:10
b <- 1:15
eg <- expand.grid(x=a,y=b)
eg$z <- eg$x^2 + eg$x * eg$y
wireframe(z ~ x+y, eg)
require(ggplot2)
## Loading required package: ggplot2
pie <- ggplot(mtcars, aes(x = factor(1), fill = factor(cyl))) +
geom_bar(width = 1, position = "fill", color = "black")
pie
pie + coord_polar(theta = "y")
require(ggplot2)
data(diamonds)
set.seed(42)
small = diamonds[sample(nrow(diamonds), 1000), ]
head(small)
## carat cut color clarity depth table price x y z
## 49345 0.71 Very Good H SI1 62.5 60 2096 5.68 5.75 3.57
## 50545 0.79 Premium H SI1 61.8 59 2275 5.97 5.91 3.67
## 15434 1.03 Ideal F SI1 62.4 57 6178 6.48 6.44 4.03
## 44792 0.50 Ideal E VS2 62.2 54 1624 5.08 5.11 3.17
## 34614 0.27 Ideal E VS1 61.6 56 470 4.14 4.17 2.56
## 27998 0.30 Premium E VS2 61.7 58 658 4.32 4.34 2.67
p = ggplot(data = small,
mapping = aes(x = carat, y = price))
p + geom_point()
p = ggplot(data=small,
mapping=aes(x=carat,
y=price,
shape=cut))
p+geom_point()
p = ggplot(data=small, mapping=aes(x=carat, y=price, shape=cut, colour=color))
p+geom_point()
ggplot(small)+geom_histogram(aes(x=price))
## stat_bin: binwidth defaulted to range/30. Use 'binwidth = x' to adjust this.
ggplot(small)+geom_histogram(aes(x=price, fill=cut))
## stat_bin: binwidth defaulted to range/30. Use 'binwidth = x' to adjust this.
ggplot(small)+geom_histogram(aes(x=price, fill=cut), position="dodge")
## stat_bin: binwidth defaulted to range/30. Use 'binwidth = x' to adjust this.
ggplot(small)+geom_bar(aes(x=clarity))
ggplot()+geom_bar(aes(x=c(LETTERS[1:3]),y=1:3), stat="identity")
ggplot(small)+geom_density(aes(x=price, colour=cut))
ggplot(small)+geom_density(aes(x=price,fill=clarity))
ggplot(small)+geom_boxplot(aes(x=cut, y=price,fill=color))
ggplot(small)+
geom_point(aes(x=carat, y=price, shape=cut, colour=color))+
scale_y_log10()+
scale_colour_manual(values=rainbow(7))
ggplot(small, aes(x=carat, y=price))+geom_point()+scale_y_log10()+stat_smooth()
## geom_smooth: method="auto" and size of largest group is >=1000, so using gam with formula: y ~ s(x, bs = "cs"). Use 'method = x' to change the smoothing method.
ggplot(small)+geom_bar(aes(x=cut, fill=cut))+coord_flip()
ggplot(small)+geom_bar(aes(x=factor(1), fill=cut))+coord_polar(theta="y")
ggplot(small)+geom_bar(aes(x=factor(1), fill=cut))+coord_polar()
ggplot(small)+geom_bar(aes(x=clarity, fill=cut))+coord_polar()
p = ggplot(mtcars, aes(x = hp, y = mpg))+
geom_point(size = 3)
p
p = ggplot(mtcars, aes(x = hp, y = mpg))+
geom_point( size = 3) +
stat_smooth(method = 'loess')
p
ggplot(small, aes(x=carat, y=price))+
geom_point(aes(colour=cut))+
scale_y_log10() +
facet_wrap(~cut)+
stat_smooth()
## geom_smooth: method="auto" and size of largest group is <1000, so using loess. Use 'method = x' to change the smoothing method.
## geom_smooth: method="auto" and size of largest group is <1000, so using loess. Use 'method = x' to change the smoothing method.
## geom_smooth: method="auto" and size of largest group is <1000, so using loess. Use 'method = x' to change the smoothing method.
## geom_smooth: method="auto" and size of largest group is <1000, so using loess. Use 'method = x' to change the smoothing method.
## geom_smooth: method="auto" and size of largest group is <1000, so using loess. Use 'method = x' to change the smoothing method.
p <- ggplot(small)+geom_boxplot(aes(x=cut, y=price,fill=color))
p + ggtitle("Price vs Cut")+xlab("Cut")+ylab("Price")
require(ggthemes)
## Loading required package: ggthemes
p + theme_wsj()
