library(ggplot2)

## 3.13

ggplot(mtcars,aes(x=mpg))+
geom_histogram(bins=10,fill="blue")+
theme_classic()

ggplot(mtcars,aes(x=cyl))+
geom_bar()+
theme_classic()

ggplot(mtcars,aes(x=disp,y=mpg))+
geom_point()+
theme_classic()

## 3.14

prob <- data.frame(label=c(rep("red",3),rep("blue",2),rep("green",4),rep("black",1)))
ggplot(prob,aes(x=as.factor(label)))+
geom_bar()

prob <- data.frame(label=c("red","blue","green","black"))
ggplot(prob,aes(x=as.factor(label),weights=c(0.3,0.2,0.4,0.1)))+
geom_bar()

## 3.15

X <- seq(-2*pi,2*pi,by=0.1)
n <- length(X)
D <- data.frame(x=rep(X,2),
type=c(rep("sin",n),rep("cos",n)),f=c(sin(X),cos(X)))

p1 <- ggplot(D,aes(x=x,y=f,colour=type))+
geom_line()+
geom_hline(yintercept=-1,size=2)+
geom_hline(yintercept=1,size=2)+
labs(y="",x="x",title="Trigonométrie",color="Fonctions")+
theme_classic()+
theme(plot.title=element_text(hjust=0.5,face="bold"),legend.position="top")
p1

ggplot(D,aes(x=x,y=f,colour=type))+
geom_line()+
geom_hline(yintercept=-1,size=2)+
geom_hline(yintercept=1,size=2)+
theme_classic()+
theme(legend.position="none")+
facet_wrap(~type)

## 3.16

n <- 100
x <- runif(n)
y <- 3+x+rnorm(n,sd=0.2)
D <- data.frame(x=x,y=y)
model.reg <- lm(y~x,data=D)

ggplot(D,aes(x=x,y=y))+
geom_point()+
geom_abline(intercept=model.reg$coefficients[1],slope=model.reg$coefficients[2])+
geom_abline(intercept=3,slope=1)+
theme_classic()

ggplot(D,aes(x=x,y=y))+
geom_point()+
geom_smooth(method="lm")+
theme_classic()

D <- data.frame(x=x,y=y,yhat=model.reg$fitted.values)
ggplot(D,aes(x=x,y=y))+
geom_point()+
geom_segment(data=D,aes(x=x,y=y,xend=x,yend=yhat))+
geom_abline(intercept=model.reg$coefficients[1],slope=model.reg$coefficients[2])+
theme_classic()

## 3.17

data(state)
states <- data.frame(state.x77,state.name=rownames(state.x77),
state.region=state.region)

# 1

q1 <- quantile(states$Income,1/3,names=FALSE)
q2 <- quantile(states$Income,2/3,names=FALSE) 
states$revenu1 <- cut(states$Income,breaks=c(min(states$Income),q1,q2,max(states$Income)),labels=c("faible","moyen","fort"),include.lowest=TRUE)
summary(states$revenu1)

# 2

ggplot(states,aes(x=Population,y=Murder))+
geom_point()+
facet_wrap(~revenu1)+
theme_classic()

# 3

ggplot(states,aes(x=Population,y=Murder))+
geom_point()+
geom_smooth(method="lm",se=FALSE)+
geom_point(aes(colour=state.region))+
facet_wrap(~revenu1)+
theme_classic()

## 3.18

# 1

library(mlbench)
data(Ozone)
help(Ozone)

# 2

Ozone$date <- as.Date(paste(Ozone$V1,Ozone$V2,"76",sep="/"),"%m/%d/%y")

# 3

ggplot(Ozone,aes(x=date,y=V4))+
geom_line()+
labs(y=expression(paste("Concentration en ",O[3],sep="")),x="Date")+
theme_classic()

# 4

ggplot(Ozone,aes(x=V4,y=V8))+
geom_point()+
labs(x=expression(paste("Concentration en ",O[3],sep="")),y="Temperature")+
theme_classic()

# 5 

Ozone$month <- as.factor(months(Ozone$date, abbreviate = TRUE))

# 6

levels(Ozone$month) <- c("jan","fév","mar","avr","mai","jui","jul","aoû","sep","oct","nov","déc")
ggplot(Ozone,aes(x=month,y=V4))+
geom_boxplot()+
labs(y=expression(paste("Concentration en ",O[3],sep="")),x="Mois")+
theme_classic()

# 7

q1 <- quantile(Ozone$V6,1/3,names=FALSE)
q2 <- quantile(Ozone$V6,2/3,names=FALSE) 
Ozone$vent <- cut(Ozone$V6,breaks=c(min(Ozone$V6),q1,q2,max(Ozone$V6)),labels=c("faible","moyen","fort"),include.lowest=TRUE)
summary(Ozone$vent)

# 8

ggplot(Ozone,aes(x=V8,y=V4,colour=vent))+
geom_point()+
labs(y=expression(paste("Concentration en ",O[3],sep="")),x="Temperature")+
facet_wrap(~month)+
theme_classic()

library(gganimate)
library(gapminder)
p <- ggplot(gapminder, aes(gdpPercap, lifeExp, size = pop, colour = country)) + 
geom_point(alpha = 0.7, show.legend = FALSE) + 
scale_colour_manual(values = country_colors) +
scale_size(range = c(2, 12)) + 
scale_x_log10() + 
facet_wrap(~continent) + 
labs(title = 'Year: {frame_time}', x = 'GDP per capita', y = 'life expectancy') + 
transition_time(year) + 
ease_aes('linear') 

animate(p, duration = 5, fps = 20, width = 200, height = 200, renderer = gifski_renderer())
anim_save("test.gif")