Chapitre 3 : Visualiser les données
Husson et al.
09/09/2018
The working directory was changed to C:/Users/husson/Dropbox/Rpourlastatetladatascience/chapitre/cartes/DONNEES inside a notebook chunk. The working directory will be reset when the chunk is finished running. Use the knitr root.dir option in the setup chunk to change the the working directory for notebook chunks.3.1 Les fonctions graphiques conventionnelles
ozone <- read.table("https://r-stat-sc-donnees.github.io/ozone.txt",header=TRUE)
ozone <- ozone[,c("T12","maxO3","vent","pluie","Vx12")]
summary(ozone) T12 maxO3 vent pluie Vx12
Min. :14.00 Min. : 42.00 Est :10 Pluie:43 Min. :-7.878
1st Qu.:18.60 1st Qu.: 70.75 Nord :31 Sec :69 1st Qu.:-3.565
Median :20.55 Median : 81.50 Ouest:50 Median :-1.879
Mean :21.53 Mean : 90.30 Sud :21 Mean :-1.611
3rd Qu.:23.55 3rd Qu.:106.00 3rd Qu.: 0.000
Max. :33.50 Max. :166.00 Max. : 6.578 3.1.1 La fonction plot
plot(maxO3~T12,data=ozone)
plot(maxO3~vent,data=ozone,xlab="Secteur du vent",ylab="Pic d’ozone")
boxplot(maxO3~vent,data=ozone)
plot(pluie~vent,data=ozone)
plot(ozone[,"T12"],ozone[,"vent"],pch=3,yaxt="n",xlab="T12",ylab="vent")
axis(side=2,at=1:4,labels=levels(ozone[,"vent"]))
plot(ozone[,"maxO3"], xlab="num.", ylab="maxO3", cex=.5, pch=16)
plot(ozone[,"maxO3"], xlab="num.", ylab="maxO3", type="l")
3.1.2 Représentation d’une distribution
hist(ozone[,"maxO3"], main="Histogramme", prob=TRUE, xlab="Ozone", col="lightblue")
plot(density(ozone[,"maxO3"]), main="Estimateur à noyau", xlab="Ozone")
plot(ozone[,"vent"])
barplot(table(ozone[,"vent"]))
3.1.3 Ajouts aux graphiques
plot(maxO3~T12,data=ozone, pch=20)
text(ozone[,"T12"],ozone[,"maxO3"], substr(rownames(ozone),5,8), cex=.75, pos=3, offset=.3)abline(v=27,lty=2)
plot(ozone[1:7,"maxO3"],type="l")
lines(ozone[8:14,"maxO3"],col="red") # ajout de la 2ème semaine
ecarty <- range(ozone[1:7,"maxO3"],ozone[8:14,"maxO3"])
plot(ozone[1:7,"maxO3"],type="l",ylim=ecarty,lty=1)
lines(ozone[8:14,"maxO3"],col="red",lty=1)
3.1.4 Graphiques en plusieurs dimensions
f <- function(x,y){10*sin(sqrt(x^2+y^2))/sqrt(x^2+y^2)}
y <- x <- seq(-10,10,length=30)
z <- outer(x,y,f)
persp(x,y,z,theta=30,phi=30,expand=0.5)
zfacette <- (z[-1,-1]+z[-1,-30]+z[-30,-1]+z[-30,-30])/4
niveaucouleur <- cut(zfacette,100)
couleurs <- heat.colors(100)[niveaucouleur]
persp(x,y,z,theta=30,phi=30,expand=0.5,col=couleurs)
library(rgl)
rgl.surface(x,y,z)library(lattice)
cloud(maxO3~T12+Vx12,type=c("p","h"),data=ozone)
plot3d(ozone[,"T12"],ozone[,"Vx12"],ozone[,"maxO3"],radius=2,xlab="T12", ylab="Vx12",zlab="maxO3",type="s")3.1.5 Exportation de graphiques
pdf("graphik.pdf")
ecarty <- range(ozone[1:7,"maxO3"],ozone[8:14,"maxO3"])
plot(ozone[1:7,"maxO3"],type="l",ylim=ecarty,lty=1)
lines(ozone[8:14,"maxO3"],col="red",lty=1)
dev.off()3.1.6 Plusieurs graphiques
par(mfrow=c(1,2))
plot(1:10,10:1,pch=0)
plot(rep(1,4),type="l")
mat <- matrix(c(1,1,2,3),nrow=2,ncol=2,byrow=TRUE)
layout(mat)
plot(1:10,10:1,pch=0)
plot(rep(1,4),type="l")plot(c(2,3,-1,0),type="b")
3.2 Les fonctions graphiques avec ggplot2
3.2.1 Premiers graphes avec ggplot2
library(ggplot2)
set.seed(1234)
diamonds2 <- diamonds[sample(nrow(diamonds),5000),]
ggplot(diamonds2)+aes(x=cut)+geom_bar()
ggplot(diamonds2)+aes(x=price)+geom_histogram()
ggplot(diamonds2)+aes(x=carat,y=price)+geom_point()
3.2.2 La grammaire ggplot
ggplot(diamonds2)+aes(x=carat,y=price,color=cut)
ggplot(diamonds2)+aes(x=carat,y=price,color=cut)+geom_point()
ggplot(diamonds2)+aes(x=carat,y=price)+geom_point(color="red")
ggplot(diamonds2)+aes(x=cut)+geom_bar(fill="blue")
D <- data.frame(X=seq(-2*pi,2*pi,by=0.01))
ggplot(D)+aes(x=X,y=sin(X))+geom_line()
ggplot(diamonds2)+aes(x=price)+geom_histogram(bins=40)
ggplot(diamonds2)+aes(x=price,y=..count..)+geom_histogram(bins=40)
ggplot(diamonds2)+aes(x=price,y=..density..)+geom_histogram(bins=40)
ggplot(diamonds2)+aes(x=price)+stat_bin(bins=40)
ggplot(diamonds2)+aes(x=price,y=..density..)+stat_bin(bins=40)
ggplot(diamonds2)+aes(x=carat,y=price)+geom_point(size=0.5)+
stat_smooth(method="loess",size=2)
ggplot(diamonds2)+aes(x=carat,y=price)+geom_point(size=0.5)+
stat_smooth(method="loess",geom="line",color="blue",size=2)
ggplot(diamonds2)+aes(x=carat,y=price)+geom_point(size=0.5)+
stat_smooth(method="loess",geom="point",color="blue",size=2)
ggplot(diamonds2)+aes(x=carat,y=price,color=cut)+geom_point()+
scale_color_manual(values=c("Fair"="black","Good"="yellow",
"Very Good"="blue","Premium"="red","Ideal"="green"))
p1 <- ggplot(diamonds2)+aes(x=cut)+geom_bar(aes(fill=cut))
p1
p1 + scale_fill_brewer(palette="Reds")
p2 <- ggplot(diamonds2)+aes(x=carat,y=price)+
geom_point(aes(color=depth))
p2
p2 + scale_color_gradient(low="red",high="yellow")
p2+scale_x_continuous(breaks=seq(0.5,3,by=0.5))+
scale_y_continuous(name="prix")+
scale_color_gradient("Profondeur")
3.2.3 Group et facets
ggplot(diamonds2)+aes(x=carat,y=price)+geom_smooth()
ggplot(diamonds2)+aes(x=carat,y=price,group=cut)+geom_smooth()
ggplot(diamonds2)+aes(x=carat,y=price,group=cut,color=cut)+
geom_smooth()
ggplot(diamonds2)+aes(x=carat,y=price)+geom_point()+
geom_smooth(method="lm")+facet_grid(color~cut)
ggplot(diamonds2)+aes(x=carat,y=price)+geom_point()+
geom_smooth(method="lm")+facet_wrap(color~cut)
ggplot(diamonds2)+aes(x=carat,y=price,color=cut)+geom_smooth()+
facet_wrap(~cut,nrow=2)
gr1 <- ggplot(diamonds2)+aes(x=cut)+geom_bar() # 1er graphe
gr2 <- ggplot(diamonds2)+aes(x=price)+geom_histogram() # 2e graphe
library(gridExtra)
grid.arrange(gr1, gr2, ncol=2, nrow=1) # organisation des graphes
3.2.4 Compléments
ggplot(diamonds2)+aes(x=carat,y=price)+geom_point()
ggplot(diamonds2,aes(x=carat,y=price))+geom_point()
ggplot(diamonds2)+geom_point(aes(x=carat,y=price))
X <- seq(-2*pi,2*pi,by=0.001)
Y1 <- cos(X)
Y2 <- sin(X)
donnees1 <- data.frame(X,Y1)
donnees2 <- data.frame(X,Y2)ggplot(donnees1)+geom_line(aes(x=X,y=Y1))+
geom_line(data=donnees2,aes(x=X,y=Y2),color="red")
library(FactoMineR) #Pour obtenir le jeu de donnees
data("decathlon")
ggplot(decathlon)+aes(x=`100m`,y=`1500m`)+geom_point()
p <- ggplot(diamonds2)+aes(x=carat,y=price,color=cut)+geom_point()
p + theme_bw()
p + theme_classic()
p + theme_grey()
p + theme_minimal()
qplot(data=diamonds2,x=carat,y=price,geom=c("point","smooth"), facets=color~cut)
monplot <- qplot(data=diamonds2,x=carat,y=price,geom=c("point","smooth"), facets=color~cut)
ggsave("mon_graphique.pdf", plot = monplot, width = 11, height = 8)3.3 Les graphiques interactifs
library(rAmCharts)
amBoxplot(maxO3 ~ vent, data = ozone, export = TRUE)data(data_stock_2)
amTimeSeries(data_stock_2, "date", c("ts1", "ts2"))ggplot(ozone) + aes(x=T12,y=maxO3,color=vent) + geom_point()
library(plotly)
ggplotly()3.4 Construire des cartes
3.4.1 Carte statique dans R
lat <- -24.66
lon <- 176.9
zoom <- 4
library(ggmap)
MaCarte <- get_map(location=c(lon,lat),zoom=zoom)
ggmap(MaCarte)
data(quakes)
ggmap(MaCarte) +
geom_point(data=quakes,aes(x=long,y=lat,colour=depth)) +
scale_color_gradient(low="yellow2",high="red4") + theme_void()
bbox <- make_bbox(long,lat,data=quakes)
bbox left bottom right top
164.5470 -39.9835 189.2530 -9.3265 MaCarteopt <- get_map(bbox) ## relancer plusieurs fois si pb de connexionbounding box given to google - spatial extent only approximate.converting bounding box to center/zoom specification. (experimental)
Map from URL : http://maps.googleapis.com/maps/api/staticmap?center=-24.655,176.9&zoom=5&size=640x640&scale=2&maptype=terrain&language=en-EN&sensor=falseggmap(MaCarteopt)
centre <- geocode("Pic de Rochebrune, Megeve, France")Information from URL : http://maps.googleapis.com/maps/api/geocode/json?address=Pic%20de%20Rochebrune,%20Megeve,%20France&sensor=falsecentre lon lat
1 6.787533 44.82231CarteRochebrune <- get_map(centre, zoom=13)Map from URL : http://maps.googleapis.com/maps/api/staticmap?center=44.822309,6.787533&zoom=13&size=640x640&scale=2&maptype=terrain&language=en-EN&sensor=falseggmap(CarteRochebrune)
3.4.3 Carte avec contours : le format shapefile
library(readr)
chomage <- read_delim("https://r-stat-sc-donnees.github.io/tauxchomage.csv",delim=";")
class(chomage)[1] "tbl_df" "tbl" "data.frame"library(sf)
# télécharger le fichier en local
dpt <- read_sf("C:/Users/husson/Dropbox/Rpourlastatetladatascience/chapitre/cartes/DONNEES/dpt")
class(dpt)[1] "sf" "tbl_df" "tbl" "data.frame"library(dplyr)
dpt2 <- inner_join(dpt,chomage,by="CODE_DEPT")
class(dpt2)[1] "sf" "tbl_df" "tbl" "data.frame"library(tidyr)
dpt3 <- dpt2 %>% select(A2006=TCHOMB1T06,A2011=TCHOMB1T11,geometry) %>%
gather("Annee","TxChomage",-geometry)
class(dpt3)[1] "sf" "tbl_df" "tbl" "data.frame"library(ggplot2)
ggplot() + geom_sf(data = dpt3, aes(fill = TxChomage)) +
facet_wrap(~Annee, nrow = 1) +
scale_fill_gradient(low="white",high="brown")+theme_bw()
library(maps)
map("state")
dp <- st_transform(dpt2, crs="+init=epsg:4326")
library(leaflet)
fr <- leaflet(data=dp) %>% setView(2.21,46.23,6) %>% addTiles()
pal <- colorNumeric(palette="inferno",domain = c(dp$TCHOMB1T06,dp$TCHOMB1T11))
frf <- fr %>% addPolygons(color=~pal(TCHOMB1T06),group ="2006") %>%
addPolygons(color=~pal(TCHOMB1T11),group="2011")
frf %>% addLayersControl(overlayGroups = c("2006", "2011"), options=
layersControlOptions(collapsed = FALSE))---
title: 'Chapitre 3 : Visualiser les données'
author: "Husson et al."
date: "09/09/2018"
output:
  html_notebook:
    toc: yes
    toc_depth: 3
    toc_float: yes
  html_document:
    toc: yes
    toc_depth: '3'
    toc_float: yes
---

```{r setup, include=FALSE}
knitr::opts_chunk$set(echo = TRUE, cache = TRUE)
```

```{r, echo=FALSE}
setwd("C:/Users/husson/Dropbox/Rpourlastatetladatascience/chapitre/cartes/DONNEES")
```
# 3.1 Les fonctions graphiques conventionnelles

```{r}
ozone <- read.table("https://r-stat-sc-donnees.github.io/ozone.txt",header=TRUE)
ozone <- ozone[,c("T12","maxO3","vent","pluie","Vx12")]
summary(ozone)
```

## 3.1.1 La fonction plot

```{r}
plot(maxO3~T12,data=ozone)
```

```{r}
plot(maxO3~vent,data=ozone,xlab="Secteur du vent",ylab="Pic d’ozone")
```

```{r}
boxplot(maxO3~vent,data=ozone)
```

```{r}
plot(pluie~vent,data=ozone)
```

```{r}
plot(ozone[,"T12"],ozone[,"vent"],pch=3,yaxt="n",xlab="T12",ylab="vent")
axis(side=2,at=1:4,labels=levels(ozone[,"vent"]))
```

```{r}
plot(ozone[,"maxO3"], xlab="num.", ylab="maxO3", cex=.5, pch=16)
```

```{r}
plot(ozone[,"maxO3"], xlab="num.", ylab="maxO3", type="l")
```

## 3.1.2 Représentation d'une distribution

```{r}
hist(ozone[,"maxO3"], main="Histogramme", prob=TRUE, xlab="Ozone", col="lightblue")
```
```{r}
plot(density(ozone[,"maxO3"]), main="Estimateur à noyau", xlab="Ozone")
```
```{r}
plot(ozone[,"vent"])
```
```{r}
barplot(table(ozone[,"vent"]))
```

## 3.1.3 Ajouts aux graphiques

```{r}
plot(maxO3~T12,data=ozone, pch=20)
text(ozone[,"T12"],ozone[,"maxO3"], substr(rownames(ozone),5,8), cex=.75, pos=3, offset=.3)
abline(v=27,lty=2)
```
```{r}
plot(ozone[1:7,"maxO3"],type="l")
lines(ozone[8:14,"maxO3"],col="red") # ajout de la 2ème semaine
```

```{r}
ecarty <- range(ozone[1:7,"maxO3"],ozone[8:14,"maxO3"])
plot(ozone[1:7,"maxO3"],type="l",ylim=ecarty,lty=1)
lines(ozone[8:14,"maxO3"],col="red",lty=1)
```
## 3.1.4 Graphiques en plusieurs dimensions

```{r}
f <- function(x,y){10*sin(sqrt(x^2+y^2))/sqrt(x^2+y^2)}
y <- x <- seq(-10,10,length=30)
z <- outer(x,y,f)
persp(x,y,z,theta=30,phi=30,expand=0.5)
zfacette <- (z[-1,-1]+z[-1,-30]+z[-30,-1]+z[-30,-30])/4
niveaucouleur <- cut(zfacette,100)
couleurs <- heat.colors(100)[niveaucouleur]
persp(x,y,z,theta=30,phi=30,expand=0.5,col=couleurs)
```

```{r,warning=FALSE,message=FALSE}
library(rgl)
rgl.surface(x,y,z)
```

```{r,warning=FALSE,message=FALSE}
library(lattice)
cloud(maxO3~T12+Vx12,type=c("p","h"),data=ozone)
```

```{r}
plot3d(ozone[,"T12"],ozone[,"Vx12"],ozone[,"maxO3"],radius=2,xlab="T12", ylab="Vx12",zlab="maxO3",type="s")
```

## 3.1.5 Exportation de graphiques

```{r,eval=FALSE}
pdf("graphik.pdf")
ecarty <- range(ozone[1:7,"maxO3"],ozone[8:14,"maxO3"])
plot(ozone[1:7,"maxO3"],type="l",ylim=ecarty,lty=1)
lines(ozone[8:14,"maxO3"],col="red",lty=1)
dev.off()
```

## 3.1.6 Plusieurs graphiques

```{r}
par(mfrow=c(1,2))
plot(1:10,10:1,pch=0)
plot(rep(1,4),type="l")
```

```{r}
mat <- matrix(c(1,1,2,3),nrow=2,ncol=2,byrow=TRUE)
layout(mat)
plot(1:10,10:1,pch=0)
plot(rep(1,4),type="l")
plot(c(2,3,-1,0),type="b")
```

# 3.2 Les fonctions graphiques avec ggplot2

## 3.2.1 Premiers graphes avec ggplot2

```{r,warning=FALSE,message=FALSE}
library(ggplot2)
set.seed(1234)
diamonds2 <- diamonds[sample(nrow(diamonds),5000),]
ggplot(diamonds2)+aes(x=cut)+geom_bar()
ggplot(diamonds2)+aes(x=price)+geom_histogram()
ggplot(diamonds2)+aes(x=carat,y=price)+geom_point()
```

## 3.2.2 La grammaire ggplot

```{r}
ggplot(diamonds2)+aes(x=carat,y=price,color=cut)
ggplot(diamonds2)+aes(x=carat,y=price,color=cut)+geom_point()
```

```{r}
ggplot(diamonds2)+aes(x=carat,y=price)+geom_point(color="red")
ggplot(diamonds2)+aes(x=cut)+geom_bar(fill="blue")
```

```{r}
D <- data.frame(X=seq(-2*pi,2*pi,by=0.01))
ggplot(D)+aes(x=X,y=sin(X))+geom_line()
```

```{r}
ggplot(diamonds2)+aes(x=price)+geom_histogram(bins=40)
ggplot(diamonds2)+aes(x=price,y=..count..)+geom_histogram(bins=40)
ggplot(diamonds2)+aes(x=price,y=..density..)+geom_histogram(bins=40)
ggplot(diamonds2)+aes(x=price)+stat_bin(bins=40)
ggplot(diamonds2)+aes(x=price,y=..density..)+stat_bin(bins=40)
```
```{r}
ggplot(diamonds2)+aes(x=carat,y=price)+geom_point(size=0.5)+
  stat_smooth(method="loess",size=2)
ggplot(diamonds2)+aes(x=carat,y=price)+geom_point(size=0.5)+
  stat_smooth(method="loess",geom="line",color="blue",size=2)
ggplot(diamonds2)+aes(x=carat,y=price)+geom_point(size=0.5)+
  stat_smooth(method="loess",geom="point",color="blue",size=2)
```

```{r}
ggplot(diamonds2)+aes(x=carat,y=price,color=cut)+geom_point()+
  scale_color_manual(values=c("Fair"="black","Good"="yellow",
                              "Very Good"="blue","Premium"="red","Ideal"="green"))
```

```{r}
p1 <- ggplot(diamonds2)+aes(x=cut)+geom_bar(aes(fill=cut))
p1
p1 + scale_fill_brewer(palette="Reds")
```
```{r}
p2 <- ggplot(diamonds2)+aes(x=carat,y=price)+
geom_point(aes(color=depth))
p2
p2 + scale_color_gradient(low="red",high="yellow")
```

 
```{r}
p2+scale_x_continuous(breaks=seq(0.5,3,by=0.5))+
  scale_y_continuous(name="prix")+
  scale_color_gradient("Profondeur")
```

## 3.2.3 Group et facets

```{r}
ggplot(diamonds2)+aes(x=carat,y=price)+geom_smooth()
ggplot(diamonds2)+aes(x=carat,y=price,group=cut)+geom_smooth()
ggplot(diamonds2)+aes(x=carat,y=price,group=cut,color=cut)+
  geom_smooth()
```

```{r}
ggplot(diamonds2)+aes(x=carat,y=price)+geom_point()+
  geom_smooth(method="lm")+facet_grid(color~cut)
ggplot(diamonds2)+aes(x=carat,y=price)+geom_point()+
  geom_smooth(method="lm")+facet_wrap(color~cut)
ggplot(diamonds2)+aes(x=carat,y=price,color=cut)+geom_smooth()+
facet_wrap(~cut,nrow=2)
```
```{r,message=FALSE,warning=FALSE}
gr1 <- ggplot(diamonds2)+aes(x=cut)+geom_bar() # 1er graphe
gr2 <- ggplot(diamonds2)+aes(x=price)+geom_histogram() # 2e graphe
library(gridExtra)
grid.arrange(gr1, gr2, ncol=2, nrow=1) # organisation des graphes
```

## 3.2.4 Compléments

```{r}
ggplot(diamonds2)+aes(x=carat,y=price)+geom_point()
ggplot(diamonds2,aes(x=carat,y=price))+geom_point()
ggplot(diamonds2)+geom_point(aes(x=carat,y=price))
```
```{r}
X <- seq(-2*pi,2*pi,by=0.001)
Y1 <- cos(X)
Y2 <- sin(X)
donnees1 <- data.frame(X,Y1)
donnees2 <- data.frame(X,Y2)
```
```{r}
ggplot(donnees1)+geom_line(aes(x=X,y=Y1))+
  geom_line(data=donnees2,aes(x=X,y=Y2),color="red")
```

```{r,message=FALSE,warning=FALSE}
library(FactoMineR) #Pour obtenir le jeu de donnees
data("decathlon")
ggplot(decathlon)+aes(x=`100m`,y=`1500m`)+geom_point()
```

```{r}
p <- ggplot(diamonds2)+aes(x=carat,y=price,color=cut)+geom_point()
p + theme_bw()
p + theme_classic()
p + theme_grey()
p + theme_minimal()
```
```{r}
qplot(data=diamonds2,x=carat,y=price,geom=c("point","smooth"), facets=color~cut)
```
```{r,eval=FALSE}
monplot <- qplot(data=diamonds2,x=carat,y=price,geom=c("point","smooth"), facets=color~cut)
ggsave("mon_graphique.pdf", plot = monplot, width = 11, height = 8)
```

# 3.3 Les graphiques interactifs

```{r,message=FALSE,warning=FALSE,eval=FALSE}
library(rAmCharts)
amBoxplot(maxO3 ~ vent, data = ozone, export = TRUE)
```

```{r,eval=FALSE}
data(data_stock_2)
amTimeSeries(data_stock_2, "date", c("ts1", "ts2"))
```

```{r,message=FALSE,warning=FALSE}
ggplot(ozone) + aes(x=T12,y=maxO3,color=vent) + geom_point()
library(plotly)
ggplotly()
```

# 3.4 Construire des cartes

## 3.4.1 Carte statique dans R

```{r,message=FALSE,warning=FALSE}
lat <- -24.66
lon <- 176.9
zoom <- 4
library(ggmap)
MaCarte <- get_map(location=c(lon,lat),zoom=zoom)
ggmap(MaCarte)
```

```{r}
data(quakes)
ggmap(MaCarte) +
  geom_point(data=quakes,aes(x=long,y=lat,colour=depth)) +
  scale_color_gradient(low="yellow2",high="red4") + theme_void()
```
```{r}
bbox <- make_bbox(long,lat,data=quakes)
bbox
MaCarteopt <- get_map(bbox)  ## relancer plusieurs fois si pb de connexion
ggmap(MaCarteopt)
```
```{r}
centre <- geocode("Pic de Rochebrune, Megeve, France")
centre
CarteRochebrune <- get_map(centre, zoom=13)  ## relancer plusieurs fois si pb de connexion
ggmap(CarteRochebrune)
```

## 3.4.2 Carte dans un navigateur

```{r,message=FALSE,warning=FALSE}
library(leaflet)
m <- leaflet()
m <- addTiles(m)
print(m)
```
```{r,include=FALSE}
m <- leaflet() %>% addTiles()
print(m)
```
```{r}
m <- leaflet() %>%
  setView(lng=6.62,lat=45.85,zoom=13) %>%
  addTiles()
print(m)
```
```{r}
m <- leaflet(data=quakes) %>%
  setView(lng=176.9, lat=-24.66, zoom=4) %>%
  addTiles()
m %>% addCircles(~long, ~lat)
pal <- colorNumeric(palette=c(low="yellow2",high="red4"), domain = quakes$depth)
pal(quakes$depth[1:3])
m %>% addTiles() %>%
  addCircles(~long,~lat,popup=~mag,color=~pal(depth))
```

```{r}
sta.paris <- read.csv("https://r-stat-sc-donnees.github.io/velib_paris_01_09_18.csv",encoding = "UTF-8")
summary(sta.paris)
```
```{r}
leaflet(data = sta.paris) %>% addTiles() %>%
  addCircleMarkers(~ lon, ~ lat,radius=8,stroke = FALSE, fillOpacity = 0.5,color="red")
```
```{r}
leaflet(data = sta.paris) %>% addTiles() %>%
  addCircleMarkers(~ lon, ~ lat,radius=8,stroke = FALSE,
fillOpacity = 0.5,color="red",popup = ~ paste("<b>
        Vélos dispos: </b>",as.character(numBikesAvailable)))
```
```{r}
leaflet(data = sta.paris) %>% addTiles() %>%
  addCircleMarkers(~ lon, ~ lat,radius=8,stroke = FALSE,
fillOpacity = 0.5,color="red",popup = ~ paste(as.character(name),
"<br/>","<b> Vélos dispos: </b>",as.character(numBikesAvailable)))
```
```{r}
SV <- geocode("Stade Velodrome Marseille")  ## relancer plusieurs fois si pb de connexion
info <- paste(sep = "<br/>", "<b><a href=’https://fr.wikipedia.org/wiki/Stade_Vélodrome’>
Stade Velodrome</a></b>", "Marseille")
leaflet() %>% addTiles() %>% addPopups(SV[1]$lon, SV[2]$lat,info,options = popupOptions(closeButton = FALSE))
```

## 3.4.3 Carte avec contours : le format shapefile

```{r,message=FALSE,warning=FALSE}
library(readr)
chomage <- read_delim("https://r-stat-sc-donnees.github.io/tauxchomage.csv",delim=";")
class(chomage)
```
```{r,message=FALSE,warning=FALSE}
library(sf)
 # télécharger le fichier en local
dpt <- read_sf("C:/Users/husson/Dropbox/Rpourlastatetladatascience/chapitre/cartes/DONNEES/dpt")
class(dpt)
```
```{r,message=FALSE,warning=FALSE}
library(dplyr)
dpt2 <- inner_join(dpt,chomage,by="CODE_DEPT")
class(dpt2)
```

```{r,message=FALSE,warning=FALSE}
library(tidyr)
dpt3 <- dpt2 %>% select(A2006=TCHOMB1T06,A2011=TCHOMB1T11,geometry) %>%
  gather("Annee","TxChomage",-geometry)
class(dpt3)
```

```{r,message=FALSE,warning=FALSE}
library(ggplot2)
ggplot() + geom_sf(data = dpt3, aes(fill = TxChomage)) +
  facet_wrap(~Annee, nrow = 1) +
  scale_fill_gradient(low="white",high="brown")+theme_bw()
```

```{r,message=FALSE,warning=FALSE}
library(maps)
map("state")
```

```{r}
dp <- st_transform(dpt2, crs="+init=epsg:4326")
library(leaflet)
fr <- leaflet(data=dp) %>% setView(2.21,46.23,6) %>% addTiles()
pal <- colorNumeric(palette="inferno",domain = c(dp$TCHOMB1T06,dp$TCHOMB1T11))
frf <- fr %>% addPolygons(color=~pal(TCHOMB1T06),group ="2006") %>%
  addPolygons(color=~pal(TCHOMB1T11),group="2011")
frf %>% addLayersControl(overlayGroups = c("2006", "2011"), options=
                           layersControlOptions(collapsed = FALSE))
```

