# Simuler des données (peut être remplacé par un vecteur de 400 valeurs

x <- rnorm(400,10,3) ; data = c()

for (i in c(0:19)) {indice = i*20;data <- c(data,sort(x[c((indice+1):(indice+10))]),sort(x[c((indice+11):(indice+20))],decreasing=T))}

# Compiler les données en une matrice m

m <- matrix(data, nrow = 20, ncol = 20,byrow=T)

# Tracer le graphique de température

packageVersion('plotly')

plot_ly( x = c("a", "b", "c"), y = c("d", "e", "f"),

    z = m, type = "heatmap")

# Aller plus loin : lien externe

# https://www.infoclimat.fr/climatologie/annee/2018/paris-montsouris/valeurs/07156.html

a2008 <- c(7.1,7.5,8,10.8,17.8,18.6,20.6,19.7,15.5,11.9,8.3,4)

a2009 <- c(2.1,5.1,8.6,13.7,16,18.4,20.9,22.1,17.7,13.3,10.6,4.7)

a2010 <- c(1.9,4.8,8.5,12.7,14,19.5,22.6,19.7,16.5,12.6,7.6,1.6)

a2011 <- c(5.3,6.9,10.1,15.8,17,18.8,18.5,20.1,18.9,14.2,10.5,7.9)

a2012 <- c(7.1,3,11.5,10.3,16.3,17.7,19.6,21.7,16.8,13.1,8.1,6.8)

a2013 <- c(4.2,3.6,5.8,11.2,12.9,17.5,22.9,20.7,17.5,14.8,7.8,6.8)

a2014 <- c(7.5,7.9,10.8,13.5,14.8,18.7,21,18.5,19,15.2,10.6,6.1)

a2015 <- c(5.3,4.9,9.1,13.4,15.1,19.6,21.7,21.8,15.6,12.3,11.5,10)

a2016 <- c(6.2,6.8,7.6,10.9,15.4,18.3,21.2,21.8,19.6,12.3,8.5,5.5)

a2017 <- c(2.9,8,11.3,11.9,17,21.8,22,20.9,16.4,15.1,8.8,6.3)

a2018 <- c(8.2,2.5,7.9,14.5,17.6,20.2,24.4,22,18,14.6,8.8,NA)

température <- data.frame(a2008,a2009,a2010,a2011,a2012,a2013,a2014,a2015,a2016,a2017,a2018)

rownames(température) <- c("Janv","Fev","Mars","Avril","Mai","Juin","Juill","Aout","Sept","Oct","Nov","Déc")

colnames(température) <- c(2008:2018)

température <- as.matrix(température)

head(température)

heatmap(température)

length(a2018)

plot(a2008,a2009)

plot_ly(x =  c(2008:2018) , y =  c("Janv","Fev","Mars","Avril","Mai","Juin","Juill","Aout","Sept","Oct","Nov","Déc"),

    z = température, type = "heatmap"

)