L'Essentiel du Package R Highcharter Pour des Graphiques Interactifs Faciles

Prérequis

# Charger les packages R requis
library(tidyverse)
library(highcharter) 

# Définir les options de highcharter
options(highcharter.theme = hc_theme_smpl(tooltip = list(valueDecimals = 2)))

Analyse de réseau

library(igraph)
N <- 40

net <- sample_gnp(N, p = 2 / N)
wc <- cluster_walktrap(net)

V(net)$label <- seq(N)
V(net)$name <- paste("I'm #", seq(N))
V(net)$page_rank <- round(page.rank(net)$vector, 2)
V(net)$betweenness <- round(betweenness(net), 2)
V(net)$degree <- degree(net)
V(net)$size <- V(net)$degree
V(net)$comm <- membership(wc)
V(net)$color <- colorize(membership(wc))

hc <- hchart(net, layout = layout_with_fr)

hc

Modèles de survie

library(survival)

data(lung)
lung <- mutate(lung, sex = ifelse(sex == 1, "Male", "Female"))
fit <- survfit(Surv(time, status) ~ sex, data = lung) 

hc <- hchart(fit, ranges = TRUE)

hc

Analyse en composantes principales

hc <- hchart(princomp(USArrests, cor = TRUE))

hc

Matrice de distance

mtcars2 <- mtcars[1:20, ]
x <- dist(mtcars2)
hc <- hchart(x)

hc

Matrice de corrélation

hc <- hchart(cor(mtcars))

hc