#' ---
#' date: false
#' output:
#' html_document:
#' toc: yes
#' ---
#+ message=FALSE, warning=FALSE, echo=FALSE, results=FALSE
library(highcharter)
library(dplyr)
options(highcharter.theme = hc_theme_smpl())
#' ### data.frame
data(mpg, package = "ggplot2")
data(diamonds, package = "ggplot2")
hchart(mpg, "point", hcaes(x = displ, y = hwy))
hchart(mpg, "point", hcaes(x = displ, y = hwy, group = class))
mpgman <- count(mpg, manufacturer)
hchart(mpgman, "bar", hcaes(x = manufacturer, y = n))
hchart(mpgman, "treemap", hcaes(x = manufacturer, value = n))
mpgman2 <- count(mpg, manufacturer, year)
hchart(mpgman2, "bar", hcaes(x = manufacturer, y = n, group = year))
#' ### numeric
hchart(c(rnorm(500), rnorm(500, 6, 2)))
#' ### histogram
hchart(hist(rbeta(300, 0.2, 4), plot = FALSE))
#' ### character
hchart(mpg$manufacturer)
#' ### factor
hchart(diamonds$cut)
#' ### ts
hchart(LakeHuron)
#' ### xts
library(quantmod)
options(download.file.method = "libcurl")
hchart(getSymbols("USD/JPY", src = "oanda", auto.assign = FALSE))
# hchart(getSymbols("YHOO", auto.assign = FALSE))
#' ### forecast
library("forecast")
hchart(forecast(auto.arima(AirPassengers), level = c(95, 80)))
hchart(forecast(ets(USAccDeaths), h = 48, level = 90))
hchart(forecast(Arima(WWWusage, c(3,1,0))))
#' ### mforecast
# hchart(forecast(ets(cbind(M = mdeaths, F = fdeaths))))
#' ### acf
hchart(acf(diff(AirPassengers), plot = FALSE))
#' ### mts
hchart(cbind(mdeaths, fdeaths))
#' ### lst
hchart(stl(co2, "per"))
#' ### ets
hchart(ets(mdeaths))
#' ### matrix
data("volcano")
hchart(volcano)
hchart(cor(mtcars))
#' ### dist
hchart(dist(mtcars[1:20, ]))
#' ### igraph
library("igraph")
net <- barabasi.game(40)
wc <- cluster_walktrap(net)
V(net)$label <- 1:40
V(net)$name <- 1:40
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))
hchart(net, layout = layout_with_fr)
#' ### survfit
library("survival")
data(lung)
hchart(survfit(Surv(time, status) ~ sex, data = lung) , ranges = TRUE)
#' ### density
hchart(density(rnorm(100)), area = TRUE)
#' ### pca
hchart(princomp(USArrests))
#' ###
hchart(glm(hwy ~ year + class + fl , data = mpg))
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