Create¶

rCharts uses a formula interface to specify plots, just like the lattice package. Here are a few examples you can try out in your R console.

Note

Every example comes with an edit button that allows you to experiment with the code online. The online playground was built using OpenCPU

First let us load the rCharts package

library(rCharts)

Polycharts¶

r1 <- rPlot(mpg ~ wt, data = mtcars, type = 'point')
r1

Edit

Standalone

NVD3¶

hair_eye = as.data.frame(HairEyeColor)
p2 <- nPlot(Freq ~ Hair, group = 'Eye',
  data = subset(hair_eye, Sex == "Female"),
  type = 'multiBarChart'
)
p2$chart(color = c('brown', 'blue', '#594c26', 'green'))
p2

Edit

Standalone

View Interactive

Morris¶

data(economics, package = "ggplot2")
econ <- transform(economics, date = as.character(date))
m1 <- mPlot(x = "date", y = c("psavert", "uempmed"), type = "Line", data = econ)
m1$set(pointSize = 0, lineWidth = 1)
m1

Edit

Standalone

Highcharts¶

h1 <- hPlot(x = "Wr.Hnd", y = "NW.Hnd",
  data = MASS::survey,
  type = c("line", "bubble", "scatter"),
  group = "Clap",
  size = "Age"
)
h1

Edit

Standalone

Rickshaw¶

usp = reshape2::melt(USPersonalExpenditure)
usp$Var2 <- as.numeric(as.POSIXct(paste0(usp$Var2, "-01-01")))
p4 <- Rickshaw$new()
p4$layer(value ~ Var2, group = "Var1", data = usp, type = "area")
p4$set(slider = TRUE)
p4

Standalone

Edit

xCharts¶

require(reshape2)
uspexp <- melt(USPersonalExpenditure)
names(uspexp)[1:2] = c('category', 'year')
x1 <- xPlot(value ~ year, group = 'category', data = uspexp,
  type = 'line-dotted')
x1

Standalone

Edit