billboarder

Htmlwidget for billboard.js

Overview

This package allow you to use billboard.js, a re-usable easy interface JavaScript chart library, based on D3 v4+.

A proxy method is implemented to smoothly update charts in shiny applications, see below for details.

Installation :

Install from CRAN with:

install.packages("billboarder")

Install development version grom GitHub with:

# install.packages("remotes")
remotes::install_github("dreamRs/billboarder")

For interactive examples & documentation, see pkgdown site : https://dreamrs.github.io/billboarder/index.html

Bar / column charts

Simple bar chart :

library("billboarder")

# data
data("prod_par_filiere")

# a bar chart !
billboarder() %>%
  bb_barchart(data = prod_par_filiere[, c("annee", "prod_hydraulique")], color = "#102246") %>%
  bb_y_grid(show = TRUE) %>%
  bb_y_axis(tick = list(format = suffix("TWh")),
            label = list(text = "production (in terawatt-hours)", position = "outer-top")) %>% 
  bb_legend(show = FALSE) %>% 
  bb_labs(title = "French hydraulic production",
          caption = "Data source: RTE (https://opendata.rte-france.com)")

Multiple categories bar chart :

library("billboarder")

# data
data("prod_par_filiere")

# dodge bar chart !
billboarder() %>%
  bb_barchart(
    data = prod_par_filiere[, c("annee", "prod_hydraulique", "prod_eolien", "prod_solaire")]
  ) %>%
  bb_data(
    names = list(prod_hydraulique = "Hydraulic", prod_eolien = "Wind", prod_solaire = "Solar")
  ) %>% 
  bb_y_grid(show = TRUE) %>%
  bb_y_axis(tick = list(format = suffix("TWh")),
            label = list(text = "production (in terawatt-hours)", position = "outer-top")) %>% 
  bb_legend(position = "inset", inset = list(anchor = "top-right")) %>% 
  bb_labs(title = "Renewable energy production",
          caption = "Data source: RTE (https://opendata.rte-france.com)")

Stacked bar charts :

library("billboarder")

# data
data("prod_par_filiere")

# stacked bar chart !
billboarder() %>%
  bb_barchart(
    data = prod_par_filiere[, c("annee", "prod_hydraulique", "prod_eolien", "prod_solaire")], 
    stacked = TRUE
  ) %>%
  bb_data(
    names = list(prod_hydraulique = "Hydraulic", prod_eolien = "Wind", prod_solaire = "Solar"), 
    labels = TRUE
  ) %>% 
  bb_colors_manual(
    "prod_eolien" = "#41AB5D", "prod_hydraulique" = "#4292C6", "prod_solaire" = "#FEB24C"
  ) %>%
  bb_y_grid(show = TRUE) %>%
  bb_y_axis(tick = list(format = suffix("TWh")),
            label = list(text = "production (in terawatt-hours)", position = "outer-top")) %>% 
  bb_legend(position = "right") %>% 
  bb_labs(title = "Renewable energy production",
          caption = "Data source: RTE (https://opendata.rte-france.com)")

Scatter plot

Classic :

library(billboarder)
library(palmerpenguins)
billboarder() %>% 
  bb_scatterplot(data = penguins, x = "bill_length_mm", y = "flipper_length_mm", group = "species") %>% 
  bb_axis(x = list(tick = list(fit = FALSE))) %>% 
  bb_point(r = 8)

You can make a bubble chart using size aes :

billboarder() %>% 
  bb_scatterplot(
    data = penguins, 
    mapping = bbaes(
      bill_length_mm, flipper_length_mm, group = species,
      size = scales::rescale(body_mass_g, c(1, 100))
    )
  ) %>% 
  bb_bubble(maxR = 25) %>% 
  bb_x_axis(tick = list(fit = FALSE))

Pie / Donut charts

library("billboarder")

# data
data("prod_par_filiere")
nuclear2016 <- data.frame(
  sources = c("Nuclear", "Other"),
  production = c(
    prod_par_filiere$prod_nucleaire[prod_par_filiere$annee == "2016"],
    prod_par_filiere$prod_total[prod_par_filiere$annee == "2016"] -
      prod_par_filiere$prod_nucleaire[prod_par_filiere$annee == "2016"]
  )
)

# pie chart !
billboarder() %>% 
  bb_piechart(data = nuclear2016) %>% 
  bb_labs(title = "Share of nuclear power in France in 2016",
          caption = "Data source: RTE (https://opendata.rte-france.com)")

Lines charts

Time serie with `Date` (and a subchart)

library("billboarder")

# data
data("equilibre_mensuel")

# line chart
billboarder() %>% 
  bb_linechart(
    data = equilibre_mensuel[, c("date", "consommation", "production")], 
    type = "spline"
  ) %>% 
  bb_x_axis(tick = list(format = "%Y-%m", fit = FALSE)) %>% 
  bb_x_grid(show = TRUE) %>% 
  bb_y_grid(show = TRUE) %>% 
  bb_colors_manual("consommation" = "firebrick", "production" = "forestgreen") %>% 
  bb_legend(position = "right") %>% 
  bb_subchart(show = TRUE, size = list(height = 30)) %>% 
  bb_labs(title = "Monthly electricity consumption and production in France (2007 - 2017)",
          y = "In megawatt (MW)",
          caption = "Data source: RTE (https://opendata.rte-france.com)")

Zoom by dragging

billboarder() %>% 
  bb_linechart(
    data = equilibre_mensuel[, c("date", "consommation", "production")], 
    type = "spline"
  ) %>% 
  bb_x_axis(tick = list(format = "%Y-%m", fit = FALSE)) %>% 
  bb_x_grid(show = TRUE) %>% 
  bb_y_grid(show = TRUE) %>% 
  bb_colors_manual("consommation" = "firebrick", "production" = "forestgreen") %>% 
  bb_legend(position = "right") %>% 
  bb_zoom(
    enabled = TRUE,
    type = "drag",
    resetButton = list(text = "Unzoom")
  ) %>% 
  bb_labs(title = "Monthly electricity consumption and production in France (2007 - 2017)",
          y = "In megawatt (MW)",
          caption = "Data source: RTE (https://opendata.rte-france.com)")

Time serie with `POSIXct` (and regions)

library("billboarder")

# data
data("cdc_prod_filiere")

# Retrieve sunrise and and sunset data with `suncalc`
library("suncalc")
sun <- getSunlightTimes(date = as.Date("2017-06-12"), lat = 48.86, lon = 2.34, tz = "CET")


# line chart
billboarder() %>% 
  bb_linechart(data = cdc_prod_filiere[, c("date_heure", "prod_solaire")]) %>% 
  bb_x_axis(tick = list(format = "%H:%M", fit = FALSE)) %>% 
  bb_y_axis(min = 0, padding = 0) %>% 
  bb_regions(
    list(
      start = as.numeric(cdc_prod_filiere$date_heure[1]) * 1000,
      end = as.numeric(sun$sunrise)*1000
    ), 
    list(
      start = as.numeric(sun$sunset) * 1000, 
      end = as.numeric(cdc_prod_filiere$date_heure[48]) * 1000
    )
  ) %>% 
  bb_x_grid(
    lines = list(
      list(value = as.numeric(sun$sunrise)*1000, text = "sunrise"),
      list(value = as.numeric(sun$sunset)*1000, text = "sunset")
    )
  ) %>% 
  bb_labs(title = "Solar production (2017-06-12)",
          y = "In megawatt (MW)",
          caption = "Data source: RTE (https://opendata.rte-france.com)")

Stacked area chart

library("billboarder")

# data
data("cdc_prod_filiere")

# area chart !
billboarder() %>% 
  bb_linechart(
    data = cdc_prod_filiere[, c("date_heure", "prod_eolien", "prod_hydraulique", "prod_solaire")], 
    type = "area"
  ) %>% 
  bb_data(
    groups = list(list("prod_eolien", "prod_hydraulique", "prod_solaire")),
    names = list("prod_eolien" = "Wind", "prod_hydraulique" = "Hydraulic", "prod_solaire" = "Solar")
  ) %>% 
  bb_legend(position = "inset", inset = list(anchor = "top-right")) %>% 
  bb_colors_manual(
    "prod_eolien" = "#238443", "prod_hydraulique" = "#225EA8", "prod_solaire" = "#FEB24C", 
    opacity = 0.8
  ) %>% 
  bb_y_axis(min = 0, padding = 0) %>% 
  bb_labs(title = "Renewable energy production (2017-06-12)",
          y = "In megawatt (MW)",
          caption = "Data source: RTE (https://opendata.rte-france.com)")

Line range

# Generate data
dat <- data.frame(
  date = seq.Date(Sys.Date(), length.out = 20, by = "day"),
  y1 = round(rnorm(20, 100, 15)),
  y2 = round(rnorm(20, 100, 15))
)
dat$ymin1 <- dat$y1 - 5
dat$ymax1 <- dat$y1 + 5

dat$ymin2 <- dat$y2 - sample(3:15, 20, TRUE)
dat$ymax2 <- dat$y2 + sample(3:15, 20, TRUE)


# Make chart : use ymin & ymax aes for range
billboarder(data = dat) %>% 
  bb_linechart(
    mapping = bbaes(x = date, y = y1, ymin = ymin1, ymax = ymax1),
    type = "area-line-range"
  ) %>% 
  bb_linechart(
    mapping = bbaes(x = date, y = y2, ymin = ymin2, ymax = ymax2), 
    type = "area-spline-range"
  ) %>% 
  bb_y_axis(min = 50)

Histogram & density

billboarder() %>%
  bb_histogram(data = rnorm(1e5), binwidth = 0.25) %>%
  bb_colors_manual()

With a grouping variable :

# Generate some data
dat <- data.frame(
  sample = c(rnorm(n = 1e4, mean = 1), rnorm(n = 1e4, mean = 2)),
  group = rep(c("A", "B"), each = 1e4), stringsAsFactors = FALSE
)
# Mean by groups
samples_mean <- tapply(dat$sample, dat$group, mean)
# histogram !
billboarder() %>%
  bb_histogram(data = dat, x = "sample", group = "group", binwidth = 0.25) %>%
  bb_x_grid(
    lines = list(
      list(value = unname(samples_mean['A']), text = "mean of sample A"),
      list(value = unname(samples_mean['B']), text = "mean of sample B")
    )
  )

Density plot with the same data :

billboarder() %>%
  bb_densityplot(data = dat, x = "sample", group = "group") %>%
  bb_x_grid(
    lines = list(
      list(value = unname(samples_mean['A']), text = "mean of sample A"),
      list(value = unname(samples_mean['B']), text = "mean of sample B")
    )
  )

Shiny interaction

Some events will trigger Shiny's inputs in application, such as click. Inputs id associated with billboarder charts use this pattern :

input$CHARTID_EVENT

Look at this example, chart id is mybbchart so you retrieve click with input$mybbchart_click :

library("shiny")
library("billboarder")

# data
data("prod_par_filiere")
prod_par_filiere_l <- reshape2::melt(data = prod_par_filiere)
prod_par_filiere_l <- prod_par_filiere_l[
  with(prod_par_filiere_l, annee == "2016" & variable != "prod_total"), 2:3
]
prod_par_filiere_l <- prod_par_filiere_l[order(prod_par_filiere_l$value), ]


# app
ui <- fluidPage(
  billboarderOutput(outputId = "mybbchart"),
  br(),
  verbatimTextOutput(outputId = "click")
)

server <- function(input, output, session) {
  
  output$mybbchart <- renderBillboarder({
    billboarder() %>%
      bb_barchart(data = prod_par_filiere_l) %>% 
      bb_y_grid(show = TRUE) %>% 
      bb_legend(show = FALSE) %>%
      bb_x_axis(categories = prod_par_filiere_l$variable, fit = FALSE) %>% 
      bb_labs(title = "French electricity generation by branch in 2016",
              y = "production (in terawatt-hours)",
              caption = "Data source: RTE (https://opendata.rte-france.com)")
  })
  
  output$click <- renderPrint({
    cat("# input$mybbchart_click$category", "\n")
    input$mybbchart_click$category
  })
  
}

shinyApp(ui = ui, server = server)

Proxy

You can modify existing charts with function billboarderProxy :

To see examples, run :

library("billboarder")
proxy_example("bar")
proxy_example("line")
proxy_example("pie")
proxy_example("gauge")

Raw API

If you wish, you can build graphs using a list syntax :

data(economics, package = "ggplot2")

# Construct a list in JSON format
params <- list(
  data = list(
    x = "x",
    json = list(
      x = economics$date,
      y = economics$psavert
    ),
    type = "spline"
  ),
  legend = list(show = FALSE),
  point = list(show = FALSE),
  axis = list(
    x = list(
      type = "timeseries",
      tick = list(
        count = 20,
        fit = TRUE,
        format = "%e %b %y"
      )
    ),
    y = list(
      label = list(
        text = "Personal savings rate"
      ),
      tick = list(
        format = htmlwidgets::JS("function(x) {return x + '%';}")
      )
    )
  )
)

# Pass the list as parameter
billboarder(params)

billboarder

Overview

Installation :

Bar / column charts

Scatter plot

Pie / Donut charts

Lines charts

Time serie with `Date` (and a subchart)

Zoom by dragging

Time serie with `POSIXct` (and regions)

Stacked area chart

Line range

Histogram & density

Shiny interaction

Proxy

Raw API

Copy Link

Version

Install

Monthly Downloads

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Functions in billboarder (0.5.0)

billboarder

Overview

Installation :

Bar / column charts

Scatter plot

Pie / Donut charts

Lines charts

Time serie with Date (and a subchart)

Zoom by dragging

Time serie with POSIXct (and regions)

Stacked area chart

Line range

Histogram & density

Shiny interaction

Proxy

Raw API

Copy Link

Version

Install

Monthly Downloads

Version

License

Issues

Pull Requests

Stars

Forks

Repository

Homepage

Maintainer

Last Published

Functions in billboarder (0.5.0)

Time serie with `Date` (and a subchart)

Time serie with `POSIXct` (and regions)