billboarder

Htmlwidget for billboard.js

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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 s