billboarder

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billboarder

Victor Perrier

2023-09-29

In the following we’ll use the insight theme (3 others are available: billboard (default), graph and datalab) and ColorBrewer Set2 as default color palette.

Bar chart

Create barcharts with bb_barchart :

library(billboarder)

# data
data("prod_par_filiere")

billboarder(data = prod_par_filiere) %>%
  bb_barchart(
    mapping = aes(x = annee, y = 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)"
  )

To create a dodge barchart, if your data are in “wide” format, you can use the following (otherwise you have to reshape your data in “long” format, with tools such as pivot_longer):

library(billboarder)

# data
data("prod_par_filiere")

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)"
  )

Same principle for 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 = "inset", inset = list(anchor = "top-right")) %>% 
  bb_labs(
    title = "Renewable energy production",
    caption = "Data source: RTE (https://opendata.rte-france.com)"
  )

Scatter plot

A classic one:

billboarder() %>% 
 bb_scatterplot(
   data = mtcars, 
   x = "wt", y = "mpg", group = "cyl",
   point_opacity = 1
 ) %>% 
  # don't display all values on x-axis
 bb_axis(x = list(tick = list(fit = FALSE))) %>% 
 bb_point(r = 5) %>% 
  # add grids
  bb_x_grid(show = TRUE) %>%
  bb_y_grid(show = TRUE)

You can make a bubble chart using size aes :

billboarder(data = mtcars) %>% 
  bb_scatterplot(
   mapping = aes(wt, mpg, group = cyl, size = scales::rescale(qsec, to = c(0.2, 7))),
   point_opacity = 1
  ) %>% 
  bb_axis(x = list(tick = list(fit = FALSE))) %>% 
  bb_x_grid(show = TRUE) %>%
  bb_y_grid(show = TRUE)

Pie charts

Create pie 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)")

You can also do donut charts :

billboarder() %>% 
  bb_donutchart(data = nuclear2016) %>% 
  bb_donut(
    title = "Share of nuclear\nin France",
    label = list(
      format = JS("function(value, ratio, id) { return id + ': ' + d3.format('.0%')(ratio);}")
    )
  ) %>% 
  bb_legend(show = FALSE) %>% 
  bb_labs(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")
sun <- data.frame(
  sunrise = as.POSIXct("2017-06-12 05:48:14"),
  sunset = as.POSIXct("2017-06-12 21:55:32")
)


# line chart
billboarder() %>% 
  bb_linechart(
    data = cdc_prod_filiere,
    mapping = aes(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")

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

Use RStudio >= 1.2.0 to display in viewer.

# 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 = aes(x = date, y = y1, ymin = ymin1, ymax = ymax1),
    type = "area-line-range"
  ) %>% 
  bb_linechart(
    mapping = aes(x = date, y = y2, ymin = ymin2, ymax = ymax2), 
    type = "area-spline-range"
  ) %>% 
  bb_y_axis(min = 50)

Histogram & density

Create histograms with a numeric vector (or data.frame) :

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")
    )
  )

Radar charts

With single serie:

data("avengers_wide")
billboarder() %>%
  bb_radarchart(
    data = avengers_wide[, 1:2]
  )

Multiple series and with data in long format:

data("avengers")
billboarder() %>%
  bb_radarchart(
    data = avengers, 
    mapping = bbaes(x = axis, y = value, group = group)
  )

Gauge

Gauge can be created with:

billboarder() %>% 
  bb_gaugechart(value = 50, color = "#112446")

or with function bauge() which had special output and render function in shiny:

bauge(round(sample.int(100, 1)), subtitle = "of total")

Multiple values gauge are also possible:

billboarder() %>%
  bb_gaugechart(
    value = c(15, 30),
    name = c("A", "B"),
    color = c("steelblue", "firebrick")
  ) %>% 
  bb_gauge(max = 60) %>% 
  bb_data(
    labels = list(colors = "#FFF")
  )

Treemap

Create a treemap chart with:

data("mpg", package = "ggplot2")
billboarder() %>% 
  bb_treemapchart(
    data = table(manufacturer = mpg$manufacturer), 
    mapping = aes(x = manufacturer, y = Freq),
    label = list(show = TRUE, threshold = 0.03),
    tile = "binary" # or "slice" or "dice"
  ) %>% 
  bb_data(
    labels = list(colors = "#FFF")
  ) %>% 
  bb_color(
    palette = colorRampPalette(c("#08519C", "#9ECAE1"))(15) #15 = n manufacturer
  )

These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.
Health stats visible at Monitor.

billboarder

Victor Perrier

2023-09-29

Bar chart

Scatter plot

Pie 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

Radar charts

Gauge

Treemap

Time serie with `Date` (and a subchart)

Time serie with `POSIXct` (and regions)