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High-Performance Open-Source Archive

Geom examples

Geom examples

This vignette shows how to use each geometry in highdir. Every example follows the same two-step workflow:

  1. hd_spec() — describe what the data means (columns, labels)
  2. hd_make() — decide how to render it (geom, backend, options)

The same hd_spec object can be passed to hd_make() multiple times with different geom types, backends, or hd_opts() objects — there is no need to rebuild the spec.

library(highdir)
# ── Age-group survey dataset (bars and pie) ────────────────────────────────────
survey <- data.frame(
  age_group = rep(c("18-24", "25-34", "35-44", "45-54", "55-64"), each = 2),
  kjonn       = rep(c("Male", "Female"), times = 5),
  pct       = c(42, 38, 55, 61, 48, 52, 60, 57, 65, 70),
  n         = c(120, 115, 200, 210, 180, 175, 160, 155, 140, 145)
)

Column chart

Column charts are the default geometry and work with both grouped and ungrouped data. The n column is shown in the highcharter tooltip alongside the percentage.

spec_col <- hd_spec(survey,
                    x     = "age_group",
                    y     = "pct",
                    group = "kjonn",
                    n     = "n")

opts_col <- hd_opts(
  title    = "Alcohol use by age group and kjonn",
  subtitle = "Source: Norwegian Directorate of Health",
  ylim     = c(0, 100),
  yint     = 20,
  ylab     = "Percentage (%)"
)

## Alternatively using ggplot2 syntax
# hd(survey, x = "age_group", y = "pct", group = "kjonn", n = "n") +
#  hd_opts(title = "Alcohol use by age group and kjonn",
#       subtitle = "Source: Norwegian Directorate of Health",
#       ylim = c(0, 100),
#       yint = 20,
#       ylab = "Percentage (%)") +
#       hd_geom_column()

Interactive figure

# Interactive (default)
hd_make(spec_col, "column", opts_col)

# alternatively
# hd(spec_col) + hd_geom_column() + hd_opts(opts_col)

Static figure

# Static ggplot2
hd_make(spec_col, "column", opts_col, backend = "ggplot2")

Column with groups

# Horizontal bars — flip applies to both backends
opts_flip <- hd_opts(
  title    = "Alcohol use by age group and kjonn",
  subtitle = "Source: Norwegian Directorate of Health",
  ylim     = c(0, 100),
  flip     = TRUE
)

hd_make(spec_col, "column", opts_flip)
hd_make(spec_col, "column", opts_flip, backend = "ggplot2")

Line chart

Line charts suit time-series data. Use smooth = TRUE (default) for spline curves, or FALSE for straight segments between points. dot_size controls the marker radius.

# ── Time-series dataset (single group) ────────────────────────────────────────
# Annual alcohol consumption estimate with 95 % confidence interval.
# data("alco1")

# ── Time-series dataset (kjonn groups) ──────────────────────────────────────────
# data("alco2")

# Single series — no group column
spec_line1 <- hd_spec(alco1,
                      x    = "year",
                      y    = "adj_mean"
                      )

opts_line <- hd_opts(
  title    = "Alcohol consumption over time",
  subtitle = "Source: Norwegian Directorate of Health",
  ylim     = c(0, 50),
  ylab = "Litres per capita"
)


# Straight segments
hd_make(spec_line1, "line", opts_line, smooth = FALSE)
hd_make(spec_line1, "line", opts_line, smooth = FALSE, backend = "ggplot2")

Interactive figure

# Spline (smooth) — default
hd_make(spec_line1, "line", opts_line)

Static

# Spline (smooth) — default
hd_make(spec_line1, "line", opts_line, backend = "ggplot2")

# Grouped by kjonn
spec_line2 <- hd_spec(alco2,
                      x     = "year",
                      y     = "adj_mean",
                      group = "kjonn")

# Larger markers, straight lines
hd_make(spec_line2, "line", opts_line, smooth = FALSE, dot_size = 6)
hd_make(spec_line2, "line", opts_line, smooth = FALSE, dot_size = 6,
        backend = "ggplot2")

# Highcharter only: custom per-group marker shapes
hd_make(spec_line2, "line", opts_line,
        line_symbols = c("circle", "square"))

Ranked bar chart

A ranked bar chart sorts bars by value so comparisons across categories are immediate. Use ranked_bar when the order of categories matters more than their nominal labels - for example, ranking regions by a health indicator.

Automatic label placement is one of the key features of this geom. In the ggplot2 backend, value labels are placed inside a bar when the bar is long enough to fit the text, and outside when the bar is too short. The cut-off is calculated per bar from the label character width relative to the axis range, so the placement is fully automatic — no manual threshold is needed.

# Regional health indicator dataset
regions <- data.frame(
  region = c("Oslo", "Viken", "Vestland", "Rogaland",
             "Trondelag", "Innlandet", "Agder",
             "Nordland", "Troms og Finnmark"),
  rate   = c(68.4, 71.2, 87.8, 64.5, 61.3, 6.1, 54.2, 49.8, 42.1),
  n      = c(402, 448, 681, 318, 297, 251, 198, 177, 148)
)
spec_rb <- hd_spec(regions,
                   x    = "region",
                   y    = "rate",
                   n    = "n")

opts_rb <- hd_opts(
  title    = "Health indicator by region",
  subtitle = "Source: Norwegian Directorate of Health",
  ylab     = "Rate per 100 000",
  flip     = TRUE
)

# Static ggplot2 — value labels placed inside or outside bars automatically
hd_make(spec_rb, "ranked_bar", opts_rb, backend = "ggplot2")

# Interactive — default ascending order (lowest bar at bottom)
hd_make(spec_rb, "ranked_bar", opts_rb)

Pass ascending = FALSE to flip the sort order so the highest-ranked category appears at the top:

hd_make(spec_rb, "ranked_bar", opts_rb, ascending = FALSE)
hd_make(spec_rb, "ranked_bar", opts_rb, ascending = FALSE, backend = "ggplot2")

Use aim to draw a dashed reference line at a target value. The line is drawn in a contrasting brand colour so it reads clearly against the bars:

# Draw a dashed target line at rate = 60
hd_make(spec_rb, "ranked_bar", opts_rb, aim = 60, backend = "ggplot2")
# Draw a dashed target line at rate = 60
hd_make(spec_rb, "ranked_bar", opts_rb, aim = 60)

Use vs to highlight one specific category with a contrasting fill colour. The argument accepts a partial string match against the x column, so vs = "Oslo" will match "Oslo" exactly, and vs = "Troms" would match "Troms og Finnmark":

# Highlight Oslo as the comparison reference
hd_make(spec_rb, "ranked_bar", opts_rb, vs = "Oslo")
hd_make(spec_rb, "ranked_bar", opts_rb, vs = "Oslo", backend = "ggplot2")

# Combine: descending order + target line + comparison highlight
hd_make(spec_rb, "ranked_bar", opts_rb,
        ascending = FALSE,
        aim       = 60,
        vs        = "Oslo")
hd_make(spec_rb, "ranked_bar", opts_rb,
        ascending = TRUE,
        aim       = 60,
        vs        = "Oslo",
        backend   = "ggplot2")

The n column in hd_spec() appends sample sizes to category labels in the ggplot2 backend ("Oslo (N=502)") and shows them in the highcharter tooltip. Omit n from hd_spec() if you do not want sample sizes displayed:

spec_rb_no_n <- hd_spec(regions,
                        x    = "region",
                        y    = "rate")

# No N= labels in ggplot2; no N line in HC tooltip
hd_make(spec_rb_no_n, "ranked_bar", opts_rb, backend = "ggplot2")
hd_make(spec_rb_no_n, "ranked_bar", opts_rb)

Arearange chart

Arearange is designed for displaying a central estimate alongside a confidence interval or min/max band. ymin and ymax are required — they name the columns that define the lower and upper bounds of the shaded band.

# Single series
spec_ar1 <- hd_spec(alco1,
                    x    = "year",
                    y    = "adj_enhet")

opts_ar <- hd_opts(
  title    = "Alcohol consumption with 95% CI",
  subtitle = "Source: Norwegian Directorate of Health",
  ylim     = c(0, 30),
  ylab = "Number of units alcohol"
)

Interactive

hd_make(spec_ar1, "arearange", opts_ar,
        ymin = "lower_enhet", ymax = "upper_enhet")

Static

hd_make(spec_ar1, "arearange", opts_ar,
        ymin = "lower_enhet", ymax = "upper_enhet", backend = "ggplot2")

# Grouped by kjonn
spec_ar2 <- hd_spec(alco2,
                    x     = "year",
                    y     = "adj_enhet",
                    group = "kjonn")

hd_make(spec_ar2, "arearange", opts_ar,
        ymin = "lower_enhet", ymax = "upper_enhet")

hd_make(spec_ar2, "arearange", opts_ar,
        ymin = "lower_95CI", ymax = "upper_95CI", backend = "ggplot2")

Pie chart

Pie charts map one categorical column to slice labels and one numeric column to slice values. Pass inner_size = "50%" (or any CSS percentage) to draw a donut chart. The group column is ignored for pie charts — use x for labels and y for values.

# Category share dataset (pie)
drinking_freq <- data.frame(
  category = c("Never", "Rarely", "Monthly", "Weekly", "Daily"),
  pct      = c(18, 25, 30, 20, 7))
  
spec_pie <- hd_spec(drinking_freq,
                    x    = "category",
                    y    = "pct")

opts_pie <- hd_opts(
  title    = "Drinking frequency",
  subtitle = "Source: Norwegian Directorate of Health",
  ylab = "Share (%)"
)

Donut

# Donut interactive
hd_make(spec_pie, "pie", opts_pie, inner_size = "50%")
# Donut static
hd_make(spec_pie, "pie", opts_pie, inner_size = "50%", backend = "ggplot2")

Solid pie

# Solid pie
hd_make(spec_pie, "pie", opts_pie)
hd_make(spec_pie, "pie", opts_pie, backend = "ggplot2")

Stacked column chart

Stacked column charts display multiple series grouped into named stacks. Each stack is a separate pile of bars; series that share the same stack accumulate on top of each other.

The data must be in long format with one row per (x, group, stack) combination:

stack is a required argument passed via ... in hd_make().

# Olympics all-time medal table
olympics <- data.frame(
  Country   = c("Norway", "Norway", "Norway",
                "Germany", "Germany", "Germany",
                "United States", "United States", "United States",
                "Canada", "Canada", "Canada"),
  Continent = c("Europe", "Europe", "Europe",
                "Europe", "Europe", "Europe",
                "North America", "North America", "North America",
                "North America", "North America", "North America"),
  Medal     = rep(c("Gold", "Silver", "Bronze"), times = 4),
  Count     = c(148, 133, 124,
                102,  98,  65,
                113, 122,  95,
                 77,  72,  80)
)

spec_st <- hd_spec(olympics,
                   x     = "Medal",
                   y     = "Count",
                   group = "Country")

opts_st <- hd_opts(
  title    = "Olympic Games all-time medal table, grouped by continent",
  subtitle = "Source: Olympics",
  ylab     = "Count medals"
)

# Interactive — stacks are separated by continent
hd_make(spec_st, "stacked_column", opts_st, stack = "Continent")
# Static ggplot2 — continents become facet panels
hd_make(spec_st, "stacked_column", opts_st,
        stack   = "Continent",
        backend = "ggplot2")

The stacking argument controls how values accumulate. "normal" (default) shows absolute values; "percent" rescales each stack to 100 % so you can compare compositions across stacks:

# 100% stacked — shows share within each continent, not absolute counts
hd_make(spec_st, "stacked_column", opts_st,
        stack    = "Continent",
        stacking = "percent")
## Static
hd_make(spec_st, "stacked_column", opts_st,
        stack    = "Continent",
        stacking = "percent",
        backend  = "ggplot2")

The same hd_opts() controls — title, axis labels, colours, and theme — work identically for stacked columns as for every other geom:

# Custom colour palette and Norwegian labels
opts_no <- hd_opts(
  title    = "Olympiske medaljer etter kontinent",
  subtitle = "Kilde: OL-statistikk",
  ylab     = "Antall medaljer",
  colors   = c("#025169", "#7C145C", "#C68803", "#047FA4")
)

hd_make(spec_st, "stacked_column", opts_no, stack = "Continent")
hd_make(spec_st, "stacked_column", opts_no,
        stack   = "Continent",
        backend = "ggplot2")

Reusing a spec across geoms

Because hd_spec() and hd_opts() are separate from hd_make(), the same data description can be rendered as any geometry — no code duplication.

# One spec, three geoms
spec_shared <- hd_spec(alco2,
                       x     = "year",
                       y     = "adj_mean",
                       group = "kjonn")

opts_shared <- hd_opts(
  title    = "Alcohol consumption by kjonn",
  subtitle = "Source: Norwegian Directorate of Health",
  ylim     = c(0, 50),
  ylab  = "Litres per capita"
)

hd_make(spec_shared, "column",    opts_shared)
hd_make(spec_shared, "line",      opts_shared)
hd_make(spec_shared, "arearange", opts_shared,
        ymin = "lower_95CI", ymax = "upper_95CI")

Reusing opts across languages

An hd_opts() object only controls presentation. Create one per language and pair it with the same spec.

opts_no <- hd_opts(
  title    = "Alkoholbruk over tid",
  subtitle = "Kilde: Helsedirektoratet",
  caption  = "Tall om alkohol",
  ylim     = c(0, 50)
)

opts_en <- hd_opts(
  title    = "Alcohol use over time",
  subtitle = "Source: Norwegian Directorate of Health",
  caption  = "Annual health report",
  ylim     = c(0, 50)
)

spec_ts <- hd_spec(alco2,
                   x     = "year",
                   y     = "adj_mean",
                   group = "kjonn")

hd_make(spec_ts, "line", opts_no)
hd_make(spec_ts, "line", opts_en)

Theming

Use hd_set_theme() once per session to apply a consistent visual style across all figures. Per-figure overrides are set via hd_opts(hc_theme = ..., gg_theme = ...).

# Session-wide defaults
hd_set_theme(
  hc_theme = "gridlight",
  gg_theme = "classic",
  colors   = c("#025169", "#7C145C", "#C68803")
)

hd_make(spec_ts, "line", opts_en)
hd_make(spec_ts, "line", opts_en, backend = "ggplot2")

# Per-figure override — does not change the session default
hd_make(spec_ts, "line",
        hd_opts(title = "Dark theme", hc_theme = "darkunica"),
        backend = "highcharter")

# Reset to package defaults
hd_set_theme(hc_theme = "default", gg_theme = "classic", colors = NULL)

Saving figures

hd_save() exports highcharter figures to HTML or JSON and ggplot2 figures to PNG, SVG, or PDF. The format is inferred from the file extension.

hc_fig <- hd_make(spec_ts, "line", opts_en)
gg_fig <- hd_make(spec_ts, "line", opts_en, backend = "ggplot2")

hd_save(hc_fig, "alcohol_line.html")
hd_save(hc_fig, "alcohol_line.json")

hd_save(gg_fig, "alcohol_line.png")
hd_save(gg_fig, "alcohol_line.svg")

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