Backends • dashboardr

dashboardr supports four chart rendering backends. The default is highcharter. You can switch to plotly, echarts4r, or ggiraph at the collection or dashboard level (e.g. with backend = "echarts4r").

Backend overview

Backend (`backend=`)	R package (CRAN link)	Licensing considerations	Approx. coverage in dashboardr
`"highcharter"`	highcharter	Free for academia/non-profit, paid for commercial use. Highcharts JS requires a separate license for commercial or government projects.	Full
`"plotly"`	plotly	Free (MIT). Fully open source for both personal and commercial applications without fees.	Well covered
`"echarts4r"`	echarts4r	Free (Apache 2.0). Open source and safe for commercial products with no licensing costs.	Decent coverage
`"ggiraph"`	ggiraph	Free (GPL-3). Standard open-source license that allows commercial use.	Low

Licensing & Usage Warnings

Highcharter: While the R wrapper is MIT licensed, the underlying Highcharts JavaScript library is a commercial product. If your dashboard is for a for-profit company or a government entity, you must purchase a license. It is free only for personal, non-commercial, or school-related projects.
Plotly & Echarts4r: These are the preferred enterprise-safe options when there is no budget for per-developer or per-site licenses. Their underlying libraries (Plotly.js and Apache ECharts) are permissive and free for commercial use.
Distribution: If you are distributing your dashboard as a standalone product, check the GPL-3 requirements for ggiraph. This license requires that any derivative works also be made open source under the same terms.

Data Setup (click to expand)

library(dashboardr)
library(dplyr)
library(gssr)
library(haven)

data(gss_all)

gss <- gss_all %>%
  select(year, age, sex, race, degree, happy) %>%
  filter(year == max(year, na.rm = TRUE)) %>%
  filter(
    happy %in% 1:3,
    !is.na(age), !is.na(sex), !is.na(race), !is.na(degree)
  ) %>%
  mutate(
    happy = droplevels(as_factor(happy)),
    degree = droplevels(as_factor(degree)),
    sex = droplevels(as_factor(sex)),
    race = droplevels(as_factor(race))
  )

Supported Backends

Backend	Package	Notes
`"highcharter"`	highcharter	Default. Cross-tab filtering support.
`"plotly"`	plotly	Plotly.js with zoom, pan, hover.
`"echarts4r"`	echarts4r	Apache ECharts with animations.
`"ggiraph"`	ggiraph + ggplot2	Interactive ggplot2 with tooltips.

Only highcharter is a hard dependency. The others are optional (Suggests):

install.packages("plotly")
install.packages("echarts4r")
install.packages(c("ggiraph", "ggplot2"))

Default: Highcharter

When no backend is specified, highcharter is used:

create_content(data = gss, type = "bar") %>%
  add_viz(x_var = "degree", title = "Education (Highcharter)") %>%
  preview()

Preview

Education (Highcharter)

Switching Backends

Set backend on individual add_viz() calls:

Plotly

create_content(data = gss, type = "bar") %>%
  add_viz(x_var = "degree", title = "Education (Plotly)", backend = "plotly") %>%
  preview()

Preview

Education (Plotly)

ECharts

create_content(data = gss, type = "bar") %>%
  add_viz(x_var = "degree", title = "Education (ECharts)", backend = "echarts4r") %>%
  preview()

Preview

Education (ECharts)

ggiraph

create_content(data = gss, type = "bar") %>%
  add_viz(x_var = "degree", title = "Education (ggiraph)", backend = "ggiraph") %>%
  preview()

Preview

Education (ggiraph)

Collection-Wide Backend

Set backend in create_content() so all add_viz() calls inherit it:

create_content(data = gss, type = "bar", backend = "plotly") %>%
  add_viz(x_var = "degree", title = "Education") %>%
  add_viz(x_var = "race", title = "Race") %>%
  preview()

Preview

Education

Race

Dashboard-Wide Backend

Set backend in create_dashboard() for the entire project. Individual charts can still override:

proj <- create_dashboard(
  title = "Plotly Dashboard",
  output_dir = tempdir(),
  backend = "plotly"
) %>%
  add_dashboard_page(page_title = "Overview", data = gss) %>%
  add_viz(type = "bar", x_var = "degree") %>%
  add_viz(type = "bar", x_var = "race") %>%
  # Override one chart back to highcharter
  add_viz(type = "bar", x_var = "sex", backend = "highcharter")

Validation with print(check = TRUE)

print(collection, check = TRUE) validates parameters and column names. This works regardless of which backend is set:

content <- create_content(data = gss, type = "bar") %>%
  add_viz(x_var = "degree", title = "Highcharter Bar", backend = "highcharter") %>%
  add_viz(x_var = "race", title = "Plotly Bar", backend = "plotly") %>%
  add_viz(x_var = "sex", title = "ECharts Bar", backend = "echarts4r")

print(content, check = TRUE)
#> -- Content Collection ──────────────────────────────────────────────────────────
#> 3 items | ✔ data: 3118 rows x 6 cols
#> 
#> • [Viz] Highcharter Bar (bar) x=degree
#> • [Viz] Plotly Bar (bar) x=race
#> • [Viz] ECharts Bar (bar) x=sex

Backend validity is checked at render time when you call preview() or generate_dashboard(), not during validation.

Limitations

Feature parity: All backends support core features (titles, colors, labels, tooltips).
Chart type coverage: Most viz types support all 4 backends. A few exceptions exist (e.g., viz_gauge() does not support ggiraph, viz_waffle() does not support echarts4r).