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Supported Plot Types

Source: vignettes/supported_types.Rmd
supported_types.Rmd

The following plot types are supported.

Any ggplot2 geom is supported, but we added support for some other, more advanced plots.

Column / Bar

Used for comparing discrete categories through rectangular bars.

diamonds |> # from the ggplot2 package
  plot2(x = cut,
        y = n())

In plot2, bar types are horizontal alternatives for column types (just like MS Excel):

diamonds |>
  plot2(x = cut,
        y = n(),
        type = "bar")

Line

Used for visualising trends over ordered intervals.

pressure |> # from base R
  plot2(x = temperature,
        y = pressure,
        type = "line")

pressure |>
  plot2(x = temperature,
        y = pressure,
        type = "line-point")

Point

Used for displaying individual observations in a two dimensional space.

iris |> # from base R
  plot2()
#> ℹ Using category = Species
#> ℹ Using type = "point" since both axes are numeric
#> ℹ Using x = Sepal.Length
#> ℹ Using y = Sepal.Width

diamonds |>
  plot2(x = carat,
        y = price,
        category = cut,
        type = "point")

Area

Used for emphasising cumulative magnitudes across continuous domains.

pressure |>
  plot2(x = temperature,
        y = pressure,
        type = "area")

airquality |>
  plot2(x = Day, 
        y = Wind, 
        category = Month,
        category.character = TRUE,
        stacked_fill= TRUE,
        type = "area")

Boxplot / Violin

Used for summarising and comparing distributions with focus on spread and density.

iris |>
  plot2(x = Species,
        type = "violin")
#> ℹ Using y = Sepal.Length

iris |>
  plot2(x = Species,
        y = where(is.double),
        type = "boxplot")
#> ℹ Using y = c(Petal.Length, Petal.Width, Sepal.Length, Sepal.Width)

Histogram

Used for visualising the frequency distribution of continuous variables.

diamonds |>
  plot2(x = price,
        type = "hist")
#> ℹ Assuming smooth = TRUE for type = "histogram"
#> ℹ Using binwidth = 841 based on data
#> ✖ Values set in x.limits are of class numeric, while the values on x are of
#> class integer

Geo (sf)

Used for mapping spatial data encoded as simple features.

netherlands |> # from this plot2 package
  plot2()
#> ℹ Assuming datalabels.centroid = TRUE. Set to FALSE for a point-on-surface
#> placing of datalabels.
#> ℹ Using category = area_km2
#> ℹ Using datalabels = province

Beeswarm

Used for showing distributions of individual observations without overlap.

iris |>
  plot2(x = Species,
        y = Sepal.Length,
        type = "beeswarm")

Back-to-back

Used for contrasting two mirrored groups across shared categories.

admitted_patients |> # from this plot2 package
  plot2(x = age_group,
        y = n(),
        facet = ward,
        type = "back-to-back")

admitted_patients |> # from this plot2 package
  plot2(x = age_group,
        y = n(),
        y.limits = c(0, 60),
        category = gender,
        facet = ward,
        type = "back-to-back")

Dumbbell

Used for highlighting changes or differences between paired values.

diamonds |>
  dplyr::filter(cut %in% c("Fair", "Very Good")) |>
  plot2(x = cut(carat, 6),
        y = median(price),
        category = cut,
        type = "dumbbell")

Sankey

Used for depicting flows or transitions between connected stages.

Titanic |> # from base R
  plot2(x = c(Age, Class, Survived),
        category = Sex,
        type = "sankey")
#> ℹ Assuming sankey.remove_axes = TRUE
#> ! Input class 'table' was transformed using `as.data.frame()`

UpSet

Used for analysing intersections among multiple sets with scalable clarity.

movies |> # from the ggplot2movies package
  plot2(x = c(Action, Animation, Comedy, Drama, Romance),
        type = "upset")
#> ℹ Using summarise_function = sum for UpSet plot
#> ℹ Using y = 1

movies |>
  plot2(x = c(Action, Animation, Comedy, Drama, Romance),
        y = median(rating),
        y.title = "Median Rating",
        x.sort = TRUE,
        type = "upset")
#> ℹ Using summarise_function = sum for UpSet plot