32  Stream plots

This graph is largely complete and just needs final proof reading.


This graph requires:

✅ a categorical variable

✅ a numeric (continuous) variable

✅ a numeric (date) variable

32.1 Description

Stream graphs display how a numerical variable (typically on the y axis) changes over time (on the x axis) across levels of a categorical variable. These graphs are handy if the numerical value varies wildly (or isn’t always present) over the time measurement.

Categorical groups are differentiated by color layers, with the area of the layer representing the change in y value. In ggplot2, we can create stream graphs using ggstream.

32.2 Set up

PACKAGES:

Install packages.

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remotes::install_github("davidsjoberg/ggstream")
install.packages("ggplot2movies")
library(ggstream)
library(ggplot2movies)
library(ggplot2)

DATA:

We’re going to use only the mpaa, year, and budget columns from ggplot2movies::movies, then drop all missing values (we have to remove special missing characters from mpaa).

We’ll then convert mpaa to an ordered factor, then group by year and mpaa to calculate the average budget and filter to only those movies after 1984.

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movies_stream <- ggplot2movies::movies |> 
    dplyr::select(mpaa, year, budget) |> 
    tidyr::drop_na() |> 
    dplyr::filter(mpaa != "") |> 
    dplyr::mutate(mpaa = factor(mpaa, 
                            levels = c("NC-17", "R", 
                                       "PG-13", "PG"), 
                            ordered = TRUE)) |> 
    dplyr::group_by(year, mpaa) |> 
    dplyr::summarise(
        avg_budget = mean(budget, na.rm = TRUE)) |> 
    dplyr::ungroup() |> 
    dplyr::filter(year > 1984)
#> `summarise()` has grouped output by 'year'. You
#> can override using the `.groups` argument.
dplyr::glimpse(movies_stream)
#> Rows: 47
#> Columns: 3
#> $ year       <int> 1986, 1989, 1989, 1990, 1991,…
#> $ mpaa       <ord> R, R, PG-13, R, R, PG, R, R, …
#> $ avg_budget <dbl> 17250000, 787000, 39250000, 3…

32.3 Grammar

CODE:

  • Create labels with labs()

    • Use paste0() in the subtitle to automatically update the year when the movies_stream changes

  • Initialize the graph with ggplot() and provide data

  • Map year to the x, avg_budget to y, and mpaa to fill

  • Add the geom_stream() layer

  • Adjust the y axis with scale_y_continuous() and scales::dollar

  • Finally, move the legend with theme(legend.position = "bottom")

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labs_stream <- labs(
    title = "20 years of movie budgets", 
    subtitle = 
        paste0("movies between", 
            min(movies_stream$year), 
            " and ", 
            max(movies_stream$year)),
    x = "Year", 
    y = "Average Movie Budget")

ggp2_stream <- ggplot(data = movies_stream, 
    mapping = aes(x = year, 
                  y = avg_budget, 
                  fill = mpaa)) + 
    ggstream::geom_stream() + 
    scale_y_continuous(labels = scales::dollar)
ggp2_stream + 
    labs_stream + 
    theme(legend.position = "bottom")

GRAPH:

32.4 More info

The ggstream package has multiple arguments for adjusting the shape and look of the categorical levels (and text labels).

32.4.1 Type

  • Map mpaa to fill (wrapped in forcats::fct_rev())

  • We can adjust the look of the graph by setting the type argument in geom_stream()

    • type: change type from "mirror" (the default) to "proportional"

  • Adjust colors:

    • scale_fill_manual(): Add colors as a named vector to values

  • Finally, add the fill to the labels and move the legend with theme(legend.position = "bottom")

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ggp2_stream_prp <- ggplot(data = movies_stream, 
    mapping = aes(x = year, 
                  y = avg_budget, 
                  fill = forcats::fct_rev(mpaa))) + 
    ggstream::geom_stream(type = "proportional") + 
    scale_y_continuous(labels = scales::percent) + 
  scale_fill_manual(
      values = c("PG-13" = "#0bd3d3", 
                 "PG" = "#f890e7", 
                 "R" = "#282b2d", 
                 "NC-17" = "#772953"))
ggp2_stream_prp + 
    labs_stream + 
    labs(fill = "mpaa") + 
    theme(legend.position = "bottom")

32.4.2 Sorting

To change how the categorical areas are sorted, adjust the sorting argument to either "none", "onset", or "inside_out"

  • sorting: set the sorting method to "inside_out" in both ggstream::geom_stream() and ggstream::geom_stream_label()

  • type: change the type to "ridge" in both ggstream::geom_stream() and ggstream::geom_stream_label()

We can also add text labels using ggstream::geom_stream_label():

  • Map mpaa (wrapped in forcats::fct_rev()) to label globally

  • Inside ggstream::geom_stream_label():

    • set color to white ("#ffffff") and the size to 2.7

    • Remove the legend with show.legend = FALSE

Colors:

  • Use scale_colour_manual() and scale_fill_manual() to manually set the values using a named vector

    • Change the y axis to US dollars using scale_y_continuous() and scales::dollar

  • Finally, add the fill to the labels and move the legend with theme(legend.position = "bottom")

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ggp2_stream_srt <- ggplot(data = movies_stream, 
    mapping = aes(x = year, 
                  y = avg_budget, 
                  fill = fct_rev(mpaa),
                  label = fct_rev(mpaa))) + 
    ggstream::geom_stream(
        type = "ridge", 
        sorting = "inside_out") + 
    ggstream::geom_stream_label(
        type = "ridge", 
        sorting = "inside_out", 
        color = "#ffffff", 
        size = 2.7,
        show.legend = FALSE) +
    scale_colour_manual(
      values = c("PG-13" = "#0bd3d3", 
                 "PG" = "#f890e7", 
                 "R" = "#282b2d", 
                 "NC-17" = "#772953")) +
    scale_fill_manual(
      values = c("PG-13" = "#0bd3d3", 
                 "PG" = "#f890e7", 
                 "R" = "#282b2d", 
                 "NC-17" = "#772953")) + 
    scale_y_continuous(labels = scales::dollar) 
    
ggp2_stream_srt + 
    labs_stream + 
    labs(fill = "mpaa") + 
    theme(legend.position = "bottom")