ggstream
The goal of ggstream is to create a simple but powerful implementation
of streamplot/streamgraph in ggplot2. A streamplot is a stacked area
plot mostly used for time series.
Installation
Install ggstream from CRAN:
install.packages("ggstream")Or you can install the development version of ggstream from github with:
remotes::install_github("davidsjoberg/ggstream")Examples
Type: Mirror
The characteristic streamplot which creates a symmetrical area chart around the x axis.
Type: Ridge
Which is equivalent to a stacked area chart.
Type: Proportional
The type proportional shows the share of each group in percent.
Stacked like the ridge
type.
Great community uses
By CΓ©dric Scherer. Code
here.
By Georgios Karamanis. Code here.
Basic usage
This is a basic example:
library(ggstream)
ggplot(blockbusters, aes(year, box_office, fill = genre)) +
geom_stream()
ggstream also features a custom labeling geom that places decent
default labels.
ggplot(blockbusters, aes(year, box_office, fill = genre)) +
geom_stream() +
geom_stream_label(aes(label = genre))
Add some pimp
library(cowplot)
library(paletteer)
library(dplyr)
library(colorspace)
blockbusters %>%
ggplot(aes(year, box_office, fill = genre, label = genre, color = genre)) +
geom_stream(extra_span = 0.013, type = "mirror", n_grid = 3000, bw = .78) +
geom_stream_label(size = 4, type = "mirror", n_grid = 1000) +
cowplot::theme_minimal_vgrid(font_size = 18) +
theme(legend.position = "none") +
scale_colour_manual(values = paletteer::paletteer_d("dutchmasters::milkmaid") %>% colorspace::darken(.8)) +
scale_fill_manual(values = paletteer::paletteer_d("dutchmasters::milkmaid") %>% colorspace::lighten(.2)) +
labs(title = "Box office per genre 1977-2019",
x = NULL,
y = "Current dollars, billions")
Advanced parameters
bw
The main parameter to adjust in geom_stream is probably the bandwidth,
or bw. A lower bandwidth creates a more bumpy plot and a higher
bandwidth smooth out some variation. Below is an illustration of how
different bandwidths affect the stream plot.
library(patchwork)
base <- ggplot(blockbusters, aes(year, box_office, fill = genre)) +
theme(legend.position = "none")
(base + geom_stream(bw = 0.5) + ggtitle("bw = 0.5")) /
(base + geom_stream() + ggtitle("Default (bw = 0.75)")) /
(base + geom_stream(bw = 1) + ggtitle("bw = 1"))
Extra Span
Another important parameter to adjust is extra_span. This parameter
adjust if a larger range than the range of the data which can help if
the edges of the stream plot grows too large due in the estimation
function. The additional range is set to y = 0 which forces the area
towards zero. The cut-off can include the extra range or fit the data.
Too illustrate this rather unintuitive parameter some variations are
shown below. The transparent areas show the full estimation, and the
solid area is the final plot.
base <- ggplot(blockbusters, aes(year, box_office, fill = genre)) +
theme(legend.position = "none") +
xlim(1970, 2028)
(base + geom_stream() + ggtitle("Default")) /
(base + geom_stream(extra_span = 0.001) + geom_stream(extra_span = 0.001, true_range = "none", alpha = .3) + ggtitle("extra_span = 0.001")) /
(base + geom_stream(extra_span = .1) + geom_stream(extra_span = .1, true_range = "none", alpha = .3) + ggtitle("extra_span = .1")) /
(base + geom_stream(extra_span = .2) + geom_stream(extra_span = .2, true_range = "none", alpha = .3) + ggtitle("extra_span = .2")) /
(base + geom_stream(extra_span = .2, true_range = "none") + ggtitle("extra_span = .2 and true_range = \"none\""))
Sorting
Another feature of stream plots is the sorting of groups in the
stacking. The default of ggstream is to stack as factor order of the
fill aesthetics. However, ggstream supports two other stackning
sorting options. The onset and inside_out.
library(patchwork)
set.seed(123)
df <- map_dfr(1:30, ~{
x <- 1:sample(1:70, 1)
tibble(x = x + sample(1:150, 1)) %>%
mutate(y = sample(1:10, length(x), replace = T),
k = .x %>% as.character())
})
p <- df %>%
ggplot(aes(x, y, fill = k)) +
theme_void() +
theme(legend.position = "none")
p1 <- p +
geom_stream(color = "black") +
ggtitle("None (Default)")
p2 <- p + geom_stream(color = "black", sorting = "inside_out") +
ggtitle("Inside out")
p3 <- p +
geom_stream(color = "black", sorting = "onset") +
ggtitle("Onset")
p1 / p2 / p3
Final remarks
The ggstream package provides some flexible ways to make stream plots
but with decent defaults. However, due to the complexity of the
underlying smoothing/estimation it should be used carefully and mostly
for fun too illustrate major trends.
If you find a bug or have ideas for additional feature you are more than welcome to open an issue.