ggpointless is a small extension of the ggplot2 package
that provides additional layers:
geom_pointless() &
stat_pointless()geom_lexis() & stat_lexis()geom_chaikin() & stat_chaikin()geom_pointless() is a layer to easily add minimal
emphasis to your plots. The function takes it’s power from
stat_pointless(), which does all the work, but is not
usually in the spotlight.
library(ggplot2)
library(ggpointless)
x <- seq(-pi, pi, length.out = 100)
y <- outer(x, 1:5, function(x, y) sin(x * y))
df1 <- data.frame(
var1 = x,
var2 = rowSums(y)
)
p <- ggplot(df1, aes(x = var1, y = var2))
p + geom_pointless(location = c("first", "last", "minimum", "maximum"))As you see, just adding geom_pointless() to
ggplot(...) is not terribly useful on its own but when it
teams up with geom_line() and friends, hopefully.
p <- p + geom_line()
p + geom_pointless(location = "all", size = 3)geom_pointless() behaves like geom_point()
does with the addition of a location argument. You can set
it to "first", "last" (the default),
"minimum", "maximum", and "all";
where "all" is just shorthand to select
"first", "last", "minimum" and
"maximum".
In addition, you can use the computed variable location
and map it to an aesthetic, e.g. color.
p + geom_pointless(aes(color = after_stat(location)),
location = "all",
size = 3
) +
theme(legend.position = "bottom")The locations are determined in the order in which they appear in the
data, like geom_path() does compared to
geom_line(). This can be seen in the next example, with
sample data kindly taken from the geomtextpath
package:
x <- seq(5, -1, length.out = 1000) * pi
spiral <- data.frame(
var1 = sin(x) * 1:1000,
var2 = cos(x) * 1:1000
)
p <- ggplot(spiral) +
geom_path() +
coord_equal(xlim = c(-1000, 1000), ylim = c(-1000, 1000)) +
theme(legend.position = "none")
p + aes(x = var1, y = var2) +
geom_pointless(aes(color = after_stat(location)),
location = "all",
size = 3
) +
labs(subtitle = "orientation = 'x'")
p + aes(y = var1, x = var2) +
geom_pointless(aes(color = after_stat(location)),
location = "all",
size = 3
) +
labs(subtitle = "orientation = 'y'")As you see from the first of the last two examples
"first" and "minimum" overlap, and
"first" wins over "minimum". If
location is set to "all", then the order in
which points are plotted from top to bottom is: "first"
> "last" > "minimum" >
"maximum".
Otherwise, the order is determined as specified in the
location argument, which also applies to the order of the
legend key labels.
cols <- c(
"first" = "#f8766d",
"last" = "#7cae00",
"minimum" = "#00bfc4",
"maximum" = "#c77cff"
)
df2 <- data.frame(
var1 = 1:2,
var2 = 1:2
)
p <- ggplot(df2, aes(x = var1, y = var2)) +
geom_path() +
coord_equal() +
scale_color_manual(values = cols)
# same as location = 'all'
p + geom_pointless(aes(color = after_stat(location)),
location = c("first", "last", "minimum", "maximum"),
size = 3
) +
labs(subtitle = "same as location = 'all'")# reversed order
p + geom_pointless(aes(color = after_stat(location)),
location = c("maximum", "minimum", "last", "first"),
size = 3
) +
labs(subtitle = "custom order")# same as location = 'all' again
p + geom_pointless(aes(color = after_stat(location)),
location = c("maximum", "minimum", "last", "first", "all"),
size = 3
) +
labs(subtitle = "same as location = 'all' again")Just like all stat_* functions,
stat_pointless() has a default geom, which is
"point". This means in reverse that you can highlight
e.g. minimum and maximum in another way, for example with a horizontal
line.
set.seed(42)
ggplot(data.frame(x = 1:10, y = sample(1:10)), aes(x, y)) +
geom_line() +
stat_pointless(
aes(yintercept = y, color = after_stat(location)),
location = c("minimum", "maximum"),
geom = "hline"
) +
guides(color = guide_legend(reverse = TRUE))geom_lexis() draws a lifeline for an event from it’s
start to it’s end. The required aesthetics are x and
xend. Here is an example:
df1 <- data.frame(
key = c("A", "B", "B", "C", "D"),
x = c(0, 1, 6, 5, 6),
y = c(5, 4, 10, 8, 10)
)
p <- ggplot(df1, aes(x = x, xend = y, color = key)) +
coord_equal()
p + geom_lexis()Also, if there is a gap in an event a horizontal line is drawn, which
can be hidden setting gap_filler = FALSE.
p + geom_lexis(gap_filler = FALSE)You can further style the appearance of your plot using the
additional arguments. If you e.g. want to make a visual distinction
between the ascending lines and the connecting lines, use
after_stat() to map the type variable to the
linetype aesthetic (or any other aesthetic). The variable
type is created by geom_lexis() and takes two
values: “solid” and “dotted”; so you might also want to call
scale_linettype_identity.
p + geom_lexis(
aes(linetype = after_scale(type)),
point_show = FALSE
) +
scale_linetype_identity()You see the coordinates on the vertical y-axis show the difference
between x and xend aesthetics. The “magic” of
geom_lexis() happens in stat_lexis() when the
input data is transformed and the calculations are performed.
df1 <- data.frame(
start = c(2019, 2021),
end = c(2022, 2022),
key = c("A", "B")
)
ggplot(df1, aes(x = start, xend = end, group = key)) +
geom_lexis() +
coord_fixed()Keeping in mind that dates are internally represented as the number of days, and the POSIXct class in turn represents seconds since some origin, the y-scale values in the next plots should come as no surprise.
# Date
fun <- function(i, class) as.Date(paste0(i, "-01-01"))
df1[, c("start", "end")] <- lapply(df1[, c("start", "end")], fun)
p1 <- ggplot(df1, aes(x = start, xend = end, group = key)) +
geom_lexis() +
labs(y = "days") +
coord_fixed()
# POSIXct
df2 <- df1
df2[, c("start", "end")] <- lapply(df2[, c("start", "end")], as.POSIXct)
p2 <- ggplot(df2, aes(x = start, xend = end, group = key)) +
geom_lexis() +
labs(y = "seconds") +
coord_fixed()
p1
p2In order to change the breaks and labels of the vertical scale to, say, years, we make the assumption that 1 year has 365.25 days, or 365.25 * 86400 seconds.
# years, roughly
p1 +
scale_y_continuous(
breaks = 0:3 * 365.25, # or for p2: 0:3*365.25*86400
labels = function(i) floor(i / 365.25) # floor(i / 365.25*86400)
) +
labs(y = "years")geom_chaikin() is a function to smooth ragged paths. The
algorithm iteratively cut’s off sharp corners.
set.seed(42)
dat <- data.frame(
x = seq.int(10),
y = sample(15:30, 10)
)
p1 <- ggplot(dat, aes(x, y)) +
geom_line(linetype = "12")
p1 +
geom_chaikin()In addition, ggpointless contains the following data
sets:
co2_ml : CO2 records
taken at Mauna Loacovid_vac : COVID-19
Cases and Deaths by Vaccination Statusfemale_leaders : Elected
and appointed female heads of state and governmentSee the vignette("examples")
for possible use cases.