脊线密度图顶部的线被截断
Line at the top of a ridgeline density plot is cut off
为什么绘图的顶部被截断了,我该如何解决?我增加了边距,但没有任何区别。
查看 1854 年的曲线,位于左侧驼峰的最顶部。看起来这条线在驼峰顶部更细。对我来说,将大小更改为 0.8 没有帮助。
这是生成此示例所需的代码:
library(tidyverse)
library(ggridges)
t2 <- structure(list(Date = c("1853-01", "1853-02", "1853-03", "1853-04",
"1853-05", "1853-06", "1853-07", "1853-08", "1853-09", "1853-10",
"1853-11", "1853-12", "1854-01", "1854-02", "1854-03", "1854-04",
"1854-05", "1854-06", "1854-07", "1854-08", "1854-09", "1854-10",
"1854-11", "1854-12"), t = c(-5.6, -5.3, -1.5, 4.9, 9.8, 17.9,
18.5, 19.9, 14.8, 6.2, 3.1, -4.3, -5.9, -7, -1.3, 4.1, 10, 16.8,
22, 20, 16.1, 10.1, 1.8, -5.6), year = c("1853", "1853", "1853",
"1853", "1853", "1853", "1853", "1853", "1853", "1853", "1853",
"1853", "1854", "1854", "1854", "1854", "1854", "1854", "1854",
"1854", "1854", "1854", "1854", "1854")), row.names = c(NA, -24L
), class = c("tbl_df", "tbl", "data.frame"), .Names = c("Date",
"t", "year"))
# Density plot -----------------------------------------------
jj <- ggplot(t2, aes(x = t, y = year)) +
stat_density_ridges(
geom = "density_ridges_gradient",
quantile_lines = TRUE,
size = 1,
quantiles = 2) +
theme_ridges() +
theme(
plot.margin = margin(t = 1, r = 1, b = 0.5, l = 0.5, "cm")
)
# Build ggplot and extract data
d <- ggplot_build(jj)$data[[1]]
# Add geom_ribbon for shaded area
jj +
geom_ribbon(
data = transform(subset(d, x >= 20), year = group),
aes(x, ymin = ymin, ymax = ymax, group = group),
fill = "red",
alpha = 0.5)
正在添加
scale_y_discrete(expand = c(0.01, 0))
成功了。
一些评论者说他们无法重现这个问题,但它确实存在。如果我们增加行大小更容易看到:
library(ggridges)
library(ggplot2)
ggplot(iris, aes(x = Sepal.Length, y = Species)) +
geom_density_ridges(size = 2)
这是关于 ggplot 如何扩展离散尺度的 属性。密度线超出了 ggplot 使用的正常附加扩展值(其大小是从 "setosa" 基线到 x 轴的距离)。在这种情况下,ggplot 会进一步扩展轴,但只会恰好扩展到最大数据点。因此,线的一半在最大点处超出了绘图区域,而那一半被截断了。
即将推出的 ggplot2 2.3.0(目前可通过 github 获得)将有两种处理此问题的新方法。首先,可以在坐标系中设置clip = "off"
,让线条超出绘图范围:
ggplot(iris, aes(x = Sepal.Length, y = Species)) +
geom_density_ridges(size = 2) +
coord_cartesian(clip = "off")
其次,您可以分别扩展比例尺的底部和顶部。对于离散尺度,我更喜欢加性扩展,我认为在这种情况下我们希望使下限值小于默认值但上限值要大得多:
ggplot(iris, aes(x = Sepal.Length, y = Species)) +
geom_density_ridges(size = 2) +
scale_y_discrete(expand = expand_scale(add = c(0.2, 1.5)))
为什么绘图的顶部被截断了,我该如何解决?我增加了边距,但没有任何区别。
查看 1854 年的曲线,位于左侧驼峰的最顶部。看起来这条线在驼峰顶部更细。对我来说,将大小更改为 0.8 没有帮助。
这是生成此示例所需的代码:
library(tidyverse)
library(ggridges)
t2 <- structure(list(Date = c("1853-01", "1853-02", "1853-03", "1853-04",
"1853-05", "1853-06", "1853-07", "1853-08", "1853-09", "1853-10",
"1853-11", "1853-12", "1854-01", "1854-02", "1854-03", "1854-04",
"1854-05", "1854-06", "1854-07", "1854-08", "1854-09", "1854-10",
"1854-11", "1854-12"), t = c(-5.6, -5.3, -1.5, 4.9, 9.8, 17.9,
18.5, 19.9, 14.8, 6.2, 3.1, -4.3, -5.9, -7, -1.3, 4.1, 10, 16.8,
22, 20, 16.1, 10.1, 1.8, -5.6), year = c("1853", "1853", "1853",
"1853", "1853", "1853", "1853", "1853", "1853", "1853", "1853",
"1853", "1854", "1854", "1854", "1854", "1854", "1854", "1854",
"1854", "1854", "1854", "1854", "1854")), row.names = c(NA, -24L
), class = c("tbl_df", "tbl", "data.frame"), .Names = c("Date",
"t", "year"))
# Density plot -----------------------------------------------
jj <- ggplot(t2, aes(x = t, y = year)) +
stat_density_ridges(
geom = "density_ridges_gradient",
quantile_lines = TRUE,
size = 1,
quantiles = 2) +
theme_ridges() +
theme(
plot.margin = margin(t = 1, r = 1, b = 0.5, l = 0.5, "cm")
)
# Build ggplot and extract data
d <- ggplot_build(jj)$data[[1]]
# Add geom_ribbon for shaded area
jj +
geom_ribbon(
data = transform(subset(d, x >= 20), year = group),
aes(x, ymin = ymin, ymax = ymax, group = group),
fill = "red",
alpha = 0.5)
正在添加
scale_y_discrete(expand = c(0.01, 0))
成功了。
一些评论者说他们无法重现这个问题,但它确实存在。如果我们增加行大小更容易看到:
library(ggridges)
library(ggplot2)
ggplot(iris, aes(x = Sepal.Length, y = Species)) +
geom_density_ridges(size = 2)
这是关于 ggplot 如何扩展离散尺度的 属性。密度线超出了 ggplot 使用的正常附加扩展值(其大小是从 "setosa" 基线到 x 轴的距离)。在这种情况下,ggplot 会进一步扩展轴,但只会恰好扩展到最大数据点。因此,线的一半在最大点处超出了绘图区域,而那一半被截断了。
即将推出的 ggplot2 2.3.0(目前可通过 github 获得)将有两种处理此问题的新方法。首先,可以在坐标系中设置clip = "off"
,让线条超出绘图范围:
ggplot(iris, aes(x = Sepal.Length, y = Species)) +
geom_density_ridges(size = 2) +
coord_cartesian(clip = "off")
其次,您可以分别扩展比例尺的底部和顶部。对于离散尺度,我更喜欢加性扩展,我认为在这种情况下我们希望使下限值小于默认值但上限值要大得多:
ggplot(iris, aes(x = Sepal.Length, y = Species)) +
geom_density_ridges(size = 2) +
scale_y_discrete(expand = expand_scale(add = c(0.2, 1.5)))