geomtextpath | 成功让你的ggplot注释拥有傲人曲线！~

1写在前面

最近的世界杯结果的确是让人大跌眼镜🕶️, 日本队🇯🇵先后击败世界杯冠军, 德国队🇩🇪和西班牙队🇪🇸, 韩国队🇰🇷逆转葡萄牙🇵🇹, 踩着乌拉圭🇺🇾进入淘汰赛(请韩国队🇰🇷自觉感谢裁判), 让无数人站上天台😂.
不过大家要是看看这几十年日本足球⚽️的发展也就不会觉得奇怪了, 就算有一天日本队将梦想照进现实,捧起大力神杯🏆, 我也不觉得有什么奇怪的.
还是祝各亚洲球队取得好成绩, 也祝梅西和C罗在顶峰相遇, 人生不留遗憾😘.

接着是这一期的教程, 最近用了一下geomtextpath, 是个不错的ggplot2扩展包, 让你的geom_text卷起来吧.😂

主要函数有2个, geom_textpath和geom_labelpath, 我们逐一介绍吧.🧐

2用到的包

rm(list = ls())
# remotes::install_github("AllanCameron/geomtextpath", quiet = TRUE)
library(tidyverse)
library(geomtextpath)
library(ggsci)
library(RColorBrewer)
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3等价函数

这里我们给大家补充一下在使用text或者label时, 用到的包内对等函数, 不同figure可以选用对应的text或者label.😉

4示例数据

示例数据我们就用大名鼎鼎的Orange和iris吧.

4.1 示例数据一

dat1 <- Orange
DT::datatable(dat1)
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4.2 示例数据二

dat2 <- iris
DT::datatable(dat2)
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5标注曲线

5.1 简单绘图

这里用到的是geom_textline函数, 一起看一下吧.🥳

dat1 %>% 
dplyr::filter(., Tree == 1) %>% 
  ggplot(aes(x = age, y = circumference)) +
  geom_textline(label = "This is my text oh oh oh!", 
                size = 4, vjust = -0.2,
                linewidth = 1, linecolor = "red4", linetype = 2, 
                color = "deepskyblue4")
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5.2 进阶绘图

有时候我们还想标注上复杂的公式, 大家可以这样试一下.😏

lab <- expression(paste("y = ", frac(1, sigma*sqrt(2*pi)), " ",
                            plain(e)^{frac(-(x-mu)^2, 2*sigma^2)}))

df <- data.frame(x = seq(-2, 0, len = 100),
                 y = dnorm(seq(-2, 0, len = 100)),
                 z = as.character(lab))
df %>% 
ggplot(aes(x, y)) + 
  geom_textpath(aes(label = z), 
                vjust = -0.2, hjust = 0.1, 
                size = 8, parse = T)
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6标注densityplot

6.1 绘图

这里用到的是geom_textdensity函数, 我们再改一下颜色和主题.🤗

dat2 %>% 
ggplot(aes(x = Sepal.Width, colour = Species, label = Species)) +
  geom_textdensity(size = 6, fontface = 2, hjust = 0.2, vjust = 0.3) +
  scale_color_npg()+
  theme_bw()+
  theme(panel.background = element_blank(),
        panel.grid = element_blank(),
        legend.position = "none")
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6.2 更改文字位置

我们试着把他们改到最大值处.😂

dat2 %>% 
ggplot(aes(x = Sepal.Width, colour = Species, label = Species)) +
  scale_color_npg()+
  theme_bw()+
  theme(panel.background = element_blank(),
        panel.grid = element_blank(),
        legend.position = "none")+
  
  geom_textdensity(size = 5, fontface = 2, spacing = 50,
                   vjust = -0.2, hjust = "ymax") +
  ylim(c(0, 1.5))
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7标注趋势线

这里用到的是geom_labelsmooth函数, method可选如下:👇

• "lm";
• "glm";
• "gam";
• "loess";

dat2 %>% 
ggplot(aes(x = Sepal.Width, y = Petal.Width, color = Species)) +
  geom_point(alpha = 0.3) +
  geom_labelsmooth(aes(label = Species), 
                   text_smoothing = 30, 
                   fill = "#F6F6FF", # label背景色
                   method = "loess", 
                   formula = y ~ x,
                   size = 4, linewidth = 1, 
                   boxlinewidth = 0.3) +
  scale_color_npg()+
  theme_bw()+
  theme(panel.background = element_blank(),
        panel.grid = element_blank(),
        legend.position = "none")
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Note! 这里你也可以使用geom_textsmooth, 大家自己试一下有什么区别吧.😗

8标注contour lines

我们试着在contour lines上标注一下吧, 用到的函数是eom_textcontour.🤩

df <- expand.grid(x = seq(nrow(volcano)), y = seq(ncol(volcano)))
df$z <- as.vector(volcano)

ggplot(df, aes(x, y, z = z)) + 
  geom_contour_filled(bins = 6, alpha = 0.8) + 
  geom_textcontour(bins = 6, size = 2.5, straight = T) + 
  scale_fill_manual(values = colorRampPalette(brewer.pal(9,"Greens"))(9))+
  theme_bw()+
  theme(panel.background = element_blank(),
        panel.grid = element_blank(),
        legend.position = "none")
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9标注阈值线

我们在这里可以使用geom_texthline, geom_textvline, geom_textabline来进行各种阈值线的绘制.🤓

dat2 %>% 
ggplot(aes(Sepal.Length, Sepal.Width)) + 
  geom_point() + 
  geom_texthline(yintercept = 3, 
                 label = "hline", 
                 hjust = 0.8, color = "red4") +
  geom_textvline(xintercept = 6, 
                 label = "vline", 
                 hjust = 0.8, color = "blue4",
                 linetype = 2, vjust = 1.3) +
  geom_textabline(slope = 15, intercept = -100, 
                  label = "abline", 
                  color = "green4", hjust = 0.6, vjust = -0.2)+
  theme_bw()+
  theme(panel.background = element_blank(),
        panel.grid = element_blank(),
        legend.position = "none")
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10标注统计学差异

我们常规使用直线或者直接标注*的方式来展示统计学差异.😘
这里我们试试换一种方式来绘图吧.🫢

dat2 %>% 
ggplot(aes(x = Species, y = Sepal.Length)) + 
  geom_boxplot(aes(fill = Species))+
  geom_textcurve(data = data.frame(x = 1, xend = 2, 
                                   y = 8.72, yend = 8.52), 
                 aes(x, y, xend = xend, yend = yend), 
                 hjust = 0.35, ncp = 20,
                 curvature = -0.8, 
                 label = "significant difference") +
  scale_y_continuous(limits = c(4, 14))+
  scale_fill_aaas()+
  theme_bw()+
  theme(panel.background = element_blank(),
        panel.grid = element_blank(),
        legend.position = "none")
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11test_smoothing

有的时候画出来的figure过于尖锐, 不够平滑, 这样就不好标注了, 这里也提供了text_smoothing参数用来解决这个问题, 0 (none) to 100 (maximum), 大家试一下吧, 我在这里先做一个示范.😘

dat2 %>% 
ggplot(aes(Sepal.Length, Petal.Length)) +
  geom_textline(linecolour = "red4", size = 4, vjust = -7.5,
                label = "smooth_text", text_smoothing = 40)
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12复杂绘图的坐标轴改变

12.1 初步绘图

我们先画个图, 然后我们再把坐标coord_polar().🤩

p <- data.frame(x1 = c(seq(0, 10/6 * pi, pi/3),
                  seq(0, 10/6 * pi, 2*pi/3)),
           y1 = c(rep(2, 6), rep(-1, 3)),
           x2 = c(seq(0, 10/6 * pi, pi/3)  + pi/3,
                  seq(0, 10/6 * pi, 2*pi/3) + 2*pi/3),
           y2 = c(rep(4, 6), rep(2, 3)),
           group = letters[c(1:6, (1:3) * 2)],
           alpha = c(rep(1, 6), rep(0.4, 3))) |>
  ggplot(aes(x1, y1)) +
  geom_rect(aes(xmin = x1, xmax = x2, ymin = y1, ymax = y2, fill = group,
                alpha = alpha),
            color = "white", size = 2) +
  geom_textpath(data = data.frame(x1 = seq(0, 2 * pi, length = 300),
           y1 = rep(0.5, 300),
           label = rep(c("A and B", "C and D", "E and F"), each = 100)),
           aes(label = label), linetype = 0, size = 8,
           upright = TRUE) +
  geom_textpath(data = data.frame(x1 = seq(0, 2 * pi, length = 300),
           y1 = rep(3, 300),
           label = rep(c("apple", "banana", "cucumber", "durian",
                         "egg", "flower"), 
                       each = 50)),
           aes(label = label), linetype = 0, size = 4.6, color = "white",
           upright = TRUE) +
  scale_y_continuous(limits = c(-5, 4)) +
  scale_x_continuous(limits = c(0, 2*pi)) +
  scale_fill_npg()+
  scale_alpha_identity() +
  theme_void() +
  theme(legend.position = "none") 

p
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12.2 更改坐标系

这个包非常强大, 大家完全不用担心使用coord_polar()后, 文字的位置会有改变, 请放心使用!😂

p + coord_polar()
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Nice! 这种图无论是在研究型paper还是Review中使用, 都是可以拉高水平的图🌟~

12.3 直接使用coord_curvedpolar()

在这种polar式的坐标系中, 如果标注的文字太长, 我们可以使用coord_curvedpolar(), 要比coord_polar()合适, 大家试一试吧.😉

df <- data.frame(x = c("Apple label", "Banana label",
                       "Cucumber label", "Durian label"),
                 y = c(7, 10, 12, 5))

p <- ggplot(df, aes(x, y, fill = x)) + 
      geom_col(width = 0.5) +
      scale_fill_npg()+
      theme(axis.text.x = element_text(size = 15),
            legend.position = "none")
p
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搞定!~🥳🥳🥳

p + coord_curvedpolar()
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点个在看吧各位~ ✐.ɴɪᴄᴇ ᴅᴀʏ 〰