Appendix D — ggplot2 速查表

资料来源：https://rstudio.github.io/cheatsheets/

D.1 基础

ggplot2 基于图形语法理念，即所有图形都可以通过相同组件构建：数据集、坐标系和几何对象（代表数据点的视觉标记）。

library(ggplot2)

要展示数值，需要将数据中的变量映射到几何对象的视觉属性（美学映射），如大小、颜色以及x和y位置。

使用以下模板构建图形：

ggplot(data = <数据>) +
  <几何对象函数>(mapping = aes(<映射关系>),
  stat = <统计变换>,
  position = <位置调整>) +
  <坐标系函数> +
  <分面函数> +
  <比例尺函数> +
  <主题函数>

数据、几何对象函数和美学映射是必需项。统计变换、位置调整以及坐标系、分面、比例尺和主题函数为可选项，会提供合理的默认值。

  • ggplot(data = mpg, aes(x = cty, y = hwy)): 初始化绘图，后续通过添加图层完成。每个图层添加一个几何对象函数。
ggplot(data = mpg, aes(x = cty, y = hwy)) + 
  geom_point()

  • last_plot(): 返回上一次绘制的图形。
last_plot()

  • ggsave("plot.png", width = 5, height = 5): 将最后一次绘制的图形保存为5英寸x5英寸的”plot.png”文件。文件类型自动匹配扩展名。
ggsave("plot.png", width = 5, height = 5)

D.2 美学映射

常用美学属性值：

  • color和fill: 字符串（"red", "#RRGGBB"）

  • linetype: 整数或字符串（0 = "blank"，1 = "solid"，2 = "dashed"，3 = "dotted"，4 = "dotdash"， 5 = "longdash"，6 = "twodash"）

  • size: 整数（点的大小和文本尺寸，单位为毫米）

  • linewidth: 整数（线的宽度，单位为毫米）

  • shape: 整数/形状名称或单个字符（"a"）

    形状整数/名称对应关系：0 = "square open", 1 = "circle open", 2 = "triangle open", 3 = "plus", 4 = "cross", 5 = "diamond open", 6 = "triangle down open", 7 = "square cross", 8 = "asterisk", 9 = "diamond plus", 10 = "circle plus", 11 = "star", 12 = "square plus", 13 = "circle cross", 14 = "square triangle", 15 = "square", 16 = "circle", 17 = "triangle", 18 = "diamond", 19 = "circle small", 20 = "bullet", 21 = "circle filled", 22 = "square filled", 23 = "diamond filled", 24 = "triangle filled", 25 = "triangle down filled"

D.3 几何对象

使用几何对象函数表示数据点，通过几何对象的美学属性映射变量。每个函数返回一个图层。

D.3.1 图形基元

a <- ggplot(economics, aes(date, unemploy))
b <- ggplot(seals, aes(x = long, y = lat))
  • a + geom_blank() 和 a + expand_limits(): 确保所有图形的范围包含所有值。
a + geom_blank()

  • b + geom_curve(aes(yend = lat + 1, xend = long + 1), curvature = 1): 绘制从(x, y)到(xend, yend)的曲线。aes()参数：x, xend, y, yend, alpha, angle, color, curvature, linetype, size
b + geom_curve(aes(yend = lat + 1, xend = long + 1), curvature = 1)

  • a + geom_path(lineend = "butt", linejoin = "round", linemitre = 1): 按数据出现顺序连接观测点。aes()参数：x, y, alpha, color, group, linetype, size
a + geom_path(lineend = "butt", linejoin = "round", linemitre = 1)

  • a + geom_polygon(aes(alpha = 50)): 将点连接成多边形。aes()参数：x, y, alpha, color, fill, group, subgroup, linetype, size
a + geom_polygon(aes(alpha = 50))

  • b + geom_rect(aes(xmin = long, ymin = lat, xmax = long + 1, ymax = lat + 1)): 通过四个角点绘制矩形。aes()参数：xmax, xmin, ymax, ymin, alpha, color, fill, linetype, size
b + geom_rect(aes(xmin = long, ymin = lat, xmax = long + 1, ymax = lat + 1))

  • a + geom_ribbon(aes(ymin = unemploy - 900, ymax = unemploy + 900)): 为每个x绘制从ymin到ymax的区间。aes()参数：x, ymax, ymin, alpha, color, fill, group, linetype, size
a + geom_ribbon(aes(ymin = unemploy - 900, ymax = unemploy + 900))

D.3.1.1 线段

常用美学属性：x, y, alpha, color, linetype, size, linewidth

  • b + geom_abline(aes(intercept = 0, slope = 1)): 绘制指定斜率和截距的参考线
b + geom_abline(aes(intercept = 0, slope = 1))

  • b + geom_hline(aes(yintercept = lat)): 绘制水平参考线
b + geom_hline(aes(yintercept = lat))

  • b + geom_vline(aes(xintercept = long)): 绘制垂直参考线
b + geom_vline(aes(xintercept = long))

  • b + geom_segment(aes(yend = lat + 1, xend = long + 1)): 绘制两点间直线
b + geom_segment(aes(yend = lat + 1, xend = long + 1))

  • b + geom_spoke(aes(angle = 1:1155, radius = 1)): 使用极坐标绘制线段
b + geom_spoke(aes(angle = 1:1155, radius = 1))

D.3.2 单变量-连续型

c <- ggplot(mpg, aes(hwy))
c2 <- ggplot(mpg)
  • c + geom_area(stat = "bin"): 面积图
c + geom_area(stat = "bin")

  • c + geom_density(kernel = "gaussian"): 核密度估计曲线
c + geom_density(kernel = "gaussian")

  • c + geom_dotplot(): 点图
c + geom_dotplot()

  • c + geom_freqpoly(): 频率多边形
c + geom_freqpoly()

  • c + geom_histogram(binwidth = 5): 直方图
c + geom_histogram(binwidth = 5)

  • c2 + geom_qq(aes(sample = hwy)): Q-Q图
c2 + geom_qq(aes(sample = hwy))

D.3.3 单变量-离散型

d <- ggplot(mpg, aes(fl))
  • d + geom_bar(): 条形图
d + geom_bar()

D.3.4 双变量-连续型

e <- ggplot(mpg, aes(cty, hwy))
  • e + geom_label(aes(label = cty), nudge_x = 1, nudge_y = 1): 带背景的文字标注
e + geom_label(aes(label = cty), nudge_x = 1, nudge_y = 1)

  • e + geom_point(): 散点图
e + geom_point()

  • e + geom_quantile(): 分位数回归线
e + geom_quantile()

  • e + geom_rug(sides = "bl"): 地毯图
e + geom_rug(sides = "bl")

  • e + geom_smooth(method = lm): 平滑条件均值
e + geom_smooth(method = lm)

  • e + geom_text(aes(label = cty), nudge_x = 1, nudge_y = 1): 文字标注
e + geom_text(aes(label = cty), nudge_x = 1, nudge_y = 1)

D.3.5 双变量-离散型与连续型

f <- ggplot(mpg, aes(class, hwy))
  • f + geom_col(): 柱状图
f + geom_col()

  • f + geom_boxplot(): 箱线图
f + geom_boxplot()

  • f + geom_dotplot(binaxis ="y", stackdir = "center"): 点图
f + geom_dotplot(binaxis ="y", stackdir = "center")

  • f + geom_violin(scale = "area"): 小提琴图
f + geom_violin(scale = "area")

D.3.6 双变量-离散型

g <- ggplot(diamonds, aes(cut, color))
  • g + geom_count(): 计数点图
g + geom_count()

  • e + geom_jitter(height = 2, width = 2): 抖动点图
e + geom_jitter(height = 2, width = 2)

D.3.7 双变量-连续型双变量分布

h <- ggplot(diamonds, aes(carat, price))
  • h + geom_bin2d(binwidth = c(0.25, 500)): 二维分箱热图
h + geom_bin2d(binwidth = c(0.25, 500))

  • h + geom_density_2d(): 二维核密度等高线
h + geom_density_2d()

  • h + geom_hex(): 六边形分箱热图
h + geom_hex()

D.3.8 双变量-连续型函数

i <- ggplot(economics, aes(date, unemploy))
  • i + geom_area(): 面积图
i + geom_area()

  • i + geom_line(): 折线图
i + geom_line()

  • i + geom_step(direction = "hv"): 阶梯图
i + geom_step(direction = "hv")

D.3.9 双变量-误差可视化

df <- data.frame(grp = c("A", "B"), fit = 4:5, se = 1:2)
j <- ggplot(df, aes(grp, fit, ymin = fit - se, ymax = fit + se))
  • j + geom_crossbar(fatten = 2): 交叉条
j + geom_crossbar(fatten = 2)

  • j + geom_errorbar(): 误差条
j + geom_errorbar()

  • j + geom_linerange(): 线条范围
j + geom_linerange()

  • j + geom_pointrange(): 点范围
j + geom_pointrange()

D.3.10 双变量-地图

根据数据中的简单要素绘制几何对象：

nc <- sf::st_read(system.file("shape/nc.shp", package = "sf"))
Reading layer `nc' from data source 
  `/Users/gaoch/Library/Caches/org.R-project.R/R/renv/cache/v5/macos/R-4.4/aarch64-apple-darwin20/sf/1.0-19/fe02eec2f6b3ba0e24afe83d5ccfb528/sf/shape/nc.shp' 
  using driver `ESRI Shapefile'
Simple feature collection with 100 features and 14 fields
Geometry type: MULTIPOLYGON
Dimension:     XY
Bounding box:  xmin: -84.32385 ymin: 33.88199 xmax: -75.45698 ymax: 36.58965
Geodetic CRS:  NAD27
ggplot(nc) +
  geom_sf(aes(fill = AREA))

D.3.11 三变量

seals$z <- with(seals, sqrt(delta_long^2 + delta_lat^2))
l <- ggplot(seals, aes(long, lat))
  • l + geom_contour(aes(z = z)): 等高线图
l + geom_contour(aes(z = z))

  • l + geom_contour_filled(aes(fill = z)): 填充等高线图
l + geom_contour_filled(aes(z = z))

  • l + geom_raster(aes(fill = z), hjust = 0.5, vjust = 0.5, interpolate = FALSE): 栅格图
l + geom_raster(aes(fill = z), hjust = 0.5, vjust = 0.5, interpolate = FALSE)

  • l + geom_tile(aes(fill = z)): 瓦片图
l + geom_tile(aes(fill = z))

D.4 统计变换

通过统计变换构建图层的另一种方式。统计变换会生成新的绘图变量（如计数、比例）。使用after_stat(变量名)语法将统计变量映射到美学属性。

i + stat_density_2d(aes(fill = after_stat(level)), geom = "polygon")

D.5 比例尺

使用scales包覆盖默认设置。比例尺将数据值映射到美学属性的视觉值。

n <- d + geom_bar(aes(fill = fl))
n + scale_fill_manual(
  values = c("skyblue", "royalblue", "blue", "navy"),
  limits = c("d", "e", "p", "r"),
  breaks =c("d", "e", "p", "r"),
  name = "fuel", 
  labels = c("D", "E", "P", "R")
)

D.6 坐标系

u <- d + geom_bar()
  • u + coord_cartesian(xlim = c(0, 5)): 笛卡尔坐标系
u + coord_cartesian(xlim = c(0, 5))

  • u + coord_fixed(ratio = 1/2): 固定比例坐标系
u + coord_fixed(ratio = 1/2)

  • u + coord_polar(theta = "x", direction = 1): 极坐标系
u + coord_polar(theta = "x", direction = 1)

D.7 位置调整

位置调整决定如何排列重叠的几何对象。

s <- ggplot(mpg, aes(fl, fill = drv))
  • s + geom_bar(position = "dodge"): 并列排列
s + geom_bar(position = "dodge")

  • s + geom_bar(position = "fill"): 堆叠标准化
s + geom_bar(position = "fill")

D.8 主题

  • u + theme_bw(): 白底网格主题
u + theme_bw()

  • u + theme_classic(): 经典无网格主题
u + theme_classic()

D.9 分面

分面根据离散变量值将图形划分为子图。

t <- ggplot(mpg, aes(cty, hwy)) + geom_point()
  • t + facet_grid(. ~ fl): 列分面
t + facet_grid(. ~ fl)

  • t + facet_wrap(~ fl): 环绕分面
t + facet_wrap(~ fl)

D.10 标签与图例

使用labs()添加图形标签：

t + labs(x = "城市油耗", y = "高速油耗", 
         title = "油耗关系图", 
         subtitle = "城市与高速公路油耗对比",
         caption = "数据来源: mpg数据集")

D.11 缩放

  • t + coord_cartesian(xlim = c(0, 100), ylim = c(10,20)): 无损缩放
t + coord_cartesian(xlim = c(0, 30), ylim = c(10,40))