R 语言绘图教程案例

下面给你整理一套最常用、最实用、直接能跑代码的 R 语言绘图案例，全部基于 ggplot2（行业标准），从基础到进阶，适合做报告、论文、数据分析展示。
你复制代码到 RStudio 就能直接出图。

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一、环境准备（先运行）

library(ggplot2)
library(dplyr)
data(iris)    # 经典鸢尾花数据
data(mtcars)  # 汽车数据

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1. 散点图（最常用）

基础散点图

ggplot(iris, aes(x = Sepal.Length, y = Sepal.Width)) +
  geom_point() +
  labs(title = "花萼长度 vs 花萼宽度", x = "长度", y = "宽度")

分组上色 + 拟合曲线

ggplot(iris, aes(x = Sepal.Length, y = Sepal.Width, color = Species)) +
  geom_point(size = 2, alpha = 0.7) +
  geom_smooth(method = "lm", se = FALSE) +
  theme_minimal()

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2. 柱状图 / 条形图

简单柱状图

ggplot(mtcars, aes(x = factor(cyl))) +
  geom_bar(fill = "steelblue") +
  labs(title = "不同气缸数车辆数量", x = "气缸数", y = "数量")

分组柱状图

ggplot(mtcars, aes(x = factor(cyl), fill = factor(am))) +
  geom_bar(position = "dodge") +
  labs(title = "变速箱与气缸分布", fill = "变速箱(0/1)")

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3. 箱线图（看分布、异常值）

ggplot(iris, aes(x = Species, y = Petal.Length, fill = Species)) +
  geom_boxplot() +
  theme_bw() +
  labs(title = "不同品种花瓣长度分布")

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4. 直方图 / 密度图

直方图

ggplot(iris, aes(x = Petal.Length)) +
  geom_histogram(bins = 20, fill = "orange", alpha = 0.7)

分组密度图

ggplot(iris, aes(x = Petal.Length, fill = Species)) +
  geom_density(alpha = 0.5)

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5. 折线图（时序/趋势）

# 构造简单时序数据
df <- data.frame(
  time = 1:20,
  value = cumsum(rnorm(20))
)

ggplot(df, aes(x = time, y = value)) +
  geom_line(color = "red", linewidth = 1) +
  geom_point()

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6. 热图（相关性矩阵）

cor_df <- round(cor(iris[,1:4]), 2)
cor_df <- as.data.frame(as.table(cor_df))

ggplot(cor_df, aes(x = Var1, y = Var2, fill = Freq)) +
  geom_tile() +
  geom_text(aes(label = Freq), color = "white") +
  scale_fill_gradient2(low = "blue", high = "red") +
  theme_minimal()

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7. 小提琴图（比箱线图更详细）

ggplot(iris, aes(x = Species, y = Sepal.Width, fill = Species)) +
  geom_violin(alpha = 0.6) +
  geom_jitter(size = 0.5)

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8. 分面图（多子图一起展示）

ggplot(iris, aes(x = Sepal.Length, y = Petal.Length)) +
  geom_point() +
  facet_wrap(~Species) +
  theme_bw()

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9. 误差棒图（均值±标准差）

iris_sum <- iris %>%
  group_by(Species) %>%
  summarise(
    mean = mean(Petal.Length),
    sd = sd(Petal.Length)
  )

ggplot(iris_sum, aes(x = Species, y = mean, fill = Species)) +
  geom_col() +
  geom_errorbar(aes(ymin = mean - sd, ymax = mean + sd), width = 0.2)

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10. 饼图（ggplot 风格）

df <- table(mtcars$cyl) %>% data.frame()
colnames(df) <- c("cyl", "count")

ggplot(df, aes(x = "", y = count, fill = factor(cyl))) +
  geom_col() +
  coord_polar("y") +
  theme_void()

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11. 高级：气泡图（散点+大小+颜色）

ggplot(mtcars, aes(x = wt, y = mpg, size = hp, color = factor(cyl))) +
  geom_point(alpha = 0.7) +
  scale_size(range = c(2, 10)) +
  labs(title = "重量-油耗-马力-气缸")

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12. 论文级美化模板（通用）

ggplot(iris, aes(x = Sepal.Length, y = Petal.Length, color = Species)) +
  geom_point(size = 2) +
  geom_smooth(method = "lm", se = T) +
  labs(
    title = "鸢尾花性状关系",
    subtitle = "花萼长度与花瓣长度线性回归",
    x = "Sepal Length (cm)",
    y = "Petal Length (cm)",
    color = "Species"
  ) +
  theme_classic() +
  theme(
    plot.title = element_text(hjust = 0.5),
    plot.subtitle = element_text(hjust = 0.5)
  )

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