参考资料：根据《R数据科学(中文完整版）》第一章内容总结。

1、散点图

library(pacman)
p_load(tidyverse)
df <- read.csv("./data_set/class.csv",header = T) %>% tbl_df();str(df)

## Classes 'tbl_df', 'tbl' and 'data.frame':    19 obs. of  5 variables:
##  $ name  : Factor w/ 19 levels "Alfred","Alice",..: 2 3 5 10 11 12 15 16 17 1 ...
##  $ sex   : Factor w/ 2 levels "F","M": 1 1 1 1 1 1 1 1 1 2 ...
##  $ age   : int  13 13 14 12 12 15 11 15 14 14 ...
##  $ height: num  56.5 65.3 64.3 56.3 59.8 66.5 51.3 62.5 62.8 69 ...
##  $ weight: num  84 98 90 77 84.5 ...

p1 <- ggplot(df,aes(height,weight,col = sex)) + # shape = sex
  geom_point() +
  theme_get() +
  labs(title = "",x="身高(CM)",y="体重(KG)") +
  theme(plot.title = element_text(hjust = 0.5),legend.position = "none") +
  scale_x_continuous(breaks = seq(55,70,5),
                     labels = seq(55,70,5) * 2.54) +
  scale_y_continuous(breaks = seq(50,150,25),
                     labels = round(seq(50,150,25) * 0.45,0)) +
  geom_smooth(data = df %>% filter(sex=="F"),se=T,formula = y ~ x,method = "loess");p1

散点图

2、分面

p2 <- ggplot(df) + 
  geom_point(aes(height,weight)) +
  theme_get() +
  labs(title = "",x="身高(CM)",y="体重(KG)") +
  theme(plot.title = element_text(hjust = 0.5)) +
  scale_x_continuous(breaks = seq(55,70,5),
                     labels = seq(55,70,5) * 2.54) +
  scale_y_continuous(breaks = seq(50,150,25),
                     labels = round(seq(50,150,25) * 0.45,0)) +
  facet_wrap(~ sex,ncol = 2);p2 # facet_grid(drv ~ cyl)

分面

3、柱状图

p3 <- ggplot(df) + 
  #  position="identity"将每个对象直接显示在图中,"fill"效果与堆叠相似，但每组堆叠条形具有同样的高度
  # "dodge"将每组中的条形依次并列放置
  # "jitter" 为每个数据点添加一个很小的随机扰动，或者使用geom_jitter()
  geom_bar(stat = "identity",aes(reorder(name,weight),weight),fill="dodgerblue") + 
  theme_get() +
  labs(title = "",x="",y="体重") +
  theme(plot.title = element_text(hjust = 0.5)) +
  coord_flip();p3

柱状图

4、箱线图

p4 <- ggplot(df) + 
  geom_boxplot(aes(sex,weight),fill=c("violetred","dodgerblue")) + 
  labs(title = "",x="",y="") +
  theme(plot.title = element_text(hjust = 0.5)) +
  coord_flip();p4

箱线图

5、饼图

df.sex <- table(df$sex) %>% as.data.frame()
label = paste(df.sex$Var1, "(", round(df.sex$Freq / sum(df.sex$Freq) * 100,2), "%)", sep = "")

# 创建空白主题
blank_theme <- theme_minimal()+
  theme(
  axis.title.x = element_blank(),
  axis.title.y = element_blank(),
  panel.border = element_blank(),
  panel.grid=element_blank(),
  axis.ticks = element_blank(),
  axis.text = element_blank(),
  plot.title=element_text(size=14, face="bold", hjust = 0.5)
  )

p5 <- ggplot(df.sex,aes(x="",y=Freq,fill=Var1)) + 
  geom_bar(stat = "identity",width = 10) + # width>=1去除中心杂点
  coord_polar(theta = "y", start=0) +
  blank_theme +
  scale_fill_manual(values=c("violetred","dodgerblue")) + # 手动填充颜色
  geom_text(aes(y = Freq/2 + c(0, cumsum(Freq)[-length(Freq)]), 
            label = label), size=5) +
  theme(legend.position = "none") + # 去掉图例
  labs(title = "",x="",y="");p5  # 标签设为空

饼图

6、统计变换

p6 <- ggplot(df) + 
  stat_summary(aes(sex,height),fun.ymin = min,fun.ymax = max,fun.y = mean,na.rm = T) +
  theme_get() +
  labs(title = "性别体重分布图",x="性别",y="体重") +
  theme(plot.title = element_text(hjust = 0.5),legend.position = "top");p6

统计变换

7、拼图

p_load(patchwork)

(p1 | p4) /
  p2

拼图一

p4 + p5 + plot_layout(nrow = 1)

拼图二