如何用cowplot与ggplot排列多图并添加自定义图表元素

以下是满足你需求的完整代码，实现了蓝色背景主标题栏、子图标题上方添加说明文本，同时适配RMarkdown HTML和PNG输出：

library(tidyverse)
library(ggrepel)
library(ggtext)
library(lubridate)
library(scales)
library(cowplot)

# ---------------------- 数据准备（原代码保留） ----------------------
#plot 1 data
date <- as.Date(c("2019-09-01","2019-10-01","2019-11-01","2019-12-01",
                  "2020-01-01","2020-02-01","2020-03-01","2020-04-01"))
year <- c(2019,2019,2019,2019,2020,2020,2020,2020)
month <- c("Sep", "Oct", "Nov", "Dec", "Jan", "Feb", "Mar", "Apr")
sales <- c(100,200,600,200,100,100,800,100)
df <- data.frame(date,year,month,sales)
df <- add_row(df, date = as.Date("2019-08-15"), year = 2019, month = "Aug", sales = NA)
df <- add_row(df, date = as.Date("2020-04-15"), year = 2020, month = "Apr", sales = NA)
df <- df %>% arrange(date)

#plot 1 without facet years
data_start <- df %>% filter(row_number()==2)
data_mid <- df %>% filter(row_number()==5)
data_end <- df %>% filter(row_number()==9)

p1 <- ggplot(df, aes(x = date, y = sales)) +
  geom_line() +
  scale_x_date(
    date_labels = "%b",
    date_breaks = "month", 
    expand = c(0, 0)) +
  theme_bw() +
  theme(
    strip.placement = "outside",
    strip.background = element_rect(fill = NA, colour = NA),
    panel.spacing = unit(0, "lines"),
    axis.line = element_line(colour = "gray"),
    panel.grid.major = element_blank(),
    panel.grid.minor = element_blank(),
    panel.border = element_blank(),
    panel.background = element_blank(),
    axis.title.x = element_text(colour = "gray"),
    axis.title.y = element_text(hjust=1, colour = "gray"),
    axis.text.x = element_text(colour = "gray"),
    axis.text.y = element_text(colour = "gray"),
    strip.text.x = element_text(colour = "gray", size = 9),
    plot.title = element_markdown(size = 14),
    plot.subtitle = element_text(size=10, colour = "#818589"),
    plot.margin = margin(10,0,0,0, "pt")) + # 微调顶部margin避免和说明文本重叠
  geom_point(data_start, mapping=aes(x=date,y=sales), colour="blue", size=2) +
  geom_point(data_mid, mapping=aes(x=date,y=sales), colour="blue", size=2) +
  geom_point(data_end, mapping=aes(x=date,y=sales), colour="blue", size=2) +
  geom_text(data=subset(df, year == 2019 & month == "Sep"), 
            aes(date,sales,label=sales),
            position = position_dodge(width = 1),
            vjust = -1, hjust=1,size = 5) +
  geom_text(data=subset(df, year == 2019 & month == "Dec"), 
            aes(date,sales,label=sales),
            position = position_dodge(width = 1),
            vjust = 2, size = 5) + 
  geom_text(data=subset(df, year == 2020 & month == "Apr"), 
            aes(date,sales,label=sales),
            position = position_dodge(width = 1),
            vjust = 0, hjust=0,size = 5) +
  geom_text_repel(data=df[5, ], label="Total number of dollars circulation", nudge_y=500, nudge_x=10, color = "blue") +
  labs(title = "Chocolate sales",
       y = "Number of Units", 
       x = "2019                                                   2020")

#plot 2
choco_date <- as.Date(c("2021-01-01","2021-01-01","2021-01-01",
                        "2022-01-01","2022-01-01","2022-01-01"))
choco_type <- c("Dark", "Milk", "White","Dark", "Milk", "White")
choco_sales <- c(1000,600,100,800,400,200)
df2 <- data.frame(choco_date,choco_type,choco_sales)

dark_start <- df2 %>% filter(choco_type == "Dark" & choco_date == "2021-01-01")
milk_start <- df2 %>% filter(choco_type == "Milk" & choco_date == "2021-01-01")
white_start <- df2 %>% filter(choco_type == "White" & choco_date == "2021-01-01")
dark_end <- df2 %>% filter(choco_type == "Dark" & choco_date == "2022-01-01")
milk_end <- df2 %>% filter(choco_type == "Milk" & choco_date == "2022-01-01")
white_end <- df2 %>% filter(choco_type == "White" & choco_date == "2022-01-01")

p2 <- df2 %>% ggplot(aes(x=choco_date, y=choco_sales, color = choco_type)) + 
  geom_line() +
  geom_point(dark_start, mapping=aes(x=choco_date,y=choco_sales), colour="red", size=2) +
  geom_point(milk_start, mapping=aes(x=choco_date,y=choco_sales), colour="green", size=2) +
  geom_point(white_start, mapping=aes(x=choco_date,y=choco_sales), colour="blue", size=2) +
  geom_point(dark_end, mapping=aes(x=choco_date,y=choco_sales), colour="red", size=2) +
  geom_point(milk_end, mapping=aes(x=choco_date,y=choco_sales), colour="green", size=2) +
  geom_point(white_end, mapping=aes(x=choco_date,y=choco_sales), colour="blue", size=2) +
  geom_text(data=df2[1, ], label = "1000", nudge_x = -30, size = 3) +
  geom_text(data=df2[2, ], label = "600", nudge_x = -25, size = 3) +
  geom_text(data=df2[3, ], label = "100", nudge_x = -25, size = 3)+
  geom_text(data=df2[4, ], label = "800 Dark", nudge_x = 105, size = 3) +
  geom_text(data=df2[5, ], label = "400 Milk", nudge_x = 100, size = 3) +
  geom_text(data=df2[6, ], label = "200 White", nudge_x = 110, size = 3) +
  geom_text(data=df2[1, ], label = "- 20%", nudge_x = 200, nudge_y = -40, size = 3) +
  geom_text(data=df2[2, ], label = "- 33%", nudge_x = 200, nudge_y = -40, size = 3) +
  geom_text(data=df2[3, ], label = "100%", nudge_x = 200, nudge_y = 100, size = 3)+
  scale_x_date(breaks = as.Date(c("2021-01-01", "2022-01-01")),
               labels = c("Last Month \n Mar-20", "This Month \n Apr-20")) +
  labs(title = "Number of chocolate bars sold",
       subtitle = "# OF UNITS           % CHANGE") +
  theme_bw() +
  theme(strip.placement = "outside",
        strip.background = element_rect(fill=NA,colour="grey50"),
        panel.spacing=unit(0,"cm"), 
        axis.line.y = element_blank(),
        axis.line.x = element_line(),
        axis.ticks.y = element_blank(),
        axis.text.y = element_blank(),
        panel.grid.major = element_blank(),
        panel.grid.minor = element_blank(),
        panel.border = element_blank(),
        panel.background = element_blank(),
        aspect.ratio = 1,
        plot.title = element_text(color = "black", size = 14),
        plot.subtitle = element_text(color = "dark gray", size = 8),
        legend.position = "none",
        plot.margin = margin(10,0,0,0, "pt")) + # 微调顶部margin
  coord_fixed(ratio = 1, clip = 'off') +
  labs(x="",y="")

# ---------------------- 定制化需求实现 ----------------------
# 1. 为每个子图添加标题上方的说明文本
# 子图1的说明文本
p1_note <- ggdraw() +
  draw_label(
    "**月度销售趋势**：展示2019-2020年巧克力销售的月度波动及关键节点",
    size = 10,
    x = 0,
    hjust = 0
  ) +
  theme(plot.margin = margin(0,0,5,7, "pt"))

# 组合说明文本和子图1
p1_with_note <- plot_grid(
  p1_note, p1,
  ncol = 1,
  rel_heights = c(0.1, 1)
)

# 子图2的说明文本
p2_note <- ggdraw() +
  draw_label(
    "**品类销售对比**：对比黑巧、牛奶巧、白巧的销量变化及同比变动",
    size = 10,
    x = 0,
    hjust = 0
  ) +
  theme(plot.margin = margin(0,0,5,7, "pt"))

# 组合说明文本和子图2
p2_with_note <- plot_grid(
  p2_note, p2,
  ncol = 1,
  rel_heights = c(0.1, 1)
)

# 2. 合并两个带说明的子图
combined_plots <- plot_grid(
  p1_with_note, p2_with_note,
  rel_widths = c(2,1)
)

# 3. 创建带蓝色背景的主标题栏
main_title <- ggdraw() +
  # 先绘制蓝色背景矩形
  draw_rect(
    x = 0, y = 0, width = 1, height = 1,
    fill = "#1E88E5", color = NA
  ) +
  # 再绘制白色标题文字
  draw_label(
    "巧克力销售数据分析",
    fontface = 'bold',
    size = 16,
    color = "white",
    x = 0,
    hjust = 0
  ) +
  theme(
    plot.margin = margin(10,10,10,10, "pt")
  )

# 最终合并标题和子图
final_plot <- plot_grid(
  main_title, combined_plots,
  ncol = 1,
  rel_heights = c(0.1, 1)
)

# 输出图表
final_plot

关键修改说明 #

• 子图说明文本：用ggdraw()创建独立的文本块，通过plot_grid()和子图垂直拼接，rel_heights控制文本和子图的高度比例，plot.margin调整间距避免重叠。
• 蓝色背景主标题：先调用draw_rect()绘制覆盖整个标题区域的蓝色背景，再叠加白色标题文字，确保视觉层级正确；调整plot.margin让标题有足够留白。
• 适配RMarkdown HTML：所有margin单位统一用pt，避免不同输出格式的间距差异；保留ggtext支持的 markdown 格式文本，让说明文本更清晰。

内容的提问来源于stack exchange，提问作者OTA