ggplot2优雅的进行批量绘图-多版本代码

library(tidyverse)
library(patchwork)
library(ggbeeswarm)
library(ggsci)
library(multcompView)
library(magrittr)
library(wesanderson)

# 读取数据并进行过滤和选择
dat <- read_tsv("data.txt") %>% 
  filter(continent != "Oceania") %>% 
  select(2, 3, 4)

years <- unique(dat$year)
city_plots = list()

for (i in years) {
  plot <- ggplot(dat %>% filter(year == i),
                 aes(x = continent,y = lifeExp,fill = continent)) +
    geom_violin(scale = "width", width = 0.8) +
    geom_quasirandom(aes(colour = continent), size = 0.5) +
    labs(x = NULL, y = NULL, title = as.character(i)) +
    scale_fill_manual(values=wes_palette("GrandBudapest2",type="discrete"))+
    scale_color_npg() +
    theme(legend.position = "none",
          plot.title = element_text(hjust = 0.5, vjust = 0.5, color = "black", face = "bold", size = 10),
          panel.background = element_blank(),
          axis.line = element_line(color = "black"),
          axis.line.x.top = element_line(color = "black"), 
          axis.text.x.top = element_blank(),
          axis.ticks.y.right = element_blank(),
          axis.text.y.right = element_blank(),
          axis.ticks.x.top = element_blank(),
          axis.text.y = element_text(color = "black", size = 6, face = "bold"),
          axis.text.x = element_text(color = "black", size = 6, face = "bold")) +
    guides(x.sec = "axis", y.sec = "axis")
  # 保存绘图对象到列表
  city_plots[[as.character(i)]] <- plot
  
  # 打印并保存每个年份的图
  #print(plot)
  ggsave(filename = paste0("plot_", i, ".pdf"), plot = plot,
        # width = 3.9, height = 2.53,
         units = "in", dpi = 300)
}

# 使用patchwork包将所有图拼接在一起
wrap_plots(city_plots, nrow = 3)

dat <- read_tsv("data.txt") %>% 
  filter(continent != "Oceania") %>% 
  select(2, 3, 4)

# 获取唯一年份列表
years <- unique(dat$year)
# 创建绘图函数
create_plot <- function(i) {
  plot <- ggplot(dat %>% filter(year == i),aes(x = continent,
                     y = lifeExp, fill = continent)) +
    geom_violin(scale = "width", width = 0.8) +
    geom_quasirandom(aes(colour = continent), size = 0.5) +
    labs(x = NULL, y = NULL, title = as.character(i)) +
    scale_fill_manual(values=wes_palette("GrandBudapest2",type="discrete"))+
    scale_color_npg() +
    theme(legend.position = "none",
          plot.title = element_text(hjust = 0.5, vjust = 0.5, color = "black", face = "bold", size = 10),
          panel.background = element_blank(),
          axis.line = element_line(color = "black"),
          axis.line.x.top = element_line(color = "black"), 
          axis.text.x.top = element_blank(),
          axis.ticks.y.right = element_blank(),
          axis.text.y.right = element_blank(),
          axis.ticks.x.top = element_blank(),
          axis.text.y = element_text(color = "black", size = 6, face = "bold"),
          axis.text.x = element_text(color = "black", size = 6, face = "bold")) +
    guides(x.sec = "axis", y.sec = "axis")
  
  ggsave(filename = paste0("plot_", i, ".pdf"), plot = plot, width = 3.9, height = 2.53, units = "in", dpi = 300)
  
  return(plot)
}

# 使用 map 函数生成每个年份的图表并打印
city_plots <- map(years, ~ {
  plot <- create_plot(.x)
#  print(plot)
  plot
})

# 使用 patchwork 包将所有图拼接在一起
wrap_plots(city_plots, nrow = 3)

# 读取数据并进行过滤和选择
data <- read_tsv("data.txt") %>% 
  filter(continent != "Oceania") %>% 
  select(2, 3, 4)

# 创建绘图函数
make_plot <- function(data) {
  ggplot(data, aes(x = continent, y = lifeExp.x, fill = continent)) +
    geom_violin(scale = "width", width = 0.8) +
    geom_quasirandom(aes(colour = continent), size = 0.5) +
    geom_text(aes(label = Tukey, y = lifeExp.y + 3)) +
    labs(x = NULL, y = NULL, title = unique(data$year)) +
    scale_fill_manual(values=wes_palette("GrandBudapest2",type="discrete"))+
    scale_color_npg() +
    theme(legend.position = "none",
          plot.title = element_text(hjust = 0.5, vjust = 0.5, color = "black", face = "bold", size = 10),
          panel.background = element_blank(),
          axis.line = element_line(color = "black"),
          axis.line.x.top = element_line(color = "black"), 
          axis.text.x.top = element_blank(),
          axis.ticks.y.right = element_blank(),
          axis.text.y.right = element_blank(),
          axis.ticks.x.top = element_blank(),
          axis.text.y = element_text(color = "black", size = 6, face = "bold"),
          axis.text.x = element_text(color = "black", size = 6, face = "bold")) +
    guides(x.sec = "axis", y.sec = "axis")
}

# 分割数据并进行ANOVA和Tukey HSD检验
aov_data <- data %>%
  split(.$year) %>%
  map_dfr(~{
    anova <- aov(lifeExp ~ continent, data = .x)
    tukey_result <- TukeyHSD(anova)
    cld <- multcompLetters4(anova, tukey_result)
    
    # 提取显著性标记并添加到数据中
    .x %>%
      group_by(continent, year) %>%
      summarise(lifeExp = max(lifeExp), .groups = 'drop') %>%
      mutate(Tukey = cld$continent$Letters)
  })

# 合并并绘图
plots <- data %>%
  left_join(aov_data, by = c("continent", "year")) %>%
  split(.$year) %>%
  map(make_plot)

walk2(plots, names(plots), ~ggsave(filename = paste0("plot_aov-", .y, ".pdf"),
                                   plot = .x,
                                   width = 3.9, height = 2.53, units = "in", dpi = 300))
# 使用patchwork包将所有图拼接在一起
wrap_plots(plots, ncol = 4, nrow = 3)