使用ggplot2制作并列图表

还可以考虑使用ggpubr包中的ggarrange函数。它有很多好处，包括在绘图之间对齐坐标轴和将常见的图例合并成一个。

根据我的经验，如果您正在尝试在循环中生成图形，gridExtra:grid.arrange非常完美。

简短的代码片段：

gridExtra::grid.arrange(plot1, plot2, ncol = 2)

** 更新此评论以展示如何在for循环中使用grid.arrange()来为分类变量的不同因素生成图表。

for (bin_i in levels(athlete_clean$BMI_cat)) {

plot_BMI <- athlete_clean %>% filter(BMI_cat == bin_i) %>% group_by(BMI_cat,Team) %>% summarize(count_BMI_team = n()) %>% 
          mutate(percentage_cbmiT = round(count_BMI_team/sum(count_BMI_team) * 100,2)) %>% 
          arrange(-count_BMI_team) %>% top_n(10,count_BMI_team) %>% 
          ggplot(aes(x = reorder(Team,count_BMI_team), y = count_BMI_team, fill = Team)) +
            geom_bar(stat = "identity") +
            theme_bw() +
            # facet_wrap(~Medal) +
            labs(title = paste("Top 10 Participating Teams with \n",bin_i," BMI",sep=""), y = "Number of Athletes", 
                 x = paste("Teams - ",bin_i," BMI Category", sep="")) +
            geom_text(aes(label = paste(percentage_cbmiT,"%",sep = "")), 
                      size = 3, check_overlap = T,  position = position_stack(vjust = 0.7) ) +
            theme(axis.text.x = element_text(angle = 00, vjust = 0.5), plot.title = element_text(hjust = 0.5), legend.position = "none") +
            coord_flip()

plot_BMI_Medal <- athlete_clean %>% 
          filter(!is.na(Medal), BMI_cat == bin_i) %>% 
          group_by(BMI_cat,Team) %>% 
          summarize(count_BMI_team = n()) %>% 
          mutate(percentage_cbmiT = round(count_BMI_team/sum(count_BMI_team) * 100,2)) %>% 
          arrange(-count_BMI_team) %>% top_n(10,count_BMI_team) %>% 
          ggplot(aes(x = reorder(Team,count_BMI_team), y = count_BMI_team, fill = Team)) +
            geom_bar(stat = "identity") +
            theme_bw() +
            # facet_wrap(~Medal) +
            labs(title = paste("Top 10 Winning Teams with \n",bin_i," BMI",sep=""), y = "Number of Athletes", 
                 x = paste("Teams - ",bin_i," BMI Category", sep="")) +
            geom_text(aes(label = paste(percentage_cbmiT,"%",sep = "")), 
                      size = 3, check_overlap = T,  position = position_stack(vjust = 0.7) ) +
            theme(axis.text.x = element_text(angle = 00, vjust = 0.5), plot.title = element_text(hjust = 0.5), legend.position = "none") +
            coord_flip()

gridExtra::grid.arrange(plot_BMI, plot_BMI_Medal, ncol = 2)

}

从上面的循环中，以下是一个示例图。上述循环将为所有BMI类别级别生成多个图。 示例图片 如果您希望在for循环中看到更全面地使用grid.arrange()，请查看https://rpubs.com/Mayank7j_2020/olympic_data_2000_2016。

上述解决方案可能不太高效，如果您想使用循环绘制多个ggplot图（例如：使用循环创建具有不同Y轴值的多个ggplot图），这是分析未知（或大型）数据集的必要步骤（例如，当您想要绘制数据集中所有变量的计数时）。
下面的代码展示了如何使用上述提到的“multiplot()”来实现这一点，其源代码在此处：http://www.cookbook-r.com/Graphs/Multiple_graphs_on_one_page_(ggplot2):

plotAllCounts <- function (dt){   
  plots <- list();
  for(i in 1:ncol(dt)) {
    strX = names(dt)[i]
    print(sprintf("%i: strX = %s", i, strX))
    plots[[i]] <- ggplot(dt) + xlab(strX) +
      geom_point(aes_string(strX),stat="count")
  }

  columnsToPlot <- floor(sqrt(ncol(dt)))
  multiplot(plotlist = plots, cols = columnsToPlot)
}

现在运行该函数-使用ggplot在一页上打印所有变量的计数

dt = ggplot2::diamonds
plotAllCounts(dt)

需要注意的一件事是：
在使用ggplot时，通常会在循环中使用aes(get(strX))，但在上面的代码中，使用aes_string(strX)代替aes(get(strX))将不会绘制所需的图表。相反，它将绘制许多次最后一个图表。我还没有弄清原因——可能与aes和aes_string在ggplot中的调用有关。

否则，希望您会发现此函数有用。

cowplot 包提供了一种适合出版的方式，让你以一种不错的方式完成这个任务。

x <- rnorm(100)
eps <- rnorm(100,0,.2)
A = qplot(x,3*x+eps, geom = c("point", "smooth"))+theme_gray()
B = qplot(x,2*x+eps, geom = c("point", "smooth"))+theme_gray()
cowplot::plot_grid(A, B, labels = c("A", "B"), align = "v")