使用tidyverse:

x <- rnorm(100)
eps <- rnorm(100,0,.2)
df <- data.frame(x, eps) %>% 
  mutate(p1 = 3*x+eps, p2 = 2*x+eps) %>% 
  tidyr::gather("plot", "value", 3:4) %>% 
  ggplot(aes(x = x , y = value)) + 
    geom_point() + 
    geom_smooth() + 
    facet_wrap(~plot, ncol =2)

df

您可以使用温斯顿张的R食谱下面的多绘图函数

multiplot(plot1, plot2, cols=2)

multiplot <- function(..., plotlist=NULL, cols) {
    require(grid)

    # Make a list from the ... arguments and plotlist
    plots <- c(list(...), plotlist)

    numPlots = length(plots)

    # Make the panel
    plotCols = cols                          # Number of columns of plots
    plotRows = ceiling(numPlots/plotCols) # Number of rows needed, calculated from # of cols

    # Set up the page
    grid.newpage()
    pushViewport(viewport(layout = grid.layout(plotRows, plotCols)))
    vplayout <- function(x, y)
        viewport(layout.pos.row = x, layout.pos.col = y)

    # Make each plot, in the correct location
    for (i in 1:numPlots) {
        curRow = ceiling(i/plotCols)
        curCol = (i-1) %% plotCols + 1
        print(plots[[i]], vp = vplayout(curRow, curCol ))
    }

}

使用tidyverse:

x <- rnorm(100)
eps <- rnorm(100,0,.2)
df <- data.frame(x, eps) %>% 
  mutate(p1 = 3*x+eps, p2 = 2*x+eps) %>% 
  tidyr::gather("plot", "value", 3:4) %>% 
  ggplot(aes(x = x , y = value)) + 
    geom_point() + 
    geom_smooth() + 
    facet_wrap(~plot, ncol =2)

df

cowplot软件包以适合出版的方式为您提供了一种很好的方法。

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")

根据我的经验，网格。如果您试图在循环中生成情节，那么Arrange工作得很好。

简短代码片段:

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

**更新此注释以展示如何在for循环中使用grid.arrange()为类别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)

}

下面包含了上面for循环中的一个样例图。 上述循环将为BMI类别的所有级别生成多个图。

样本图像

如果您希望在for循环中看到grid.arrange()的更全面的使用，请访问https://rpubs.com/Mayank7j_2020/olympic_data_2000_2016

Ggplot2基于网格图形，网格图形提供了在页面上安排图形的不同系统。par(mfrow…)命令并没有直接的对等物，因为网格对象(称为grobs)不一定是立即绘制的，但在转换为图形输出之前，可以作为常规R对象存储和操作。这比现在绘制基础图形的模型具有更大的灵活性，但策略必然略有不同。

我编写grid.arrange()是为了提供一个尽可能接近par(mfrow)的简单接口。在其最简单的形式中，代码看起来像:

library(ggplot2)
x <- rnorm(100)
eps <- rnorm(100,0,.2)
p1 <- qplot(x,3*x+eps)
p2 <- qplot(x,2*x+eps)

library(gridExtra)
grid.arrange(p1, p2, ncol = 2)

在这个小插图中详细介绍了更多的选项。

一个常见的抱怨是，图不一定是对齐的，例如，当它们有不同大小的轴标签时，但这是通过设计:网格。Arrange没有尝试处理特殊情况下的ggplot2对象，并将它们与其他grobs(例如，晶格图)同等对待。它只是将抓取放在矩形布局中。

对于ggplot2对象的特殊情况，我编写了另一个函数ggarrange，该函数具有类似的接口，它尝试对齐绘图面板(包括分面图)，并尝试尊重用户定义的纵横比。

library(egg)
ggarrange(p1, p2, ncol = 2)

这两个函数都与ggsave()兼容。对于不同选项的一般概述和一些历史背景，本小插图提供了额外的信息。