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")

是的，我认为你需要适当地安排你的数据。一种方法是:

X <- data.frame(x=rep(x,2),
                y=c(3*x+eps, 2*x+eps),
                case=rep(c("first","second"), each=100))

qplot(x, y, data=X, facets = . ~ case) + geom_smooth()

我相信在plyr或重塑中有更好的技巧——我仍然没有真正跟上速度 哈德利设计的这些强大的软件包。

还可以考虑ggpubr包中的ggarrange。它有很多好处，包括在情节之间对齐轴和将常见图例合并为一个图例的选项。

使用重塑包可以完成如下操作。

library(ggplot2)
wide <- data.frame(x = rnorm(100), eps = rnorm(100, 0, .2))
wide$first <- with(wide, 3 * x + eps)
wide$second <- with(wide, 2 * x + eps)
long <- melt(wide, id.vars = c("x", "eps"))
ggplot(long, aes(x = x, y = value)) + geom_smooth() + geom_point() + facet_grid(.~ variable)

还有一个多面板图形包是值得一提的。看看这个答案。

library(ggplot2)
theme_set(theme_bw())

q1 <- ggplot(mtcars) + geom_point(aes(mpg, disp))
q2 <- ggplot(mtcars) + geom_boxplot(aes(gear, disp, group = gear))
q3 <- ggplot(mtcars) + geom_smooth(aes(disp, qsec))
q4 <- ggplot(mtcars) + geom_bar(aes(carb))

library(magrittr)
library(multipanelfigure)
figure1 <- multi_panel_figure(columns = 2, rows = 2, panel_label_type = "none")
# show the layout
figure1

figure1 %<>%
  fill_panel(q1, column = 1, row = 1) %<>%
  fill_panel(q2, column = 2, row = 1) %<>%
  fill_panel(q3, column = 1, row = 2) %<>%
  fill_panel(q4, column = 2, row = 2)
figure1

# complex layout
figure2 <- multi_panel_figure(columns = 3, rows = 3, panel_label_type = "upper-roman")
figure2

figure2 %<>%
  fill_panel(q1, column = 1:2, row = 1) %<>%
  fill_panel(q2, column = 3, row = 1) %<>%
  fill_panel(q3, column = 1, row = 2) %<>%
  fill_panel(q4, column = 2:3, row = 2:3)
figure2

由reprex包(v0.2.0.9000)于2018-07-06创建。

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