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

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

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。

基于网格的解决方案的一个缺点。他们的一个缺点是很难像大多数期刊要求的那样用字母(A, B等)来标记这些图。

我写了cowplot包来解决这个(和其他一些)问题，特别是函数plot_grid():

library(cowplot)

iris1 <- ggplot(iris, aes(x = Species, y = Sepal.Length)) +
  geom_boxplot() + theme_bw()

iris2 <- ggplot(iris, aes(x = Sepal.Length, fill = Species)) +
  geom_density(alpha = 0.7) + theme_bw() +
  theme(legend.position = c(0.8, 0.8))

plot_grid(iris1, iris2, labels = "AUTO")

plot_grid()返回的对象是另一个ggplot2对象，你可以像往常一样用ggsave()保存它:

p <- plot_grid(iris1, iris2, labels = "AUTO")
ggsave("plot.pdf", p)

或者，你可以使用cowplot函数save_plot()，它是ggsave()的一个薄包装，可以很容易地获得组合图的正确尺寸，例如:

p <- plot_grid(iris1, iris2, labels = "AUTO")
save_plot("plot.pdf", p, ncol = 2)

(ncol = 2参数告诉save_plot()有两个并排的图像，而save_plot()使保存的图像宽度增加一倍。)

有关如何在网格中安排图的更深入描述，请参阅此小插图。还有一个小插图解释如何用一个共享的传说来制作情节。

一个常见的混淆点是cowplot包更改了默认的ggplot2主题。这个包之所以这样做，是因为它最初是为内部实验室使用而编写的，我们从不使用默认主题。如果这导致问题，您可以使用以下三种方法之一来解决它们:

1. 为每个情节手动设置主题。我认为始终为每个情节指定一个特定的主题是一个很好的实践，就像我在上面的示例中对+ theme_bw()所做的那样。如果您指定了一个特定的主题，那么默认主题并不重要。

2. 将默认主题恢复为ggplot2默认。你可以用一行代码做到这一点:

theme_set(theme_gray())

3.调用cowplot函数而不附加包。你也可以不调用library(cowplot)或require(cowplot)，而是通过前置cowplot::来调用cowplot函数。例如，上面使用ggplot2默认主题的示例将变成:

## Commented out, we don't call this
# library(cowplot)

iris1 <- ggplot(iris, aes(x = Species, y = Sepal.Length)) +
  geom_boxplot()

iris2 <- ggplot(iris, aes(x = Sepal.Length, fill = Species)) +
  geom_density(alpha = 0.7) +
  theme(legend.position = c(0.8, 0.8))

cowplot::plot_grid(iris1, iris2, labels = "AUTO")

更新:

从cowplot 1.0开始，默认的ggplot2主题不再更改。 从ggplot2 3.0.0开始，可以直接对图进行标记，参见这里的示例。

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。