使用嵌套循环，创建几个 ggplots 的一页聚合

Question

这是@Duck 非常有帮助地回答了上一个问题。 (R ggpot: Arranging on one page several ggplots created with a loop / name each plot differenly)。我希望将几个由循环创建的 ggplots 放在一页上。我做了一个模型循环，@Duck 提供了一种方法来做到这一点。它工作得非常好，以至于我的胃口更大，现在想将整个事情嵌套在另一个循环中，创建几个这样的页面（每个页面都显示来自不同年份的数据）。但是效果不好。

这是@Duck 建议的，在一页上的几个情节：

  library(dplyr)
  library(ggplot2)
  library(patchwork)
  #Create list
  List <- list()
  cylinder<-unique(mtcars$cyl)
  #Loop
  for (value in seq_along(cylinder)) {
    m<-mtcars%>%
      filter(cyl==cylinder[value])%>%
      group_by (gear)%>%
      summarise(number=n(), average=mean(mpg), se=sd(mpg))
    print(m) # reporting the numbers
    
    a<-m%>%
      mutate(gear=factor(gear, levels=unique(gear)))%>%
      ggplot()+
      geom_bar(aes(x=gear, y=average), stat = 'identity', fill ='red') +
      geom_errorbar( aes(x= gear, ymin=average-se, ymax=average+se), width=0.2, colour="black", alpha=1, size=1) +
      xlab("gears") + ylab("average mpg") +
      ggtitle (paste( "cyliner:", value ))+
      theme(axis.ticks.x=element_blank())  
    print(a)
    List[[value]]<-a}
  #Wrap plots
  wrap_plots(List,nrow = 1)
   

不错！现在，为了简单起见，假设我们只想要三个相同的。这是我尝试嵌套它的方法。

  library(dplyr)
  library(ggplot2)
  library(patchwork)
repeats<-c(1,2,3)  # for the outer loop. 
  List <- list()
  cylinder<-unique(mtcars$cyl)

    #Loop 1:
  
  for (pic in seq_along(repeats)) {
    
    # loop 2:
  
  for (value in seq_along(cylinder)) 
# same code:
    m<-mtcars%>%
      filter(cyl==cylinder[value])%>%
      group_by (gear)%>%
      summarise(number=n(), average=mean(mpg), se=sd(mpg))
    print(m) # reporting the numbers
    
    a<-m%>%
      mutate(gear=factor(gear, levels=unique(gear)))%>%
      ggplot()+
      geom_bar(aes(x=gear, y=average), stat = 'identity', fill ='red') +
      geom_errorbar( aes(x= gear, ymin=average-se, ymax=average+se), width=0.2, colour="black", alpha=1, size=1) +
      xlab("gears") + ylab("average mpg") +
      ggtitle (paste( "cyliner:", value ))+
      theme(axis.ticks.x=element_blank())  
    print(a)
    List[[value]]<-a}
  #Wrap plots
  wrap_plots(List,nrow = 1)

    List[[value]]<-a }
  #Wrap plots
  wrap_plots(List,nrow = 1)

      print (List) #reporter
  List <- list()
  print(List) #reporter
  
  }  

判断列表被填充多少次，然后归零，编译正确地通过循环。但是只制作了 6 个单图（不是 9 个），并且没有包含三个包裹图的页面。建议？

Answer 1

这可以这样实现：

1. 使用循环时，我更喜欢将绘图代码放在单独的函数中。使检查、调试和生成（在我看来）更清晰、更清晰的代码变得更容易。因此，我将绘图代码放在函数 make_plot 中。

2. 我为您的内部循环做了同样的事情。我创建了一个函数

   inner_loop <- function(x) {
     # Create list
     List <- lapply(seq_along(cylinder), make_plot)
     # Wrap plots
     wrap_plots(List, nrow = 1)  
   }
   

循环遍历cylinders，为每个值绘制一个图并将图粘合在一起。

1. 在这些初始步骤之后，我们可以轻松地循环 repeats，调用函数 inner_loop，最后将生成的页面列表粘合在一起：

repeats <- c(1,2,3)  # for the outer loop. 
pages <- lapply(repeats, inner_loop)
wrap_plots(pages, ncol = 1)  

完全可重现的代码：

library(dplyr)
library(ggplot2)
library(patchwork)

cylinder <- unique(mtcars$cyl)

make_plot <- function(value) {
  m <- mtcars %>%
    filter(cyl == cylinder[value]) %>%
    group_by(gear) %>%
    summarise(number = n(), average = mean(mpg), se = sd(mpg))
  #print(m) # reporting the numbers
  
  m %>%
    mutate(gear = factor(gear, levels = unique(gear))) %>%
    ggplot() +
    geom_bar(aes(x = gear, y = average), stat = "identity", fill = "red") +
    geom_errorbar(aes(x = gear, ymin = average - se, ymax = average + se), width = 0.2, colour = "black", alpha = 1, size = 1) +
    xlab("gears") +
    ylab("average mpg") +
    ggtitle(paste("cylinder:", value)) +
    theme(axis.ticks.x = element_blank())
}

inner_loop <- function(x) {
  # Create list
  List <- lapply(seq_along(cylinder), make_plot)
  # Wrap plots
  wrap_plots(List, nrow = 1)  
}

# Outer loop
repeats <- c(1,2,3)  # for the outer loop. 
pages <- lapply(repeats, inner_loop)

wrap_plots(pages, ncol = 1)  

编辑 我稍微修改了示例和代码。现在，外部循环在gears 上循环。 gear 的值作为参数传递给内部循环和绘图函数，可用于相应地过滤数据集。结果你有九个不同的情节。每个齿轮一排，圆柱为一列。

library(dplyr)
library(ggplot2)
library(patchwork)

make_plot <- function(value, .gear) {
  m <- mtcars %>%
    filter(cyl == cylinder[value], gear %in% .gear) %>%
    group_by(gear) %>%
    summarise(number = n(), average = mean(mpg), se = sd(mpg))
  
  m %>%
    mutate(gear = factor(gear, levels = unique(gear))) %>%
    ggplot() +
    geom_bar(aes(x = gear, y = average), stat = "identity", fill = "red") +
    geom_errorbar(aes(x = gear, ymin = average - se, ymax = average + se), width = 0.2, colour = "black", alpha = 1, size = 1) +
    xlab("gears") +
    ylab("average mpg") +
    ggtitle(paste("cylinder:", value)) +
    theme(axis.ticks.x = element_blank())
}

inner_loop <- function(gear) {
  # Create list
  List <- lapply(seq_along(cylinder), make_plot, .gear = gear)
  # Wrap plots
  wrap_plots(List, nrow = 1)  
}

# Outer loop
cylinder <- unique(mtcars$cyl)
gears <- unique(mtcars$gear)
 # for the outer loop. 
pages <- lapply(gears, inner_loop)

wrap_plots(pages, ncol = 1) 

Answer 2

正如我所写，我最初要做的任务是在我的数据库中每年重复相同的操作；因此，我的例子可能过于简单化。更好的方法是查看汽缸 # 对每个齿轮 # 的 mpg 的影响。虽然这不是我认为的方向（循环内循环），但我可以通过将 Duck 的代码作为函数放入其中来避免重复每个齿轮的代码，正如 Stefan 所建议的那样。

library(dplyr)
library(ggplot2)
library(patchwork)

vls<-unique(mtcars$gear) #these are the values for the different plots.

mltplot<-function(geer) {
  
 # the original code, with additional filtering for a specific gear:
    
  #Create list
  List <- list()
  cylinder<-unique(mtcars$cyl)
  #Loop
  for (value in seq_along(cylinder)) {
    m<-mtcars%>%
      filter(gear==geer)%>% # filtering, we look at one gear at a time.
      filter(cyl==cylinder[value])%>%
      group_by (gear)%>%
      summarise(number=n(), average=mean(mpg), se=sd(mpg))
    print(m) # reporting the numbers
    
    a<-m%>%
      mutate(gear=factor(gear, levels=unique(gear)))%>%
      ggplot()+
      geom_bar(aes(x=gear, y=average), stat = 'identity', fill ='red') +
      geom_errorbar( aes(x= gear, ymin=average-se, ymax=average+se), width=0.2, colour="black", alpha=1, size=1) +
      xlab("gears") + ylab("average mpg") +
      ggtitle (paste( "cyliner:", value ))+
      theme(axis.ticks.x=element_blank())  
    
    List[[value]]<-a}
  #Wrap plots
  wrap_plots(List,nrow = 1)
  
}

现在我可以要求：

mltplot (vls[1])
mlrplor{vls[2])
mltplot{val[3[)