问题

I have translated the code portion for you:

我有一个类似下面的数据框。
```R
sakubun<-read.csv("https://www.iwanami.co.jp/files/moreinfo/0298960/sakubun259f.csv",
                  row.names=1, 
                  fileEncoding = 'cp932')
sakubunp<-sakubun/rowSums(sakubun)

我对这个数据框进行了聚类分析，并尝试绘制结果。聚类分析有两个不同的部分，每个部分都有多种方法。因此，结果可以是多种模式，就像这些方法组合在一起一样多。

我想在一张图片中显示多个结果，以及应用的组合。我成功地将结果放入了列表对象中。
我的代码如下所示。

d.method <- c("euclidean", "maximum", "manhattan", "canberra", "binary", "minkowski")
c.method <- c("ward.D", "ward.D2", "single", "complete", "average", "mcquitty", "median", "centroid")

result_list <- list()

for (i in 1:length(d.method)) {
  for (j in 1:length(c.method)) {
    cl_plot <- hclust(dist(sakubunp[, -32], method = d.method[i]), method = c.method[j]) %>% 
      stats::as.dendrogram() %>% 
      dendextend::as.ggdend() %>% 
      ggplot2::ggplot()
    
    result_list[[paste(d.method[i], c.method[j], sep = "_")]] <- cl_plot
  }
}

在我的数据中，"d.method" 可以有六种方法，"c.method" 可以有八种方法，所以会有 6x8=48 种图形模式，我想将它们排列成矩阵形式（列 = d.method，行 = c.method）。

但是，我无法找到如何将它们放入图中，使用 ggplot::facet_grid() 函数。我该如何制作这样的图形？

Please note that I've translated the code portion as requested, and you can continue working with the code to achieve your desired plot.

<details>
<summary>英文:</summary>

I have a dataframe like below.

sakubun<-read.csv("https://www.iwanami.co.jp/files/moreinfo/0298960/sakubun259f.csv",
row.names=1,
fileEncoding = 'cp932')
sakubunp<-sakubun/rowSums(sakubun)

I applied cluster analysis to this data frame and try to plot the result. Cluster analysis has two different part and each has multiple methods. So the result can be a multitude of patterns, as many as the combination of these methods applied together.

I want to show multiple results as well as which combination is applied in one picture.
I succeeded in putting the results into list object. 
My code is as following

d.method <- c("euclidean", "maximum", "manhattan", "canberra", "binary", "minkowski")
c.method <- c("ward.D", "ward.D2", "single", "complete", "average", "mcquitty", "median", "centroid")

result_list <- list()

for (i in 1:length(d.method)) {
for (j in 1:length(c.method)) {
cl_plot <- hclust(dist(sakubunp[, -32], method = d.method[i]), method = c.method[j]) %>%
stats::as.dendrogram() %>%
dendextend::as.ggdend() %>%
ggplot2::ggplot()

result_list[[paste(d.method[i], c.method[j], sep = "_")]] <- cl_plot

}
}

In my data, there can be six methods for "d.method" and eight for "c.method", so there will be 6x8=48 patterns of plots and I want to arrange them into a matrix-form (column = d.method, row = c.method).

But i can't find how to put them into plots, with using `ggplot::facet_grid()` function. How can I make such a plot?

</details>


# 答案1
**得分**: 1

以下是翻译好的内容：

如果不需要使用ggplot的facet功能，您可以使用基本的R图形参数`mfrow`，并按照以下方式遍历您的数据框，每行一个图：
- 创建一个包含每种方法组合的数据框`d`：
```R
d.method <- c("euclidean", "maximum", "manhattan", "canberra", "binary", "minkowski")
c.method <- c("ward.D", "ward.D2", "single", "complete", "average", "mcquitty", "median", "centroid")

d <- 
  setNames(expand.grid(d.method, c.method),
           c('d.method', 'c.method')
           )

> d |> head(2)
   d.method c.method
1 euclidean   ward.D
2   maximum   ward.D

• 使用mfrow准备一个多图面板（最好将绘图窗口调整得很大，根据需要调整绘图参数：?par）：

par(mfrow = c(8, 6),
    mar = rep(1, 4) ## 减小外边距（facets之间的间隔）
    )

• 为每一行创建一个hclust对象（记得将结果包装成一个列表），然后进行一次mutate绘图：

library(dplyr)

d |>
  rowwise() |>
  mutate(clust = 
           ## 您可以将任何内容（如聚类结果）打包到*列表*列中：
           list( 
             iris[1:4] |>
             dist(method = d.method) |>
             hclust(method = c.method)
           ),
         plot(clust,
              main = paste(c.method, d.method, sep = ', ') , sub = '', 
              labels = FALSE,
              xlab = '', ylab = '')
         )

d.method <- c("euclidean", "maximum", "manhattan", "canberra", "binary", "minkowski")
c.method <- c("ward.D", "ward.D2", "single", "complete", "average", "mcquitty", "median", "centroid")

d <- 
  setNames(expand.grid(d.method, c.method),
           c('d.method', 'c.method')
           )

> d |> head(2)
   d.method c.method
1 euclidean   ward.D
2   maximum   ward.D

par(mfrow = c(8, 6),
    mar = rep(1, 4) ## decrease outer margin (= gap between facets)
    )

library(dplyr)

d |>
  rowwise() |>
  mutate(clust = 
           ## you can pack anything (like a cluster result) in a *list* column:
           list( 
             iris[1:4] |>
             dist(method = d.method) |>
             hclust(method = c.method)
           ),
         plot(clust,
              main = paste(c.method, d.method, sep =', ') , sub = '', 
              labels = FALSE,
              xlab = '', ylab = '')
         )