R使用数据框中的circlize制作圆/弦图

Question

我想使用circlize包制作一个和弦图.我有一个包含四列汽车的数据框.2个第一列包含有关汽车频段和所拥有车型的信息,以及受访者迁移到的品牌和型号的下两列.

这是一个简单的数据框示例:

   Brand_from model_from Brand_to Model_to
1:      VOLVO        s80      BMW  5series
2:        BMW    3series      BMW  3series
3:      VOLVO        s60    VOLVO      s60
4:      VOLVO        s60    VOLVO      s80
5:        BMW    3series     AUDI       s4
6:       AUDI         a4      BMW  3series
7:       AUDI         a5     AUDI       a5

将它变成和弦图会很棒.我在帮助中找到了一个有效的例子但是我无法将我的数据转换成正确的格式以制作情节.此代码来自circlize包中的帮助.这产生了一层,我想我需要两个,品牌和型号.

mat = matrix(1:18, 3, 6)
rownames(mat) = paste0("S", 1:3)
colnames(mat) = paste0("E", 1:6)

rn = rownames(mat)
cn = colnames(mat)
factors = c(rn, cn)
factors = factor(factors, levels = factors)
col_sum = apply(mat, 2, sum)
row_sum = apply(mat, 1, sum)
xlim = cbind(rep(0, length(factors)), c(row_sum, col_sum))

par(mar = c(1, 1, 1, 1))
circos.par(cell.padding = c(0, 0, 0, 0))
circos.initialize(factors = factors, xlim = xlim)
circos.trackPlotRegion(factors = factors, ylim = c(0, 1), bg.border = NA,
                       bg.col = c("red", "green", "blue", rep("grey", 6)), track.height = 0.05,
                       panel.fun = function(x, y) {
                         sector.name = get.cell.meta.data("sector.index")
                         xlim = get.cell.meta.data("xlim")
                         circos.text(mean(xlim), 1.5, sector.name, adj = c(0.5, 0))
})

col = c("#FF000020", "#00FF0020", "#0000FF20")
for(i in seq_len(nrow(mat))) {
  for(j in seq_len(ncol(mat))) {
    circos.link(rn[i], c(sum(mat[i, seq_len(j-1)]), sum(mat[i, seq_len(j)])),
                cn[j], c(sum(mat[seq_len(i-1), j]), sum(mat[seq_len(i), j])),
                col = col[i], border = "white")
  }
}
circos.clear()

此代码生成以下图表:

理想的结果就像这个例子,但我想要汽车品牌,而不是大陆,而在内圈,属于该品牌的汽车模型

Answer 1

当我稍微更新一下软件包时,现在有了一种更简单的方法.如果有人对此感兴趣,我会在这里给出另一个答案.

在circlize的最新几个版本中,chordDiagram()接受邻接矩阵和邻接列表作为输入,这意味着,现在您可以提供包含与函数的成对关系的数据帧.此外,还有一个highlight.sector()功能可以同时突出显示或标记多个扇区.

我将实现之前显示的情节,但代码更短:

df = read.table(textConnection("
 brand_from model_from brand_to model_to
      VOLVO        s80      BMW  5series
        BMW    3series      BMW  3series
      VOLVO        s60    VOLVO      s60
      VOLVO        s60    VOLVO      s80
        BMW    3series     AUDI       s4
       AUDI         a4      BMW  3series
       AUDI         a5     AUDI       a5
"), header = TRUE, stringsAsFactors = FALSE)

brand = c(structure(df$brand_from, names=df$model_from),
          structure(df$brand_to,names= df$model_to))
brand = brand[!duplicated(names(brand))]
brand = brand[order(brand, names(brand))]
brand_color = structure(2:4, names = unique(brand))
model_color = structure(2:8, names = names(brand))

对于价值brand,brand_color并且model_color是:

> brand
     a4      a5      s4 3series 5series     s60     s80
 "AUDI"  "AUDI"  "AUDI"   "BMW"   "BMW" "VOLVO" "VOLVO"
> brand_color
 AUDI   BMW VOLVO
    2     3     4
> model_color
     a4      a5      s4 3series 5series     s60     s80
      2       3       4       5       6       7       8

这一次,我们只添加一个额外的轨道,用于放置线条和品牌名称.而且你也可以发现输入变量实际上是一个数据帧(df[, c(2, 4)]).

library(circlize)
gap.degree = do.call("c", lapply(table(brand), function(i) c(rep(2, i-1), 8)))
circos.par(gap.degree = gap.degree)

chordDiagram(df[, c(2, 4)], order = names(brand), grid.col = model_color,
    directional = 1, annotationTrack = "grid", preAllocateTracks = list(
        list(track.height = 0.02))
)

与之前相同,手动添加模型名称:

circos.trackPlotRegion(track.index = 2, panel.fun = function(x, y) {
    xlim = get.cell.meta.data("xlim")
    ylim = get.cell.meta.data("ylim")
    sector.index = get.cell.meta.data("sector.index")
    circos.text(mean(xlim), mean(ylim), sector.index, col = "white", cex = 0.6, facing = "inside", niceFacing = TRUE)
}, bg.border = NA)

最后,我们按highlight.sector()功能添加行和品牌名称.这里的值sector.index可以是长度大于1的向量,线(或薄矩形)将覆盖所有指定的扇区.将在行业中间添加标签,并通过text.vjust选项控制基本位置.

for(b in unique(brand)) {
  model = names(brand[brand == b])
  highlight.sector(sector.index = model, track.index = 1, col = brand_color[b], 
    text = b, text.vjust = -1, niceFacing = TRUE)
}

circos.clear()

Answer 2

这里的关键是将您的数据转换为矩阵(邻接矩阵,其中行对应于'from',列对应于'to').

df = read.table(textConnection("
 Brand_from model_from Brand_to Model_to
      VOLVO        s80      BMW  5series
        BMW    3series      BMW  3series
      VOLVO        s60    VOLVO      s60
      VOLVO        s60    VOLVO      s80
        BMW    3series     AUDI       s4
       AUDI         a4      BMW  3series
       AUDI         a5     AUDI       a5
"), header = TRUE, stringsAsFactors = FALSE)

from = paste(df[[1]], df[[2]], sep = ",")
to = paste(df[[3]], df[[4]], sep = ",")

mat = matrix(0, nrow = length(unique(from)), ncol = length(unique(to)))
rownames(mat) = unique(from)
colnames(mat) = unique(to)
for(i in seq_along(from)) mat[from[i], to[i]] = 1

价值mat是

> mat
            BMW,5series BMW,3series VOLVO,s60 VOLVO,s80 AUDI,s4 AUDI,a5
VOLVO,s80             1           0         0         0       0       0
BMW,3series           0           1         0         0       1       0
VOLVO,s60             0           0         1         1       0       0
AUDI,a4               0           1         0         0       0       0
AUDI,a5               0           0         0         0       0       1

然后将矩阵发送到chordDiagram指定order和directional.手动规范order是为了确保将相同的品牌组合在一起.

par(mar = c(1, 1, 1, 1))
chordDiagram(mat, order = sort(union(from, to)), directional = TRUE)
circos.clear()

为了使图形更复杂,您可以创建品牌名称跟踪,品牌识别跟踪,模型名称跟踪.此外,我们可以设定品牌之间的差距大于每个品牌内部.

1套 gap.degree

circos.par(gap.degree = c(2, 2, 8, 2, 8, 2, 8))

在绘制和弦图之前,我们创建了两个空轨道,一个用于品牌名称,一个用于通过preAllocateTracks参数识别.

par(mar = c(1, 1, 1, 1))
chordDiagram(mat, order = sort(union(from, to)),
    direction = TRUE, annotationTrack = "grid", preAllocateTracks = list(
        list(track.height = 0.02),
        list(track.height = 0.02))
)

3将模型名称添加到注释轨道(默认情况下创建此轨道,左右图中的较粗轨道.请注意,这是从外圆到内部的第三条轨道)

circos.trackPlotRegion(track.index = 3, panel.fun = function(x, y) {
    xlim = get.cell.meta.data("xlim")
    ylim = get.cell.meta.data("ylim")
    sector.index = get.cell.meta.data("sector.index")
    model = strsplit(sector.index, ",")[[1]][2]
    circos.text(mean(xlim), mean(ylim), model, col = "white", cex = 0.8, facing = "inside", niceFacing = TRUE)
}, bg.border = NA)

4添加品牌识别线.由于品牌涵盖多个行业,我们需要手动计算行(弧)的起始和结束程度.在下面, rou1并且rou2是第二轨道中的两个边界的高度.标识线在第二轨道中绘制.

all_sectors = get.all.sector.index()
rou1 = get.cell.meta.data("yplot", sector.index = all_sectors[1], track.index = 2)[1]
rou2 = get.cell.meta.data("yplot", sector.index = all_sectors[1], track.index = 2)[2]

start.degree = get.cell.meta.data("xplot", sector.index = all_sectors[1], track.index = 2)[1]
end.degree = get.cell.meta.data("xplot", sector.index = all_sectors[3], track.index = 2)[2]
draw.sector(start.degree, end.degree, rou1, rou2, clock.wise = TRUE, col = "red", border = NA)

首先得到极坐标系中文本的坐标,然后通过映射到数据坐标系reverse.circlize.请注意您将坐标映射回的单元格,并且您绘制文本的单元格应该是同一个单元格.

m = reverse.circlize( (start.degree + end.degree)/2, 1, sector.index = all_sectors[1], track.index = 1)
circos.text(m[1, 1], m[1, 2], "AUDI", cex = 1.2, facing = "inside", adj = c(0.5, 0), niceFacing = TRUE, 
    sector.index = all_sectors[1], track.index = 1)

对于另外两个品牌,使用相同的代码.

start.degree = get.cell.meta.data("xplot", sector.index = all_sectors[4], track.index = 2)[1]
end.degree   = get.cell.meta.data("xplot", sector.index = all_sectors[5], track.index = 2)[2]
draw.sector(start.degree, end.degree, rou1, rou2, clock.wise = TRUE, col = "green", border = NA)
m = reverse.circlize( (start.degree + end.degree)/2, 1, sector.index = all_sectors[1], track.index = 1)
circos.text(m[1, 1], m[1, 2], "BMW", cex = 1.2, facing = "inside", adj = c(0.5, 0), niceFacing = TRUE, 
    sector.index = all_sectors[1], track.index = 1)

start.degree = get.cell.meta.data("xplot", sector.index = all_sectors[6], track.index = 2)[1]
end.degree  = get.cell.meta.data("xplot", sector.index = all_sectors[7], track.index = 2)[2]
draw.sector(start.degree, end.degree, rou1, rou2, clock.wise = TRUE, col = "blue", border = NA)
m = reverse.circlize( (start.degree + end.degree)/2, 1, sector.index = all_sectors[1], track.index = 1)
circos.text(m[1, 1], m[1, 2], "VOLVO", cex = 1.2, facing = "inside", adj = c(0.5, 0), niceFacing = TRUE, 
    sector.index = all_sectors[1], track.index = 1)

circos.clear()

如果要设置颜色,请转到包装插图,如果需要,还可以使用circos.axis在绘图上添加轴.