Question

我一直在聚集我的一些价值观，然后将它们分组。然后，我使用ggplot2绘制一些密度图并覆盖聚类。示例图片如下： enter image description here

对于群集中的每个组，我绘制密度图并覆盖它们。密度图中的着色对应于聚类中的分组。

我的问题是，我已根据分组手动拆分数据并将它们放在各自的文本表中（请参阅下面的代码）。这是非常低效的，并且对于大型数据集来说可能非常繁琐。如何在ggplot2中动态绘制密度图而不将聚类分离到各自的文本表中？

原始输入表在拆分之前看起来像这样：

scores <- read.table(textConnection("
file        max        min        avg               lowest
132         5112.0     6520.0     5728.0            5699.0
133         4720.0     6064.0     5299.0            5277.0
5           4617.0     5936.0     5185.0            5165.0
1           4384.0     5613.0     4917.0            4895.0
1010        5008.0     6291.0     5591.0            5545.0
104         4329.0     5554.0     4858.0            4838.0
105         4636.0     5905.0     5193.0            5165.0
35          4304.0     5578.0     4842.0            4831.0
36          4360.0     5580.0     4891.0            4867.0
37          4444.0     5663.0     4979.0            4952.0
31          4328.0     5559.0     4858.0            4839.0
39          4486.0     5736.0     5031.0            5006.0
32          4334.0     5558.0     4864.0            4843.0
"), header=TRUE)

我用来生成情节的代码：请注意，将基本图形与网格组合仍然无法正常工作

library(ggplot2)
library(grid)

layout(matrix(c(1,2,3,1,4,5), 2, 3, byrow = TRUE))

# deﬁne function to create multi-plot setup (nrow, ncol)
vp.setup <- function(x,y){
grid.newpage()
pushViewport(viewport(layout = grid.layout(x,y)))
}

# deﬁne function to easily access layout (row, col)
vp.layout <- function(x,y){
viewport(layout.pos.row=x, layout.pos.col=y)
}

vp.setup(2,3)

file_vals <- read.table(textConnection("
file        avg_vals
133         1.5923
132         1.6351
1010        1.6532
104         1.6824
105         1.6087
39          1.8694
32          1.9934
31          1.9919
37          1.8638
36          1.9691
35          1.9802
1           1.7283
5           1.7637
"), header=TRUE)

red <- read.table(textConnection("
file        max        min        avg               lowest
31          4328.0     5559.0     4858.0            4839.0
32          4334.0     5558.0     4864.0            4843.0
36          4360.0     5580.0     4891.0            4867.0
35          4304.0     5578.0     4842.0            4831.0
"), header=TRUE)

blue <- read.table(textConnection("
file        max        min        avg               lowest
133         4720.0     6064.0     5299.0            5277.0
105         4636.0     5905.0     5193.0            5165.0
104         4329.0     5554.0     4858.0            4838.0
132         5112.0     6520.0     5728.0            5699.0
1010        5008.0     6291.0     5591.0            5545.0
"), header=TRUE)

green <- read.table(textConnection("
file        max        min        avg               lowest
39          4486.0     5736.0     5031.0            5006.0
37          4444.0     5663.0     4979.0            4952.0
5           4617.0     5936.0     5185.0            5165.0
1           4384.0     5613.0     4917.0            4895.0
"), header=TRUE)


# Perform Cluster
d <- dist(file_vals$avg_vals, method = "euclidean")
fit <- hclust(d, method="ward")
plot(fit, labels=file_vals$file)
groups <- cutree(fit, k=3)

cols = c('red', 'blue', 'green', 'purple', 'orange', 'magenta', 'brown', 'chartreuse4','darkgray','cyan1')
rect.hclust(fit, k=3, border=cols)


# Desnity plots
dat = rbind(data.frame(Cluster='Red', max_vals = red$max), data.frame(Cluster='Blue', max_vals = blue$max), data.frame(Cluster='Green', max_vals = green$max))
max = (ggplot(dat,aes(x=max_vals)))
max = max + geom_density(aes(fill=factor(Cluster)), alpha=.3) + xlim(c(3500, 5500)) + scale_fill_manual(values=c("red",'blue',"green"))
max = max + labs(fill = 'Clusters')
print(max, vp=vp.layout(1,2))

dat = rbind(data.frame(Cluster='Red', min_vals = red$min), data.frame(Cluster='Blue', min_vals = blue$min), data.frame(Cluster='Green', min_vals = green$min))
min = (ggplot(dat,aes(x=min_vals)))
min = min + geom_density(aes(fill=factor(Cluster)), alpha=.3) + xlim(c(5000, 7000)) + scale_fill_manual(values=c("red",'blue',"green"))
min = min + labs(fill = 'Clusters')
print(min, vp=vp.layout(1,3))

dat = rbind(data.frame(Cluster='Red', avg_vals = red$avg), data.frame(Cluster='Blue', avg_vals = blue$avg), data.frame(Cluster='Green', avg_vals = green$avg))
avg = (ggplot(dat,aes(x=avg_vals)))
avg = avg + geom_density(aes(fill=factor(Cluster)), alpha=.3) + xlim(c(4000, 6000)) + scale_fill_manual(values=c("red",'blue',"green"))
avg = avg + labs(fill = 'Clusters')
print(avg, vp=vp.layout(2,2))

dat = rbind(data.frame(Cluster='Red', lowest_vals = red$lowest), data.frame(Cluster='Blue', lowest_vals = blue$lowest), data.frame(Cluster='Green', lowest_vals = green$lowest))
lowest = (ggplot(dat,aes(x=lowest_vals)))
lowest = lowest + geom_density(aes(fill=factor(Cluster)), alpha=.3) + xlim(c(4000, 6000)) + scale_fill_manual(values=c("red",'blue',"green"))
lowest = lowest + labs(fill = 'Clusters')
print(lowest, vp=vp.layout(2,3))

Answer 1

通过这种方式，您可以使用4个面板自动创建所需的绘图。

首先，数据：

scores <- read.table(textConnection("
file        max        min        avg               lowest
132         5112.0     6520.0     5728.0            5699.0
133         4720.0     6064.0     5299.0            5277.0
5           4617.0     5936.0     5185.0            5165.0
1           4384.0     5613.0     4917.0            4895.0
1010        5008.0     6291.0     5591.0            5545.0
104         4329.0     5554.0     4858.0            4838.0
105         4636.0     5905.0     5193.0            5165.0
35          4304.0     5578.0     4842.0            4831.0
36          4360.0     5580.0     4891.0            4867.0
37          4444.0     5663.0     4979.0            4952.0
31          4328.0     5559.0     4858.0            4839.0
39          4486.0     5736.0     5031.0            5006.0
32          4334.0     5558.0     4864.0            4843.0
"), header=TRUE)

file_vals <- read.table(textConnection("
file        avg_vals
                                   133         1.5923
                                   132         1.6351
                                   1010        1.6532
                                   104         1.6824
                                   105         1.6087
                                   39          1.8694
                                   32          1.9934
                                   31          1.9919
                                   37          1.8638
                                   36          1.9691
                                   35          1.9802
                                   1           1.7283
                                   5           1.7637
                                   "), header=TRUE)

两个数据框都可以合并为一个：

dat <- merge(scores, file_vals, by = "file")

适用于：

d <- dist(dat$avg_vals, method = "euclidean")
fit <- hclust(d, method="ward")
groups <- cutree(fit, k=3)
cols <- c('red', 'blue', 'green', 'purple', 'orange', 'magenta', 'brown', 'chartreuse4','darkgray','cyan1')

添加一个带有颜色名称的列（基于拟合）：

dat$group <- cols[groups]

从长格式重塑数据：

dat_re <- reshape(dat, varying = c("max", "min", "avg", "lowest"), direction = "long", drop = c("file", "avg_vals"), v.names = "value", idvar = "group", times = c("max", "min", "avg", "lowest"), new.row.names = seq(nrow(scores) * 4))

简介：

p <- (ggplot(dat_re ,aes(x = value))) +
geom_density(aes(fill = group), alpha=.3) +
scale_fill_manual(values=cols) +
labs(fill = 'Clusters') +
facet_wrap( ~ time)

print(p)

enter image description here

颜色密度通过簇组绘制在ggplot2中

1 个答案: