我一直在聚集一些价值观，然后将它们分组。然后，我使用ggplot2绘制一些密度图并覆盖群集。下面是一个示例图像：
对于群集中的每个组，我绘制一个密度图并覆盖它们。密度图中的颜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了数据，并将它们放在自己的单独文本表中（请参见下面的代码）。这是非常低效的，对于大数据集可能非常繁琐。如何在ggplot2中动态绘制密度图，而又不将群集分为各自的单独文本表？
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)
我用来生成绘图的代码：
请注意，将基本图形与网格结合仍无法正常工作
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))
最佳答案
这样，您可以使用4个面板自动创建所需的图。
一，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)
可以将两个数据帧合并为一个：
dat <- merge(scores, file_vals, by = "file")
适合：
d <- dist(dat$avg_vals, method = "euclidean")
fit <- hclust(d, method="ward")
groups <- cutree(fit, k=3)
cols <- c('red', 'blue', 'green', 'purple', 'orange', 'magenta', 'brown', 'chartreuse4','darkgray','cyan1')
添加带有颜色名称的列（基于适合度）：
dat$group <- cols[groups]
将数据从宽格式重整为长格式：
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))
情节：
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)
关于r - ggplot2中按群集组的颜色密度图，我们在Stack Overflow上找到一个类似的问题：https://stackoverflow.com/questions/12357067/