[英]How to plot an exploratory decision tree in R
让我们假设一群人在时间和3个时间点被跟踪,他们被问到是否想成为法官。 在此期间,他们将改变他们的意见。 我希望以图形方式显示意见的变化,以便在时间内成为判断/不判断。 这是一个如何显示它的想法:
以下是如何阅读情节:
例如:
(a)118人不想在高中和大学期间成为法官,但在实践中他们决定成为法官。
(b)直到练习695决定成为法官,但在练习后400成为法官,295做了其他事情。
主要思想是探索哪种决策路径存在以及哪种决策路径最常用。
我有几个问题:
有什么建议么?
编辑1:
我发现了一个类似于上图的情节:河图,例如,参见R library riverplot或R blogger 。 河流图的缺点是在交叉点处,各个线程或路径都会丢失。
以下是数据:
library(reshape2)
library(ggplot2)
# Data
wide <- data.frame( grp = 1:8,
time1_orig = rep(8,8)
, time2_orig = rep(c(4,12), each = 4)
, time3_orig = rep(c(2,6,10,14), each = 2)
, time4_orig = seq(1,15,2)
, n = c(409,118,38,33,147,22,295,400) # number of persion
, d = c(1,0,1,0,1,0,1,0) # decision
)
wide
grp time1_orig time2_orig time3_orig time4_orig n d
1 1 8 4 2 1 409 1
2 2 8 4 2 3 118 0
3 3 8 4 6 5 38 1
4 4 8 4 6 7 33 0
5 5 8 12 10 9 147 1
6 6 8 12 10 11 22 0
7 7 8 12 14 13 295 1
8 8 8 12 14 15 400 0
接下来是数据转换得到的情节:
w <- 500
wide$time1 <- wide$time1_orig + (cumsum(wide$n)-(wide$n)/2)/w
wide$time2 <- wide$time2_orig + (cumsum(wide$n)-(wide$n)/2)/w
wide$time3 <- wide$time3_orig + (cumsum(wide$n)-(wide$n)/2)/w
wide$time4 <- wide$time4_orig + (cumsum(wide$n)-(wide$n)/2)/w
long<- melt(wide[,-c(2:5)], id = c("d","grp","n"))
long$d<-as.character(long$d)
str(long)
这是ggplot:
gg1 <- ggplot(long, aes(x=variable, y=value, group=grp, colour=d)) +
geom_line (aes(size=n),position=position_dodge(height=c(0.5))) +
geom_text(aes(label=c( "1462","" ,"" ,"" ,"" ,"" ,"" ,""
,"" ,"" ,"598","" ,"" ,"864","" ,""
,"527" ,"" ,"" ,"71" ,"169","" ,"" ,"695"
,"409" ,"118","38" ,"33" ,"147","22" ,"295","400"
)
, size = 300, vjust= -1.5)
) +
scale_colour_manual(name="",labels=c("Yes", "No"),values=c("royalblue","black")) +
theme(legend.position = c(0,1),legend.justification = c(0, 1),
legend.text = element_text( size=12),
axis.text = element_text( size=12),
axis.title = element_text( size=15),
plot.title = element_text( size=15)) +
guides(lwd="none") +
labs(x="", y="Consider a judge career as an option:") +
scale_y_discrete(labels="") +
scale_x_discrete(labels = c( "during high school"
, "during university"
, "during practice"
, ""
)
)
gg1
我找到了一个解决方案,感谢图书riverplot
,它给了我这个情节:
这是代码:
library("riverplot")
# Create nodes
nodes <- data.frame( ID = paste(rep(c("O","C","R","D"),c(1,2,4,8)),c(1,1:2,1:4,1:8),sep="")
, x = rep(0:3, c(1,2,4,8))
, y = c(8, 12,4,14, 10,6,2, 15,13,11,9,7,5,3,1)
, labels = c("1462","864","598","695","169","71","527","400","295","22","147","33","38","118","409")
, col = rep("lightblue", 15)
, stringsAsFactors= FALSE
)
# Create edges
edges <- data.frame( N1 = paste(rep(c("O","C","R"), c(2,4,8)), rep(c(1,2,1,4:1) , each=2), sep="")
, N2 = paste(rep(c("C","R","D"), c(2,4,8)), c(c(2:1,4:1,8:1)), sep="")
)
edges$Value <- as.numeric(nodes$labels[2:15])
edges$col <- NA
edges$col <- rep(c("black","royalblue"), 7)
edges$edgecol <- "col"
# Create nodes/edges object
river <- makeRiver(nodes, edges)
# Define styles
style <-default.style()
style[["edgestyle"]]<-"straight"
# Plot
plot(river, default_style= style, srt=0, nsteps=200, nodewidth = 3)
# Add label
names <- data.frame (Time = c(" ", "during high school", "during university", "during practive")
,hi = c(0,0,0,0)
,wi = c(0,1,2,3)
)
with( names, text( wi, hi, Time) )
可以选择绘制一系列分类信息:
TraMineR - 挖掘序列数据
TraMineR:用于探索序列数据的工具箱
TraMineR是一个R-package,用于挖掘,描述和可视化状态或事件序列,以及更一般的离散顺序数据。 其主要目的是分析社会科学中的传记纵向数据,例如描述职业或家庭轨迹的数据。 然而,其大多数特征也适用于非时间数据,例如文本或DNA序列
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