大家好，我希望能够获得有关三元图（“vcd”）的一些提示。
我有这个数据框：
a <- c(0.1, 0.5, 0.5, 0.6, 0.2, 0, 0, 0.004166667, 0.45)
b <- c(0.75,0.5,0,0.1,0.2,0.951612903,0.918103448,0.7875,0.45)
c <- c(0.15,0,0.5,0.3,0.6,0.048387097,0.081896552,0.208333333,0.1)
d <- c(500,2324.90,2551.44,1244.50, 551.22,-644.20,-377.17,-100, 2493.04)
df <- data.frame(a, b, c, d)
我正在构建一个三元图：
ternaryplot(df[,1:3], df$d)
我该如何映射连续变量d，以获得类似于以下结果的输出？
我曾经需要解决一个类似的问题，这部分促使我编写了一个扩展ggplot2的三元图软件包。该软件包可在CRAN上获得。
此问题的输出：
构建以上内容的代码：
#Orignal Data as per Question
a <- c(0.1, 0.5,0.5, 0.6, 0.2, 0 , 0 , 0.004166667, 0.45)
b <- c(0.75,0.5,0 , 0.1, 0.2, 0.951612903,0.918103448, 0.7875 , 0.45)
c <- c(0.15,0 ,0.5, 0.3, 0.6, 0.048387097,0.081896552, 0.208333333, 0.10)
d <- c(500,2324.90,2551.44,1244.50, 551.22,-644.20,-377.17,-100, 2493.04)
df <- data.frame(a, b, c, d)
#For labelling each point.
df$id <- 1:nrow(df)
#Build Plot
ggtern(data=df,aes(x=c,y=a,z=b),aes(x,y,z)) +
stat_density2d(geom="polygon",
n=400,
aes(fill=..level..,
weight=d,
alpha=abs(..level..)),
binwidth=100) +
geom_density2d(aes(weight=d,color=..level..),
n=400,
binwidth=100) +
geom_point(aes(fill=d),color="black",size=5,shape=21) +
geom_text(aes(label=id),size=3) +
scale_fill_gradient(low="yellow",high="red") +
scale_color_gradient(low="yellow",high="red") +
theme_tern_rgbw() +
theme(legend.justification=c(0,1), legend.position=c(0,1)) +
guides(fill = guide_colorbar(order=1),
alpha= guide_legend(order=2),
color="none") +
labs( title= "Ternary Plot and Filled Contour",
fill = "Value, V",alpha="|V - 0|")
#Save Plot
ggsave("TernFilled.png")
这可能不是最优雅的方法，但它能够工作（从头开始，而且没有使用ternaryplot，因为我无法弄清楚如何做到这一点）。
a<- c (0.1, 0.5, 0.5, 0.6, 0.2, 0, 0, 0.004166667, 0.45)
b<- c (0.75,0.5,0,0.1,0.2,0.951612903,0.918103448,0.7875,0.45)
c<- c (0.15,0,0.5,0.3,0.6,0.048387097,0.081896552,0.208333333,0.1)
d<- c (500,2324.90,2551.44,1244.50, 551.22,-644.20,-377.17,-100, 2493.04)
df<- data.frame (a, b, c)
# First create the limit of the ternary plot:
plot(NA,NA,xlim=c(0,1),ylim=c(0,sqrt(3)/2),asp=1,bty="n",axes=F,xlab="",ylab="")
segments(0,0,0.5,sqrt(3)/2)
segments(0.5,sqrt(3)/2,1,0)
segments(1,0,0,0)
text(0.5,(sqrt(3)/2),"c", pos=3)
text(0,0,"a", pos=1)
text(1,0,"b", pos=1)
# The biggest difficulty in the making of a ternary plot is to transform triangular coordinates into cartesian coordinates, here is a small function to do so:
tern2cart <- function(coord){
coord[1]->x
coord[2]->y
coord[3]->z
x+y+z -> tot
x/tot -> x # First normalize the values of x, y and z
y/tot -> y
z/tot -> z
(2*y + z)/(2*(x+y+z)) -> x1 # Then transform into cartesian coordinates
sqrt(3)*z/(2*(x+y+z)) -> y1
return(c(x1,y1))
}
# Apply this equation to each set of coordinates
t(apply(df,1,tern2cart)) -> tern
# Intrapolate the value to create the contour plot
resolution <- 0.001
require(akima)
interp(tern[,1],tern[,2],z=d, xo=seq(0,1,by=resolution), yo=seq(0,1,by=resolution)) -> tern.grid
# And then plot:
image(tern.grid,breaks=c(-1000,0,500,1000,1500,2000,3000),col=rev(heat.colors(6)),add=T)
contour(tern.grid,levels=c(-1000,0,500,1000,1500,2000,3000),add=T)
points(tern,pch=19)
我先前的回答使用了密度估计方法。这里是一种使用线性回归的方法。
df <- data.frame(a, b, c, d)
ggtern(df,aes(a,c,b)) +
geom_interpolate_tern(aes(value=d,fill=..level..),
binwidth=500,
colour="white") +
geom_point(aes(fill=d),color="black",shape=21,size=3) +
scale_fill_gradient(low="yellow",high="red") +
theme(legend.position=c(0,1),legend.justification=c(0,1)) +
labs(fill="Value, d")
#Rename header
names(SI) [6] <- "WATER%"
names(SI) [7] <- "VEGETATION%"
names(SI) [8] <- "SOIL%"
#pdf(file="prova_ternary12.pdf", width = 5, height =5)
##++++++++++++++++++++++++++++++
install.packages("colourschemes", repos="http://R-Forge.R-project.org")
library(colourschemes)
rs = rampInterpolate ( limits =c(-0.8 , 0.8),
ramp = c("red4", "red", "orangered", "orange", "darkgoldenrod1", "white",
"cyan2", "blue", "darkblue", "blueviolet", "purple3") )
rs(-0.8)
rs(-0.6000)
rs(-0.4)
rs(-0.2)
rs(0)
rs(0.2)
rs(0.4)
rs(0.6000)
rs(0.8000)
#++++++++++++++++++++++++++++++
#TERNARYPLOT (vcd)
library(vcd)
png(file="ternary.png", width=800, height=800)
ternaryplot(
SI[,6:8],
bg = "lightgray",
grid_color = "black",
labels_color = "black",
dimnames_position = c("corner"),
#dimnames = 10,
newpage = T,
#dimnames_color = "green",
border = "black",
pop=T,
#SI$MEAN_b2b6.tm,
col=rs(SI$MEAN_b2b6.TM_V2),
#col = ifelse(SI$MEAN_b1b6.tm > 0, "blue", "#cd000020"),
pch=13, cex=.4, prop_size = F,
labels = c("outside"),
#size=SI$MEAN_b1b6.tm,
main="b4b6 -TM data-")
import plotly.figure_factory as ff
import numpy as np
Al = np.array([0. , 0. , 0., 0., 1./3, 1./3, 1./3, 2./3, 2./3, 1.])
Cu = np.array([0., 1./3, 2./3, 1., 0., 1./3, 2./3, 0., 1./3, 0.])
Y = 1 - Al - Cu
# synthetic data for mixing enthalpy
# See https://pycalphad.org/docs/latest/examples/TernaryExamples.html
enthalpy = (Al - 0.01) * Cu * (Al - 0.52) * (Cu - 0.48) * (Y - 1)**2
fig = ff.create_ternary_contour(np.array([Al, Y, Cu]), enthalpy,
pole_labels=['Al', 'Y', 'Cu'],
interp_mode='cartesian')
fig.show()
RSiteSearch("三元轮廓")看看是否有帮助？另外，使用library("sos"); findFn("三元轮廓")。 - Ben Bolker