我遇到了麻烦geom_text，我有两组点，它们的颜色不同，并使用线性模型为两组点拟合了一条线。
当我尝试添加直线方程、皮尔逊相关性和斯皮尔曼相关性时，图例中会出现一个字母，并且文本会变得像素化。我该如何纠正？
当我单独绘制它们时，我没有遇到这个问题，但我将它们集中在一起。
library(ggplot2)
library(reshape2)
lm_eqn = function(df){
m = lm(area ~ Mass, df);
eq <- substitute(italic(y) == a + b %.% italic(x)*","~~italic(r)^2~"="~r2,
list(a = format(coef(m)[1], digits = 2),
b = format(coef(m)[2], digits = 2),
r2 = format(summary(m)$r.squared, digits = 3)))
as.character(as.expression(eq));
}
lm_eqn_PA = function(df){
m = lm(area ~ Mass, df);
eq <- substitute(italic(y) == a + b %.% italic(x)*","~~italic(r)^2~"="~r2,
list(a = format(coef(m)[1], digits = 2),
b = format(coef(m)[2], digits = 2),
r2 = format(summary(m)$r.squared, digits = 3)))
as.character(as.expression(eq));
}
dat <- read.table("../../mass_vs_area/mass_vs_area.txt",header=TRUE)
area <- read.table("../results/all_values.txt", header=TRUE)
all_dat <- merge(area,dat,by="file")
all_dat1 <- data.frame(file=all_dat$file,Mass=all_dat$Mass,area=all_dat$area,Cluster=c("Experimental"))
all_dat2 <- data.frame(file=all_dat$file,Mass=all_dat$Mass,area=all_dat$PA,Cluster=c("PA"))
plot_dat = rbind(all_dat1,all_dat2)
data.label.equation1 <- data.frame(x = 250,y = 20000,label = paste("Exp_eq.: ",lm_eqn(all_dat1)))
data.label.pearson1 <- data.frame(x = 250, y = 21000,label = paste("Exp_Peasron: ",format(cor(all_dat1$Mass,all_dat1$area),digits=3)))
data.label.spearman1 <- data.frame(x = 250, y = 22000, label = paste("Exp_Spearman: ",format(cor(all_dat1$Mass,all_dat1$area,method="spearman"),digits=3)))
data.label.equation2 <- data.frame(x = 250,y = 19000,label = paste("PA_eq.: ",lm_eqn_PA(all_dat2)))
data.label.pearson2 <- data.frame(x = 250, y = 18000,label = paste("PA_Peasron: ",format(cor(all_dat2$Mass,all_dat2$area),digits=3)))
data.label.spearman2 <- data.frame(x = 250, y = 17000, label = paste("PA_Spearman: ",format(cor(all_dat2$Mass,all_dat2$area,method="spearman"),digits=3)))
plotter <- (qplot(data=plot_dat, x=Mass, y=area, colour=factor(plot_dat$Cluster)) + geom_point(shape=1) + geom_smooth(method="lm") + scale_colour_discrete(name="Cross-Section Type"))
plotter <- plotter + labs(x = "Mass kDa" ,y = expression(paste('CCS (',Å^2,')')))
plotter <- plotter + geom_text(data = data.label.equation1, aes(x = x , y = y, label = label) ,parse=TRUE)
plotter <- plotter + geom_text(data = data.label.pearson1, aes(x = x , y = y , label = label ),parse=TRUE)
plotter <- plotter + geom_text(data = data.label.spearman1, aes(x = x , y = y , label = label ),parse=TRUE)
plotter <- plotter + geom_text(data = data.label.equation2, aes(x = x , y = y , label = label ),parse=TRUE)
plotter <- plotter + geom_text(data = data.label.pearson2, aes(x = x , y = y , label = label ),parse=TRUE)
plotter <- plotter + geom_text(data = data.label.spearman2, aes(x = x , y = y , label = label ),parse=TRUE)
print(plotter)
编辑数据框如下：
all_dat1 table:
file Mass area Cluster
1 1bsy 18 1660 Experimental
2 1fi9 12 1240 Experimental
3 1gr5_edited 801 20900 Experimental
4 1gyk_edit 125 7030 Experimental
5 1hwz 336 12800 Experimental
6 1jyc_edited 103 5550 Experimental
7 1pkn_model 237 10300 Experimental
8 1ttr_model 56 3410 Experimental
9 2a3y_model 250 10400 Experimental
10 2hcy 143 6940 Experimental
11 3blg_model 37 2850 Experimental
12 3fdc_model 64 3640 Experimental
13 4f5sA 69 4090 Experimental
all_dat2 table:
file Mass area Cluster
1bsy 18 1428 PA
1fi9 12 1119 PA
1gr5_edited 801 21518 PA
1gyk_edit 125 5592 PA
1hwz 336 10759 PA
1jyc_edited 103 4649 PA
1pkn_model 237 8689 PA
1ttr_model 56 2793 PA
2a3y_model 250 8514 PA
2hcy 143 7255 PA
3blg_model 37 2399 PA
3fdc_model 64 2833 PA
4f5sA 69 3871 PA