y = hwy, color = cty)) + geom_point(size = 5) + scale_color_continuous(low = "blu...
geom_point() +scale_color_gradient(low = "#f0cf61", high = "#371722") +scale_size_continuous(range = c(1, 4)) +theme_classic() + labs(x = "Sepal Length", y = "Sepal Width", title = "Iris Sepal") + theme(plot.title = element_text(size = 15, face = "bold", hjust = ...
p1 <- ggplot(df, aes(y, x)) + geom_point()+scale_x_continuous(labels = scales::percent,name="percent")p2 <- ggplot(df, aes(y, x)) + geom_point()+scale_x_continuous(labels = scales::dollar,name="dollar")grid.arrange(p1,p2,ncol=2) scales::percent、scales::dollar分别指定x轴...
scale_size_continuous(breaks = c(1,2,3,4,5,6,7,8)) + scale_x_continuous(limits = symmetric_limits) + scale_y_continuous(limits = symmetric_limits) + theme_minimal() + geom_text(aes(label = ifelse(CDD_e > quantile(CDD_e,probs = .95,na.rm=TRUE) | R95p_e > quantile(R95p_...
p+scale_x_continuous(name="X name(distance = 3)",limits=c(0,40),breaks=breaks_width(3))->p3 p3 p/p1/p2/p3 ## 更改x坐标刻度的名称 p+scale_x_continuous(name="Currency",breaks=breaks_width(5),labels=label_number(prefix="USD ")) ...
▲scale_ +美学映射(color、size、shape、x、y等)+_continuous/discrete等 我们以scale_size_manual()为例,看一下scale修改图形大小映射关系时的情况: 主题scale_() 除了scale_*_*()函数族,另一个对图形细节更重要的函数就是theme()函数,它可以让我们近乎随心所欲地修改我们图片的外观细节,其实我们在上一讲中...
scale_colour_gray() scale_colour_hue() scale_colour_brewer() scale_colour_manual() 基于大小属性: scale_size_discrete() scale_size_continuous() scale_size_manual() scale_size_area() 基于形状属性: scale_shape_discrete() scale_shape_continuous() ...
scale_size_continuous(range=c(1,10)) 1. 2. 3. 4. 5. 6. image.png 接下来就是美化 灰色背景不太喜欢,去掉 AI检测代码解析 ggplot(df,aes(x=ID,y=log2FC,size=pvalue, color=Class,shape=group))+ geom_point(alpha=0.5)+ scale_size_continuous(range=c(1,10))+ ...
直接使用scale_x_continuous()/scale_y_continuous()或者xlim()/ylim()就可以实现。 这个方法很好用,尤其是遇到画气泡图、散点图,发生图形显示不全的情况,只要增加下坐标轴范围就解决了! 代码语言:javascript 代码运行次数:0 运行 AI代码解释 p1<-p+scale_y_continuous(limits=c(0,5000))p2<-p+ylim(c(0,...
# 代码来自 http://sape.inf.usi.ch/quick-reference/ggplot2/shaped=data.frame(p=c(0:25,32:127))ggplot() +scale_y_continuous(name="") +scale_x_continuous(name="") +scale_shape_identity() +geom_point(data=d, mapping=aes(x=p%%16, y=p%/%16, shape=p), size=5, fill="red") ...