ggplot(birthwt,aes(x=bwt,fill=smoke))+ geom_histogram(position = 'identity',alpha=0.4) #3.2.2 使用分面 #各声部歌手身高的分布,根据voice.part因子水平进行颜色填充 p414 #str(singer) data(singer,package='lattice') ggplot(singer,aes(x=
ggplot2包中绘制点图的函数有两个:geom_point和 geom_dotplot,当使用geom_dotplot绘图时,point的形状是dot,不能改变点的形状,因此,geom_dotplot 叫做散点图(Scatter Plot),通过绘制点来呈现数据的分布,对点分箱的方法有两种:点密度(dot-density )和直方点(histodot)。当使用点密度分箱(bin)方式时,分箱的位...
ggplot2 cheat sheet 这里应该用直方图 movie%>% ggplot(aes(x=Profit_num))+ geom_histogram() 出图 可见利润呈现明显的右倾分布(right skewed)。中位数小于平均数,大部分电影都是低利润。 对于一些不呈现正态分布的样本数据,利用中心极限定理,进行样本分布,可以获得呈正态分布的样本。 比如用到上节讲到的重复...
ggplot2包中绘制点图的函数有两个:geom_point和 geom_dotplot,当使用geom_dotplot绘图时,point的形状是dot,不能改变点的形状,因此,geom_dotplot 叫做散点图(Scatter Plot),通过绘制点来呈现数据的分布,对点分箱的方法有两种:点密度(dot-density )和直方点(histodot)。当使用点密度分箱(bin)方式时,分箱的位...
ggplot(gapminder, aes(year, lifeExp)) + geom_line(aes(group = country), alpha = 0.2) + geom_smooth(se = FALSE, size = 1.2)3.1.3 标度 通常ggplot2 会自动根据输入变量选择最优的坐标刻度方案,若要手动设置或调整,就需要用到标度函数:scale_<MAPPING>_<KIND>(). 标度函数控制几何对象中的标度映...
If the number of group or variable you have is relatively low, you can display all of them on the same axis, using a bit of transparency to make sure you do not hide any data. Note: with 2 groups, you can also build a mirror histogram # library library(ggplot2) library(dplyr) ...
(x="Estimated effect of minor haplotype", y="Frequency") p1 p3<-ggplot(data=dat01,aes(x=`Variance Explained`*100))+ geom_histogram(bins = 30,color="black",fill="grey")+ scale_x_continuous(breaks = seq(0,0.006,by=0.001))+ scale_y_continuous(breaks = seq(0,6000,by=2000))+ ...
stack.bar.label.position<-function(x){x<-rev(x)new.x<-vector()for(iin1:length(x)){if(i==1){new.x<-append(new.x,x[i]/2)}else{new.x<-append(new.x,sum(x[1:i-1])+x[i]/2)}}return(new.x)}ggplot(data=fig3b,aes(x=var1,y=value))+geom_bar(stat="identity",position=...
The functiongeom_histogram()is used. You can also add a line for the mean using the functiongeom_vline. Related Book: GGPlot2 Essentials for Great Data Visualization in R Prepare the data The data below will be used : set.seed(1234) df <- data.frame( sex=factor(rep(c("F",...
position="identity") # Interleaved histograms ggplot2.histogram(data=weight, xName='weight', groupName='sex', legendPosition="top", alpha=0.5, position="dodge") #stacked histograms ggplot2.histogram(data=weight, xName='weight', groupName='sex', legendPosition="top", alpha=0.5, position="...