R Normal Distribution +ggplot_r

Randnorm<-rnorm (3000)
# #rnorm (3000) produces 3,000 positive too many distributions
Randdensity<-dnorm (Randnorm)
# # # #dnorm (randnorm) to find its density function value
Ggplot (Data.frame (x=randnorm,y=randdensity)) +aes (x=x,y=y) +geom_point () +labs (x= "Random Normal varables", y= " Randdensity ")
# #将这个你太分分布数以及对应的密度函数值作为x, y-axis values, and draw point graphs
P<-ggplot (Data.frame (x=randnorm,y=randdensity)) +aes (x=x,y=y) +geom_point () +labs (x= "Random Normal Varables", y= " Randdensity ")
# #变量p作为该段代码的引用
Neg1seq<-seq (From=min (randnorm), to=-1,by=0.1)
# #生成一段序列, the starting value is min (randnorm), the end is to=-1, step 0.1
Lessthanneg1<-data.frame (X=neg1seq,y=dnorm (NEG1SEQ))
# #将序列跟该段序列对应的密度函数值作为数据框元素
Lessthanneg1<-rbind (c (min (random), 0), Lessthanneg1,c (Max (lessthanneg1$x), 0)
# #将最小值, the middle, the maximum value of the row and data box
P+geom_polygon (Data=lessthanneg1,aes (x=x,y=y))
# #geom_polygon进行填充, fill the part of the curve with the X-week
Randprob<-pnorm (Randnorm)
# #pnorm () distribution function
Ggplot (Data.frame (X=randnorm,y=randprob)) +aes (x=x,y=y) +geom_point () +labs (x= "Random Normal varables", y= " Randdensity ")
# #绘制分布函数
# #分布函数求积分为密度函数图形