1

enter image description here

This is the my data

data <- data.frame(x1 = c(344,347,351,351,355))

I need to plot the empirical cumulative distribution function based on the data above,and here is my solution

point<-na.omit(data$x1)
point<-point[!duplicated(point)]
point<-point[order(point)]

prob<-ecdf(data$x1)
prob<-prob(data$x1)
prob<-prob[order(prob)]
prob<-na.omit(prob)
x1<-data$x1[order(data$x1)]
x1<-c(340,344,347,351,355)
x2<-c(344,347,351,355,360)
prob2<-prob[!duplicated(prob)]

data1<-cbind(x1,x2,prob,point,prob2)
data1<-as.data.frame(data1)

ggplot(data=data1)+
  geom_segment(mapping = aes(x = x1,xend = x2,y = prob,yend = prob))+
  scale_x_continuous(limits = c(340,360))+
  scale_y_continuous(limits = c(0,1))+
  geom_point(mapping = aes(x = point ,y = prob2))

I think this is too complicated and it is not like the plot on the image, but i really dont know how to simplify it without using base plot()(or the stepfun() because it looks not so well). I've been thinking for several hours ,really appreciate your help!

M--
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Enzo Paolo
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  • What data defines the y-axis values? Please post a more complete example, including what you have tried so far. – jdobres Mar 03 '22 at 14:24

1 Answers1

1

You could do it manually by creating a little data frame of the values you want to plot.

library(ggplot2)

data <- c(344, 347, 351, 351, 355)

df <- data.frame(x    = c(-Inf, data), 
                 xend = c(data, Inf), 
                 y    = c(0, 0.2, 0.4, 0.4, 0.8, 1))

ggplot(df, aes(x, y)) + 
  geom_segment(aes(xend = xend, yend = y), size = 1) +
  geom_point(aes(x = xend), shape = 21, fill = "white", size = 3) +
  theme_classic()

enter image description here

Allan Cameron
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