在x轴上显示点图的计数 [英] showing count on x-axis for dot plot

问题描述

我想要一个点图，该图在x轴上显示计数.如何获得下面的点图以显示x-asax上的计数?

谢谢.

  date = seq(as.Date("2016/1/5")，as.Date("2016/1/11")，"day")值= c(11,11,12,12,13,14,14)dat = data.frame(日期=日期，值=值)达特库(ggplot2)库(ggplot2)ggplot(dat，aes(x =值))+ geom_dotplot(binwidth = .8)+scale_y_discrete(breaks = seq(1，max(table(dat $ value))+ 2,1)，labels = seq(1，max(table(dat $ value))+ 2,1))#使用scale_y离散变量进行尝试，但没有任何作用 

解决方案

ylim(0，A)提供所需的内容，其中A是计算1.00密度所需的堆叠点数.我们可以计算出A的确切值(但有点复杂；通过逻辑方法可以得出近似值).(我引用

I'd like to have a dot plot that shows the count on the x-axis. How can you get the dotplot below to show the count on the x-asix?

Thank you.

    date = seq(as.Date("2016/1/5"), as.Date("2016/1/11"), "day")
    value = c(11,11,12,12,13,14,14)
    dat =data.frame(date = date, value = value)
    dat
    library(ggplot2)
    library(ggplot2)
ggplot(dat, aes(x = value)) + geom_dotplot(binwidth = .8) +
  scale_y_discrete(breaks= seq(1,max(table(dat$value))+2,1), 
                   labels = seq(1,max(table(dat$value))+2,1) ) #tried using scale_y discrete but it does nothing

解决方案

ylim(0, A) gives what you want, where A is the number of stacked dots necessary to count 1.00 density. We can calculate the exact value of A (but a little complexly ; Dialogical approach gives you approximate value). (I reffered to post1, post2, and post3)

library(ggplot2); library(grid)

date = seq(as.Date("2016/1/5"), as.Date("2016/1/12"), "day")
value = c(11,11,12,12,13,14,14,14)
dat =data.frame(date = date, value = value)

### base plot
g <- ggplot(dat, aes(x = value)) + geom_dotplot(binwidth = 0.8) + coord_flip()
g  # output to read parameter

### calculation of width and height of panel
grid.ls(view=TRUE,grob=FALSE)
seekViewport('panel.3-4-3-4')
real_width <- convertWidth(unit(1,'npc'), 'inch', TRUE)
real_height <- convertHeight(unit(1,'npc'), 'inch', TRUE)

### calculation of other values
height_coordinate_range <- diff(ggplot_build(g)$panel$ranges[[1]]$y.range)
real_binwidth <- real_height / height_coordinate_range * 0.8  # 0.8 is the argument binwidth
num_balls <- real_width / 1.1 / real_binwidth  # the number of stacked balls. 1.1 is expanding value.

g + ylim(0, num_balls)

# The dirty balls border probably comes from my environment. 

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