Normalizing Y-Axis in Histograms in R Ggplot to Proportion

Normalizing y-axis in histograms in R ggplot to proportion

Note that ..ncount.. rescales to a maximum of 1.0, while ..count.. is the non scaled bin count.

ggplot(mydataframe, aes(x=value)) +
  geom_histogram(aes(y=..count../sum(..count..)))

Which gives:

Normalizing y-axis in histograms in R ggplot to proportion by group

Like this? [edited based on OP's comment]

ggplot(all,aes(x=value,fill=dataset))+
  geom_histogram(aes(y=0.5*..density..),
                 alpha=0.5,position='identity',binwidth=0.5)

Using y=..density.. scales the histograms so the area under each is 1, or sum(binwidth*y)=1. As a result, you would use y = binwidth*..density.. to have y represent the fraction of the total in each bin. In your case, binwidth=0.5.

IMO this is a little easier to interpret:

ggplot(all,aes(x=value,fill=dataset))+
  geom_histogram(aes(y=0.5*..density..),binwidth=0.5)+
  facet_wrap(~dataset,nrow=2)

Normalizing y-axis in density plots in R ggplot to proportion by group

For those still interested. The answer is rather simple. First create a separate column with the relative group sizes and use that column in ggplot.

unique_episodes = bp_combi %>% group_by(dataset) %>% count(dataset)
data2 = merge(x = bp_combi, y = unique_episodes, by = "dataset", all.x = TRUE)


combi_dens = ggplot(bp_combi, 
                    aes(x=value,,
                        y=(..count..)/n*1000, fill=dataset)) +
  geom_density(bw = 1, alpha=0.4, size = 1.5 )

how can I plot a histogramme with y axis representing proportion of observations in a bin with geom_histogram?

I think this is what you're looking for:

ggplot(data=diamonds, aes(x=carat)) +  
  geom_histogram(aes(y = stat(count/sum(count))), 
  binwidth = 0.1, position="identity", 
  fill =  "white", colour = "black")

How can I scale histogram between 0 and 1 in ggplot2?

geom_histogram(data = df1, aes(y = ..ncount..,x=meanf,fill = "g", color="g"))

should do it.

If you want both histograms be normalized by the same divisor:

First get the y-range of the original histogram first. Refer here

ggobj <- ggplot() + 
  geom_histogram(data = df1, aes(x=meanf,fill = "g", color="g"), alpha = 0.6,binwidth = 0.02)+
  geom_histogram(data = df2, aes(x=meanf,fill = "b", color="b"), alpha = 0.4,binwidth = 0.02)

y_max <- ggplot_build(ggobj)$panel$ranges[[1]]$y.range[2] 

Then recreate your histogram and scale it with the y_range that you got.

p <- ggplot() + 
      geom_histogram(data = df1, aes(y_max=y_max, y=..count../y_max,x=meanf,fill = "g", color="g"), alpha = 0.6,binwidth = 0.02)+
      geom_histogram(data = df2, aes(y_max=y_max, y=..count../y_max,x=meanf,fill = "b", color="b"), alpha = 0.4,binwidth = 0.02)

Plotting the proportion of a categorial variable on the y-axis in R using ggplot with a numerical x-axis

You can summarize the data with dplyr and then plot the summarized data frame rather than the original data frame

library(dplyr)
library(ggplot2)

df %>% 
  group_by(age) %>% 
  summarise(p = mean(result == 'y')) %>% 
  ggplot(aes(x = age, y = p)) +
    geom_point() + 
    geom_line()

Show percent in ggplot histogram

The issue is that the labels are placed at y=..count... To solve your issue use y=..count../sum(..count..) in stat_bin too.

Making use of ggplot2::mpg as example data:

library(ggplot2)
library(dplyr)

mpg %>%
  ggplot(aes(x = hwy)) +
  geom_histogram(aes(y = (..count..)/sum(..count..)),binwidth=6) +
  scale_y_continuous(labels = scales::percent)

mpg %>%
  ggplot(aes(x = hwy)) +
  geom_histogram(aes(y = (..count..)/sum(..count..)),binwidth=6) +
  stat_bin(binwidth=6, geom='text', color='white', aes(y = ..count../sum(..count..), label = scales::percent((..count..)/sum(..count..))),position=position_stack(vjust = 0.5))+
  scale_y_continuous(labels = scales::percent)

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