Multiple Histograms with Ggplot2 - Position

multiple histograms with ggplot2 - position

ggplot2 works best with "long" data, where all the data is in a single data frame and different groups are described by other variables in the data frame. To that end

DF <- rbind(data.frame(fill="blue", obs=dataset1$obs),
            data.frame(fill="green", obs=dataset2$obs),
            data.frame(fill="red", obs=dataset3$obs),
            data.frame(fill="orange", obs=dataset3$obs))

where I've added a fill column which has the values that you used in your histograms. Given that, the plot can be made with:

ggplot(DF, aes(x=obs, fill=fill)) +
  geom_histogram(binwidth=1, colour="black", position="dodge") +
  scale_fill_identity()

where position="dodge" now works.

You don't have to use the literal fill color as the distinction. Here is a version that uses the dataset number instead.

DF <- rbind(data.frame(dataset=1, obs=dataset1$obs),
            data.frame(dataset=2, obs=dataset2$obs),
            data.frame(dataset=3, obs=dataset3$obs),
            data.frame(dataset=4, obs=dataset3$obs))
DF$dataset <- as.factor(DF$dataset)
ggplot(DF, aes(x=obs, fill=dataset)) +
  geom_histogram(binwidth=1, colour="black", position="dodge") +
  scale_fill_manual(breaks=1:4, values=c("blue","green","red","orange"))

This is the same except for the legend.

Overlaying histograms with ggplot2 in R

Your current code:

ggplot(histogram, aes(f0, fill = utt)) + geom_histogram(alpha = 0.2)

is telling ggplot to construct one histogram using all the values in f0 and then color the bars of this single histogram according to the variable utt.

What you want instead is to create three separate histograms, with alpha blending so that they are visible through each other. So you probably want to use three separate calls to geom_histogram, where each one gets it's own data frame and fill:

ggplot(histogram, aes(f0)) + 
    geom_histogram(data = lowf0, fill = "red", alpha = 0.2) + 
    geom_histogram(data = mediumf0, fill = "blue", alpha = 0.2) +
    geom_histogram(data = highf0, fill = "green", alpha = 0.2) +

Here's a concrete example with some output:

dat <- data.frame(xx = c(runif(100,20,50),runif(100,40,80),runif(100,0,30)),yy = rep(letters[1:3],each = 100))

ggplot(dat,aes(x=xx)) + 
    geom_histogram(data=subset(dat,yy == 'a'),fill = "red", alpha = 0.2) +
    geom_histogram(data=subset(dat,yy == 'b'),fill = "blue", alpha = 0.2) +
    geom_histogram(data=subset(dat,yy == 'c'),fill = "green", alpha = 0.2)

which produces something like this:

Edited to fix typos; you wanted fill, not colour.

ggplot two histograms in one plot

You can reference some of the other calculated values from stat functions using a notation that you may have seen before: ..value... I'm not sure the proper name for these or where you can find a list documented, but sometimes these are called "special variables" or "calculated aesthetics".

In this case, the default calculated aesthetic on the y axis for geom_histogram() is ..count... When comparing distributions of different total N size, it's useful to use ..density... You can access ..density.. by passing it to the y aesthetic directly in the geom_histogram() function.

First, here's an example of two histograms with vastly different sizes (similar to OP's question):

library(ggplot2)

set.seed(8675309)
df <- data.frame(
 x = c(rnorm(1000, -1, 0.5), rnorm(100000, 3, 1)),
 group = c(rep("A", 1000), rep("B", 100000))
)

ggplot(df, aes(x, fill=group)) + theme_classic() +
  geom_histogram(
    alpha=0.2, color='gray80',
    position="identity", bins=80)

And here's the same plot using ..density..:

ggplot(df, aes(x, fill=group)) + theme_classic() +
  geom_histogram(
    aes(y=..density..), alpha=0.2, color='gray80',
    position="identity", bins=80)

Combine multiple histograms ggplot

You need to pivot your data into long format:

ggplot(tidyr::pivot_longer(MD3[1:2], 1:2),
       aes(x = value, fill = name)) +
  geom_bar(position = 'dodge') +
  scale_fill_brewer(palette = 'Set1') +
  theme_light()

You can even plot all your columns this way with no extra effort

ggplot(tidyr::pivot_longer(MD3, tidyr::everything()),
       aes(x = value, fill = name)) +
  geom_bar(position = 'dodge') +
  scale_fill_brewer(palette = 'Set1') +
  theme_light()

If you need to change the labels in the legend and x axis, use labs

ggplot(tidyr::pivot_longer(MD3[1:2], 1:2),
       aes(x = value, fill = name)) +
  geom_bar(position = 'dodge') +
  scale_fill_brewer(palette = 'Set1') +
  theme_light() +
  labs(x = 'My x variables', fill = 'My categories')

To remove NA values, filter them out of your data frame to start with:

ggplot(subset(tidyr::pivot_longer(MD3[1:2], 1:2), !is.na(value)),
       aes(x = value, fill = name)) +
  geom_bar(position = 'dodge') +
  scale_fill_brewer(palette = 'Set1') +
  theme_light() +
  labs(x = 'My x variables', fill = 'My categories')

ggplot2: plotting multiple histograms in the same page, but one with inverted coordinates

Like this maybe:

ggplot(data = diamonds) + 
    geom_histogram(aes(x = x,y = ..count..)) + 
    geom_histogram(aes(x = x,y = -..count..))

FYI - I couldn't remember exactly how I'd done this in the past, so I Googled "ggplot2 inverted histogram" and clicked on the first hit, a StackOverflow question.

I'm not sure exactly how the proto object that stat_bin returns is structured, but the new variables are in there somewhere. The way this works is that geom_histogram itself calls stat_bin to perform the binning, and so it has access to the computed variables, which we can map to the y variable.

Multiple Relative frequency histogram in R, ggplot

Below are some basic example with the build-in iris dataset. The relative part is obtained by multiplying the density with the binwidth.

library(ggplot2)

ggplot(iris, aes(Sepal.Length, fill = Species)) +
  geom_histogram(aes(y = after_stat(density * width)),
                 position = "identity", alpha = 0.5)
#> `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.

ggplot(iris, aes(Sepal.Length)) +
  geom_histogram(aes(y = after_stat(density * width))) +
  facet_wrap(~ Species)
#> `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.

^{Created on 2022-03-07 by the reprex package (v2.0.1)}

Plot multiple histograms in one using ggplot2 in R

You're not really plotting histograms, you're just plotting a bar chart that looks kind of like a histogram. I personally think this is a good case for faceting:

library(ggplot2)
library(reshape2) # for melt()
melt_df <- melt(df)
head(melt_df) # so you can see it

ggplot(melt_df, aes(Date,value,fill=Date)) + 
 geom_bar() + 
 facet_wrap(~ variable)

However, I think in general, that changes over time are much better represented by a line chart:

ggplot(melt_df,aes(Date,value,group=variable,color=variable)) + geom_line() 

I want to plot multiple histogram per site

Something like this?

library(tidyverse)
DF %>% 
  pivot_longer(
    cols = c(mol, an, crust)
  ) %>% 
  ggplot(aes(x=factor(Site), y=value, fill=name))+
  geom_col(position = position_dodge()) 

OR

library(tidyverse)
DF %>% 
  pivot_longer(
    cols = c(mol, an, crust)
  ) %>% 
  ggplot(aes(x=Site, y=value, fill=name))+
  geom_col(position = position_dodge()) +
  facet_wrap(.~Site, scales = "free")

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