16.5 Histograms

Simple histogram on one sample (using the df2 data frame):

ggplot(data=df1, mapping=aes(x=sample1)) + geom_histogram()

## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.

Histogram on more samples (using df_long):

ggplot(data=df_long, mapping=aes(x=value)) + geom_histogram()

## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.

Split the data per sample (“variable” column that represents here the samples):

ggplot(data=df_long, mapping=aes(x=value, fill=variable)) + geom_histogram()

## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.

By default, the histograms are stacked: change to position dodge (side by side):

phist <- ggplot(data=df_long, mapping=aes(x=value, fill=variable)) + 
    geom_histogram(position='dodge')
phist

## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.

HANDS-ON

Going back to the rock dataset:

• Create a histogram of the rocks perimeter.
• Add a density plot to the histogram, following instructions from this post

Answer

# Create a histogram of the rocks **perimeter**.
ggplot(data=rock, mapping=aes(x=peri)) + geom_histogram()
# Add a density plot to the histogram
ggplot(data=rock, mapping=aes(x=peri)) + 
    geom_histogram(aes(y=..density..)) +
    geom_density(alpha=.2, fill="lightblue")