10.1 Histograms and density plots

10.1.1 Histogram

We can use one of our previous objects to create a histogram. You can get it back with:

rnaseq2 <- read_csv("DataViz_source_files-main/files/GSE150029_rnaseq_log2_long.csv")

ggplot(rnaseq2, aes(x=log2_counts)) + 
  geom_histogram()

We can split the histogram per sample:

ggplot(rnaseq2, aes(x=log2_counts, color=sample)) + 
  geom_histogram() 

Set position to “identity”, so histograms will not be on top of each other:

ggplot(rnaseq2, aes(x=log2_counts, color=sample)) + 
  geom_histogram(position="identity")

Set alpha (transparency to 0.2):

ggplot(rnaseq2, aes(x=log2_counts, color=sample)) + 
  geom_histogram(position="identity", alpha=0.2)

Express as density instead of counts:

ggplot(rnaseq2, aes(x=log2_counts, color=sample)) + 
  geom_histogram(aes(y=after_stat(density)), position="identity", alpha=0.2) +
  theme_classic()

10.1.2 Density plot

The structure is pretty much the same:

ggplot(rnaseq2, aes(x=log2_counts, color=sample)) + 
  geom_density(alpha=0.2) +
  theme_classic()

You can use fill instead:

ggplot(rnaseq2, aes(x=log2_counts, fill=sample)) + 
  geom_density(alpha=0.2) +
  theme_classic()

10.1.3 Histogram + density

As we combined geom_boxplot and geom_violin in a previous exercise, we can also combine geom_histogram and geom_density:

ggplot(rnaseq2, aes(x=log2_counts, color=sample)) + 
  geom_histogram(aes(y=after_stat(density)), position="identity", alpha=0.2) +
  geom_density(alpha=0.2) +
  theme_classic()