Scatterplot

Let’s start with a scatterplot. A scatter plot provides a graphical view of the relationship between two sets of numbers. We don’t have a variable in our metadata that is a continous variable, so there is nothing to plot it against but we can plot the values against their index values just to demonstrate the function.

plot(genome_size)

Each point represents a clone and the value on the x-axis is the clone index in the file, where the values on the y-axis correspond to the genome size for the clone. For any plot you can customize many features of your graphs (fonts, colors, axes, titles) through graphic options For example, we can change the shape of the data point using pch.

plot(genome_size, pch=8)

We can add a title to the plot by assigning a string to main:

plot(genome_size, pch=8, main="Scatter plot of genome sizes")

Histogram

Another way to visualize the distribution of genome sizes is to use a histogram, we can do this buy using the hist function:

hist(genome_size)

Boxplot

Using additional information from our metadata, we can use plots to compare values between the different citrate mutant status using a boxplot. A boxplot provides a graphical view of the median, quartiles, maximum, and minimum of a data set.

# Boxplot
boxplot(genome_size ~ cit, metadata)

Similar to the scatterplots above, we can pass in arguments to add in extras like plot title, axis labels and colors.

boxplot(genome_size ~ cit, metadata,  col=c("pink","purple", "darkgrey"),
        main="Average expression differences between celltypes", ylab="Expression")

Histogram

To plot a histogram we require another geometric object geom_bar, which requires a statistical transformation. Some plot types (such as scatterplots) do not require transformations, each point is plotted at x and y coordinates equal to the original value. Other plots, such as boxplots, histograms, prediction lines etc. need to be transformed, and usually has a default statistic that can be changed via the stat_bin argument.

ggplot(metadata) +
  geom_bar(aes(x = genome_size))

Try plotting with the default value and compare it to the plot using the binwidth values. How do they differ?

ggplot(metadata) +
  geom_bar(aes(x = genome_size), stat = "bin", binwidth=0.05)

## Warning: `geom_bar()` no longer has a `binwidth` parameter. Please use
## `geom_histogram()` instead.

Boxplot

Now that we have all the required information on let’s try plotting a boxplot similar to what we had done using the base plot functions at the start of this lesson. We can add some additional layers to include a plot title and change the axis labels. Explore the code below and all the different layers that we have added to understand what each layer contributes to the final graphic.

ggplot(metadata) +
  geom_boxplot(aes(x = cit, y = genome_size, fill = cit)) +
  ggtitle('Boxplot of genome size by citrate mutant type') +
  xlab('citrate mutant') +
  ylab('genome size') +
  theme(panel.grid.major = element_line(size = .5, color = "grey"),
          axis.text.x = element_text(angle=45, hjust=1),
          axis.title = element_text(size = rel(1.5)),
          axis.text = element_text(size = rel(1.25)))

Writing figures to file

There are two ways in which figures and plots can be output to a file (rather than simply displaying on screen). The first (and easiest) is to export directly from the RStudio ‘Plots’ panel, by clicking on Export when the image is plotted. This will give you the option of png or pdf and selecting the directory to which you wish to save it to. The second option is to use R functions in the console, allowing you the flexibility to specify parameters to dictate the size and resolution of the output image. Some of the more popular formats include pdf(), png.

Initialize a plot that will be written directly to a file using pdf, png etc. Within the function you will need to specify a name for your image, and the with and height (optional). Then create a plot using the usual functions in R. Finally, close the file using the dev.off() function. There are also bmp, tiff, and jpeg functions, though the jpeg function has proven less stable than the others.

pdf("figure/boxplot.pdf")

ggplot(example_data) +
  geom_boxplot(aes(x = cit, y =....) +
  ggtitle(...) +
  xlab(...) +
  ylab(...) +
  theme(panel.grid.major = element_line(...),
          axis.text.x = element_text(...),
          axis.title = element_text(...),
          axis.text = element_text(...)

dev.off()

Resources:

We have only scratched the surface here. To learn more, see the ggplot reference site, and Winston Chang’s excellent Cookbook for R site. Though slightly out of date, ggplot2: Elegant Graphics for Data Anaysis is still the definative book on this subject. Much of the material here was adpapted from Introduction to R graphics with ggplot2 Tutorial at IQSS.