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12_boxplot_exercise.md

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Plotting and data visualization in R
Mary Piper, Meeta Mistry, Radhika Khetani
Wednesday, December 4, 2019

Approximate time: 60 minutes

Learning Objectives

  • Generate the box plot using ggplot2

Generating a Boxplot with ggplot2

A boxplot provides a graphical view of the distribution of data based on a five number summary:

  • The top and bottom of the box represent the (1) first and (2) third quartiles (25th and 75th percentiles, respectively).
  • The line inside the box represents the (3) median (50th percentile).
  • The whiskers extending above and below the box represent the (4) maximum, and (5) minimum of a data set.
  • The whiskers of the plot reach the minimum and maximum values that are not outliers.

In this case, outliers are determined using the interquartile range (IQR), which is defined as: Q3 - Q1. Any values that exceeds 1.5 x IQR below Q1 or above Q3 are considered outliers and are represented as points above or below the whiskers.

1. Boxplot!

Generate a boxplot using the data in the new_metadata dataframe. Create a ggplot2 code chunk with the following instructions:

  1. Use the geom_boxplot() layer to plot the differences in sample means between the Wt and KO genotypes.
  2. Use the fill aesthetic to look at differences in sample means between the celltypes within each genotype.
  3. Add a title to your plot.
  4. Add labels, 'Genotype' for the x-axis and 'Mean expression' for the y-axis.
  5. Make the following theme() changes:
    • Use the theme_bw() function to make the background white.
    • Change the size of your axes labels to 1.25x larger than the default.
    • Change the size of your plot title to 1.5x larger than default.
    • Center the plot title.

After running the above code the boxplot should look something like that provided below.

2. Changing the order of genotype on the Boxplot

Let's say you wanted to have the "Wt" boxplots displayed first on the left side, and "KO" on the right. How might you go about doing this?

To do this, your first question should be - How does ggplot2 determine what to place where on the X-axis?

  • The order of the genotype on the X axis is in alphabetical order.
  • To change it, you need to make sure that the genotype column is a factor
  • And, the factor levels for that column are in the order you want on the X-axis
  1. Factor the new_metadata$genotype column without creating any extra variables/objects and change the levels to c("Wt", "KO")
  2. Re-run the boxplot code chunk you created for the "Boxplot!" exercise above.

3. Changing default colors

You can color the boxplot differently by using some specific layers:

  1. Add a new layer scale_color_manual(values=c("purple","orange")).
    • Do you observe a change?
  2. Replace scale_color_manual(values=c("purple","orange")) with scale_fill_manual(values=c("purple","orange")).
    • Do you observe a change?
    • In the scatterplot we drew in class, add a new layer scale_color_manual(values=c("purple","orange")), do you observe a difference?
    • What do you think is the difference between scale_color_manual() and scale_fill_manual()?
  3. Back in your boxplot code, change the colors in the scale_fill_manual() layer to be your 2 favorite colors.
    • Are there any colors that you tried that did not work?

We have a separate lesson about using color palettes from the package RColorBrewer, if you are interested.

You are not restricted to using colors by writing them out as character vectors. You have the choice of a lot of colors in R, and you can do so by using their hexadecimal code. For example, "#FF0000" would be red and "#00FF00" would be green similarly, "#FFFFFF" would be white and "#000000" would be black. Click here for more information about color palettes in R or you can use this helpful cheatsheet that lists many popular colors in R.

OPTIONAL Exercise:

  • Find the hexadecimal code for your 2 favourite colors (from exercise 3 above) and replace the color names with the hexadecimal codes within the ggplot2 code chunk.

This lesson has been developed by members of the teaching team at the Harvard Chan Bioinformatics Core (HBC). These are open access materials distributed under the terms of the Creative Commons Attribution license (CC BY 4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.