Data Science in Omics Introduction
An OMICS Course Part 1: Shell, Organizing and Genomics
This is a follow up to Sexual Dimorophism Exploration.
Having done some basic visualization of the Lislevand et al. 2007 dataset of bird size measures you realize that you’ll need to do some data manipulation to really get at the questions you want to answer.
In Sexual Dimorophism Exploration you created a plot of the histograms of female and male masses by family. This resulted in a lot of plots, but many of them had low sample sizes.
Use dplyr to filter out null values for mass, group the the data by family,
and then filter the grouped data to return data only for families with more
than 25 species. Add a comment to the top of the block of code describing
what it does.
Now, remake your original graph using only the data on families with greater than 25 species.
Sexual size dimorphism doesn’t seem to show up clearly when visually comparing the distributions of male and female masses across species. Maybe the differences among species are too large relative to the differences between sexes to see what is happening; so, you decide to calculate the difference between male and female masses for each species and look at the distribution of those values for all species in the data.
In the original data frame, use mutate() to create a new column which is the
relative size difference between female and male masses
(F_mass - M_mass) / F_mass
and then make a single histogram that shows all of the species-level differences. Add a vertical line at 0 difference for reference.
Combine the two other tasks to produce histograms of the relative size difference for each family, only including families with more than 25 species.
Save the figure from task 3 as a jpg file with the name
sexual_dimorphism_histogram.jpg.
