Previous versions of this material were also presented at:
* APS, San Antonio, Texas, 2017
For workshops, we subset the information available elsewhere in this book to the pages in this section specifically for the workshop. This provides a convenient way to reduce the contents of a book to the essential portions we feel can be reasonably covered in a several hour long workshop. However, the cost of brevity is a lack of detail. While this section may be used as an entry point, learning about genomic analyses in R, details may be found elsewhere in the book.
Create an RStudio “Project”
Open RStudio and under the “File” dropdown menu select “New Project…”. A “New Project” window should pop up where you should select the option to create your project in a “New Directory”. Select the “New Project” option. You will be asked where on your filesystem you’d like your project and what to name your project. We’ll name our project “APS_Workshop”. This should create a directory for your project that will contain a file named “APS_Workshop.Rproj”. When you double click this file it should spawn a new session of RStudio and will use that directory as your working directory.
Download the example datasets
We have placed example data files at the OSF site for Population genomics in R workshop. You can download them by copying and pasting the below code into your R console.
You should be able to validate that the files have been downloaded with the below code.
##  "pinfsc50_filtered.vcf.gz" "population_data.gbs.txt" "prubi_gbs.vcf.gz"
APS_Workshopand copy and paste the below command into the R console.
This should output some tests to the console. It should also generate a report file called
apstest.txt. The report file should look exactly like this file. If your results are different, and you don’t understand why, send us an email with the report as an attachment.
Some aspects of analyzing genetic data are rather technical. Others are more stylistic. An example is the presentation of the data. The presentation of data may include choices in color schemes and sometimes the perspective on the data. Below is a little example that you can copy and paste into your R console. Explore how changing the number in
set.seed() changes the plot. Remember to execute the
plot_poppr_msn(partial_clone, myMsn, palette = brewer.pal(n=4, name = "Set1")) function again. There are other examples that are commented out (i.e., the lines begin with
# so they are not executed). Try removing the comment character (
#) and see how the different parameters affect the plot. This example should validate that you have successfully installed
poppr and hopefully provides a fun example that may inspire you to explore more options.
library(RColorBrewer) library(viridisLite) library(poppr) data(partial_clone) myMsn <- bruvo.msn(partial_clone, include.ties = TRUE, showplot = FALSE) set.seed(9) plot_poppr_msn(partial_clone, myMsn, palette = brewer.pal(n=4, name = "Set1")) #plot_poppr_msn(partial_clone, myMsn, palette = magma(n=4, begin = 0.2, end = 0.8)) #plot_poppr_msn(partial_clone, myMsn, palette = plasma(n=4))
After you’ve completed the workshop, please fill out this quick survey to provide us with feedback. Thank you!