How to create an exercise solution blog post

Last updated on Feb 11, 2020 5 min read instructions, blogdown, exercises, github

Each of you will be responsible once or twice over the semester to create a blog post that provides a clean, clearly-presented solution to the in-class exercise for the week. This blog post provides the technical instructions for writing and submitting that exercise.

Your exercise solution should be posted before the next class meeting. Since it will need to be reviewed by the faculty before it can be officially posted, please plan to submit it by the Tuesday after the class for your exercise. Student assignments for the exercises are given in the Schedule section of our course website.

Overview of creating a post

You will be submitting your exercise solution as a blog post. Creating one for our website will follow all the same steps as creating a blog post for a vocabulary list, just with different content. Please read the post on creating a vocabulary list and follow the steps there to:

Update your fork of the website
Make a new blog post
Use RMarkdown syntax to write the blog post
Submit the blog post

Content for the blog post

The blog post should provide a walk-through of the solution to that week’s in-course exercise. We have posted an example for the exercise for Chapter 1 to give you an idea of what you should aim to write.

Generally, this exercise will be a resource for everyone in the class, to make sure they’ve understood the exercise, as well as to see how someone else tackled the problem. Your solution should cover all parts of the exercise (for example, if there’s a part A and B, you should cover both). You can start by writing it as you would if you were assigned the exercise as a homework problem, but then you should do a second step of revision to provide some context and dig a bit deeper into how you tackled the question. Since we are only requiring you to write up exercise answers once or twice over the semester (rather than submitting homework for exercises every week), we expect this product to be more in-depth and polished than a typical homework solution.

First, make sure that you have provided text explaining what the exercise asks for, in case the reader hasn’t recently read the exercise prompt. Second, please add a few details either about how you tackled the problem through code or how the statistical principles covered in the exercise could apply to other problems you’ve come across in your research or coursework.

To help in preparing your post, plan to spend the exercise time in class during the week of your exercise visiting the different groups of students working on the exercise. You can talk to them about how they’re approaching the problem, how they interpret it, etc., to help you develop your own answer.

Tips

Be sure to refresh yourself on all the Markdown formatting tags you can use to improve the appearance of your post. Be sure to include things like section headings and italics or bold as appropriate. RStudio’s website has some nice cheatsheets on RMarkdown that can help.
Make sure you include R code if appropriate. If you put parentheses around an assignment expression in R, it will print out the assigned object and make the assignment in the same call—you might find this useful in writing concise code while still showing what’s in the objects you create.
Use the $ and $$ tags in RMarkdown to include mathematical equations in your blog post when appropriate.
If you need to read in a dataset for R code in your blog post, save it in the website directory’s “content/post/example_datasets” subdirectory. If your data comes from an online source or from an R library, you won’t need to do this, only if you need a “local” copy of the datafile to run your RMarkdown code.
You are welcome to draw from (and cite) other statistics textbooks or dictionaries if you’d like to in explaining the problem and your approach to it.
For the code, look at vignettes and helpfiles, especially for packages you are not familiar with.
For a lot of Bioconductor packages, object-oriented programming is used pretty heavily. This means that associated data in R packages will often be stored in a format that you haven’t used yet. Look up more information on data classes used in your exercise if you aren’t familiar with them. You can use the class function to determine the class of an object as well as the name of the package that defines that class. The str function is often helpful for exploring a data object class, as well. Many of the Bioconductor object classes will have special accessor methods, which are functions that allow you to extract certain elements from the object—check the helpfile for the object class, as these methods are often listed there with examples.
Googling can also be very helpful for learning more about functions, packages, and datasets in R, especially if you don’t yet know what package the item is from.
Most Bioconductor packages have very nice vignettes available online and from your R session once you have installed the package. These are a great place to start to find out more about how to use the functions and object classes that come with the package.