Using R Markdown

R markdown

R markdown is a very convenient and useful tool for communicating R output. It allows us to write text directly into a file, which will then be rendered into a presentable format, and it also allows us to embed R code into our document, which will be processed when we “knit” the document, with the results presented in the “knitted” output.

We have used R markdown files extensively in the course so far, so hopefully the format is now familiar to you. If you need a refresher on the basics of R markdown, this cheat sheet from R studio is very helpful. I suggest you download it and keep it open on your desktop while completing your homeworks.

R markdown and project reporting

I want to emphasise to all of you that the purpose of R markdown is to communicate results. It is not intended to experiment with code or to run very long and complex models. That is what R scripts are for. You should limit your code in R markdown to what is necessary for communication. In the case of your homeworks, you need to communicate:

1. That you know the correct code.
2. That you have produced the correct results.

To do this, you do not necessarily need to produce the results from the code inside the R markdown file. Instead:

• You can run the code in an R script and save the output using the saveRDS() function.
• You can then read that output into the R markdown file using the readRDS() function inside a code chunk.
• You can include the original code that you used to create the output inside a separate chunk which does not evaluate during knitting by adding the eval = FALSE option to the brackets above that chunk.

You do not need to do this for every line of code, and most code is fine to run inside R markdown. You should use your own judgement though to determine which code it is foolish to run inside R markdown: very intensive machine learning algorithms will certainly fall into this category.

In general, I recommend you:

1. Experiment with your code first in a separate R script.
2. Once you have the correct, minimal code necessary, create a single R script which contains the complete workflow of the analysis.
3. If running this script produces the correct results, you can then save any objects which result from computationally intensive algorithms using saveRDS().
4. Finally, you can copy-paste the relevant sections of code from your final R script into the R markdown file, remembering to include the eval = FALSE option for those chunks which contain computationally intensive algorithms, and instead using readRDS() in a separate chunk to read in the object which you already created.