- #Download rmarkdown how to
- #Download rmarkdown pdf
- #Download rmarkdown update
- #Download rmarkdown software
- #Download rmarkdown code
For those of you who have already downloaded everything, skip this next section. Before we dive into that, though, we need to download the software. We will touch on both of these concepts today. We learned in class that the choice of statistical learning technique we will use to estimate the function that connects Y and X is driven by (1) what we want to do with the data and (2) characteristics of the data set itself. The goals of today's lab are to (1) introduce RStudio and RMarkdown, (2) practice downloading data and (3) conduct EDA in R.
#Download rmarkdown how to
They will be review on how to set-up R and RMarkdown. Go through and answer the questions, but feel free to skim the first few sections. Dalzell, you will be able to skip much of this set-up and introductory section. If you have questions, please ask! If you have taken MST 256 with Dr. No prior experience with RMarkdown is assumed. The fact that you have familiarity with R is something you can put on your resume, and can be beneficial when applying for internships and jobs.įor those who are new to R and RMarkdown, welcome! This lab is intended to be an introduction to this material so we are all on the same page as we start the class. We will work with R because it is (1) free, (2) widely-used in industry and academia and (3) highly adaptable to multiple disciplines. R, STATA, SAS, Python, MatLab and Excel are just a few examples.
Why do we need to learn a computing language at all? In practice, real statistical analysis is performed using some sort of computing language. We will build the skills we need as we move through the class. We assume you have no experience with R as you come into this course, so don't worry if this language is new to you.
#Download rmarkdown pdf
RStudio allows us to see visualizations, explore stored data sets and create PDF documents all within the same interface.
We will be working with R through an interface called RStudio.
#Download rmarkdown software
Welcome to Statistical Learning! Throughout this course, we will be using a statistical software called R to practice the statistical concepts discussed in class, to analyze real data and to make informed conclusions. Complete all Questions and submit final PDF under Assignments in Sakai.
#Download rmarkdown code
All the code can be seen (and used!) here. ```Īnd that’s it! With a little work upfront we can now stylize different languages in the same markdown file and apply this to a bookdown, blogpost, or even slides. We also set eval=FALSE because R does not know how to compile SAS code (without other packages like SASMarkdown) and these are just for show. Now all we need to do is add some code to our markdown file! By adding class='SAS' to the chunk arguments these chunks will be stylized appropriately. Don’t be intimidated by the minified JavaScript file - there is a vector of keywords in the file which you can simply add words to! As you can see in the image below, we’ve added data and descending to the list. When using SAS styling in presentations, our team uncovered that some keywords are missing from the SAS file and a special color wasn’t being applied.
#Download rmarkdown update
Feel free to play with these and find one that suits your style! Update JavaScript Code as Needed For the CSS argument I chose a “dark mode” file from within the folder of different styles. This is important because HTML is read top to bottom. The in_header argument allows us to put our scripts in the beginning of the HTML. With our header file and CSS folder, we can now go into our RMarkdown and source these both within our YAML:. This is kind of like starting an R script with a call to your favorite library before you can use a function from it. The first line sources the JavaScript file we included, and the second line calls the function from the file. We need to create a new file called header.html that does two things:
Add header.htmlīy outputting our RMarkdown as HTML we can add scripts to the head of our document to be used throughout. I copied both of these into the RProject with my markdown. The downloaded zip includes the JavaScript file and an entire folder of different highlighting styles. Once you have the languages you’d like to include highlighting for, download! Add Files to Project For our purposes I selected both R and SAS. This will bring you to a new page with an enormous selection of coding languages for highlighting syntax. Head over to and click on the latest version. This article will walk you through the 6 steps needed to add SAS formatting to your R Markdown using highlight.js: You can specify several coding languages when creating code chunks in R Markdown, but adding SAS formatting takes a little work. SAS is a major player in the clinical trial world, and as R enthusiasts here at Atorus, finding ways that SAS and R can be symbiotic when used together in tools like R Markdown proves quite useful.
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