This project is intended to be a course to learn RMarkdown. It contains a number of RMarkdown lessons (located in the Lessons folder), each ending with some exercises. Anyone taking this course should try to come up with solutions to the exercises before sneaking into the Solutions folder.
- 00_Setup: Packages one needs to install to use RMarkdown
- 01_GettingStarted: Making first steps in RMarkdown
- 02_MovingForward: Going one step further ...
- 03_KeepingCalm: Discovering obstacles and overcome them ...
It is intended to provide two further lessons on
- 05_UsingPapaja: Using an RMarkdown template for APA-style manuscripts
- 06_Collaborating: Using GitHub and Overleaf to collaborate with others
To take this course, it is useful to have some command of the markdown language in general and of LaTeX as well as of the statistical computing software R that is best used together with RStudio. Below, we first give some recommendations for introducing oneself in markdown and LaTeX. Then we outline where to download R and RStudio, and how to get acquainted with this software and with RMarkdown.
As many explanatory or documenting files on this repository are written as markdown documents (just as this one), it may be worthwhile to have a look at the following resource to correctly create/edit markdown files:
Data analysis in this project will be done using the R software for statistical computing (and in fact much much more), so if you are not familiar with R, download it at:
by clicking on the download R link in the first paragraph on that site.
The R Project provides an Introduction to R, but it is not a short read. For a denser overview on the basics of R, you may want to use the cheat sheet below:
It is recommended to use R with RStudio as frontend, it makes working with R so much easier, and it’s free. So far, I could not find a both useful and visually appealing introduction, just ask Google if you do not find RStudio's GUI self-explaining.
We will use R together with R Markdown to analyse and report the data of this project. R Markdown enables one to analyze data and report it in one instance and in a dynamic manner. That is, if one later makes some analysis decision that will affect all downstream analyses (such as removing an outlier), the document will be dynamically updated including all now perhaps changed results. A helpful guide to R Markdown is given below, again together with a cheat sheet.
Because we will also use the R package papaja dedicated to use R Markdown for writing scientific articles in APA style, one needs to install it as described on the author's GitHub site and it may pay to familiarize yourself with the manual:
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