Data Processing in R, Lecture Notes
Chapter 1 Getting Started
(08 December, 2020, 00:33)
1.1 Initial Remarks
- The purpose of these lecture notes is to allow you to quickly revisit concepts covered in class. On their own, they likely won’t form a sufficient introduction to R.
- In the following, I will draw on the following resources.
- The great introduction materials developed at the University of Glasgow: https://psyteachr.github.io/, in particular ‘Data Skills for Reproducible Science’.
- The also pretty great introduction to R and statistics by Danielle Navarro available here.
- Matt Crump’s ‘Answering Questions with Data’.
- Primers on a variety of topics: https://rstudio.cloud/learn/primers
- Cheat sheets on a variety of topics: https://rstudio.cloud/learn/cheat-sheets
- I also recommend the following tutorials:
1.2 Specific useful cheat sheets and primers
1.3 Installing and Setting up RStudio
Currently, the most convenient interface to R is the RStudio GUI. You can either install it locally, or use it online on RStudio Cloud.
1.3.1 Local RStudio installation
- Typically, RStudio is run on a local computer.
Please download R and RStudio separately from:
1.3.2 RStudio Cloud
You can use the RStudio cloud service for free after getting an account on RStudio Cloud.
1.4 Basics of the RStudio environment
Please take a look at the following thorough introduction to the RStudio environment.
Please watch the following video by Lisa DeBruine:
1.5 Special blocks in the following
- In the following, R code blocks will appear as follows. The first block shows the R code, the second block the second block (with text in bold), the R output.
##  21.0 21.0 22.8 21.4 18.7
- Grey boxes of this type introduce new functions (at least sometimes).
Computes the average of a vector of numbers.
xThe vector to average over.
1.6 Lecture Notes Source Code
- You can always download the most up-to-date version of these lecture notes from github at this URL: https://github.com/plogacev/ling411_lecture_notes_R