Summary
This one-day course is set out to improve your R skills and make you a more efficient programmer. In particular, you will:
- become better at file management with R
- learn all about piping operators
- understand what functional programming means
- get an overview of string processing and regular expressions
- get to know new tools that help you tidy data
- learn how to manipulate data frames efficiently
- be able to routinely split-apply-combine your data
- learn to establish a debugging workflow
This course focuses more on recent advances in R than expert knowledge you're hardly likely to ever apply in your daily workflow. Ultimately, the goal is to help you improve your data processing workflow. To that end, you will updated on the following new and/or popular packages:
plyr
, for consistent split-apply-combine functionalitydplyr
, for data frame manipulationstringr
andstringi
, for string processingmagrittr
, for pipingtidyr
, for tidying data framesbroom
, for tidying model outputjanitor
, for basic data tidying and examinations
Event
Social Science Data Lab, MZES Mannheim
Date and Venue
Wednesday, January 18, 2017, MZES A Building, Room A-231
Instructor
Simon Munzert (website, Twitter)
Requirements
Working knowledge of R is a necessary prerequisite. You're assumed to be familiar with fundamentals such as how to operate with different object types in R, how to work with the apply family (apply(), sapply() etc.), and how to program your own basic functions.
Are you prepared for taking this course? Take a look at the basic R vocabulary listed here. Are you aware of at least 60% to 80% of the functions? Then you're prepared. I plan to conduct a poll with participants before the workshop takes place to determine which topics you're already familiar with and which should be covered.
Resources
The materials presented in this workshop were developed on the basis of several resources, including:
- Hadley Wickham's "Advanced R" book. A free version of the book is available here; a physical copy can be purchased here.
- Garrett Grolemund and Hadley Wickham's "R for Data Science" book. A free version of the book is available here; a physical copy can be purchased here.
Admittedly, while much of the content presented here may be useful to you even if you started to learn R many years ago and lost track of the more recent developments, it is, at list in part, utterly boring. So here's some entertaining content on the Web that is well suited for procrastination between the sessions:
- hipsteR: re-educating people who learned R before it was cool by Karl Broman
- Rbitrary Standards by Oliver Keyes
- Awesome R
- R Data Science Tutorials, curated by Ujjwal Karn