Advanced R and Recent Advances in R

General information

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 functionality
• dplyr, for data frame manipulation
• stringr and stringi, for string processing
• magrittr, for piping
• tidyr, for tidying data frames
• broom, for tidying model output
• janitor, 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