3 Learning Resources
3.1 New to R Kickstart your learning and career with these 6 steps!
3.1.1 New to R Kickstart your learning and career with these 6 steps! – paulvanderlaken.com (Paul van der Laken, 2017)
- Create a directory for your R learning stuff somewhere on your computer.
- Download A (very) short introduction to R by Paul Torfs and Claudia Bauer
- Read the introduction and follow the steps. It will help you install all R software on your own computer and familiarize you with the standard data types.
- References/Cheat Sheets - Many standard functions exist in R and after a while you will remember them by heart. For now, it’s good to have a dictionary or references close by hand. Download and read the cheat sheets for:
- R Base (Mhairi McNeill) and
- R Base Functions (Tom Short).
- Because you’ll be writing most of your R scripts in RStudio, it’s also recommended to have an:
- RStudio Cheatsheet as well as an
- RStudio Keyboard Shortcuts Cheatsheet by hand.
- Swirl - Go through the exercises in the Swirl package and take 1: R Programming: The basics of programming in R .
- Open up your RStudio and enter the two lines of code below in your console window.
install.packages('swirl') #download swirl packagelibrary(swirl) #load in swirl package- Swirl (webpage) will automatically start and after a couple of prompts you will be able to choose the learning course called 1: R Programming: The basics of programming in R.
- This course consists of 15 modules via which you will master the basics of R in the environment itself. Start with module 1 and complete between one to three modules per day, so that you finish the swirl course in a week.
- YaRrr! The Pirate’s Guide to R (Phillips, 2017) starting in chapter 3.
- OK, you should now be familiar with the basics of R. However, knowledge is crystallized via repetition. I therefore suggest, you walk through the book YaRrr! The Pirate’s Guide to R (Phillips, 2017) starting in chapter 3. It’s a fun book and will provide you with more knowledge on how to program custom functions, loops, and some basic statistical modelling techniques – the thing R was actually designed for.
- R for Data Science (Grolemund & Wickham, 2017)
- By now, you can say you might say you are an adapt R programmer with statistical modelling experience. However, you have been working with base R functions mostly, knowledge of which is a must-have to really understand the language. In practice, R programmers rely strongly on developed packages nevertheless. A very useful group of packages is commonly referred to as the tidyverse. You will be amazed at how much this set of packages simplifies working in R. The next step therefore, is to work through the book R for Data Science (Grolemund & Wickham, 2017).
- You are now several steps and a couple of weeks further. You possess basic knowledge of the R language, know how to write scripts in RStudio, are capable of programming in base R as well as using the advanced functionality of the tidyverse, and you have even made a start with some basic statistical modelling. It’s time to set you loose in the wonderful world of the R community. If you had not done this earlier, you should get accounts on / subscribe to /
- Stack Overflow
- Cross Validated
- R Help Mailing List
- R Bloggers
- paulvanderlaken.com - Data Science, Machine Learning, & Visualization
- On Twitter, have a look at #rstats and,
- On reddit, subscribe to the rstats, rstudio, and statistics threads.
- Stack Overflow
- Continuing Education
- At this time, I can’t but advise you to return to the R Resources Overview (Paul van der Laken, 2017) and to continue broadening your R programming skills.
- Pick materials in the area that interests you:
- If you want to become a hardcore programmer, this Coursera R programming course may better suit you and you will want to learn Efficient R Programming (Gillespie & Lovelace, 2017).
- Relatedly, if you want to become a program developer, building functions and packages, you also want to consider the above resources and simultaneously master Software Development in R (Peng, Kross, & Anderson, 2017).
- If you like visualization, look into
- Relately, if you like interactive visualizations, you will want to look at the above as well as
- R Shiny and
- HTML Widgets
- If you want to become a data scientist,
- focus on machine learning via this Data School course on statistical learning (Hastie & Tibshirani, 2014).
- If you prefer a shorter, practical introduction, try this Kaggle Competition Titanic walkthrough on Youtube.
- If you like automation and reporting, start with
- If you’re more interested in text analysis and text mining, knowledge of
- Regular Expressions is a must-have and
- a good additional start would be the book on Tidy Text Mining (Silges & Robinson, 2017)
3.2 R for Data Science
3.2.1 R for Data Science
3.3 Other Learning Resources
YaRrr! The Pirate's Guide to R
R resources (free courses, books, tutorials, & cheat sheets) – paulvanderlaken.com
- Quoting, enquoting, !!, etc.
Using R for psychological research - A simple guide to an elegant language
- Appears to be a good overall reference
- owners/maintainers of the psych package
The R class R programming for biologists
- Looks like a good introduction/tutorial to R, RStudio, etc.
Using R and psych for personality and psychological research
- Psych package
Data Analytics Classroom
A First Course in Statistical Programming with R
3.4 Git
3.4.1 git ready: learn git one commit at a time
git ready: learn git one commit at a time (Quaranto, 2009)
- Some good intro resources. Dated 2009, so possibly old.
- 6/12-13: read the Beginner section
- Next Steps: read the Intermediate and Advanced sections, and possibly look through the Resources