UK R Courses

Over the next few months we’re running a number of R, Stan and Scala courses around the UK.

June (London)

• Mon Jun 26 – Introduction to R
• Tue Jun 27 – Statistical Modelling with R
• Wed Jun 28 – Programming with R
• Thu Jun 29 (2-day course) – Advanced R Programming

August (Belfast)

• Thu Aug 10 – Programming with R
• Fri Aug 11 – Building an R Package

September 2017 (Newcastle)

• Mon Sep 11 – Introduction to R
• Tue Sep 12- Statistical Modelling with R
• Wed Sep 13- Programming with R
• Thu Sep 14- Advanced Graphics with R
• Fri Sep 15- Automated Reporting (first steps towards Shiny)
• Mon Sep 18 (5-day course) – Bioconductor

October (Glasgow)

• Wed Oct 25 – Advanced Graphics with R
• Thu Oct 26 (2-day course) – Advanced R Programming

November (London)

• Mon Nov 06 (1-day course) – Efficient R Programming
• Tue Nov 07 (1-day course) – R for Big Data

December (London/Newcastle)

• Thu Dec 07 (2-day course) – Introduction to Bayesian Inference using RStan (Newcastle)
• Mon Dec 11 (3-day course) – Scala for Statistical Computing and Data Science (London)

See the website for course descriptions. Any questions, feel free to contact me: colin@jumpingrivers.com

On site courses available on request.

R Courses at London, Leeds and Newcastle

Over the next few months we’re running a number of R courses at London, Leeds and Newcastle.

• September 2016 (Newcastle)
  • Sept 12th: Introduction to R
  • Sept 13th: Statistical modelling
  • Sept 14th: Programming with R
  • Sept 15th: Efficient R: speeding up your code
  • Sept 16th: Advanced graphics
• October 2016 (London)
  • Oct 3rd: Introduction to R
  • Oct 4th: Programming with R
  • Oct 5, 6th: Predictive analytics
  • Oct 7th: Building an R package
• November 2016 (Leeds)
  • Nov 21st, 22nd: Predictive analytics
  • Nov 23rd: Building an R package
• December 2016 (London)
  • December 5th, 6th: Advanced programming. Held at the Royal Statistical Society (booking form).
• January 2017 (Newcastle)
  • Jan 16th: Introduction to R
  • Jan 17th: Statistical modelling
  • Jan 18th: Programming with R
  • Jan 19th: Efficient R: speeding up your code
  • Jan 20th: Advanced graphics

See the website for course description. Any questions, feel free to contact me: csgillespie@gmail.com

On site courses available on request.

R Courses at Newcastle

Over the next two months I’m running a number of R courses at Newcastle University.

• May 2016
  • May 10th, 11th: Predictive Analytics
  • May 16th – 20th: Bioconductor
  • May 23rd, 24th: Advanced programming
• June 2016
  • June 8th: R for Big Data
  • June 9th: Interactive graphics with Shiny

Since these courses are on  advanced topics, numbers are limited (there’s only a couple of places left on Predictive Analytics). If you are interested in attending, sign up as soon as possible.

Getting to Newcastle is easy. The airport is 10 minutes from the city centre and has direct flights to the main airport hubs: Schiphol, Heathrow, and Paris.  The courses at Newcastle attract participants from around the world; at the April course, we had representatives from North America, Sweden, Germany,  Romania and Geneva.

Cost: The courses cost around £130 per day (more than half the price of certain London courses!)

 

Onsite courses available on request.

UK R Courses – 2012

The School of Mathematics & Statistics at Newcastle University (UK), are again running some R courses. In January, 2012, we will run:

• January 16th: Introduction to R;
• January 17th: Programming with R;
• January 18th & 19th: Advanced graphics with R.

The courses aren’t aimed at teaching statistics, rather they aim to go through the fundemental concepts of R programming. Further information is available at the course website. If you have any questions, feel free to contact me: colin.gillespie@newcastle.ac.uk

 

Bespoke courses are also on request.

R books for undergraduate students

In a recent post, I asked for suggestions for introductory R computing books. In particular, I was looking for books that:

• Assume no prior knowledge of programming.
• Assume very little knowledge of statistics. For example, no regression.
• Are cheap, since they are for undergraduate students.

Some of my cons aren’t really downsides as such. Rather, they just indicate that the books aren’t suitable for this particular audience. A prime example is “R in a Nutshell”.

I ended up recommending five books to the first year introductory R class.

Recommended Books

• A first course in statistical programming with R (Braun & Murdoch)
  • Pros: I quite like this book (hence the reason I put it on my list). It has a nice collection of exercises, it “looks nice” and doesn’t assume knowledge of programming. It also doesn’t assume (or try to teach) any statistics.
  • Cons: When describing for loops and functions the examples aren’t very statistical. For example, it uses Fibonacci sequences in the while loop section and the sieve of Eratosthenes for if statements.
• An introduction to R (Venables & Smith)
  • Pros: Simple, short and to the point. Free copies available. Money from the book goes to the R project.
  • Cons: More a R reference guide than a textbook.
• A Beginner´s Guide to R by Zuur.
  • Pros: Assumes no prior knowledge. Proceeds through concepts slowly and carefully.
  • Cons: Proceeds through concepts very slowly and carefully.
• R in a Nutshell by Adler.
  • I completely agree with the recent review by Robin Wilson: “Very comprehensive and very useful, but not good for a beginner. Great book though – definitely has a place on my bookshelf.”
  • Pros: An excellent reference.
  • Cons: Only suitable for students with a previous computer background.
• Introduction to Scientific Programming and Simulation Using R by Jones, Maillardet and Robinson.
  • Pros: A nice book that teaches R programming. Similar to the Braun & Murdoch book.
  • Cons: A bit pricey in comparison to the other books.

Books not being recommended

These books were mentioned in the comments of the previous post.

• The Basics of S-PLUS by Krause & Olson.
  • Most students struggle with R. Introducing a similar, but slightly different language is too sadistic.
• Software for Data Analysis: Programming with R by Chambers.
  • Assumed some previous statistical knowledge.
• Bayesian Computation with R by Albert.
  • Not suitable for first year students who haven’t taken any previous statistics courses.
• R Graphics by Paul Murrell
  • I know graphics are important, but a whole book for an undergraduate student might be too much. I did toy with the idea of recommending this book, but I thought that five recommendations were more than sufficient.
• ggplot2 by Hadley Wickham.
  • Great book, but our students don’t encounter ggplot2 in their undergraduate course.

Online Resources

• Introduction to Probability and Statistics by Kerns
  • Suitable for a combined R and statistics course. But I don’t really do much stats in this module.
• The R Programming wikibook (a work in progress).
  • Will give the students this link.
• Biological Data Analysis Using R by Rodney J. Dyer. Available under the CC license.
  • Nice resource. Possibly a little big for this course (I know that this is very picky, but I had to draw the line somewhere). Will probably use it for future courses.
• Hadley Wickham’s devtools wiki (a work in progress).
  • Assumes a good working knowledge of R
• The R Inferno by Patrick Burns
  • Good book, but too advanced for students who have never programmed before.
• Introduction to S programming
  • It’s in french – this may or may not be a good thing depending on your point of view 😉