R COURSES

R is a (free) open source programming language and software environment for statistical computing and graphics. It is used by many different companies, including: Google, Facebook, Bank of America and many firms involved in Biomedical research.

Throughout the year, we run a number of courses:

The aim of these courses isn't to teach statistics, rather to introduce fundamental concepts of programming with R.

COURSE DATES

January 2015

February 2015

March 2015

April 2015

May 2015

June 2015

September 2015

FEES

  • Course 1: Introduction to R: £140
  • Course 2: Statistical modelling: £180
  • Course 3: Programming with R: £180
  • Course 4: Advanced programming (two days): £360
  • Course 5: Advanced graphics with R: £180
  • Course 6: Efficient R programming: £180
  • Course 7: Building an R package: £180
  • Course 8: Five day bioconductor course: £800
  • Course 9: R for Six Sigma professionals: £900
  • Course 10: Predictive analytics: £450
  • Course 11: Spatial data analysis with R (two days): £360

Discounts

  • External academics and charities: 25% discount
  • Newcastle academics: 35% discount
  • If three people register from the same institution, the fourth place is free


If you qualify for a discount, choose below and select the number of attendees for whom you would like to calculate costs - the fees above will update accordingly.

No discount
External academics and charities: 25% discount
Newcastle academics: 35% discount

Number of attendees (assumes all are from the same institution)

If you have any questions, please contact the course organiser.

Cancellations

Cancellations up to 14 days before the course start date will incur a 30% cancellation fee. For later cancellations, or non attendance, the full course fee will be charged.

REGISTRATION

How to register:



Events

No discount
External academics and charities: 25% discount
Newcastle academics: 35% discount
Number of attendees (assumes all are from the same institution)

Total: £

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CONTACT

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Address

School of Mathematics &Statistics
Herschel Building
Newcastle University
Newcastle upon Tyne
NE1 7RU
UK

General Office

+44 (0) 191 208 5371

Email

colin.gillespie@ncl.ac.uk (for all enquiries)

COURSE DESCRIPTIONS

Introduction to R

This is a one day intensive course on R. This course will be a mixture of lectures and computer practicals. The main focus will be to introduce fundamental R concepts.

No prior programming knowledge of any kind is assumed. This course is suitable for a wide range of applicants e.g., biologists, statisticians, engineers, students.

Course outline:

  • Introduction to R: A brief overview of the background and features of the R statistical programming system.
  • Entering Data: A description of how to import and export data from R.
  • Data types: A summary of R's data types.
  • R environment: A description of the R environment including the R working directory, creating/using scripts, saving data and results.
  • R Graphics: Creating, editing and storing graphics in R.
  • Manipulating data in R: Describing how data can be manipulated in R using logical operators.

Course structure

This course will be structured as follows:

  • 8:30 -- 9:00: Registration and coffee
  • 9:00 -- 10:30: Lecture
  • 10:30 -- 11:00: Coffee break
  • 11:00 -- 12:00: Lecture
  • 12:00 -- 1:00 Lunch (not provided)
  • 1:00 -- 2:00 Practical 1
  • 2:00 -- 2:40: Lecture
  • 2:40 -- 3:00: Coffee break
  • 3:00 -- 4:30: Practical 2

These times are intended to give a flavour of how the course is run and are subject to change.

Comments from previous courses

  • Clear explanations; combination of theory and practice is excellent.
  • Good pace, good split of practical and lecture.
  • Excellent introduction to R!
  • Nice friendly environment.
  • Almost one to one! Great teaching, good lectures.

Presenter

Dr Colin Gillespie, Statistics Lecturer in the School of Mathematics &Statistics.

Introduction to statistical modelling in R

This is a one day intensive course on modelling in R. This course will be a mixture of lectures and computer practicals.

Prior knowledge: it will be assumed that participants are familiar with R. For example, inputting data, basic visualisation and data frames. Attending the introduction to R courses will provide a sufficient background. This course is suitable to a wide range of applicants e.g., biologists, statisticians, engineers, students.

Course outline:

  • Basic hypothesis testing: examples include one-sample t-test, one-sample Wilcoxon signed-rank test, independent two-sample t-test, Mann-Whitney test, two-sample t-test for paired samples, Wilcoxon signed-rank test.
  • ANOVA tables: 1-way and 2-way tables.
  • Simple and multiple linear regression: including model diagnostics.
  • Clustering: hierarchical clustering, kmeans.
  • Principal components analysis: plotting and scaling data

Course structure

This course will be structured as follows:

  • 8:30 -- 9:00: Registration and coffee
  • 9:00 -- 10:30: Lecture
  • 10:30 -- 10:45: Coffee break
  • 10:45 -- 12:15: Practical 1
  • 12:15 -- 1:15: Lunch (not provided)
  • 1:15 -- 2:40: Lecture
  • 2:40 -- 3:00: Coffee break
  • 3:00 -- 4:30: Practical 2

These times are intended to give a flavour of how the course is run and are subject to change.

Comments from previous courses

  • The balance between lectures and practicals was good.
  • Great help during the practicals.
  • High quality lecture materials.

Presenter

Dr Colin Gillespie, Statistics Lecturer in the School of Mathematics &Statistics.

Programming with R

This is a one day intensive course on R. The course will be a mixture of lectures and computer practicals. The main focus of the course is R programming techniques, such as functions, for loops and conditional expressions.

The course follows on from the Introduction to R course. It is assumed that all students have attended this course (or have equivalent skills). This course is suitable to a wide range of applicants e.g., biologists, statisticians, engineers, students.

Course outline

  • Vector operations: details of R's vectors operations.
  • Conditionals: using "if" and "else" statements in R
  • Functions: what is function is, how are they used, and how can we construct our own functions.
  • Looping in R: an introduction to the concept of looping in R. In particular "for" and "while" loops.
  • The apply functions: apply, tapply and other members of the apply family.

Course structure

This course will be structured as follows:

  • 8:30 -- 9:00: Registration and coffee
  • 9:00 -- 10:30: Lecture
  • 10:30 -- 11:00: Coffee break
  • 11:00 -- 12:00: Practical 1
  • 12:00 -- 1:00: Lunch (not provided)
  • 1:00 -- 2:00: Lecture
  • 2:00 -- 4:30: Practical 2 (with a coffee break)

These times are intended to give a flavour of how the course is run and are subject to change.

Comments from previous courses

  • We started from the beginning and achieved a lot by the end. I'm not scared of R anymore. It was actually fun!
  • You cover all the aspects that we need to learn to get started.

Presenter

Dr Colin Gillespie, Statistics Lecturer in the School of Mathematics &Statistics.

Advanced programming

This is a two day intensive course on R. The course will be a mixture of lectures and computer practicals. The main focus of the course is advanced R programming techniques, such as S3/S4 objects, reference classes and function closures.

The course follows on from the Programming with R course. It is assumed that all students have attended this course (or have equivalent skills). This course is suitable to a wide range of applicants e.g. biologists, statisticians, engineers, students.

Course outline:

  • Functions:
    • Scoping rules (including lexical scope)
    • The ... argument
    • Functions as first class objects
    • Functions closures and mutable states
    • Argument matching
  • Customising your workspace
    • The Rprofile and Renviron files
  • Dealing with errors
    • Messages, warnings and errors
    • Using try and tryCatch effectively
  • S3 classes:
    • Introduction to object oriented programming
    • Constructing S3 objects
    • Drawbacks
  • S4 and reference classes:
    • Creating and using S4 and reference classes
    • Differences between S3 and S4

Presenter

Dr Colin Gillespie, Statistics Lecturer in the School of Mathematics &Statistics.

Advanced graphics

This is a one day intensive course on advanced graphics with R. The standard plotting commands in R are known as the Base graphics. In this course, we cover more advanced graphics packages - in particular, ggplot2. The ggplot2 package can create very advanced and informative graphics. For example:


A basic knowledge of R is assumed for this course. In particular, attendees should be familiar with the topics covered in the first course.This course will be a mixture of lectures and computer practicals. The goal is to enable participants to apply the techniques covered to their own data. This course is suitable to a wide range of applicants e.g., biologists, statisticians, engineers, students.

Course outline

  • The grammar of graphics
  • Mastering the grammar
  • Groups, geoms, stats and layers
  • Scales, axes and legends
  • Facets

Course structure

This course will be structured as follows:

  • 8:30 -- 9:00: Registration and coffee
  • 9:00 -- 9:30: Lecture
  • 9:30 -- 10:30: Practical 1
  • 10:30 -- 11:00: Coffee break
  • 11:00 -- 12:15: Lecture
  • 12:15 -- 1:30: Lunch
  • 2:45 -- 3:15: Coffee break
  • 1:30 -- 4:30: Practical 2 & Lecture

Comments from previous courses

  • Very clear lectures and handouts.
  • Good overview of the main topics. Also gave advice on how to find out about other features that may be needed above the standards.
  • The ability to ask more general questions about our data in the practical.

Presenter

Dr Colin Gillespie, Statistics Lecturer in the School of Mathematics &Statistics.

Efficient R programming

This is a one day intensive course on efficient R programming. This course will be a mixture of lectures and computer practicals.This course is aimed at anyone who uses R, but wants tips and techniques on speeding up their code.

Prior knowledge: it will be assumed that participants are familiar with R. For example, inputting data, basic visualisation and data frames. Attending the introduction to R will be sufficient. This course is suitable to a wide range of applicants e.g. biologists, statisticians, engineers, students.

Course outline:

  • Why is your code slow? Code profiling: which part of the code should you optimise.
  • Efficient data structures: object growth and memory allocation.
  • Avoiding loops: accessing the underlying C code faster.
  • Parallel computing: an introduction to multi-core computing.

Course structure

This course will be structured as follows:

  • 8:30 -- 9:00: Registration and coffee
  • 9:00 -- 9:45: Lecture
  • 9:45 -- 10:30: Practical 1
  • 10:30 -- 11:00: Coffee break
  • 11:00 -- 12:00: Lecture
  • 12:00 -- 1:00: Lunch
  • 1:00 -- 2:00: Practical 2
  • 2:00 -- 2:40: Lecture
  • 2:40 -- 3:00: Coffee break
  • 3:00 -- 4:30: Practical 3

These times are intended to give a flavour of how the course is run and are subject to change.

Presenter

Dr Colin Gillespie, Statistics Lecturer in the School of Mathematics & Statistics.

Building an R package

This is a one day intensive course on building an package. This course will be a mixture of lectures and computer practicals. The main focus will be getting a working R package ready for distribution. It is assumed that all applicants have a basic knowledge of R.

Course outline:

Participants can bring their own code or they can use the provided example code to write a fully functional R package.

  • Why create an R package.
  • What is in an R package.
  • Writing documentation with roxygen.
  • Creating packages with rstudio.
  • Distributing your package.

Participants will need to bring their own laptop.

Course structure

This course will be structured as follows:

  • 9:00 -- 9:30 Registration and coffee
  • 9:30 -- 12:15: Lecture & practical session
  • 12:15 -- 1:15 Lunch (not provided)
  • 1:15 -- 2:40 Lecture & practical
  • 2:40 -- 3:00: Coffee break
  • 3:00 -- 4:30: Practical session

These times are intended to give a flavour of how the course is run and are subject to change.

Presenter

Dr Colin Gillespie, Statistics Lecturer in the School of Mathematics & Statistics.

Five day Bioconductor course

Course outline:

This is a five day intensive course on R and Bioconductor. The course will be a mixture of lectures and computer practicals. The final day provide participants an opportunity to analyse their own data.

No prior programming knowledge of any kind is assumed.

Course structure:

This course will be structured as follows:

  • Day 1: Introduction to R
    • Standard R data types, base graphics, Manipulating data
  • Day 2: Bioconductor input/output
    • What is Bioconductor
    • Installing packages
    • Loading Affymetrix and Illumina data into R
    • Data quality checks
  • Day 3:
    • Object oriented programming in R
    • Microarray data analysis including Limma, RankProd
  • Day 4: Clustering, ArrayExpress, GO stats
  • Day 5: RNAseq and Analysis of participants' data

Presenters

Dr Colin Gillespie, Statistics Lecturer in the School of Mathematics &Statistics.

Dr Simon Cockell, Newcastle Bioinformatics Support Unit

Dr Matthew Bashton, Newcastle Bioinformatics Support Unit

R for Six Sigma professionals

Course outline:

This is a three day conversion course for Six Sigma professionals who wish to use the well-established statistical software package R in their project work. As the price of standard software packages increases, switching to R can give significant business advantage.

No prior knowledge of programming or R is necessary for this course. After this course, participants will be able to produce professional quality graphics and carry out advanced Six Sigma analysis, including SPC charts, correlation and comparative tests.

No prior programming knowledge of any kind is assumed.

Course structure:

This course will be structured as follows:

  • Review of Six Sigma statistical techniques
  • Getting started with R
  • Graphics, including control charts, scatter plots, boxplots, histograms, Pareto charts
  • Statistical tests in R: t-tests, non-parametric, regression, ANOVA tables

Comments from previous courses

  • Clear explanations; combination of theory and practice is excellent.
  • Almost one to one! Great teaching, good lectures.
  • The ability to ask more general questions about our data in the practicals.

Presenters

Dr Shirley Coleman, Industrial Statistics Research Unit.

Dr Colin Gillespie, Statistics Lecturer in the School of Mathematics &Statistics.

Predictive analytics

Course outline:

This is a two day intensive course on using the R programming language for predictive analytics. This course will be a mixture of lectures and computer practicals.

It will be assumed that participants are familiar with R. For example inputting data, basic visualisation, basic data structures and use of functions. Attending the introduction to R course will provide a sufficient background.

Course structure:

This course will be structured as follows:

  • Introduction to analytics: a general introduction into analytics and some of the techniques that are in common use.
  • Simple regression problems: simple and multiple linear regression and model diagnostics.
  • Classification: KNN, clustering, logistic regression, Linear Discriminant analysis and associated diagnostics.
  • Model selection: various model selection procedures, subset selection, shrinkage.
  • Advanced regression techniques: polynomial regression, splines, local regression, GAMs, trees and random forests.

Presenters

Dr Colin Gillespie, Statistics Lecturer in the School of Mathematics &Statistics.

Mr Jamie Owen, R trainer in the School of Mathematics &Statistics.

Spatial data analysis with R

Course outline:

As spatial datasets get larger more sophisticated software needs to be harnessed for their analysis. R is now a widely used open source software platform for working with spatial data thanks to its powerful analysis and visualisation packages. The first day of the course introduces the basics of how R can be used for spatial data.

The second day demonstrates the many useful features that are hidden away in package documentation. get_map and getData, from ggmap and raster packages, for example, allow users to download data from anywhere in the world into R directly. Participants will be introduced to functionality in R that is very difficult to achieve in other software, such as the clustering of points into polygons and geographically weighted regression. The focus is on the principles rather than the specific methods, providing participants with the understanding needed to apply R's powerful suite of geographical tools to their own problems.

It is expected that partipants have basic R experience, e.g. attending, Course 1, Introduction to R. The course will be hands-on and applied with short introductory lectures to each of the topics, followed by practical sessions loading and analysing real spatial datasets.

Course structure:

This course will be structured as follows:

  • Introducing R as a GIS
  • The structure of spatial objects in R
  • Loading and interrogating spatial data
  • Visualising spatial datasets
  • Acquiring external data with R
  • Point pattern analysis and spatial interpolation
  • Geographical models in R

Presenters

Dr Colin Gillespie, Statistics Lecturer in the School of Mathematics &Statistics.

Mr Robin Lovelace, Leeds University.

ON SITE TRAINING

Thank you for your interest in onsite R training. Please use the form below and we will get back to you as soon as possible.

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ABOUT

Presenter

Dr Colin Gillespie is a statistics lecturer at Newcastle University. He has been using R since 1999 and teaching R programming for the last eight years. Colin has authored a number of R packages and regularly answers R questions on stackoverflow.

Location

The courses at held at Newcastle University, UK. The University is located in the city centre. Situated in the North East of England, Newcastle has excellent transport links with major UK and international cities.

Easy to get to


The main University travel page provides more details.