Module 1: Introduction to R

Dates - Venue - Description - Target audience - Exam
Course prerequisites - Teacher - Course material - Fees - Enrol

Dates

October 10, 13, 17 and 20, 2016, from 5.30 pm tot 9 pm.

Venue

Faculty of Science, Site Sterre, Krijgslaan 281, building S9, Ghent

Description

R is a flexible environment for statistical computing and graphics, which is becoming increasingly popular as a tool to get insight in often complex data. While in some ways similar to other programming languages (such as C, Java and Perl), R is particularly suited for data analysis because ready-made functions are available for a wide variety of statistical (classical statistical tests, linear and nonlinear modeling, timeseries analysis, classification, clustering, ...) and graphical techniques.

The base R program can be extended with user-submitted packages, which means new techniques are often implemented in R before being available in other software. This is one of the reasons why R is becoming the de facto standard in certain fields such as bioinformatics (Bioconductor) and financial services.

This course introduces the use of the R environment for the implementation of data management, data exploration, basic statistical analysis and automation of procedures.

It starts with a description of the R GUI, the use of the command line and an overview of basic data structures. The application of standard procedures to import data or to export results to external files will be illustrated.

Creation of new variables, subsetting, merging and stacking of data sets will be covered in the data management section. Exploration of the data by histograms, box plots, scatter plots, summary numbers, correlation coefficients and cross-tabulations will be performed.

Simple statistical procedures that will be covered are:

  • comparisons of observed group means (t-test, ANOVA and their non-parametric versions) and proportions
  • test for independence in 2-way cross tables and linear regression (focusing on the R-implementation of the statistical methods that are the subject of other modules of the statistics series)

Finally, installing new packages and automation of analysis procedures will also be discussed.

Practical sessions and specific exercises will be provided to allow participants to practice their R skills in interaction with the teacher.

Target audience

This course targets professionals and investigators from diverse areas with little to no R-programming experience who wish to start using R for their data manipulation, data exploration or statistical analysis.

Exam

There is no exam connected to this module. Participants receive a certificate of attendance via e-mail at the end of the course.

To qualify for reimbursement from the UGent Doctoral Schools one must attend all classes. Additional conditions and procedure.

Course prerequisites

The course is open to all interested persons.

Knowledge of basic statistical concepts and experience with other programming languages are considered advantages, but not required for learning the R language.

Teachers

Foto van Ineke van GrembergheDr. Ineke van Gremberghe is post-doctoral fellow at Ghent University. She obtained a master degree in Biotechnology, a PhD in Biology and a master degree in Statistical Data Analysis at Ghent University. She works as FLAMES coordinator and statistical consultant for Stat-Gent Crescendo. She has experience in statistical data analysis of different types of data (data visualisation, linear mixed models, causal mediation analysis, multivariate methods) and in R programming.

 

 

Foto van Emmanuel AbatihDr. Emmanuel Abatih is post-doctoral fellow at Ghent University and works as statistical consultant for FIRE and Stat-Gent Crescendo. He served as statistical analyst at the Institute of Public Health in Brussels in 2005 and obtained a PhD in Life Sciences in 2008 at the University of Copenhagen. He worked for the Institute of Tropical Medicine in Antwerp, as post-doctoral assistant on topics including: space-time analysis, diagnostic test evaluation, transmission dynamic modeling and risk analysis. He can rely on several years of experience as teacher in a wide range of courses in statistics and epidemiology. He has (co-)supervised over 30 masters and 7 PhD students and has experience with R, python, SATSCAN, SAS, SPSS and STATA.

Course material

Copies of slides.

Recommended but optional handbook:
"R for Dummies", J. Meys and A. de Vries, 2nd ed. (2015), Wiley, ISBN 978-1119055808.

Fees

Different prices apply, depending on your main type of employment.

EmploymentModule 1BookExam
Industry/Private sector1 325 30 n/a
Non-profit, government, university outside AUGent2 175 30 n/a
(Doctoral) student outside AUGent2 125 30 n/a

1 If three or more employees from the same company enrol simultaneously for this course a reduction of 10% on the module price is taken into account.

2 AUGent staff and AUGent doctoral students who pay through use of an SAP internal order/invoice can participate at these special rates.

Enrol for this course