Getting Started with R Software for Data Analysis


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 is part of a larger course series in Data Analysis consisting of 19 individual modules. Find more information and enroll for this module via

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.

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Getting Started with R Software for Data Analysis

  • Type of course: This is an on campus course, with blended learning options.
  • Dates & times: October 5, 12, 19 and 26, 2021, from 5.30 pm to 9 pm
  • Venue: UGent, Faculty of Sciences, Campus Sterre, Krijgslaan 281, building S9, 9000 Gent
  • 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/certificate: There is no exam connected to this module. Participants receive a certificate of attendance via e-mail at the end of the course.
  • 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.
  • Funding: => Our academy is recognised as a service provider for the 'KMO-portefeuille'. In this way small and middle sized businesses located in the Flanders region can save up to 30% on the registration fee for our courses. You can request this subsidy via up until 14 calender days after the course has started. => UGent PhD students can apply for a full refund from their Doctoral School.
  • Reduction: => If two or more employees from the same company enrol simultaneously for this course a reduction of 20% on the module price is taken into account starting from the second enrolment => Reduced prices apply to coworkers in governmental institutions, non-profit organisations and higher eduction as well as for students and the unemployed.
  • Enrolling for this course is possible via the IPVW-ICES website.