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:
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.
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.
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.
There is no exam connected to this module. If you attend all four classes you will receive a certificate of attendance via e-mail at the end of the course.
This module is part of the microcredential 'Data Analysis in R: Basics and Beyond' that consists of three modules:
If you are planning on registering for all three modules, consider enrolling for the microcredential instead. Read more...
This is an on campus course. We offer blended learning options if, exceptionally, you can't attend a session on campus.
October 21, 24, 28 & 31, 2024, from 5.30 pm to 9 pm.
Faculty of Science, Campus Sterre, Krijgslaan 281, 9000 Ghent, Building S9, 3th floor, Auditorium 3.
Access to slides and data files.
The participation fee is 620 EUR for participants from the private sector. Reduced prices apply to students and staff from non-profit, social profit, and government organizations.
*If two or more employees from the same company enrol simultaneously for this course a reduction of 20% on the course fee is taken into account starting from the second enrolment.
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As UGent PhD student you can incorporate this 'transferable skills seminar: research & valorization' in your Doctoral Training Program (DTP). To get a refund of the registration fee from your Doctoral School (DS) please follow these strict rules and take the necessary action in time. Open a dossier on the DS website (Application for Registration) for this course as fast as possible.
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Information on "KMO-portefeuille": https://www.ugent.be/nl/opleidingen/levenslang-leren/kmo
Academy for Lifelong Learning (IPVW)
Faculty of Sciences