• Advanced Courses in Life Sciences

    Header of Statistics at Transmitting Science

Online Course – 3rd Edition

Statistical Analyses with R

February 21st-25th, 2022


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This course will be delivered live online

Online live sessions from Monday to Friday, from 15:00 to 19:00 (Madrid time zone).

Course Statistical Computing for Environmental Science with R and RstudioCourse overview

The course is given in four modules covering exploratory data analysis, univariate and multivariate statistical techniques and a final discussion session where students will work and discuss their projects:

  • Module I. Programming in R and Rstudio
  • Module II. Exploratory Data Analysis (EDA)
  • Module III. Univariate Statistical Analysis (UniStat)
  • Module IV. Multivariate Statistical Analysis (MultiStat)

This course will cover some advanced issues in most statistical computing workflow for Life Sciences. Together with the statistical analysis, we will cover several aspects of data structure and workspace management, and visualization techniques using R and Rstudio.

Check the full program here

This course is of intermediate level. Some basic knowledge of R and statistics is highly recommended.


Basic knowledge of Statistics and R.

All participants must have a computer (Windows, Macintosh, Linux). Webcam and headphones are strongly recommended, as well as a good internet connection.




February 21st-25th, 2022

Schedule and Course length

Online live sessions from Monday to Friday, from 15:00 to 19:00 (Madrid time zone).

The rest of the time will be taught with exercises, to be done by the participants between the live sessions.

26 hours

This course is equivalent to 1 ECTS (European Credit Transfer System) at the Life Science Zurich Graduate School.

The recognition of ECTS by other institutions depends on each university or school.




Places are limited to 18 participants and will be occupied by strict registration order.

Participants who have completed the course will receive a certificate at the end of it.

Haris Saslis coordinator for Transmitting Science

Dr. Haris Saslis
Transmitting Science


Module I. Programming in R and Rstudio

  • Along with this model, we will review the most commonly used files in any statistical programming workflow like the scripts (.R) the allocated memory (.RData) and the novel Rstudio projects (.Rproj) for establishing dedicated working directories, workspace, history, and source documents.

Module II. Exploratory Data Analysis (EDA)

  • During any statistical programming workflow, almost half of the time must be given to data exploration. However, not every exploration is a valid exercise. Here we will review the most common assumption in a classical statistical analysis like normality, heterogeneity and independence in the data.

Module III. Univariate Statistical Analysis (UniStat)

  • In module III we will review the classic univariate approach for statistics like te linear models and their extensions. The most common analysis like ANOVA, ANCOVA or Regression analysis will be covered. For those common cases in ecology, where the linear models fail (e.g. non-negative data in count/abundance data) we will present some extensions covering the Generalized Linear Models (GLM) for count and presence/absence data and we will review some insights in Generalized Additive Models (GAM).

Module IV. Multivariate Statistical Analysis (MultiStat)

  • Along with this fourth module, we will cover the analysis of multivariate data. We will review two different approaches to understand community (multivariate) data based on different ordination techniques. Thus, two approaches from unconstrained ordination, like Principal Component Analysis (PCA) and non-Metric Multidimensional Scaling (nMDS) will help us to reveal patterns along with our community data. Finally, we will use two approaches from constrained ordination like Redundancy Analysis (RDA) and Canonical Correspondence Analysis (CCA) to understand the role of some environmental (explanatory) variables over the community structure in our community Data.


  • Course Fee
  • Early bird (until December 31st, 2021):
  • 476 €
    (380.8 € for Ambassador Institutions)
  • Regular (after December 31st, 2021):
  • 570 €
    (456 € for Ambassador Institutions)
  • Price is VAT included.
    After registration you will receive confirmation of your acceptance in the course. Payment is not required during registration.

You can check the list of Ambassador Institutions. If you want your institution to become a Transmitting Science Ambassador please contact us at communication@transmittingscience.com


Discounts are not cumulative and apply only on the Course Fee. We offer the possibility of paying in two instalments (contact courses@transmittingscience.com).

Former participants will have a 5 % discount on the Course Fee.

20 % discount on the Course Fee is offered for members of some organizations (Ambassador Institutions). If you want to apply to this discount please indicate it in the Registration form (proof will be asked later).

Unemployed scientists living in the country were the course will be held, as well as PhD students based in that country without any grant or scholarship to develop their PhD, could benefit from a 40 % discount on the Course Fee. If you want to ask for this discount, please contact the course coordinator. That would apply for a maximum of 2 places and they will be covered by strict inscription order.