Participant profile

Doctoral students of the UPV/EHU.

Preferably first or second year PhD students (students with priority).

Calendar

Biscay Campus: May-June 2025

Duration / Timetable

25 hours (4 hour clases run over 6 weeks, 5 hours the first class)

Time: 8:30 to 10:30 and 11:00 to 13:00 (to 14:00 the first class)

Attendance Requirement

Students will be expected to attend 100% of the clases and submitting all practical work assignments (see points 3 and 5 of the Basic regulations for participation in transversal training activities organised by the Doctoral School).

Language

English

Modality

Face-to-face

Pre-requisites

It would be good for the students to have some minor knowledge about R (e.g. reading files or creating simple plots), although the course will cover the basics as well. The students will work on their own laptops. R and RStudio should be installed in the computers before the start of the course.

Links to download the software required for the course: 

https://cran.r-project.org/

https://posit.co/download/rstudio-desktop/

Location and dates

Speaker, Trainer and Profile

Aitor Larrañaga Arrizabalaga. Profesor Titular in the Faculty of Science and Technology. He gives classes in the degree of Biology and in the Master in Biodiversity, Functioning and Management of Ecosystems He has a long experience teaching statistics with R.

Maite Arroita Azkarate. Assistant Lecturer in the Faculty of Science and Technology. She teaches in the degrees of Biology and Environmental Sciences, as well as in the master's degree in Biodiversity, Functioning and Management of Ecosystems. Her postdoctoral research focused on historic changes in rivers, for which she analyzed 20 years of high-frequency data from several sites. Therefore, she has a strong experience in data manipulation and representation using R.

Group size

12

Registration

Objectives

The main objective of this course is to initiate the students to the manipulation and representation of data in R. The basics will be covered and useful packages to manipulate dataset will be shown. The students will end up being able to use their own data and extract descriptive statistics, create plots and apply statistical routines to support or reject their hypotheses. Although statistical tools will be used in the course, the main objective of the course will be learning data manipulation with R and statistical concepts will not be explained in depth.

Competences to be acquired by the doctoral student:

Ability to critically analyse, evaluate and synthesise new and complex ideas.

Format

The teachers will use the 4-hour session to cover the basics of the language R and to use packages that are useful for data manipulation, creation of plots and basic statistical analysis. After the 4-hour sessions, the teachers will send assignments to students to practice the learned techniques. The students will be encouraged to use their own datasets as examples for the exercises. The assignments will be corrected in the next class before starting with new content.

Content

• 1) Basic concepts of the R working environment
• 2) Packages within the tidyverse environment: ggplot2, dplyr, tidyr and readr
• 3) Packages within the tidyverse environment: ggplot2, dplyr, tidyr and readr
• 4) Packages within the tidyverse environment: ggplot2, dplyr, tidyr and readr
• 5) Using your own datasets to create summaries, plots and statistical analyses
• 6) Report results using R Markdown