Kaisa_2012_3_photo by Veikko Somerpuro

1.4.2020 at 09:00 - 26.4.2020 at 23:59


Here is the course’s teaching schedule. Check the description for possible other schedules.

Mon 4.5.2020
13:00 - 15:00
Wed 6.5.2020
13:00 - 15:00
Fri 8.5.2020
13:00 - 15:00
Mon 11.5.2020
13:00 - 15:00
Wed 13.5.2020
13:00 - 15:00
Fri 15.5.2020
13:00 - 15:00
Mon 18.5.2020
13:00 - 15:00
Wed 20.5.2020
13:00 - 15:00


All students in the Doctoral Programme in Food Chain and Health

The participants do not need to have any prior knowledge in R or programming. Participants should have some knowledge of statistical modelling, especially regression analysis.

Monday 4th May: Webinar

Tuesday 5th May: Pre-recorded video lecture
Running commands

Functions and packages

Writing scripts

Tuesday 5th May: (Optional) Office hours
help with getting started if you’re stuck

Wednesday 6th May: Webinar
group discussion

Thursday 7th May: Pre-recorded video lecture
Reading in data

Looking at your data (descriptive statistics, basic plots)

Saving data and plots

Friday 8th May: Webinar: group discussion

Monday 11th May: Pre-recorded video lecture
Tidying data using tidyverse

Simple statistical tests

Prettier plots using ggplot2

Tuesday 12th May: (Optional) Office hours
chance to discuss your own data & get answers to any questions specific to your project

Wednesday 13th May: Pre-recorded video lecture
Editing data from wide to long (and vice versa)

Multiple linear regression

Logistic regression

Comparing regression models

Thursday 14th May: Webinar
group discussion

Final task, DL: 25th May: Participants are expected to apply the lessons to their own project and produce three outputs (publication-quality figures or statistical analyses) using an exclusively R workflow.

This course will cover the basics of using R for statistical analysis. The course will act a practical primer for incorporating R into a research workflow. Topics covered include accessing and using R, managing R packages, writing scripts and debugging. Through practical examples, the participants will learn how to read in data, inspect and tidy data, run statistical analyses, create publication ready plots, and save the results. The aim of the course is to provide the participants with basic knowledge and skills in using R for statistical analysis. A maximum number of 20 participants can attend the course.

Pre-course questionary, lectures and exercises and final task after the course

The course is 2 credits. You need to complete all the exercises and tasks of the course. Evaluation is Pass or Fail.

Pre-course questionary, lectures and exercises and final task after the course. Online course.

Maijaliisa Erkkola, Juulia Suvilehto and Mia Vehkaoja