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
Functions and packages
Tuesday 5th May: (Optional) Office hours
help with getting started if you’re stuck
Wednesday 6th May: Webinar
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
Comparing regression models
Thursday 14th May: Webinar
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