This course is offered in collaboration with the Graduate School of Production Ecology and Resource Conservation of The Netherlands. Registrations are open for everyone. If you are a PhD student, postdoc or staff of any of the affiliated Graduate Schools you might be eligible for a discount. Please consult the registration page for more information
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Aim of the course
The aim of this course is to introduce the R programming language and how to use it to perform statistical inference and data visualization. Participants will be introduced to the R language syntax, basic types of data in R, how to explore data through descriptive statistics and visualization and how to apply a selection of basic techniques for statistical inference. The focus of the course is on the application of these techniques with R, and not on the underlying statistical theory which can be learnt in other courses. The course will be a combination of lectures, interactive live coding, individual exercises and self-study.
Learning Outcomes
Getting familiar with programming in a dynamic language in general and with R specifically..
Introduce participants to the RStudio IDE (scripts, projects, customizing RStudio)
First, participants will get to know the R language syntax, how to write proper code for solving a given problem. They will learn how to work with variables to store data, and how to apply functions to data.
This will be followed by a strong foundation on the basic types of data in R (vectors, matrices, lists, data frames) and how to work with them (access data, modify, filter). A good understanding on R data types facilitates further learning and understanding of R code
Next, participants will learn how to import and export data and will get to know the test datasets that R provides for practicing their skills. This will include understanding how to prepare data in a spreadsheet such that it can be imported efficiently into R
Participants will also learn how to visualize data using the basic R plotting tools and we will cover common statistical plots (scatter plots, bar plots, box plots, histograms), with an emphasis on exploratory data analysis (detecting outliers, visualize correlations and patterns) as well as visualizing results of statistical models. Participants will also learn how to customize the appearance of plots (labels, colors, legends, margins) so that they are publication ready.
Finally, participants will be taught how to apply basic techniques of statistical inference from experimental data, including analysis of variance, linear models, t-test and testing the hypothesis behind these models.
Course setup
The course is spread across 4 days of 6 hours each, and takes place on a dedicated Microsoft Teams group that will be created for the course. Each day of the course will be broken into three sections by a lunch break (1 hour) and two shorter breaks (20 minutes). Each section is chaired by one of the instructors who will share his computer screen with the rest of participants. During a section, theoretical concepts will be taught via a presentation, mixed with live coding practice, interaction with participants as well as short exercises (2 – 3 minutes each) performed by participants on their own. The other instructor will be answering questions on the chat of the Teams group.
