We start with the data structures present in R (vectors, matrices, lists, data frames) and how to perform simple operations with them. Then we learn how to import the data in R and how to save the output of your analyses.
The next step would be to learn logical operations and key elements to navigate through datasets. Once the students have familiarised themselves with these basic concepts we then move to more advanced programming concepts such as understanding and writing of functions, string operations, and list operations.
Besides these technical skills, the students will learn how to compute descriptive statistics and how to produce a visual representation of data in R. Then, the linear regression model is introduced, a widely used model with two main purposes: modeling relationships among the data and predicting future observations. After that, the linear model will be extended to the generalised linear framework, in order to analyse non-normally distributed variables. All the statistical theory will be briefly covered first and then applied examples using R will be presented, such that the students can learn the methods by applying them.
Every day consists of short lectures with examples, and exercises in which the students can apply what they have learned right away. At the end of the course, there will be an assignment that will be graded. The focus in the exercises and assignment is the coding in R and how to apply and interpret generalised linear regression models. By the end of the course, the students will be acquainted with various popular R packages, can write their own functions, and use attractive plots to present their data.