19th-22nd September 2022

This course will introduce scientists and practitioners interested in applying statistical approaches in their daily routine using R as a working environment. Participants will be introduced into the mysteries of R and R Studio while learning how to perform common statistical analyses. After a short introduction on R and its principles, the focus will be on questions that could be addressed using common statistical analyses, both for descriptive statistics and for statistical inference.

The course is aimed at early-career researchers (PhD students, early postdocs) or any other types of practitioners interested in widening their analytical toolbox. The course is structured in a way that even an inexperienced and naïve attendee could take advantage of the possibilities offered by the inclusion of statistical analyses using R. There will be a mix of lectures and hands-on practical exercises using R as a freely available software and online resources.
The course is devoted to beginners with no prior knowledge in statistics, programming, and R language, but with a keen interest in using R as a platform for statistical analyses. All scripts will be carefully explained to allow all attendees understanding the rationale and usage of the statistical approaches.

1. Understand how to read, interpret and write scripts in R.

2. Learn statistical tools to address common questions in research activities.

3. An introduction to efficient, readable and reproducible analyses

4. Being comfortable with using R when performing both descriptive and inferential statistics.

 Monday – Classes from 2 to 8 pm Berlin time

Session 1: Getting to know R

We will introduce the basic principles of R. Together, we will learn about R data objects, simple commands, how to use and write functions, and get comfortable dealing with errors. We will start writing our first scripts, generate outputs and learn how to organise data projects for reproducibility.

Session 2: Working with data

In this session, we will introduce the tidyverse range of packages and how they can be used for data science. We will progress through a data workflow example, learning how to import, check and clean data. We will introduce different types of data and the approaches to understanding and summarise them. We will also develop insights from our data using descriptive statistics of centrality and dispersion.

 

Tuesday – Classes from 2 to 8 pm Berlin time

Session 3: Data visualisation

We will continue with our data workflow in the afternoon and start building graphs with the ggplot2 R package. We will cover how to create accurate, clear and beautiful graphs and the rationale behind using data visualisations to understand our data.

Session 4: Executable manuscripts with Rmarkdown

In the afternoon we will break free from word documents, and learn how to make reproducible reports directly from our code with Rmarkdown. Documents produced with Rmarkdown, allow analyses to be included easily - and make an unbroken link between raw data, analysis & and a published report.

 
Wednesday– Classes from 2 to 8 pm Berlin time

Sessions 5 and 6:

We will spend day three learning common statistical tests, focusing on learning how many of these are simply special cases of the general or generalized linear model. We will go through multiple case studies for application and robust model testing, balancing theory and application. We will discuss the process of turning model outputs into results summaries.

 
Thursday– Classes from 2 to 8 pm Berlin time

Sessions 7:

An introduction to producing DRY (Don't Repeat Yourself) code. Combining functions and iterations while balancing this with creating readable and reproducible analyses.

Session 8:

Each attendee will present a brief introduction to their research in this session and how we can use R to produce a reproducible workflow.

The session is followed by a general discussion about the course.