Due to the COVID-19 outbreak, this course will be held online

This course aims at giving the students abilities in R programming that go beyond basic R usage. This includes both learning important frameworks as well as tips & tricks and coding style.

The students should have a basic skill set in R. They should be able to write simple programs comfortably, install packages from CRAN and BioConductor, be comfortable with their preferred coding environment and basic data import and export functions.

Basic skills in statistics are necessary. The students should understand the concepts of statistical hypothesis testing and p-values. However, an in-depth introduction to these concepts will also be provided.

Learning how to code is most effective if applied to a real problem. The students are highly encouraged to bring their own ideas for programs. A few ideas, however, will be provided.

The students will learn:

•             good coding practices

•             sanitizing data, the tidyverse

•             advanced graphics in R (both base R graphics and ggplot2)

•             the R modelling interface

•             using R for manuscript writing

•             how to create own R packages

Each day will be split in several short blocks: a short (20 minutes) lecture often combined with demonstrations, 35 minutes of discussion, individual excercise and problem solving, 5 minutes break.

There will be time for supervised work, i.e. solving questions and problems or working on your own projects, while you will still be able to ask questions or request help.

At the end of each day, there will be a concise summary, as well as time for questions, comments and queries.

 Monday-2-8 pm Berlin time

–            Introduction

–            Good coding practices and common fails" and discussion (30 minutes)

–            Hands on sessions: Introducing data sanitization with tidyverse: (4 hours)

–            Supervised work / exercise: you will be asked to clean up a very messy data set (2 hours) and given additional exercises. Furthermore, you are encouraged to work with your data.

–            Summary

Cancellation Policy:

> 30  days before the start date = 30% cancellation fee

< 30 days before the start date= No Refund.

Physalia-courses cannot be held responsible for any travel fees, accommodation or other expenses incurred to you as a result of the cancellation.