Advanced programming in R

Dates

17th- 21st January 2022

Where

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

 

Summary

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.

Prerequisites

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.

 

 

 

Outcomes

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

 

 

scheme

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.

 

Program

 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

 

 

 

Instructor

 

 Dr. January Weiner

 

COst overview

Package 1

 

 

 

 

 

  480 €


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.