Hi, we have different R courses for biologists in Berlin: https://www.physalia-courses.org/courses-workshops/
Geting started with R: https://www.physalia-courses.org/courses-workshops/course13/
This course aims at overcoming those challenges by providing solid basics in R. At the end of the course, participants should feel much more at ease writing a computer script in the R language which covers the entire spectrum of a statistical analysis: reading data, editing them, plotting them, and analysing them. Because linear models are the dominant statistical tool in many fields, the part of the course focusing on analyses per se (see schedule) will focus on those, but principles seen during the class should greatly help those interested in other kind of analyses as well. The course will be presented over five days and will mix explanations and guided exercises. Students are free to practice with their own datasets during the course.
Advanced R Programming: https://www.physalia-courses.org/courses-workshops/course26/
This course will introduce techniques for programming in R, including how to work with a variety of data structures, write functions and package code for distribution. Each lesson will be interactive with hands-on activities to solidify concepts covered in lectures. The week will conclude with a package development practicum, during which attendees will have the opportunity to implement methods covered in class for a project of their own design.
We have also a course on "Bioinformatics with R and Bioconductor": https://www.physalia-courses.org/courses-workshops/course19/
This course will provide biologists and bioinformaticians with practical statistical and data analysis skills to perform rigorous analysis of high-throughput biological data. The course assumes some familiarity with genomics and with R programming, but does not assume prior statistical training. It covers the statistical concepts necessary to design experiments and analyze high-dimensional data generated by high-throughput sequencing, including: exploratory data analysis, principal components analysis, unsupervised clustering, batch effects, linear modeling for differential expression, gene set analysis