Max Planck Gesellschaft
Max Planck Gesellschaft

R course: Advanced modules

Category: Skill course
Credit points: 0.2 per day

On this page... (hide)

  1.   1.  Concept
  2.   2.  Place and time
  3.   3.  What you need to prepare
  4.   4.  Agenda
    1.   4.1  The Tidyverse
    2.   4.2  Programming
    3.   4.3  Speeding up your computation
  5.   5.  Feedback

1.  Concept

This modular seminar addresses everybody who is interested in improving existing R skills.

There will be

  • a little theory
  • focus on technical aspects (not on interpretation)
  • time for questions and answers
  • hands-on training based on the requests you write in the comment field of the registration form. Feel free to approach us with your data

Prerequisites: Basic knowledge of a language of scientific computing: R, Matlab (exercises will be in R) which are taught in the course 'R - The basics'

Lecturer: Thomas Wutzler, Nora Linscheid, David Martini, Daniel Pabon

2.  Place and time

  • When? September 14-16, 2020, start: 9:30 am and 13:30 pm
  • Where? MPI for Biogeochemistry, lecture hall


More R… Here is a list of useful online resources to accelerate your progress.

3.  What you need to prepare

Please make sure that a recent version of R and RStudio is running on your laptop.
You can download the most recent version here: http://www.r-project.org/

RStudio is a new open-source integrated development environment that runs on all platforms. It nicely combines console, script editor, working directory, plots etc. into a an uncluttered layout that you can easily navigate. You need to have R installed before you can use RStudio as a development environment.

Please also make sure that you can access the internet via WLAN (BGC-users, if you have a BGC-account; BGC-guests, if you don't have an account)

Course material are avaialable on owncloud. Please contact Stefanie to get the password.

There is also a discourse group.

4.  Agenda

4.1  The Tidyverse

  • get data in the right shape using tidyr
  • data science using dplyr
  • working with subsets of data using nest and map
  • graphically explore datasets using ggplot

4.2  Programming

  • Organizing your code + R notebook
  • Writing R-packages
  • Documentation using Roxygen
  • Locating bugs: Debugging

4.3  Speeding up your computation

  • locating bottlenecks and vectorizing
  • twRCourses/inst/Performance (createTable.R)
  • some older material in twRCourses/inst/parallel

Optional

  • Working with your on data or FAQ

5.  Feedback

Find the feedback of the participants here. Please note, that statistics and statements should not be taken as an exhaustive or exclusive list.



This page was last modified on November 07, 2020, at 07:28 PM

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