University of Oslo

Jun 6-7, 2017

9:00 am - 4:00 pm

Instructors: Ana Costa Conrado, Christian Wilhelm Mohr, Dmytro Perepølkin, Halfdan Rydbeck, Raoul Wolf

Helpers: Fatima Heinicke

General Information

This is not a full Software Carpentry, but an R tidyverse workshop based on Software Carpentry material.

Who: The course is aimed at graduate students and other researchers. You don't need to have any previous knowledge of the tools that will be presented at the workshop.

Where: Seminarrom 123, Vilhelm Bjerknes' hus, Moltke Moes vei 35, 0851 Oslo. Get directions with OpenStreetMap or Google Maps.

When: Jun 6-7, 2017. Add to your Google Calendar.

Requirements: Participants must bring a laptop with a Mac, Linux, or Windows operating system (not a tablet, Chromebook, etc.) that they have administrative privileges on. They should have a few specific software packages installed (listed below). They are also required to abide by Software Carpentry's Code of Conduct.

Accessibility: We are committed to making this workshop accessible to everybody. The workshop organisers have checked that:

Materials will be provided in advance of the workshop and large-print handouts are available if needed by notifying the organizers in advance. If we can help making learning easier for you (e.g. sign-language interpreters, lactation facilities) please get in touch and we will attempt to provide them.

Contact: Please email raoul.wolf@ibv.uio.no for more information.

Message from the organizers: We would like to encourage participants to commit to two full days of intensive learning. We are running a experimental "deep dive" into the subject and we are trying to learn if the dense two-day format is better for absorbing and practicing the material. Think about it not as a typical workshop, but rather as a mini-bootcamp, where you come to immerse yourself into data analysis with R and, hopefully, come out with some practical and immediately applicable skills for making sense of your data. We would like to see the participants who have immediate data analysis problems they are trying to solve, and would like to prioritize the teaching effort to those who need urgent assistance and help to get started. Provided that this experiment is successful, we will be bringing more of similar exciting offerings to you in the fall. Thank you for your cooperation and understanding.


Schedule and syllabus

Day 1

09:00 Introduction to RStudio, GitHub and tidyverse
10:00 Elegant data visualisations with ggplot2
10:45 Coffee
11:15 Elegant data visualisations with ggplot2 (continued)
12:00 Lunch break
13:00 Data manipulation with dplyr
14:00 Coffee
14:15 Easily arrange data with tidyr
15:45 Version control with GitHub
16:00 Final assignment and wrap-up

Day 2

09:00 Vectors and lists explained
10:00 Coffee
10:30 Functional programming with purrr
11:30 Elegant modelling pipelines with modelr
12:00 Lunch break
13:00 Cross-validation in tidyverse
14:00 Coffee
14:15 Cross-validation in tidyverse (continued)
15:00 Wrap-up

Setup

To participate in this workshop, you will need access to the software described below. In addition, you will need an up-to-date web browser.

We maintain a list of common issues that occur during installation as a reference for instructors that may be useful on the Configuration Problems and Solutions wiki page.

R

R is a programming language that is especially powerful for data exploration, visualization, and statistical analysis. To interact with R, we use RStudio.

Windows

Video Tutorial

Install R by downloading and running this .exe file from CRAN. Also, please install the RStudio IDE. Note that if you have separate user and admin accounts, you should run the installers as administrator (right-click on .exe file and select "Run as administrator" instead of double-clicking). Otherwise problems may occur later, for example when installing R packages.

Mac OS X

Video Tutorial

Install R by downloading and running this .pkg file from CRAN. Also, please install the RStudio IDE.

Linux

You can download the binary files for your distribution from CRAN. Or you can use your package manager (e.g. for Debian/Ubuntu run sudo apt-get install r-base and for Fedora run sudo yum install R). Also, please install the RStudio IDE.