Data Handling with R

In particular the course will teach you how to write more ordered code that can be easily reused and incorporated into other projects and to deal with the automation of data and code handling tasks. This will allow you to save time and handle bigger datasets.

General information

• Language:  English
• Course code: PNG0090
• Points: 3 ECTS

Prerequisites

A basic knowledge of the R language. For example, being able to read files, perform simple manipulation and indexing of R objects, reading and understanding R help files. Admitted to PhD-studies.

Learning outcomes

After completing the course, students should be able to:

1. Write reproducible code
   1. Basic use of GitHub and Rmarkdown.
   2. Write code that can be reused yourself or used and modified by a complete stranger
   3. Standardize code and data structure
2. Confidently manipulate data and R-objects, allowing you to avoid using spreadsheet programs for data cleaning and processing.
   1. Understand data manipulation tools such as the apply family
   2. Index, group and aggregate data
   3. Write simple loops and functions
3. Check for and fix errors in data and code
   1. Data cleaning and error checking (tests such as look for outliers, NAs, patterns)
   2. Diagnostic plotting
   3. Basic functions for debugging and most common errors

Contact for application and further information

Apply for the course no later than 2 March 2026 by sending an email to course organiser Alistair Auffret (alistair.auffret@slu.se).

Time table

• 9 March: Kick-off workshop: student and teacher introductions. Connecting to course platforms  
• 25 March: Workshop: Theme 1
• 9 April: Workshop: Theme 2
• 24 April: Workshop: Theme 3

Each workshop is 3 hours long. The time of day will be determined at a later point, depending on if the workshops are in person, online or both (but separately). In-person workshops will be at the Department of Ecology, SLU Uppsala, online meetings will be on Zoom. Please note that participants will get learning materials in advance and are expected to complete exercises before each scheduled workshop. 

Content

The course will start with a short introductory workshop. Thereafter the content will be split across the three themes of the learning outcomes above. In each theme, the students will be given some learning materials and a task to complete. Students will have the opportunity to communicate online among themselves and with the teachers using asynchronous tools while working on each theme, before a workshop where students will discuss the assignments and work on additional tasks. 

Introduction

The course will start with a short introduction where students and teachers will get to know each other and the structure of the course will be explained. Time will then be given for students to install and connect to the relevant software that will be used during the course.

1. Write reproducible code

Individual study

Here students will acquaint themselves with the very basics of using GitHub for code backup, archiving, sharing and editing, uploading their work to a course project site. Working in small groups, students will practice writing reproducible code using one of two sample data sets. The idea is that, while the functions used in the exercise should already be familiar to the students the students will write the code in a reproducible way that with very little editing allows the same code to be used on an alternative data set by a stranger. 

Workshop

Students and teachers will discuss the students’ experiences in writing reproducible code and additional tasks will be provided.

2. Confidently manipulate data and R-objects

Individual study

Study material will teach the students different ways to manipulate large and heterogeneous data sets in a reproducible way. Students will then be given one of two data sets and be asked to organize the data in a specific way. As in the first theme, students will work via GitHub in small groups, checking and commenting on each other’s code for clarity and reproducibility. 

Workshop

Students and teachers will discuss the students’ experiences in writing reproducible code and additional tasks will be provided.

3. Check for and fix errors in data and code

Individual study

In this theme, students will learn some ways to identify and deal with errors in code and/or data sets. They will then receive a ‘buggy’ data set (optional: own data set), and using these skills and the knowledge gained in the rest of the course to clean and restructure the data in order to produce a set of specified figures. Again, students will work via GitHub in small groups, checking and commenting on each other’s code for clarity and reproducibility. 

Workshop

Students and teachers will discuss the students’ experiences in writing reproducible code and additional tasks will be provided.

Examination

To receive course credits, students are required to have completed all individual exercises and played an active part in all workshops.

Literature

Online learning materials will be distributed during the course.

Additional Information

The course is organized as part of the of the NJ-faculty research schools Ecology – basics and applications and Focus on Soils and Water