sponsored by Helmholtz Information & Data Science Academy (HIDA)

in cooperation with Core Facility Statistical Consulting  at Helmholtz Zentrum München - German Research Center for Environmental Health (HMGU)

Graphics with R

This course covers methods in customizing classical R graphics. Additionally, an introduction to ggplot2, an advanced and powerful tool for building graphics with R, is provided.

In this course you will learn how to create refined, meaningful graphs in R to visually describe your research outputs.

This is an intermediate level course. So we expect pre-knowledge in R especially loading and subsetting of data sets.

Content

• Modifying base R graphics:
  This includes font modification, customization of axes and legends, specifying color schemes, adding multiple layers to one graphic and arranging multiple figures to one graphic (layout).
   
• Introduction to ggplot2:
  The R package ggplot2 provides a different “grammar” of building figures with R. It is convenient for complex self-contained figures. The course covers the basic principles of using ggplot2. Further adjustments on the axis, titles, legends, color scheme and layout will be discussed.

The course provides the necessary theoretical background to use R, hands-on examples with best-practice solutions, and practical exercises.

Target Group

When should I join this course?

Are you unsatisfied with the default layout of base R graphics and want to know how to adjust them to look nicer?
In this course we provide some insights how R deals with graphics and how to personalize them.

Do you want to know what is the miracle behind ggplot2 graphics and how to use it?
We will start with explaining ggplot2 from scratch. Such that afterwards you know why people are so keen on using ggplot2 for data science.

Is interactive learning important to you? Would you like to have a learning experience in which you can try things on your own yet still have an expert support you?
Our teaching philosophy combines self-learning with explanatory sessions. We purposefully do not (pre)record our sessions as we find that it is easier to learn if you have somebody available to answer your questions directly.

When should I not join this course?

Are you an absolute newcomer to R and never used it before?
Then we recommend to first visit an introductory R course.

Are you interested in one specific solution for a definite problem?
Then we recommend a consulting session, since the idea of the course is to give an overview on many different topics.

Do you want to write your own statistical analysis program, using tools like loops, apply, or matrix multiplication?
Then you can skip this course as it is aimed at using graphics.