This lesson is being piloted (Beta version)

R EDA

Please note: These pages are under construction! That is a permanent condition, but at the moment they are under more construction than they will ever be.

The aim of KUB-Datalab is to teach students, teachers and researchers basic concepts, skills and tools for working with data so they can get more done in less time. And with less pain.

This is the next step after an introduction to R for complete beginners. We assume that you have some knowledge of R, equivalent to the material covered in our introductory course.

In this workshop we investigate a dataset together, and let our curiosity guide us in analysing and visualizing the data, using both elementary statistical methods and graphs.

Regardless of our curiosity the concepts covered on this site, will be covered.

Getting Started

Our teaching is hands-on, so participants are encouraged to use their own computers to ensure the proper setup of tools for an efficient workflow.

To get started, follow the directions in the “Setup” tab to download data to your computer and follow any installation instructions.

Prerequisites

We are going to assume that you have some familiarity with R and RStudio, equivalent to the topics covered in our introductory course, R for absolute beginners. We are going to use the datamanipulation tools from the tidyverse a lot - so please brush up on the pipe %>% if you have forgotten about it.

Schedule

Setup Download files required for the lesson
00:00 1. Before we Start What is EDA?
How to get ready to do data analysis?
How to get the data we are working with?
00:15 2. Getting to know the data How do I import data?
How are the data distributed?
Are there correlations between variables?
00:55 3. Exploring with summary statistics How do I get means, medians, IQRs and other summary statistics on my data?
How do I get summary statistics for different groups in my data?
01:35 4. Joining data How do I import data from other sheets in a spreadsheet?
How do I enrich tables with additional data?
What is a join?
02:05 5. Boxplots and linear regressions How do I make a Boxplot?
How do I make a linear regression?
04:00 6. What is the next step? What do I do now?
What is the next step?
04:10 Finish

The actual schedule may vary slightly depending on the topics and exercises chosen by the instructor.