This lecture is part of the "Machine Learning in R" graduate course held at University of Münster, School of Business and Economics (winter term 2021/22). 🎓

Slides: https://simonschoe.github.io/introduction-to-the-tidyverse/

This lecture teaches you important tools for working with tabular data sets in R. It introduces and showcases a suite of packages which ease your data science workflow in terms of data import, data cleaning, data transformation and data visualization.

More specifically, after this lecture you will

• be familiar with the main tools of the tidyverse and how it differs from base R,
• know your way around in working with the core packages of the tidyverse for importing, tidying, transforming and visualizing data,
• be proficient in processing (non-tidy) data of any shape and quality,
• be able to produce high-quality, fully customizable visualizations,
• have improved your overall data literacy.

1 Learning Objectives

2 Introduction to the tidyverse

2.1 What is the tidyverse
2.2 The Concept of Tidy Data

3 palmerpenguins: Palmer Archipelago (Antarctica) Penguin Data

4 The Core tidyverse Packages

4.1 magrittr: A Forward-Pipe Operator for R
4.2 tibble: Simple Data Frames
4.3 readr: Read Rectangular Text Data
4.4 tidyr: Tidy Messy Data
4.5 dplyr: A Grammar of Data Manipulation
4.6 purrr: Functional Programming Tools
4.7 ggplot2: Create Elegant Data Visualisations Using the Grammar of Graphics