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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). 🎓

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Introduction to the Tidyverse

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/

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Contents

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.

Agenda

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

About

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). 🎓

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