Leverage your R Skills: Data Wrangling, Plotting and Reporting with Tidyverse and Quarto

This course covers the most essential tools from 3 tidyverse core-packages.
The lectures provide insight on how to align the data structures with the analysis-goals and link this with the implementation by presenting corresponding code alongside. Hands-on exercises let familiarise with the concepts and code, guided by an experienced data scientist.

A well designed and executed analysis can only reach full impact if it is well reported. From a technical side, this implies a focus on structure, reproducibility and avoiding errors. Quarto – Rmarkdown’s successor – is a platform independent reporting system that works for R, Python and Julia. It allows seamless integration of analysis (code) and text. This greatly improves reproducibility, reduces copy-paste- and other errors and provides tools for automation. Starting from the basics, we will explore the possibilities of the different output formats (pdf, html, docx) and touch upon more advanced features like interactive plots, working with labelled data and avoiding copy-paste with repeated sections and reports.

• Data transforming and summarizing with dplyr: narrowing in on observations of interest, creating new variables that are functions of existing variables, and calculating a set of summary statistics (like counts or means)
• Data visualization with ggplot2: creating more informative graphs (e.g., scatter plot, bar plot, histogram, smoother/regression line, …) in an elegant and efficient way. Arranging multiple plots on a grid
• Data ingest and tidying with tidyr: storing it in a consistent form that matches the semantics of the dataset with the way it is stored.
• Extra tools for programming: Merging and comparing two datasets based on various matching or filtering criterion. Other useful tools for R programming.
• Introduction of the basic framework of Quarto and output format-specific possibilities with a focus on tables, figures and general layout..
• Exploring some general approaches for automation (using self-built templates for report-sections or complete reports), working with labelled data and a quick way to interactive plots.

• A systematic training guide in basics of R. If you never used R or RStudio before, we highly recommend you to take Module 1 of this year's program which will guide you to be familiar with the R environment for the implementation of data management and exploration tasks.
• Big data. This course focuses on small, in-memory datasets as you can’t tackle big data easily unless you have experience with small data.
• Statistics. Although you will see many basic statistics in this course, the main focus is on R and the tidyverse tools instead of explaining the statistical concepts.

This module is part of the microcredential 'Data Analysis in R: Basics and Beyond'that consists of two modules:

• Getting Started with R Software for Data Analysis
• Leverage your R Skills: Data Wrangling, Plotting and Reporting with Tidyverse and Quatro.