Leverage your R Skills: Data Wrangling, Plotting and Reporting with Tidyverse and Quarto
Description
Tidyverse is a collection of R-packages for data science that share a common design philosophy. The goal of this course is to get you up to speed with the most essential tidyverse tools for data-preparation and -exploration. After attending this course, you’ll have the tools to tackle a wide variety of data wrangling and visualization challenges, using the best parts of tidyverse.
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.
What you will learn:
- 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.
Not included in this course:
- 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.
Course prerequisites
The course is open to all interested persons. Basic R skills as provided in Module 2 (Getting started with R software for data-analysis) of this year's program are strongly advised.
Exam / Certificate
There is no exam connected to this module. Participants who attend all 4 classes receive a certificate of attendance via e-mail at the end of the course.
Type of course
This is an on campus and an online course.
Schedule
2/12 9u-16u Online
3/12 13u-16u On campus
6/12 13u-16u Online
10/12 9u-12u On campus
Venue
Faculty of Science, Campus Sterre, Krijgslaan 281, building S1, 9000 Gent
Course material
All course materials e.g., lecture slides, data, R scripts, exercises and solutions, will be made available at least one day before the start of the course as an RStudio project.
Microcredential
This module is part of the microcredential 'Data Analysis in R: Basics and Beyond' that consists of two modules:
- Module 2 - Getting Started with R Software for Data Analysis
- Module 6 - Leverage your R Skills: Data Wrangling, Plotting and Reporting with Tidyverse and Quatro.
If you are planning on registering for all two modules, consider enrolling for the microcredential instead. Read more...
Fees
The participation fee is 825 EUR for participants from the private sector. Reduced prices apply to students and staff from non-profit, social profit, and government organizations. An exam fee of 35 EUR will be applied.
- Industry, private sector, profession*: € 1040
- Non profit, government, higher education staff, (Doctoral) students, unemployed: € 468
*If two or more employees from the same company enrol simultaneously for this course a reduction of 20% on the course fee is taken into account starting from the second enrolment.
Registration
To register, add the course below to your shopping cart and proceed to checkout.
Is this your first registration for a Beta Academy course? In that case, you will need to create an account first. Afterward, you will receive a confirmation email to activate your account on the academy platform. You do not have to click on the activation link but can immediately return to your shopping cart to complete your course registration. If you do not receive a confirmation email for your course order, please contact our Science Academy at science-academy@ugent.be.
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UGent PhD students
Doctoral School pays for your course on the condition that you sign the attendance list for each lesson. If you are absent, please notify our academy in advance by email and provide the necessary documents.
By registering for a course or event organized by the Science Academy, you agree to the cancellation procedure that you can find on our website
KMO-portefeuille
Information on "KMO-portefeuille": https://www.ugent.be/nl/opleidingen/levenslang-leren/kmo
Organisation
Science Academy (IPVW)
Faculty of Science
science-academy@ugent.be
