Leverage your R Skills: Data Wrangling & Plotting and reporting-M6

DA24-25-M6

Engels

Description

Tidyverse is a collection of R-packages used for data wrangling and visualization that share a common design philosophy. The goal of this course is to get you up to speed with the most up-to-date and essential tidyverse tools for data 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 R tidyverse.

This course covers the most essential tools from 3 main R tidyverse packages that are frequently used in general data analysis procedure.
Lectures with R code demonstrations are blended with hands-on exercises which allows you to try out the tools you’ve seen in the class under guides.

R offers many first class features for statistics and data science. One of these, is certainly Rmarkdown, that allows seamless integration of analysis (code) and text. This greatly improves reproducibility, reduces copy-paste- and others errors and enhances possibilities for automation.

R markdown offers three main types of output: pdf, html and docx. The first session introduces the basic framework, the output-specific possibilities, the bookdown-extension and the easy and rewarding move to its recent less platform dependent successor Quarto.

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, the output-specific possibilities, the bookdown-extension and the easy and rewarding move to its recent less platform dependent successor Quarto.
Exploring some general approaches for automation (using self-built templates for report-sections or complete reports) and presents Officedown. The latter is less flexible than Rmarkdown, but offers more options for docx-output.

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.

Target audience

This course targets anyone who wants to use R for data processing and needs to produce professional looking graphs and/or summary statistics.

Course prerequisites

The course is open to all interested persons. Basic R skills as provided in Module 2 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 with Tidyverse

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*: € 825
Non profit, government, higher education staff: € 620
(Doctoral) students, unemployed: € 370

*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

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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.

We follow the No Show polity of the Doctoral School!