Leverage your R Skills: Data Wrangling & Plotting and reporting
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.
Practical information:
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- Prerequisites: Basic R skills are strongly advised
- Price: €370-€825
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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.
Teachers / speakers
Dr. Limin Liu is a postdoc researcher at the Center for Statistics at Ghent University. She studied social work and sociology in Beijing and Berlin where she has accumulated several years of empirical research experience in the field of sociology of eduction, social mobility and stratification. Upon her social science research experience, she achieved a Master in Statistical Data Analysis at Ghent University. Since 2019, she has joined the team of statistician consultants and focused on both qualitative and quantitative research methods. She is also teaching for FLAMES and ICES in many qualitative and quantitative data analysis software courses.
Dries Reynders studied Physics and Statistical Data Analysis at Ghent University. As a consultant for Stat-Gent, he has experience with a wide range of statistical methods and with clearly and efficiently communicating and reporting the results to varying audiences.