M6 - Leverage your R Skills: Data Wrangling & Plotting with Tidyverse

Type of Course - Dates - Venue - Description - Target audience - Exam - IMPORTANT: Incorporation in DTP and reimbursement by DS
Course prerequisites - Teacher - Course material - Fees - Enrol

Type of course

 This is an on campus course, with blended learning options.

Dates

Two afternoons in January 2022: January 25 and 27, 2022, from 1.30 pm to 4.30 pm.
Please note: the deadline for UGent PhD students who want a refund to open a dossier on the DS website (Application for Recognition) is December 24, 2021.

Venue

 To be confirmed.

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.

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.

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.

Exam

There is no exam connected to this module. Participants receive a certificate of attendance via e-mail at the end of the course.

Incorporation in DTP and reimbursement from DS for UGent PhD students

As a UGent PhD student, to be able to incorporate this 'transferable skills seminar, cluster research & valorization' in your Doctoral Training Program (DTP) and get a reimbursement of the registration fee from your Doctoral School (DS) you need to follow strict rules: please take the necessary action in time. The deadline to open a dossier on the DS website (Application for Recognition) for this course is December 24, 2021. Please note that opening a dossier does not mean that you are enrolled. You still need to enrol via the registration form on this site.

Course prerequisites

The course is open to all interested persons. Basic R skills as provided in Module 1 of this year's program are advised.

Teacher

Foto Limin LiuDr. 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 research experience in the field of sociology of eduction, social mobility and stratification. Upon her empirical research experience, she achieved a Master in Statistical Data Analysis at Ghent University. Since 2019, she has joined the team of statisticians and focused on both qualitative and quantitative research methods. She is experienced in guiding beginners from both academic and industry environment.

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.

Fees

A different price applies, depending on your main type of employment.

Employment Module 6
Industry/Private sector1    240
Non-profit, government, higher education staff2    180
(Doctoral) students, retired, unemployed2     80

1 If two or more employees from the same company enrol simultaneously for this course a reduction of 20% on the module price is taken into account starting from the second enrolment.

2 UGent staff and UGent doctoral students who pay internally via SAP or internal transfer can participate at these special rates.

Enrol for this course