Data wrangling and visualization with R tidyverse – ONLINE

Data wrangling and visualization with R tidyverse – ONLINE

  • Day 1
    29/03/2022
    1:00 pm - 4:00 pm
  • Day 2
    31/03/2022
    1:00 pm - 4:00 pm

Course details

statistics course | level: intermediate | register now
for questions related to this event, contact ugent@flames-statistics.com
affiliation: Ghent University


Abstract

The 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.

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 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.
• 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.

What you won’t learn:
• A systematic training guide in basics of R. If you never used R or RStudio before, we highly recommend you to take the ICES course “Introduction to R” 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 structure
The content of this course will cover the most essential tools from 3 main R tidyverse packages that are frequently used in general data analysis procedure.
• Session 1
o Introduction
o Data transforming and summarizing with dplyr
o Lab 1 (Exercise dplyr)
o Data ingest and tidying with tidyr
o Lab 2 (Exercise tidyr)
o Extra tools for programming
• Session 2
o Data visualization with ggplot2
o Lab 1 (Exercise ggplot2)
o Extra for ggplot
This course blends lectures with R code demonstrations and hands-on exercises which allows you to try out the tools you’ve seen in the class under guides.
The course materials e.g., lecture slides, data, r scripts, exercises and solutions, are piled into an RStudio project. All the materials will be provided to you latest 1 day before the start of the module with a mail notification. Thus, it is recommended to install R and RStudio beforehand:
• R: https://cran.r-project.org
• RStudio: https://rstudio.com/products/rstudio/download/
Open recourse for your own learning and discovery on tidyverse:
• https://www.tidyverse.org


Prerequisites

The course is open to all interested persons with some basic experience using R or other programming languages.


Background readings

Open recourse for your own learning and discovery on tidyverse:
• https://www.tidyverse.org


Fee

PhD's or postdocs of a Flemish university: free of charge
Other academics, Non-profit/Public sector: 150 €
Private sector: 400 €

If you are a PhD student, do not register with the ticket of a paying category because your registration will not be correct.


Venue

Online workshop: via MS Teams


Instructor

dr. Limin Liu is a sociologist and statistician working at Ghent University as a Post-doc at the Center for Statistics and a lecturer at Flames training network for Methodology and Statistics. She has delivered this tidyverse training module several times and is experienced in guiding the beginners from both academic and industry environment


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