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.
This course is part of a larger course series in Data Analysis consisting of 19 individual modules. Find more information and enroll for this module via www.ipvw-ices.ugent.be
Program
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.
