Transforming Data with R and the Tidyverse

Introduction

In this one-day hands-on workshop, RStudio certified instructor Jeroen Janssens will walk you through the so-called tidyverse to transform data. The tidyverse is an ecosystem of R packages that share an underlying design philosophy, grammar, and data structures.

We'll start at the beginning, with importing CSV data using readr and spreadsheets using readxl. We'll cover the most important functions from dplyr and tidyr for generic data wrangling and cleaning. We'll also look at dealing with dates, factors, and textual data specifically using the packages lubridate, forcats, and stringr, respectively. Note that this workshop does not cover ggplot2; for that we recommend our one-day workshop Data Visualisation with R and ggplot2.

By the end of this workshop, you'll have a good understanding of the tidyverse ecosystem and you'll be able to apply many of its packages to your own data.

Schedule

• The concept of tidy data
• Filtering rows
• Selecting columns
• Replacing values
• Handling missing values
• Cleaning column names
• Making groups
• Computing summary statistics
• Pivoting
• Dealing with dates, factors, and textual data

Prerequisites

You're expected to have some experience with programming in R and RStudio. Our workshop Programming in R is one option that can help you with that.

Recommended preparation

Participants are kindly requested to have the following items installed prior to the start of the workshop:

• R version 4.0 or later
• RStudio v1.3 or later
• The latest version the tidyverse, by running: install.packages("tidyverse"), dependencies = TRUE)

Clients

I’ve previously delivered this workshop at:

• DPD
• Dutch Institute for Clinical Auditing
• EQUATE Petrochemical Company ركة ايكويت للبتروكيماويات
• Gemeente Nijmegen
• KPN
• T-Mobile

Testimonials

"Jeroen organised for KPN a ten-week course on Data Science with R. The combination of training, on-site coaching, and remote support ensured that our analysts are applying the new knowledge and skills in their daily projects. For instance, they're now capable to implement complex predictive models using R. We're looking forward to the follow-up course on Advanced Machine Learning."

--Wouter Egberink, Manager Commercial Analytics, KPN