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ONLINE COURSE – Data wrangling using R and Rstudio (DWRS01) This course will be delivered live

3rd September 2020 - 4th September 2020

This course will now be delivered live by video link in light of travel restrictions due to the COVID-19 (Coronavirus) outbreak.

This is a ‘LIVE COURSE’ – the instructor will be delivering lectures and coaching attendees through the accompanying computer practical’s via video link, a good internet connection is essential.

TIME ZONE – Western European Time – however all sessions will be recorded and made available allowing attendees from different time zones to follow a day behind with an additional 1/2 days support after the official course finish date (please email oliverhooker@psstatistics.com for full details or to discuss how we can accommodate you).

Course Overview:

In this two day course, we provide a comprehensive practical introduction to data wrangling using R. In particular, we focus on tools provided by R’s tidyverse, including dplyr, tidyr, purrr, etc. Data wrangling is the art of taking raw and messy data and formating and cleaning it so that data analysis and visualization etc may be performed on it. Done poorly, it can be a time consuming, labourious, and error-prone. Fortunately, the tools provided by R’s tidyverse allow us to do data wrangling in a fast, efficient, and high-level manner, which can have dramatic consequence for ease and speed with which we analyse data. On Day 1 of this course, having covered how to read data of different types into R, we cover in detail all the dplyr tools such as select, filter, mutate, etc. Here, we will also cover the pipe operator (%>%) to create data wrangling pipelines that take raw messy data on the one end and return cleaned tidy data on the other. On Day 2, we cover how to perform descriptive or summary statistics on our data using dplyr’s summarize and group_by functions. We then turn to combining and merging data. Here, we will consider how to concatenate data frames, including concatenating all data files in a folder, as well as cover the powerful SQL like join operations that allow us to merge information in different data frames. The final topic we will consider is how to “pivot” data from a “wide” to “long” format and back using tidyr’s pivot_longer and pivot_wider.

To find out more or to book online via our sister company (PS statistics) use the link below…

ONLINE COURSE – Data wrangling using R and Rstudio (DWRS01) This course will be delivered live


3rd September 2020
4th September 2020


Delivered remotely (United Kingdom)
Western European Time, United Kingdom + Google Map