Advanced R

Available Dates

Course Location Starting Date
Advanced R Virtual via Zoom 28 April 2026 View
Advanced R Virtual via Zoom 02 June 2026 View

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This course follows on from the introductory course in familiarising you with the core R language

In this course we focus on extending your language knowledge to include more advanced filtering and data maniupulation. We look at data restructuring if your original data isn't in the right format, and the summarisation of datasets with repeated values. We also show how to deal with awkward data - files that don't import cleanly, have missing values or are inconsistently annotated. This course will make you a more practial and proficient R programmer.

Pre-Course Requirements & Suggestions

This course assumes that you have knowledge or skills equivalent to those taught in the following courses.

Introduction to R

Please ask us if you're unsure if you have the necessary knowledge or skills for this course.

Course Content

Importing problematic data
We will look in more depth at the read_ functions in the ReadR package. We'll look at how to cure common import problems such as mis-detected column types or files with additional headers on them.
Selecting and Sorting
We are going to extend our knowledge of the dplyr package for selecting and filtering data. Here we review piped operations, and look at new functions for sorting and deduplicating, as well as some more advanced ways to select columns to work on.
More clever filtering
The dplyr package provides the filter function for picking out rows based on your data. In this section we look at more clever things we can do with filter. We'll look at new operators and functions to allow more complex filtering of your data.
Restructuring data into 'tidy' format
We will look at what you can do if your data doesn't arrive with the 'tidy' structure which R works best with. We will look at restructuing data between 'long' and 'wide' formats, both of which may be needed during an analysis.
Mutating, Grouping and Summarising
We look at how to modify datasets by either adding new columns computed from existing data, or by using grouped operations to summarise rows which are replicates of each other.
Extending and Joining
In this final tidyvserse session we will look at how to extend existing datasets with more data. We do this both through the addition of more rows/columns, but also look at combining related datasets with joining operations.
Writing Custom Functions
Writing your own functions is a great way to reuse code in more than one place in your script. Here we show the structure of a custom function and how it can be written to play nicely with other tidyverse functions.