Data Wrangling


  • Loading data from files
  • Manipulating Data Frames with dplyr
    • Selecting columns with select()
    • Filtering rows with filter()
    • Sorting with arrange()
    • Adding and changing columns with mutate() and transmute()
    • Aggregation with group_by() and summarise()
    • Assorted other functions from dplyr
  • Pivoting with tidyr
    • Long versus wide data formats
    • Going wide with spread()
    • Getting long with gather()
    • Splitting compound columns using separate() and extract()
    • Explicit missing values with complete()
    • Handling columns of data frames with nest() and unnest()
  • Functional programming with purrr
    • Mapping functions of a single variable with map()
    • Mapping to a specific data type
    • Mapping functions of two variables with map2()
    • Mapping functions with many variables using pmap()
    • Leveraging side effects with walk(), walk2() and pwalk()
    • Repeating with delays using insistently() and slowly()

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Training Philosophy

Our training emphasises practical skills. So, although you'll be learning concepts and theory, you'll see how everything is applied in the real world. We will work through examples and exercises based on real datasets.


All you'll need is a computer with a browser and a decent internet connection. We'll be using an online development environment. This means that you can focus on learning and not on solving technical problems.

Of course, we are happy to help you get your local environment set up too! You can start by following these instructions.


The training package includes access to
  • our online development environment and
  • detailed course material (slides and scripts).

Return to our list of courses.