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Programming with the Tidyverse

A Code Clinic tutorial for staff and students at the Oxford Big Data Institute

R’s Tidyverse packages make interactive data exploration and modelling fast and fluid (and fun!?). The flip side of this ease of interrogating your data and building models is that using tidyr/dplyr/ggplot code indirectly (i.e. non-interactively within functions or loops) is more challenging. This code clinic will demonstrate the issues that arise when trying to use dplyr/tidyr verbs non-interactively and show you a number of recipes to solve common problems encountered in programming with the Tidyverse.

Intended Audience

Intermediate users of R who are familiar with the Tidyverse functions but are new to, or struggling with, using these within their own functions and scripts.

Topics to be covered:

Tidy evaluation (non-standard evaluation) Data-masking and indirection Tidy selection Developing functions based on dplyr/tidyr/ggplot

Learning Objectives

  1. Understand the issues with writing functions that incorporate Tidyverse functions
  2. Understand the two forms of non-standard evaluation used in the Tidyverse: Data Masking and Tidy Selection
  3. Gain a set of code techniques to solve the problems described above

Background Knowledge

  • Be familiar with R, RStudio
  • Be familiar with the Tidyverse – especially have some experience using dplyr, tidyr, and ggplot packages and the %>% syntax
  • Understand the basics of writing functions in R and have written at least one function of your own.

Installation Requirements

  1. Install R (versions > 4.0) and RStudio installed ((We recommend using RStudio version 1.4 or later as your IDE to interact with R)
  2. Install the following R packages: install.packages(c(“tidyverse”, “gapminder”, "palmerpenguins", "testthat"))