Intermediate R: Advanced data wrangling, functions, and control flow

Session 7

September 26, 2023

Learning objectives

• Write a data wrangling script using tidy verbs
• Convert a tailor-made script to a function
• Use if-else logic to generalize functions

Part I: Data wrangling refresher

Picking up from last week

• Commit last week’s work to GitHub and push
• Merge your work to main!
• Create a new branch for today’s work and pull

Data wrangling objectives:

• Write a data wrangling script using the functions we covered last week
  • Use mutate to convert units of columns
  • Use summarize to calculate group-wise summary statistics
  • Use pivot_wider to create cross-tabs
• Use “tidy selection” helpers to reduce repetition in this workflow!

Part II: Functions

What are functions in R?

• We use pre-written functions constantly.
• Automated “recipes” for executing code
• Inputs (“arguments”) and outputs

Why write our own functions?

• Avoid repetition (+ mistakes)
• Enhance portability + reproducibility

Writing a function objectives

• Convert our data wrangling script into a function
• Break this function into smaller, human-readable functions
• Make this function flexible to apply to other data

Anatomy of a function

# Defining a function
# a_function will be the name of the function
# arguments go within the ()
# code goes in the curly braces

a_function <- function(an_argument) {
  # code to execute goes here
  
  # whatever is inside return() comes out of the funciton
  return(final_output) 
}

# Run the code to create the function
# Then you can use it:
# a_function(<some_input>)
# 

Part III: Conditionals

What are conditionals?

• if and ifelse statements
• Balance readability and flexibility in functions

Conditionals in R

• == for “is equal to”
• != for “not equal to:
• <, > for less than/greater than
• <=, >= for less than or equal to/greater than or equal to
• is.na() for is it NA?
• %in% for, is this value in a vector of possibilities
• …and more!

Conditionals objectives

• Add ifelse logic to our data wrangling function

Finishing up

• Commit your changes and push
• Open a pull request.

Homework

• None, except for…

Show and tell (Late October)

• An opportunity to apply workshop skills to your own work.
• You could apply different parts to different projects, or more integratively to one single project.
• Optional, but encouraged!

Some inspirations

• Turn the project folder for one of your current research project into a git/GitHub repo and/or R project
• Modify an R script to use functions, conditionals, or best-practices for code formatting
• Improve directory organization for project or your entire computer!