Vectorized functions

Learning Objectives

This week, students will:

• Learn to run functions on multiple datasets by using:
  • vectorized functions
  • apply functions
  • the function rowwise() from dplyr
• Understand that the group_by() function from dplyr runs the same function on multiple data sets
• Learn the difference between these four methods for code reusing
• Manipulate character strings with the package stringr and paste()

Practice Objectives

• Creating functions
• Deafult values for arguments
• Reading tables
• Using functions from dplyr and ggplot2

Setup your RStudio project (5 min)

• 🎗️ Structuring your files into a project is a best practice for good data science!
• Open your RStudio project for the class; I called mine “fall-2022”.
• Open a new file, name it “apply.Rmd”, and save it to your “documents” folder.

Vectorized functions (5 min)

Exercise 1: Calculating the mass of a bunch of dinosaurs (35 min)

sapply() (5 min)

Exercise 2: Conditioning on length (15 min)

mapply() (5 min)

Exercise 3: A data set of dinosaur lengths (30 min)

Discussion (20 min)

• How does using the functions you practiced today (vectorized and apply functions) is different from creating your own (regular) functions?
  • Creating and using a regular function:
    1. avoids repeating a group of code
    2. can be applied to a small number of values in a given amount of time
  • Using vectorized functions and apply functions:
    1. avoids repeating the same funcion over and over with different values
    2. can be applied to an almost infinite number of values in seconds