Day 1: Functions in R

Learning Objectives

This week, students will:

• learn the parts of a function

Practice Objectives

• Reading and writing tables
• Reading and plotting phylogenies

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 “spring2023”.
• Open a new file, name it “functions.Rmd”, and save it to your “documents” folder.

Writing functions (10 min)

• In this lesson we will use the concept of “mass” to learn how to create functions.
• Mass describes the density and type of atoms in any given object.
• The SI unit of mass is the kilogram (kg), though mass can also be measured in grams (gr), ounces (oz), and pounds (lb).
• Reminder - Mass is not the same as weight:
  • Weight is the measure of the force of gravity on an object
  • It is hence a relative measure
  • Mass is an absolute measure
  • The mass of an object will never change, but the weight of an item can change based on its location.

• Formulas to convert between different units of mass:

Pounds to kilograms	Kilograms to pounds

Exercise 1: Converting between units of mass

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Using and modifying functions (20 min)

• The length of an organism is strongly correlated with its body mass.
• This allometric relationship takes the form of mass equals to parameter “a” multiplied by “length” elevated to the power of parameter “b”:

mass = a * length^b

• Parameters a and b vary among biological groups.
• Scientists use this formula to estimate the mass of organisms for which we only have length measurememts. For example:
  • trees, we cannot weigh them unless we unroot them,
  • extinct creatures such as dinosaurs, as we cannot get the living weight of something that is fossilized.
• The following function uses the formula mass = a * length^b to estimate the mass of an organisms belonging to Theropoda dinosaurs in kg, based on its length in meters and the set of parameter values a and b that have been estimated for that biological group, a = 0.73 and b = 3.63, by Seebacher (2001).
  • Take 3 min to type the following function in:

    get_mass_from_length_theropoda <- function(length){
      mass <- 0.73 * length ^ 3.63
      return(mass)
    }
    

Exercise 2: The weight of dinosaurs

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Why do we use functions in programming? (15 min)

Discussion in small groups (5 min)

• Discuss in small groups: Based on your experience with coding so far and after watching the introductory videos, how does creating your own functions would improve your coding workflow?

• Individually, work on your Rmd file:

  • Write a second level subtitle for the introductory section.
  • List and justify three reasons why using functions is useful in data science.

Discussion in full (10 min)

• How does creating your own functions will improve your code?

• Makes code more understandable:
  • Code is shorter, easier to remember
  • Code is more organized for you and others, so it is easier to read
  • Code is grouped conceptually, easier to understand
  • Code is more manageable, invites you to be intentional on the code that you are writing
  • Allows to be more in control of the outputs
• Makes code reusable:
  • It allows reusing code for other parts of a project or a future project
  • It is less error prone than copy-pasting code
    • If it occurs in more than one place, it will eventually be wrong somewhere.
  • It is more efficient than copy-pasting code
    • Functions are written to be reusable.

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Defining argument values in a function (20 min)

• In the previous exercise, you created the function get_mass_from_length() which is a more flexible form of get_mass_from_length_theropoda(), by allowing a and b to be passed as arguments.
• Still, for some organisms we don’t have specific values of a and b. In this case, we have to use values of a and b that can be applied generally.
• We can give this general values as default values for some or all arguments in any function that we are creating.

Exercise 3: The general form of a formula

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Combining functions (20 min)

• The metric system is the standard approach used in scientific practice.
• To communicate scientific results to a broader audience, it might be more impactful to use different units (at least in some countries).

Exercise 4: Facilitating impactful science

Homework:

Instructions

• Open a new file called “functions-after.Rmd” that is saved in your “documents” folder.
• Write the code to solve the following exercises in R chunks.
• Add comments to each line of code explaining with your own words what the code is doing.
• Once you are finished, knit to PDF.
• Git add, commit and push the new files (PDF and Rmd) to your remote repository.

Exercise 5: Creating a function for the Portal data set

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Day 2: Making choices inside R functions

Review from last class (10 min)

• Exercise 4 from last class: Combining functions
  • Combining functions in a nested way
  • Calling functions inside functions
• Homework

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 “choices.Rmd”, and save it to your “documents” folder.

Review: Logical and conditional statements (15 min)

• Statements that return logical values (TRUE or FALSE)
  • logical operators
  • conditional operators
  • logical functions

Exercise 6: Practice logical and conditional statements

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Basic if statement (10 min)

• A simple if statement allows us to choose between a single option and its alternative.

Exercise 7: Handling one choice

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The if/else statement (10 min)

• An else statement allows us to choose between two options and its alternative.

Exercise 8: Handling 2 choices

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The if/else if/else statement (10 min)

• An else if statement allows us to choose between three or more options and an alternative.

Exercise 9: Handling 3 choices or more

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Using conditions inside functions (30 min)

• We can use conditions inside functions
• Conditions alter the behavior of a functions
• Conditions give us more control on the behavior of a function

Exercise 10: Value of y by age class (10 min)

• Conditions allow us to be even more efficient in reusing code
  • For example, we can consolidate all the functions we created to get the mass of a dinosaur into a single one.

Exercise 11: Mass estimates by biological group (20 min)

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Homework:

Instructions

• Open a new file called “choices-after.Rmd” that is saved in your “documents” folder.
• Write the code to solve the following exercises in R chunks.
• Add comments to each line of code explaining with your own words what the code is doing.
• Once you are finished, knit to PDF.
• Git add, commit and push the new files (PDF and Rmd) to your remote repository.

Exercise 12: Load or download a file?

Exercise 13: A function for the UHURU data set

Exercise 14: Energy conversion challenge