Functions · Data Carpentry for Biologists

Learning Objectives

Following this assignment students should be able to:

use, modify, and write custom functions

use the output of one function as the input of another

Reading

Topics
- Functions
Readings
- Creating functions

Lecture Notes

Setup

install.packages(c('dplyr', 'ggplot2', 'readr'))
download.file("https://ndownloader.figshare.com/files/2292172",
  "surveys.csv")
download.file("https://ndownloader.figshare.com/files/3299474",
  "plots.csv")
download.file("https://ndownloader.figshare.com/files/3299483",
  "species.csv")

Lecture Notes

Exercises

Writing Functions (15 pts)

1. Copy the following function (which converts weights in pounds to weights in grams) into your assignment and replace the ________ with the variable names for the input and output.
```
convert_pounds_to_grams <- function(________) {
    grams = 453.6 * pounds
    return(________)
}
```
Use the function to calculate how many grams there are in 3.75 pounds.

2. Copy the following function (which converts temperatures in Fahrenheit to temperatures in Celsius) into your assignment and replace the ________ with the needed commands and variable names so that the function returns the calculated value for Celsius.
```
convert_fahrenheit_to_celsius <- ________(________) {
    celsius = (fahrenheit - 32) * 5 / 9
    ________(________)
}
```
Use the function to calculate the temperature in Celsius if the temperature in Fahrenheit is 80°F.

3. Write a function named double that takes a number as input and outputs that number multiplied by 2. Run it with an input of 512.

4. Write a function named prediction that takes three arguments, x, a, and b, and returns y using y = a + b * x (like a prediction from a simple linear model). Run it with x = 12, a = 6, and b = 0.8.
Expected outputs for Writing Functions
Use and Modify (15 pts)

The length of an organism is typically strongly correlated with its body mass. This is useful because it allows us to estimate the mass of an organism even if we only know its length. This relationship generally takes the form:

mass = a * length^b

Where the parameters a and b vary among groups. This allometric approach is regularly used to estimate the mass of dinosaurs since we cannot weigh something that is only preserved as bones.

The following function estimates the mass of an organism in kg based on its length in meters for a particular set of parameter values, those for Theropoda (where a has been estimated as 0.73 and b has been estimated as 3.63; Seebacher 2001).
```
get_mass_from_length_theropoda <- function(length){
  mass <- 0.73 * length ^ 3.63
  return(mass)
}
```
1. Use this function to print out the mass of a Theropoda that is 16 m long based on its reassembled skeleton.
2. Create a new version of this function called get_mass_from_length() that takes length, a and b as arguments and uses the following code to estimate the mass mass <- a * length ^ b. Use this function to estimate the mass of a Sauropoda (a = 214.44, b = 1.46) that is 26 m long.
Expected outputs for Use and Modify
Writing Functions 2 (15 pts)

1. Copy the following function (which converts weights in pounds to weights in grams and rounds them) into your assignment. Replace the ________ with the variable names for the input and output. Replace __ with a number so that by default the function will round the output to one decimal place.
```
convert_pounds_to_grams <- function(________, numdigits = __) {
  grams <- 453.6 * pounds
  rounded <- round(grams, digits = numdigits)
  return(________)
}
```
Use the function to calculate how many grams there are in 4.3 pounds using the default for the number of decimal places.

2. Write a function called get_height_from_weight that takes three arguments, weight, a, and b, and returns an estimate of height using height = a * weight ^ b (a prediction from a power model). Give it default arguments of a = 12 and b = 0.38. There should be no default value for weight. Use the default argument values (by passing only the value of weight to the function) to calculate height when weight = 42.

3. Call the function from (2) setting weight to 42, a to 6, and b to 0.5.

4. The function in (2) assumes that the weight is provided in grams. Use the functions from (1) and (2) in combination to estimate the height for an animal that weighs 2 pounds using the default value for a, but changing the value for b to 0.32.
Expected outputs for Writing Functions 2
Default Arguments (15 pts)

The following function estimates the mass of an organism in kg based on its length in meters and a set of parameter values. For some types of dinosaurs we don’t have specific values of a and b, so we have to use general values that can be applied to a number of different species.
```
get_mass_from_length_theropoda <- function(length, a, b){
  mass <- a * length ^ b
  return(mass)
}
```
Rewrite this function so that its arguments have default values of a = 39.9 and b = 2.6 (the average values from Seebacher 2001).
1. Use this function to estimate the mass of a Sauropoda (a = 214.44, b = 1.46) that is 22 m long (by setting a and b when calling the function).
2. Use this function to estimate the mass of a dinosaur from an unknown taxonomic group that is 16m long. Only pass the function length, not a and b, so that the default values are used.
Expected outputs for Default Arguments
Combining Functions (15 pts)

Write two functions:
- One called get_mass_from_length() that takes length (in m), a and b as arguments, has the following default arguments a = 39.9 and b = 2.6, uses the following code to estimate the mass (in kg) mass <- a * length ^ b, and returns it. (This function is the answer to the Default Arguments exercise, so feel free to copy over your answer if you’ve done that exercise).
- One called convert_kg_to_pounds that converts kilograms into pounds (pounds = 2.205 * kg)
1. Use these two functions (each function should be called separately) to estimate the weight, in pounds, of a 12 m long Stegosaurus with a = 10.95 and b = 2.64 (The estimated a and b values for Stegosauria from Seebacher 2001).
2. Use these two functions (each function should be called separately) to estimate the weight, in pounds, of a 4 m long dinosaur using the default parameters.
Expected outputs for Combining Functions
Writing Tidyverse Functions (15 pts)

1. Copy the following vectors into R and combine them into a data frame named count_data with columns named state, count, area, and site.
```
state_vector <- c("FL", "FL", "FL", "FL", "GA", "GA", "GA", "GA", "SC", "SC", "SC", "SC")
site_vector <- c("A", "B", "C", "D", "A", "B", "C", "D", "A", "B", "C", "D")
count_vector <- c(9, 16, 3, 10, 2, 26, 5, 8, 17, 8, 2, 6)
area_vector <- c(3, 5, 1.9, 2.7, 2, 2.6, 6.2, 4.5, 8, 4, 1, 3)
```
2. Write a function that takes two arguments: 1) a data frame with a count column and an area column; and 2) a column in that data frame to color the points by. Have the function make a plot with area on the x-axis and count on the y-axis and the points colored by the column you provided as an argument. Set the size of the points to 3. Use the function to make a scatter plot of count as a function of area for the count_data data frame with the points colored by the state column.

3. Use the function from (2) to make a scatter plot of count as a function of area for the count_data data frame with the points colored by the site column.
Expected outputs for Writing Tidyverse Functions
Check That Your Code Runs (10 pts)

Sometimes you think you’re code runs, but it only actually works because of something else you did previously. To make sure it actually runs you should save your work and then run it in a clean environment.

Follow these steps in RStudio to make sure your code really runs:

1. Restart R (see above) by clicking Session in the menu bar and selecting Restart R:

2. If the Environment tab isn’t empty click on the broom icon to clear it:

The Environment tab should now say “Environment Is Empty”:

3. Rerun your entire homework assignment using “Source with Echo” to make sure it runs from start to finish and produces the expected results.

Expected outputs for Check That Your Code Runs
Portal Species Time-Series Challenge (Challenge - optional)

If surveys.csv, species.csv, and plots.csv are not available in your workspace download them:

Write a function that:
- Takes four arguments - 1) a data frame (where each row is one individual and there is a genus and a species column); 2) a column to use as a time column (e.g., year); 3) a genus_name argument for choosing which genus to plot; and 4) a species_name argument for choosing which species to plot.
- Makes a plot using ggplot2 with the time on the y-axis and the count of the number of individuals (i.e., the number of rows) observed for that time for the species indicated by the genus_name and species_name arguments. The plot should display the data as blue points (with size = 2) connected by blue lines (with linewidth = 1). The y-axis label Number of Individuals
1. Use your function, and the data in surveys.csv and species.csv, to plot the time-series for time = year, genus_name = "Dipodomys" and species_name = "merriami"
2. Use your function, and the data in surveys.csv and species.csv, to plot the time-series for time = month, genus_name = "Chaetodipus" and species_name = "penicillatus" (this plot will show the average seasonal pattern of Chaetodipus penicillatus abundances)
3. Use your function, and the data from plots.csv, surveys.csv and species.csv, to plot the time-series for time = year, genus_name = "Chaetodipus" and species_name = "baileyi" only on the "Control" plots.
Expected outputs for Portal Species Time-Series Challenge

Assignment submission & checklist