Programming Fundamentals 1 · 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

understand and use the basic relational operators

use an if statement to evaluate conditionals

Reading

Topics
- Functions
- Conditionals
Readings
- Creating functions
- Software Carpentry lesson on making choices

Lecture Notes

Exercises

Writing Functions (5 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 (10 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
Combining Functions (10 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
Choice Operators (10 pts)

Create the following variables.
```
w <- 10.2
x <- 1.3
y <- 2.8
z <- 17.5
colors <- c("red", "blue", "green")
masses <- c(45.2, 36.1, 27.8, 81.6, 42.4)
dna1 <- "attattaggaccaca"
dna2 <- "attattaggaacaca"
```
Use them to print whether or not the following statements are TRUE or FALSE.
1. w is greater than 10
2. "green" is in colors
3. x is greater than y
4. Each value in masses is greater than 40.
5. 2 * x + 0.2 is equal to y
6. dna1 is the same as dna2
7. dna1 is not the same as dna2
8. w is greater than x, or y is greater than z
9. x times w is between 13.2 and 13.5 (there is no way to indicated “between” in R so to do this we have to separately check if the number is greater than the minimum value and less than the maximum value, combining these two conditions with &)
10. Each mass in masses is between 30 and 50.
Expected outputs for Choice Operators
Simple If Statement (10 pts)

To determine if a file named thesis_data.csv exists in your working directory you can use the code to get a list of available files and directories:
```
list.files()
```
1. Use the %in% operator to write a conditional statement that checks to see if thesis_data.csv is in this list.
2. Write an if statement that loads the file using read_csv() only if the file exists.
3. Add an else clause that prints “OMG MY THESIS DATA IS MISSING. NOOOO!!!!” if the file doesn’t exist.
4. Make sure your actual thesis data is backed up.
Expected outputs for Simple If Statement
Size Estimates by Name (20 pts)

You’re going to write a function to estimate a dinosaur’s mass based on its length. The general form of the equation for doing this is:

mass <- a * length ^ b

The parameters a and b vary by the group of dinosaurs, so you decide to create a function that lets you specify which dinosaur group you need to estimate the size of by name and then have the function automatically choose the right parameters.

Create a new function get_mass_from_length_by_name() that takes two arguments, the length and the name of the dinosaur group. Inside this function use if/else if/else statements to check to see if the name is one of the following values and if so use the associated equation to estimate the species mass:
- Stegosauria: mass = 10.95 * length ^ 2.64 (Seebacher 2001)
- Theropoda: mass = 0.73 * length ^ 3.63 (Seebacher 2001)
- Sauropoda: mass = 214.44 * length ^ 1.46 (Seebacher 2001)
If the name is not any of these values the function should return NA.

Run the function for:
1. A Stegosauria that is 10 meters long.
2. A Theropoda that is 8 meters long.
3. A Sauropoda that is 12 meters long.
4. A Ankylosauria that is 13 meters long.
Challenge (optional): If the name is not one of values that have a and b values print warning that it doesn’t know how to convert that group that includes that groups name in a message like “No known estimation for Ankylosauria”. You can use the function warning("your warning text") to print a warning and the function paste() to combine text with a value from a variable paste("My name is", name). Doing this successfully will modify your answer to (4), which is fine.

Challenge (optional): Change your function so that it uses two different equations for Stegosauria. When Stegosauria is greater than 8 meters long use the equation above. When it is less than 8 meters long use mass = 8.5 * length ^ 2.8`. Run the function for a Stegosauria that is 6 meters long.
Expected outputs for Size Estimates by Name
DNA or RNA (15 pts)

Write a function that determines if a sequence of base pairs is DNA, RNA, or if it is not possible to tell given the sequence provided. RNA has the base Uracil ("u") instead of the base Thymine ("t"), so sequences with u’s are RNA, sequences with t’s are DNA, and sequences with neither are unknown.

You can check if a string contains a character (or a longer substring) in R using the str_detect function from the stringr package: str_detect(string, substring), which will return TRUE if substring is present in string. So str_detect(sequence, "u") will check if the string in the sequence variable has the base u.

Name the function dna_or_rna() and have it take sequence as an argument. Have the function return one of three outputs: "DNA", "RNA", or "UNKNOWN". Call the function on each of the following sequences.
```
seq1 <- "ttgaatgccttacaactgatcattacacaggcggcatgaagcaaaaatatactgtgaaccaatgcaggcg"
seq2 <- "gauuauuccccacaaagggagugggauuaggagcugcaucauuuacaagagcagaauguuucaaaugcau"
seq3 <- "gaaagcaagaaaaggcaggcgaggaagggaagaagggggggaaacc"
```
Challenge (optional): Figure out how to make your function work with both upper and lower case letters, or even strings with mixed capitalization.
Expected outputs for DNA or RNA
Climate Space Rewrite (20 pts)

This is a follow up to Climate Space.

Producing a plot of occurrences on the available climate space for each of the three species required a lot of repetition of very similar code. Whenever this happens, it is usually an indication that a function could be used instead. Such functions reduce the repetition in producing the three species plots, which enables you to save time and prevent errors by not having to rewrite the same code multiple times.
1. Create a function to download occurrence data and extract the corresponding climate data, which should return a dataset of all the bioclim variables for a single species. Because the latitude and longitude columns for each occurrence dataset have different names you can select and set them to the same name using the column index, instead of the column name, to get only those columns (e.g., select(longitude = 2, latitude = 3).
2. Create a second function for plotting the occurrences for a single species onto the available climate space, then use this function to generate separate plots for each of the three tree species.
Expected outputs for Climate Space Rewrite

Assignment submission & checklist