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


Lecture Notes

  1. Functions
  2. Conditionals


  1. Writing Functions (5 pts)

    Write a function that converts pounds to grams (there are 453.592 grams in one pound). It should take a value in pounds as the input and return the equivalent value in grams (i.e., the number of pounds times 453.592). Use that function to calculate how many grams there are in 3.75 pounds.

    [click here for output]
  2. 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 * Lengthb

    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
    1. Add a comment to this function so that you know what it does.
    2. Use this function to print out the mass of a Spinosaurus that is 16 m long based on its reassembled skeleton. Spinosaurus is a predator that is bigger, and therefore, by definition, cooler, than that stupid Tyrannosaurus that everyone likes so much.
    3. Create a new version of this function called get_mass_from_length() that estimates the mass of an organism in kg based on its length in meters by taking length, a, and b as parameters. To be clear we want to pass the function all 3 values that it needs to estimate a mass as parameters. This makes it much easier to reuse for all of the non-theropod species. Use this new function to estimate the mass of a Sauropoda (a = 214.44, b = 1.46) that is 26 m long.
    [click here for output]
  3. Combining Functions (10 pts)

    This is a follow up to Use and Modify.

    Measuring things using the metric system is the standard approach for scientists, but when communicating your results more broadly it may be useful to use different units (at least in some countries). Write a function that converts kilograms into pounds (there are 2.205 pounds in a kilogram). Use that function along with your dinosaur mass function from Use and Modify to estimate the weight, in pounds, of a 12 m long Stegosaurus (12 m is about as big as they come and nothing gets folks excited like a giant dinosaur). In Stegosauria, a has been estimated as 10.95 and b has been estimated as 2.64 (Seebacher 2001).

    [click here for output]
  4. Choice Operators (10 pts)

    Create the following variables.

    w <- 10.2
    x <- 1.3
    y <- 2.8
    z <- 17.5
    dna1 <- "attattaggaccaca"
    dna2 <- "attattaggaacaca"
    colors <- c("green", "pink", "red")

    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. 2 * x + 0.2 is equal to y
    5. dna1 is the same as dna2
    6. dna1 is not the same as dna2
    7. w is greater than x, and y is greater than z
    8. x times w is between 13.2 and 13.5
    9. dna1 is longer than 5 bases (use nchar() to figure out how long a string is), or z is less than w * x
    [click here for output]
  5. 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:

    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.
    [click here for output]
  6. Size Estimates by Name (20 pts)

    This is a follow up to Use and Modify.

    To make it even easier to work with your dinosaur size estimation functions 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 set a and b to the appropriate values.

    Once the function has assigned a and b have it run get_mass_from_length() with the appropriate values and return the estimated mass.

    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.

    Challenge (optional): If the name doesn’t match any of these values have the function return NA and print out a message that it doesn’t know how to convert that group.

    [click here for output]
  7. DNA or RNA (15 pts)

    Write a function, dna_or_rna(), which takes a sequence as an argument, that determines if a sequence of base pairs is DNA, RNA, or if it is not possible to tell given the sequence provided. Since all the function will know about the material is the sequence the only way to tell the difference between DNA and RNA is that RNA has the base Uracil ("u") instead of the base Thymine ("t"). You can check if a string contains a character (or a longer substring) in R using grepl(substring, string). Have the function return one of three outputs: "DNA", "RNA", or "UNKNOWN". Use the function to test each of the following sequences.

    seq1 <- "ttgaatgccttacaactgatcattacacaggcggcatgaagcaaaaatatactgtgaaccaatgcaggcg"
    seq2 <- "gauuauuccccacaaagggagugggauuaggagcugcaucauuuacaagagcagaauguuucaaaugcau"
    seq3 <- "gaaagcaagaaaaggcaggcgaggaagggaagaagggggggaaacc"

    Optional: For a little extra challenge make your function work with both upper and lower case letters, or even strings with mixed capitalization

    [click here for output]
  8. 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 each species. Because the latitude and longitude columns for each occurrence dataset will be different, generalize them 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 species occurrences onto the available climate space, then use this function to generate a plot for each of the three tree species.

    [click here for output] [click here for output] [click here for output]