Data Carpentry for Biologists

Making Choices

Assignment

Learning Objectives

Following this assignment students should be able to:

  • understand and use the basic relational operators
  • use an if statement to evaluate conditionals
  • use if statements with functions

Reading

Lecture Notes

  1. Conditionals
  2. Floating Point
  3. Style

Exercises

  1. 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
    10. Each mass in masses is between 30 and 50.
    Expected outputs for Choice Operators

  2. Basic If Statements (20 pts)

    1. Complete (i.e., copy into your code and them modify) the following if statement so that if age_class is equal to “sapling” it sets y <- 10.

    age_class = "sapling"
if (){
  
}
y

    2. Complete the following if statement so that if age_class is equal to “sapling” it sets y <- 10 and if age_class is equal to “seedling” it sets y <- 5.

    age_class = "seedling"
if (){
  
}
y

    3. Complete the following if statement so that if age_class is equal to “sapling” it sets y <- 10 and if age_class is equal to “seedling” it sets y <- 5 and if age_class is something else then it sets the value of y <- 0.

    age_class = "adult"
if (){
  
}
y

    4. Convert your if/else if/ else statement from (3) into a function that takes age_class as an argument and returns y. Call this function 5 times, once with each of the following values for age_class: “sapling”, “seedling”, “adult”, “mature”, “established”.

    Expected outputs for Basic If Statements

  3. If Statements In Functions (20 pts)

    1. Write a function named double_if_small that takes a number as input and returns the number multiplied by 2 if the input is less than 26 and returns just the number (not multiplied by two) if the input is greater than or equal to 26. Call the function with 10 as the input.
    2. Call the function from (1) with 30 as the input.
    3. Write a function called prediction that takes a single argument x. If x is both greater than 0 and less than 15 then return y = 6 + 0.8 * x. If x is both greater than 15 and less than 30 then return y = 5 + 0.75 * x. In all other cases return y = NA. Call the function with 5 as the input.
    4. Call the function from (3) with 26 as the input.
    5. Call the function from (3) with -2 as the input.
    Expected outputs for If Statements In Functions

  4. 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 a and b values to estimate the species mass using these equations:

    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 values of a and b for Stegosauria. When Stegosauria is greater than 8 meters long use the equation above. When it is less than 8 meters long use a = 8.5 and b = 2.8. Run the function for a Stegosauria that is 6 meters long.

    Expected outputs for Size Estimates by Name

  5. DNA or RNA (20 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

  6. 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:

    Screenshot showing clicking session from the menu bar and selecting Restart R

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

    Screenshot showing the Environment tab with the cursor hovering over the broom icon

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

    Screenshot showing the Environment tab with only the words 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.

    Screenshot showing the RStudio Source with Echo item hovered in the Source dropdown

    Expected outputs for Check That Your Code Runs

  7. Load or Download File (Challenge - optional)

    With large data files it can be useful to only download the file if it hasn’t already been downloaded. One way to do this is to check if the file name exists in your working directory. If it does then load it, if not then download it. You can use the list.files() function to get a list of files and directories in the working directory and the download.file(url, filename) function to download the file at a url to a specific filename.

    1. Write a conditional statement that checks if surveys.csv exists in the working directory, if it doesn’t then downloads it from https://ndownloader.figshare.com/files/2292172 using download.file(), and finally loads the file into a data frame and displays the first few rows using the head() function. The url needs to be in quotes since it is character data.

    2. Make a version of this conditional statement that is a function, where the name of the file is the first argument and the link for downloading the file is the second argument. The function should return the resulting data frame. Add some documentation to the top of the function describing what it does. Call this function using “species.csv” as the file name and https://ndownloader.figshare.com/files/3299483 as the link. Print the first few rows of the resulting data frame using head().

    Expected outputs for Load or Download File

  8. Unit Conversion Challenge (Challenge - optional)

    Measures of the amount of energy used by biological processes are critical to understanding many aspects of biology from cellular physiology to ecosystem ecology. There are many different units for energy use and their utilization varies across methods, research areas, and lab groups. Write a function, convert_energy_units(energy_value, input_unit, output_unit) to convert units between the following energy values - Joules(J), Kilojoules(KJ), Calories(CAL), and Kilocalories (KCAL; this is unit used for labeling the amount of energy contained in food). A Kilojoule is 1000 Joules, a Calorie is 4.1868 Joules, a Kilocalorie is 4186.8 Joules. An example of a call to this function would look like:

    energy_in_cal <- 200
energy_in_j <- convert_energy_units(energy_in_cal, "CAL", "J")

    Make this function more efficient by linking if else statements. If either the input unit or the output unit do not match the five types given above, have the function return NA and display a warning (using the warning() function) - “Cannot convert:” + the name of the unit provided.

    Instead of writing an individual conversion between each of the different currencies (which would require 12 if statements) you could choose to convert all of the input units to a common scale and then convert from that common scale to the output units. This approach is especially useful since we might need to add new units later and this will be much easier using this approach.

    Use your function to answer the following questions:

    1. What is the daily metabolic energy used by a human (~2500 KCALs) in Joules.
    2. How many times more energy does a common seal use than a human? The common seal uses ~52,500 KJ/day (Nagy et al. 1999). Use the daily human metabolic cost given above.
    3. How many ergs (ERG) are there in one kilocalorie. Since we didn’t include the erg conversion this should trigger our ‘Cannot convert’ message
    Expected outputs for Unit Conversion Challenge

Assignment submission & checklist