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

• Topics

• Functions
• Conditionals

1. -- Writing Functions --

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.

2. -- Use and Modify --

The length of an organism is typically strongly correlated with it’s 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 it’s 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. 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 it’s 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 it’s 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.
3. -- Nested Functions --

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).

4. -- Choice Operators --

Create the following variables.

``````w <- 10.2
x <- 1.3
y <- 2.8
z <- 17.5
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. `w` + `x` is less than 15
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`
5. -- Simple If Statement --

To determine if a file named `thesis_data.csv` exists in your working directory you can use the code:

``````file.exists('thesis_data.csv')
``````

This code returns TRUE if the files exists and FALSE if it does not.

1. Write an `if` statement that loads the file using `read.csv()` only if the file exists.
2. Add an `else` clause that prints “OMG MY THESIS DATA IS MISSING. NOOOO!!!!” if the file doesn’t exist.
6. -- Complete the Code --

The following function is intended to check if two geographic points are close to one another. If they are it should return `TRUE`. If they aren’t, it should return `FALSE`. Two points are considered near to each other if the absolute value of the difference in their latitudes is less than one and the absolute value of the difference in their longitudes is less than one.

1. Fill in the `_________` in the function to make it work.

``````near <- function(lat1, long1, lat2, long2){
# Check if two geographic points are near each other
if ((abs(lat1 - lat2) < 1) & (_________){
near <- TRUE
} else {
near <- _________
}
return(near)
}
``````
2. Improve the documentation for the function so that it is clear what near means and what output the user should expect.
3. Check if Point 1 (latitude = 29.65, longitude = -82.33) is near Point 2 (latitude = 41.74, longitude = -111.83).
4. Check if Point 1 (latitude = 29.65, longitude = -82.33) is near Point 2 (latitude = 30.5, longitude = -82.8).
5. Create a new version of the function that improves it by allowing the user to pass in a parameter that sets what “near” means. To avoid changing the existing behavior of the function (since some of your lab mates are using it already) give the parameter a default value of 1.
6. Improve the documentation for the new function so that it reflects this new behavior
7. Check if Point 1 (latitude = 48.86, longitude = 2.35) is near Point 2 (latitude = 41.89, longitude = 2.5), when near is set to 7.
7. -- Choices with Functions --

The UHURU experiment in Kenya has conducted a survey of Acacia drepanolobium among each of their ungulate exclosure treatments. Data for the survey is available here in a tab delimited (`"\t"`) format. Each of the individuals surveyed were measured for branch circumference (`CIRC`) and canopy width (`AXIS1`) and was identified for the associated ant-symbiont species present (`ANT`).

The following function takes a subset of the data for a given `ANT` symbiont and evaluates the linear regression (`lm()`) for a given relationship, returning the symbiont `species` used for the subset and the `r2` of the model.

``````report_rsquared <- function(data, species, formula){
subset <- dplyr::filter(data, ANT == species)
test <- lm(formula, data = subset)
rsquared <- round(summary(test)\$r.squared, 3)
output <- data.frame(species = species, r2 = rsquared)
return(output)
}
``````
1. Execute the function using the UHURU data and specifying `species = "CM"` and `formula = "AXIS1~CIRC"`.
2. Modify the function so that it also determines `if()` the `rsquared` is significant based on a given `threshold`. The modified function should `return()` the `species`, `rsquared` and a `significance` value of `"S"` for a relationship with an `rsquared > threshold` or `"NS"` for an ```rsquared < threshold```.
3. Execute your modified function for `species` of `"CM"`, `"CS"`, and `"TP"` given a `threshold = 0.667`.