Day 1: Tasks & activities

Problems, gut checks, R Projects, and a function

Section 1. Practice problems

Evaluate the following:

  1. By hand: Evaluate \(f(x,y)=x^2-1+3xy\) at \(x=1\) and \(y = 0\)

  2. In the R Console: given \(G(t,z)=3.1(t-4.2)^2 + 0.06z\), find the value of \(G(1, 2.5)\) (you do NOT need to write a function - just do the calculation in R using R as a calculator)

Units:

  1. You are combining information from multiple sources to estimate the total annual oil spilled in a watershed, reported by different companies. The following are reported for the year:
  • Company A: 14.6 barrels spilled (with 9.3 barrels recovered)
  • Company B: 692 liters spilled (94% recovered)
  • Company C: 18.1 gallons spilled (0% recovered)

What is the total unrecovered amount of oil spilled into the watershed that year, in barrels. Use R & Google here for calculations.

Average slope

  1. By hand: If an urchin population in a study plot in 1990 is 432, and in 2004 is 801, what is the average rate of population increase? Find the slope, then write a statement about the average increase (including units).

Section 2: Projects and functions

  1. Create a new R project (named eds212-day1-tasks)
  2. Add a new Quarto document to the project
  3. Press Render (save this in your project as fish-length-weight.qmd)
  4. Delete everything below the first code chunk
  5. Attach the {tidyverse} package
  6. Play around with markdown in the Quarto doc. Try adding text with at least the following:
  • Headers of varying size
  • Italics
  • Bold
  • Superscripts / subscripts
  • Bulletpoints
  • An image (google how!)
  1. Add a new code chunk
  2. Within the R code chunk, write a function to estimate fish standard weight, given parameters \(a\), \(b\), and fish length \(L\) as inputs. “Standard weight” is how much we expect a fish to weigh, give the species and fish length, and the nonlinear relationship is given by:

\[W=aL^b\]

where L is total fish length (centimeters), W is the expected fish weight (grams), and a and b are species-dependent parameter values. i. Test out your function to find the mass (g) for a 60 cm fish of the following species (parameter estimates from Peyton et al. (2016)):

  • Milkfish: a = 0.0905, b = 2.52
  • Giant trevally: a = 0.0353, b = 3.05
  • Great barracuda: a = 0.0181, b = 3.27
Citation

Peyton, K. A., T. S. Sakihara, L. K. Nishiura, T. T. Shindo, T. E. Shimoda, S. Hau, A. Akiona, and K. Lorance. 2016. “Length–Weight Relationships for Common Juvenile Fishes and Prey Species in Hawaiian Estuaries.” Journal of Applied Ichthyology 32 (3): 499–502. https://doi.org/10.1111/jai.12957.

  1. Make and store (as fish_length) a sequence of size ranges from 0 to 200 cm, by increments of 1 cm
  2. Estimate the weight for giant barracudas along that entire range (given the parameters above). Store the output as barracuda_weight.
  3. Bind the lengths and estimated barracuda weights into a single data frame using data.frame()
  4. Create a ggplot graph of predicted length versus weight for giant barracuda
  5. Update the graph axis labels and title
  6. Render the .qmd. Make sure everything is saved.
  7. Close your project. Reopen the project, and ensure that you can re-run the entire Quarto document (reproducibility check).


End Activity Session (Day 1)