Data Science with R for the Life Sciences

Repeating Things 1

Assignment

Learning Objectives

Following this assignment students should be able to:

  • use and create vectorized functions
  • use the apply family of functions for iteration
  • integrate custom functions with dplyr for iteration

Reading

Lecture Notes

Place this code at the start of the assignment to load all the required packages.

library(dplyr)
library(ggplot2)

Exercises

  1. Size Estimates Vectorized (30 pts)

    This is a followup to Use and Modify.

    1. Write a function named mass_from_length_theropoda() that takes length as an argument to get an estimate of mass values for the dinosaur Theropoda. Use the equation mass <- 0.73 * length^3.63. Copy the data below into R and pass the entire vector to your function to calculate the estimated mass for each dinosaur.

      theropoda_lengths <- c(17.8013631070471, 20.3764452071665, 14.0743486294308, 25.65782386974, 26.0952008049675, 20.3111541103134, 17.5663244372533, 11.2563431277577, 20.081903202614, 18.6071626441984, 18.0991894513166, 23.0659685685892, 20.5798853467837, 25.6179254233558, 24.3714331573996, 26.2847248252537, 25.4753783544473, 20.4642089867304, 16.0738256364701, 20.3494171706583, 19.854399305869, 17.7889814608919, 14.8016421998303, 19.6840911485379, 19.4685885050906, 24.4807784966691, 13.3359960054899, 21.5065994598917, 18.4640304608411, 19.5861532398676, 27.084751999756, 18.9609366301798, 22.4829168046521, 11.7325716149514, 18.3758846100456, 15.537504851634, 13.4848751773738, 7.68561192214935, 25.5963348603783, 16.588285389794)

    2. Create a new version of the function named mass_from_length() to use the equation mass <- a * length^b and take length, a and b as arguments. In the function arguments, set the default values for a to 0.73 and b to 3.63. If you run this function with just the length data from Part 1, you should get the same result as Part 1. Copy the data below into R and call your function using the vector of lengths from Part 1 (above) and these vectors of a and b values to estimate the mass for the dinosaurs using different values of a and b.

      a_values <- c(0.759, 0.751, 0.74, 0.746, 0.759, 0.751, 0.749, 0.751, 0.738, 0.768, 0.736, 0.749, 0.746, 0.744, 0.749, 0.751, 0.744, 0.754, 0.774, 0.751, 0.763, 0.749, 0.741, 0.754, 0.746, 0.755, 0.764, 0.758, 0.76, 0.748, 0.745, 0.756, 0.739, 0.733, 0.757, 0.747, 0.741, 0.752, 0.752, 0.748)

      b_values <- c(3.627, 3.633, 3.626, 3.633, 3.627, 3.629, 3.632, 3.628, 3.633, 3.627, 3.621, 3.63, 3.631, 3.632, 3.628, 3.626, 3.639, 3.626, 3.635, 3.629, 3.642, 3.632, 3.633, 3.629, 3.62, 3.619, 3.638, 3.627, 3.621, 3.628, 3.628, 3.635, 3.624, 3.621, 3.621, 3.632, 3.627, 3.624, 3.634, 3.621)

    3. Create a data frame for this data using dino_data <- data.frame(theropoda_lengths, a_values, b_values). Use dplyr to add a new masses column to this data frame (using mutate() and your function) and print the result to the console.

    Expected outputs for Size Estimates Vectorized: 1

  2. Size Estimates With Maximum (30 pts)

    This is a followup to Part 1 Size Estimates Vectorized.

    Create a new version of your mass_from_length_theropoda() function from Part 1 of Size Estimates Vectorized called mass_from_length_max(). This function should only calculate a mass if the value of length passed to the function is less than 20. If length is greater than 20 return NA instead. Use sapply() and this new function to estimate the mass for the theropoda_lengths data from Size Estimates Vectorized.

    Expected outputs for Size Estimates With Maximum: 1

  3. Size Estimates By Name Apply (40 pts)

    This is a followup to Size Estimates by Name.

    Download the data on dinosaur lengths with species names into your data folder and import it using read.csv().

    Write a function get_mass_from_length_by_name() that uses the equation mass <- a * length^b to estimate the size of a dinosaur from its length. This function should take 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.

    If the name is not any of these values set a = NA and b = NA.

    1. Use this function and mapply() to calculate the estimated mass for each dinosaur. You’ll need to pass the data to mapply() as single vectors or columns, not the whole data frame.

    2. Using dplyr, add a new masses column to the data frame (using rowwise(), mutate() and your function) and print the result to the console.

    3. Using ggplot, make a histogram of dinosaur masses with one subplot for each species (using facet_wrap()).

    Expected outputs for Size Estimates By Name Apply: 1 2

  4. Tree Biomass Challenge (optional)

    Understanding the total amount of biomass (the total mass of all individuals) in forests is important for understanding the global carbon budget and how the earth will respond to increases in carbon dioxide emissions.

    We don’t normally measure the mass of a tree, but take a measurement of the diameter or circumference of the trunk and then estimate mass using equations like M = 0.124 * D2.53.

    1. Estimate tree biomass for each species in a 96 hectare area of the Western Ghats in India using the following steps.

    • Download the data and load the data into R.
    • Write a function that takes a vector of tree diameters as an argument and returns a vector of tree masses.
    • Create a dplyr pipeline that
      • Adds a new column (using mutate and your function) that contains masses calculated from the diameters
      • Groups the data frame into species using the SpCode column
      • And then calculates biomass (i.e., the sum of the masses) for each species (using summarize)
      • Stores the result as a data frame
    • Display the resulting data frame

    2. Plot a histogram of the species biomass values you just calculated.

    • Use 10 bins in the histogram (using the bins argument)
    • Use a log10 scale for the x axis (using scale_x_log10)
    • Change the x axis label to Biomass and the y axis label to Number of Species (using labs)
    Expected outputs for Tree Biomass Challenge: 1 2

  5. Crown Volume Calculation (optional)

    The UHURU experiment in Kenya has conducted a survey of Acacia and other tree species in ungulate exclosure treatments. Data for the tree data is available here in a tab delimited ("\t") format. Each of the individuals surveyed were measured for tree height (HEIGHT) and canopy size in two directions (AXIS_1 and AXIS_2). Read these data in using the following code:

    tree_data <- read.csv("https://ndownloader.figshare.com/files/5629536",
                 sep = '\t',
                 na.strings = c("dead", "missing", "MISSING",
                                "NA", "?", "3.3."))

    You want to estimate the crown volumes for the different species and have developed equations for species in the Acacia genus:

    volume = 0.16 * HEIGHT^0.8 * pi * AXIS_1 * AXIS_2

    and the Balanites genus:

    volume = 1.2 * HEIGHT^0.26 * pi * AXIS_1 * AXIS_2

    For all other genera you’ll use a general equation developed for trees:

    volume = 0.5 * HEIGHT^0.6 * pi * AXIS_1 * AXIS_2

    1. Write a function called tree_volume_calc that calculates the canopy volume for the Acacia species in the dataset. To do so, use an if statement in combination with the str_detect() function from the stringr R package. The code str_detect(SPECIES, "Acacia") will return TRUE if the string stored in this variable contains the word “Acacia” and FALSE if it does not. This function will have to take the following arguments as input: SPECIES, HEIGHT, AXIS_1, AXIS_2. Then run the following line:

      tree_volume_calc("Acacia_brevispica", 2.2, 3.5, 1.12)

    2. Expand this function to additionally calculate canopy volumes for other types of trees in this dataset by adding if/else statements and including the volume equations for the Balanites genus and other genera. Then run the following lines:

      tree_volume_calc("Balanites", 2.2, 3.5, 1.12) tree_volume_calc("Croton", 2.2, 3.5, 1.12)

    3. Now get the canopy volumes for all the trees in the tree_data dataframe and add them as a new column to the data frame. You can do this using tree_volume_calc() and either mapply() or using dplyr with rowwise and mutate.

    Expected outputs for Crown Volume Calculation: 1 2 3

Assignment submission & checklist