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

• Topics

  • Iteration
  • Style

• Readings

  • Introduction to apply, lapply, sapply, and tapply
  • Hadley Wickham’s Style Guide

Lecture Notes

• Iteration using vectorization and apply
• Style

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Exercises

1. Vectorized Genus Extraction (15 pts)

   The following code extracts the genus from strings that are scientific names (include both genus and species). The str_extract function is from the stringr package, which is great for working with strings.

   waterbird <- "cygnus olor"
   str_extract(waterbird, "\\w+")
   

   str_extract is a vectorized function meaning it can take a multiple species names as input and return one genera for each species.

   1. Copy and modify the code above to display a vector of genera for the following vector of species names:

   waterbirds <- c("cygnus olor", "aix sponsa", "anas acuta")
   

   2. Copy the code below to create a data frame and then add a new genus column to that data frame that contains the just the genus (the first word in each pair). Display the data frame.

   bird_data <- data.frame(species = c("cygnus olor", "aix sponsa", "anas acuta"))
   

   [click here for output]

2. Species Name Capitalization with Apply (15 pts)

   You have some data with species names that are stored in capital letters. You want to capitalize them properly so that the genus starts with a capital letter and the other letters are lower case. The str_to_sentence function from the stringr package can do this:

   library(stringr)
   
   species <- "CYGNUS OLOR"
   species_clean <- str_to_sentence(species)
   

   1. Use sapply and str_to_sentence to produce a vector of properly capitalized species names from the following vector of species names:

   species <- c("CYGNUS OLOR", "AIX SPONSA", "ANAS ACUTA")
   

   2. Replace sapply with lapply to get the answer as a list instead of a vector.

   3. Use lapply to get the properly capitalized species and the use unlist to convert the result to a vector.

   Note: this exercise doesn’t technically require the use of an apply function, but we’re going to use one to keep our first use of apply simple.

   [click here for output]

3. Size Estimates Vectorized (20 pts)

   This is a followup to Use and Modify.

   1. Write a function 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. Rewrite the function to use the equation mass <- a * length^b and take length, a and b as arguments. Set the default values for a to 0.73 and b to 3.63 so that the code from (1) still works. Copy the data below into R and call your function using vector of lengths (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). Using dplyr add a new masses column to this data frame (using mutate and your function).

   [click here for output]

4. Size Estimates By Name Apply (20 pts)

   This is a followup to Size Estimates by Name.

   Download and import data on dinosaur lengths with species names.

   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.

   • Stegosauria: a = 10.95 and b = 2.64 (Seebacher 2001).
   • Theropoda: a = 0.73 and b = 3.63 (Seebacher 2001).
   • Sauropoda: a = 214.44 and b = 1.46 (Seebacher 2001).

   If the name is not any of these values set a and b to 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).

   3. Make a histogram of of dinosaur masses with one subplot for each species (using facet_wrap).

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

5. Tree Biomass Challenge (30 pts)

   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 * D^2.53.

   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. (Thanks to vector math this function is basically just the equation above).
   • 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)
   [click here for output] [click here for output]

6. 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("http://www.esapubs.org/archive/ecol/E095/064/TREE_SURVEYS.txt",
                    sep = '\t',
                    na.strings = c("dead", "missing", "MISSING",
                                   "NA", "?", "3.3."),
                    stringsAsFactors = FALSE)
   

   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.

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

7. Climate Space Iteration (optional)

   This is a follow up to Climate Space Rewrite.

   Using the functions you created in Climate Space Rewrite iterate over the following list of species to create one plot per species from the list. Include a title for each plot that is the species name using the ggtitle() function. You can use any type of automated iteration that we’ve learned.

   species <- c("Juniperus occidentalis", "Quercus alba", "Picea glauca", "Ceiba pentandra", "Quercus rubra", "Larrea tridentata", "Opuntia pusilla")
   

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

Assignment submission & checklist