Data Science in Omics Introduction
An OMICS Course Part 1: Shell, Organizing and Genomics
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
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)
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)
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.
