Data Carpentry for Biologists

Tree Growth (Loops)

The UHURU experiment in Kenya has conducted a survey of Acacia and other tree species in ungulate exclosure treatments. Each of the individuals surveyed were measured for tree height (HEIGHT), circumference (CIRC) and canopy size in two directions (AXIS_1 and AXIS_2). If the file TREE_SURVEYS.txt isn’t already in your working directory, download the data file here.

Read the 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."))

  1. Write a function named get_growth() that takes two inputs, a vector of sizes and a vector of years, and calculates the average annual growth rate. Pseudo-code for calculating this rate is (size_in_last_year - size_in_first_year) / (last_year - first_year). Test this function by running get_growth(c(40.2, 42.6, 46.0), c(2020, 2021, 2022)).

  2. Use dplyr and this function to get the growth for each individual tree along with information about the TREATMENT that tree occurs on. Trees are identified by a unique value in the ORIGINAL_TAG column. Don’t include information for cases where a TREATMENT is not known (e.g., where it is NA).

  3. Using ggplot the output from (2) make a histogram of growth rates for each TREATMENT, which each TREATMENT in it’s own facet. Use geom_vline() to add a vertical line at 0 to help indicate which trees are getting bigger vs. smaller. Include good axis labels.

  4. Create a single function called compare_growth() that combines your work in (2) and (3). It should take the arguments:df (the data frame being used), measure (the column that contains the size measurement to measure growth on; we used CIRC), tag_column (the name of the column with the unique tag; we used ORIGINAL_TAG), sample_column (the name of the column indicating different samples, we used YEAR), and facet_column (the name of the column to use to determine which groups to make histograms for, we used TREATMENT). Use the function to recreate your original plot using compare_growth(tree_data, CIRC, ORIGINAL_TAG, YEAR, TREATMENT). Then use the function to create a similar plot showing growth faceted SPECIES, using SURVEY as the sample_column, and AXIS_1 as the measure by running compare_growth(tree_data, AXIS_1, ORIGINAL_TAG, SURVEY, SPECIES).

Expected outputs for Tree Growth: 1 2 3 4