In-class Activity (20 min): Acacia and ants

Exercise 1. Scatterplots

1. Make a scatter plot with CIRC on the x axis and AXIS1 (the maximum canopy width) on the y axis. Label the x axis “Circumference” and the y axis “Canopy Diameter”.
2. The same plot as (1), but with both axes scaled logarithmically (using scale_x_log10() and scale_y_log10()).
3. The same plot as (1), but with points colored based on the ANT column (the species of ant symbiont living with the acacia)
4. The same plot as (3)), but instead of different colors show different species of ant (values of ANT column) each in a separate subplot.
5. The same plot as (4) but add a simple model of the data by adding geom_smooth().

Exercise 2. Histograms

1. Make a bar plot of the number of acacia with each mutualist ant species (using the ANT column).
2. Make a histogram of the height of acacia (using the HEIGHT column). Label the x axis “Height (m)” and the y axis “Number of acacia trees”.
3. Make a plot that shows histograms of both AXIS1 and AXIS2. Due to the way the data is structured you’ll need to add a 2nd geom_histogram() layer that specifies a new aesthetic. To make it possible to see both sets of bars you’ll need to make them transparent with the optional argument alpha = 0.3. Set the color for AXIS1 to “red” and AXIS2 to “black” using the fill = argument. Label the x axis “Canopy Diameter(m)” and the y axis “Number of Acacia”.
4. Use facet_wrap() to make the same plot as (3) but with one subplot for each experimental treatment in TREATMENT. Set the number of bins in the histogram to 10.

Once your are done with the exercises:

1. Save your .Rmd file and knit to PDF.
2. Add the two files, commit and push to GitHub
3. Let your instructor know that changes have been published on GitHub

---

---

Home exercises (20 min): Acacia vs trees

Exercise 3.

1. Download the file TREE_SURVEYS.txt and save it to your “data-raw” folder
2. Read the file with the function read_tsv from the package readr and assign it to an object called trees:

   trees <- read_tsv("TREE_SURVEYS.txt",
                  col_types = list(HEIGHT = col_double(),
                                   AXIS_2 = col_double()))
   

3. Add a new column to the trees data frame that is named canopy_area and contains the estimated canopy area calculated as the value in the AXIS_1 column times the value in the AXIS_2 column.
4. Subset the trees data frame with just the SURVEY, YEAR, SITE, and canopy_area columns.
5. Make a scatter plot with canopy_area on the x axis and HEIGHT on the y axis. Color the points by TREATMENT and create a subplot per species uding the function facet_wrap(). This will plot the points for each variable in the SPECIES column in a separate subplot. Label the x axis “Canopy Area (m)” and the y axis “Height (m)”. Make the point size 2.
6. That’s a big outlier in the plot from (2). 50 by 50 meters is a little too big for a real acacia tree, so filter the data to remove any values for AXIS_1 and AXIS_2 that are over 20 and update the data frame. Then, remake the graph.
7. For this question you will use the package dplyr and the pipe operator %>%. To learn more about the pipe operator and how to use it, watch this introductory video. Using the data without the outlier – i.e., the data generated in (6), create a data fram called abundance that shows how the abundance of each species has been changing through time. To do this, use the functions group_by(), summarize(), and n() to make a data frame with YEAR, SPECIES, and a species_abundance column that has the number of individuals per species per year. For guidance, look at the examples of the functions group_by() (using help(group_by) and summarize() (using help(summarize)). Print out the abundance data frame.
8. Using the data frame generated in (7), make a line plot with points (by using geom_line() in addition to geom_point()) with YEAR on the x axis and abundance on the y axis with one subplot per species. To let you see each trend clearly, let the scale for the y axis vary among plots by adding scales = "free_y" as an optional argument to facet_wrap().

Exercise 4.

We want to compare the circumference to height relationship in acacia to the same relationship for all trees in the region. These data are stored in two different tables. Make a graph with the relationship between CIRC and HEIGHT for all trees as gray circles in the background and the same relationship for acacia as red circles plotted on top of the gray circles. Scale both axes logarithmically. Include a linear model fitting for both sets of data, trying different linear models specified using the argument method =. Provide clear labels for the axes.

Your plot should look something like this.

Once your are done with the exercises:

1. Save your .Rmd file and knit to PDF.
2. Add the two files, commit and push to GitHub
3. Let your instructor know that changes have been published on GitHub