### Learning Objectives

Following this assignment students should be able to:

• understand the basic plot function of `ggplot2`
• import ‘messy’ data with missing values and extra lines
• execute and visualize a regression analysis

• Topics

• `ggplot`

### Setup

``````install.packages(c('dplyr', 'ggplot2', 'readr'))
"ACACIA_DREPANOLOBIUM_SURVEY.txt")
"TREE.txt")
"Mammal_lifehistories_v2.txt")
``````

### Lecture Notes

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

``````library(dplyr)
library(ggplot2)
``````

### Exercises

1. #### Acacia and Ants (20 pts)

An experiment in Kenya has been exploring the influence of large herbivores on plants.

Check to see if `ACACIA_DREPANOLOBIUM_SURVEY.txt` is in your workspace. If not, download it. Read it into R using the following command:

``````acacia <- read.csv("ACACIA_DREPANOLOBIUM_SURVEY.txt", sep="\t", na.strings = c("dead"))
``````
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`) each in a separate subplot.
5. The same plot as (4) but add a simple model of the data by adding `geom_smooth`.
Expected outputs for Acacia and Ants: 1 2 3 4 5
2. #### Mass vs Metabolism (20 pts)

The relationship between the body size of an organism and its metabolic rate is one of the most well studied and still most controversial areas of organismal physiology. We want to graph this relationship in the Artiodactyla using a subset of data from a large compilation of body size data (Savage et al. 2004). You can copy and paste this data frame into your program:

``````size_mr_data <- data.frame(
body_mass = c(32000, 37800, 347000, 4200, 196500, 100000,
4290, 32000, 65000, 69125, 9600, 133300, 150000, 407000,
115000, 67000,325000, 21500, 58588, 65320, 85000, 135000,
20500, 1613, 1618),
metabolic_rate = c(49.984, 51.981, 306.770, 10.075, 230.073,
148.949, 11.966, 46.414, 123.287, 106.663, 20.619, 180.150,
200.830, 224.779, 148.940, 112.430, 286.847, 46.347,
142.863, 106.670, 119.660, 104.150, 33.165, 4.900, 4.865),
family = c("Antilocapridae", "Antilocapridae", "Bovidae",
"Bovidae", "Bovidae", "Bovidae", "Bovidae", "Bovidae",
"Bovidae", "Bovidae", "Bovidae", "Bovidae", "Bovidae",
"Camelidae", "Camelidae", "Canidae", "Cervidae",
"Cervidae", "Cervidae", "Cervidae", "Cervidae", "Suidae",
"Tayassuidae", "Tragulidae", "Tragulidae"))
``````

Make the following plots with appropriate axis labels:

1. A plot of body mass vs. metabolic rate
2. A plot of body mass vs. metabolic rate, with log10 scaled axes (this stretches the axis, but keeps the numbers on the original scale), and the point size set to 3.
3. The same plot as (2), but with the different families indicated using color.
4. The same plot as (2), but with the different families each in their own subplot.
Expected outputs for Mass vs Metabolism: 1 2 3 4
3. #### Acacia and Ants Histograms (20 pts)

An experiment in Kenya has been exploring the influence of large herbivores on plants.

Check to see if `ACACIA_DREPANOLOBIUM_SURVEY.txt` is in your workspace. If not, download it. Read it into R using the following command:

``````acacia <- read.csv("data/ACACIA_DREPANOLOBIUM_SURVEY.txt", sep="\t", na.strings = c("dead"))
``````
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”.
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 treatment. Set the number of bins in the histogram to 10.
Expected outputs for Acacia and Ants Histograms: 1 2 3 4
4. #### Acacia and Ants Data Manipulation (20 pts)

An experiment in Kenya has been exploring the influence of large herbivores on plants.

Check to see if `TREE_SURVEYS.txt` is in your workspace. If not, download `TREE_SURVEYS.txt`. Use `read_tsv` from the `readr` package to read in the data using the following command:

``````trees <- read_tsv("TREE_SURVEYS.txt",
col_types = list(HEIGHT = col_double(),
AXIS_2 = col_double()))
``````
1. Update the `trees` data frame with a new column named `canopy_area` that contains the estimated canopy area calculated as the value in the `AXIS_1` column times the value in the `AXIS_2` column. Show output of the `trees` data frame with just the `SURVEY`, `YEAR`, `SITE`, and `canopy_area` columns.
2. Make a scatter plot with `canopy_area` on the x axis and `HEIGHT` on the y axis. Color the points by `TREATMENT` and plot the points for each value 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.
3. That’s a big outlier in the plot from (2). 50 by 50 meters is a little too big for a real Acacia, 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.
4. Using the data without the outlier (i.e., the data generated in (3)), find out how the abundance of each species has been changing through time. Use `group_by`, `summarize`, and `n` to make a data frame with `YEAR`, `SPECIES`, and an `abundance` column that has the number of individuals in each species in each year. Print out this data frame.
5. Using the data frame generated in (4), 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 seen 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`.
Expected outputs for Acacia and Ants Data Manipulation: 1 2 3 4 5
5. #### Graphing Data From Multiple Tables (optional)

An experiment in Kenya has been exploring the influence of large herbivores on plants.

Check to see if `ACACIA_DREPANOLOBIUM_SURVEY.txt` and `TREE_SURVEYS.txt` is in your workspace. If not, download `ACACIA_DREPANOLOBIUM_SURVEY.txt` and `TREE_SURVEYS.txt` Install the `readr` package and use `read_tsv` to read in the data using the following commands:

``````library(readr)
col_types = list(HEIGHT = col_double(),
AXIS_2 = col_double()))
``````

We want to compare the circumference to height relationship in acacia and to the same relationship for trees in the region. These data are stored in two different tables. Make a graph with the relationship between `CIRC` and `HEIGHT` for the trees as gray circles in the background and the same relationship for acacia as red circles plotted on top of the grah circles. Scale the both axes logarithmically. Inlude linear models for both sets of data. Provide clear labels for the axes.

Expected outputs for Graphing Data From Multiple Tables: 1
6. #### Adult vs Newborn Size (20 pts)

Larger organisms have larger offspring. We want to explore the form of this relationship in mammals.

Check to see if `Mammal_lifehistories_v2.txt` is in your working directory. If not download it from the web. This is tab delimited data, so you’ll want to use `sep = "\t"` as an optional argument when calling `read.csv()`. The `\t` is how we indicate a tab character to R (and most other programming languages).

When you import the data there are some extra blank lines at the end of this file. Get rid of them by using the optional `read.csv()` argument `nrows = 1440` to import only the first 1440 rows.

Missing data in this file is specified by `-999` and `-999.00`. Tell R that these are null values using the optional `read.csv()` argument, `na.strings = c("-999", "-999.00")`. This will stop them from being plotted.

1. Graph adult mass vs. newborn mass. Label the axes with clearer labels than the column names.
2. It looks like there’s a regular pattern here, but it’s definitely not linear. Let’s see if log-transformation straightens it out. Graph adult mass vs. newborn mass, with both axes scaled logarithmically. Label the axes.
3. This looks like a pretty regular pattern, so you wonder if it varies among different groups. Graph adult mass vs. newborn mass, with both axes scaled logarithmically, and the data points colored by order. Label the axes.
4. Coloring the points was useful, but there are a lot of points and it’s kind of hard to see what’s going on with all of the orders. Use `facet_wrap` to create a subplot for each order.
5. Now let’s visualize the relationships between the variables using a simple linear model. Create a new graph like your faceted plot, but using `geom_smooth` to fit a linear model to each order. You can do this using the optional argument `method = "lm"` in `geom_smooth`.
Expected outputs for Adult vs Newborn Size: 1 2 3 4 5

Assignment submission & checklist